mirror of
https://github.com/OpenBB-finance/OpenBB.git
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[V5] Remove openbb-alpha-vantage (#7522)
* delete alpha vantage * more files --------- Co-authored-by: Theodore Aptekarev <aptekarev@gmail.com>
This commit is contained in:
Danglewood
14
assets/extensions/provider.json
vendored
14
assets/extensions/provider.json
vendored
@@ -1,18 +1,4 @@
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[
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{
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"packageName": "openbb-alpha-vantage",
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"optional": true,
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"reprName": "Alpha Vantage",
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"description": "Alpha Vantage provides realtime and historical\nfinancial market data through a set of powerful and developer-friendly data APIs\nand spreadsheets. From traditional asset classes (e.g., stocks, ETFs, mutual funds)\nto economic indicators, from foreign exchange rates to commodities,\nfrom fundamental data to technical indicators, Alpha Vantage\nis your one-stop-shop for enterprise-grade global market data delivered through\ncloud-based APIs, Excel, and Google Sheets. ",
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"credentials": [
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"alpha_vantage_api_key"
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],
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"deprecatedCredentials": {
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"API_KEY_ALPHAVANTAGE": "alpha_vantage_api_key"
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},
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"website": "https://www.alphavantage.co",
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"instructions": "Go to: https://www.alphavantage.co/support/#api-key\n\n\n\nFill out the form, pass Captcha, and click on, \"GET FREE API KEY\"."
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},
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{
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"packageName": "openbb-benzinga",
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"optional": false,
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@@ -54,7 +54,6 @@ openbb-regulators = { path = "./extensions/regulators", develop = true }
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openbb-mcp-server = { path = "./extensions/mcp_server", develop = true, markers = "python_version >= '3.10'" }
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# Community dependencies
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openbb-alpha-vantage = { path = "./providers/alpha_vantage", optional = true, develop = true }
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openbb-cboe = { path = "./providers/cboe", optional = true, develop = true }
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openbb-deribit = { path = "./providers/deribit", optional = true, develop = true }
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openbb-ecb = { path = "./providers/ecb", optional = true, develop = true }
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@@ -1097,17 +1097,6 @@ def test_equity_compare_groups(params, headers):
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@pytest.mark.parametrize(
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"params",
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[
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(
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{
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"adjustment": "unadjusted",
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"extended_hours": True,
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"provider": "alpha_vantage",
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"symbol": "AAPL",
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"start_date": "2023-01-01",
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"end_date": "2023-06-06",
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"interval": "15m",
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}
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),
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(
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{
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"provider": "cboe",
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@@ -1880,17 +1869,7 @@ def test_equity_market_snapshots(params, headers):
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@pytest.mark.parametrize(
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"params",
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[
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({"symbol": "AAPL", "limit": 5, "provider": "fmp"}),
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(
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{
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"symbol": "AAPL",
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"period": "quarter",
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"limit": 5,
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"provider": "alpha_vantage",
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}
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),
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],
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[({"symbol": "AAPL", "limit": 5, "provider": "fmp"})],
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)
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@pytest.mark.integration
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def test_equity_fundamental_historical_eps(params, headers):
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@@ -1038,17 +1038,6 @@ def test_equity_compare_groups(params, obb):
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@pytest.mark.parametrize(
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"params",
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[
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(
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{
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"adjustment": "unadjusted",
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"extended_hours": True,
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"provider": "alpha_vantage",
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"symbol": "AAPL",
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"start_date": "2023-01-01",
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"end_date": "2023-06-06",
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"interval": "15m",
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}
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),
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(
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{
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"provider": "cboe",
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@@ -1769,14 +1758,6 @@ def test_equity_market_snapshots(params, obb):
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"params",
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[
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({"symbol": "AAPL", "limit": 5, "provider": "fmp"}),
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(
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{
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"symbol": "AAPL",
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"period": "quarter",
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"limit": 5,
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"provider": "alpha_vantage",
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}
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),
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],
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)
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@pytest.mark.integration
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@@ -55,17 +55,6 @@ def test_etf_search(params, headers):
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@pytest.mark.parametrize(
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"params",
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[
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(
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{
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"adjustment": "unadjusted",
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"extended_hours": True,
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"provider": "alpha_vantage",
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"symbol": "SPY",
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"start_date": "2023-01-01",
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"end_date": "2023-06-06",
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"interval": "15m",
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}
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),
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(
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{
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"provider": "cboe",
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@@ -52,17 +52,6 @@ def test_etf_search(params, obb):
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@pytest.mark.parametrize(
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"params",
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[
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(
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{
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"adjustment": "unadjusted",
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"extended_hours": True,
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"provider": "alpha_vantage",
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"symbol": "SPY",
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"start_date": "2023-01-01",
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"end_date": "2023-06-06",
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"interval": "15m",
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}
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),
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(
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{
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"provider": "cboe",
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@@ -1,13 +0,0 @@
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# OpenBB Alpha Vantage Provider
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This extension integrates the [Alpha Vantage](https://www.alphavantage.co/) data provider into the OpenBB Platform.
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## Installation
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To install the extension:
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```bash
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pip install openbb-alpha-vantage
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```
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Documentation available [here](https://docs.openbb.co/platform/developer_guide/contributing).
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@@ -1 +0,0 @@
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"""Alpha Vantage Provider."""
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@@ -1,26 +0,0 @@
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"""Alpha Vantage Provider module."""
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from openbb_alpha_vantage.models.equity_historical import AVEquityHistoricalFetcher
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from openbb_alpha_vantage.models.historical_eps import AVHistoricalEpsFetcher
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from openbb_core.provider.abstract.provider import Provider
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alpha_vantage_provider = Provider(
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name="alpha_vantage",
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website="https://www.alphavantage.co",
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description="""Alpha Vantage provides realtime and historical
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financial market data through a set of powerful and developer-friendly data APIs
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and spreadsheets. From traditional asset classes (e.g., stocks, ETFs, mutual funds)
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to economic indicators, from foreign exchange rates to commodities,
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from fundamental data to technical indicators, Alpha Vantage
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is your one-stop-shop for enterprise-grade global market data delivered through
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cloud-based APIs, Excel, and Google Sheets. """,
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credentials=["api_key"],
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fetcher_dict={
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"EquityHistorical": AVEquityHistoricalFetcher,
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"HistoricalEps": AVHistoricalEpsFetcher,
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"EtfHistorical": AVEquityHistoricalFetcher,
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},
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repr_name="Alpha Vantage",
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deprecated_credentials={"API_KEY_ALPHAVANTAGE": "alpha_vantage_api_key"},
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instructions='Go to: https://www.alphavantage.co/support/#api-key\n\n\n\nFill out the form, pass Captcha, and click on, "GET FREE API KEY".', # noqa: E501 pylint: disable=line-too-long
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)
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@@ -1 +0,0 @@
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"""Alpha Vantage models."""
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@@ -1,308 +0,0 @@
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"""Alpha Vantage Equity Historical Price Model."""
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# pylint: disable=unused-argument
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from datetime import datetime
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from typing import Any, Literal
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from warnings import warn
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from openbb_core.provider.abstract.fetcher import Fetcher
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from openbb_core.provider.standard_models.equity_historical import (
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EquityHistoricalData,
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EquityHistoricalQueryParams,
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)
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from openbb_core.provider.utils.descriptions import (
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DATA_DESCRIPTIONS,
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QUERY_DESCRIPTIONS,
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)
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from pydantic import (
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Field,
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NonNegativeFloat,
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PositiveFloat,
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)
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class AVEquityHistoricalQueryParams(EquityHistoricalQueryParams):
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"""Alpha Vantage Equity Historical Price Query.
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Source: https://www.alphavantage.co/documentation/#time-series-data
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"""
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__json_schema_extra__ = {
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"symbol": {"multiple_items_allowed": True},
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"interval": {"choices": ["1m", "5m", "15m", "30m", "60m", "1d", "1W", "1M"]},
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}
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interval: Literal["1m", "5m", "15m", "30m", "60m", "1d", "1W", "1M"] = Field(
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default="1d",
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description=QUERY_DESCRIPTIONS.get("interval", ""),
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)
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adjustment: Literal["splits_only", "splits_and_dividends", "unadjusted"] = Field(
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description="The adjustment factor to apply. 'splits_only' is not supported for intraday data.",
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default="splits_only",
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)
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extended_hours: bool = Field(
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description="Include Pre and Post market data.",
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default=False,
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)
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class AVEquityHistoricalData(EquityHistoricalData):
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"""Alpha Vantage Equity Historical Price Data."""
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__alias_dict__ = {
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"adj_close": "adjusted_close",
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"dividend": "dividend_amount",
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"split_ratio": "split_factor",
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}
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adj_close: PositiveFloat | None = Field(
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default=None, description=DATA_DESCRIPTIONS.get("adj_close", "")
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)
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dividend: NonNegativeFloat | None = Field(
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default=None,
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description="Dividend amount, if a dividend was paid.",
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)
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split_ratio: NonNegativeFloat | None = Field(
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default=None,
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description="Split coefficient, if a split occurred.",
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)
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class AVEquityHistoricalFetcher(
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Fetcher[
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AVEquityHistoricalQueryParams,
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list[AVEquityHistoricalData],
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]
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):
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"""Transform the query, extract and transform the data from the AlphaVantage endpoints."""
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@staticmethod
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def transform_query(params: dict[str, Any]) -> AVEquityHistoricalQueryParams:
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"""Transform the query."""
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# pylint: disable=import-outside-toplevel
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from dateutil.relativedelta import relativedelta
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transformed_params = params
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now = datetime.now().date()
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if params.get("start_date") is None:
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transformed_params["start_date"] = now - relativedelta(years=1)
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if params.get("end_date") is None:
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transformed_params["end_date"] = now
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return AVEquityHistoricalQueryParams(**transformed_params)
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@staticmethod
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async def aextract_data(
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query: AVEquityHistoricalQueryParams,
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credentials: dict[str, str] | None,
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**kwargs: Any,
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) -> list[dict]:
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"""Return the raw data from the Alpha Vantage endpoint."""
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# pylint: disable=import-outside-toplevel
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import asyncio # noqa
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from io import BytesIO # noqa
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from openbb_alpha_vantage.utils.helpers import ( # noqa
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INTERVALS_DICT,
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calculate_adjusted_prices,
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get_interval,
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)
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from openbb_core.provider.utils.helpers import ( # noqa
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amake_request,
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amake_requests,
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get_querystring,
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)
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from pandas import date_range, read_csv, to_datetime # noqa
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api_key = credentials.get("alpha_vantage_api_key") if credentials else ""
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intraday = False
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interval = get_interval(query.interval)
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query_str = get_querystring(
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query.model_dump(by_alias=True),
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[
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"start_date",
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"end_date",
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"interval",
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"symbol",
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"adjustment",
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"extended_hours",
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],
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)
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function = INTERVALS_DICT[query.interval[-1]]
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if query.adjustment != "unadjusted":
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function += "_ADJUSTED"
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query_str += f"&function={function}&outputsize=full&datatype=csv"
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if "INTRADAY" in function:
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query_str += f"&interval={interval}&extended_hours={str(query.extended_hours).lower()}"
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intraday = True
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results = []
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async def callback(response, _):
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"""Use callback function to process the response."""
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try:
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result = await response.json()
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if "Information" in result:
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warn(str(result.get("Information")))
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except Exception as _:
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# TODO: This is hacky, find a better solution.
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return await response.read()
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return await response.read()
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async def intraday_callback(response, _):
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"""Use callback function to process the intraday response."""
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symbol = response.url.query.get("symbol", None)
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data = await response.read()
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if data:
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df = read_csv(BytesIO(data))
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if len(df) > 0:
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df.rename(
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columns={
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"timestamp": "date",
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},
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inplace=True,
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)
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if len(query.symbol.split(",")) > 1:
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df["symbol"] = symbol
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results.extend(df.to_dict("records"))
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if not data:
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warn(f"Symbol Error: No data found for {symbol}")
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return results
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async def get_one(symbol, intraday: bool = False):
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"""Get data for one symbol."""
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if intraday is True:
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adjusted = query.adjustment != "unadjusted"
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if query.adjustment == "splits_only":
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warn(
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"Intraday does not support 'splits_only'. Using 'splits_and_dividends' instead."
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)
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url = (
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f"https://www.alphavantage.co/query?{query_str.replace('_ADJUSTED', '')}"
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f"&symbol={symbol}&adjusted={str(adjusted).lower()}"
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)
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dates = (
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date_range(start=query.start_date, end=query.end_date)
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.strftime("%Y-%m")
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.unique()
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.tolist()
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)
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urls = [f"{url}&month={date}&apikey={api_key}" for date in dates]
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return await amake_requests(urls, response_callback=intraday_callback)
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# We will resample the intervals ourselves to get the correct data.
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url = (
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f"https://www.alphavantage.co/query?{query_str.replace('MONTHLY', 'DAILY').replace('WEEKLY', 'DAILY')}"
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f"&symbol={symbol}&apikey={api_key}"
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)
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result = await amake_request(url, response_callback=callback)
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if not result:
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warn(f"Symbol Error: No data found for {symbol}")
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if result:
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data = read_csv(BytesIO(result)) # type: ignore
|
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if len(data) > 0:
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data.rename(
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columns={
|
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"timestamp": "date",
|
||||
"dividend_amount": "dividend",
|
||||
"adjusted close": "adj_close",
|
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"dividend amount": "dividend",
|
||||
"adjusted_close": "adj_close",
|
||||
"split_coefficient": "split_factor",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
if "date" in data.columns:
|
||||
data["date"] = data["date"].apply(to_datetime)
|
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data.set_index("date", inplace=True)
|
||||
# The returned data when 'adjusted=true' from the API does not return a usable OHLCV data set.
|
||||
# We need to calculate the adjusted prices manually.
|
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if query.adjustment != "unadjusted":
|
||||
temp = data.copy()
|
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temp["dividend_factor"] = (
|
||||
temp["close"] - temp["dividend"]
|
||||
) / temp["close"]
|
||||
temp["volume_factor"] = temp["split_factor"]
|
||||
temp["split_factor"] = 1 / temp["split_factor"]
|
||||
adj_cols = ["open", "high", "low", "close", "volume"]
|
||||
divs = query.adjustment == "splits_and_dividends"
|
||||
for col in adj_cols:
|
||||
divs = False if col == "volume" else divs
|
||||
if col in temp.columns:
|
||||
temp = calculate_adjusted_prices(temp, col, divs)
|
||||
temp["adj_dividend"] = (
|
||||
temp["adj_close"] * (1 - temp["dividend_factor"])
|
||||
if query.adjustment == "splits_only"
|
||||
else temp["close"] * (1 - temp["dividend_factor"])
|
||||
)
|
||||
data["open"] = round(temp["adj_open"], 4)
|
||||
data["high"] = round(temp["adj_high"], 4)
|
||||
data["low"] = round(temp["adj_low"], 4)
|
||||
data["close"] = round(temp["adj_close"], 4)
|
||||
data["volume"] = round(temp["adj_volume"]).astype(int)
|
||||
data["dividend"] = round(temp["adj_dividend"], 4)
|
||||
data.drop(columns=["adj_close"], inplace=True)
|
||||
# Resample the daily data for the interval requested.
|
||||
freq = ""
|
||||
agg_dict = {
|
||||
"open": "first",
|
||||
"high": "max",
|
||||
"low": "min",
|
||||
"close": "last",
|
||||
"volume": "sum",
|
||||
"dividend": "sum",
|
||||
"split_factor": "prod",
|
||||
}
|
||||
if query.adjustment == "unadjusted":
|
||||
agg_dict.pop("dividend")
|
||||
agg_dict.pop("split_factor")
|
||||
if query.interval == "1M":
|
||||
freq = "M"
|
||||
if query.interval == "1W":
|
||||
freq = "W-FRI"
|
||||
if freq in ["M", "W-FRI"]:
|
||||
data = data.resample(freq).agg({**agg_dict})
|
||||
if len(query.symbol.split(",")) > 1:
|
||||
data["symbol"] = symbol
|
||||
|
||||
data = data.reset_index()
|
||||
if intraday is False:
|
||||
data["date"] = data["date"].dt.date
|
||||
|
||||
results.extend(data.to_dict("records"))
|
||||
|
||||
return results
|
||||
|
||||
tasks = [get_one(symbol, intraday) for symbol in query.symbol.split(",")]
|
||||
await asyncio.gather(*tasks)
|
||||
|
||||
return results
|
||||
|
||||
@staticmethod
|
||||
def transform_data(
|
||||
query: AVEquityHistoricalQueryParams, data: list[dict], **kwargs: Any
|
||||
) -> list[AVEquityHistoricalData]:
|
||||
"""Transform the data to the standard format."""
|
||||
# pylint: disable=import-outside-toplevel
|
||||
from pandas import to_datetime
|
||||
|
||||
if data == []:
|
||||
return []
|
||||
if "{" in data[0]:
|
||||
warn(str(data[0]["{"].strip()))
|
||||
return []
|
||||
return [
|
||||
AVEquityHistoricalData.model_validate(d)
|
||||
for d in sorted(
|
||||
data,
|
||||
key=(
|
||||
lambda x: (x["date"], x["symbol"]) if "symbol" in x else x["date"]
|
||||
),
|
||||
)
|
||||
if query.start_date <= to_datetime(d["date"]).date() <= query.end_date
|
||||
]
|
||||
@@ -1,164 +0,0 @@
|
||||
"""AlphaVantage Historical EPS Model."""
|
||||
|
||||
# pylint: disable=unused-argument
|
||||
|
||||
from datetime import date as dateType
|
||||
from typing import Any, Literal
|
||||
from warnings import warn
|
||||
|
||||
from openbb_core.provider.abstract.fetcher import Fetcher
|
||||
from openbb_core.provider.standard_models.historical_eps import (
|
||||
HistoricalEpsData,
|
||||
HistoricalEpsQueryParams,
|
||||
)
|
||||
from openbb_core.provider.utils.descriptions import QUERY_DESCRIPTIONS
|
||||
from openbb_core.provider.utils.errors import EmptyDataError
|
||||
from pydantic import Field, field_validator
|
||||
|
||||
|
||||
class AlphaVantageHistoricalEpsQueryParams(HistoricalEpsQueryParams):
|
||||
"""
|
||||
AlphaVantage Historical EPS Query Params.
|
||||
|
||||
Source: https://www.alphavantage.co/documentation/#earnings
|
||||
"""
|
||||
|
||||
__json_schema_extra__ = {"symbol": {"multiple_items_allowed": True}}
|
||||
|
||||
period: Literal["annual", "quarter"] = Field(
|
||||
default="quarter", description=QUERY_DESCRIPTIONS.get("period", "")
|
||||
)
|
||||
limit: int | None = Field(
|
||||
default=None, description=QUERY_DESCRIPTIONS.get("limit", "")
|
||||
)
|
||||
|
||||
|
||||
class AlphaVantageHistoricalEpsData(HistoricalEpsData):
|
||||
"""AlphaVantage Historical EPS Data."""
|
||||
|
||||
__alias_dict__ = {
|
||||
"date": "fiscalDateEnding",
|
||||
"eps_actual": "reportedEPS",
|
||||
"eps_estimated": "estimatedEPS",
|
||||
"surprise_percent": "surprisePercentage",
|
||||
"reported_date": "reportedDate",
|
||||
}
|
||||
|
||||
surprise: float | None = Field(
|
||||
default=None,
|
||||
description="Surprise in EPS (Actual - Estimated).",
|
||||
)
|
||||
surprise_percent: float | str | None = Field(
|
||||
default=None,
|
||||
description="EPS surprise as a normalized percent.",
|
||||
json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100},
|
||||
)
|
||||
reported_date: dateType | None = Field(
|
||||
default=None,
|
||||
description="Date of the earnings report.",
|
||||
)
|
||||
report_time: str | None = Field(
|
||||
default=None,
|
||||
description="Time of day when the earnings report was released, e.g., 'post-market'.",
|
||||
)
|
||||
|
||||
@field_validator(
|
||||
"eps_estimated",
|
||||
"eps_actual",
|
||||
"surprise",
|
||||
mode="before",
|
||||
check_fields=False,
|
||||
)
|
||||
@classmethod
|
||||
def validate_null(cls, v):
|
||||
"""Clean None returned as a string."""
|
||||
return None if str(v).strip() == "None" or str(v) == "0" else v
|
||||
|
||||
@field_validator("surprise_percent", mode="before", check_fields=False)
|
||||
@classmethod
|
||||
def normalize_percent(cls, v):
|
||||
"""Normalize percent values."""
|
||||
if isinstance(v, str) and v == "None" or str(v) == "0":
|
||||
return None
|
||||
return float(v) / 100
|
||||
|
||||
|
||||
class AVHistoricalEpsFetcher(
|
||||
Fetcher[AlphaVantageHistoricalEpsQueryParams, list[AlphaVantageHistoricalEpsData]]
|
||||
):
|
||||
"""AlphaVantage Historical EPS Fetcher."""
|
||||
|
||||
@staticmethod
|
||||
def transform_query(params: dict[str, Any]) -> AlphaVantageHistoricalEpsQueryParams:
|
||||
"""Transform the query params."""
|
||||
return AlphaVantageHistoricalEpsQueryParams(**params)
|
||||
|
||||
@staticmethod
|
||||
async def aextract_data(
|
||||
query: AlphaVantageHistoricalEpsQueryParams,
|
||||
credentials: dict[str, str] | None,
|
||||
**kwargs: Any,
|
||||
) -> list[dict]:
|
||||
"""Return the raw data from the AlphaVantage endpoint."""
|
||||
# pylint: disable=import-outside-toplevel
|
||||
from openbb_core.provider.utils.helpers import (
|
||||
ClientResponse,
|
||||
ClientSession,
|
||||
amake_requests,
|
||||
)
|
||||
|
||||
api_key = credentials.get("alpha_vantage_api_key") if credentials else ""
|
||||
BASE_URL = "https://www.alphavantage.co/query?function=EARNINGS&"
|
||||
# We are allowing multiple symbols to be passed in the query, so we need to handle that.
|
||||
symbols = query.symbol.split(",")
|
||||
urls = [f"{BASE_URL}symbol={symbol}&apikey={api_key}" for symbol in symbols]
|
||||
results: list = []
|
||||
messages: list = []
|
||||
|
||||
# We need to make a custom callback function for this async request.
|
||||
async def response_callback(response: ClientResponse, _: ClientSession):
|
||||
"""Response callback function."""
|
||||
symbol = response.url.query.get("symbol", None)
|
||||
data = await response.json()
|
||||
target = (
|
||||
"annualEarnings" if query.period == "annual" else "quarterlyEarnings"
|
||||
)
|
||||
message = data.get("Information", "") # type: ignore
|
||||
if message:
|
||||
messages.append(message)
|
||||
warn(f"Symbol Error for {symbol}: {message}")
|
||||
result: list = []
|
||||
# If data is returned, append it to the results list.
|
||||
if data:
|
||||
result = [
|
||||
{
|
||||
"symbol": symbol,
|
||||
**d,
|
||||
}
|
||||
for d in data.get(target, []) # type: ignore
|
||||
]
|
||||
if query.limit is not None:
|
||||
results.extend(result[: query.limit])
|
||||
else:
|
||||
results.extend(result)
|
||||
# If no data is returned, raise a warning and move on to the next symbol.
|
||||
if not data:
|
||||
warn(f"Symbol Error: No data found for {symbol}")
|
||||
|
||||
await amake_requests(urls, response_callback, **kwargs) # type: ignore
|
||||
|
||||
if not results:
|
||||
raise EmptyDataError(f"No data was returned -> \n{messages[-1]}")
|
||||
|
||||
return results
|
||||
|
||||
@staticmethod
|
||||
def transform_data(
|
||||
query: AlphaVantageHistoricalEpsQueryParams,
|
||||
data: list[dict],
|
||||
**kwargs: Any,
|
||||
) -> list[AlphaVantageHistoricalEpsData]:
|
||||
"""Transform the raw data into the standard model."""
|
||||
if not data:
|
||||
raise EmptyDataError("No data found.")
|
||||
return [AlphaVantageHistoricalEpsData.model_validate(d) for d in data]
|
||||
@@ -1 +0,0 @@
|
||||
"""Alpha Vantage utilities."""
|
||||
@@ -1,99 +0,0 @@
|
||||
"""Alpha Vantage Helpers Module."""
|
||||
|
||||
from datetime import datetime
|
||||
from typing import TYPE_CHECKING, Any
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from pandas import DataFrame
|
||||
|
||||
INTERVALS_DICT = {
|
||||
"m": "TIME_SERIES_INTRADAY",
|
||||
"d": "TIME_SERIES_DAILY",
|
||||
"W": "TIME_SERIES_WEEKLY",
|
||||
"M": "TIME_SERIES_MONTHLY",
|
||||
}
|
||||
|
||||
|
||||
def get_interval(value: str) -> str:
|
||||
"""Get the intervals for the Alpha Vantage API."""
|
||||
intervals = {
|
||||
"m": "min",
|
||||
"d": "day",
|
||||
"W": "week",
|
||||
"M": "month",
|
||||
}
|
||||
|
||||
return f"{value[:-1]}{intervals[value[-1]]}"
|
||||
|
||||
|
||||
def extract_key_name(key):
|
||||
"""Extract the alphabetical part of the key using regex."""
|
||||
# pylint: disable=import-outside-toplevel
|
||||
import re
|
||||
|
||||
match = re.search(r"\d+\.\s+([a-z]+)", key, re.I)
|
||||
return match.group(1) if match else key
|
||||
|
||||
|
||||
def filter_by_dates(
|
||||
data: list[dict[str, Any]], start_date: datetime, end_date: datetime
|
||||
) -> list[dict[str, Any]]:
|
||||
"""Filter the data by start and end dates."""
|
||||
return list(
|
||||
filter(
|
||||
lambda x: start_date
|
||||
<= datetime.strptime(x["date"], "%Y-%m-%d").date()
|
||||
<= end_date,
|
||||
data,
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
def calculate_adjusted_prices(
|
||||
df: "DataFrame", column: str, dividends: bool = False
|
||||
) -> "DataFrame":
|
||||
"""Calculate the split-adjusted prices, or split and dividend adjusted prices.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
df: DataFrame
|
||||
DataFrame with unadjusted OHLCV values + split_factor + dividend
|
||||
column: str
|
||||
The column name to adjust.
|
||||
dividends: bool
|
||||
Whether to adjust for both splits and dividends. Default is split-adjusted only.
|
||||
|
||||
Returns
|
||||
-------
|
||||
DataFrame
|
||||
DataFrame with adjusted prices.
|
||||
"""
|
||||
# pylint: disable=import-outside-toplevel
|
||||
from numpy import zeros
|
||||
|
||||
df = df.copy()
|
||||
adj_column = "adj_" + column
|
||||
|
||||
# Reverse the DataFrame order, sorting by date in descending order
|
||||
df.sort_index(ascending=False, inplace=True)
|
||||
|
||||
price_col = df[column]
|
||||
split_col = df["volume_factor"] if column == "volume" else df["split_factor"]
|
||||
dividend_col = df["dividend"] if dividends else zeros(len(price_col))
|
||||
adj_price_col = zeros(len(df.index))
|
||||
adj_price_col[0] = price_col.iloc[0]
|
||||
|
||||
for i in range(1, len(price_col)):
|
||||
adj_price_col[i] = adj_price_col[i - 1] + adj_price_col[i - 1] * (
|
||||
(
|
||||
(price_col.iloc[i] * split_col.iloc[i - 1])
|
||||
- price_col.iloc[i - 1]
|
||||
- dividend_col[i - 1]
|
||||
)
|
||||
/ price_col.iloc[i - 1]
|
||||
)
|
||||
df[adj_column] = adj_price_col
|
||||
|
||||
# Change the DataFrame order back to dates ascending
|
||||
df.sort_index(ascending=True, inplace=True)
|
||||
return df
|
||||
1959
openbb_platform/providers/alpha_vantage/poetry.lock
generated
vendored
1959
openbb_platform/providers/alpha_vantage/poetry.lock
generated
vendored
File diff suppressed because it is too large
Load Diff
@@ -1,19 +0,0 @@
|
||||
[tool.poetry]
|
||||
name = "openbb-alpha-vantage"
|
||||
version = "1.6.1"
|
||||
description = "Alpha Vantage extension for OpenBB"
|
||||
authors = ["OpenBB Team <hello@openbb.co>"]
|
||||
license = "AGPL-3.0-only"
|
||||
readme = "README.md"
|
||||
packages = [{ include = "openbb_alpha_vantage" }]
|
||||
|
||||
[tool.poetry.dependencies]
|
||||
python = ">=3.10,<4"
|
||||
openbb-core = "^1.6.10"
|
||||
|
||||
[build-system]
|
||||
requires = ["poetry-core"]
|
||||
build-backend = "poetry.core.masonry.api"
|
||||
|
||||
[tool.poetry.plugins."openbb_provider_extension"]
|
||||
alpha_vantage = "openbb_alpha_vantage:alpha_vantage_provider"
|
||||
@@ -1 +0,0 @@
|
||||
"""Alpha Vantage tests."""
|
||||
@@ -1,316 +0,0 @@
|
||||
interactions:
|
||||
- request:
|
||||
body: null
|
||||
headers:
|
||||
Accept:
|
||||
- application/json
|
||||
Accept-Encoding:
|
||||
- gzip, deflate
|
||||
Connection:
|
||||
- keep-alive
|
||||
method: GET
|
||||
uri: https://www.alphavantage.co/query?adjusted=true&apikey=MOCK_API_KEY&datatype=csv&extended_hours=false&function=TIME_SERIES_INTRADAY&interval=15min&month=2023-01&outputsize=full&symbol=AAPL
|
||||
response:
|
||||
body:
|
||||
string: "timestamp,open,high,low,close,volume\r\n2023-01-31 15:45:00,142.4710,143.3640,142.1380,143.3200,6111148\r\n2023-01-31
|
||||
15:30:00,142.3220,142.6090,142.3120,142.4800,1466236\r\n2023-01-31 15:15:00,142.7790,142.7980,142.2770,142.3310,1409505\r\n2023-01-31
|
||||
15:00:00,142.8880,143.0660,142.6600,142.7730,1206452\r\n2023-01-31 14:45:00,142.9580,143.0260,142.8230,142.8830,852602\r\n2023-01-31
|
||||
14:30:00,142.8490,143.0160,142.8040,142.9720,856467\r\n2023-01-31 14:15:00,142.7340,142.9170,142.6250,142.8480,705206\r\n2023-01-31
|
||||
14:00:00,142.6450,142.9370,142.5450,142.7440,951014\r\n2023-01-31 13:45:00,142.4610,142.7680,142.4160,142.6540,909265\r\n2023-01-31
|
||||
13:30:00,142.4410,142.5590,142.3170,142.4650,611083\r\n2023-01-31 13:15:00,142.4560,142.4700,142.1780,142.4410,1006968\r\n2023-01-31
|
||||
13:00:00,142.5610,142.6360,142.3270,142.4550,655584\r\n2023-01-31 12:45:00,142.5460,142.6880,142.4560,142.5600,630828\r\n2023-01-31
|
||||
12:30:00,142.5410,142.6680,142.4160,142.5550,590808\r\n2023-01-31 12:15:00,142.3220,142.6680,142.2620,142.5600,974220\r\n2023-01-31
|
||||
12:00:00,142.2820,142.3950,141.9990,142.3070,929554\r\n2023-01-31 11:45:00,142.3520,142.5140,142.1780,142.2850,844479\r\n2023-01-31
|
||||
11:30:00,142.5560,142.6680,142.2970,142.3260,969895\r\n2023-01-31 11:15:00,142.2330,142.6190,142.1280,142.5500,1187882\r\n2023-01-31
|
||||
11:00:00,142.1930,142.4600,142.0090,142.2470,1107755\r\n2023-01-31 10:45:00,142.1600,142.3210,141.8200,142.1870,1388137\r\n2023-01-31
|
||||
10:30:00,142.2130,142.3800,141.9790,142.1670,1226566\r\n2023-01-31 10:15:00,142.1040,142.3700,141.6520,142.1970,1825953\r\n2023-01-31
|
||||
10:00:00,141.6770,142.2110,141.4330,142.1080,2150277\r\n2023-01-31 09:45:00,141.7070,142.7780,141.4030,141.6760,2822577\r\n2023-01-31
|
||||
09:30:00,141.7160,142.4800,141.3040,141.7110,4394928\r\n2023-01-30 15:45:00,142.4070,142.5490,141.8700,142.0580,4775426\r\n2023-01-30
|
||||
15:30:00,142.0840,142.4600,141.9990,142.4110,1792886\r\n2023-01-30 15:15:00,142.0840,142.1620,141.9490,142.0830,1765526\r\n2023-01-30
|
||||
15:00:00,142.2130,142.4900,142.0490,142.0780,1650201\r\n2023-01-30 14:45:00,142.2280,142.2810,141.9010,142.2170,1669205\r\n2023-01-30
|
||||
14:30:00,142.1780,142.5460,142.0890,142.2320,1432781\r\n2023-01-30 14:15:00,142.4070,142.4350,142.0490,142.1770,1464175\r\n2023-01-30
|
||||
14:00:00,142.3920,142.6190,142.2970,142.4110,998241\r\n2023-01-30 13:45:00,142.1930,142.4500,142.0790,142.4010,1198176\r\n2023-01-30
|
||||
13:30:00,142.5110,142.5190,142.0290,142.1970,1309283\r\n2023-01-30 13:15:00,142.5110,142.8470,142.4260,142.5230,1064570\r\n2023-01-30
|
||||
13:00:00,142.4310,142.6780,142.3270,142.5200,875111\r\n2023-01-30 12:45:00,142.6400,142.6490,142.1880,142.4410,1400651\r\n2023-01-30
|
||||
12:30:00,142.6950,142.8120,142.4960,142.6440,1181401\r\n2023-01-30 12:15:00,142.9780,142.9910,142.4910,142.6990,1609852\r\n2023-01-30
|
||||
12:00:00,143.1960,143.2840,142.1680,142.9820,1400170\r\n2023-01-30 11:45:00,142.8780,143.2150,142.5160,143.2090,1185883\r\n2023-01-30
|
||||
11:30:00,142.8880,143.2540,142.7840,142.8840,1110391\r\n2023-01-30 11:15:00,142.9680,143.3440,142.7240,142.9020,1375517\r\n2023-01-30
|
||||
11:00:00,143.0080,143.1850,142.7440,142.9770,1391660\r\n2023-01-30 10:45:00,142.6300,143.0560,142.5850,143.0170,1923936\r\n2023-01-30
|
||||
10:30:00,143.2060,143.2540,142.3860,142.6560,2033741\r\n2023-01-30 10:15:00,143.6230,143.6520,142.8630,143.2100,2312830\r\n2023-01-30
|
||||
10:00:00,144.1890,144.3670,143.5290,143.6180,1980967\r\n2023-01-30 09:45:00,143.7520,144.2870,143.1820,144.1990,2799333\r\n2023-01-30
|
||||
09:30:00,143.9660,144.5650,143.5190,143.7380,4153218\r\n2023-01-27 15:45:00,145.6890,145.7180,144.8000,144.9780,4867213\r\n2023-01-27
|
||||
15:30:00,145.9470,146.2340,145.6640,145.7230,2420991\r\n2023-01-27 15:15:00,145.8880,145.9760,145.6140,145.9620,1956807\r\n2023-01-27
|
||||
15:00:00,145.8930,146.1650,145.8130,145.8920,1674321\r\n2023-01-27 14:45:00,145.6790,146.0260,145.6350,145.9020,1811395\r\n2023-01-27
|
||||
14:30:00,145.7780,145.8470,145.5050,145.6830,1394494\r\n2023-01-27 14:15:00,145.5150,145.9560,145.5050,145.7740,1614957\r\n2023-01-27
|
||||
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10:00:00,143.7230,144.4960,143.3990,144.1700,2810931\r\n2023-01-27 09:45:00,143.0670,144.2280,143.0620,143.7170,3425862\r\n2023-01-27
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|
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@@ -1,48 +0,0 @@
|
||||
"""Test the Alpha Vantage fetchers."""
|
||||
|
||||
from datetime import date
|
||||
|
||||
import pytest
|
||||
from openbb_alpha_vantage.models.equity_historical import AVEquityHistoricalFetcher
|
||||
from openbb_alpha_vantage.models.historical_eps import AVHistoricalEpsFetcher
|
||||
from openbb_core.app.service.user_service import UserService
|
||||
|
||||
test_credentials = UserService().default_user_settings.credentials.model_dump(
|
||||
mode="json"
|
||||
)
|
||||
|
||||
|
||||
@pytest.fixture(scope="module")
|
||||
def vcr_config():
|
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"""VCR configuration."""
|
||||
return {
|
||||
"filter_headers": [("User-Agent", None)],
|
||||
"filter_query_parameters": [
|
||||
("apikey", "MOCK_API_KEY"),
|
||||
],
|
||||
}
|
||||
|
||||
|
||||
@pytest.mark.record_http
|
||||
def test_av_equity_historical_fetcher(credentials=test_credentials):
|
||||
"""Test the Alpha Vantage Equity Historical fetcher."""
|
||||
params = {
|
||||
"symbol": "AAPL",
|
||||
"start_date": date(2023, 1, 1),
|
||||
"end_date": date(2023, 1, 10),
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||||
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||||
}
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|
||||
fetcher = AVEquityHistoricalFetcher()
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||||
result = fetcher.test(params, credentials)
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||||
assert result is None
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||||
|
||||
|
||||
@pytest.mark.record_http
|
||||
def test_av_historical_eps_fetcher(credentials=test_credentials):
|
||||
"""Test the Alpha Vantage Historical Earnings fetcher."""
|
||||
params = {"symbol": "AAPL,MSFT", "period": "quarter", "limit": 4}
|
||||
|
||||
fetcher = AVHistoricalEpsFetcher()
|
||||
result = fetcher.test(params, credentials)
|
||||
assert result is None
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||||
@@ -2,7 +2,6 @@
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||||
|
||||
test_credentials: dict[str, tuple[str, str]] = {
|
||||
"benzinga": ("token", "MOCK_TOKEN"),
|
||||
"alpha_vantage": ("apikey", "MOCK_API_KEY"),
|
||||
"fmp": ("apikey", "MOCK_API_KEY"),
|
||||
"nasdaq": ("x-api-token", "MOCK_API_KEY"),
|
||||
"fred": ("api_key", "MOCK_API_KEY"),
|
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|
||||
1
openbb_platform/pyproject.toml
vendored
1
openbb_platform/pyproject.toml
vendored
@@ -64,7 +64,6 @@ openbb-technical = { version = "^1.6.2", optional = true }
|
||||
openbb-mcp-server = { version = "^1.4.1", optional = true }
|
||||
|
||||
[tool.poetry.extras]
|
||||
alpha_vantage = ["openbb-alpha-vantage"]
|
||||
cboe = ["openbb-cboe"]
|
||||
charting = ["openbb-charting"]
|
||||
deribit = ["openbb-deribit"]
|
||||
|
||||
Reference in New Issue
Block a user