diff --git a/noxfile.py b/noxfile.py index 9c305b66e32..d8b2091473f 100644 --- a/noxfile.py +++ b/noxfile.py @@ -2,7 +2,7 @@ import nox test_locations = [ "openbb_sdk/tests", - "openbb_sdk/sdk/core/tests", + "openbb_sdk/sdk", "openbb_sdk/providers", ] diff --git a/openbb_sdk/providers/alpha_vantage/tests/.gitkeep b/openbb_sdk/extensions/charting/integration/.gitkeep similarity index 100% rename from openbb_sdk/providers/alpha_vantage/tests/.gitkeep rename to openbb_sdk/extensions/charting/integration/.gitkeep diff --git a/openbb_sdk/extensions/crypto/integration/test_crypto_api.py b/openbb_sdk/extensions/crypto/integration/test_crypto_api.py new file mode 100644 index 00000000000..54d67068e31 --- /dev/null +++ b/openbb_sdk/extensions/crypto/integration/test_crypto_api.py @@ -0,0 +1,98 @@ +import pytest +import requests +from openbb_provider.utils.helpers import get_querystring + + +def get_token(): + return requests.post( + "http://0.0.0.0:8000/api/v1/account/token", + data={"username": "openbb", "password": "openbb"}, + timeout=5, + ) + + +@pytest.fixture(scope="session") +def headers(): + access_token = get_token().json()["access_token"] + return {"Authorization": f"Bearer {access_token}"} + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "BTCUSD", "start_date": "2023-01-01", "end_date": "2023-06-06"}), + ( + { + "interval": "1min", + "provider": "fmp", + "symbol": "BTCUSD", + "start_date": "2023-01-01", + "end_date": "2023-01-02", + } + ), + ( + { + "interval": "1day", + "provider": "fmp", + "symbol": "BTCUSD", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "multiplier": 1, + "timespan": "minute", + "sort": "desc", + "limit": 49999, + "adjusted": True, + "provider": "polygon", + "symbol": "BTCUSD", + "start_date": "2023-01-01", + "end_date": "2023-01-02", + } + ), + ( + { + "multiplier": 1, + "timespan": "day", + "sort": "desc", + "limit": 49999, + "adjusted": True, + "provider": "polygon", + "symbol": "BTCUSD", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "5m", + "period": "max", + "provider": "yfinance", + "symbol": "BTCUSD", + "start_date": "2023-01-01", + "end_date": "2023-01-02", + } + ), + ( + { + "interval": "1d", + "period": "max", + "provider": "yfinance", + "symbol": "BTCUSD", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_crypto_load(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/crypto/load?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 diff --git a/openbb_sdk/extensions/crypto/integration/test_crypto_python.py b/openbb_sdk/extensions/crypto/integration/test_crypto_python.py new file mode 100644 index 00000000000..d30bd1a8050 --- /dev/null +++ b/openbb_sdk/extensions/crypto/integration/test_crypto_python.py @@ -0,0 +1,89 @@ +"""Test crypto extension.""" + +import pytest +from openbb import obb +from openbb_core.app.model.obbject import OBBject + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "symbol": "BTCUSD", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "1min", + "provider": "fmp", + "symbol": "BTCUSD", + "start_date": "2023-01-01", + "end_date": "2023-01-02", + } + ), + ( + { + "interval": "1day", + "provider": "fmp", + "symbol": "BTCUSD", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "multiplier": 1, + "timespan": "minute", + "sort": "desc", + "limit": 49999, + "adjusted": True, + "provider": "polygon", + "symbol": "BTCUSD", + "start_date": "2023-01-01", + "end_date": "2023-01-02", + } + ), + ( + { + "multiplier": 1, + "timespan": "day", + "sort": "desc", + "limit": 49999, + "adjusted": True, + "provider": "polygon", + "symbol": "BTCUSD", + "start_date": "2023-01-01", + "end_date": "2023-01-02", + } + ), + ( + { + "interval": "5m", + "period": "max", + "provider": "yfinance", + "symbol": "BTCUSD", + "start_date": "2023-01-01", + "end_date": "2023-01-02", + } + ), + ( + { + "interval": "1d", + "period": "max", + "provider": "yfinance", + "symbol": "BTCUSD", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_crypto_load(params): + result = obb.crypto.load(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 diff --git a/openbb_sdk/extensions/econometrics/integration/test_econometrics_api.py b/openbb_sdk/extensions/econometrics/integration/test_econometrics_api.py new file mode 100644 index 00000000000..c2cb290c636 --- /dev/null +++ b/openbb_sdk/extensions/econometrics/integration/test_econometrics_api.py @@ -0,0 +1,122 @@ +import pytest +import requests +from openbb_provider.utils.helpers import get_querystring + + +def get_token(): + return requests.post( + "http://0.0.0.0:8000/api/v1/account/token", + data={"username": "openbb", "password": "openbb"}, + timeout=5, + ) + + +@pytest.fixture(scope="session") +def headers(): + access_token = get_token().json()["access_token"] + return {"Authorization": f"Bearer {access_token}"} + + +@pytest.mark.parametrize( + "params", + [({"data": ""})], +) +@pytest.mark.integration +def test_econometrics_corr(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/econometrics/corr?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"data": "", "y_column": "", "x_columns": ""})], +) +@pytest.mark.integration +def test_econometrics_ols_summary(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/econometrics/ols_summary?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"data": "", "y_column": "", "x_columns": ""})], +) +@pytest.mark.integration +def test_econometrics_dwat(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/econometrics/dwat?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"data": "", "y_column": "", "x_columns": "", "lags": ""})], +) +@pytest.mark.integration +def test_econometrics_bgot(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/econometrics/bgot?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"data": "", "columns": ""})], +) +@pytest.mark.integration +def test_econometrics_coint(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/econometrics/coint?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"data": "", "y_column": "", "x_column": "", "lag": ""})], +) +@pytest.mark.integration +def test_econometrics_granger(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/econometrics/granger?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"data": "", "column": "", "regression": ""})], +) +@pytest.mark.integration +def test_econometrics_unitroot(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/econometrics/unitroot?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 diff --git a/openbb_sdk/providers/benzinga/tests/.gitkeep b/openbb_sdk/extensions/econometrics/tests/.gitkeep similarity index 100% rename from openbb_sdk/providers/benzinga/tests/.gitkeep rename to openbb_sdk/extensions/econometrics/tests/.gitkeep diff --git a/openbb_sdk/extensions/economy/integration/test_economy_api.py b/openbb_sdk/extensions/economy/integration/test_economy_api.py new file mode 100644 index 00000000000..47f390f41ff --- /dev/null +++ b/openbb_sdk/extensions/economy/integration/test_economy_api.py @@ -0,0 +1,351 @@ +import pytest +import requests +from openbb_provider.utils.helpers import get_querystring + + +def get_token(): + return requests.post( + "http://0.0.0.0:8000/api/v1/account/token", + data={"username": "openbb", "password": "openbb"}, + timeout=5, + ) + + +@pytest.fixture(scope="session") +def headers(): + access_token = get_token().json()["access_token"] + return {"Authorization": f"Bearer {access_token}"} + + +@pytest.mark.parametrize( + "params", + [({"index": "dowjones"})], +) +@pytest.mark.integration +def test_economy_const(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/const?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "countries": ["portugal", "spain"], + "units": "growth_same", + "frequency": "monthly", + "harmonized": True, + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ) + ], +) +@pytest.mark.integration +def test_economy_cpi(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/cpi?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "DJI", "start_date": "2023-01-01", "end_date": "2023-06-06"}), + ( + { + "interval": "1m", + "provider": "cboe", + "symbol": "AAVE100", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "1d", + "provider": "cboe", + "symbol": "AAVE100", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "1min", + "provider": "fmp", + "symbol": "^DJI", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "1day", + "provider": "fmp", + "symbol": "^DJI", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "timespan": "minute", + "sort": "desc", + "limit": 49999, + "adjusted": True, + "multiplier": 1, + "provider": "polygon", + "symbol": "NDX", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "timespan": "day", + "sort": "desc", + "limit": 49999, + "adjusted": True, + "multiplier": 1, + "provider": "polygon", + "symbol": "NDX", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "1m", + "period": "max", + "prepost": True, + "rounding": True, + "provider": "yfinance", + "symbol": "DJI", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "1d", + "period": "max", + "prepost": True, + "rounding": True, + "provider": "yfinance", + "symbol": "DJI", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_economy_index(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/index?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "BUKBUS", "start_date": "2023-01-01", "end_date": "2023-06-06"}), + ( + { + "interval": "1m", + "provider": "cboe", + "symbol": "BUKBUS", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "1d", + "provider": "cboe", + "symbol": "BUKBUS", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_economy_european_index(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/european_index?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"symbol": "BUKBUS"})], +) +@pytest.mark.integration +def test_economy_european_index_constituents(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/european_index_constituents?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({}), ({"europe": True, "provider": "cboe"})], +) +@pytest.mark.integration +def test_economy_available_indices(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/available_indices?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({})], +) +@pytest.mark.integration +def test_economy_risk(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/risk?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + ({"query": "DJ", "symbol": True}), + ( + { + "europe": True, + "provider": "cboe", + "query": "DJ", + "symbol": True, + } + ), + ], +) +@pytest.mark.integration +def test_economy_index_search(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/index_search?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"region": "US"})], +) +@pytest.mark.integration +def test_economy_index_snapshots(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/index_snapshots?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"query": "grain"})], +) +@pytest.mark.integration +def test_economy_cot_search(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/cot_search?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + ({}), + ( + { + "code": "13874P", + "data_type": "FO", + "legacy_format": True, + "report_type": "ALL", + "measure": "CR", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "transform": "diff", + "provider": "quandl", + } + ), + ], +) +@pytest.mark.integration +def test_economy_cot(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/cot?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "series_name": "PE Ratio by Month", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "collapse": "monthly", + "transform": "diff", + } + ) + ], +) +@pytest.mark.integration +def test_economy_sp500_multiples(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/sp500_multiples?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 diff --git a/openbb_sdk/extensions/economy/integration/test_economy_python.py b/openbb_sdk/extensions/economy/integration/test_economy_python.py new file mode 100644 index 00000000000..ed5c307877d --- /dev/null +++ b/openbb_sdk/extensions/economy/integration/test_economy_python.py @@ -0,0 +1,329 @@ +"""Test economy extension.""" + +import pytest +from openbb import obb +from openbb_core.app.model.obbject import OBBject + + +@pytest.mark.parametrize( + "params", + [ + ({"index": "dowjones"}), + ], +) +@pytest.mark.integration +def test_economy_const(params): + result = obb.economy.const(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "countries": ["portugal", "spain"], + "units": "growth_same", + "frequency": "monthly", + "harmonized": True, + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_economy_cpi(params): + result = obb.economy.cpi(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "symbol": "DJI", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "1m", + "provider": "cboe", + "symbol": "AAVE100", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "1d", + "provider": "cboe", + "symbol": "AAVE100", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "1min", + "provider": "fmp", + "symbol": "^DJI", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "1day", + "provider": "fmp", + "symbol": "^DJI", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "timespan": "minute", + "sort": "desc", + "limit": 49999, + "adjusted": True, + "multiplier": 1, + "provider": "polygon", + "symbol": "NDX", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "timespan": "day", + "sort": "desc", + "limit": 49999, + "adjusted": True, + "multiplier": 1, + "provider": "polygon", + "symbol": "NDX", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "5m", + "period": "max", + "prepost": True, + "rounding": True, + "provider": "yfinance", + "symbol": "DJI", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "1d", + "period": "max", + "prepost": True, + "rounding": True, + "provider": "yfinance", + "symbol": "DJI", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_economy_index(params): + result = obb.economy.index(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "symbol": "BUKBUS", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "1m", + "provider": "cboe", + "symbol": "BUKBUS", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "1d", + "provider": "cboe", + "symbol": "BUKBUS", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_economy_european_index(params): + result = obb.economy.european_index(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "BUKBUS"}), + ], +) +@pytest.mark.integration +def test_economy_european_index_constituents(params): + result = obb.economy.european_index_constituents(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({}), + ({"europe": True, "provider": "cboe"}), + ], +) +@pytest.mark.integration +def test_economy_available_indices(params): + result = obb.economy.available_indices(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({}), + ], +) +@pytest.mark.integration +def test_economy_risk(params): + result = obb.economy.risk(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"query": "DJ", "symbol": True}), + ( + { + "europe": True, + "provider": "cboe", + "query": "AA", + "symbol": True, + } + ), + ], +) +@pytest.mark.integration +def test_economy_index_search(params): + result = obb.economy.index_search(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"region": "US"}), + ], +) +@pytest.mark.integration +def test_economy_index_snapshots(params): + result = obb.economy.index_snapshots(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"query": "grain"}), + ], +) +@pytest.mark.integration +def test_economy_cot_search(params): + result = obb.economy.cot_search(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({}), + ( + { + "code": "13874P", + "data_type": "FO", + "legacy_format": True, + "report_type": "ALL", + "measure": "CR", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "transform": "diff", + "provider": "quandl", + } + ), + ], +) +@pytest.mark.integration +def test_economy_cot(params): + result = obb.economy.cot(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "series_name": "PE Ratio by Month", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "collapse": "monthly", + "transform": "diff", + } + ), + ], +) +@pytest.mark.integration +def test_economy_sp500_multiples(params): + result = obb.economy.sp500_multiples(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + assert len(result.results) > 0 diff --git a/openbb_sdk/extensions/fixedincome/integration/test_fixedincome_api.py b/openbb_sdk/extensions/fixedincome/integration/test_fixedincome_api.py new file mode 100644 index 00000000000..3a96790948c --- /dev/null +++ b/openbb_sdk/extensions/fixedincome/integration/test_fixedincome_api.py @@ -0,0 +1,202 @@ +import pytest +import requests +from openbb_provider.utils.helpers import get_querystring + + +def get_token(): + return requests.post( + "http://0.0.0.0:8000/api/v1/account/token", + data={"username": "openbb", "password": "openbb"}, + timeout=5, + ) + + +@pytest.fixture(scope="session") +def headers(): + access_token = get_token().json()["access_token"] + return {"Authorization": f"Bearer {access_token}"} + + +@pytest.mark.parametrize( + "params", + [({"start_date": "2023-01-01", "end_date": "2023-06-06"})], +) +@pytest.mark.integration +def test_fixedincome_treasury(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/fixedincome/treasury?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"date": "2023-01-01", "inflation_adjusted": True})], +) +@pytest.mark.integration +def test_fixedincome_ycrv(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/fixedincome/ycrv?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + ({"start_date": "2023-01-01", "end_date": "2023-06-06"}), + ( + { + "period": "overnight", + "provider": "fred", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_sofr(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/fixedincome/sofr?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + ({"start_date": "2023-01-01", "end_date": "2023-06-06"}), + ( + { + "parameter": "volume_weighted_trimmed_mean_rate", + "provider": "fred", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_estr(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/fixedincome/estr?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + ({"start_date": "2023-01-01", "end_date": "2023-06-06"}), + ( + { + "parameter": "rate", + "provider": "fred", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_sonia(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/fixedincome/sonia?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + ({"start_date": "2023-01-01", "end_date": "2023-06-06"}), + ( + { + "parameter": "overnight", + "provider": "fred", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_ameribor(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/fixedincome/ameribor?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + ({"start_date": "2023-01-01", "end_date": "2023-06-06"}), + ( + { + "parameter": "weekly", + "provider": "fred", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_fed(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/fixedincome/fed?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({}), ({"long_run": True, "provider": "fred"})], +) +@pytest.mark.integration +def test_fixedincome_projections(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/fixedincome/projections?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"start_date": "2023-01-01", "end_date": "2023-06-06"})], +) +@pytest.mark.integration +def test_fixedincome_iorb(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/fixedincome/iorb?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 diff --git a/openbb_sdk/extensions/fixedincome/integration/test_fixedincome_python.py b/openbb_sdk/extensions/fixedincome/integration/test_fixedincome_python.py new file mode 100644 index 00000000000..4ad652b3d91 --- /dev/null +++ b/openbb_sdk/extensions/fixedincome/integration/test_fixedincome_python.py @@ -0,0 +1,171 @@ +"""Test fixedincome extension.""" +import pytest +from openbb import obb +from openbb_core.app.model.obbject import OBBject + + +@pytest.mark.parametrize( + "params", + [ + ({"start_date": "2023-01-01", "end_date": "2023-06-06"}), + ], +) +@pytest.mark.integration +def test_fixedincome_treasury(params): + result = obb.fixedincome.treasury(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"date": "2023-01-01", "inflation_adjusted": True}), + ], +) +@pytest.mark.integration +def test_fixedincome_ycrv(params): + result = obb.fixedincome.ycrv(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"start_date": "2023-01-01", "end_date": "2023-06-06"}), + ( + { + "period": "overnight", + "provider": "fred", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_sofr(params): + result = obb.fixedincome.sofr(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"start_date": "2023-01-01", "end_date": "2023-06-06"}), + ( + { + "parameter": "volume_weighted_trimmed_mean_rate", + "provider": "fred", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_estr(params): + result = obb.fixedincome.estr(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"start_date": "2023-01-01", "end_date": "2023-06-06"}), + ( + { + "parameter": "rate", + "provider": "fred", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_sonia(params): + result = obb.fixedincome.sonia(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"start_date": "2023-01-01", "end_date": "2023-06-06"}), + ( + { + "parameter": "overnight", + "provider": "fred", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_ameribor(params): + result = obb.fixedincome.ameribor(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"start_date": "2023-01-01", "end_date": "2023-06-06"}), + ( + { + "parameter": "weekly", + "provider": "fred", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_fed(params): + result = obb.fixedincome.fed(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({}), + ({"long_run": True, "provider": "fred"}), + ], +) +@pytest.mark.integration +def test_fixedincome_projections(params): + result = obb.fixedincome.projections(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"start_date": "2023-01-01", "end_date": "2023-06-06"}), + ], +) +@pytest.mark.integration +def test_fixedincome_iorb(params): + result = obb.fixedincome.iorb(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 diff --git a/openbb_sdk/extensions/forex/integration/test_forex_api.py b/openbb_sdk/extensions/forex/integration/test_forex_api.py new file mode 100644 index 00000000000..fa2880bbdb4 --- /dev/null +++ b/openbb_sdk/extensions/forex/integration/test_forex_api.py @@ -0,0 +1,127 @@ +import pytest +import requests +from openbb_provider.utils.helpers import get_querystring + + +def get_token(): + return requests.post( + "http://0.0.0.0:8000/api/v1/account/token", + data={"username": "openbb", "password": "openbb"}, + timeout=5, + ) + + +@pytest.fixture(scope="session") +def headers(): + access_token = get_token().json()["access_token"] + return {"Authorization": f"Bearer {access_token}"} + + +@pytest.mark.parametrize( + "params", + [ + ({}), + ( + { + "symbol": "EURUSD", + "date": "2023-01-01", + "search": "USD", + "active": True, + "order": "asc", + "sort": "ticker", + "limit": 1000, + "provider": "polygon", + } + ), + ], +) +@pytest.mark.integration +def test_forex_pairs(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/forex/pairs?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "EURUSD", "start_date": "2023-01-01", "end_date": "2023-06-06"}), + ( + { + "interval": "1min", + "provider": "fmp", + "symbol": "EURUSD", + "start_date": "2023-01-01", + "end_date": "2023-01-02", + } + ), + ( + { + "interval": "1day", + "provider": "fmp", + "symbol": "EURUSD", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "multiplier": 1, + "timespan": "minute", + "sort": "desc", + "limit": 49999, + "adjusted": True, + "provider": "polygon", + "symbol": "EURUSD", + "start_date": "2023-01-01", + "end_date": "2023-01-02", + } + ), + ( + { + "multiplier": 1, + "timespan": "day", + "sort": "desc", + "limit": 49999, + "adjusted": True, + "provider": "polygon", + "symbol": "EURUSD", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "5m", + "period": "max", + "provider": "yfinance", + "symbol": "EURUSD", + "start_date": "2023-01-01", + "end_date": "2023-01-02", + } + ), + ( + { + "interval": "1d", + "period": "max", + "provider": "yfinance", + "symbol": "EURUSD", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_forex_load(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/forex/load?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 diff --git a/openbb_sdk/extensions/forex/integration/test_forex_python.py b/openbb_sdk/extensions/forex/integration/test_forex_python.py new file mode 100644 index 00000000000..21df465e5ee --- /dev/null +++ b/openbb_sdk/extensions/forex/integration/test_forex_python.py @@ -0,0 +1,116 @@ +"""Test forex extension.""" +import datetime + +import pytest +from openbb import obb +from openbb_core.app.model.obbject import OBBject + + +@pytest.mark.parametrize( + "params", + [ + ({}), + ( + { + "symbol": "EURUSD", + "date": datetime.date(2023, 9, 25), + "search": "USD", + "active": True, + "order": "asc", + "sort": "ticker", + "limit": 1000, + "provider": "polygon", + } + ), + ], +) +@pytest.mark.integration +def test_forex_pairs(params): + result = obb.forex.pairs(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "symbol": "EURUSD", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "1min", + "provider": "fmp", + "symbol": "EURUSD", + "start_date": "2023-01-01", + "end_date": "2023-01-02", + } + ), + ( + { + "interval": "1day", + "provider": "fmp", + "symbol": "EURUSD", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "multiplier": 1, + "timespan": "minute", + "sort": "desc", + "limit": 49999, + "adjusted": True, + "provider": "polygon", + "symbol": "EURUSD", + "start_date": "2023-01-01", + "end_date": "2023-01-02", + } + ), + ( + { + "multiplier": 1, + "timespan": "day", + "sort": "desc", + "limit": 49999, + "adjusted": True, + "provider": "polygon", + "symbol": "EURUSD", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "5m", + "period": "max", + "provider": "yfinance", + "symbol": "EURUSD", + "start_date": "2023-01-01", + "end_date": "2023-01-02", + } + ), + ( + { + "interval": "1d", + "period": "max", + "provider": "yfinance", + "symbol": "EURUSD", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_forex_load(params): + result = obb.forex.load(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 diff --git a/openbb_sdk/extensions/futures/integration/test_futures_api.py b/openbb_sdk/extensions/futures/integration/test_futures_api.py new file mode 100644 index 00000000000..2cd5d3638c6 --- /dev/null +++ b/openbb_sdk/extensions/futures/integration/test_futures_api.py @@ -0,0 +1,70 @@ +import pytest +import requests +from openbb_provider.utils.helpers import get_querystring + + +def get_token(): + return requests.post( + "http://0.0.0.0:8000/api/v1/account/token", + data={"username": "openbb", "password": "openbb"}, + timeout=5, + ) + + +@pytest.fixture(scope="session") +def headers(): + access_token = get_token().json()["access_token"] + return {"Authorization": f"Bearer {access_token}"} + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "symbol": "ES", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "expiration": "2024-06", + } + ), + ( + { + "interval": "1d", + "period": "1d", + "prepost": True, + "adjust": True, + "back_adjust": True, + "provider": "yfinance", + "symbol": "ES", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "expiration": "2024-06", + } + ), + ], +) +@pytest.mark.integration +def test_futures_load(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/futures/load?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"symbol": "AAPL", "date": "2023-01-01"})], +) +@pytest.mark.integration +def test_futures_curve(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/futures/curve?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 diff --git a/openbb_sdk/extensions/futures/integration/test_futures_python.py b/openbb_sdk/extensions/futures/integration/test_futures_python.py new file mode 100644 index 00000000000..9490a84c5ba --- /dev/null +++ b/openbb_sdk/extensions/futures/integration/test_futures_python.py @@ -0,0 +1,53 @@ +"""Test futures extension.""" +import pytest +from openbb import obb +from openbb_core.app.model.obbject import OBBject + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "symbol": "ES", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "expiration": "2024-06", + } + ), + ( + { + "interval": "1d", + "period": "1d", + "prepost": True, + "adjust": True, + "back_adjust": True, + "provider": "yfinance", + "symbol": "ES", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "expiration": "2024-06", + } + ), + ], +) +@pytest.mark.integration +def test_futures_load(params): + result = obb.futures.load(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL", "date": "2023-01-01"}), + ], +) +@pytest.mark.integration +def test_futures_curve(params): + result = obb.futures.curve(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 diff --git a/openbb_sdk/extensions/news/integration/test_news_api.py b/openbb_sdk/extensions/news/integration/test_news_api.py new file mode 100644 index 00000000000..6e6cecbbf11 --- /dev/null +++ b/openbb_sdk/extensions/news/integration/test_news_api.py @@ -0,0 +1,54 @@ +import pytest +import requests +from openbb_provider.utils.helpers import get_querystring + + +def get_token(): + return requests.post( + "http://0.0.0.0:8000/api/v1/account/token", + data={"username": "openbb", "password": "openbb"}, + timeout=5, + ) + + +@pytest.fixture(scope="session") +def headers(): + access_token = get_token().json()["access_token"] + return {"Authorization": f"Bearer {access_token}"} + + +@pytest.mark.parametrize( + "params", + [ + ({"limit": 20}), + ( + { + "display": "full", + "date": "2023-01-01", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "updated_since": 1, + "published_since": 1, + "sort": "created", + "order": "desc", + "isin": "US0378331005", + "cusip": "037833100", + "channels": "General", + "topics": "AAPL", + "authors": "Benzinga Insights", + "content_types": "headline", + "provider": "benzinga", + "limit": 20, + } + ), + ], +) +@pytest.mark.integration +def test_news_globalnews(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/news/globalnews?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 diff --git a/openbb_sdk/extensions/news/integration/test_news_python.py b/openbb_sdk/extensions/news/integration/test_news_python.py new file mode 100644 index 00000000000..753a0f5a9bb --- /dev/null +++ b/openbb_sdk/extensions/news/integration/test_news_python.py @@ -0,0 +1,38 @@ +"""Test news extension.""" + +import pytest +from openbb import obb +from openbb_core.app.model.obbject import OBBject + + +@pytest.mark.parametrize( + "params", + [ + ({"limit": 20}), + ( + { + "display": "full", + "date": "2023-01-01", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "updated_since": 900000, + "published_since": 900000, + "sort": "created", + "order": "desc", + "isin": "US0378331005", + "cusip": "037833100", + "channels": "General", + "topics": "car", + "authors": "Benzinga Insights", + "content_types": "Car", + "provider": "benzinga", + "limit": 20, + } + ), + ], +) +@pytest.mark.integration +def test_news_globalnews(params): + result = obb.news.globalnews(**params) + assert result + assert isinstance(result, OBBject) diff --git a/openbb_sdk/extensions/qa/integration/test_qa_api.py b/openbb_sdk/extensions/qa/integration/test_qa_api.py new file mode 100644 index 00000000000..d3841892947 --- /dev/null +++ b/openbb_sdk/extensions/qa/integration/test_qa_api.py @@ -0,0 +1,167 @@ +import pytest +import requests +from openbb_provider.utils.helpers import get_querystring + + +def get_token(): + return requests.post( + "http://0.0.0.0:8000/api/v1/account/token", + data={"username": "openbb", "password": "openbb"}, + timeout=5, + ) + + +@pytest.fixture(scope="session") +def headers(): + access_token = get_token().json()["access_token"] + return {"Authorization": f"Bearer {access_token}"} + + +@pytest.mark.parametrize( + "params", + [({"data": "", "target": ""})], +) +@pytest.mark.integration +def test_qa_normality(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/qa/normality?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"data": "", "target": ""})], +) +@pytest.mark.integration +def test_qa_capm(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/qa/capm?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"data": "", "target": "", "threshold_start": "", "threshold_end": ""})], +) +@pytest.mark.integration +def test_qa_om(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/qa/om?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"data": "", "target": "", "window": ""})], +) +@pytest.mark.integration +def test_qa_kurtosis(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/qa/kurtosis?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"data": "", "target": "", "fuller_reg": "", "kpss_reg": ""})], +) +@pytest.mark.integration +def test_qa_unitroot(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/qa/unitroot?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"data": "", "target": "", "rfr": "", "window": ""})], +) +@pytest.mark.integration +def test_qa_sh(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/qa/sh?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"data": "", "target": "", "target_return": "", "window": "", "adjusted": ""})], +) +@pytest.mark.integration +def test_qa_so(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/qa/so?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"data": "", "target": "", "window": ""})], +) +@pytest.mark.integration +def test_qa_skew(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/qa/skew?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"data": "", "target": "", "window": "", "quantile_pct": ""})], +) +@pytest.mark.integration +def test_qa_quantile(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/qa/quantile?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"data": "", "target": ""})], +) +@pytest.mark.integration +def test_qa_summary(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/qa/summary?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 diff --git a/openbb_sdk/extensions/stocks/integration/test_stocks_api.py b/openbb_sdk/extensions/stocks/integration/test_stocks_api.py new file mode 100644 index 00000000000..fa3edf05938 --- /dev/null +++ b/openbb_sdk/extensions/stocks/integration/test_stocks_api.py @@ -0,0 +1,822 @@ +import pytest +import requests +from openbb_provider.utils.helpers import get_querystring + + +def get_token(): + return requests.post( + "http://0.0.0.0:8000/api/v1/account/token", + data={"username": "openbb", "password": "openbb"}, + timeout=5, + ) + + +@pytest.fixture(scope="session") +def headers(): + access_token = get_token().json()["access_token"] + return {"Authorization": f"Bearer {access_token}"} + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL", "period": "annual", "limit": 12}), + ( + { + "type": "reported", + "year": 2023, + "provider": "intrinio", + "symbol": "AAPL", + "period": "annual", + "limit": 12, + } + ), + ( + { + "company_name": "Apple Inc", + "company_name_search": "AAPL", + "sic": "35719904", + "include_sources": True, + "order": "asc", + "sort": "filing_date", + "provider": "polygon", + "symbol": "AAPL", + "period": "annual", + "limit": 12, + } + ), + ], +) +@pytest.mark.integration +def test_stocks_fa_balance(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/fa/balance?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"symbol": "AAPL", "limit": 10})], +) +@pytest.mark.integration +def test_stocks_fa_balance_growth(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/fa/balance_growth?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"start_date": "2023-01-01", "end_date": "2023-06-06"})], +) +@pytest.mark.integration +def test_stocks_fa_cal(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/fa/cal?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL", "period": "annual", "limit": 12}), + ( + { + "type": "reported", + "year": 2023, + "provider": "intrinio", + "symbol": "AAPL", + "period": "annual", + "limit": 12, + } + ), + ( + { + "company_name": "Apple Inc", + "company_name_search": "AAPL", + "sic": "35719904", + "include_sources": True, + "order": "asc", + "sort": "filing_date", + "provider": "polygon", + "symbol": "AAPL", + "period": "annual", + "limit": 12, + } + ), + ], +) +@pytest.mark.integration +def test_stocks_fa_cash(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/fa/cash?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"symbol": "AAPL", "limit": 10})], +) +@pytest.mark.integration +def test_stocks_fa_cash_growth(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/fa/cash_growth?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"symbol": "AAPL"})], +) +@pytest.mark.integration +def test_stocks_fa_comp(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/fa/comp?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"start_date": "2023-01-01", "end_date": "2023-06-06"})], +) +@pytest.mark.integration +def test_stocks_fa_comsplit(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/fa/comsplit?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"symbol": "AAPL"})], +) +@pytest.mark.integration +def test_stocks_fa_divs(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/fa/divs?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"symbol": "AAPL", "limit": 50})], +) +@pytest.mark.integration +def test_stocks_fa_earning(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/fa/earning?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"symbol": "AAPL"})], +) +@pytest.mark.integration +def test_stocks_fa_emp(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/fa/emp?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"symbol": "AAPL", "period": "annual", "limit": 30})], +) +@pytest.mark.integration +def test_stocks_fa_est(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/fa/est?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL", "period": "annual", "limit": 12}), + ( + { + "type": "reported", + "year": 2023, + "provider": "intrinio", + "symbol": "AAPL", + "period": "annual", + "limit": 12, + } + ), + ( + { + "company_name": "Apple Inc", + "company_name_search": "AAPL", + "sic": "35719904", + "include_sources": True, + "order": "asc", + "sort": "filing_date", + "provider": "polygon", + "symbol": "AAPL", + "period": "annual", + "limit": 12, + } + ), + ], +) +@pytest.mark.integration +def test_stocks_fa_income(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/fa/income?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"symbol": "AAPL", "limit": 10, "period": "annual"})], +) +@pytest.mark.integration +def test_stocks_fa_income_growth(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/fa/income_growth?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "symbol": "AAPL", + "transactionType": ["P-Purchase"], + "reportingCik": 1, + "companyCik": 1, + "page": 1, + } + ) + ], +) +@pytest.mark.integration +def test_stocks_fa_ins(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/fa/ins?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"symbol": "AAPL", "include_current_quarter": True, "date": "2023-01-01"})], +) +@pytest.mark.integration +def test_stocks_fa_ins_own(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/fa/ins_own?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"symbol": "AAPL", "period": "annual", "limit": 100})], +) +@pytest.mark.integration +def test_stocks_fa_metrics(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/fa/metrics?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"symbol": "AAPL"})], +) +@pytest.mark.integration +def test_stocks_fa_mgmt(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/fa/mgmt?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"symbol": "AAPL"})], +) +@pytest.mark.integration +def test_stocks_fa_overview(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/fa/overview?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"symbol": "AAPL", "date": "2023-01-01", "page": 1})], +) +@pytest.mark.integration +def test_stocks_fa_own(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/fa/own?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"symbol": "AAPL"})], +) +@pytest.mark.integration +def test_stocks_fa_pt(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/fa/pt?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"symbol": "AAPL"}), ({"with_grade": True, "provider": "fmp", "symbol": "AAPL"})], +) +@pytest.mark.integration +def test_stocks_fa_pta(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/fa/pta?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"symbol": "AAPL", "period": "annual", "limit": 12})], +) +@pytest.mark.integration +def test_stocks_fa_ratios(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/fa/ratios?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"symbol": "AAPL", "period": "annual", "structure": "flat"})], +) +@pytest.mark.integration +def test_stocks_fa_revgeo(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/fa/revgeo?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"symbol": "AAPL", "period": "annual", "structure": "flat"})], +) +@pytest.mark.integration +def test_stocks_fa_revseg(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/fa/revseg?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"symbol": "AAPL", "type": "1", "page": 1, "limit": 100})], +) +@pytest.mark.integration +def test_stocks_fa_sec(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/fa/sec?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"symbol": "AAPL"})], +) +@pytest.mark.integration +def test_stocks_fa_shrs(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/fa/shrs?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"symbol": "AAPL"})], +) +@pytest.mark.integration +def test_stocks_fa_split(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/fa/split?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"symbol": "AAPL", "year": 2023, "quarter": 1})], +) +@pytest.mark.integration +def test_stocks_fa_transcript(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/fa/transcript?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"symbol": "AAPL"})], +) +@pytest.mark.integration +def test_stocks_ca_peers(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/ca/peers?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL"}), + ({"date": "2023-01-01", "provider": "intrinio", "symbol": "AAPL"}), + ], +) +@pytest.mark.integration +def test_stocks_options_chains(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/options/chains?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL", "start_date": "2023-01-01", "end_date": "2023-06-06"}), + ( + { + "period": "daily", + "interval": "60min", + "adjusted": True, + "extended_hours": True, + "month": "2023-03", + "outputsize": "full", + "provider": "alpha_vantage", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "1m", + "provider": "cboe", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-01-02", + } + ), + ( + { + "interval": "1d", + "provider": "cboe", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "1min", + "provider": "fmp", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-01-02", + } + ), + ( + { + "interval": "1day", + "provider": "fmp", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "timezone": "UTC", + "interval_size": "30m", + "provider": "intrinio", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-01-02", + } + ), + ( + { + "timezone": "UTC", + "source": "realtime", + "interval_size": "60m", + "provider": "intrinio", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-01-02", + } + ), + ( + { + "multiplier": 1, + "timespan": "minute", + "sort": "desc", + "limit": 49999, + "adjusted": True, + "provider": "polygon", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-01-02", + } + ), + ( + { + "multiplier": 1, + "timespan": "day", + "sort": "desc", + "limit": 49999, + "adjusted": True, + "provider": "polygon", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "1m", + "period": "max", + "prepost": True, + "actions": True, + "auto_adjust": True, + "back_adjust": True, + "progress": True, + "ignore_tz": True, + "rounding": True, + "repair": True, + "keepna": True, + "group_by": "column", + "provider": "yfinance", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-01-02", + } + ), + ( + { + "interval": "1d", + "period": "max", + "prepost": True, + "actions": True, + "auto_adjust": True, + "back_adjust": True, + "progress": True, + "ignore_tz": True, + "rounding": True, + "repair": True, + "keepna": True, + "group_by": "column", + "provider": "yfinance", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_stocks_load(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/load?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbols": "AAPL,MSFT", "limit": 20}), + ( + { + "display": "full", + "date": "2023-01-01", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "updated_since": 1, + "published_since": 1, + "sort": "created", + "order": "desc", + "isin": "US0378331005", + "cusip": "037833100", + "channels": "General", + "topics": "AAPL", + "authors": "Benzinga Insights", + "content_types": "headline", + "provider": "benzinga", + "symbols": "AAPL,MSFT", + "limit": 20, + } + ), + ( + { + "published_utc": "2023-01-01", + "order": "desc", + "provider": "polygon", + "symbols": "AAPL", + "limit": 20, + } + ), + ], +) +@pytest.mark.integration +def test_stocks_news(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/news?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"symbol": "AAPL", "limit": 100})], +) +@pytest.mark.integration +def test_stocks_multiples(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/multiples?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"query": "AAPl", "ticker": True})], +) +@pytest.mark.integration +def test_stocks_search(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/search?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL"}), + ({"source": "iex", "provider": "intrinio", "symbol": "AAPL"}), + ], +) +@pytest.mark.integration +def test_stocks_quote(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/quote?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"symbol": "AAPL"})], +) +@pytest.mark.integration +def test_stocks_info(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/stocks/info?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 diff --git a/openbb_sdk/extensions/stocks/integration/test_stocks_python.py b/openbb_sdk/extensions/stocks/integration/test_stocks_python.py new file mode 100644 index 00000000000..8c241f2b9a8 --- /dev/null +++ b/openbb_sdk/extensions/stocks/integration/test_stocks_python.py @@ -0,0 +1,779 @@ +"""Test stocks extension.""" +import pytest +from openbb import obb +from openbb_core.app.model.obbject import OBBject + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL", "period": "annual", "limit": 12}), + ( + { + "type": "reported", + "year": 2023, + "provider": "intrinio", + "symbol": "AAPL", + "period": "annual", + "limit": 12, + } + ), + ( + { + "company_name": "Apple Inc", + "company_name_search": "AAPL", + "sic": "35719904", + "include_sources": True, + "order": "asc", + "sort": "filing_date", + "provider": "polygon", + "symbol": "AAPL", + "period": "annual", + "limit": 12, + } + ), + ], +) +@pytest.mark.integration +def test_stocks_fa_balance(params): + result = obb.stocks.fa.balance(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL", "limit": 10}), + ], +) +@pytest.mark.integration +def test_stocks_fa_balance_growth(params): + result = obb.stocks.fa.balance_growth(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_stocks_fa_cal(params): + result = obb.stocks.fa.cal(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL", "period": "annual", "limit": 12}), + ( + { + "type": "reported", + "year": 2023, + "provider": "intrinio", + "symbol": "AAPL", + "period": "annual", + "limit": 12, + } + ), + ( + { + "company_name": "Apple Inc", + "company_name_search": "AAPL", + "sic": "35719904", + "include_sources": True, + "order": "asc", + "sort": "filing_date", + "provider": "polygon", + "symbol": "AAPL", + "period": "annual", + "limit": 12, + } + ), + ], +) +@pytest.mark.integration +def test_stocks_fa_cash(params): + result = obb.stocks.fa.cash(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL", "limit": 10}), + ], +) +@pytest.mark.integration +def test_stocks_fa_cash_growth(params): + result = obb.stocks.fa.cash_growth(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL"}), + ], +) +@pytest.mark.integration +def test_stocks_fa_comp(params): + result = obb.stocks.fa.comp(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_stocks_fa_comsplit(params): + result = obb.stocks.fa.comsplit(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL"}), + ], +) +@pytest.mark.integration +def test_stocks_fa_divs(params): + result = obb.stocks.fa.divs(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL", "limit": 50}), + ], +) +@pytest.mark.integration +def test_stocks_fa_earning(params): + result = obb.stocks.fa.earning(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL"}), + ], +) +@pytest.mark.integration +def test_stocks_fa_emp(params): + result = obb.stocks.fa.emp(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL", "period": "annual", "limit": 30}), + ], +) +@pytest.mark.integration +def test_stocks_fa_est(params): + result = obb.stocks.fa.est(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL", "period": "annual", "limit": 12}), + ( + { + "type": "reported", + "year": 2023, + "provider": "intrinio", + "symbol": "AAPL", + "period": "annual", + "limit": 12, + } + ), + ( + { + "company_name": "Apple Inc", + "company_name_search": "AAPL", + "sic": "35719904", + "include_sources": True, + "order": "asc", + "sort": "filing_date", + "provider": "polygon", + "symbol": "AAPL", + "period": "annual", + "limit": 12, + } + ), + ], +) +@pytest.mark.integration +def test_stocks_fa_income(params): + result = obb.stocks.fa.income(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL", "limit": 10, "period": "annual"}), + ], +) +@pytest.mark.integration +def test_stocks_fa_income_growth(params): + result = obb.stocks.fa.income_growth(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "symbol": "AAPL", + "transactionType": ["P-Purchase"], + "reportingCik": 1, + "companyCik": 1, + "page": 1, + } + ), + ], +) +@pytest.mark.integration +def test_stocks_fa_ins(params): + result = obb.stocks.fa.ins(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "symbol": "AAPL", + "include_current_quarter": True, + "date": "2023-01-01", + } + ), + ], +) +@pytest.mark.integration +def test_stocks_fa_ins_own(params): + result = obb.stocks.fa.ins_own(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL", "period": "annual", "limit": 100}), + ], +) +@pytest.mark.integration +def test_stocks_fa_metrics(params): + result = obb.stocks.fa.metrics(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL"}), + ], +) +@pytest.mark.integration +def test_stocks_fa_mgmt(params): + result = obb.stocks.fa.mgmt(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL"}), + ], +) +@pytest.mark.integration +def test_stocks_fa_overview(params): + result = obb.stocks.fa.overview(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL", "date": "2023-01-01", "page": 1}), + ], +) +@pytest.mark.integration +def test_stocks_fa_own(params): + result = obb.stocks.fa.own(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL"}), + ], +) +@pytest.mark.integration +def test_stocks_fa_pt(params): + result = obb.stocks.fa.pt(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL"}), + ({"with_grade": True, "provider": "fmp", "symbol": "AAPL"}), + ], +) +@pytest.mark.integration +def test_stocks_fa_pta(params): + result = obb.stocks.fa.pta(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL", "period": "annual", "limit": 12}), + ], +) +@pytest.mark.integration +def test_stocks_fa_ratios(params): + result = obb.stocks.fa.ratios(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL", "period": "annual", "structure": "flat"}), + ], +) +@pytest.mark.integration +def test_stocks_fa_revgeo(params): + result = obb.stocks.fa.revgeo(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL", "period": "annual", "structure": "flat"}), + ], +) +@pytest.mark.integration +def test_stocks_fa_revseg(params): + result = obb.stocks.fa.revseg(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL", "type": "1", "page": 1, "limit": 100}), + ], +) +@pytest.mark.integration +def test_stocks_fa_sec(params): + result = obb.stocks.fa.sec(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL"}), + ], +) +@pytest.mark.integration +def test_stocks_fa_shrs(params): + result = obb.stocks.fa.shrs(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL"}), + ], +) +@pytest.mark.integration +def test_stocks_fa_split(params): + result = obb.stocks.fa.split(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL", "year": 2023, "quarter": 1}), + ], +) +@pytest.mark.integration +def test_stocks_fa_transcript(params): + result = obb.stocks.fa.transcript(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL"}), + ], +) +@pytest.mark.integration +def test_stocks_ca_peers(params): + result = obb.stocks.ca.peers(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL"}), + ({"date": "2023-01-01", "provider": "intrinio", "symbol": "AAPL"}), + ], +) +@pytest.mark.integration +def test_stocks_options_chains(params): + result = obb.stocks.options.chains(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "period": "daily", + "interval": "60min", + "adjusted": True, + "extended_hours": True, + "month": "2023-03", + "outputsize": "full", + "provider": "alpha_vantage", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "1m", + "provider": "cboe", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-01-02", + } + ), + ( + { + "interval": "1d", + "provider": "cboe", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "1min", + "provider": "fmp", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-01-02", + } + ), + ( + { + "interval": "1day", + "provider": "fmp", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "timezone": "UTC", + "interval_size": "30m", + "provider": "intrinio", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-01-02", + } + ), + ( + { + "timezone": "UTC", + "source": "realtime", + "interval_size": "60m", + "provider": "intrinio", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-01-02", + } + ), + ( + { + "multiplier": 1, + "timespan": "minute", + "sort": "desc", + "limit": 49999, + "adjusted": True, + "provider": "polygon", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-01-02", + } + ), + ( + { + "multiplier": 1, + "timespan": "day", + "sort": "desc", + "limit": 49999, + "adjusted": True, + "provider": "polygon", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "1m", + "period": "max", + "prepost": True, + "actions": True, + "auto_adjust": True, + "back_adjust": True, + "progress": True, + "ignore_tz": True, + "rounding": True, + "repair": True, + "keepna": True, + "group_by": "column", + "provider": "yfinance", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-01-02", + } + ), + ( + { + "interval": "1d", + "period": "max", + "prepost": True, + "actions": True, + "auto_adjust": True, + "back_adjust": True, + "progress": True, + "ignore_tz": True, + "rounding": True, + "repair": True, + "keepna": True, + "group_by": "column", + "provider": "yfinance", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_stocks_load(params): + result = obb.stocks.load(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbols": "AAPL,MSFT", "limit": 20}), + ( + { + "display": "full", + "date": "2023-01-01", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "updated_since": 1, + "published_since": 1, + "sort": "created", + "order": "desc", + "isin": "US0378331005", + "cusip": "037833100", + "channels": "General", + "topics": "AAPL", + "authors": "Benzinga Insights", + "content_types": "headline", + "provider": "benzinga", + "symbols": "AAPL,MSFT", + "limit": 20, + } + ), + ( + { + "published_utc": "2023-01-01", + "order": "desc", + "provider": "polygon", + "symbols": "AAPL", + "limit": 20, + } + ), + ], +) +@pytest.mark.integration +def test_stocks_news(params): + result = obb.stocks.news(**params) + assert result + assert isinstance(result, OBBject) + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL", "limit": 100}), + ], +) +@pytest.mark.integration +def test_stocks_multiples(params): + result = obb.stocks.multiples(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"query": "AAPL", "ticker": True}), + ], +) +@pytest.mark.integration +def test_stocks_search(params): + result = obb.stocks.search(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL"}), + ({"source": "iex", "provider": "intrinio", "symbol": "AAPL"}), + ], +) +@pytest.mark.integration +def test_stocks_quote(params): + result = obb.stocks.quote(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"symbol": "AAPL"}), + ], +) +@pytest.mark.integration +def test_stocks_info(params): + result = obb.stocks.info(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 diff --git a/openbb_sdk/extensions/ta/integration/test_ta_api.py b/openbb_sdk/extensions/ta/integration/test_ta_api.py new file mode 100644 index 00000000000..a94e747ae24 --- /dev/null +++ b/openbb_sdk/extensions/ta/integration/test_ta_api.py @@ -0,0 +1,507 @@ +import pytest +import requests +from openbb_provider.utils.helpers import get_querystring + + +def get_token(): + return requests.post( + "http://0.0.0.0:8000/api/v1/account/token", + data={"username": "openbb", "password": "openbb"}, + timeout=5, + ) + + +@pytest.fixture(scope="session") +def headers(): + access_token = get_token().json()["access_token"] + return {"Authorization": f"Bearer {access_token}"} + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "data": "", + "index": "", + "length": "", + "mamode": "", + "drift": "", + "offset": "", + } + ) + ], +) +@pytest.mark.integration +def test_ta_atr(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/ta/atr?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "data": "", + "index": "", + "close_column": "", + "period": "", + "start_date": "", + "end_date": "", + } + ) + ], +) +@pytest.mark.integration +def test_ta_fib(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/ta/fib?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"data": "", "index": "", "offset": ""})], +) +@pytest.mark.integration +def test_ta_obv(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/ta/obv?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"data": "", "index": "", "length": "", "signal": ""})], +) +@pytest.mark.integration +def test_ta_fisher(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/ta/fisher?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"data": "", "index": "", "fast": "", "slow": "", "offset": ""})], +) +@pytest.mark.integration +def test_ta_adosc(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/ta/adosc?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "data": "", + "target": "", + "index": "", + "length": "", + "std": "", + "mamode": "", + "offset": "", + } + ) + ], +) +@pytest.mark.integration +def test_ta_bbands(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/ta/bbands?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"data": "", "target": "", "index": "", "length": "", "offset": ""})], +) +@pytest.mark.integration +def test_ta_zlma(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/ta/zlma?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"data": "", "index": "", "length": "", "scalar": ""})], +) +@pytest.mark.integration +def test_ta_aroon(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/ta/aroon?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"data": "", "target": "", "index": "", "length": "", "offset": ""})], +) +@pytest.mark.integration +def test_ta_sma(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/ta/sma?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "data": "", + "index": "", + "target": "", + "show_all": "", + "asint": "", + "offset": "", + } + ) + ], +) +@pytest.mark.integration +def test_ta_demark(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/ta/demark?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"data": "", "index": "", "anchor": "", "offset": ""})], +) +@pytest.mark.integration +def test_ta_vwap(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/ta/vwap?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"data": "", "target": "", "index": "", "fast": "", "slow": "", "signal": ""})], +) +@pytest.mark.integration +def test_ta_macd(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/ta/macd?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"data": "", "target": "", "index": "", "length": "", "offset": ""})], +) +@pytest.mark.integration +def test_ta_hma(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/ta/hma?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"data": "", "index": "", "lower_length": "", "upper_length": "", "offset": ""})], +) +@pytest.mark.integration +def test_ta_donchian(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/ta/donchian?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "data": "", + "index": "", + "conversion": "", + "base": "", + "lagging": "", + "offset": "", + "lookahead": "", + } + ) + ], +) +@pytest.mark.integration +def test_ta_ichimoku(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/ta/ichimoku?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"data": "", "index": "", "target": "", "period": ""})], +) +@pytest.mark.integration +def test_ta_clenow(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/ta/clenow?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"data": "", "index": "", "offset": ""})], +) +@pytest.mark.integration +def test_ta_ad(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/ta/ad?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"data": "", "index": "", "length": "", "scalar": "", "drift": ""})], +) +@pytest.mark.integration +def test_ta_adx(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/ta/adx?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"data": "", "target": "", "index": "", "length": "", "offset": ""})], +) +@pytest.mark.integration +def test_ta_wma(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/ta/wma?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"data": "", "index": "", "length": "", "scalar": ""})], +) +@pytest.mark.integration +def test_ta_cci(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/ta/cci?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "data": "", + "target": "", + "index": "", + "length": "", + "scalar": "", + "drift": "", + } + ) + ], +) +@pytest.mark.integration +def test_ta_rsi(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/ta/rsi?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "data": "", + "index": "", + "fast_k_period": "", + "slow_d_period": "", + "slow_k_period": "", + } + ) + ], +) +@pytest.mark.integration +def test_ta_stoch(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/ta/stoch?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "data": "", + "index": "", + "length": "", + "scalar": "", + "mamode": "", + "offset": "", + } + ) + ], +) +@pytest.mark.integration +def test_ta_kc(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/ta/kc?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"data": "", "index": "", "length": ""})], +) +@pytest.mark.integration +def test_ta_cg(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/ta/cg?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "data": "", + "index": "", + "lower_q": "", + "upper_q": "", + "model": "", + "is_crypto": "", + } + ) + ], +) +@pytest.mark.integration +def test_ta_cones(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/ta/cones?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [({"data": "", "target": "", "index": "", "length": "", "offset": ""})], +) +@pytest.mark.integration +def test_ta_ema(params, headers): + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/ta/ema?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 diff --git a/openbb_sdk/extensions/tests/utils/integration_test_generator.py b/openbb_sdk/extensions/tests/utils/integration_test_generator.py new file mode 100644 index 00000000000..a124fc84248 --- /dev/null +++ b/openbb_sdk/extensions/tests/utils/integration_test_generator.py @@ -0,0 +1,184 @@ +"""Integration test generator.""" +from pathlib import Path, PosixPath +from typing import Any, Dict, List, Literal, get_origin + +from pydantic.fields import ModelField +from sdk.core.openbb_core.app.provider_interface import ProviderInterface +from sdk.core.openbb_core.app.router import CommandMap + +cm = CommandMap(coverage_sep=".") + + +def find_extensions(): + """Find extensions.""" + filter_ext = ["tests", "ta", "qa", "econometrics", "charting"] + extensions = [x for x in Path("openbb_sdk/extensions").iterdir() if x.is_dir()] + extensions = [x for x in extensions if x.name not in filter_ext] + return extensions + + +def create_integration_test_files(extensions: List[PosixPath]) -> None: + """Create integration test files for the python interface.""" + for extension in extensions: + extension_name = extension.name + test_file_name = f"test_{extension_name}_python.py" + test_file = extension / "integration" / test_file_name + if not test_file.exists(): + with open(test_file, "w") as f: + f.write( + f'''"""Test {extension_name} extension.""" +import pytest +from openbb import obb +from openbb_core.app.model.obbject import OBBject + ''' + ) + + +def get_model_params(param_fields: Dict[str, ModelField]) -> Dict[str, Any]: + """Get the test params for the fetcher based on the required standard params.""" + test_params: Dict[str, Any] = {} + for field_name, field in param_fields.items(): + if field.default: + test_params[field_name] = field.default + elif not field.default: + example_dict = { + "symbol": "AAPL", + "symbols": "AAPL,MSFT", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "country": "Portugal", + "date": "2023-01-01", + "countries": ["portugal", "spain"], + } + if field_name in example_dict: + test_params[field_name] = example_dict[field_name] + elif field.type_ == str: + test_params[field_name] = "TEST_STRING" + elif field.type_ == int: + test_params[field_name] = 1 + elif field.type_ == float: + test_params[field_name] = 1.0 + elif field.type_ == bool: + test_params[field_name] = True + elif get_origin(field.type_) is Literal: + test_params[field_name] = field.type_.__args__[0] + + return test_params + + +def get_test_params( + model_name: str, provider_interface_map: Dict[str, Any] +) -> List[Dict[str, Any]]: + """Get the test params for the integration test.""" + test_params_list: List[Dict[str, Any]] = [] + available_providers = provider_interface_map[model_name].keys() + standard_params = get_model_params( + param_fields=provider_interface_map[model_name]["openbb"]["QueryParams"][ + "fields" + ] + ) + + test_params_list.append(standard_params) + + for provider in available_providers: + if provider != "openbb": + test_params: Dict[str, Any] = get_model_params( + param_fields=provider_interface_map[model_name][provider][ + "QueryParams" + ]["fields"] + ) + if test_params: + test_params["provider"] = provider + test_params.update(standard_params) + test_params_list.append(test_params) + + return test_params_list + + +def add_test_commands_to_file( # pylint: disable=W0102 + extensions: List[PosixPath], + commands_model: Dict[str, str] = cm.commands_model, +) -> None: + """Add test commands to file.""" + provider_interface = ProviderInterface() + provider_interface_map = provider_interface.map + + template = """\n\n@pytest.mark.parametrize( + "params", + [ + {params} + ], +) +@pytest.mark.integration +def test_{test_name}(**params): + result = obb.{command_name}(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 +""" + for extension in extensions: + extension_name = extension.name + test_file_name = f"test_{extension_name}_python.py" + test_file = extension / "integration" / test_file_name + with open(test_file, "a") as f: + for command in commands_model: + if extension_name in command and command.startswith( + f".{extension_name}." + ): + extension_name = command.split(".")[1] + + if len(command.split(".")) > 3: + test_name = ( + extension_name + + "_" + + command.split(".")[2] + + "_" + + command.split(".")[3] + ) + elif len(command.split(".")) == 3: + test_name = extension_name + "_" + command.split(".")[2] + + command_name = command.split(".")[-1] + if "_" in command_name: + full_command_name = test_name.replace("_", ".", 2) + # The code below double checks if the full command name is correct. + # This eliminates edge cases. + fix_full_command_name = full_command_name.split(".") + for i, part in enumerate(fix_full_command_name): + if part == command_name.split("_")[0]: + fix_full_command_name[i] = command_name + fix_full_command_name.pop() + + full_command_name = ".".join(fix_full_command_name) + + else: + full_command_name = test_name.replace("_", ".") + + test_params_list = get_test_params( + model_name=commands_model[command], + provider_interface_map=provider_interface_map, + ) + + params = "" + for test_params in test_params_list: + params += f"({test_params}),\n" + + read_file = open(test_file) # noqa + if full_command_name not in read_file.read(): + f.write( + template.format( + test_name=test_name, + command_name=full_command_name, + params=params, + ) + ) + + +def write_integration_test() -> None: + """Write integration test.""" + extensions = find_extensions() + create_integration_test_files(extensions) + add_test_commands_to_file(extensions) + + +write_integration_test() diff --git a/openbb_sdk/extensions/tests/utils/integration_tests_api_generator.py b/openbb_sdk/extensions/tests/utils/integration_tests_api_generator.py new file mode 100644 index 00000000000..8c2de7d9aff --- /dev/null +++ b/openbb_sdk/extensions/tests/utils/integration_tests_api_generator.py @@ -0,0 +1,135 @@ +import os +from typing import Dict, List, Type, get_type_hints + +import requests +from extensions.tests.utils.integration_test_generator import get_test_params +from openbb_core.app.provider_interface import ProviderInterface +from openbb_core.app.router import CommandMap + + +def get_http_method(api_paths: Dict[str, dict], route: str): + route_info = api_paths.get(route, None) + if not route_info: + return route_info + return list(route_info.keys())[0] + + +def get_post_flat_params(hints: Dict[str, Type]): + return list(hints.keys()) + + +def write_init_test_template(path: str): + template = """ +import pytest +import requests +from openbb_provider.utils.helpers import get_querystring + + +def get_token(): + return requests.post( + "http://0.0.0.0:8000/api/v1/account/token", + data={"username": "openbb", "password": "openbb"}, + timeout=5, + ) + + +@pytest.fixture(scope="session") +def headers(): + access_token = get_token().json()["access_token"] + return {"Authorization": f"Bearer {access_token}"} +""" + + with open(path, "w") as f: + f.write(template) + + +def write_test_w_template(params_list: List[Dict[str, str]], route: str, path: str): + params_str = ",\n".join([f"({params})" for params in params_list]) + + template = f""" +@pytest.mark.parametrize( + "params", + [{params_str}], +) +@pytest.mark.integration +def test_{route.replace("/", "_")[1:]}(params, headers): + params = {{p: v for p, v in params.items() if v}} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1{route}?{{query_str}}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 +""" + + with open(path, "a") as f: + f.write(template) + + +def test_exists(route: str, path: str): + with open(path) as f: + return route.replace("/", "_")[1:] in f.read() + + +def write_integration_tests( + command_map: CommandMap, + provider_interface: ProviderInterface, + api_paths: Dict[str, dict], +) -> List[str]: + commands_not_found = [] + + commandmap_map = command_map.map + commandmap_models = command_map.commands_model + provider_interface_map = provider_interface.map + + for route in commandmap_map: + http_method = get_http_method(api_paths, f"/api/v1{route}") + + menu = route.split("/")[1] + path = os.path.join( + "openbb_sdk", "extensions", menu, "integration", f"test_{menu}_api.py" + ) + if not os.path.exists(path): + write_init_test_template(path=path) + + if not http_method: + commands_not_found.append(route) + else: + hints = get_type_hints(commandmap_map[route]) + hints.pop("cc", None) + hints.pop("return", None) + + params_list = ( + [{k: "" for k in get_post_flat_params(hints)}] + if http_method == "post" + else get_test_params( + model_name=commandmap_models[route], + provider_interface_map=provider_interface_map, + ) + ) + + if not test_exists(route=route, path=path): + write_test_w_template(params_list=params_list, route=route, path=path) + + return commands_not_found + + +if __name__ == "__main__": + r = requests.get("http://0.0.0.0:8000/openapi.json", timeout=5).json() + + if not r: + raise Exception("Could not get openapi.json") + + command_map = CommandMap() + provider_interface = ProviderInterface() + + commands_not_found_in_openapi = write_integration_tests( + command_map=command_map, + provider_interface=provider_interface, + api_paths=r["paths"], + ) + + if commands_not_found_in_openapi: + print( # noqa + f"Commands not found in openapi.json: {commands_not_found_in_openapi}" + ) diff --git a/openbb_sdk/openbb/package/__extensions__.py b/openbb_sdk/openbb/package/__extensions__.py index f55f965b650..198cf925498 100644 --- a/openbb_sdk/openbb/package/__extensions__.py +++ b/openbb_sdk/openbb/package/__extensions__.py @@ -9,25 +9,38 @@ class Extensions(Container): """ Routers: /crypto + /econometrics /economy /fixedincome /forex + /futures /news + /qa /stocks + /ta Extensions: - crypto@0.1.0a2 + - econometrics@0.1.0a2 - economy@0.1.0a2 - fixedincome@0.1.0a2 - forex@0.1.0a2 + - futures@0.1.0a2 - news@0.1.0a2 + - openbb_charting@0.1.0a2 + - qa@0.1.0a2 - stocks@0.1.0a2 + - ta@0.1.0a2 + - alpha_vantage@0.1.0a2 - benzinga@0.1.0a2 + - cboe@0.1.0a2 - fmp@0.1.0a2 - fred@0.1.0a2 - intrinio@0.1.0a2 - - polygon@0.1.0a2 """ + - polygon@0.1.0a2 + - quandl@0.1.0a2 + - yfinance@0.1.0a2 """ # fmt: on def __repr__(self) -> str: return self.__doc__ or "" @@ -38,6 +51,12 @@ Extensions: return crypto.ROUTER_crypto(command_runner=self._command_runner) + @property + def econometrics(self): # route = "/econometrics" + from . import econometrics + + return econometrics.ROUTER_econometrics(command_runner=self._command_runner) + @property def economy(self): # route = "/economy" from . import economy @@ -56,14 +75,32 @@ Extensions: return forex.ROUTER_forex(command_runner=self._command_runner) + @property + def futures(self): # route = "/futures" + from . import futures + + return futures.ROUTER_futures(command_runner=self._command_runner) + @property def news(self): # route = "/news" from . import news return news.ROUTER_news(command_runner=self._command_runner) + @property + def qa(self): # route = "/qa" + from . import qa + + return qa.ROUTER_qa(command_runner=self._command_runner) + @property def stocks(self): # route = "/stocks" from . import stocks return stocks.ROUTER_stocks(command_runner=self._command_runner) + + @property + def ta(self): # route = "/ta" + from . import ta + + return ta.ROUTER_ta(command_runner=self._command_runner) diff --git a/openbb_sdk/openbb/package/crypto.py b/openbb_sdk/openbb/package/crypto.py index 71118fae87f..15eabe985ba 100644 --- a/openbb_sdk/openbb/package/crypto.py +++ b/openbb_sdk/openbb/package/crypto.py @@ -38,7 +38,7 @@ class ROUTER_crypto(Container): description="End date of the data, in YYYY-MM-DD format." ), ] = None, - provider: Union[Literal["fmp", "polygon"], None] = None, + provider: Union[Literal["fmp", "polygon", "yfinance"], None] = None, **kwargs ) -> OBBject[List]: """Crypto Historical Price. @@ -51,14 +51,14 @@ class ROUTER_crypto(Container): Start date of the data, in YYYY-MM-DD format. end_date : Union[datetime.date, None, str] End date of the data, in YYYY-MM-DD format. - provider : Union[Literal['fmp', 'polygon'], None] + provider : Union[Literal['fmp', 'polygon', 'yfinance'], None] The provider to use for the query, by default None. If None, the provider specified in defaults is selected or 'fmp' if there is no default. timeseries : Union[pydantic.types.NonNegativeInt, None] Number of days to look back. (provider: fmp) - interval : Literal['1min', '5min', '15min', '30min', '1hour', '4hour', '1day'] - Data granularity. (provider: fmp) + interval : Union[Literal['1min', '5min', '15min', '30min', '1hour', '4hour', '1day'], Literal['1m', '2m', '5m', '15m', '30m', '60m', '90m', '1h', '1d', '5d', '1wk', '1mo', '3mo']] + Data granularity. (provider: fmp, yfinance) multiplier : PositiveInt Multiplier of the timespan. (provider: polygon) timespan : Literal['minute', 'hour', 'day', 'week', 'month', 'quarter', 'year'] @@ -69,13 +69,15 @@ class ROUTER_crypto(Container): The number of data entries to return. (provider: polygon) adjusted : bool Whether the data is adjusted. (provider: polygon) + period : Literal['1d', '5d', '1mo', '3mo', '6mo', '1y', '2y', '5y', '10y', 'ytd', 'max'] + Period of the data to return. (provider: yfinance) Returns ------- OBBject results : List[CryptoHistorical] Serializable results. - provider : Union[Literal['fmp', 'polygon'], None] + provider : Union[Literal['fmp', 'polygon', 'yfinance'], None] Provider name. warnings : Optional[List[Warning_]] List of warnings. diff --git a/openbb_sdk/openbb/package/econometrics.py b/openbb_sdk/openbb/package/econometrics.py new file mode 100644 index 00000000000..65f6a887123 --- /dev/null +++ b/openbb_sdk/openbb/package/econometrics.py @@ -0,0 +1,519 @@ +### THIS FILE IS AUTO-GENERATED. DO NOT EDIT. ### + +from typing import Dict, List, Literal, Union + +import openbb_provider +import openbb_provider.abstract.data +import pandas +import pydantic +import pydantic.types +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.static.container import Container +from openbb_core.app.static.filters import filter_inputs +from pydantic import validate_arguments + + +class ROUTER_econometrics(Container): + """/econometrics + bgot + coint + corr + dwat + granger + ols + ols_summary + panelbols + panelfd + panelfmac + panelols + panelpols + panelre + unitroot + """ + + def __repr__(self) -> str: + return self.__doc__ or "" + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def bgot( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + y_column: str, + x_columns: List[str], + lags: pydantic.types.PositiveInt = 1, + ) -> OBBject[openbb_provider.abstract.data.Data]: + """Perform Breusch-Godfrey Lagrange Multiplier tests for residual autocorrelation. + + Parameters + ---------- + data: List[Data] + Input dataset. + y_column: str + Target column. + x_columns: str + List of columns to use as exogenous variables. + lags: PositiveInt + Number of lags to use in the test. + Returns + ------- + OBBject[Data] + OBBject with the results being the score from the test. + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + y_column=y_column, + x_columns=x_columns, + lags=lags, + ) + + return self._command_runner.run( + "/econometrics/bgot", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def coint( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + columns: List[str], + ) -> OBBject[openbb_provider.abstract.data.Data]: + """Show co-integration between two timeseries using the two step Engle-Granger test. + + Parameters + ---------- + data: List[Data] + Input dataset. + columns: List[str] + Data columns to check cointegration + maxlag: PositiveInt + Number of lags to use in the test. + Returns + ------- + OBBject[Data] + OBBject with the results being the score from the test. + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + columns=columns, + ) + + return self._command_runner.run( + "/econometrics/coint", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def corr( + self, data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame] + ) -> OBBject[List]: + """Get the corrlelation matrix of an input dataset. + + Parameters + ---------- + data : List[Data] + Input dataset. + + Returns + ------- + OBBject[List[Data]] + Correlation matrix. + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + ) + + return self._command_runner.run( + "/econometrics/corr", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def dwat( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + y_column: str, + x_columns: List[str], + ) -> OBBject[Dict]: + """Perform Durbin-Watson test for autocorrelation + + Parameters + ---------- + data: List[Data] + Input dataset. + y_column: str + Target column. + x_columns: str + List of columns to use as exogenous variables. + + Returns + ------- + OBBject[Data] + OBBject with the results being the score from the test. + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + y_column=y_column, + x_columns=x_columns, + ) + + return self._command_runner.run( + "/econometrics/dwat", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def granger( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + y_column: str, + x_column: str, + lag: pydantic.types.PositiveInt = 3, + ) -> OBBject[openbb_provider.abstract.data.Data]: + """Perform Granger causality test to determine if X "causes" y. + + Parameters + ---------- + data: List[Data] + Input dataset. + y_column: str + Target column. + x_column: str + Columns to use as exogenous variables. + lag: PositiveInt + Number of lags to use in the test. + Returns + ------- + OBBject[Data] + OBBject with the results being the score from the test. + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + y_column=y_column, + x_column=x_column, + lag=lag, + ) + + return self._command_runner.run( + "/econometrics/granger", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def ols( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + y_column: str, + x_columns: List[str], + ) -> OBBject[Dict]: + """Perform OLS regression. This returns the model and results objects from statsmodels. + + Parameters + ---------- + data: List[Data] + Input dataset. + y_column: str + Target column. + x_columns: str + List of columns to use as exogenous variables. + + Returns + ------- + OBBject[Dict] + OBBject with the results being model and results objects. + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + y_column=y_column, + x_columns=x_columns, + ) + + return self._command_runner.run( + "/econometrics/ols", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def ols_summary( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + y_column: str, + x_columns: List[str], + ) -> OBBject[openbb_provider.abstract.data.Data]: + """Perform OLS regression. This returns the summary object from statsmodels. + + Parameters + ---------- + data: List[Data] + Input dataset. + y_column: str + Target column. + x_columns: str + List of columns to use as exogenous variables. + + Returns + ------- + OBBject[Dict] + OBBject with the results being summary object. + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + y_column=y_column, + x_columns=x_columns, + ) + + return self._command_runner.run( + "/econometrics/ols_summary", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def panelbols( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + y_column: str, + x_columns: List[str], + ) -> OBBject[Dict]: + """Perform a Between estimator regression on panel data + + Parameters + ---------- + data: List[Data] + Input dataset. + y_column: str + Target column. + x_columns: str + List of columns to use as exogenous variables. + + Returns + ------- + OBBject[Dict] + OBBject with the fit model returned + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + y_column=y_column, + x_columns=x_columns, + ) + + return self._command_runner.run( + "/econometrics/panelbols", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def panelfd( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + y_column: str, + x_columns: List[str], + ) -> OBBject[Dict]: + """Perform a first-difference estimate for panel data + + Parameters + ---------- + data: List[Data] + Input dataset. + y_column: str + Target column. + x_columns: str + List of columns to use as exogenous variables. + + Returns + ------- + OBBject[Dict] + OBBject with the fit model returned + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + y_column=y_column, + x_columns=x_columns, + ) + + return self._command_runner.run( + "/econometrics/panelfd", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def panelfmac( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + y_column: str, + x_columns: List[str], + ) -> OBBject[Dict]: + """Fama-MacBeth estimator for panel data + + Parameters + ---------- + data: List[Data] + Input dataset. + y_column: str + Target column. + x_columns: str + List of columns to use as exogenous variables. + + Returns + ------- + OBBject[Dict] + OBBject with the fit model returned + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + y_column=y_column, + x_columns=x_columns, + ) + + return self._command_runner.run( + "/econometrics/panelfmac", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def panelols( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + y_column: str, + x_columns: List[str], + ) -> OBBject[Dict]: + """One- and two-way fixed effects estimator for panel data + + Parameters + ---------- + data: List[Data] + Input dataset. + y_column: str + Target column. + x_columns: str + List of columns to use as exogenous variables. + + Returns + ------- + OBBject[Dict] + OBBject with the fit model returned + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + y_column=y_column, + x_columns=x_columns, + ) + + return self._command_runner.run( + "/econometrics/panelols", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def panelpols( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + y_column: str, + x_columns: List[str], + ) -> OBBject[Dict]: + """Perform a Pooled coefficvient estimator regression on panel data + + Parameters + ---------- + data: List[Data] + Input dataset. + y_column: str + Target column. + x_columns: str + List of columns to use as exogenous variables. + + Returns + ------- + OBBject[Dict] + OBBject with the fit model returned + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + y_column=y_column, + x_columns=x_columns, + ) + + return self._command_runner.run( + "/econometrics/panelpols", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def panelre( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + y_column: str, + x_columns: List[str], + ) -> OBBject[Dict]: + """Perform One-way Random Effects model for panel data + + Parameters + ---------- + data: List[Data] + Input dataset. + y_column: str + Target column. + x_columns: str + List of columns to use as exogenous variables. + + Returns + ------- + OBBject[Dict] + OBBject with the fit model returned + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + y_column=y_column, + x_columns=x_columns, + ) + + return self._command_runner.run( + "/econometrics/panelre", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def unitroot( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + column: str, + regression: Literal["c", "ct", "ctt"] = "c", + ) -> OBBject[openbb_provider.abstract.data.Data]: + """Perform Augmented Dickey-Fuller unit root test. + + Parameters + ---------- + data: List[Data] + Input dataset. + column: str + Data columns to check unit root + regression: str + Regression type to use in the test. Either "c" for constant only, "ct" for constant and trend, or "ctt" for + constant, trend, and trend-squared. + Returns + ------- + OBBject[Data] + OBBject with the results being the score from the test. + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + column=column, + regression=regression, + ) + + return self._command_runner.run( + "/econometrics/unitroot", + **inputs, + ) diff --git a/openbb_sdk/openbb/package/economy.py b/openbb_sdk/openbb/package/economy.py index 290573d4918..c9327fe489d 100644 --- a/openbb_sdk/openbb/package/economy.py +++ b/openbb_sdk/openbb/package/economy.py @@ -15,9 +15,16 @@ class ROUTER_economy(Container): """/economy available_indices const + cot + cot_search cpi + european_index + european_index_constituents index + index_search + index_snapshots risk + sp500_multiples """ def __repr__(self) -> str: @@ -25,23 +32,25 @@ class ROUTER_economy(Container): @validate_arguments def available_indices( - self, provider: Union[Literal["fmp"], None] = None, **kwargs + self, provider: Union[Literal["cboe", "fmp", "yfinance"], None] = None, **kwargs ) -> OBBject[List]: """Lists of available indices from a provider. Parameters ---------- - provider : Union[Literal['fmp'], None] + provider : Union[Literal['cboe', 'fmp', 'yfinance'], None] The provider to use for the query, by default None. - If None, the provider specified in defaults is selected or 'fmp' if there is + If None, the provider specified in defaults is selected or 'cboe' if there is no default. + europe : bool + Filter for European indices. False for US indices. (provider: cboe) Returns ------- OBBject results : List[AvailableIndices] Serializable results. - provider : Union[Literal['fmp'], None] + provider : Union[Literal['cboe', 'fmp', 'yfinance'], None] Provider name. warnings : Optional[List[Warning_]] List of warnings. @@ -56,11 +65,34 @@ class ROUTER_economy(Container): Name of the index. currency : Optional[str] Currency the index is traded in. + isin : Optional[str] + ISIN code for the index. Valid only for European indices. (provider: cboe) + region : Optional[str] + Region for the index. Valid only for European indices (provider: cboe) + symbol : Optional[str] + Symbol for the index. (provider: cboe, yfinance) + description : Optional[str] + Description for the index. Valid only for US indices. (provider: cboe) + data_delay : Optional[int] + Data delay for the index. Valid only for US indices. (provider: cboe) + open_time : Optional[time] + Opening time for the index. Valid only for US indices. (provider: cboe) + close_time : Optional[time] + Closing time for the index. Valid only for US indices. (provider: cboe) + time_zone : Optional[str] + Time zone for the index. Valid only for US indices. (provider: cboe) + tick_days : Optional[str] + The trading days for the index. Valid only for US indices. (provider: cboe) + tick_frequency : Optional[str] + The frequency of the index ticks. Valid only for US indices. (provider: cboe) + tick_period : Optional[str] + The period of the index ticks. Valid only for US indices. (provider: cboe) stock_exchange : Optional[str] Stock exchange where the index is listed. (provider: fmp) exchange_short_name : Optional[str] Short name of the stock exchange where the index is listed. (provider: fmp) - """ # noqa: E501 + code : Optional[str] + ID code for keying the index in the OpenBB Terminal. (provider: yfinance)""" # noqa: E501 inputs = filter_inputs( provider_choices={ @@ -146,6 +178,162 @@ class ROUTER_economy(Container): **inputs, ) + @validate_arguments + def cot( + self, provider: Union[Literal["quandl"], None] = None, **kwargs + ) -> OBBject[List]: + """Lookup Commitment of Traders Reports by series ID. + + Parameters + ---------- + provider : Union[Literal['quandl'], None] + The provider to use for the query, by default None. + If None, the provider specified in defaults is selected or 'quandl' if there is + no default. + code : str + + CFTC series code. Use search_cot() to find the code. + Codes not listed in the curated list, but are published by on the Nasdaq Data Link website, are valid. + Certain symbols, such as "ES=F", or exact names are also valid. + Default report is: S&P 500 Consolidated (CME)) + (provider: quandl) + data_type : Union[Literal['F', 'FO', 'CITS'], None] + + The type of data to reuturn. Default is "FO". + + F = Futures only + + FO = Futures and Options + + CITS = Commodity Index Trader Supplemental. Only valid for commodities. + (provider: quandl) + legacy_format : Union[bool, None] + Returns the legacy format of report. Default is False. (provider: quandl) + report_type : Union[Literal['ALL', 'CHG', 'OLD', 'OTR'], None] + + The type of report to return. Default is "ALL". + + ALL = All + + CHG = Change in Positions + + OLD = Old Crop Years + + OTR = Other Crop Years + (provider: quandl) + measure : Union[Literal['CR', 'NT', 'OI', 'CHG'], None] + + The measure to return. Default is None. + + CR = Concentration Ratios + + NT = Number of Traders + + OI = Percent of Open Interest + + CHG = Change in Positions. Only valid when data_type is "CITS". + (provider: quandl) + start_date : Union[datetime.date, None] + The start date of the time series. Defaults to all. (provider: quandl) + end_date : Union[datetime.date, None] + The end date of the time series. Defaults to the most recent data. (provider: quandl) + transform : Union[Literal['diff', 'rdiff', 'cumul', 'normalize'], None] + Transform the data as w/w difference, percent change, cumulative, or normalize. (provider: quandl) + + Returns + ------- + OBBject + results : List[COT] + Serializable results. + provider : Union[Literal['quandl'], None] + Provider name. + warnings : Optional[List[Warning_]] + List of warnings. + chart : Optional[Chart] + Chart object. + metadata: Optional[Metadata] + Metadata info about the command execution. + + COT + ---""" # noqa: E501 + + inputs = filter_inputs( + provider_choices={ + "provider": provider, + }, + standard_params={}, + extra_params=kwargs, + ) + + return self._command_runner.run( + "/economy/cot", + **inputs, + ) + + @validate_arguments + def cot_search( + self, + query: typing_extensions.Annotated[ + str, OpenBBCustomParameter(description="Search query.") + ] = "", + provider: Union[Literal["quandl"], None] = None, + **kwargs + ) -> OBBject[List]: + """Fuzzy search and list of curated Commitment of Traders Reports series information. + + Parameters + ---------- + query : str + Search query. + provider : Union[Literal['quandl'], None] + The provider to use for the query, by default None. + If None, the provider specified in defaults is selected or 'quandl' if there is + no default. + + Returns + ------- + OBBject + results : List[COTSearch] + Serializable results. + provider : Union[Literal['quandl'], None] + Provider name. + warnings : Optional[List[Warning_]] + List of warnings. + chart : Optional[Chart] + Chart object. + metadata: Optional[Metadata] + Metadata info about the command execution. + + COTSearch + --------- + code : Optional[str] + CFTC Code of the report. + name : Optional[str] + Name of the underlying asset. + category : Optional[str] + Category of the underlying asset. + subcategory : Optional[str] + Subcategory of the underlying asset. + units : Optional[str] + The units for one contract. + symbol : Optional[str] + Trading symbol representing the underlying asset.""" # noqa: E501 + + inputs = filter_inputs( + provider_choices={ + "provider": provider, + }, + standard_params={ + "query": query, + }, + extra_params=kwargs, + ) + + return self._command_runner.run( + "/economy/cot_search", + **inputs, + ) + @validate_arguments def cpi( self, @@ -296,6 +484,174 @@ class ROUTER_economy(Container): **inputs, ) + @validate_arguments + def european_index( + self, + symbol: typing_extensions.Annotated[ + Union[str, List[str]], + OpenBBCustomParameter(description="Symbol to get data for."), + ], + start_date: typing_extensions.Annotated[ + Union[datetime.date, None, str], + OpenBBCustomParameter( + description="Start date of the data, in YYYY-MM-DD format." + ), + ] = None, + end_date: typing_extensions.Annotated[ + Union[datetime.date, None, str], + OpenBBCustomParameter( + description="End date of the data, in YYYY-MM-DD format." + ), + ] = None, + provider: Union[Literal["cboe"], None] = None, + **kwargs + ) -> OBBject[List]: + """Get historical close values for select European indices. + + Parameters + ---------- + symbol : Union[str, List[str]] + Symbol to get data for. + start_date : Union[datetime.date, None, str] + Start date of the data, in YYYY-MM-DD format. + end_date : Union[datetime.date, None, str] + End date of the data, in YYYY-MM-DD format. + provider : Union[Literal['cboe'], None] + The provider to use for the query, by default None. + If None, the provider specified in defaults is selected or 'cboe' if there is + no default. + interval : Union[Literal['1d', '1m'], None] + Data granularity. (provider: cboe) + + Returns + ------- + OBBject + results : List[EuropeanIndexHistorical] + Serializable results. + provider : Union[Literal['cboe'], None] + Provider name. + warnings : Optional[List[Warning_]] + List of warnings. + chart : Optional[Chart] + Chart object. + metadata: Optional[Metadata] + Metadata info about the command execution. + + EuropeanIndexHistorical + ----------------------- + date : Optional[datetime] + The date of the data. + close : Optional[float] + The close price of the symbol. + open : Optional[float] + Opening price for the interval. Only valid when interval is 1m. (provider: cboe) + high : Optional[float] + High price for the interval. Only valid when interval is 1m. (provider: cboe) + low : Optional[float] + Low price for the interval. Only valid when interval is 1m. (provider: cboe) + utc_datetime : Optional[datetime] + UTC datetime. Only valid when interval is 1m. (provider: cboe)""" # noqa: E501 + + inputs = filter_inputs( + provider_choices={ + "provider": provider, + }, + standard_params={ + "symbol": ",".join(symbol) if isinstance(symbol, list) else symbol, + "start_date": start_date, + "end_date": end_date, + }, + extra_params=kwargs, + ) + + return self._command_runner.run( + "/economy/european_index", + **inputs, + ) + + @validate_arguments + def european_index_constituents( + self, + symbol: typing_extensions.Annotated[ + Union[str, List[str]], + OpenBBCustomParameter(description="Symbol to get data for."), + ], + provider: Union[Literal["cboe"], None] = None, + **kwargs + ) -> OBBject[List]: + """Get current levels for constituents of select European indices. + + Parameters + ---------- + symbol : Union[str, List[str]] + Symbol to get data for. + provider : Union[Literal['cboe'], None] + The provider to use for the query, by default None. + If None, the provider specified in defaults is selected or 'cboe' if there is + no default. + + Returns + ------- + OBBject + results : List[EuropeanIndexConstituents] + Serializable results. + provider : Union[Literal['cboe'], None] + Provider name. + warnings : Optional[List[Warning_]] + List of warnings. + chart : Optional[Chart] + Chart object. + metadata: Optional[Metadata] + Metadata info about the command execution. + + EuropeanIndexConstituents + ------------------------- + symbol : Optional[str] + Symbol of the constituent company in the index. + price : Optional[float] + Current price of the constituent company in the index. + open : Optional[float] + The open price of the symbol. + high : Optional[float] + The high price of the symbol. + low : Optional[float] + The low price of the symbol. + close : Optional[float] + The close price of the symbol. + volume : Optional[float] + The volume of the symbol. + prev_close : Optional[float] + Previous closing price. (provider: cboe) + change : Optional[float] + Change in price. (provider: cboe) + change_percent : Optional[float] + Change in price as a percentage. (provider: cboe) + tick : Optional[str] + Whether the last sale was an up or down tick. (provider: cboe) + last_trade_timestamp : Optional[datetime] + Last trade timestamp for the symbol. (provider: cboe) + exchange_id : Optional[int] + The Exchange ID number. (provider: cboe) + seqno : Optional[int] + Sequence number of the last trade on the tape. (provider: cboe) + asset_type : Optional[str] + Type of asset. (provider: cboe)""" # noqa: E501 + + inputs = filter_inputs( + provider_choices={ + "provider": provider, + }, + standard_params={ + "symbol": ",".join(symbol) if isinstance(symbol, list) else symbol, + }, + extra_params=kwargs, + ) + + return self._command_runner.run( + "/economy/european_index_constituents", + **inputs, + ) + @validate_arguments def index( self, @@ -315,7 +671,7 @@ class ROUTER_economy(Container): description="End date of the data, in YYYY-MM-DD format." ), ] = None, - provider: Union[Literal["fmp", "polygon"], None] = None, + provider: Union[Literal["cboe", "fmp", "polygon", "yfinance"], None] = None, **kwargs ) -> OBBject[List]: """Get historical levels for an index. @@ -328,14 +684,14 @@ class ROUTER_economy(Container): Start date of the data, in YYYY-MM-DD format. end_date : Union[datetime.date, None, str] End date of the data, in YYYY-MM-DD format. - provider : Union[Literal['fmp', 'polygon'], None] + provider : Union[Literal['cboe', 'fmp', 'polygon', 'yfinance'], None] The provider to use for the query, by default None. - If None, the provider specified in defaults is selected or 'fmp' if there is + If None, the provider specified in defaults is selected or 'cboe' if there is no default. + interval : Union[Literal['1d', '1m'], None, Literal['1min', '5min', '15min', '30min', '1hour', '4hour', '1day'], Literal['1m', '2m', '5m', '15m', '30m', '60m', '90m', '1h', '1d', '5d', '1wk', '1mo', '3mo']] + Use interval, 1m, for intraday prices during the most recent trading period. (provider: cboe); Data granularity. (provider: fmp); Data granularity. (provider: yfinance) timeseries : Union[pydantic.types.NonNegativeInt, None] Number of days to look back. (provider: fmp) - interval : Literal['1min', '5min', '15min', '30min', '1hour', '4hour', '1day'] - Data granularity. (provider: fmp) timespan : Literal['minute', 'hour', 'day', 'week', 'month', 'quarter', 'year'] Timespan of the data. (provider: polygon) sort : Literal['asc', 'desc'] @@ -346,13 +702,19 @@ class ROUTER_economy(Container): Whether the data is adjusted. (provider: polygon) multiplier : PositiveInt Multiplier of the timespan. (provider: polygon) + period : Literal['1d', '5d', '1mo', '3mo', '6mo', '1y', '2y', '5y', '10y', 'ytd', 'max'] + Period of the data to return. (provider: yfinance) + prepost : bool + Include Pre and Post market data. (provider: yfinance) + rounding : bool + Round prices to two decimals? (provider: yfinance) Returns ------- OBBject results : List[MajorIndicesHistorical] Serializable results. - provider : Union[Literal['fmp', 'polygon'], None] + provider : Union[Literal['cboe', 'fmp', 'polygon', 'yfinance'], None] Provider name. warnings : Optional[List[Warning_]] List of warnings. @@ -375,6 +737,12 @@ class ROUTER_economy(Container): The close price of the symbol. volume : Optional[NonNegativeInt] The volume of the symbol. + calls_volume : Optional[float] + Number of calls traded during the most recent trading period. Only valid if interval is 1m. (provider: cboe) + puts_volume : Optional[float] + Number of puts traded during the most recent trading period. Only valid if interval is 1m. (provider: cboe) + total_options_volume : Optional[float] + Total number of options traded during the most recent trading period. Only valid if interval is 1m. (provider: cboe) adj_close : Optional[float] Adjusted Close Price of the symbol. (provider: fmp) unadjusted_volume : Optional[float] @@ -408,6 +776,174 @@ class ROUTER_economy(Container): **inputs, ) + @validate_arguments + def index_search( + self, + query: typing_extensions.Annotated[ + str, OpenBBCustomParameter(description="Search query.") + ] = "", + symbol: typing_extensions.Annotated[ + Union[bool, List[str]], + OpenBBCustomParameter(description="Whether to search by ticker symbol."), + ] = False, + provider: Union[Literal["cboe"], None] = None, + **kwargs + ) -> OBBject[List]: + """Search for indices. + + Parameters + ---------- + query : str + Search query. + symbol : Union[bool, List[str]] + Whether to search by ticker symbol. + provider : Union[Literal['cboe'], None] + The provider to use for the query, by default None. + If None, the provider specified in defaults is selected or 'cboe' if there is + no default. + europe : bool + Filter for European indices. False for US indices. (provider: cboe) + + Returns + ------- + OBBject + results : List[IndexSearch] + Serializable results. + provider : Union[Literal['cboe'], None] + Provider name. + warnings : Optional[List[Warning_]] + List of warnings. + chart : Optional[Chart] + Chart object. + metadata: Optional[Metadata] + Metadata info about the command execution. + + IndexSearch + ----------- + symbol : Optional[str] + Symbol of the index. + name : Optional[str] + Name of the index. + isin : Optional[str] + ISIN code for the index. Valid only for European indices. (provider: cboe) + region : Optional[str] + Region for the index. Valid only for European indices (provider: cboe) + description : Optional[str] + Description for the index. (provider: cboe) + data_delay : Optional[int] + Data delay for the index. Valid only for US indices. (provider: cboe) + currency : Optional[str] + Currency for the index. (provider: cboe) + time_zone : Optional[str] + Time zone for the index. Valid only for US indices. (provider: cboe) + open_time : Optional[time] + Opening time for the index. Valid only for US indices. (provider: cboe) + close_time : Optional[time] + Closing time for the index. Valid only for US indices. (provider: cboe) + tick_days : Optional[str] + The trading days for the index. Valid only for US indices. (provider: cboe) + tick_frequency : Optional[str] + Tick frequency for the index. Valid only for US indices. (provider: cboe) + tick_period : Optional[str] + Tick period for the index. Valid only for US indices. (provider: cboe)""" # noqa: E501 + + inputs = filter_inputs( + provider_choices={ + "provider": provider, + }, + standard_params={ + "query": query, + "symbol": ",".join(symbol) if isinstance(symbol, list) else symbol, + }, + extra_params=kwargs, + ) + + return self._command_runner.run( + "/economy/index_search", + **inputs, + ) + + @validate_arguments + def index_snapshots( + self, + region: typing_extensions.Annotated[ + Union[Literal["US", "EU"], None], + OpenBBCustomParameter( + description="The region to return. Currently supports US and EU." + ), + ] = "US", + provider: Union[Literal["cboe"], None] = None, + **kwargs + ) -> OBBject[List]: + """Get current levels for all indices from a provider. + + Parameters + ---------- + region : Union[Literal['US', 'EU'], None] + The region to return. Currently supports US and EU. + provider : Union[Literal['cboe'], None] + The provider to use for the query, by default None. + If None, the provider specified in defaults is selected or 'cboe' if there is + no default. + + Returns + ------- + OBBject + results : List[IndexSnapshots] + Serializable results. + provider : Union[Literal['cboe'], None] + Provider name. + warnings : Optional[List[Warning_]] + List of warnings. + chart : Optional[Chart] + Chart object. + metadata: Optional[Metadata] + Metadata info about the command execution. + + IndexSnapshots + -------------- + symbol : Optional[str] + Symbol of the index. + name : Optional[str] + Name of the index. + currency : Optional[str] + Currency of the index. + price : Optional[float] + Current price of the index. + open : Optional[float] + Opening price of the index. + high : Optional[float] + Highest price of the index. + low : Optional[float] + Lowest price of the index. + close : Optional[float] + Closing price of the index. + prev_close : Optional[float] + Previous closing price of the index. + change : Optional[float] + Change of the index. + change_percent : Optional[float] + Change percent of the index. + isin : Optional[str] + ISIN code for the index. Valid only for European indices. (provider: cboe) + last_trade_timestamp : Optional[datetime] + Last trade timestamp for the index. (provider: cboe)""" # noqa: E501 + + inputs = filter_inputs( + provider_choices={ + "provider": provider, + }, + standard_params={ + "region": region, + }, + extra_params=kwargs, + ) + + return self._command_runner.run( + "/economy/index_snapshots", + **inputs, + ) + @validate_arguments def risk( self, provider: Union[Literal["fmp"], None] = None, **kwargs @@ -458,3 +994,133 @@ class ROUTER_economy(Container): "/economy/risk", **inputs, ) + + @validate_arguments + def sp500_multiples( + self, + series_name: typing_extensions.Annotated[ + Literal[ + "Shiller PE Ratio by Month", + "Shiller PE Ratio by Year", + "PE Ratio by Year", + "PE Ratio by Month", + "Dividend by Year", + "Dividend by Month", + "Dividend Growth by Quarter", + "Dividend Growth by Year", + "Dividend Yield by Year", + "Dividend Yield by Month", + "Earnings by Year", + "Earnings by Month", + "Earnings Growth by Year", + "Earnings Growth by Quarter", + "Real Earnings Growth by Year", + "Real Earnings Growth by Quarter", + "Earnings Yield by Year", + "Earnings Yield by Month", + "Real Price by Year", + "Real Price by Month", + "Inflation Adjusted Price by Year", + "Inflation Adjusted Price by Month", + "Sales by Year", + "Sales by Quarter", + "Sales Growth by Year", + "Sales Growth by Quarter", + "Real Sales by Year", + "Real Sales by Quarter", + "Real Sales Growth by Year", + "Real Sales Growth by Quarter", + "Price to Sales Ratio by Year", + "Price to Sales Ratio by Quarter", + "Price to Book Value Ratio by Year", + "Price to Book Value Ratio by Quarter", + "Book Value per Share by Year", + "Book Value per Share by Quarter", + ], + OpenBBCustomParameter( + description="The name of the series. Defaults to 'PE Ratio by Month'." + ), + ] = "PE Ratio by Month", + start_date: typing_extensions.Annotated[ + Union[str, None], + OpenBBCustomParameter( + description="The start date of the time series. Format: YYYY-MM-DD" + ), + ] = "", + end_date: typing_extensions.Annotated[ + Union[str, None], + OpenBBCustomParameter( + description="The end date of the time series. Format: YYYY-MM-DD" + ), + ] = "", + collapse: typing_extensions.Annotated[ + Union[Literal["daily", "weekly", "monthly", "quarterly", "annual"], None], + OpenBBCustomParameter( + description="Collapse the frequency of the time series." + ), + ] = "monthly", + transform: typing_extensions.Annotated[ + Union[Literal["diff", "rdiff", "cumul", "normalize"], None], + OpenBBCustomParameter(description="The transformation of the time series."), + ] = None, + provider: Union[Literal["quandl"], None] = None, + **kwargs + ) -> OBBject[List]: + """Historical S&P 500 multiples and Shiller PE ratios. + + Parameters + ---------- + series_name : Literal['Shiller PE Ratio by Month', 'Shiller PE Ratio by Year', 'PE Rat... + The name of the series. Defaults to 'PE Ratio by Month'. + start_date : Union[str, None] + The start date of the time series. Format: YYYY-MM-DD + end_date : Union[str, None] + The end date of the time series. Format: YYYY-MM-DD + collapse : Union[Literal['daily', 'weekly', 'monthly', 'quarterly', 'annual'... + Collapse the frequency of the time series. + transform : Union[Literal['diff', 'rdiff', 'cumul', 'normalize'], None] + The transformation of the time series. + provider : Union[Literal['quandl'], None] + The provider to use for the query, by default None. + If None, the provider specified in defaults is selected or 'quandl' if there is + no default. + + Returns + ------- + OBBject + results : List[SP500Multiples] + Serializable results. + provider : Union[Literal['quandl'], None] + Provider name. + warnings : Optional[List[Warning_]] + List of warnings. + chart : Optional[Chart] + Chart object. + metadata: Optional[Metadata] + Metadata info about the command execution. + + SP500Multiples + -------------- + date : Optional[str] + The date data for the time series. + value : Optional[float] + The data value for the time series.""" # noqa: E501 + + inputs = filter_inputs( + provider_choices={ + "provider": provider, + }, + standard_params={ + "series_name": series_name, + "start_date": start_date, + "end_date": end_date, + "collapse": collapse, + "transform": transform, + }, + extra_params=kwargs, + ) + + return self._command_runner.run( + "/economy/sp500_multiples", + **inputs, + ) diff --git a/openbb_sdk/openbb/package/extension_map.json b/openbb_sdk/openbb/package/extension_map.json index bd468205a5d..fd19f57c957 100644 --- a/openbb_sdk/openbb/package/extension_map.json +++ b/openbb_sdk/openbb/package/extension_map.json @@ -1,17 +1,26 @@ { "openbb_core_extension": [ "crypto@0.1.0a2", + "econometrics@0.1.0a2", "economy@0.1.0a2", "fixedincome@0.1.0a2", "forex@0.1.0a2", + "futures@0.1.0a2", "news@0.1.0a2", - "stocks@0.1.0a2" + "openbb_charting@0.1.0a2", + "qa@0.1.0a2", + "stocks@0.1.0a2", + "ta@0.1.0a2" ], "openbb_provider_extension": [ + "alpha_vantage@0.1.0a2", "benzinga@0.1.0a2", + "cboe@0.1.0a2", "fmp@0.1.0a2", "fred@0.1.0a2", "intrinio@0.1.0a2", - "polygon@0.1.0a2" + "polygon@0.1.0a2", + "quandl@0.1.0a2", + "yfinance@0.1.0a2" ] } \ No newline at end of file diff --git a/openbb_sdk/openbb/package/forex.py b/openbb_sdk/openbb/package/forex.py index 48a0d8d57fe..71128f4a697 100644 --- a/openbb_sdk/openbb/package/forex.py +++ b/openbb_sdk/openbb/package/forex.py @@ -39,7 +39,7 @@ class ROUTER_forex(Container): description="End date of the data, in YYYY-MM-DD format." ), ] = None, - provider: Union[Literal["fmp", "polygon"], None] = None, + provider: Union[Literal["fmp", "polygon", "yfinance"], None] = None, **kwargs ) -> OBBject[List]: """Forex Intraday Price. @@ -52,12 +52,12 @@ class ROUTER_forex(Container): Start date of the data, in YYYY-MM-DD format. end_date : Union[datetime.date, None, str] End date of the data, in YYYY-MM-DD format. - provider : Union[Literal['fmp', 'polygon'], None] + provider : Union[Literal['fmp', 'polygon', 'yfinance'], None] The provider to use for the query, by default None. If None, the provider specified in defaults is selected or 'fmp' if there is no default. - interval : Literal['1min', '5min', '15min', '30min', '1hour', '4hour', '1day'] - Data granularity. (provider: fmp) + interval : Union[Literal['1min', '5min', '15min', '30min', '1hour', '4hour', '1day'], Literal['1m', '2m', '5m', '15m', '30m', '60m', '90m', '1h', '1d', '5d', '1wk', '1mo', '3mo']] + Data granularity. (provider: fmp, yfinance) multiplier : PositiveInt Multiplier of the timespan. (provider: polygon) timespan : Literal['minute', 'hour', 'day', 'week', 'month', 'quarter', 'year'] @@ -68,13 +68,15 @@ class ROUTER_forex(Container): The number of data entries to return. (provider: polygon) adjusted : bool Whether the data is adjusted. (provider: polygon) + period : Literal['1d', '5d', '1mo', '3mo', '6mo', '1y', '2y', '5y', '10y', 'ytd', 'max'] + Period of the data to return. (provider: yfinance) Returns ------- OBBject results : List[ForexHistorical] Serializable results. - provider : Union[Literal['fmp', 'polygon'], None] + provider : Union[Literal['fmp', 'polygon', 'yfinance'], None] Provider name. warnings : Optional[List[Warning_]] List of warnings. diff --git a/openbb_sdk/openbb/package/futures.py b/openbb_sdk/openbb/package/futures.py new file mode 100644 index 00000000000..c0da379851f --- /dev/null +++ b/openbb_sdk/openbb/package/futures.py @@ -0,0 +1,187 @@ +### THIS FILE IS AUTO-GENERATED. DO NOT EDIT. ### + +import datetime +from typing import List, Literal, Union + +import typing_extensions +from openbb_core.app.model.custom_parameter import OpenBBCustomParameter +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.static.container import Container +from openbb_core.app.static.filters import filter_inputs +from pydantic import validate_arguments + + +class ROUTER_futures(Container): + """/futures + curve + load + """ + + def __repr__(self) -> str: + return self.__doc__ or "" + + @validate_arguments + def curve( + self, + symbol: typing_extensions.Annotated[ + Union[str, List[str]], + OpenBBCustomParameter(description="Symbol to get data for."), + ], + date: typing_extensions.Annotated[ + Union[datetime.date, None], + OpenBBCustomParameter(description="Historical date to search curve for."), + ] = None, + provider: Union[Literal["cboe", "yfinance"], None] = None, + **kwargs + ) -> OBBject[List]: + """Futures Historical Price. + + Parameters + ---------- + symbol : Union[str, List[str]] + Symbol to get data for. + date : Union[datetime.date, None] + Historical date to search curve for. + provider : Union[Literal['cboe', 'yfinance'], None] + The provider to use for the query, by default None. + If None, the provider specified in defaults is selected or 'cboe' if there is + no default. + + Returns + ------- + OBBject + results : List[FuturesCurve] + Serializable results. + provider : Union[Literal['cboe', 'yfinance'], None] + Provider name. + warnings : Optional[List[Warning_]] + List of warnings. + chart : Optional[Chart] + Chart object. + metadata: Optional[Metadata] + Metadata info about the command execution. + + FuturesCurve + ------------ + expiration : Optional[str] + Futures expiration month. + price : Optional[float] + The close price of the symbol. + symbol : Optional[str] + The trading symbol for the tenor of future. (provider: cboe)""" # noqa: E501 + + inputs = filter_inputs( + provider_choices={ + "provider": provider, + }, + standard_params={ + "symbol": ",".join(symbol) if isinstance(symbol, list) else symbol, + "date": date, + }, + extra_params=kwargs, + ) + + return self._command_runner.run( + "/futures/curve", + **inputs, + ) + + @validate_arguments + def load( + self, + symbol: typing_extensions.Annotated[ + Union[str, List[str]], + OpenBBCustomParameter(description="Symbol to get data for."), + ], + start_date: typing_extensions.Annotated[ + Union[datetime.date, None, str], + OpenBBCustomParameter( + description="Start date of the data, in YYYY-MM-DD format." + ), + ] = None, + end_date: typing_extensions.Annotated[ + Union[datetime.date, None, str], + OpenBBCustomParameter( + description="End date of the data, in YYYY-MM-DD format." + ), + ] = None, + expiration: typing_extensions.Annotated[ + Union[str, None], + OpenBBCustomParameter(description="Future expiry date with format YYYY-MM"), + ] = None, + provider: Union[Literal["yfinance"], None] = None, + **kwargs + ) -> OBBject[List]: + """Futures Historical Price. + + Parameters + ---------- + symbol : Union[str, List[str]] + Symbol to get data for. + start_date : Union[datetime.date, None, str] + Start date of the data, in YYYY-MM-DD format. + end_date : Union[datetime.date, None, str] + End date of the data, in YYYY-MM-DD format. + expiration : Union[str, None] + Future expiry date with format YYYY-MM + provider : Union[Literal['yfinance'], None] + The provider to use for the query, by default None. + If None, the provider specified in defaults is selected or 'yfinance' if there is + no default. + interval : Union[Literal['1m', '2m', '5m', '15m', '30m', '60m', '90m', '1h', '1d', '5d', '1wk', '1mo', '3mo'], None] + Data granularity. (provider: yfinance) + period : Union[Literal['1d', '5d', '1mo', '3mo', '6mo', '1y', '2y', '5y', '10y', 'ytd', 'max'], None] + Period of the data to return. (provider: yfinance) + prepost : bool + Include Pre and Post market data. (provider: yfinance) + adjust : bool + Adjust all the data automatically. (provider: yfinance) + back_adjust : bool + Back-adjusted data to mimic true historical prices. (provider: yfinance) + + Returns + ------- + OBBject + results : List[FuturesHistorical] + Serializable results. + provider : Union[Literal['yfinance'], None] + Provider name. + warnings : Optional[List[Warning_]] + List of warnings. + chart : Optional[Chart] + Chart object. + metadata: Optional[Metadata] + Metadata info about the command execution. + + FuturesHistorical + ----------------- + date : Optional[datetime] + The date of the data. + open : Optional[float] + The open price of the symbol. + high : Optional[float] + The high price of the symbol. + low : Optional[float] + The low price of the symbol. + close : Optional[float] + The close price of the symbol. + volume : Optional[float] + The volume of the symbol.""" # noqa: E501 + + inputs = filter_inputs( + provider_choices={ + "provider": provider, + }, + standard_params={ + "symbol": ",".join(symbol) if isinstance(symbol, list) else symbol, + "start_date": start_date, + "end_date": end_date, + "expiration": expiration, + }, + extra_params=kwargs, + ) + + return self._command_runner.run( + "/futures/load", + **inputs, + ) diff --git a/openbb_sdk/openbb/package/module_map.json b/openbb_sdk/openbb/package/module_map.json index 6c5584bf269..f28bc01722b 100644 --- a/openbb_sdk/openbb/package/module_map.json +++ b/openbb_sdk/openbb/package/module_map.json @@ -2,12 +2,34 @@ "__extensions__": "", "crypto": "/crypto", "crypto_load": "/crypto/load", + "econometrics": "/econometrics", + "econometrics_bgot": "/econometrics/bgot", + "econometrics_coint": "/econometrics/coint", + "econometrics_corr": "/econometrics/corr", + "econometrics_dwat": "/econometrics/dwat", + "econometrics_granger": "/econometrics/granger", + "econometrics_ols": "/econometrics/ols", + "econometrics_ols_summary": "/econometrics/ols_summary", + "econometrics_panelbols": "/econometrics/panelbols", + "econometrics_panelfd": "/econometrics/panelfd", + "econometrics_panelfmac": "/econometrics/panelfmac", + "econometrics_panelols": "/econometrics/panelols", + "econometrics_panelpols": "/econometrics/panelpols", + "econometrics_panelre": "/econometrics/panelre", + "econometrics_unitroot": "/econometrics/unitroot", "economy": "/economy", "economy_available_indices": "/economy/available_indices", "economy_const": "/economy/const", + "economy_cot": "/economy/cot", + "economy_cot_search": "/economy/cot_search", "economy_cpi": "/economy/cpi", + "economy_european_index": "/economy/european_index", + "economy_european_index_constituents": "/economy/european_index_constituents", "economy_index": "/economy/index", + "economy_index_search": "/economy/index_search", + "economy_index_snapshots": "/economy/index_snapshots", "economy_risk": "/economy/risk", + "economy_sp500_multiples": "/economy/sp500_multiples", "fixedincome": "/fixedincome", "fixedincome_ameribor": "/fixedincome/ameribor", "fixedincome_estr": "/fixedincome/estr", @@ -21,8 +43,22 @@ "forex": "/forex", "forex_load": "/forex/load", "forex_pairs": "/forex/pairs", + "futures": "/futures", + "futures_curve": "/futures/curve", + "futures_load": "/futures/load", "news": "/news", "news_globalnews": "/news/globalnews", + "qa": "/qa", + "qa_capm": "/qa/capm", + "qa_kurtosis": "/qa/kurtosis", + "qa_normality": "/qa/normality", + "qa_om": "/qa/om", + "qa_quantile": "/qa/quantile", + "qa_sh": "/qa/sh", + "qa_skew": "/qa/skew", + "qa_so": "/qa/so", + "qa_summary": "/qa/summary", + "qa_unitroot": "/qa/unitroot", "stocks": "/stocks", "stocks_ca": "/stocks/ca", "stocks_ca_peers": "/stocks/ca/peers", @@ -55,10 +91,39 @@ "stocks_fa_shrs": "/stocks/fa/shrs", "stocks_fa_split": "/stocks/fa/split", "stocks_fa_transcript": "/stocks/fa/transcript", + "stocks_info": "/stocks/info", "stocks_load": "/stocks/load", "stocks_multiples": "/stocks/multiples", "stocks_news": "/stocks/news", "stocks_options": "/stocks/options", "stocks_options_chains": "/stocks/options/chains", - "stocks_quote": "/stocks/quote" + "stocks_quote": "/stocks/quote", + "stocks_search": "/stocks/search", + "ta": "/ta", + "ta_ad": "/ta/ad", + "ta_adosc": "/ta/adosc", + "ta_adx": "/ta/adx", + "ta_aroon": "/ta/aroon", + "ta_atr": "/ta/atr", + "ta_bbands": "/ta/bbands", + "ta_cci": "/ta/cci", + "ta_cg": "/ta/cg", + "ta_clenow": "/ta/clenow", + "ta_cones": "/ta/cones", + "ta_demark": "/ta/demark", + "ta_donchian": "/ta/donchian", + "ta_ema": "/ta/ema", + "ta_fib": "/ta/fib", + "ta_fisher": "/ta/fisher", + "ta_hma": "/ta/hma", + "ta_ichimoku": "/ta/ichimoku", + "ta_kc": "/ta/kc", + "ta_macd": "/ta/macd", + "ta_obv": "/ta/obv", + "ta_rsi": "/ta/rsi", + "ta_sma": "/ta/sma", + "ta_stoch": "/ta/stoch", + "ta_vwap": "/ta/vwap", + "ta_wma": "/ta/wma", + "ta_zlma": "/ta/zlma" } \ No newline at end of file diff --git a/openbb_sdk/openbb/package/qa.py b/openbb_sdk/openbb/package/qa.py new file mode 100644 index 00000000000..3227e3f4029 --- /dev/null +++ b/openbb_sdk/openbb/package/qa.py @@ -0,0 +1,375 @@ +### THIS FILE IS AUTO-GENERATED. DO NOT EDIT. ### + +from typing import List, Literal, Union + +import openbb_provider +import openbb_qa.qa_models +import pandas +import pydantic +import pydantic.types +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.static.container import Container +from openbb_core.app.static.filters import filter_inputs +from pydantic import validate_arguments + + +class ROUTER_qa(Container): + """/qa + capm + kurtosis + normality + om + quantile + sh + skew + so + summary + unitroot + """ + + def __repr__(self) -> str: + return self.__doc__ or "" + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def capm( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + target: str, + ) -> OBBject[openbb_qa.qa_models.CAPMModel]: + """Capital Asset Pricing Model.""" # noqa: E501 + + inputs = filter_inputs( + data=data, + target=target, + ) + + return self._command_runner.run( + "/qa/capm", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def kurtosis( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + target: str, + window: pydantic.types.PositiveInt, + ) -> OBBject[List]: + """Kurtosis. + + Parameters + ---------- + data : List[Data] + Time series data. + target : str + Target column name. + window : PositiveInt + Window size. + + Returns + ------- + OBBject[List[Data]] + Kurtosis. + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + target=target, + window=window, + ) + + return self._command_runner.run( + "/qa/kurtosis", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def normality( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + target: str, + ) -> OBBject[openbb_qa.qa_models.NormalityModel]: + """ + Normality Statistics. + + - **Kurtosis**: whether the kurtosis of a sample differs from the normal distribution. + - **Skewness**: whether the skewness of a sample differs from the normal distribution. + - **Jarque-Bera**: whether the sample data has the skewness and kurtosis matching a normal distribution. + - **Shapiro-Wilk**: whether a random sample comes from a normal distribution. + - **Kolmogorov-Smirnov**: whether two underlying one-dimensional probability distributions differ. + + Parameters + ---------- + data : List[Data] + Time series data. + target : str + Target column name. + + Returns + ------- + OBBject[NormalityModel] + Normality tests summary. See qa_models.NormalityModel for details. + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + target=target, + ) + + return self._command_runner.run( + "/qa/normality", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def om( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + target: str, + threshold_start: float = 0.0, + threshold_end: float = 1.5, + ) -> OBBject[List]: + """Omega Ratio. + + Parameters + ---------- + data : List[Data] + Time series data. + target : str + Target column name. + threshold_start : float, optional + Start threshold, by default 0.0 + threshold_end : float, optional + End threshold, by default 1.5 + + Returns + ------- + OBBject[List[OmegaModel]] + Omega ratios. + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + target=target, + threshold_start=threshold_start, + threshold_end=threshold_end, + ) + + return self._command_runner.run( + "/qa/om", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def quantile( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + target: str, + window: pydantic.types.PositiveInt, + quantile_pct: pydantic.types.NonNegativeFloat = 0.5, + ) -> OBBject[List]: + """Quantile.""" # noqa: E501 + + inputs = filter_inputs( + data=data, + target=target, + window=window, + quantile_pct=quantile_pct, + ) + + return self._command_runner.run( + "/qa/quantile", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def sh( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + target: str, + rfr: float = 0.0, + window: pydantic.types.PositiveInt = 252, + ) -> OBBject[List]: + """Sharpe Ratio. + + Parameters + ---------- + data : List[Data] + Time series data. + target : str + Target column name. + rfr : float, optional + Risk-free rate, by default 0.0 + window : PositiveInt, optional + Window size, by default 252 + + Returns + ------- + OBBject[List[Data]] + Sharpe ratio. + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + target=target, + rfr=rfr, + window=window, + ) + + return self._command_runner.run( + "/qa/sh", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def skew( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + target: str, + window: pydantic.types.PositiveInt, + ) -> OBBject[List]: + """Skewness. + + Parameters + ---------- + data : List[Data] + Time series data. + target : str + Target column name. + window : PositiveInt + Window size. + + Returns + ------- + OBBject[List[Data]] + Skewness. + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + target=target, + window=window, + ) + + return self._command_runner.run( + "/qa/skew", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def so( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + target: str, + target_return: float = 0.0, + window: pydantic.types.PositiveInt = 252, + adjusted: bool = False, + ) -> OBBject[List]: + """Sortino Ratio. + + For method & terminology see: http://www.redrockcapital.com/Sortino__A__Sharper__Ratio_Red_Rock_Capital.pdf + + Parameters + ---------- + data : List[Data] + Time series data. + target : str + Target column name. + target_return : float, optional + Target return, by default 0.0 + window : PositiveInt, optional + Window size, by default 252 + adjusted : bool, optional + Adjust sortino ratio to compare it to sharpe ratio, by default False + + Returns + ------- + OBBject[List[Data]] + Sortino ratio. + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + target=target, + target_return=target_return, + window=window, + adjusted=adjusted, + ) + + return self._command_runner.run( + "/qa/so", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def summary( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + target: str, + ) -> OBBject[openbb_qa.qa_models.SummaryModel]: + """Summary. + + Parameters + ---------- + data : List[Data] + Time series data. + target : str + Target column name. + + Returns + ------- + OBBject[SummaryModel] + Summary table. + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + target=target, + ) + + return self._command_runner.run( + "/qa/summary", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def unitroot( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + target: str, + fuller_reg: Literal["c", "ct", "ctt", "nc", "c"] = "c", + kpss_reg: Literal["c", "ct"] = "c", + ) -> OBBject[openbb_qa.qa_models.UnitRootModel]: + """Unit Root Test. + + Augmented Dickey-Fuller test for unit root. + Kwiatkowski-Phillips-Schmidt-Shin test for unit root. + + Parameters + ---------- + data : List[Data] + Time series data. + target : str + Target column name. + fuller_reg : Literal["c", "ct", "ctt", "nc", "c"] + Regression type for ADF test. + kpss_reg : Literal["c", "ct"] + Regression type for KPSS test. + + Returns + ------- + OBBject[UnitRootModel] + Unit root tests summary. + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + target=target, + fuller_reg=fuller_reg, + kpss_reg=kpss_reg, + ) + + return self._command_runner.run( + "/qa/unitroot", + **inputs, + ) diff --git a/openbb_sdk/openbb/package/stocks.py b/openbb_sdk/openbb/package/stocks.py index 60162780f88..8685268e21e 100644 --- a/openbb_sdk/openbb/package/stocks.py +++ b/openbb_sdk/openbb/package/stocks.py @@ -17,11 +17,13 @@ class ROUTER_stocks(Container): """/stocks /ca /fa + info load multiples news /options quote + search """ def __repr__(self) -> str: @@ -39,6 +41,121 @@ class ROUTER_stocks(Container): return stocks_fa.ROUTER_stocks_fa(command_runner=self._command_runner) + @validate_arguments + def info( + self, + symbol: typing_extensions.Annotated[ + Union[str, List[str]], + OpenBBCustomParameter(description="Symbol to get data for."), + ], + provider: Union[Literal["cboe"], None] = None, + **kwargs + ) -> OBBject[List]: + """Get general price and performance metrics of a stock. + + Parameters + ---------- + symbol : Union[str, List[str]] + Symbol to get data for. + provider : Union[Literal['cboe'], None] + The provider to use for the query, by default None. + If None, the provider specified in defaults is selected or 'cboe' if there is + no default. + + Returns + ------- + OBBject + results : List[StockInfo] + Serializable results. + provider : Union[Literal['cboe'], None] + Provider name. + warnings : Optional[List[Warning_]] + List of warnings. + chart : Optional[Chart] + Chart object. + metadata: Optional[Metadata] + Metadata info about the command execution. + + StockInfo + --------- + symbol : Optional[str] + Symbol to get data for. + name : Optional[str] + Name associated with the ticker symbol. + price : Optional[float] + Last transaction price. + open : Optional[float] + Opening price of the stock. + high : Optional[float] + High price of the current trading day. + low : Optional[float] + Low price of the current trading day. + close : Optional[float] + Closing price of the most recent trading day. + change : Optional[float] + Change in price over the current trading period. + change_percent : Optional[float] + Percent change in price over the current trading period. + prev_close : Optional[float] + Previous closing price. + type : Optional[str] + Type of asset. (provider: cboe) + exchange_id : Optional[int] + The Exchange ID number. (provider: cboe) + tick : Optional[str] + Whether the last sale was an up or down tick. (provider: cboe) + bid : Optional[float] + Current bid price. (provider: cboe) + bid_size : Optional[float] + Bid lot size. (provider: cboe) + ask : Optional[float] + Current ask price. (provider: cboe) + ask_size : Optional[float] + Ask lot size. (provider: cboe) + volume : Optional[float] + Stock volume for the current trading day. (provider: cboe) + iv30 : Optional[float] + The 30-day implied volatility of the stock. (provider: cboe) + iv30_change : Optional[float] + Change in 30-day implied volatility of the stock. (provider: cboe) + last_trade_timestamp : Optional[datetime] + Last trade timestamp for the stock. (provider: cboe) + iv30_annual_high : Optional[float] + The 1-year high of implied volatility. (provider: cboe) + hv30_annual_high : Optional[float] + The 1-year high of realized volatility. (provider: cboe) + iv30_annual_low : Optional[float] + The 1-year low of implied volatility. (provider: cboe) + hv30_annual_low : Optional[float] + The 1-year low of realized volatility. (provider: cboe) + iv60_annual_high : Optional[float] + The 60-day high of implied volatility. (provider: cboe) + hv60_annual_high : Optional[float] + The 60-day high of realized volatility. (provider: cboe) + iv60_annual_low : Optional[float] + The 60-day low of implied volatility. (provider: cboe) + hv60_annual_low : Optional[float] + The 60-day low of realized volatility. (provider: cboe) + iv90_annual_high : Optional[float] + The 90-day high of implied volatility. (provider: cboe) + hv90_annual_high : Optional[float] + The 90-day high of realized volatility. (provider: cboe)""" # noqa: E501 + + inputs = filter_inputs( + provider_choices={ + "provider": provider, + }, + standard_params={ + "symbol": ",".join(symbol) if isinstance(symbol, list) else symbol, + }, + extra_params=kwargs, + ) + + return self._command_runner.run( + "/stocks/info", + **inputs, + ) + @validate_arguments def load( self, @@ -58,7 +175,11 @@ class ROUTER_stocks(Container): description="End date of the data, in YYYY-MM-DD format." ), ] = None, - provider: Union[Literal["fmp", "intrinio", "polygon"], None] = None, + chart: bool = False, + provider: Union[ + Literal["alpha_vantage", "cboe", "fmp", "intrinio", "polygon", "yfinance"], + None, + ] = None, **kwargs ) -> OBBject[List]: """Load stock data for a specific ticker. @@ -71,14 +192,26 @@ class ROUTER_stocks(Container): Start date of the data, in YYYY-MM-DD format. end_date : Union[datetime.date, None, str] End date of the data, in YYYY-MM-DD format. - provider : Union[Literal['fmp', 'intrinio', 'polygon'], None] + chart : bool + Whether to create a chart or not, by default False. + provider : Union[Literal['alpha_vantage', 'cboe', 'fmp', 'intrinio', 'polygo... The provider to use for the query, by default None. - If None, the provider specified in defaults is selected or 'fmp' if there is + If None, the provider specified in defaults is selected or 'alpha_vantage' if there is no default. + period : Union[Literal['intraday', 'daily', 'weekly', 'monthly'], Literal['1d', '5d', '1mo', '3mo', '6mo', '1y', '2y', '5y', '10y', 'ytd', 'max']] + Period of the data to return. (provider: alpha_vantage, yfinance) + interval : Union[Literal['1min', '5min', '15min', '30min', '60min'], None, Literal['1d', '1m'], Literal['1min', '5min', '15min', '30min', '1hour', '4hour', '1day'], Literal['1m', '2m', '5m', '15m', '30m', '60m', '90m', '1h', '1d', '5d', '1wk', '1mo', '3mo']] + Data granularity. (provider: alpha_vantage, cboe, fmp, yfinance) + adjusted : Union[bool, None] + Output time series is adjusted by historical split and dividend events. (provider: alpha_vantage, polygon) + extended_hours : Union[bool, None] + Extended trading hours during pre-market and after-hours. (provider: alpha_vantage) + month : Union[str, None] + Query a specific month in history (in YYYY-MM format). (provider: alpha_vantage) + outputsize : Union[Literal['compact', 'full'], None] + Compact returns only the latest 100 data points in the intraday time series; full returns trailing 30 days of the most recent intraday data if the month parameter (see above) is not specified, or the full intraday data for a specific month in history if the month parameter is specified. (provider: alpha_vantage) timeseries : Union[pydantic.types.NonNegativeInt, None] Number of days to look back. (provider: fmp) - interval : Literal['1min', '5min', '15min', '30min', '1hour', '4hour', '1day'] - Data granularity. (provider: fmp) timezone : Union[Literal['Africa/Algiers', 'Africa/Cairo', 'Africa/Casablanca', 'Africa/Harare', 'Africa/Johannesburg', 'Africa/Monrovia', 'Africa/Nairobi', 'America/Argentina/Buenos_Aires', 'America/Bogota', 'America/Caracas', 'America/Chicago', 'America/Chihuahua', 'America/Denver', 'America/Godthab', 'America/Guatemala', 'America/Guyana', 'America/Halifax', 'America/Indiana/Indianapolis', 'America/Juneau', 'America/La_Paz', 'America/Lima', 'America/Lima', 'America/Los_Angeles', 'America/Mazatlan', 'America/Mexico_City', 'America/Mexico_City', 'America/Monterrey', 'America/Montevideo', 'America/New_York', 'America/Phoenix', 'America/Regina', 'America/Santiago', 'America/Sao_Paulo', 'America/St_Johns', 'America/Tijuana', 'Asia/Almaty', 'Asia/Baghdad', 'Asia/Baku', 'Asia/Bangkok', 'Asia/Bangkok', 'Asia/Chongqing', 'Asia/Colombo', 'Asia/Dhaka', 'Asia/Dhaka', 'Asia/Hong_Kong', 'Asia/Irkutsk', 'Asia/Jakarta', 'Asia/Jerusalem', 'Asia/Kabul', 'Asia/Kamchatka', 'Asia/Karachi', 'Asia/Karachi', 'Asia/Kathmandu', 'Asia/Kolkata', 'Asia/Kolkata', 'Asia/Kolkata', 'Asia/Kolkata', 'Asia/Krasnoyarsk', 'Asia/Kuala_Lumpur', 'Asia/Kuwait', 'Asia/Magadan', 'Asia/Muscat', 'Asia/Muscat', 'Asia/Novosibirsk', 'Asia/Rangoon', 'Asia/Riyadh', 'Asia/Seoul', 'Asia/Shanghai', 'Asia/Singapore', 'Asia/Srednekolymsk', 'Asia/Taipei', 'Asia/Tashkent', 'Asia/Tbilisi', 'Asia/Tehran', 'Asia/Tokyo', 'Asia/Tokyo', 'Asia/Tokyo', 'Asia/Ulaanbaatar', 'Asia/Urumqi', 'Asia/Vladivostok', 'Asia/Yakutsk', 'Asia/Yekaterinburg', 'Asia/Yerevan', 'Atlantic/Azores', 'Atlantic/Cape_Verde', 'Atlantic/South_Georgia', 'Australia/Adelaide', 'Australia/Brisbane', 'Australia/Darwin', 'Australia/Hobart', 'Australia/Melbourne', 'Australia/Melbourne', 'Australia/Perth', 'Australia/Sydney', 'Etc/UTC', 'UTC', 'Europe/Amsterdam', 'Europe/Athens', 'Europe/Belgrade', 'Europe/Berlin', 'Europe/Berlin', 'Europe/Bratislava', 'Europe/Brussels', 'Europe/Bucharest', 'Europe/Budapest', 'Europe/Copenhagen', 'Europe/Dublin', 'Europe/Helsinki', 'Europe/Istanbul', 'Europe/Kaliningrad', 'Europe/Kiev', 'Europe/Lisbon', 'Europe/Ljubljana', 'Europe/London', 'Europe/London', 'Europe/Madrid', 'Europe/Minsk', 'Europe/Moscow', 'Europe/Moscow', 'Europe/Paris', 'Europe/Prague', 'Europe/Riga', 'Europe/Rome', 'Europe/Samara', 'Europe/Sarajevo', 'Europe/Skopje', 'Europe/Sofia', 'Europe/Stockholm', 'Europe/Tallinn', 'Europe/Vienna', 'Europe/Vilnius', 'Europe/Volgograd', 'Europe/Warsaw', 'Europe/Zagreb', 'Pacific/Apia', 'Pacific/Auckland', 'Pacific/Auckland', 'Pacific/Chatham', 'Pacific/Fakaofo', 'Pacific/Fiji', 'Pacific/Guadalcanal', 'Pacific/Guam', 'Pacific/Honolulu', 'Pacific/Majuro', 'Pacific/Midway', 'Pacific/Midway', 'Pacific/Noumea', 'Pacific/Pago_Pago', 'Pacific/Port_Moresby', 'Pacific/Tongatapu'], None] Returns trading times in this timezone. (provider: intrinio) source : Union[Literal['realtime', 'delayed', 'nasdaq_basic'], None] @@ -97,15 +230,33 @@ class ROUTER_stocks(Container): Sort order of the data. (provider: polygon) limit : PositiveInt The number of data entries to return. (provider: polygon) - adjusted : bool - Output time series is adjusted by historical split and dividend events. (provider: polygon) + prepost : bool + Include Pre and Post market data. (provider: yfinance) + actions : bool + Include actions. (provider: yfinance) + auto_adjust : bool + Adjust all OHLC data automatically. (provider: yfinance) + back_adjust : bool + Attempt to adjust all the data automatically. (provider: yfinance) + progress : bool + Show progress bar. (provider: yfinance) + ignore_tz : bool + When combining from different timezones, ignore that part of datetime. (provider: yfinance) + rounding : bool + Round to two decimal places? (provider: yfinance) + repair : bool + Detect currency unit 100x mixups and attempt repair. (provider: yfinance) + keepna : bool + Keep NaN rows returned by Yahoo? (provider: yfinance) + group_by : Literal['ticker', 'column'] + Group by ticker or column. (provider: yfinance) Returns ------- OBBject results : List[StockHistorical] Serializable results. - provider : Union[Literal['fmp', 'intrinio', 'polygon'], None] + provider : Union[Literal['alpha_vantage', 'cboe', 'fmp', 'intrinio', 'polygon', 'yfinance'], None] Provider name. warnings : Optional[List[Warning_]] List of warnings. @@ -130,6 +281,18 @@ class ROUTER_stocks(Container): The volume of the symbol. vwap : Optional[PositiveFloat] Volume Weighted Average Price of the symbol. + adjusted_close : Optional[PositiveFloat] + The adjusted close price of the symbol. (provider: alpha_vantage) + dividend_amount : Optional[NonNegativeFloat] + Dividend amount paid for the corresponding date. (provider: alpha_vantage) + split_coefficient : Optional[NonNegativeFloat] + Split coefficient for the corresponding date. (provider: alpha_vantage) + calls_volume : Optional[float] + Number of calls traded during the most recent trading period. Only valid if interval is 1m. (provider: cboe) + puts_volume : Optional[float] + Number of puts traded during the most recent trading period. Only valid if interval is 1m. (provider: cboe) + total_options_volume : Optional[float] + Total number of options traded during the most recent trading period. Only valid if interval is 1m. (provider: cboe) adj_close : Optional[float] Adjusted Close Price of the symbol. (provider: fmp) unadjusted_volume : Optional[float] @@ -162,6 +325,7 @@ class ROUTER_stocks(Container): "end_date": end_date, }, extra_params=kwargs, + chart=chart, ) return self._command_runner.run( @@ -180,6 +344,7 @@ class ROUTER_stocks(Container): Union[int, None], OpenBBCustomParameter(description="The number of data entries to return."), ] = 100, + chart: bool = False, provider: Union[Literal["fmp"], None] = None, **kwargs ) -> OBBject[List]: @@ -191,6 +356,8 @@ class ROUTER_stocks(Container): Symbol to get data for. limit : Union[int, None] The number of data entries to return. + chart : bool + Whether to create a chart or not, by default False. provider : Union[Literal['fmp'], None] The provider to use for the query, by default None. If None, the provider specified in defaults is selected or 'fmp' if there is @@ -342,6 +509,7 @@ class ROUTER_stocks(Container): "limit": limit, }, extra_params=kwargs, + chart=chart, ) return self._command_runner.run( @@ -359,7 +527,10 @@ class ROUTER_stocks(Container): Union[pydantic.types.NonNegativeInt, None], OpenBBCustomParameter(description="Number of results to return per page."), ] = 20, - provider: Union[Literal["benzinga", "fmp", "intrinio", "polygon"], None] = None, + chart: bool = False, + provider: Union[ + Literal["benzinga", "fmp", "intrinio", "polygon", "yfinance"], None + ] = None, **kwargs ) -> OBBject[List]: """Get news for one or more stock tickers. @@ -370,7 +541,9 @@ class ROUTER_stocks(Container): Comma separated list of symbols. limit : Union[pydantic.types.NonNegativeInt, None] Number of results to return per page. - provider : Union[Literal['benzinga', 'fmp', 'intrinio', 'polygon'], None] + chart : bool + Whether to create a chart or not, by default False. + provider : Union[Literal['benzinga', 'fmp', 'intrinio', 'polygon', 'yfinance... The provider to use for the query, by default None. If None, the provider specified in defaults is selected or 'benzinga' if there is no default. @@ -410,7 +583,7 @@ class ROUTER_stocks(Container): OBBject results : List[StockNews] Serializable results. - provider : Union[Literal['benzinga', 'fmp', 'intrinio', 'polygon'], None] + provider : Union[Literal['benzinga', 'fmp', 'intrinio', 'polygon', 'yfinance'], None] Provider name. warnings : Optional[List[Warning_]] List of warnings. @@ -455,10 +628,18 @@ class ROUTER_stocks(Container): Image URL. (provider: polygon) keywords : Optional[List[str]] Keywords in the article (provider: polygon) - publisher : Optional[PolygonPublisher] - Publisher of the article. (provider: polygon) + publisher : Union[PolygonPublisher, NoneType, str] + Publisher of the article. (provider: polygon, yfinance) tickers : Optional[List[str]] - Tickers covered in the article. (provider: polygon)""" # noqa: E501 + Tickers covered in the article. (provider: polygon) + uuid : Optional[str] + Unique identifier for the news article (provider: yfinance) + type : Optional[str] + Type of the news article (provider: yfinance) + thumbnail : Optional[List[T]] + Thumbnail related data to the ticker news article. (provider: yfinance) + related_tickers : Optional[str] + Tickers related to the news article. (provider: yfinance)""" # noqa: E501 inputs = filter_inputs( provider_choices={ @@ -469,6 +650,7 @@ class ROUTER_stocks(Container): "limit": limit, }, extra_params=kwargs, + chart=chart, ) return self._command_runner.run( @@ -624,3 +806,70 @@ class ROUTER_stocks(Container): "/stocks/quote", **inputs, ) + + @validate_arguments + def search( + self, + query: typing_extensions.Annotated[ + str, OpenBBCustomParameter(description="Search query.") + ] = "", + ticker: typing_extensions.Annotated[ + bool, + OpenBBCustomParameter(description="Whether to search by ticker symbol."), + ] = False, + provider: Union[Literal["cboe"], None] = None, + **kwargs + ) -> OBBject[List]: + """Search for a company or stock ticker. + + Parameters + ---------- + query : str + Search query. + ticker : bool + Whether to search by ticker symbol. + provider : Union[Literal['cboe'], None] + The provider to use for the query, by default None. + If None, the provider specified in defaults is selected or 'cboe' if there is + no default. + + Returns + ------- + OBBject + results : List[StockSearch] + Serializable results. + provider : Union[Literal['cboe'], None] + Provider name. + warnings : Optional[List[Warning_]] + List of warnings. + chart : Optional[Chart] + Chart object. + metadata: Optional[Metadata] + Metadata info about the command execution. + + StockSearch + ----------- + symbol : Optional[str] + Symbol to get data for. + name : Optional[str] + Name of the company. + dpm_name : Optional[str] + Name of the primary market maker. (provider: cboe) + post_station : Optional[str] + Post and station location on the CBOE trading floor. (provider: cboe)""" # noqa: E501 + + inputs = filter_inputs( + provider_choices={ + "provider": provider, + }, + standard_params={ + "query": query, + "ticker": ticker, + }, + extra_params=kwargs, + ) + + return self._command_runner.run( + "/stocks/search", + **inputs, + ) diff --git a/openbb_sdk/openbb/package/stocks_fa.py b/openbb_sdk/openbb/package/stocks_fa.py index b1d18dd62fc..07e3f2613bf 100644 --- a/openbb_sdk/openbb/package/stocks_fa.py +++ b/openbb_sdk/openbb/package/stocks_fa.py @@ -63,7 +63,7 @@ class ROUTER_stocks_fa(Container): pydantic.types.NonNegativeInt, OpenBBCustomParameter(description="The number of data entries to return."), ] = 12, - provider: Union[Literal["fmp", "intrinio", "polygon"], None] = None, + provider: Union[Literal["fmp", "intrinio", "polygon", "yfinance"], None] = None, **kwargs ) -> OBBject[List]: """Balance Sheet. @@ -76,7 +76,7 @@ class ROUTER_stocks_fa(Container): Period of the data to return. limit : NonNegativeInt The number of data entries to return. - provider : Union[Literal['fmp', 'intrinio', 'polygon'], None] + provider : Union[Literal['fmp', 'intrinio', 'polygon', 'yfinance'], None] The provider to use for the query, by default None. If None, the provider specified in defaults is selected or 'fmp' if there is no default. @@ -122,7 +122,7 @@ class ROUTER_stocks_fa(Container): OBBject results : List[BalanceSheet] Serializable results. - provider : Union[Literal['fmp', 'intrinio', 'polygon'], None] + provider : Union[Literal['fmp', 'intrinio', 'polygon', 'yfinance'], None] Provider name. warnings : Optional[List[Warning_]] List of warnings. @@ -498,7 +498,7 @@ class ROUTER_stocks_fa(Container): pydantic.types.NonNegativeInt, OpenBBCustomParameter(description="The number of data entries to return."), ] = 12, - provider: Union[Literal["fmp", "intrinio", "polygon"], None] = None, + provider: Union[Literal["fmp", "intrinio", "polygon", "yfinance"], None] = None, **kwargs ) -> OBBject[List]: """Cash Flow Statement. @@ -511,7 +511,7 @@ class ROUTER_stocks_fa(Container): Period of the data to return. limit : NonNegativeInt The number of data entries to return. - provider : Union[Literal['fmp', 'intrinio', 'polygon'], None] + provider : Union[Literal['fmp', 'intrinio', 'polygon', 'yfinance'], None] The provider to use for the query, by default None. If None, the provider specified in defaults is selected or 'fmp' if there is no default. @@ -557,7 +557,7 @@ class ROUTER_stocks_fa(Container): OBBject results : List[CashFlowStatement] Serializable results. - provider : Union[Literal['fmp', 'intrinio', 'polygon'], None] + provider : Union[Literal['fmp', 'intrinio', 'polygon', 'yfinance'], None] Provider name. warnings : Optional[List[Warning_]] List of warnings. @@ -1301,7 +1301,7 @@ class ROUTER_stocks_fa(Container): pydantic.types.NonNegativeInt, OpenBBCustomParameter(description="The number of data entries to return."), ] = 12, - provider: Union[Literal["fmp", "intrinio", "polygon"], None] = None, + provider: Union[Literal["fmp", "intrinio", "polygon", "yfinance"], None] = None, **kwargs ) -> OBBject[List]: """Income Statement. @@ -1314,7 +1314,7 @@ class ROUTER_stocks_fa(Container): Period of the data to return. limit : NonNegativeInt The number of data entries to return. - provider : Union[Literal['fmp', 'intrinio', 'polygon'], None] + provider : Union[Literal['fmp', 'intrinio', 'polygon', 'yfinance'], None] The provider to use for the query, by default None. If None, the provider specified in defaults is selected or 'fmp' if there is no default. @@ -1360,7 +1360,7 @@ class ROUTER_stocks_fa(Container): OBBject results : List[IncomeStatement] Serializable results. - provider : Union[Literal['fmp', 'intrinio', 'polygon'], None] + provider : Union[Literal['fmp', 'intrinio', 'polygon', 'yfinance'], None] Provider name. warnings : Optional[List[Warning_]] List of warnings. diff --git a/openbb_sdk/openbb/package/stocks_options.py b/openbb_sdk/openbb/package/stocks_options.py index 1d569b10d94..409f5b376ca 100644 --- a/openbb_sdk/openbb/package/stocks_options.py +++ b/openbb_sdk/openbb/package/stocks_options.py @@ -25,7 +25,7 @@ class ROUTER_stocks_options(Container): Union[str, List[str]], OpenBBCustomParameter(description="Symbol to get data for."), ], - provider: Union[Literal["intrinio"], None] = None, + provider: Union[Literal["cboe", "intrinio"], None] = None, **kwargs ) -> OBBject[List]: """Get the complete options chain for a ticker. @@ -34,9 +34,9 @@ class ROUTER_stocks_options(Container): ---------- symbol : Union[str, List[str]] Symbol to get data for. - provider : Union[Literal['intrinio'], None] + provider : Union[Literal['cboe', 'intrinio'], None] The provider to use for the query, by default None. - If None, the provider specified in defaults is selected or 'intrinio' if there is + If None, the provider specified in defaults is selected or 'cboe' if there is no default. date : Union[str, None] Date for which the options chains are returned. (provider: intrinio) @@ -46,7 +46,7 @@ class ROUTER_stocks_options(Container): OBBject results : List[OptionsChains] Serializable results. - provider : Union[Literal['intrinio'], None] + provider : Union[Literal['cboe', 'intrinio'], None] Provider name. warnings : Optional[List[Warning_]] List of warnings. @@ -108,7 +108,29 @@ class ROUTER_stocks_options(Container): theta : Optional[float] Theta of the option. vega : Optional[float] - Vega of the option.""" # noqa: E501 + Vega of the option. + bid_size : Optional[int] + Bid size for the option. (provider: cboe) + ask_size : Optional[int] + Ask size for the option. (provider: cboe) + theoretical : Optional[float] + Theoretical value of the option. (provider: cboe) + last_trade_price : Optional[float] + Last trade price of the option. (provider: cboe) + tick : Optional[str] + Whether the last tick was up or down in price. (provider: cboe) + prev_close : Optional[float] + Previous closing price of the option. (provider: cboe) + change : Optional[float] + Change in price of the option. (provider: cboe) + change_percent : Optional[float] + Change, in percent, of the option. (provider: cboe) + rho : Optional[float] + Rho of the option. (provider: cboe) + last_trade_timestamp : Optional[datetime] + Last trade timestamp of the option. (provider: cboe) + dte : Optional[int] + Days to expiration for the option. (provider: cboe)""" # noqa: E501 inputs = filter_inputs( provider_choices={ diff --git a/openbb_sdk/openbb/package/ta.py b/openbb_sdk/openbb/package/ta.py new file mode 100644 index 00000000000..c18832c3694 --- /dev/null +++ b/openbb_sdk/openbb/package/ta.py @@ -0,0 +1,1486 @@ +### THIS FILE IS AUTO-GENERATED. DO NOT EDIT. ### + +from typing import List, Literal, Union + +import openbb_provider +import pandas +import pydantic +import pydantic.types +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.static.container import Container +from openbb_core.app.static.filters import filter_inputs +from pydantic import validate_arguments + + +class ROUTER_ta(Container): + """/ta + ad + adosc + adx + aroon + atr + bbands + cci + cg + clenow + cones + demark + donchian + ema + fib + fisher + hma + ichimoku + kc + macd + obv + rsi + sma + stoch + vwap + wma + zlma + """ + + def __repr__(self) -> str: + return self.__doc__ or "" + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def ad( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + index: str = "date", + offset: int = 0, + ) -> OBBject[List]: + """ + The Accumulation/Distribution Line is similar to the On Balance + Volume (OBV), which sums the volume times +1/-1 based on whether the close is + higher than the previous close. The Accumulation/Distribution indicator, however + multiplies the volume by the close location value (CLV). The CLV is based on the + movement of the issue within a single bar and can be +1, -1 or zero. + + + The Accumulation/Distribution Line is interpreted by looking for a divergence in + the direction of the indicator relative to price. If the Accumulation/Distribution + Line is trending upward it indicates that the price may follow. Also, if the + Accumulation/Distribution Line becomes flat while the price is still rising (or falling) + then it signals an impending flattening of the price. + + Parameters + ---------- + data : List[Data] + List of data to be used for the calculation. + index : str, optional + Index column name to use with `data`, by default "date". + offset : int, optional + Offset of the AD, by default 0. + + Returns + ------- + OBBject[List[Data]] + + Examples + -------- + >>> from openbb import obb + >>> stock_data = obb.stocks.load(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> ad_data = obb.ta.ad(data=stock_data.results,offset=0) + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + index=index, + offset=offset, + ) + + return self._command_runner.run( + "/ta/ad", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def adosc( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + index: str = "date", + fast: pydantic.types.PositiveInt = 3, + slow: pydantic.types.PositiveInt = 10, + offset: int = 0, + ) -> OBBject[List]: + """ + Accumulation/Distribution Oscillator, also known as the Chaikin Oscillator + is essentially a momentum indicator, but of the Accumulation-Distribution line + rather than merely price. It looks at both the strength of price moves and the + underlying buying and selling pressure during a given time period. The oscillator + reading above zero indicates net buying pressure, while one below zero registers + net selling pressure. Divergence between the indicator and pure price moves are + the most common signals from the indicator, and often flag market turning points. + + Parameters + ---------- + data : List[Data] + List of data to be used for the calculation. + fast : PositiveInt, optional + Number of periods to be used for the fast calculation, by default 3. + slow : PositiveInt, optional + Number of periods to be used for the slow calculation, by default 10. + offset : int, optional + Offset to be used for the calculation, by default 0. + + Returns + ------- + OBBject[List[Data]] + + Examples + -------- + >>> from openbb import obb + >>> stock_data = obb.stocks.load(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> adosc_data = obb.ta.adosc(data=stock_data.results, fast=3, slow=10, offset=0) + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + index=index, + fast=fast, + slow=slow, + offset=offset, + ) + + return self._command_runner.run( + "/ta/adosc", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def adx( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + index: str = "date", + length: int = 50, + scalar: float = 100.0, + drift: int = 1, + chart: bool = False, + ) -> OBBject[List]: + """ + The ADX is a Welles Wilder style moving average of the Directional Movement Index (DX). + The values range from 0 to 100, but rarely get above 60. To interpret the ADX, consider + a high number to be a strong trend, and a low number, a weak trend. + + Parameters + ---------- + data : List[Data] + List of data to be used for the calculation. + index : str, optional + Index column name to use with `data`, by default "date". + length : int, optional + Number of periods for the ADX, by default 50. + scalar : float, optional + Scalar value for the ADX, by default 100.0. + drift : int, optional + Drift value for the ADX, by default 1. + + Returns + ------- + OBBject[List[Data]] + The calculated data. + + Examples + -------- + >>> from openbb import obb + >>> stock_data = obb.stocks.load(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> adx_data = obb.ta.adx(data=stock_data.results,length=50,scalar=100.0,drift=1) + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + index=index, + length=length, + scalar=scalar, + drift=drift, + chart=chart, + ) + + return self._command_runner.run( + "/ta/adx", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def aroon( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + index: str = "date", + length: int = 25, + scalar: int = 100, + chart: bool = False, + ) -> OBBject[List]: + """ + The word aroon is Sanskrit for "dawn's early light." The Aroon + indicator attempts to show when a new trend is dawning. The indicator consists + of two lines (Up and Down) that measure how long it has been since the highest + high/lowest low has occurred within an n period range. + + When the Aroon Up is + staying between 70 and 100 then it indicates an upward trend. When the Aroon Down + is staying between 70 and 100 then it indicates an downward trend. A strong upward + trend is indicated when the Aroon Up is above 70 while the Aroon Down is below 30. + Likewise, a strong downward trend is indicated when the Aroon Down is above 70 while + the Aroon Up is below 30. Also look for crossovers. When the Aroon Down crosses above + the Aroon Up, it indicates a weakening of the upward trend (and vice versa). + + Parameters + ---------- + data : List[Data] + List of data to be used for the calculation. + index: str, optional + Index column name to use with `data`, by default "date". + length : int, optional + Number of periods to be used for the calculation, by default 25. + scalar : int, optional + Scalar to be used for the calculation, by default 100. + + Returns + ------- + OBBject[List[Data]] + The calculated data. + + Examples + -------- + >>> from openbb import obb + >>> stock_data = obb.stocks.load(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> aroon_data = obb.ta.aroon(data=stock_data.results, length=25, scalar=100) + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + index=index, + length=length, + scalar=scalar, + chart=chart, + ) + + return self._command_runner.run( + "/ta/aroon", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def atr( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + index: str = "date", + length: pydantic.types.PositiveInt = 14, + mamode: Literal["rma", "ema", "sma", "wma"] = "rma", + drift: pydantic.types.NonNegativeInt = 1, + offset: int = 0, + ) -> OBBject[List]: + """ + Average True Range is used to measure volatility, especially volatility caused by + gaps or limit moves. + + Parameters + ---------- + data : List[Data] + List of data to apply the indicator to. + index : str, optional + Index column name, by default "date" + length : PositiveInt, optional + It's period, by default 14 + mamode : Literal["rma", "ema", "sma", "wma"], optional + Moving average mode, by default "rma" + drift : NonNegativeInt, optional + The difference period, by default 1 + offset : int, optional + How many periods to offset the result, by default 0 + + Returns + ------- + OBBject[List[Data]] + List of data with the indicator applied. + + Examples + -------- + >>> from openbb import obb + >>> stock_data = obb.stocks.load(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> atr_data = obb.ta.atr(data=stock_data.results) + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + index=index, + length=length, + mamode=mamode, + drift=drift, + offset=offset, + ) + + return self._command_runner.run( + "/ta/atr", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def bbands( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + target: str = "close", + index: str = "date", + length: int = 50, + std: pydantic.types.NonNegativeFloat = 2, + mamode: Literal["sma", "ema", "wma", "rma"] = "sma", + offset: int = 0, + ) -> OBBject[List]: + """ + Bollinger Bands consist of three lines. The middle band is a simple + moving average (generally 20 periods) of the typical price (TP). The upper and lower + bands are F standard deviations (generally 2) above and below the middle band. + The bands widen and narrow when the volatility of the price is higher or lower, + respectively. + + Bollinger Bands do not, in themselves, generate buy or sell signals; + they are an indicator of overbought or oversold conditions. When the price is near the + upper or lower band it indicates that a reversal may be imminent. The middle band + becomes a support or resistance level. The upper and lower bands can also be + interpreted as price targets. When the price bounces off of the lower band and crosses + the middle band, then the upper band becomes the price target. + + Parameters + ---------- + data : List[Data] + List of data to be used for the calculation. + target : str + Target column name. + index : str, optional + Index column name to use with `data`, by default "date". + length : int, optional + Number of periods to be used for the calculation, by default 50. + std : NonNegativeFloat, optional + Standard deviation to be used for the calculation, by default 2. + mamode : Literal["sma", "ema", "wma", "rma"], optional + Moving average mode to be used for the calculation, by default "sma". + offset : int, optional + Offset to be used for the calculation, by default 0. + + Returns + ------- + OBBject[List[Data]] + The calculated data. + + Examples + -------- + >>> from openbb import obb + >>> stock_data = obb.stocks.load(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> bbands = obb.ta.bbands( + >>> data=stock_data.results, target="close", length=50, std=2, mamode="sma", offset=0 + >>> ) + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + target=target, + index=index, + length=length, + std=std, + mamode=mamode, + offset=offset, + ) + + return self._command_runner.run( + "/ta/bbands", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def cci( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + index: str = "date", + length: pydantic.types.PositiveInt = 14, + scalar: pydantic.types.PositiveFloat = 0.015, + ) -> OBBject[List]: + """ + The CCI is designed to detect beginning and ending market trends. + The range of 100 to -100 is the normal trading range. CCI values outside of this + range indicate overbought or oversold conditions. You can also look for price + divergence in the CCI. If the price is making new highs, and the CCI is not, + then a price correction is likely. + + Parameters + ---------- + data : List[Data] + The data to use for the CCI calculation. + index : str, optional + Index column name to use with `data`, by default "date". + length : PositiveInt, optional + The length of the CCI, by default 14. + scalar : PositiveFloat, optional + The scalar of the CCI, by default 0.015. + + Returns + ------- + OBBject[List[Data]] + The CCI data. + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + index=index, + length=length, + scalar=scalar, + ) + + return self._command_runner.run( + "/ta/cci", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def cg( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + index: str = "date", + length: pydantic.types.PositiveInt = 14, + ) -> OBBject[List]: + """ + The Center of Gravity indicator, in short, is used to anticipate future price movements + and to trade on price reversals as soon as they happen. However, just like other oscillators, + the COG indicator returns the best results in range-bound markets and should be avoided when + the price is trending. Traders who use it will be able to closely speculate the upcoming + price change of the asset. + + Parameters + ---------- + data : List[Data] + The data to use for the COG calculation. + index : str, optional + Index column name to use with `data`, by default "date" + length : PositiveInt, optional + The length of the COG, by default 14 + + Returns + ------- + OBBject[List[Data]] + The COG data. + + Examples + -------- + >>> from openbb import obb + >>> stock_data = obb.stocks.load(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> cg_data = obb.ta.cg(data=stock_data.results, length=14) + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + index=index, + length=length, + ) + + return self._command_runner.run( + "/ta/cg", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def clenow( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + index: str = "date", + target: str = "adj_close", + period: pydantic.types.PositiveInt = 90, + ) -> OBBject[List]: + """ + Clenow Volatility Adjusted Momentum. + + Parameters + ---------- + data : List[Data] + List of data to be used for the calculation. + index : str, optional + Index column name to use with `data`, by default "date". + target : str, optional + Target column name, by default "adj_close". + period : PositiveInt, optional + Number of periods for the momentum, by default 90. + + Returns + ------- + OBBject[List[Data]] + The calculated data. + + Examples + -------- + >>> from openbb import obb + >>> stock_data = obb.stocks.load(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> clenow_data = obb.ta.clenow(data=stock_data.results,period=90) + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + index=index, + target=target, + period=period, + ) + + return self._command_runner.run( + "/ta/clenow", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def cones( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + index: str = "date", + lower_q: float = 0.25, + upper_q: float = 0.75, + model: Literal[ + "STD", + "Parkinson", + "Garman-Klass", + "Hodges-Tompkins", + "Rogers-Satchell", + "Yang-Zhang", + ] = "STD", + is_crypto: bool = False, + ) -> OBBject[List]: + """Calculate the realized volatility quantiles over rolling windows of time. + + The model for calculating volatility is selectable. + + Parameters + ---------- + data : List[Data] + The data to use for the calculation. + index : str, optional + Index column name to use with `data`, by default "date" + lower_q : float, optional + The lower quantile value for calculations + upper_q : float, optional + The upper quantile value for calculations + model : Literal["STD", "Parkinson", "Garman-Klass", "Hodges-Tompkins", "Rogers-Satchell", "Yang-Zhang"], optional + The model used to calculate realized volatility + + Standard deviation measures how widely returns are dispersed from the average return. + It is the most common (and biased) estimator of volatility. + + Parkinson volatility uses the high and low price of the day rather than just close to close prices. + It is useful for capturing large price movements during the day. + + Garman-Klass volatility extends Parkinson volatility by taking into account the opening and closing price. + As markets are most active during the opening and closing of a trading session; + it makes volatility estimation more accurate. + + Hodges-Tompkins volatility is a bias correction for estimation using an overlapping data sample. + It produces unbiased estimates and a substantial gain in efficiency. + + Rogers-Satchell is an estimator for measuring the volatility with an average return not equal to zero. + Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term, + mean return not equal to zero. + + Yang-Zhang volatility is the combination of the overnight (close-to-open volatility). + It is a weighted average of the Rogers-Satchell volatility and the open-to-close volatility. + is_crypto : bool, optional + Whether the data is crypto or not. If True, volatility is calculated for 365 days instead of 252 + + Returns + ------- + OBBject[List[Data]] + The cones data. + + Examples + -------- + >>> from openbb import obb + >>> stock_data = obb.stocks.load(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> cones_data = obb.ta.cones(data=stock_data.results, lower_q=0.25, upper_q=0.75, model="STD") + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + index=index, + lower_q=lower_q, + upper_q=upper_q, + model=model, + is_crypto=is_crypto, + ) + + return self._command_runner.run( + "/ta/cones", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def demark( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + index: str = "date", + target: str = "close", + show_all: bool = False, + asint: bool = False, + offset: int = 0, + ) -> OBBject[List]: + """ + Demark sequential indicator + + Parameters + ---------- + data : List[Data] + List of data to be used for the calculation. + index : str, optional + Index column name to use with `data`, by default "date". + target : str, optional + Target column name, by default "close". + show_all : bool, optional + Show 1 - 13. If set to False, show 6 - 9 + asint : bool, optional + If True, fill NAs with 0 and change type to int, by default False + offset : int, optional + How many periods to offset the result + + Returns + ------- + OBBject[List[Data]] + The calculated data. + + Examples + -------- + >>> from openbb import obb + >>> stock_data = obb.stocks.load(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> demark_data = obb.ta.demark(data=stock_data.results,offset=0) + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + index=index, + target=target, + show_all=show_all, + asint=asint, + offset=offset, + ) + + return self._command_runner.run( + "/ta/demark", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def donchian( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + index: str = "date", + lower_length: pydantic.types.PositiveInt = 20, + upper_length: pydantic.types.PositiveInt = 20, + offset: int = 0, + ) -> OBBject[List]: + """ + Donchian Channels are three lines generated by moving average + calculations that comprise an indicator formed by upper and lower + bands around a midrange or median band. The upper band marks the + highest price of a security over N periods while the lower band + marks the lowest price of a security over N periods. The area + between the upper and lower bands represents the Donchian Channel. + + Parameters + ---------- + data : List[Data] + List of data to be used for the calculation. + index : str, optional + Index column name to use with `data`, by default "date". + lower_length : PositiveInt, optional + Number of periods for the lower band, by default 20. + upper_length : PositiveInt, optional + Number of periods for the upper band, by default 20. + offset : int, optional + Offset of the Donchian Channel, by default 0. + + Returns + ------- + OBBject[List[Data]] + The calculated data. + + Examples + -------- + >>> from openbb import obb + >>> stock_data = obb.stocks.load(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> donchian_data = obb.ta.donchian(data=stock_data.results,lower_length=20,upper_length=20,offset=0) + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + index=index, + lower_length=lower_length, + upper_length=upper_length, + offset=offset, + ) + + return self._command_runner.run( + "/ta/donchian", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def ema( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + target: str = "close", + index: str = "date", + length: int = 50, + offset: int = 0, + chart: bool = False, + ) -> OBBject[List]: + """ + The Exponential Moving Average is a staple of technical + analysis and is used in countless technical indicators. In a Simple Moving + Average, each value in the time period carries equal weight, and values outside + of the time period are not included in the average. However, the Exponential + Moving Average is a cumulative calculation, including all data. Past values have + a diminishing contribution to the average, while more recent values have a greater + contribution. This method allows the moving average to be more responsive to changes + in the data. + + Parameters + ---------- + data : List[Data] + The data to use for the calculation. + target : str + Target column name. + index : str, optional + Index column name to use with `data`, by default "date" + length : int, optional + The length of the calculation, by default 50. + offset : int, optional + The offset of the calculation, by default 0. + + Returns + ------- + OBBject[List[Data]] + The calculated data. + + Examples + -------- + >>> from openbb import obb + >>> stock_data = obb.stocks.load(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> ema_data = obb.ta.ema(data=stock_data.results,target="close",length=50,offset=0) + + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + target=target, + index=index, + length=length, + offset=offset, + chart=chart, + ) + + return self._command_runner.run( + "/ta/ema", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def fib( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + index: str = "date", + close_column: Literal["close", "adj_close"] = "close", + period: pydantic.types.PositiveInt = 120, + start_date: Union[str, None] = None, + end_date: Union[str, None] = None, + ) -> OBBject[List]: + """Create Fibonacci Retracement Levels. + + Parameters + ---------- + data : List[Data] + List of data to apply the indicator to. + index : str, optional + Index column name, by default "date" + period : PositiveInt, optional + Period to calculate the indicator, by default 120 + + Returns + ------- + OBBject[List[Data]] + List of data with the indicator applied. + + Examples + -------- + >>> from openbb import obb + >>> stock_data = obb.stocks.load(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> fib_data = obb.ta.fib(data=stock_data.results, period=120) + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + index=index, + close_column=close_column, + period=period, + start_date=start_date, + end_date=end_date, + ) + + return self._command_runner.run( + "/ta/fib", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def fisher( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + index: str = "date", + length: pydantic.types.PositiveInt = 14, + signal: pydantic.types.PositiveInt = 1, + ) -> OBBject[List]: + """ + The Fisher Transform is a technical indicator created by John F. Ehlers + that converts prices into a Gaussian normal distribution.1 The indicator + highlights when prices have moved to an extreme, based on recent prices. + This may help in spotting turning points in the price of an asset. It also + helps show the trend and isolate the price waves within a trend. + + Parameters + ---------- + data : List[Data] + List of data to apply the indicator to. + index : str, optional + Index column name, by default "date" + length : PositiveInt, optional + Fisher period, by default 14 + signal : PositiveInt, optional + Fisher Signal period, by default 1 + + Returns + ------- + OBBject[List[Data]] + List of data with the indicator applied. + + Examples + -------- + >>> from openbb import obb + >>> stock_data = obb.stocks.load(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> fisher_data = obb.ta.fisher(data=stock_data.results, length=14, signal=1) + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + index=index, + length=length, + signal=signal, + ) + + return self._command_runner.run( + "/ta/fisher", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def hma( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + target: str = "close", + index: str = "date", + length: int = 50, + offset: int = 0, + chart: bool = False, + ) -> OBBject[List]: + """ + The Hull Moving Average solves the age old dilemma of making a moving average + more responsive to current price activity whilst maintaining curve smoothness. + In fact the HMA almost eliminates lag altogether and manages to improve smoothing + at the same time. + + Parameters + ---------- + data : List[Data] + List of data to be used for the calculation. + target : str + Target column name. + index : str, optional + Index column name to use with `data`, by default "date". + length : int, optional + Number of periods for the HMA, by default 50. + offset : int, optional + Offset of the HMA, by default 0. + + Returns + ------- + OBBject[List[Data]] + The calculated data. + + Examples + -------- + >>> from openbb import obb + >>> stock_data = obb.stocks.load(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> hma_data = obb.ta.hma(data=stock_data.results,target="close",length=50,offset=0) + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + target=target, + index=index, + length=length, + offset=offset, + chart=chart, + ) + + return self._command_runner.run( + "/ta/hma", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def ichimoku( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + index: str = "date", + conversion: pydantic.types.PositiveInt = 9, + base: pydantic.types.PositiveInt = 26, + lagging: pydantic.types.PositiveInt = 52, + offset: pydantic.types.PositiveInt = 26, + lookahead: bool = False, + ) -> OBBject[List]: + """ + The Ichimoku Cloud, also known as Ichimoku Kinko Hyo, is a versatile indicator that + defines support and resistance, identifies trend direction, gauges momentum and provides + trading signals. Ichimoku Kinko Hyo translates into "one look equilibrium chart". With + one look, chartists can identify the trend and look for potential signals within that trend. + + Parameters + ---------- + data : List[Data] + List of data to be used for the calculation. + index : str, optional + Index column name to use with `data`, by default "date". + conversion : PositiveInt, optional + Number of periods for the conversion line, by default 9. + base : PositiveInt, optional + Number of periods for the base line, by default 26. + lagging : PositiveInt, optional + Number of periods for the lagging span, by default 52. + offset : PositiveInt, optional + Number of periods for the offset, by default 26. + lookahead : bool, optional + drops the Chikou Span Column to prevent potential data leak + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + index=index, + conversion=conversion, + base=base, + lagging=lagging, + offset=offset, + lookahead=lookahead, + ) + + return self._command_runner.run( + "/ta/ichimoku", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def kc( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + index: str = "date", + length: pydantic.types.PositiveInt = 20, + scalar: pydantic.types.PositiveFloat = 20, + mamode: Literal["ema", "sma", "wma", "hma", "zlma"] = "ema", + offset: pydantic.types.NonNegativeInt = 0, + ) -> OBBject[List]: + """ + Keltner Channels are volatility-based bands that are placed + on either side of an asset's price and can aid in determining + the direction of a trend.The Keltner channel uses the average + true range (ATR) or volatility, with breaks above or below the top + and bottom barriers signaling a continuation. + + Parameters + ---------- + data : List[Data] + The data to use for the Keltner Channels calculation. + index : str, optional + Index column name to use with `data`, by default "date" + length : PositiveInt, optional + The length of the Keltner Channels, by default 20 + scalar : PositiveFloat, optional + The scalar to use for the Keltner Channels, by default 20 + mamode : Literal["ema", "sma", "wma", "hma", "zlma"], optional + The moving average mode to use for the Keltner Channels, by default "ema" + offset : NonNegativeInt, optional + The offset to use for the Keltner Channels, by default 0 + + Returns + ------- + OBBject[List[Data]] + The Keltner Channels data. + + Examples + -------- + >>> from openbb import obb + >>> stock_data = obb.stocks.load(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> kc_data = obb.ta.kc(data=stock_data.results, length=20, scalar=20, ma_mode="ema", offset=0) + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + index=index, + length=length, + scalar=scalar, + mamode=mamode, + offset=offset, + ) + + return self._command_runner.run( + "/ta/kc", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def macd( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + target: str = "close", + index: str = "date", + fast: int = 12, + slow: int = 26, + signal: int = 9, + chart: bool = False, + ) -> OBBject[List]: + """ + The Moving Average Convergence Divergence (MACD) is the difference + between two Exponential Moving Averages. The Signal line is an Exponential Moving + Average of the MACD. + + The MACD signals trend changes and indicates the start + of new trend direction. High values indicate overbought conditions, low values + indicate oversold conditions. Divergence with the price indicates an end to the + current trend, especially if the MACD is at extreme high or low values. When the MACD + line crosses above the signal line a buy signal is generated. When the MACD crosses + below the signal line a sell signal is generated. To confirm the signal, the MACD + should be above zero for a buy, and below zero for a sell. + + Parameters + ---------- + data : List[Data] + List of data to be used for the calculation. + target : str + Target column name. + fast : int, optional + Number of periods for the fast EMA, by default 12. + slow : int, optional + Number of periods for the slow EMA, by default 26. + signal : int, optional + Number of periods for the signal EMA, by default 9. + + Returns + ------- + OBBject[List[Data]] + The calculated data. + + Examples + -------- + >>> from openbb import obb + >>> stock_data = obb.stocks.load(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> macd_data = obb.ta.macd(data=stock_data.results,target="close",fast=12,slow=26,signal=9) + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + target=target, + index=index, + fast=fast, + slow=slow, + signal=signal, + chart=chart, + ) + + return self._command_runner.run( + "/ta/macd", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def obv( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + index: str = "date", + offset: int = 0, + ) -> OBBject[List]: + """ + The On Balance Volume (OBV) is a cumulative total of the up and + down volume. When the close is higher than the previous close, the volume is added + to the running total, and when the close is lower than the previous close, the volume + is subtracted from the running total. + + To interpret the OBV, look for the OBV + to move with the price or precede price moves. If the price moves before the OBV, + then it is a non-confirmed move. A series of rising peaks, or falling troughs, in the + OBV indicates a strong trend. If the OBV is flat, then the market is not trending. + + Parameters + ---------- + data : List[Data] + List of data to apply the indicator to. + index : str, optional + Index column name, by default "date" + offset : int, optional + How many periods to offset the result, by default 0. + + Returns + ------- + OBBject[List[Data]] + List of data with the indicator applied. + + Examples + -------- + >>> from openbb import obb + >>> stock_data = obb.stocks.load(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> obv_data = obb.ta.obv(data=stock_data.results, offset=0) + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + index=index, + offset=offset, + ) + + return self._command_runner.run( + "/ta/obv", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def rsi( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + target: str = "close", + index: str = "date", + length: int = 14, + scalar: float = 100.0, + drift: int = 1, + chart: bool = False, + ) -> OBBject[List]: + """ + The Relative Strength Index (RSI) calculates a ratio of the + recent upward price movements to the absolute price movement. The RSI ranges + from 0 to 100. The RSI is interpreted as an overbought/oversold indicator when + the value is over 70/below 30. You can also look for divergence with price. If + the price is making new highs/lows, and the RSI is not, it indicates a reversal. + + Parameters + ---------- + data : List[Data] + The data to use for the RSI calculation. + target : str + Target column name. + index : str, optional + Index column name to use with `data`, by default "date" + length : int, optional + The length of the RSI, by default 14 + scalar : float, optional + The scalar to use for the RSI, by default 100.0 + drift : int, optional + The drift to use for the RSI, by default 1 + + Returns + ------- + OBBject[List[Data]] + The RSI data. + + Examples + -------- + >>> from openbb import obb + >>> stock_data = obb.stocks.load(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> rsi_data = obb.ta.rsi(data=stock_data.results, target="close", length=14, scalar=100.0, drift=1) + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + target=target, + index=index, + length=length, + scalar=scalar, + drift=drift, + chart=chart, + ) + + return self._command_runner.run( + "/ta/rsi", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def sma( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + target: str = "close", + index: str = "date", + length: int = 50, + offset: int = 0, + chart: bool = False, + ) -> OBBject[List]: + """ + Moving Averages are used to smooth the data in an array to + help eliminate noise and identify trends. The Simple Moving Average is literally + the simplest form of a moving average. Each output value is the average of the + previous n values. In a Simple Moving Average, each value in the time period carries + equal weight, and values outside of the time period are not included in the average. + This makes it less responsive to recent changes in the data, which can be useful for + filtering out those changes. + + Parameters + ---------- + data : List[Data] + List of data to be used for the calculation. + target : str + Target column name. + index : str, optional + Index column name to use with `data`, by default "date". + length : int, optional + Number of periods to be used for the calculation, by default 50. + offset : int, optional + Offset from the current period, by default 0. + + Returns + ------- + OBBject[List[Data]] + The calculated data. + + Examples + -------- + >>> from openbb import obb + >>> stock_data = obb.stocks.load(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> sma_data = obb.ta.sma(data=stock_data.results,target="close",length=50,offset=0) + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + target=target, + index=index, + length=length, + offset=offset, + chart=chart, + ) + + return self._command_runner.run( + "/ta/sma", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def stoch( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + index: str = "date", + fast_k_period: pydantic.types.NonNegativeInt = 14, + slow_d_period: pydantic.types.NonNegativeInt = 3, + slow_k_period: pydantic.types.NonNegativeInt = 3, + ) -> OBBject[List]: + """ + The Stochastic Oscillator measures where the close is in relation + to the recent trading range. The values range from zero to 100. %D values over 75 + indicate an overbought condition; values under 25 indicate an oversold condition. + When the Fast %D crosses above the Slow %D, it is a buy signal; when it crosses + below, it is a sell signal. The Raw %K is generally considered too erratic to use + for crossover signals. + + Parameters + ---------- + data : List[Data] + The data to use for the Stochastic Oscillator calculation. + index : str, optional + Index column name to use with `data`, by default "date". + fast_k_period : NonNegativeInt, optional + The fast %K period, by default 14. + slow_d_period : NonNegativeInt, optional + The slow %D period, by default 3. + slow_k_period : NonNegativeInt, optional + The slow %K period, by default 3. + + Returns + ------- + OBBject[List[Data]] + The Stochastic Oscillator data. + + Examples + -------- + >>> from openbb import obb + >>> stock_data = obb.stocks.load(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> stoch_data = obb.ta.stoch(data=stock_data.results, fast_k_period=14, slow_d_period=3, slow_k_period=3) + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + index=index, + fast_k_period=fast_k_period, + slow_d_period=slow_d_period, + slow_k_period=slow_k_period, + ) + + return self._command_runner.run( + "/ta/stoch", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def vwap( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + index: str = "date", + anchor: str = "D", + offset: int = 0, + ) -> OBBject[List]: + """ + The Volume Weighted Average Price that measures the average typical price + by volume. It is typically used with intraday charts to identify general direction. + + Parameters + ---------- + data : List[Data] + List of data to be used for the calculation. + index : str, optional + Index column name to use with `data`, by default "date". + anchor : str, optional + Anchor period to use for the calculation, by default "D". + See Timeseries Offset Aliases below for additional options: + https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases + offset : int, optional + Offset from the current period, by default 0. + + Returns + ------- + OBBject[List[Data]] + The calculated data. + + Examples + -------- + >>> from openbb import obb + >>> stock_data = obb.stocks.load(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> vwap_data = obb.ta.vwap(data=stock_data.results,anchor="D",offset=0) + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + index=index, + anchor=anchor, + offset=offset, + ) + + return self._command_runner.run( + "/ta/vwap", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def wma( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + target: str = "close", + index: str = "date", + length: int = 50, + offset: int = 0, + chart: bool = False, + ) -> OBBject[List]: + """ + A Weighted Moving Average puts more weight on recent data and less on past data. + This is done by multiplying each bar’s price by a weighting factor. Because of its + unique calculation, WMA will follow prices more closely than a corresponding Simple + Moving Average. + + Parameters + ---------- + data : List[Data] + The data to use for the calculation. + target : str + Target column name. + index : str, optional + Index column name to use with `data`, by default "date". + length : int, optional + The length of the WMA, by default 50. + offset : int, optional + The offset of the WMA, by default 0. + + Returns + ------- + OBBject[List[Data]] + The WMA data. + + Examples + -------- + >>> from openbb import obb + >>> stock_data = obb.stocks.load(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> wma_data = obb.ta.wma(data=stock_data.results, target="close", length=50, offset=0) + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + target=target, + index=index, + length=length, + offset=offset, + chart=chart, + ) + + return self._command_runner.run( + "/ta/wma", + **inputs, + ) + + @validate_arguments(config=dict(arbitrary_types_allowed=True)) + def zlma( + self, + data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame], + target: str = "close", + index: str = "date", + length: int = 50, + offset: int = 0, + chart: bool = False, + ) -> OBBject[List]: + """ + The zero lag exponential moving average (ZLEMA) indicator + was created by John Ehlers and Ric Way. The idea is do a + regular exponential moving average (EMA) calculation but + on a de-lagged data instead of doing it on the regular data. + Data is de-lagged by removing the data from "lag" days ago + thus removing (or attempting to) the cumulative effect of + the moving average. + + Parameters + ---------- + data : List[Data] + List of data to be used for the calculation. + target : str + Target column name. + index : str, optional + Index column name to use with `data`, by default "date". + length : int, optional + Number of periods to be used for the calculation, by default 50. + offset : int, optional + Offset to be used for the calculation, by default 0. + + Returns + ------- + OBBject[List[Data]] + The calculated data. + + Examples + -------- + >>> from openbb import obb + >>> stock_data = obb.stocks.load(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> zlma_data = obb.ta.zlma(data=stock_data.results, target="close", length=50, offset=0) + """ # noqa: E501 + + inputs = filter_inputs( + data=data, + target=target, + index=index, + length=length, + offset=offset, + chart=chart, + ) + + return self._command_runner.run( + "/ta/zlma", + **inputs, + ) diff --git a/openbb_sdk/providers/cboe/tests/.gitkeep b/openbb_sdk/providers/cboe/tests/.gitkeep deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/openbb_sdk/providers/fmp/tests/.gitkeep b/openbb_sdk/providers/fmp/tests/.gitkeep deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/openbb_sdk/providers/fred/tests/.gitkeep b/openbb_sdk/providers/fred/tests/.gitkeep deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/openbb_sdk/providers/intrinio/tests/.gitkeep b/openbb_sdk/providers/intrinio/tests/.gitkeep deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/openbb_sdk/providers/polygon/tests/.gitkeep b/openbb_sdk/providers/polygon/tests/.gitkeep deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/openbb_sdk/providers/quandl/tests/.gitkeep b/openbb_sdk/providers/quandl/tests/.gitkeep deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/openbb_sdk/providers/yfinance/openbb_yfinance/models/futures_historical.py b/openbb_sdk/providers/yfinance/openbb_yfinance/models/futures_historical.py index b7a704e2eff..15dfda1cbd9 100644 --- a/openbb_sdk/providers/yfinance/openbb_yfinance/models/futures_historical.py +++ b/openbb_sdk/providers/yfinance/openbb_yfinance/models/futures_historical.py @@ -122,8 +122,7 @@ class YFinanceFuturesHistoricalFetcher( else 0 ) if query.start_date: - data.set_index("date", inplace=True) - data.index = to_datetime(data.index) + data.index = to_datetime(data.index).tz_convert(None) start_date_dt = datetime.combine(query.start_date, datetime.min.time()) end_date_dt = datetime.combine(query.end_date, datetime.min.time()) @@ -134,7 +133,7 @@ class YFinanceFuturesHistoricalFetcher( ] data.reset_index(inplace=True) - data.rename(columns={"index": "date"}, inplace=True) + data.rename(columns={"index": "Date"}, inplace=True) return data.to_dict("records") diff --git a/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_balance_sheet_fetcher.yaml b/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_balance_sheet_fetcher.yaml index 0a88b094a26..3c69c4d6c82 100644 --- a/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_balance_sheet_fetcher.yaml +++ b/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_balance_sheet_fetcher.yaml @@ -5,7 +5,7 @@ interactions: Accept: - '*/*' Accept-Encoding: - - gzip, deflate + - gzip, deflate, br Connection: - keep-alive method: GET @@ -13,93 +13,93 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAAM1dW1PcyJL+LzwbRV2lkt+wwXPY8IUwzNmHjXmQuwvQTrfEqtXYPhP+75vqC1Sp - ckASWT28jIEYsj6qv8rKe/111JZLu7JNaVdHb/86auxqvWiP3v7PX0dL2xbdj1Y/l9/qBfzo6OTk - 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finance-company-fundamentals-api--mtls-production-sg3.finance-k8s.svc.yahoo.local:4080/* + - finance-company-fundamentals-api--mtls-production-ir2.finance-k8s.svc.yahoo.local:4080/* x-envoy-upstream-service-time: - - '59' + - '42' x-request-id: - - cde03e0f-cfde-4090-a6a2-99515fca03fa + - bfacfa9b-fd6e-4698-bd06-46960e9b0726 x-yahoo-request-id: - - 2bl3j0higk0t8 + - 0c9mhqpih5imb y-rid: - - 2bl3j0higk0t8 + - 0c9mhqpih5imb status: code: 200 message: OK diff --git a/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_cash_flow_statement_fetcher.yaml b/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_cash_flow_statement_fetcher.yaml index 2642c211a24..3f0d9429b5c 100644 --- a/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_cash_flow_statement_fetcher.yaml +++ b/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_cash_flow_statement_fetcher.yaml @@ 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- 5qfqqkhih5imb status: code: 200 message: OK diff --git a/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_crypto_historical_fetcher.yaml b/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_crypto_historical_fetcher.yaml index e23bdaf08d4..8565be70168 100644 --- a/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_crypto_historical_fetcher.yaml +++ b/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_crypto_historical_fetcher.yaml @@ -5,14 +5,14 @@ interactions: Accept: - '*/*' Accept-Encoding: - - gzip, deflate + - gzip, deflate, br Connection: - keep-alive method: GET uri: https://query2.finance.yahoo.com/v8/finance/chart/BTC-USD?interval=1d&range=1d response: body: - string: '{"chart":{"result":[{"meta":{"currency":"USD","symbol":"BTC-USD","exchangeName":"CCC","instrumentType":"CRYPTOCURRENCY","firstTradeDate":1410912000,"regularMarketTime":1695154980,"gmtoffset":0,"timezone":"UTC","exchangeTimezoneName":"UTC","regularMarketPrice":27160.805,"chartPreviousClose":26774.715,"priceHint":2,"currentTradingPeriod":{"pre":{"timezone":"UTC","start":1695081600,"end":1695081600,"gmtoffset":0},"regular":{"timezone":"UTC","start":1695081600,"end":1695167940,"gmtoffset":0},"post":{"timezone":"UTC","start":1695167940,"end":1695167940,"gmtoffset":0}},"dataGranularity":"1d","range":"1d","validRanges":["1d","5d","1mo","3mo","6mo","1y","2y","5y","10y","ytd","max"]},"timestamp":[1695154980],"indicators":{"quote":[{"low":[26683.6328125],"high":[27457.267578125],"close":[27160.8046875],"volume":[13805281280],"open":[26774.71484375]}],"adjclose":[{"adjclose":[27160.8046875]}]}}],"error":null}}' + string: 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+ - 64e62c46-e5d4-485a-b9e0-a053f478661d x-yahoo-request-id: - - 3p1b27pigk0t4 + - 40169ltih5im8 y-rid: - - 3p1b27pigk0t4 + - 40169ltih5im8 status: code: 200 message: OK diff --git a/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_forex_historical_fetcher.yaml b/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_forex_historical_fetcher.yaml index a4e1ecb43e1..72381872907 100644 --- a/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_forex_historical_fetcher.yaml +++ b/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_forex_historical_fetcher.yaml @@ -5,14 +5,14 @@ interactions: Accept: - '*/*' Accept-Encoding: - - gzip, deflate + - gzip, deflate, br Connection: - keep-alive method: GET uri: https://query2.finance.yahoo.com/v8/finance/chart/EURUSD=X?interval=1d&range=1d response: body: - string: 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+ string: '{"chart":{"result":[{"meta":{"currency":"USD","symbol":"EURUSD=X","exchangeName":"CCY","instrumentType":"CURRENCY","firstTradeDate":1070236800,"regularMarketTime":1695730335,"gmtoffset":3600,"timezone":"BST","exchangeTimezoneName":"Europe/London","regularMarketPrice":1.061,"chartPreviousClose":1.0594,"priceHint":4,"currentTradingPeriod":{"pre":{"timezone":"BST","start":1695682800,"end":1695682800,"gmtoffset":3600},"regular":{"timezone":"BST","start":1695682800,"end":1695769140,"gmtoffset":3600},"post":{"timezone":"BST","start":1695769140,"end":1695769140,"gmtoffset":3600}},"dataGranularity":"1d","range":"1d","validRanges":["1d","5d","1mo","3mo","6mo","1y","2y","5y","10y","ytd","max"]},"timestamp":[1695730335],"indicators":{"quote":[{"close":[1.0610079765319824],"high":[1.0610079765319824],"volume":[0],"open":[1.0594342947006226],"low":[1.057305932044983]}],"adjclose":[{"adjclose":[1.0610079765319824]}]}}],"error":null}}' headers: Age: - '0' @@ -33,25 +33,25 @@ interactions: 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nmefmmL0dSdeyVjyl0B159SzeYwptCBu9e37PBksmkmiFN1NVDO+kj+wrUubxOvjIjw822BLXoIk + j42zRUUGWKav+N7wis/f0SgyLfzJpcOSdImNHLcR0Yw67aVflhH3HFXU7uHX0ecyBPEWKBnWSQ4p + sZz0VeIIh1D872z+n5VvBE2wf9RhAWPrUyIyt7yZLBrqU0iSJEfniEMRaIGkQ25W1NrJlFfPbWTa + d8xYlA/G9hPXICEDlcPrWfciroLX0ob2/RmhpNuPKwEaDm8hlNFi+a2s5lJ0p5x3KFIPrJrHWW2K + zaaCJRyBXPcQTnHbtzERyyq1Rvi7dN5m0u4waa+tnbutQzEyot2p1kn4ivLlVxb9ZADCQe3HFNEU + hJgExWekrwRopXj2ghaB7lwTwmfW1CzTCRq/ikOZBzN5SsTaHjSro/hMIubYTj5GWXJiptirPQYU + 7DGJsX6pRaaQAR+z9bqFd8KbTbU9rvVonZg9Cw1Yh64brBJFbs+TpfCSK0jtV3xzGATw1u40wi42 + LiaqvGskiAU2m6yJz2xwyvR+e7ye8ilnGpxl6igN6pkzM7ZsfP3QaD/2aRjkCKBB63ULz/JsncJd + q7jz1iHtMNYV1EImUXf6848Iv3IDOihx5/Fg1pWkyvqBJMOk0vbZeopCNrzY8XeOkMIBbPwr3NzL + cYUmvvVQekqk2drNU12XoG00h57J0qDlhWcQoQib+tCR6EYkgpol0xR+nr94GBUuwAxxDjwyPbRW + HwaZZtpYfPB0mcxSH0R0StIRI/DBzMcFjg/9/DQOmOxmKvf2bOvoRvbqtgzOt9Pd23dho3PtFUux + 4D9nK9qvrsPCmUNQwdkZIAiF0mq8YuUkAVaSlcP7pA3lpjCMYqiPNrrVpwyRhcrbgOoPiagPocUD + Y8azZku/PuMgaAk3wC3G3PVNUNXwe9aiEDLbcsHHBlZncB6DJdzR7KYS0NFkPvaSNSU9aa4kvHwX + cSRY/Sfpb0o8gCH+FAPfus9zIiQXEGyxuOfktu33XtP8uu5xkvhlAifJieLxXp/EA3yc+HKQhn6e + kX+m//vOjMII785gOIcCOVzaqBjy5dCd+JRTk/h5Cx2SNmO/dy3Gd8VGEofpqnBv7AMYsll80XQ8 + bOhvI3u3TgvXWkbPsfa/BxYmhU7nmiORA55SmLT2/aHUobHc/vx7n0PxZQgwm3CMfAp8Hm0KM9l6 + T6j+9zl0586ssosKZtoeG6mv58932RaT+RESXx3aojS5hf1sdWUMH2HwKvyqIycMB8rWYUhM6+1y + Fgbb191Q/fMt9xbAx89ZE8ZzMEH6bNCOqN0Td1Ycgc8hUVxLlH7o6jmyxEAM4QxXxqHShdHz1Yjj + c+hMzFZo/YxE8+1+skbrypgmnwKCXKGWTDCTnFWm+5cZlf2xrsIaH1puFvkH8V7ukC1JVvL7XDMl + d35U3edzjd+byd991S884+fBnmvO/cqZ0jRGgAl2r0Nn/k0kHfiwQ7zH9MbV+eTbbx7DwL5t68sX + x/1t6MEc0k75gfW45+/MXT69C3ISvplDZzyMjf681PWHUwQBppif259DplbhDoTMXW8O09mYPUwd + tEe3k2Aoid71XB2RlFJhptHJww0PobAIS6m//XPpOs98qQfkPPNi2JXtlw7Rw+gyuCzXDUsHYDTB + emipZy2Pi2Ivbj71VoAoT6Y4S1nWKAb0ldtPz4IpuuaRd496/Vvi+XD/qG2Jl/FeXcBqHWPzi9yT + YrCKF1U/Ctga89NaEvn/TZB+KsH6Q/w+eNFIG6a0VZ+VHWlJhc0y8gZixtSikkAT3OjA3rUjz0f8 + rss9q/IMVSMMDYa1Gbj8sy3J9N7as3qCRG04LudQLiQDF44Tc/QqCawCG3H+UlReZTKM7lc8VUZm + SaeWabpVeYPzffdeX990JMiQ58HoBPYYIN0JO+7gOGELcVh7Css9bMxzp5oZVdAvTKY3+5ccvNN9 + n8JOWEZaWVTLdHFnRzNeZriELDVSNLnYCTg6IKC/GtB3AuqrmkLUtn5+Ssx7VJ6k7OTna8dMTURt + E6ZVoYP+cb2fLOFus9jBHIk8s6GX3ukrVPOID44c7zYozHPU8EXbvRg0E1HKvuwQo7ea80hGXz2p + XcW8XNwjrSiZzYgmmNSM0Qmu1uw6Ik0AzoI5UxU6pekUl8apTPqF1nkIMsrtku9CQJV2c6yPtM6e + hMwsjKDFOF6GKZk3HeB6xlrp8wwZFq71VeZXeSeHTMo6lXnbyEuGdNhDOgGiRXoSPXl3rmsH6S65 + JlpSNnX5UDiw05Oqka8CVQFoh/H2dMVRklLgKaG/c/pixLanQyRf6qDehAlb9j8Lb3Uzd146EZDl + ATmdmcpfzT6ra0xge9HajeArmw5q59Jq+xziIIoDF/OKvx4QNQ8qDTynaAXiaU5rrgr/7vjAO/Ez + Ex7myLHlG5DOZbCppyBMjSAUJW0KhVm4Hsimu7rP8oNvxsy7XtokzRMsDIt8IHtkj3gJW7czDGSl + Ma8jwYW7SShh8KcWD+JYUZOWt2e9X0eklxWzUNmEn2oES6e7pX67l0igo1Ws08pjlYTHpunTx2Oc + TElTqteLdGFA+3WI7fx1x0t2P/q6xLFreD7WLdxjQPsTiIeJSFpkvmpnrO+iMk5o9r6NTNdNJcAv + tY22PZ52JIS0qawHJylBkhGYu3XuIe9/t3EbGKjEP2szzcnojJJAdMbZo2PfY1CiiiekPlq/CElm + 748z1Tq9b0UcQ78tkuHPQzc0eGpuW8vVnfMVo2pkt8x98ylxInH/UIjmyItPBasLnNonpPSW8mRj + 2wCIJXYj547V0jXaEhQDhohoHv0SMvvibV6LQw4p7vyDesQWXJcu51fEy5vFBq64mdU+qGyv1hx7 + rGe0jWsFY0pqXLJkXj03NgciJ/qXHQS/zBsjcYjap2KlC/p+3NjeVNkrMMc++bhjThpndBxxvkeb + gdxxla++9Xz/lI2b2yzCVsudVRiRIz7pxK0UiqH7ME4x2mN2v7TXjj7QMIwTzVkQTcZvaMW251aF + mwLKB1Fc1P0DyEuv3ZMXXI/xek6Pv1DjokBmr89wn0i1i84yx22qXwmTHoYReufGRUlBVAeWiD9B + SV4N1AkGg/XuvsVug0iBKcDf2i6XyBXjonB3WqwN0xV9Uj5ss9Zbpe7Syabwbw6nJSatU/iKVX6F + uMikMHKfd/DIygqxtegufQ5bfMqW4SSe8MfQHxPIUb+QIcTc9qa91YfYZydqqq8hNWtZmHgXW2Bz + NR+zkCoa+CLALHnS99quyI+k48kOtEOYrS4/KTDecNNjXnlX63WNS6GN1x6Lk3q9wvNCtnE3arEA + YRswB1qpRysE5yX/Sv/dactAVzrKy3FKFpqq+P5ZM0mR27mg8rRe41xNgCxqXvUuN75heQtMecuO + zGF992jTz14bUKfYGsYb+C+TI1eNbTECZHtzfYV4LtlHaETmIeoVCPwpeMCnI4OSejRmnGWlV5/C + 1h3hy23ysUem+qzR9A6TLpfcqAZx5L2ozID0tqjAuIYTIlWuw/wJxcUkaa27xU3GbTa7vctIfIrn + K67lPVqLmesnOImr0dnTJJyhZ/GpC4PJz/ajus961jzFlwjKz17QSn43ei0meZ2DPBk+eOej4Jxi + 7uy5JHn560mDlFoy12GSCNBDIBOpuY9HlAwNWIt00E/onWYHuaSoGjZLb+P+q7i5ROEk1oBP2dmH + zJzvZLB3+etSxcCtB/0aypV8OSKcpQcyR2y5XOnK9rNoijQ8H5IyBk9csOeW5IYJfJUt9jr8AORf + HXszyXqKeHNficl/q9pvTpE6kZ+961MrBqT6hSDH4XiRvjt10fI9SfL4HrkZ1NN9iHKACP0cQw4z + gvjlc07plRb5a40cc6RDYxXvoX6PvkSPdUSp0n6NU14EKqnq/PLlLw0bXf997fKGSnSjGDoSkr0O + j+G3qYoMC9YNy8iIYHO4h3yps+kOEWNbvad+6+L0373n//qf//U///O//vm//t///b//v//9f/0/ + nDL/53/+f3QJVrl4aAgA headers: Age: - - '0' + - '1' Connection: - keep-alive Expect-CT: @@ -3740,21 +3742,21 @@ interactions: content-type: - application/json;charset=utf-8 date: - - Tue, 19 Sep 2023 20:25:09 GMT + - Tue, 26 Sep 2023 12:12:56 GMT server: - ATS vary: - Origin,Accept-Encoding x-envoy-decorator-operation: - - finance-chart-api--mtls-production-sg3.finance-k8s.svc.yahoo.local:4080/* + - finance-chart-api--mtls-production-ir2.finance-k8s.svc.yahoo.local:4080/* x-envoy-upstream-service-time: - - '34' + - '35' x-request-id: - - 6925c008-41fd-40ef-a855-41baee8d31ab + - 5df45b62-d20d-4da4-bd42-9173ce64630c x-yahoo-request-id: - - 4c03q5pigk0t5 + - 02eu2blih5im9 y-rid: - - 4c03q5pigk0t5 + - 02eu2blih5im9 status: code: 200 message: OK diff --git a/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_futures_historical_fetcher.yaml b/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_futures_historical_fetcher.yaml index 5aec977884b..ce272b1ee31 100644 --- a/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_futures_historical_fetcher.yaml +++ b/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_futures_historical_fetcher.yaml @@ -5,17 +5,17 @@ interactions: Accept: - '*/*' Accept-Encoding: - - gzip, deflate + - gzip, deflate, br Connection: - keep-alive method: GET uri: https://query2.finance.yahoo.com/v8/finance/chart/ES=F?interval=1d&range=1d response: body: - string: '{"chart":{"result":[{"meta":{"currency":"USD","symbol":"ES=F","exchangeName":"CME","instrumentType":"FUTURE","firstTradeDate":969249600,"regularMarketTime":1695311200,"gmtoffset":-14400,"timezone":"EDT","exchangeTimezoneName":"America/New_York","regularMarketPrice":4401.5,"chartPreviousClose":4447.0,"priceHint":2,"currentTradingPeriod":{"pre":{"timezone":"EDT","start":1695268800,"end":1695268800,"gmtoffset":-14400},"regular":{"timezone":"EDT","start":1695268800,"end":1695355140,"gmtoffset":-14400},"post":{"timezone":"EDT","start":1695355140,"end":1695355140,"gmtoffset":-14400}},"dataGranularity":"1d","range":"1d","validRanges":["1d","5d","1mo","3mo","6mo","1y","2y","5y","10y","ytd","max"]},"timestamp":[1695311200],"indicators":{"quote":[{"low":[4395.0],"close":[4401.5],"volume":[993779],"open":[4445.0],"high":[4447.0]}],"adjclose":[{"adjclose":[4401.5]}]}}],"error":null}}' + string: '{"chart":{"result":[{"meta":{"currency":"USD","symbol":"ES=F","exchangeName":"CME","instrumentType":"FUTURE","firstTradeDate":969249600,"regularMarketTime":1695729778,"gmtoffset":-14400,"timezone":"EDT","exchangeTimezoneName":"America/New_York","regularMarketPrice":4363.5,"chartPreviousClose":4378.75,"priceHint":2,"currentTradingPeriod":{"pre":{"timezone":"EDT","start":1695700800,"end":1695700800,"gmtoffset":-14400},"regular":{"timezone":"EDT","start":1695700800,"end":1695787140,"gmtoffset":-14400},"post":{"timezone":"EDT","start":1695787140,"end":1695787140,"gmtoffset":-14400}},"dataGranularity":"1d","range":"1d","validRanges":["1d","5d","1mo","3mo","6mo","1y","2y","5y","10y","ytd","max"]},"timestamp":[1695729778],"indicators":{"quote":[{"close":[4363.5],"high":[4382.0],"volume":[201094],"open":[4382.0],"low":[4344.0]}],"adjclose":[{"adjclose":[4363.5]}]}}],"error":null}}' headers: Age: - - '1' + - '0' Connection: - keep-alive Expect-CT: @@ -33,25 +33,25 @@ interactions: cache-control: - public, max-age=10, stale-while-revalidate=20 content-length: - - '884' + - '885' content-type: - application/json;charset=utf-8 date: - - Thu, 21 Sep 2023 15:56:39 GMT + - Tue, 26 Sep 2023 12:12:58 GMT server: - ATS vary: - Origin,Accept-Encoding x-envoy-decorator-operation: - - finance-chart-api--mtls-production-sg3.finance-k8s.svc.yahoo.local:4080/* + - finance-chart-api--mtls-production-ir2.finance-k8s.svc.yahoo.local:4080/* x-envoy-upstream-service-time: - - '3' + - '2' x-request-id: - - f2c31509-0b1d-46ea-ada0-f23aa55a3a6d + - 7dbae154-1c52-40db-a974-c783d6c9d5bc x-yahoo-request-id: - - 45o2061igopto + - 4itskcpih5ima y-rid: - - 45o2061igopto + - 4itskcpih5ima status: code: 200 message: OK @@ -61,17 +61,17 @@ interactions: Accept: - '*/*' Accept-Encoding: - - gzip, deflate + - gzip, deflate, br Connection: - keep-alive method: GET uri: https://query2.finance.yahoo.com/v8/finance/chart/ES=F?events=div%2Csplits%2CcapitalGains&includePrePost=False&interval=1d&period1=MOCK_PERIOD_1&period2=MOCK_PERIOD_2 response: body: - string: '{"chart":{"result":[{"meta":{"currency":"USD","symbol":"ES=F","exchangeName":"CME","instrumentType":"FUTURE","firstTradeDate":969249600,"regularMarketTime":1695311200,"gmtoffset":-14400,"timezone":"EDT","exchangeTimezoneName":"America/New_York","regularMarketPrice":4401.5,"chartPreviousClose":3846.0,"priceHint":2,"currentTradingPeriod":{"pre":{"timezone":"EDT","start":1695268800,"end":1695268800,"gmtoffset":-14400},"regular":{"timezone":"EDT","start":1695268800,"end":1695355140,"gmtoffset":-14400},"post":{"timezone":"EDT","start":1695355140,"end":1695355140,"gmtoffset":-14400}},"dataGranularity":"1d","range":"","validRanges":["1d","5d","1mo","3mo","6mo","1y","2y","5y","10y","ytd","max"]},"timestamp":[1672722000,1672808400,1672894800,1672981200,1673240400,1673326800],"indicators":{"quote":[{"low":[3814.5,3836.5,3822.5,3819.0,3909.75,3891.5],"high":[3906.75,3896.25,3885.5,3928.75,3973.25,3943.75],"close":[3846.0,3874.5,3829.0,3915.5,3913.75,3940.75],"volume":[1782473,1912155,1679973,2026154,1697887,1520955],"open":[3895.0,3842.75,3871.0,3833.0,3918.5,3914.5]}],"adjclose":[{"adjclose":[3846.0,3874.5,3829.0,3915.5,3913.75,3940.75]}]}}],"error":null}}' + string: '{"chart":{"result":[{"meta":{"currency":"USD","symbol":"ES=F","exchangeName":"CME","instrumentType":"FUTURE","firstTradeDate":969249600,"regularMarketTime":1695729778,"gmtoffset":-14400,"timezone":"EDT","exchangeTimezoneName":"America/New_York","regularMarketPrice":4363.5,"chartPreviousClose":3846.0,"priceHint":2,"currentTradingPeriod":{"pre":{"timezone":"EDT","end":1695700800,"start":1695700800,"gmtoffset":-14400},"regular":{"timezone":"EDT","end":1695787140,"start":1695700800,"gmtoffset":-14400},"post":{"timezone":"EDT","end":1695787140,"start":1695787140,"gmtoffset":-14400}},"dataGranularity":"1d","range":"","validRanges":["1d","5d","1mo","3mo","6mo","1y","2y","5y","10y","ytd","max"]},"timestamp":[1672722000,1672808400,1672894800,1672981200,1673240400,1673326800],"indicators":{"quote":[{"low":[3814.5,3836.5,3822.5,3819.0,3909.75,3891.5],"open":[3895.0,3842.75,3871.0,3833.0,3918.5,3914.5],"high":[3906.75,3896.25,3885.5,3928.75,3973.25,3943.75],"volume":[1782473,1912155,1679973,2026154,1697887,1520955],"close":[3846.0,3874.5,3829.0,3915.5,3913.75,3940.75]}],"adjclose":[{"adjclose":[3846.0,3874.5,3829.0,3915.5,3913.75,3940.75]}]}}],"error":null}}' headers: Age: - - '0' + - '1' Connection: - keep-alive Expect-CT: @@ -93,21 +93,21 @@ interactions: content-type: - application/json;charset=utf-8 date: - - Thu, 21 Sep 2023 15:56:40 GMT + - Tue, 26 Sep 2023 12:12:58 GMT server: - ATS vary: - Origin,Accept-Encoding x-envoy-decorator-operation: - - finance-chart-api--mtls-production-sg3.finance-k8s.svc.yahoo.local:4080/* + - finance-chart-api--mtls-production-ir2.finance-k8s.svc.yahoo.local:4080/* x-envoy-upstream-service-time: - - '5' + - '3' x-request-id: - - 3ec9768d-f778-4c28-93d2-a4b5e4a44c4b + - bb8c4bf3-b256-4fde-9bb4-f49d36757303 x-yahoo-request-id: - - 3uh35g9igopto + - 1q5gtbtih5imb y-rid: - - 3uh35g9igopto + - 1q5gtbtih5imb status: code: 200 message: OK diff --git a/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_income_statement_fetcher.yaml b/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_income_statement_fetcher.yaml index e17aa26e057..3780dd00448 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2lp9ldtigk0t8 + - 0s8cljhih5imc status: code: 200 message: OK diff --git a/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_major_indices_historical_fetcher.yaml b/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_major_indices_historical_fetcher.yaml index 3daf2ad5eca..3c0005e0406 100644 --- a/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_major_indices_historical_fetcher.yaml +++ b/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_major_indices_historical_fetcher.yaml @@ -5,14 +5,14 @@ interactions: Accept: - '*/*' Accept-Encoding: - - gzip, deflate + - gzip, deflate, br Connection: - keep-alive method: GET uri: https://query2.finance.yahoo.com/v8/finance/chart/SPY?interval=1d&range=1d response: body: - string: '{"chart":{"result":[{"meta":{"currency":"USD","symbol":"SPY","exchangeName":"PCX","instrumentType":"ETF","firstTradeDate":728317800,"regularMarketTime":1695153600,"gmtoffset":-14400,"timezone":"EDT","exchangeTimezoneName":"America/New_York","regularMarketPrice":442.71,"chartPreviousClose":443.63,"priceHint":2,"currentTradingPeriod":{"pre":{"timezone":"EDT","start":1695110400,"end":1695130200,"gmtoffset":-14400},"regular":{"timezone":"EDT","start":1695130200,"end":1695153600,"gmtoffset":-14400},"post":{"timezone":"EDT","start":1695153600,"end":1695168000,"gmtoffset":-14400}},"dataGranularity":"1d","range":"1d","validRanges":["1d","5d","1mo","3mo","6mo","1y","2y","5y","10y","ytd","max"]},"timestamp":[1695153600],"indicators":{"quote":[{"high":[443.2900085449219],"volume":[64338730],"open":[442.67999267578125],"close":[442.7099914550781],"low":[439.94000244140625]}],"adjclose":[{"adjclose":[442.7099914550781]}]}}],"error":null}}' + string: '{"chart":{"result":[{"meta":{"currency":"USD","symbol":"SPY","exchangeName":"PCX","instrumentType":"ETF","firstTradeDate":728317800,"regularMarketTime":1695672000,"gmtoffset":-14400,"timezone":"EDT","exchangeTimezoneName":"America/New_York","regularMarketPrice":432.23,"chartPreviousClose":430.42,"priceHint":2,"currentTradingPeriod":{"pre":{"timezone":"EDT","end":1695735000,"start":1695715200,"gmtoffset":-14400},"regular":{"timezone":"EDT","end":1695758400,"start":1695735000,"gmtoffset":-14400},"post":{"timezone":"EDT","end":1695772800,"start":1695758400,"gmtoffset":-14400}},"dataGranularity":"1d","range":"1d","validRanges":["1d","5d","1mo","3mo","6mo","1y","2y","5y","10y","ytd","max"]},"timestamp":[1695648600],"indicators":{"quote":[{"close":[432.2300109863281],"volume":[70782000],"open":[429.1700134277344],"high":[432.2699890136719],"low":[428.7200012207031]}],"adjclose":[{"adjclose":[432.2300109863281]}]}}],"error":null}}' headers: Age: - '0' @@ -33,25 +33,25 @@ interactions: cache-control: - public, max-age=10, stale-while-revalidate=20 content-length: - - '939' + - '937' content-type: - application/json;charset=utf-8 date: - - Tue, 19 Sep 2023 20:25:09 GMT + - Tue, 26 Sep 2023 12:12:57 GMT server: - ATS vary: - Origin,Accept-Encoding x-envoy-decorator-operation: - - finance-chart-api--mtls-production-sg3.finance-k8s.svc.yahoo.local:4080/* + - finance-chart-api--mtls-canary-production-ir2.finance-k8s.svc.yahoo.local:4080/* x-envoy-upstream-service-time: - - '31' + - '34' x-request-id: - - 9374c3d7-c7f5-415a-8e20-66df9f7eabbb + - 5e2e9eb0-5584-410c-a81b-0ebd382fa344 x-yahoo-request-id: - - 6j6rlqligk0t5 + - 76kv87pih5im9 y-rid: - - 6j6rlqligk0t5 + - 76kv87pih5im9 status: code: 200 message: OK @@ -61,7 +61,7 @@ interactions: Accept: - '*/*' Accept-Encoding: - - gzip, deflate + - gzip, deflate, br Connection: - keep-alive method: GET @@ -69,113 +69,115 @@ interactions: response: body: string: !!binary | - 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vary: - Origin,Accept-Encoding x-envoy-decorator-operation: - - finance-chart-api--mtls-production-sg3.finance-k8s.svc.yahoo.local:4080/* + - finance-chart-api--mtls-production-ir2.finance-k8s.svc.yahoo.local:4080/* x-envoy-upstream-service-time: - - '36' + - '34' x-request-id: - - b15db388-8092-4fbe-90df-811abbacaebf + - 644b4947-243a-4d12-aea6-39dcd0905f0c x-yahoo-request-id: - - 6cuos7higk0t6 + - 6g505jtih5im9 y-rid: - - 6cuos7higk0t6 + - 6g505jtih5im9 status: code: 200 message: OK diff --git a/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_stock_historical_fetcher.yaml b/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_stock_historical_fetcher.yaml index 9337c4df381..4638ab72b47 100644 --- a/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_stock_historical_fetcher.yaml +++ b/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_stock_historical_fetcher.yaml @@ -5,17 +5,17 @@ interactions: Accept: - '*/*' Accept-Encoding: - - gzip, deflate + - gzip, deflate, br Connection: - keep-alive method: GET uri: https://query2.finance.yahoo.com/v8/finance/chart/AAPL?interval=1d&range=1d response: body: - string: '{"chart":{"result":[{"meta":{"currency":"USD","symbol":"AAPL","exchangeName":"NMS","instrumentType":"EQUITY","firstTradeDate":345479400,"regularMarketTime":1695311644,"gmtoffset":-14400,"timezone":"EDT","exchangeTimezoneName":"America/New_York","regularMarketPrice":175.55,"chartPreviousClose":175.49,"priceHint":2,"currentTradingPeriod":{"pre":{"timezone":"EDT","start":1695283200,"end":1695303000,"gmtoffset":-14400},"regular":{"timezone":"EDT","start":1695303000,"end":1695326400,"gmtoffset":-14400},"post":{"timezone":"EDT","start":1695326400,"end":1695340800,"gmtoffset":-14400}},"dataGranularity":"1d","range":"1d","validRanges":["1d","5d","1mo","3mo","6mo","1y","2y","5y","10y","ytd","max"]},"timestamp":[1695311644],"indicators":{"quote":[{"high":[176.3000030517578],"volume":[25881397],"open":[174.5500030517578],"close":[175.5500030517578],"low":[174.4987030029297]}],"adjclose":[{"adjclose":[175.5500030517578]}]}}],"error":null}}' + string: '{"chart":{"result":[{"meta":{"currency":"USD","symbol":"AAPL","exchangeName":"NMS","instrumentType":"EQUITY","firstTradeDate":345479400,"regularMarketTime":1695672001,"gmtoffset":-14400,"timezone":"EDT","exchangeTimezoneName":"America/New_York","regularMarketPrice":176.08,"chartPreviousClose":174.79,"priceHint":2,"currentTradingPeriod":{"pre":{"timezone":"EDT","end":1695735000,"start":1695715200,"gmtoffset":-14400},"regular":{"timezone":"EDT","end":1695758400,"start":1695735000,"gmtoffset":-14400},"post":{"timezone":"EDT","end":1695772800,"start":1695758400,"gmtoffset":-14400}},"dataGranularity":"1d","range":"1d","validRanges":["1d","5d","1mo","3mo","6mo","1y","2y","5y","10y","ytd","max"]},"timestamp":[1695648600],"indicators":{"quote":[{"low":[174.14999389648438],"open":[174.1999969482422],"high":[176.97000122070312],"volume":[46119600],"close":[176.0800018310547]}],"adjclose":[{"adjclose":[176.0800018310547]}]}}],"error":null}}' headers: Age: - - '0' + - '1' Connection: - keep-alive Expect-CT: @@ -33,25 +33,25 @@ interactions: cache-control: - public, max-age=10, stale-while-revalidate=20 content-length: - - '941' + - '943' content-type: - application/json;charset=utf-8 date: - - Thu, 21 Sep 2023 15:54:06 GMT + - Tue, 26 Sep 2023 12:12:57 GMT server: - ATS vary: - Origin,Accept-Encoding x-envoy-decorator-operation: - - finance-chart-api--mtls-baseline-production-sg3.finance-k8s.svc.yahoo.local:4080/* + - finance-chart-api--mtls-production-ir2.finance-k8s.svc.yahoo.local:4080/* x-envoy-upstream-service-time: - - '39' + - '35' x-request-id: - - cb29f7e9-ce57-469d-8605-dacad0e978ca + - d7e5ecf8-8ade-41ed-a96b-dc0f049e2ff8 x-yahoo-request-id: - - 3e5oi45igopou + - 0e4vq4hih5ima y-rid: - - 3e5oi45igopou + - 0e4vq4hih5ima status: code: 200 message: OK @@ -61,7 +61,7 @@ interactions: Accept: - '*/*' Accept-Encoding: - - gzip, deflate + - gzip, deflate, br Connection: - keep-alive method: GET @@ -69,111 +69,112 @@ interactions: response: body: string: !!binary | - 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af4778c0-800f-4a0e-969d-1ad6abcd7fbd + - 1275a198-8d33-497a-a606-db0d28f9f018 x-yahoo-request-id: - - 44k8ok5igopou + - 69p8ss9ih5ima y-rid: - - 44k8ok5igopou + - 69p8ss9ih5ima status: code: 200 message: OK diff --git a/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_stock_news_fetcher.yaml b/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_stock_news_fetcher.yaml index b95592a5464..41275292459 100644 --- a/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_stock_news_fetcher.yaml +++ b/openbb_sdk/providers/yfinance/tests/record/http/test_yfinance_fetchers/test_y_finance_stock_news_fetcher.yaml @@ -5,7 +5,7 @@ interactions: Accept: - '*/*' Accept-Encoding: - - gzip, deflate + - gzip, deflate, br Connection: - keep-alive method: GET @@ -13,47 +13,44 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAAL1Z23LbOBL9FZSqUvtiSATBq990tRVbF1uSb1spFwiAEmVeFIK0LKeyNR+xL/t7 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457ldf5igk0t7 + - 6v8bd35ih5imb y-rid: - - 457ldf5igk0t7 + - 6v8bd35ih5imb status: code: 200 message: OK diff --git a/openbb_sdk/sdk/core/openbb_core/app/router.py b/openbb_sdk/sdk/core/openbb_core/app/router.py index 65009b29b68..326068a3724 100644 --- a/openbb_sdk/sdk/core/openbb_core/app/router.py +++ b/openbb_sdk/sdk/core/openbb_core/app/router.py @@ -382,6 +382,9 @@ class CommandMap: self._command_coverage = self.get_command_coverage( router=self._router, sep=coverage_sep ) + self._commands_model = self.get_commands_model( + router=self._router, sep=coverage_sep + ) @property def map(self) -> Dict[str, Callable]: @@ -395,6 +398,10 @@ class CommandMap: def command_coverage(self) -> Dict[str, List[str]]: return self._command_coverage + @property + def commands_model(self) -> Dict[str, List[str]]: + return self._commands_model + @staticmethod def get_command_map( router: Router, @@ -460,6 +467,26 @@ class CommandMap: coverage_map[rp] = providers return coverage_map + @staticmethod + def get_commands_model( + router: Router, sep: Optional[str] = None + ) -> Dict[str, List[str]]: + api_router = router.api_router + + coverage_map: Dict[Any, Any] = {} + for route in api_router.routes: + openapi_extra = getattr(route, "openapi_extra") + if openapi_extra: + model = openapi_extra.get("model", None) + if model and hasattr(route, "path"): + rp = ( + route.path if sep is None else route.path.replace("/", sep) # type: ignore + ) + if route.path not in coverage_map: # type: ignore + coverage_map[rp] = [] + coverage_map[rp] = model + return coverage_map + def get_command(self, route: str) -> Optional[Callable]: return self._map.get(route, None) diff --git a/pyproject.toml b/pyproject.toml index 0627a3e4b0b..bff792e019c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -203,13 +203,28 @@ target-version = "py38" # This is an introductory addition of ruff. We should look to adding: # D: pydocstyle, PD: pandas-vet # All options here: https://github.com/charliermarsh/ruff#supported-rules -select = ["E", "W", "F", "Q", "S", "UP", "I", "PLC", "PLE", "PLR", "PLW", "SIM", "T20"] +select = [ + "E", + "W", + "F", + "Q", + "S", + "UP", + "I", + "PLC", + "PLE", + "PLR", + "PLW", + "SIM", + "T20", +] # These ignores should be seen as temporary solutions to problems that will NEED fixed ignore = ["PLR2004", "PLR0913", "PLR0915"] exclude = ["^openbb_sdk/sdk/core/openbb_core/app/static/package/.*"] [tool.ruff.per-file-ignores] "*tests/*" = ["S101"] +"*integration/*" = ["S101"] "*init*.py" = ["F401"] "website/*" = ["T201"]