mirror of
https://github.com/OpenBB-finance/OpenBB.git
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[Feature] Pandas 3.0 - Update Library For Syntax (#7336)
* fix everything for pandas 3.0 * bump yfinance version * update core dependency python-multi-part * lint
This commit is contained in:
Danglewood
@@ -379,7 +379,7 @@ def print_rich_table( # noqa: PLR0912
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)
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if columns_to_auto_color is None and rows_to_auto_color is None:
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df = df.applymap(lambda x: return_colored_value(str(x))) # type: ignore
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df = df.map(lambda x: return_colored_value(str(x))) # type: ignore
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exceeds_allowed_columns = len(df.columns) > MAX_COLS
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exceeds_allowed_rows = len(df) > MAX_ROWS
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@@ -67,7 +67,7 @@ class OptionsChainsProperties(Data):
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# Add the underlying price to the DataFrame, or override the existing price.
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if self.last_price:
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chains_data.loc[:, "underlying_price"] = self.last_price
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chains_data["underlying_price"] = self.last_price
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if chains_data.empty:
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raise OpenBBError("Error: No validated data was found.")
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@@ -76,14 +76,14 @@ class OptionsChainsProperties(Data):
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_date = to_datetime(chains_data.eod_date)
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temp = DatetimeIndex(chains_data.expiration)
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temp_ = temp - _date # type: ignore
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chains_data.loc[:, "dte"] = [Timedelta(_temp_).days for _temp_ in temp_]
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chains_data["dte"] = [Timedelta(_temp_).days for _temp_ in temp_]
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if "dte" in chains_data.columns:
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chains_data = DataFrame(chains_data[chains_data.dte >= 0])
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if "dte" not in chains_data.columns and "eod_date" not in chains_data.columns:
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today = datetime.today().date()
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chains_data.loc[:, "dte"] = chains_data.expiration - today
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chains_data["dte"] = chains_data.expiration - today
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# Add the breakeven price for each option, and the DEX and GEX for each option, if available.
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try:
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405
openbb_platform/core/poetry.lock
generated
vendored
405
openbb_platform/core/poetry.lock
generated
vendored
@@ -1,4 +1,4 @@
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# This file is automatically @generated by Poetry 2.3.0 and should not be changed by hand.
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# This file is automatically @generated by Poetry 2.3.1 and should not be changed by hand.
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[[package]]
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name = "aiohappyeyeballs"
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@@ -660,158 +660,158 @@ type = ["mypy (<1.19) ; platform_python_implementation == \"PyPy\"", "pytest-myp
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[[package]]
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name = "multidict"
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version = "6.7.0"
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version = "6.7.1"
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description = "multidict implementation"
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optional = false
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python-versions = ">=3.9"
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groups = ["main"]
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]
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[package.dependencies]
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@@ -973,6 +973,7 @@ description = "Powerful data structures for data analysis, time series, and stat
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||||
optional = false
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||||
python-versions = ">=3.9"
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groups = ["main"]
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markers = "python_version == \"3.10\""
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files = [
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@@ -1032,11 +1033,7 @@ files = [
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]
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[package.dependencies]
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numpy = [
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{version = ">=1.22.4", markers = "python_version < \"3.11\""},
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{version = ">=1.23.2", markers = "python_version == \"3.11\""},
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{version = ">=1.26.0", markers = "python_version >= \"3.12\""},
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]
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python-dateutil = ">=2.8.2"
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pytz = ">=2020.1"
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tzdata = ">=2022.7"
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@@ -1066,6 +1063,96 @@ sql-other = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-d
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test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)"]
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xml = ["lxml (>=4.9.2)"]
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[[package]]
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name = "pandas"
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version = "3.0.0"
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description = "Powerful data structures for data analysis, time series, and statistics"
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optional = false
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python-versions = ">=3.11"
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||||
{file = "pandas-3.0.0-cp313-cp313t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:48ee04b90e2505c693d3f8e8f524dab8cb8aaf7ddcab52c92afa535e717c4812"},
|
||||
{file = "pandas-3.0.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:66f72fb172959af42a459e27a8d8d2c7e311ff4c1f7db6deb3b643dbc382ae08"},
|
||||
{file = "pandas-3.0.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:4a4a400ca18230976724a5066f20878af785f36c6756e498e94c2a5e5d57779c"},
|
||||
{file = "pandas-3.0.0-cp313-cp313t-win_amd64.whl", hash = "sha256:940eebffe55528074341a5a36515f3e4c5e25e958ebbc764c9502cfc35ba3faa"},
|
||||
{file = "pandas-3.0.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:597c08fb9fef0edf1e4fa2f9828dd27f3d78f9b8c9b4a748d435ffc55732310b"},
|
||||
{file = "pandas-3.0.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:447b2d68ac5edcbf94655fe909113a6dba6ef09ad7f9f60c80477825b6c489fe"},
|
||||
{file = "pandas-3.0.0-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:debb95c77ff3ed3ba0d9aa20c3a2f19165cc7956362f9873fce1ba0a53819d70"},
|
||||
{file = "pandas-3.0.0-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fedabf175e7cd82b69b74c30adbaa616de301291a5231138d7242596fc296a8d"},
|
||||
{file = "pandas-3.0.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:412d1a89aab46889f3033a386912efcdfa0f1131c5705ff5b668dda88305e986"},
|
||||
{file = "pandas-3.0.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:e979d22316f9350c516479dd3a92252be2937a9531ed3a26ec324198a99cdd49"},
|
||||
{file = "pandas-3.0.0-cp314-cp314-win_amd64.whl", hash = "sha256:083b11415b9970b6e7888800c43c82e81a06cd6b06755d84804444f0007d6bb7"},
|
||||
{file = "pandas-3.0.0-cp314-cp314-win_arm64.whl", hash = "sha256:5db1e62cb99e739fa78a28047e861b256d17f88463c76b8dafc7c1338086dca8"},
|
||||
{file = "pandas-3.0.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:697b8f7d346c68274b1b93a170a70974cdc7d7354429894d5927c1effdcccd73"},
|
||||
{file = "pandas-3.0.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:8cb3120f0d9467ed95e77f67a75e030b67545bcfa08964e349252d674171def2"},
|
||||
{file = "pandas-3.0.0-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:33fd3e6baa72899746b820c31e4b9688c8e1b7864d7aec2de7ab5035c285277a"},
|
||||
{file = "pandas-3.0.0-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a8942e333dc67ceda1095227ad0febb05a3b36535e520154085db632c40ad084"},
|
||||
{file = "pandas-3.0.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:783ac35c4d0fe0effdb0d67161859078618b1b6587a1af15928137525217a721"},
|
||||
{file = "pandas-3.0.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:125eb901e233f155b268bbef9abd9afb5819db74f0e677e89a61b246228c71ac"},
|
||||
{file = "pandas-3.0.0-cp314-cp314t-win_amd64.whl", hash = "sha256:b86d113b6c109df3ce0ad5abbc259fe86a1bd4adfd4a31a89da42f84f65509bb"},
|
||||
{file = "pandas-3.0.0-cp314-cp314t-win_arm64.whl", hash = "sha256:1c39eab3ad38f2d7a249095f0a3d8f8c22cc0f847e98ccf5bbe732b272e2d9fa"},
|
||||
{file = "pandas-3.0.0.tar.gz", hash = "sha256:0facf7e87d38f721f0af46fe70d97373a37701b1c09f7ed7aeeb292ade5c050f"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
numpy = {version = ">=1.26.0", markers = "python_version < \"3.14\""}
|
||||
python-dateutil = ">=2.8.2"
|
||||
tzdata = {version = "*", markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\""}
|
||||
|
||||
[package.extras]
|
||||
all = ["PyQt5 (>=5.15.9)", "SQLAlchemy (>=2.0.36)", "adbc-driver-postgresql (>=1.2.0)", "adbc-driver-sqlite (>=1.2.0)", "beautifulsoup4 (>=4.12.3)", "bottleneck (>=1.4.2)", "fastparquet (>=2024.11.0)", "fsspec (>=2024.10.0)", "gcsfs (>=2024.10.0)", "html5lib (>=1.1)", "hypothesis (>=6.116.0)", "jinja2 (>=3.1.5)", "lxml (>=5.3.0)", "matplotlib (>=3.9.3)", "numba (>=0.60.0)", "numexpr (>=2.10.2)", "odfpy (>=1.4.1)", "openpyxl (>=3.1.5)", "psycopg2 (>=2.9.10)", "pyarrow (>=13.0.0)", "pyiceberg (>=0.8.1)", "pymysql (>=1.1.1)", "pyreadstat (>=1.2.8)", "pytest (>=8.3.4)", "pytest-xdist (>=3.6.1)", "python-calamine (>=0.3.0)", "pytz (>=2024.2)", "pyxlsb (>=1.0.10)", "qtpy (>=2.4.2)", "s3fs (>=2024.10.0)", "scipy (>=1.14.1)", "tables (>=3.10.1)", "tabulate (>=0.9.0)", "xarray (>=2024.10.0)", "xlrd (>=2.0.1)", "xlsxwriter (>=3.2.0)", "zstandard (>=0.23.0)"]
|
||||
aws = ["s3fs (>=2024.10.0)"]
|
||||
clipboard = ["PyQt5 (>=5.15.9)", "qtpy (>=2.4.2)"]
|
||||
compression = ["zstandard (>=0.23.0)"]
|
||||
computation = ["scipy (>=1.14.1)", "xarray (>=2024.10.0)"]
|
||||
excel = ["odfpy (>=1.4.1)", "openpyxl (>=3.1.5)", "python-calamine (>=0.3.0)", "pyxlsb (>=1.0.10)", "xlrd (>=2.0.1)", "xlsxwriter (>=3.2.0)"]
|
||||
feather = ["pyarrow (>=13.0.0)"]
|
||||
fss = ["fsspec (>=2024.10.0)"]
|
||||
gcp = ["gcsfs (>=2024.10.0)"]
|
||||
hdf5 = ["tables (>=3.10.1)"]
|
||||
html = ["beautifulsoup4 (>=4.12.3)", "html5lib (>=1.1)", "lxml (>=5.3.0)"]
|
||||
iceberg = ["pyiceberg (>=0.8.1)"]
|
||||
mysql = ["SQLAlchemy (>=2.0.36)", "pymysql (>=1.1.1)"]
|
||||
output-formatting = ["jinja2 (>=3.1.5)", "tabulate (>=0.9.0)"]
|
||||
parquet = ["pyarrow (>=13.0.0)"]
|
||||
performance = ["bottleneck (>=1.4.2)", "numba (>=0.60.0)", "numexpr (>=2.10.2)"]
|
||||
plot = ["matplotlib (>=3.9.3)"]
|
||||
postgresql = ["SQLAlchemy (>=2.0.36)", "adbc-driver-postgresql (>=1.2.0)", "psycopg2 (>=2.9.10)"]
|
||||
pyarrow = ["pyarrow (>=13.0.0)"]
|
||||
spss = ["pyreadstat (>=1.2.8)"]
|
||||
sql-other = ["SQLAlchemy (>=2.0.36)", "adbc-driver-postgresql (>=1.2.0)", "adbc-driver-sqlite (>=1.2.0)"]
|
||||
test = ["hypothesis (>=6.116.0)", "pytest (>=8.3.4)", "pytest-xdist (>=3.6.1)"]
|
||||
timezone = ["pytz (>=2024.2)"]
|
||||
xml = ["lxml (>=5.3.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "propcache"
|
||||
version = "0.4.1"
|
||||
@@ -1404,14 +1491,14 @@ cli = ["click (>=5.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "python-multipart"
|
||||
version = "0.0.20"
|
||||
version = "0.0.22"
|
||||
description = "A streaming multipart parser for Python"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
python-versions = ">=3.10"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "python_multipart-0.0.20-py3-none-any.whl", hash = "sha256:8a62d3a8335e06589fe01f2a3e178cdcc632f3fbe0d492ad9ee0ec35aab1f104"},
|
||||
{file = "python_multipart-0.0.20.tar.gz", hash = "sha256:8dd0cab45b8e23064ae09147625994d090fa46f5b0d1e13af944c331a7fa9d13"},
|
||||
{file = "python_multipart-0.0.22-py3-none-any.whl", hash = "sha256:2b2cd894c83d21bf49d702499531c7bafd057d730c201782048f7945d82de155"},
|
||||
{file = "python_multipart-0.0.22.tar.gz", hash = "sha256:7340bef99a7e0032613f56dc36027b959fd3b30a787ed62d310e951f7c3a3a58"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@@ -1421,6 +1508,7 @@ description = "World timezone definitions, modern and historical"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
groups = ["main"]
|
||||
markers = "python_version == \"3.10\""
|
||||
files = [
|
||||
{file = "pytz-2025.2-py2.py3-none-any.whl", hash = "sha256:5ddf76296dd8c44c26eb8f4b6f35488f3ccbf6fbbd7adee0b7262d43f0ec2f00"},
|
||||
{file = "pytz-2025.2.tar.gz", hash = "sha256:360b9e3dbb49a209c21ad61809c7fb453643e048b38924c765813546746e81c3"},
|
||||
@@ -1542,6 +1630,7 @@ description = "Provider of IANA time zone data"
|
||||
optional = false
|
||||
python-versions = ">=2"
|
||||
groups = ["main"]
|
||||
markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or python_version == \"3.10\""
|
||||
files = [
|
||||
{file = "tzdata-2025.3-py2.py3-none-any.whl", hash = "sha256:06a47e5700f3081aab02b2e513160914ff0694bce9947d6b76ebd6bf57cfc5d1"},
|
||||
{file = "tzdata-2025.3.tar.gz", hash = "sha256:de39c2ca5dc7b0344f2eba86f49d614019d29f060fc4ebc8a417896a620b56a7"},
|
||||
@@ -1856,4 +1945,4 @@ type = ["pytest-mypy"]
|
||||
[metadata]
|
||||
lock-version = "2.1"
|
||||
python-versions = ">=3.10,<3.14"
|
||||
content-hash = "3c569f4de9035b7eb38bf374fac2015d5a9a664576fac7d381795b365d8ec8ee"
|
||||
content-hash = "71a69eaa28e8658925c02abb08a5967ae117c3bceea4974dc1c36d8072c88119"
|
||||
|
||||
4
openbb_platform/core/pyproject.toml
vendored
4
openbb_platform/core/pyproject.toml
vendored
@@ -14,11 +14,11 @@ packages = [
|
||||
python = ">=3.10,<3.14"
|
||||
uvicorn = "^0.40.0"
|
||||
websockets = "^15.0"
|
||||
pandas = ">=1.5.3,<3"
|
||||
pandas = ">=1.5.3"
|
||||
html5lib = "^1.1"
|
||||
fastapi = "^0.128.0"
|
||||
uuid7 = "^0.1.0"
|
||||
python-multipart = "^0.0.20"
|
||||
python-multipart = "^0.0.22"
|
||||
pydantic = "^2.12.3"
|
||||
requests = "^2.32.5"
|
||||
importlib-metadata = ">=6.8.0"
|
||||
|
||||
@@ -169,7 +169,7 @@ class DerivativesViews:
|
||||
str(d) + " Hours Ago" if d > 0 else "Current"
|
||||
for d in df["hours_ago"].unique().tolist()
|
||||
]
|
||||
df.loc[:, "date"] = df["hours_ago"].apply(
|
||||
df["date"] = df["hours_ago"].apply(
|
||||
lambda x: str(x) + " Hours Ago" if x > 0 else "Current"
|
||||
)
|
||||
figure, color_count = create_fig(figure, df, dates, color_count)
|
||||
|
||||
@@ -276,3 +276,60 @@ def test_econometrics_unit_root(params, data_type):
|
||||
result = requests.post(url, headers=get_headers(), timeout=10, data=body)
|
||||
assert isinstance(result, requests.Response)
|
||||
assert result.status_code == 200
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"params, data_type",
|
||||
[
|
||||
(
|
||||
{"data": "", "y_column": "close", "x_columns": ["high"]},
|
||||
"equity",
|
||||
),
|
||||
(
|
||||
{"data": "", "y_column": "close", "x_columns": ["high"]},
|
||||
"crypto",
|
||||
),
|
||||
],
|
||||
)
|
||||
@pytest.mark.integration
|
||||
def test_econometrics_ols_regression(params, data_type):
|
||||
"""Test the OLS regression function in econometrics extension."""
|
||||
params = {p: v for p, v in params.items() if v}
|
||||
|
||||
body = json.dumps(
|
||||
{
|
||||
"data": get_data(data_type),
|
||||
"x_columns": params.pop("x_columns"),
|
||||
}
|
||||
)
|
||||
|
||||
query_str = get_querystring(params, [])
|
||||
url = f"http://0.0.0.0:8000/api/v1/econometrics/ols_regression?{query_str}"
|
||||
result = requests.post(url, headers=get_headers(), timeout=10, data=body)
|
||||
assert isinstance(result, requests.Response)
|
||||
assert result.status_code == 200
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"params, data_type",
|
||||
[
|
||||
({"data": "", "columns": ["high", "low"]}, "equity"),
|
||||
({"data": "", "columns": ["high", "low"]}, "crypto"),
|
||||
],
|
||||
)
|
||||
@pytest.mark.integration
|
||||
def test_econometrics_variance_inflation_factor(params, data_type):
|
||||
"""Test the variance inflation factor endpoint."""
|
||||
params = {p: v for p, v in params.items() if v}
|
||||
|
||||
body = json.dumps(
|
||||
{
|
||||
"data": get_data(data_type),
|
||||
"columns": params.pop("columns"),
|
||||
}
|
||||
)
|
||||
|
||||
url = "http://0.0.0.0:8000/api/v1/econometrics/variance_inflation_factor"
|
||||
result = requests.post(url, headers=get_headers(), timeout=10, data=body)
|
||||
assert isinstance(result, requests.Response)
|
||||
assert result.status_code == 200
|
||||
|
||||
@@ -105,7 +105,7 @@ def test_econometrics_ols_regression(params, data_type, obb):
|
||||
result = obb.econometrics.ols_regression(**params)
|
||||
assert result
|
||||
assert isinstance(result, OBBject)
|
||||
assert len(result.results) > 0
|
||||
assert result.results is not None
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
|
||||
@@ -1,17 +1,77 @@
|
||||
"""Econometrics Router."""
|
||||
|
||||
# pylint: disable=too-many-lines
|
||||
|
||||
from itertools import combinations
|
||||
from typing import Literal
|
||||
from typing import Any, Literal
|
||||
|
||||
from openbb_core.app.model.example import APIEx, PythonEx
|
||||
from openbb_core.app.model.obbject import OBBject
|
||||
from openbb_core.app.router import Router
|
||||
from openbb_core.provider.abstract.data import Data
|
||||
from pydantic import PositiveInt
|
||||
from pydantic import BaseModel, PositiveInt, model_serializer
|
||||
|
||||
router = Router(prefix="", description="Econometrics analysis tools.")
|
||||
|
||||
|
||||
class OLSRegressionResults(BaseModel):
|
||||
"""OLS Regression Results that serializes statsmodels objects."""
|
||||
|
||||
model: Any
|
||||
results: Any
|
||||
|
||||
class Config:
|
||||
"""Pydantic config."""
|
||||
|
||||
arbitrary_types_allowed = True
|
||||
|
||||
@model_serializer
|
||||
def serialize_model(self) -> dict:
|
||||
"""Serialize statsmodels objects to a dictionary."""
|
||||
results = self.results
|
||||
conf_int = results.conf_int()
|
||||
conf_int_dict = (
|
||||
conf_int.to_dict()
|
||||
if hasattr(conf_int, "to_dict")
|
||||
else conf_int.to_dict("index")
|
||||
)
|
||||
return {
|
||||
"params": (
|
||||
results.params.to_dict()
|
||||
if hasattr(results.params, "to_dict")
|
||||
else dict(results.params)
|
||||
),
|
||||
"rsquared": float(results.rsquared),
|
||||
"rsquared_adj": float(results.rsquared_adj),
|
||||
"fvalue": float(results.fvalue) if results.fvalue is not None else None,
|
||||
"f_pvalue": (
|
||||
float(results.f_pvalue) if results.f_pvalue is not None else None
|
||||
),
|
||||
"aic": float(results.aic),
|
||||
"bic": float(results.bic),
|
||||
"llf": float(results.llf),
|
||||
"nobs": int(results.nobs),
|
||||
"df_model": float(results.df_model),
|
||||
"df_resid": float(results.df_resid),
|
||||
"pvalues": (
|
||||
results.pvalues.to_dict()
|
||||
if hasattr(results.pvalues, "to_dict")
|
||||
else dict(results.pvalues)
|
||||
),
|
||||
"tvalues": (
|
||||
results.tvalues.to_dict()
|
||||
if hasattr(results.tvalues, "to_dict")
|
||||
else dict(results.tvalues)
|
||||
),
|
||||
"bse": (
|
||||
results.bse.to_dict()
|
||||
if hasattr(results.bse, "to_dict")
|
||||
else dict(results.bse)
|
||||
),
|
||||
"conf_int": conf_int_dict,
|
||||
}
|
||||
|
||||
|
||||
@router.command(
|
||||
methods=["POST"],
|
||||
examples=[
|
||||
@@ -77,7 +137,6 @@ def correlation_matrix(
|
||||
|
||||
@router.command(
|
||||
methods=["POST"],
|
||||
include_in_schema=False,
|
||||
examples=[
|
||||
PythonEx(
|
||||
description="Perform Ordinary Least Squares (OLS) regression.",
|
||||
@@ -99,7 +158,7 @@ def ols_regression(
|
||||
data: list[Data],
|
||||
y_column: str,
|
||||
x_columns: list[str],
|
||||
) -> OBBject[dict]:
|
||||
) -> OBBject[OLSRegressionResults]:
|
||||
"""Perform Ordinary Least Squares (OLS) regression.
|
||||
|
||||
OLS regression is a fundamental statistical method to explore and model the relationship between a
|
||||
@@ -118,7 +177,7 @@ def ols_regression(
|
||||
|
||||
Returns
|
||||
-------
|
||||
OBBject[dict]
|
||||
OBBject[OLSRegressionResults]
|
||||
OBBject with the results being model and results objects.
|
||||
"""
|
||||
# pylint: disable=import-outside-toplevel
|
||||
@@ -133,7 +192,7 @@ def ols_regression(
|
||||
y = get_target_column(basemodel_to_df(data), y_column)
|
||||
model = sm.OLS(y, X)
|
||||
results = model.fit()
|
||||
return OBBject(results={"model": model, "results": results})
|
||||
return OBBject(results=OLSRegressionResults(model=model, results=results))
|
||||
|
||||
|
||||
@router.command(
|
||||
@@ -914,19 +973,24 @@ def panel_fmac(
|
||||
|
||||
@router.command(
|
||||
methods=["POST"],
|
||||
include_in_schema=False,
|
||||
examples=[
|
||||
PythonEx(
|
||||
description="Calculate the variance inflation factor.",
|
||||
code=[
|
||||
"stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='yfinance').to_df()", # noqa: E501 pylint: disable= C0301
|
||||
'obb.econometrics.variance_inflation_factor(data=stock_data, column="close")',
|
||||
'obb.econometrics.variance_inflation_factor(data=stock_data, columns=["open", "high", "low", "close"])', # noqa: E501 pylint: disable= C0301
|
||||
],
|
||||
),
|
||||
APIEx(
|
||||
parameters={
|
||||
"columns": ["open", "high", "low"],
|
||||
"data": APIEx.mock_data("timeseries"),
|
||||
}
|
||||
),
|
||||
],
|
||||
)
|
||||
def variance_inflation_factor(
|
||||
data: list[Data], columns: list | None = None
|
||||
data: list[Data], columns: list[str] | None = None
|
||||
) -> OBBject[list[Data]]:
|
||||
"""Calculate VIF (variance inflation factor), which tests for collinearity.
|
||||
|
||||
@@ -974,7 +1038,7 @@ def variance_inflation_factor(
|
||||
df = add_constant(dataset if columns is None else dataset[columns])
|
||||
|
||||
# Remove date and string type because VIF doesn't work for these types
|
||||
df = df.select_dtypes(exclude=["object", "datetime", "timedelta"])
|
||||
df = df.select_dtypes(exclude=["object", "datetime", "timedelta"]) # type: ignore
|
||||
|
||||
# Calculate the VIF values
|
||||
vif_values: dict = {}
|
||||
|
||||
@@ -62,7 +62,7 @@ def get_crypto_data():
|
||||
|
||||
# TODO : add more crypto providers and symbols
|
||||
symbol = random.choice(["BTCUSD"]) # noqa: S311
|
||||
provider = random.choice(["fmp"]) # noqa: S311
|
||||
provider = "fmp"
|
||||
|
||||
data["crypto_data"] = request_data(
|
||||
menu="crypto",
|
||||
|
||||
@@ -32,8 +32,8 @@ def get_stocks_data():
|
||||
symbol = random.choice(["AAPL", "NVDA", "MSFT", "TSLA", "AMZN", "V"]) # noqa: S311
|
||||
provider = random.choice(["fmp", "yfinance"]) # noqa: S311
|
||||
|
||||
data["stocks_data"] = openbb.obb.equity.price.historical(
|
||||
symbol=symbol, provider=provider
|
||||
data["stocks_data"] = openbb.obb.equity.price.historical( # type: ignore
|
||||
symbol=symbol, provider=provider # type: ignore
|
||||
).results
|
||||
return data["stocks_data"]
|
||||
|
||||
|
||||
@@ -265,7 +265,7 @@ def sortino_ratio(
|
||||
)
|
||||
|
||||
if adjusted:
|
||||
results = results.applymap(lambda x: x / sqrt(2) if isinstance(x, float) else x)
|
||||
results = results.map(lambda x: x / sqrt(2) if isinstance(x, float) else x)
|
||||
results_ = df_to_basemodel(results)
|
||||
|
||||
return OBBject(results=results_)
|
||||
|
||||
@@ -136,7 +136,7 @@ def capm(data: list[Data], target: str) -> OBBject[CAPMModel]:
|
||||
|
||||
df_target = get_target_columns(df, ["date", target])
|
||||
df_target = df_target.set_index("date")
|
||||
df_target.loc[:, "return"] = df_target.pct_change()
|
||||
df_target["return"] = df_target.pct_change()
|
||||
df_target = df_target.dropna()
|
||||
df_target.index = to_datetime(df_target.index)
|
||||
start_date = df_target.index.min().strftime("%Y-%m-%d")
|
||||
|
||||
@@ -118,7 +118,7 @@ def test_technical_atr(params, data_type, obb):
|
||||
{
|
||||
"data": "",
|
||||
"index": "date",
|
||||
"close_column": "adj_close",
|
||||
"close_column": "close",
|
||||
"period": "125",
|
||||
"start_date": "",
|
||||
"end_date": "",
|
||||
|
||||
@@ -388,7 +388,7 @@ class TA_Data:
|
||||
) from e
|
||||
|
||||
if indicator_data is not None:
|
||||
output = output.join(indicator_data).infer_objects(copy=False)
|
||||
output = output.join(indicator_data).infer_objects()
|
||||
numeric_cols = output.select_dtypes(include=["number"]).columns
|
||||
output[numeric_cols] = output[numeric_cols].interpolate("linear")
|
||||
|
||||
|
||||
@@ -192,7 +192,7 @@ class PlotlyTA(PltTA):
|
||||
indicators = ChartIndicators.from_dict(indicators or {})
|
||||
|
||||
# Apply to_datetime to the index in a way that handles daylight savings.
|
||||
df_stock.loc[:, "date"] = df_stock.index # type: ignore
|
||||
df_stock["date"] = df_stock.index # type: ignore
|
||||
df_stock["date"] = df_stock["date"].apply(pd.to_datetime)
|
||||
df_stock.index = df_stock["date"] # type: ignore
|
||||
df_stock.drop(columns=["date"], inplace=True)
|
||||
|
||||
@@ -168,7 +168,7 @@ class AVEquityHistoricalFetcher(
|
||||
inplace=True,
|
||||
)
|
||||
if len(query.symbol.split(",")) > 1:
|
||||
df.loc[:, "symbol"] = symbol
|
||||
df["symbol"] = symbol
|
||||
results.extend(df.to_dict("records"))
|
||||
if not data:
|
||||
warn(f"Symbol Error: No data found for {symbol}")
|
||||
@@ -268,7 +268,7 @@ class AVEquityHistoricalFetcher(
|
||||
if freq in ["M", "W-FRI"]:
|
||||
data = data.resample(freq).agg({**agg_dict})
|
||||
if len(query.symbol.split(",")) > 1:
|
||||
data.loc[:, "symbol"] = symbol
|
||||
data["symbol"] = symbol
|
||||
|
||||
data = data.reset_index()
|
||||
if intraday is False:
|
||||
|
||||
@@ -275,7 +275,7 @@ async def download_category_series_ids(category) -> tuple[list, dict]:
|
||||
ids = ids.rename(columns={"footnote_codes": "footnote_code"})
|
||||
ids = ids.astype(str).replace({"nan": None, "''": None, "": None})
|
||||
|
||||
ids.loc[:, "survey_name"] = SURVEY_NAMES.get(survey.upper(), None)
|
||||
ids["survey_name"] = SURVEY_NAMES.get(survey.upper(), None)
|
||||
ids = ids[ids.series_id.astype(str).str.startswith(survey.upper())]
|
||||
|
||||
if ids is None or ids.empty:
|
||||
|
||||
@@ -78,4 +78,15 @@ class CboeEquitySearchFetcher(
|
||||
query: CboeEquitySearchQueryParams, data: dict, **kwargs: Any
|
||||
) -> list[CboeEquitySearchData]:
|
||||
"""Transform the data to the standard format."""
|
||||
return [CboeEquitySearchData.model_validate(d) for d in data["results"]]
|
||||
from math import isnan # pylint: disable=import-outside-toplevel
|
||||
|
||||
def clean_nan(d: dict) -> dict:
|
||||
"""Replace nan values with None for Pydantic validation."""
|
||||
return {
|
||||
k: None if isinstance(v, float) and isnan(v) else v
|
||||
for k, v in d.items()
|
||||
}
|
||||
|
||||
return [
|
||||
CboeEquitySearchData.model_validate(clean_nan(d)) for d in data["results"]
|
||||
]
|
||||
|
||||
@@ -133,7 +133,7 @@ class DeribitOptionsChainsFetcher(
|
||||
symbols_dict: dict[str, str] = {}
|
||||
|
||||
try:
|
||||
symbols_dict = await get_options_symbols(query.symbol)
|
||||
symbols_dict = await get_options_symbols(query.symbol) # type: ignore
|
||||
except OpenBBError as e:
|
||||
raise OpenBBError(e) from e
|
||||
|
||||
@@ -268,14 +268,14 @@ class DeribitOptionsChainsFetcher(
|
||||
"high",
|
||||
"low",
|
||||
] and query.symbol.upper() in ["BTC", "ETH"]:
|
||||
df.loc[:, col] = df[col].astype(float).multiply(df.index_price).round(2)
|
||||
df[col] = (df[col].astype(float) * df["index_price"]).round(2)
|
||||
elif col in ["price_change", "mark_iv", "bid_iv", "ask_iv"]:
|
||||
df.loc[:, col] = df[col].astype(float).divide(100)
|
||||
df[col] = df[col].astype(float) / 100
|
||||
|
||||
df = df.replace({nan: None})
|
||||
df = df.sort_values(["expiration", "strike", "option_type"])
|
||||
df = df.reset_index(drop=True)
|
||||
df.loc[:, "contract_size"] = 1
|
||||
df["contract_size"] = 1
|
||||
results = df.to_dict(orient="list")
|
||||
|
||||
return DeribitOptionsChainsData.model_validate(results)
|
||||
|
||||
@@ -292,10 +292,10 @@ async def get_ohlc_data(
|
||||
.rename(columns={"ticks": "date", "cost": "volume_notional"})
|
||||
.convert_dtypes()
|
||||
)
|
||||
df.date = to_datetime(df.date, unit="ms", origin="unix", utc=True)
|
||||
df["date"] = to_datetime(df.date, unit="ms", origin="unix", utc=True)
|
||||
if interval == "1D":
|
||||
df.date = df.date.dt.date
|
||||
df.loc[:, "symbol"] = symbol
|
||||
df["symbol"] = symbol
|
||||
results.extend(
|
||||
df[
|
||||
[
|
||||
|
||||
@@ -155,14 +155,14 @@ class ECBYieldCurveFetcher(
|
||||
flattened_data = flattened_data.rename(columns={"index": "date"}).sort_values(
|
||||
"date"
|
||||
)
|
||||
flattened_data.loc[:, "maturity"] = Categorical(
|
||||
flattened_data["maturity"] = Categorical(
|
||||
flattened_data.maturity, categories=MATURITIES, ordered=True
|
||||
)
|
||||
flattened_data = flattened_data.sort_values(
|
||||
by=["date", "maturity"]
|
||||
).reset_index(drop=True)
|
||||
flattened_data.date = flattened_data.date.dt.strftime("%Y-%m-%d")
|
||||
flattened_data.rate = flattened_data.rate.astype(float).div(100)
|
||||
flattened_data["date"] = flattened_data.date.dt.strftime("%Y-%m-%d")
|
||||
flattened_data["rate"] = flattened_data.rate.astype(float).div(100)
|
||||
records = flattened_data.to_dict(orient="records")
|
||||
|
||||
return [ECBYieldCurveData.model_validate(d) for d in records]
|
||||
|
||||
@@ -100,4 +100,16 @@ class EconDbAvailableIndicatorsFetcher(
|
||||
**kwargs: Any,
|
||||
) -> list[EconDbAvailableIndicatorsData]:
|
||||
"""Transform data."""
|
||||
return [EconDbAvailableIndicatorsData.model_validate(d) for d in data]
|
||||
# pylint: disable=import-outside-toplevel
|
||||
from math import isnan
|
||||
|
||||
def clean_nan(d: dict) -> dict:
|
||||
"""Replace nan values with None for Pydantic validation."""
|
||||
return {
|
||||
k: None if isinstance(v, float) and isnan(v) else v
|
||||
for k, v in d.items()
|
||||
}
|
||||
|
||||
return [
|
||||
EconDbAvailableIndicatorsData.model_validate(clean_nan(d)) for d in data
|
||||
]
|
||||
|
||||
@@ -231,11 +231,9 @@ class EconDbYieldCurveFetcher(
|
||||
flattened_data = flattened_data.sort_values(
|
||||
by=["date", "maturity"]
|
||||
).reset_index(drop=True)
|
||||
flattened_data.loc[:, "date"] = flattened_data["date"].dt.strftime(
|
||||
"%Y-%m-%d"
|
||||
)
|
||||
flattened_data["date"] = flattened_data["date"].dt.strftime("%Y-%m-%d")
|
||||
new_df = flattened_data.copy()
|
||||
new_df.loc[:, "country"] = country
|
||||
new_df["country"] = country
|
||||
|
||||
def convert_duration(x):
|
||||
"""Convert the duration to a decimal representation of years."""
|
||||
@@ -246,7 +244,7 @@ class EconDbYieldCurveFetcher(
|
||||
return int(unit)
|
||||
return int(unit) / 12
|
||||
|
||||
new_df.loc[:, "maturity_years"] = new_df.maturity.apply(convert_duration)
|
||||
new_df["maturity_years"] = new_df.maturity.apply(convert_duration)
|
||||
|
||||
new_df = new_df.replace({nan: None})
|
||||
records = [
|
||||
|
||||
@@ -453,7 +453,7 @@ async def download_indicators(use_cache: bool = True) -> DataFrame:
|
||||
all_tickers = all_tickers[~all_tickers.short_ticker.isin(["POPNP", "M3CO"])]
|
||||
all_tickers = all_tickers[~all_tickers.short_ticker.str.contains("_")]
|
||||
country_indicators = all_tickers[all_tickers.iso.str.len() == 2].copy()
|
||||
country_indicators.loc[:, "symbol_root"] = country_indicators.apply(
|
||||
country_indicators["symbol_root"] = country_indicators.apply(
|
||||
lambda row: (
|
||||
row["short_ticker"][::-1].replace(row["iso"][::-1], "", 1)[::-1]
|
||||
if row["short_ticker"].endswith(row["iso"])
|
||||
@@ -477,7 +477,7 @@ async def download_indicators(use_cache: bool = True) -> DataFrame:
|
||||
# Fix a known incorrect value.
|
||||
tickers.loc[tickers["short_ticker"] == "GDPPCNZ", "multiplier"] = 1
|
||||
tickers.loc[tickers["short_ticker"] == "GDPPCNZ", "scale"] = "Units"
|
||||
tickers.entity = tickers.entity.str.replace("All countries", "World")
|
||||
tickers["entity"] = tickers.entity.str.replace("All countries", "World")
|
||||
tickers = tickers.fillna("N/A").replace("N/A", None).replace("nan", None)
|
||||
return tickers.sort_values(by="last_date", ascending=False)
|
||||
|
||||
|
||||
@@ -193,17 +193,17 @@ class EiaPetroleumStatusReportFetcher(
|
||||
var_name="symbol",
|
||||
).dropna()
|
||||
df = df.reset_index(drop=True)
|
||||
df.loc[:, "title"] = df.symbol.map(title_map)
|
||||
df.loc[:, "unit"] = df.title.map(lambda x: x.split(" (")[-1].split(")")[0])
|
||||
df["title"] = df.symbol.map(title_map)
|
||||
df["unit"] = df.title.map(lambda x: x.split(" (")[-1].split(")")[0])
|
||||
units = [f"({d})" for d in df.unit.unique().tolist()]
|
||||
for unit in units:
|
||||
df.title = df.title.str.replace(unit, "", regex=False).str.strip()
|
||||
df.loc[:, "table"] = table_name
|
||||
df["title"] = df.title.str.replace(unit, "", regex=False).str.strip()
|
||||
df["table"] = table_name
|
||||
df["order"] = df.groupby("date").cumcount() + 1
|
||||
df = df[["date", "table", "symbol", "order", "title", "value", "unit"]]
|
||||
df.symbol = Categorical(df.symbol, categories=symbols, ordered=True)
|
||||
df["symbol"] = Categorical(df.symbol, categories=symbols, ordered=True)
|
||||
df = df.sort_values(["date", "symbol"])
|
||||
df.date = df.date.dt.date
|
||||
df["date"] = df.date.dt.date
|
||||
|
||||
if query.start_date:
|
||||
df = df[df.date >= query.start_date]
|
||||
|
||||
@@ -176,7 +176,7 @@ class FamaFrenchRegionalPortfolioReturnsFetcher(
|
||||
)
|
||||
.copy()
|
||||
)
|
||||
flattened_data.loc[:, "measure"] = query.measure
|
||||
flattened_data["measure"] = query.measure
|
||||
|
||||
return AnnotatedResult(
|
||||
result=[
|
||||
|
||||
@@ -180,7 +180,7 @@ class FamaFrenchUSPortfolioReturnsFetcher(
|
||||
)
|
||||
.copy()
|
||||
)
|
||||
flattened_data.loc[:, "measure"] = query.measure
|
||||
flattened_data["measure"] = query.measure
|
||||
|
||||
return AnnotatedResult(
|
||||
result=[
|
||||
|
||||
@@ -85,11 +85,11 @@ class FederalReserveMoneyMeasuresFetcher(
|
||||
df = df.replace("ND", None)
|
||||
df["month"] = to_datetime(df["month"])
|
||||
df = df[
|
||||
(to_datetime(df.month) >= to_datetime(query.start_date))
|
||||
& (to_datetime(df.month) <= to_datetime(query.end_date))
|
||||
(to_datetime(df.month) >= to_datetime(query.start_date)) # type: ignore
|
||||
& (to_datetime(df.month) <= to_datetime(query.end_date)) # type: ignore
|
||||
].set_index("month")
|
||||
# Needs the date to not be in the columns
|
||||
df = df.applymap(lambda x: float(x) if x != "-" and x is not None else x)
|
||||
df = df.map(lambda x: float(x) if x != "-" and x is not None else x)
|
||||
df = df.reset_index(drop=False)
|
||||
|
||||
return df.to_dict(orient="records")
|
||||
|
||||
@@ -123,8 +123,8 @@ class FederalReservePrimaryDealerFailsFetcher(
|
||||
raise EmptyDataError("No data returned from the Federal Reserve API.")
|
||||
|
||||
df = DataFrame(data)
|
||||
df.loc[:, "title"] = df.keyid.map(FAILS_SERIES_TO_TITLE)
|
||||
df.value = df.value.astype(int)
|
||||
df["title"] = df.keyid.map(FAILS_SERIES_TO_TITLE)
|
||||
df["value"] = df.value.astype(int)
|
||||
new_df = df.pivot(index="asofdate", columns="title", values="value").copy()
|
||||
new_data = new_df.copy()
|
||||
combined_df = DataFrame()
|
||||
@@ -163,15 +163,15 @@ class FederalReservePrimaryDealerFailsFetcher(
|
||||
|
||||
new_data = new_data.T.unstack().reset_index()
|
||||
new_data.columns = ["date", "title", "value"]
|
||||
new_data.loc[:, "symbol"] = new_data.title.map(
|
||||
new_data["symbol"] = new_data.title.map(
|
||||
{v: k for k, v in FAILS_SERIES_TO_TITLE.items()}
|
||||
).replace({NA: "--"})
|
||||
new_data = new_data.dropna()
|
||||
|
||||
if query.unit == "value":
|
||||
new_data.value = new_data.value.astype(int)
|
||||
new_data["value"] = new_data.value.astype(int)
|
||||
|
||||
new_data.date = to_datetime(new_data.date).dt.date
|
||||
new_data["date"] = to_datetime(new_data.date).dt.date
|
||||
|
||||
if query.start_date:
|
||||
new_data = new_data[new_data.date >= query.start_date]
|
||||
|
||||
@@ -86,7 +86,7 @@ class FederalReserveYieldCurveFetcher(
|
||||
|
||||
df = data.copy()
|
||||
df.set_index("date", inplace=True)
|
||||
dates = query.date.split(",") if query.date else [df.index.max()]
|
||||
dates = query.date.split(",") if query.date else [df.index.max()] # type: ignore
|
||||
df.index = DatetimeIndex(df.index)
|
||||
dates_list = DatetimeIndex(dates)
|
||||
df.columns.name = "maturity"
|
||||
@@ -111,7 +111,7 @@ class FederalReserveYieldCurveFetcher(
|
||||
flattened_data = flattened_data.sort_values(
|
||||
by=["date", "maturity"]
|
||||
).reset_index(drop=True)
|
||||
flattened_data.loc[:, "date"] = flattened_data["date"].dt.strftime("%Y-%m-%d")
|
||||
flattened_data["date"] = flattened_data["date"].dt.strftime("%Y-%m-%d")
|
||||
records = flattened_data.to_dict(orient="records")
|
||||
|
||||
return [FederalReserveYieldCurveData.model_validate(d) for d in records]
|
||||
|
||||
@@ -126,7 +126,7 @@ class FMPYieldCurveFetcher(
|
||||
flattened_data = flattened_data.sort_values(
|
||||
by=["date", "maturity"]
|
||||
).reset_index(drop=True)
|
||||
flattened_data.loc[:, "date"] = flattened_data["date"].dt.strftime("%Y-%m-%d")
|
||||
flattened_data["date"] = flattened_data["date"].dt.strftime("%Y-%m-%d")
|
||||
records = flattened_data.to_dict(orient="records")
|
||||
|
||||
return [FMPYieldCurveData.model_validate(d) for d in records]
|
||||
|
||||
@@ -206,8 +206,8 @@ class FredCommoditySpotPricesFetcher(
|
||||
results = await FredSeriesFetcher.fetch_data(series_query, credentials)
|
||||
|
||||
return {
|
||||
"result": results.result,
|
||||
"metadata": results.metadata,
|
||||
"result": results.result, # type: ignore
|
||||
"metadata": results.metadata, # type: ignore
|
||||
}
|
||||
except Exception as e:
|
||||
raise OpenBBError(f"Failed to fetch data from FRED API: {e}") from e
|
||||
@@ -240,8 +240,8 @@ class FredCommoditySpotPricesFetcher(
|
||||
.sort_values(by="date")
|
||||
)
|
||||
df = df.reset_index(drop=True)
|
||||
df.loc[:, "commodity"] = df.symbol.map(title_map)
|
||||
df.loc[:, "unit"] = df.symbol.map(units_map)
|
||||
df["commodity"] = df.symbol.map(title_map)
|
||||
df["unit"] = df.symbol.map(units_map)
|
||||
records = df.to_dict(orient="records")
|
||||
|
||||
return AnnotatedResult(
|
||||
|
||||
@@ -207,6 +207,7 @@ class FredSeriesFetcher(
|
||||
records = (
|
||||
DataFrame(series)
|
||||
.filter(items=query.symbol.split(","), axis=1)
|
||||
.sort_index()
|
||||
.reset_index()
|
||||
.rename(columns={"index": "date"})
|
||||
.replace({nan: None})
|
||||
|
||||
@@ -148,9 +148,9 @@ class FredTipsYieldsFetcher(
|
||||
"series_id"
|
||||
)
|
||||
|
||||
df.loc[:, "due"] = df.title.apply(
|
||||
lambda x: x.split("Due ")[-1].strip()
|
||||
).apply(to_datetime)
|
||||
df["due"] = df.title.apply(lambda x: x.split("Due ")[-1].strip()).apply(
|
||||
to_datetime
|
||||
)
|
||||
df = df[["due", "observation_start", "observation_end", "title"]]
|
||||
return df.sort_values(by="due").reset_index() # type: ignore
|
||||
|
||||
@@ -195,13 +195,13 @@ class FredTipsYieldsFetcher(
|
||||
fetcher = FredSeriesFetcher()
|
||||
res = await fetcher.fetch_data(params=params, credentials=credentials)
|
||||
df = DataFrame([d.model_dump() for d in res.result]) # type: ignore
|
||||
meta = res.metadata # type: ignore
|
||||
meta: dict = res.metadata or {} # type: ignore
|
||||
except Exception as e:
|
||||
raise OpenBBError(e) from e
|
||||
|
||||
for k, v in title_map.items():
|
||||
if k in meta:
|
||||
meta[k]["title"] = v # type: ignore
|
||||
meta[k]["title"] = v
|
||||
|
||||
# We flatten the data and format the output with the metadata.
|
||||
|
||||
|
||||
@@ -70,11 +70,11 @@ class FREDYieldCurveFetcher(
|
||||
series_ids = ",".join(list(YIELD_CURVES[query.yield_curve_type]))
|
||||
fetcher = FredSeriesFetcher()
|
||||
data = await fetcher.fetch_data(
|
||||
{"symbol": series_ids}, {"fred_api_key": api_key}
|
||||
{"symbol": series_ids}, {"fred_api_key": api_key} # type: ignore
|
||||
)
|
||||
if not data:
|
||||
raise EmptyDataError("The request was returned empty.")
|
||||
results = [d.model_dump() for d in data.result]
|
||||
results = [d.model_dump() for d in data.result] # type: ignore
|
||||
|
||||
return results
|
||||
|
||||
@@ -88,7 +88,7 @@ class FREDYieldCurveFetcher(
|
||||
|
||||
df = DataFrame(data).set_index("date").sort_index()
|
||||
df.index = df.index.astype(str)
|
||||
dates = query.date.split(",") if query.date else [df.index.max()]
|
||||
dates = query.date.split(",") if query.date else [df.index.max()] # type: ignore
|
||||
df.index = DatetimeIndex(df.index)
|
||||
dates_list = DatetimeIndex(dates)
|
||||
maturity_dict = YIELD_CURVES[query.yield_curve_type]
|
||||
@@ -117,7 +117,7 @@ class FREDYieldCurveFetcher(
|
||||
flattened_data = flattened_data.sort_values(
|
||||
by=["date", "maturity"]
|
||||
).reset_index(drop=True)
|
||||
flattened_data.loc[:, "date"] = flattened_data["date"].dt.strftime("%Y-%m-%d")
|
||||
flattened_data["date"] = flattened_data["date"].dt.strftime("%Y-%m-%d")
|
||||
records = flattened_data.to_dict(orient="records")
|
||||
|
||||
return [FREDYieldCurveData.model_validate(d) for d in records]
|
||||
|
||||
@@ -232,4 +232,16 @@ class GovernmentUSTreasuryAuctionsFetcher(
|
||||
**kwargs: Any,
|
||||
) -> list[GovernementUSTreasuryAuctionsData]:
|
||||
"""Transform the data."""
|
||||
return [GovernementUSTreasuryAuctionsData.model_validate(d) for d in data]
|
||||
# pylint: disable=import-outside-toplevel
|
||||
from math import isnan
|
||||
|
||||
def clean_nan(d: dict) -> dict:
|
||||
"""Replace nan values with None for Pydantic validation."""
|
||||
return {
|
||||
k: None if isinstance(v, float) and isnan(v) else v
|
||||
for k, v in d.items()
|
||||
}
|
||||
|
||||
return [
|
||||
GovernementUSTreasuryAuctionsData.model_validate(clean_nan(d)) for d in data
|
||||
]
|
||||
|
||||
@@ -101,8 +101,9 @@ class GovernmentUSTreasuryPricesFetcher(
|
||||
) -> list[GovernmentUSTreasuryPricesData]:
|
||||
"""Transform the data."""
|
||||
# pylint: disable=import-outside-toplevel
|
||||
from io import StringIO # noqa
|
||||
from pandas import Index, read_csv, to_datetime # noqa
|
||||
from math import isnan # noqa
|
||||
from io import StringIO
|
||||
from pandas import Index, read_csv, to_datetime
|
||||
|
||||
try:
|
||||
if not data:
|
||||
@@ -141,7 +142,15 @@ class GovernmentUSTreasuryPricesFetcher(
|
||||
]
|
||||
if query.cusip is not None:
|
||||
results = results[results["cusip"] == query.cusip]
|
||||
|
||||
def clean_nan(d: dict) -> dict:
|
||||
"""Replace nan values with None for Pydantic validation."""
|
||||
return {
|
||||
k: None if isinstance(v, float) and isnan(v) else v
|
||||
for k, v in d.items()
|
||||
}
|
||||
|
||||
return [
|
||||
GovernmentUSTreasuryPricesData.model_validate(d)
|
||||
GovernmentUSTreasuryPricesData.model_validate(clean_nan(d))
|
||||
for d in results.to_dict("records")
|
||||
]
|
||||
|
||||
@@ -148,7 +148,7 @@ def plot_port_info_map(data: list[ImfPortInfoData]):
|
||||
|
||||
return "".join(html_parts)
|
||||
|
||||
df.loc[:, "hover_html"] = df.apply(generate_hover_html, axis=1)
|
||||
df["hover_html"] = df.apply(generate_hover_html, axis=1)
|
||||
|
||||
fig = px.scatter_map(
|
||||
df,
|
||||
|
||||
@@ -140,9 +140,7 @@ class NasdaqEquitySearchFetcher(
|
||||
]
|
||||
directory["Market Category"] = directory["Market Category"].replace(" ", None)
|
||||
results = (
|
||||
directory.infer_objects(copy=False)
|
||||
.replace({nan: None})
|
||||
.to_dict(orient="records")
|
||||
directory.infer_objects().replace({nan: None}).to_dict(orient="records")
|
||||
)
|
||||
|
||||
return [NasdaqEquitySearchData.model_validate(d) for d in results]
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -35,7 +35,7 @@ def vcr_config():
|
||||
}
|
||||
|
||||
|
||||
@pytest.mark.record_http
|
||||
@pytest.mark.record_ftp
|
||||
def test_nasdaq_equity_search_fetcher(credentials=test_credentials):
|
||||
"""Test the Nasdaq Equity Search fetcher."""
|
||||
params = {"query": "", "is_etf": True, "use_cache": False}
|
||||
|
||||
@@ -61,8 +61,9 @@ class SecSicSearchFetcher(
|
||||
) -> list[dict]:
|
||||
"""Extract data from the SEC website table."""
|
||||
# pylint: disable=import-outside-toplevel
|
||||
from aiohttp_client_cache import SQLiteBackend
|
||||
from aiohttp_client_cache import SQLiteBackend # noqa
|
||||
from aiohttp_client_cache.session import CachedSession
|
||||
from io import StringIO
|
||||
from openbb_core.app.utils import get_user_cache_directory
|
||||
from openbb_core.provider.utils.helpers import amake_request
|
||||
from openbb_sec.utils.helpers import SEC_HEADERS, sec_callback
|
||||
@@ -89,7 +90,7 @@ class SecSicSearchFetcher(
|
||||
else:
|
||||
response = await amake_request(url, headers=SEC_HEADERS, response_callback=sec_callback) # type: ignore
|
||||
|
||||
data = read_html(response)[0].astype(str)
|
||||
data = read_html(StringIO(response))[0].astype(str) # type: ignore
|
||||
if len(data) == 0:
|
||||
return results
|
||||
if query:
|
||||
|
||||
@@ -231,19 +231,19 @@ async def get_form_4_urls(
|
||||
urls: list = []
|
||||
for item in form_4:
|
||||
if (
|
||||
(not start_date or not item.filing_date)
|
||||
(not start_date or not item.filing_date) # type: ignore
|
||||
or start_date
|
||||
and item.filing_date < start_date
|
||||
and item.filing_date < start_date # type: ignore
|
||||
):
|
||||
continue
|
||||
if (
|
||||
(not end_date or not item.report_date)
|
||||
(not end_date or not item.report_date) # type: ignore
|
||||
or end_date
|
||||
and item.report_date > end_date
|
||||
and item.report_date > end_date # type: ignore
|
||||
):
|
||||
continue
|
||||
to_replace = f"{item.primary_doc.split('/')[0]}/"
|
||||
form_url = item.report_url.replace(to_replace, "")
|
||||
to_replace = f"{item.primary_doc.split('/')[0]}/" # type: ignore
|
||||
form_url = item.report_url.replace(to_replace, "") # type: ignore
|
||||
if form_url.endswith(".xml"):
|
||||
urls.append(form_url)
|
||||
|
||||
@@ -278,7 +278,7 @@ async def get_form_4_data(url) -> dict:
|
||||
response_callback=response_callback,
|
||||
timeout=30,
|
||||
) # type: ignore
|
||||
response_text = response.decode("utf-8")
|
||||
response_text = response.decode("utf-8") # type: ignore
|
||||
|
||||
if "Traffic Limit" in response_text:
|
||||
raise OpenBBError(
|
||||
@@ -296,7 +296,7 @@ async def get_form_4_data(url) -> dict:
|
||||
|
||||
return (
|
||||
xml_data.get("ownershipDocument") if xml_data.get("ownershipDocument") else {}
|
||||
)
|
||||
) # type: ignore
|
||||
|
||||
|
||||
async def parse_form_4_data( # noqa: PLR0915, PLR0912 # pylint: disable=too-many-branches
|
||||
@@ -346,10 +346,10 @@ async def parse_form_4_data( # noqa: PLR0915, PLR0912 # pylint: disable=too-ma
|
||||
"symbol": issuer.get("issuerTradingSymbol", "").upper(),
|
||||
"form": data.get("documentType"),
|
||||
"owner": (
|
||||
owners if owners else owner.get("reportingOwnerId", {}).get("rptOwnerName")
|
||||
owners if owners else owner.get("reportingOwnerId", {}).get("rptOwnerName") # type: ignore
|
||||
),
|
||||
"owner_cik": (
|
||||
ciks if ciks else owner.get("reportingOwnerId", {}).get("rptOwnerCik")
|
||||
ciks if ciks else owner.get("reportingOwnerId", {}).get("rptOwnerCik") # type: ignore
|
||||
),
|
||||
"issuer": issuer.get("issuerName"),
|
||||
"issuer_cik": issuer.get("issuerCik"),
|
||||
@@ -419,7 +419,7 @@ async def parse_form_4_data( # noqa: PLR0915, PLR0912 # pylint: disable=too-ma
|
||||
else (
|
||||
"; ".join(
|
||||
[
|
||||
footnotes.get(footnote_id)
|
||||
footnotes.get(footnote_id, "")
|
||||
for footnote_id in ids
|
||||
]
|
||||
)
|
||||
@@ -552,7 +552,7 @@ async def download_data(urls, use_cache: bool = True): # noqa: PLR0915
|
||||
|
||||
if result:
|
||||
df = DataFrame(result)
|
||||
df.loc[:, "filing_url"] = url
|
||||
df["filing_url"] = url
|
||||
df = df.replace({nan: None}).rename(columns=field_map)
|
||||
try:
|
||||
if use_cache is True:
|
||||
|
||||
@@ -168,14 +168,17 @@ async def cik_map(cik: str | int, use_cache: bool = True) -> str:
|
||||
|
||||
def get_schema_filelist(query: str = "", url: str = "", use_cache: bool = True) -> list:
|
||||
"""Get a list of schema files from the SEC website."""
|
||||
from pandas import read_html # pylint: disable=import-outside-toplevel
|
||||
# pylint: disable=import-outside-toplevel
|
||||
from io import StringIO # noqa
|
||||
from pandas import read_html
|
||||
|
||||
results: list = []
|
||||
url = url if url else f"https://xbrl.fasb.org/us-gaap/{query}"
|
||||
_url = url
|
||||
_url = url + "/" if query else _url
|
||||
response = make_request(_url)
|
||||
data = read_html(response.content)[0]["Name"].dropna()
|
||||
content = response.text
|
||||
data = read_html(StringIO(content))[0]["Name"].dropna()
|
||||
if len(data) > 0:
|
||||
data.iloc[0] = url if not query else url + "/"
|
||||
results = data.to_list()
|
||||
|
||||
@@ -36,7 +36,7 @@ async def get_13f_candidates(symbol: str | None = None, cik: str | None = None):
|
||||
params["use_cache"] = False
|
||||
params["form_type"] = "13F-HR"
|
||||
filings = await fetcher.fetch_data(params, {})
|
||||
filings = [d.model_dump() for d in filings]
|
||||
filings = [d.model_dump() for d in filings] # type: ignore
|
||||
if len(filings) == 0:
|
||||
raise OpenBBError(f"No 13F-HR filings found for {symbol if symbol else cik}.")
|
||||
|
||||
@@ -86,7 +86,7 @@ def parse_header(filing_str: str) -> dict:
|
||||
header_dict = xmltodict.parse(str(header_xml))["headerData"]
|
||||
except KeyError:
|
||||
header_xml = soup.find("type")
|
||||
header_dict = xmltodict.parse(str(header_xml)).get("type")
|
||||
header_dict = xmltodict.parse(str(header_xml)).get("type") # type: ignore
|
||||
if header_dict:
|
||||
return header_dict # type: ignore
|
||||
raise OpenBBError(
|
||||
@@ -142,7 +142,7 @@ async def parse_13f_hr(filing: str):
|
||||
info_table = soup.find_all("table")[-1]
|
||||
|
||||
parsed_xml = xmltodict.parse(
|
||||
str(info_table[0]).replace("ns1:", "").replace("n1:", "")
|
||||
str(info_table[0]).replace("ns1:", "").replace("n1:", "") # type: ignore
|
||||
)["informationTable"]["infoTable"]
|
||||
|
||||
if parsed_xml is None:
|
||||
@@ -152,23 +152,23 @@ async def parse_13f_hr(filing: str):
|
||||
+ " Documents filed before Q2 2013 are not supported."
|
||||
)
|
||||
|
||||
period_ending = get_period_ending(soup)
|
||||
period_ending = get_period_ending(soup) # type: ignore
|
||||
data = (
|
||||
DataFrame(parsed_xml)
|
||||
if isinstance(parsed_xml, list)
|
||||
else DataFrame([parsed_xml])
|
||||
)
|
||||
data.columns = data.columns.str.replace("ns1:", "")
|
||||
data.loc[:, "value"] = data["value"].astype(int)
|
||||
data["value"] = data["value"].astype(int)
|
||||
security_type: list = []
|
||||
principal_amount: list = []
|
||||
|
||||
# Unpack the nested objects
|
||||
try:
|
||||
security_type = [d.get("sshPrnamtType") for d in data["shrsOrPrnAmt"]]
|
||||
data.loc[:, "security_type"] = security_type
|
||||
data["security_type"] = security_type
|
||||
principal_amount = [int(d.get("sshPrnamt", 0)) for d in data["shrsOrPrnAmt"]]
|
||||
data.loc[:, "principal_amount"] = principal_amount
|
||||
data["principal_amount"] = principal_amount
|
||||
_ = data.pop("shrsOrPrnAmt")
|
||||
except ValueError:
|
||||
pass
|
||||
@@ -176,18 +176,18 @@ async def parse_13f_hr(filing: str):
|
||||
sole = [d.get("Sole") for d in data["votingAuthority"]]
|
||||
shared = [d.get("Shared") for d in data["votingAuthority"]]
|
||||
none = [d.get("None") for d in data["votingAuthority"]]
|
||||
data.loc[:, "voting_authority_sole"] = [int(s) if s else 0 for s in sole]
|
||||
data.loc[:, "voting_authority_shared"] = [int(s) if s else 0 for s in shared]
|
||||
data.loc[:, "voting_authority_none"] = [int(s) if s else 0 for s in none]
|
||||
data["voting_authority_sole"] = [int(s) if s else 0 for s in sole]
|
||||
data["voting_authority_shared"] = [int(s) if s else 0 for s in shared]
|
||||
data["voting_authority_none"] = [int(s) if s else 0 for s in none]
|
||||
_ = data.pop("votingAuthority")
|
||||
except ValueError:
|
||||
pass
|
||||
|
||||
if "putCall" in data.columns:
|
||||
data.loc[:, "putCall"] = data["putCall"].fillna("--")
|
||||
data["putCall"] = data["putCall"].fillna("--")
|
||||
|
||||
# Add the period ending so that the filing is identified when multiple are requested.
|
||||
data.loc[:, "period_ending"] = to_datetime(period_ending, yearfirst=False).date()
|
||||
data["period_ending"] = to_datetime(period_ending, yearfirst=False).date()
|
||||
df = DataFrame(data)
|
||||
# Aggregate the data because there are multiple entries for each security and we need the totals.
|
||||
# We break it down by CUSIP, security type, and option type.
|
||||
@@ -221,7 +221,7 @@ async def parse_13f_hr(filing: str):
|
||||
df.drop(columns=col, inplace=True)
|
||||
|
||||
total_value = df.value.sum()
|
||||
df.loc[:, "weight"] = round(df.value.astype(float) / total_value, 6)
|
||||
df["weight"] = round(df.value.astype(float) / total_value, 6)
|
||||
|
||||
return (
|
||||
df.reset_index()
|
||||
|
||||
@@ -190,7 +190,7 @@ class TmxBondPricesFetcher(
|
||||
"bondType == 'Corp'& maturityDate >= @query.maturity_date_min.strftime('%Y-%m-%d')"
|
||||
).sort_values(by=["maturityDate"])
|
||||
|
||||
data.loc[:, "issuer"] = data.issuer.str.strip()
|
||||
data["issuer"] = data.issuer.str.strip()
|
||||
|
||||
if query.maturity_date_max:
|
||||
data = data.query(
|
||||
|
||||
@@ -78,7 +78,7 @@ class TmxEtfCountriesFetcher(
|
||||
columns={"name": "country", "percent": "weight"}
|
||||
)
|
||||
if not target.empty:
|
||||
target.loc[:, "symbol"] = symbol
|
||||
target["symbol"] = symbol
|
||||
result = (
|
||||
target[["symbol", "country", "weight"]]
|
||||
.reset_index(drop=True)
|
||||
|
||||
@@ -268,4 +268,14 @@ class TmxEtfSearchFetcher(
|
||||
**kwargs: Any,
|
||||
) -> list[TmxEtfSearchData]:
|
||||
"""Transform the data to the standard format."""
|
||||
return [TmxEtfSearchData.model_validate(d) for d in data]
|
||||
# pylint: disable=import-outside-toplevel
|
||||
from math import isnan
|
||||
|
||||
def clean_nan(d: dict) -> dict:
|
||||
"""Replace nan values with None for Pydantic validation."""
|
||||
return {
|
||||
k: None if isinstance(v, float) and isnan(v) else v
|
||||
for k, v in d.items()
|
||||
}
|
||||
|
||||
return [TmxEtfSearchData.model_validate(clean_nan(d)) for d in data]
|
||||
|
||||
@@ -73,7 +73,7 @@ class TmxEtfSectorsFetcher(
|
||||
target = DataFrame.from_records(_target.iloc[0]).rename(
|
||||
columns={"name": "sector", "percent": "weight"}
|
||||
)
|
||||
target.loc[:, "symbol"] = symbol.upper()
|
||||
target["symbol"] = symbol.upper()
|
||||
result = (
|
||||
target[["symbol", "sector", "weight"]]
|
||||
.replace({float("nan"): None})
|
||||
|
||||
@@ -143,7 +143,7 @@ class TmxTreasuryPricesFetcher(
|
||||
+ " & bondType == 'Govt'"
|
||||
+ " & issuer.str.contains(@govt_type, case=False)"
|
||||
).sort_values(by=["maturityDate"])
|
||||
data.issuer = data.loc[:, "issuer"].str.strip()
|
||||
data["issuer"] = data["issuer"].str.strip()
|
||||
if query.maturity_date_max:
|
||||
data = data.query(
|
||||
"maturityDate <= @query.maturity_date_max.strftime('%Y-%m-%d')"
|
||||
|
||||
@@ -619,14 +619,22 @@ async def download_eod_chains(
|
||||
|
||||
cal = xcals.get_calendar("XTSE")
|
||||
|
||||
def _is_session(dt: str) -> bool:
|
||||
"""Check if date is a trading session.
|
||||
|
||||
Workaround for exchange_calendars bug with Pandas 3 where
|
||||
is_session() fails due to ns/us unit mismatch in _date_oob.
|
||||
"""
|
||||
return to_datetime(dt) in cal.sessions
|
||||
|
||||
if date is None:
|
||||
EOD_URL = BASE_URL + f"{symbol}&dnld=1#quotes"
|
||||
else:
|
||||
date = check_weekday(date) # type: ignore
|
||||
if cal.is_session(date) is False: # type: ignore
|
||||
if _is_session(date) is False: # type: ignore
|
||||
date = (to_datetime(date) + timedelta(days=1)).strftime("%Y-%m-%d") # type: ignore
|
||||
date = check_weekday(date) # type: ignore
|
||||
if cal.is_session(date=date) is False: # type: ignore
|
||||
if _is_session(date) is False: # type: ignore
|
||||
date = (to_datetime(date) + timedelta(days=1)).strftime("%Y-%m-%d") # type: ignore
|
||||
|
||||
EOD_URL = BASE_URL + f"{symbol}&from={date}&to={date}&dnld=1#quotes"
|
||||
|
||||
@@ -128,35 +128,35 @@ class YFinanceOptionsChainsFetcher(
|
||||
|
||||
underlying_output: dict = {
|
||||
"symbol": symbol,
|
||||
"name": underlying.get("longName"),
|
||||
"exchange": underlying.get("fullExchangeName"),
|
||||
"exchange_tz": underlying.get("exchangeTimezoneName"),
|
||||
"currency": underlying.get("currency"),
|
||||
"bid": underlying.get("bid"),
|
||||
"bid_size": underlying.get("bidSize"),
|
||||
"ask": underlying.get("ask"),
|
||||
"ask_size": underlying.get("askSize"),
|
||||
"last_price": underlying.get(
|
||||
"postMarketPrice", underlying.get("regularMarketPrice")
|
||||
"name": underlying.get("longName"), # type: ignore
|
||||
"exchange": underlying.get("fullExchangeName"), # type: ignore
|
||||
"exchange_tz": underlying.get("exchangeTimezoneName"), # type: ignore
|
||||
"currency": underlying.get("currency"), # type: ignore
|
||||
"bid": underlying.get("bid"), # type: ignore
|
||||
"bid_size": underlying.get("bidSize"), # type: ignore
|
||||
"ask": underlying.get("ask"), # type: ignore
|
||||
"ask_size": underlying.get("askSize"), # type: ignore
|
||||
"last_price": underlying.get( # type: ignore
|
||||
"postMarketPrice", underlying.get("regularMarketPrice") # type: ignore
|
||||
),
|
||||
"open": underlying.get("regularMarketOpen"),
|
||||
"high": underlying.get("regularMarketDayHigh"),
|
||||
"low": underlying.get("regularMarketDayLow"),
|
||||
"close": underlying.get("regularMarketPrice"),
|
||||
"prev_close": underlying.get("regularMarketPreviousClose"),
|
||||
"change": underlying.get("regularMarketChange"),
|
||||
"change_percent": underlying.get("regularMarketChangePercent"),
|
||||
"volume": underlying.get("regularMarketVolume"),
|
||||
"dividend_yield": float(underlying.get("dividendYield", 0)) / 100,
|
||||
"dividend_yield_ttm": underlying.get("trailingAnnualDividendYield"),
|
||||
"year_high": underlying.get("fiftyTwoWeekHigh"),
|
||||
"year_low": underlying.get("fiftyTwoWeekLow"),
|
||||
"ma_50": underlying.get("fiftyDayAverage"),
|
||||
"ma_200": underlying.get("twoHundredDayAverage"),
|
||||
"volume_avg_10d": underlying.get("averageDailyVolume10Day"),
|
||||
"volume_avg_3m": underlying.get("averageDailyVolume3Month"),
|
||||
"market_cap": underlying.get("marketCap"),
|
||||
"shares_outstanding": underlying.get("sharesOutstanding"),
|
||||
"open": underlying.get("regularMarketOpen"), # type: ignore
|
||||
"high": underlying.get("regularMarketDayHigh"), # type: ignore
|
||||
"low": underlying.get("regularMarketDayLow"), # type: ignore
|
||||
"close": underlying.get("regularMarketPrice"), # type: ignore
|
||||
"prev_close": underlying.get("regularMarketPreviousClose"), # type: ignore
|
||||
"change": underlying.get("regularMarketChange"), # type: ignore
|
||||
"change_percent": underlying.get("regularMarketChangePercent"), # type: ignore
|
||||
"volume": underlying.get("regularMarketVolume"), # type: ignore
|
||||
"dividend_yield": float(underlying.get("dividendYield", 0)) / 100, # type: ignore
|
||||
"dividend_yield_ttm": underlying.get("trailingAnnualDividendYield"), # type: ignore
|
||||
"year_high": underlying.get("fiftyTwoWeekHigh"), # type: ignore
|
||||
"year_low": underlying.get("fiftyTwoWeekLow"), # type: ignore
|
||||
"ma_50": underlying.get("fiftyDayAverage"), # type: ignore
|
||||
"ma_200": underlying.get("twoHundredDayAverage"), # type: ignore
|
||||
"volume_avg_10d": underlying.get("averageDailyVolume10Day"), # type: ignore
|
||||
"volume_avg_3m": underlying.get("averageDailyVolume3Month"), # type: ignore
|
||||
"market_cap": underlying.get("marketCap"), # type: ignore
|
||||
"shares_outstanding": underlying.get("sharesOutstanding"), # type: ignore
|
||||
}
|
||||
|
||||
return {"underlying": underlying_output, "chains": chains_output}
|
||||
@@ -178,9 +178,7 @@ class YFinanceOptionsChainsFetcher(
|
||||
records = data.get("chains", [])
|
||||
output = DataFrame(records)
|
||||
for col in ["volume", "openInterest"]:
|
||||
output[col] = (
|
||||
output[col].infer_objects(copy=False).replace({nan: 0}).astype("int64")
|
||||
)
|
||||
output[col] = output[col].infer_objects().replace({nan: 0}).astype("int64")
|
||||
|
||||
output = output.replace({nan: None})
|
||||
|
||||
|
||||
@@ -166,7 +166,7 @@ async def get_defined_screener(
|
||||
offset = 0
|
||||
|
||||
response = yf.screen(
|
||||
name,
|
||||
name, # type: ignore
|
||||
size=250,
|
||||
offset=offset,
|
||||
)
|
||||
@@ -182,7 +182,7 @@ async def get_defined_screener(
|
||||
break
|
||||
offset = len(results)
|
||||
res = yf.screen(
|
||||
name,
|
||||
name, # type: ignore
|
||||
size=250,
|
||||
offset=offset,
|
||||
)
|
||||
@@ -286,8 +286,8 @@ async def get_futures_quotes(symbols: list) -> "DataFrame":
|
||||
|
||||
df = DataFrame([d.model_dump() for d in data]) # type: ignore
|
||||
prices = df[["symbol", "bid", "ask", "prev_close"]].copy()
|
||||
prices.loc[:, "price"] = round((prices.ask + prices.bid) / 2, 2)
|
||||
prices.price = prices.price.fillna(prices.prev_close)
|
||||
prices["price"] = round((prices.ask + prices.bid) / 2, 2)
|
||||
prices["price"] = prices.price.fillna(prices.prev_close)
|
||||
prices["expiration"] = [get_expiration_month(symbol) for symbol in prices.symbol]
|
||||
|
||||
return prices[["expiration", "price"]] # type: ignore
|
||||
@@ -387,7 +387,7 @@ async def get_futures_curve( # pylint: disable=too-many-return-statements
|
||||
flattened_data = flattened_data.sort_values(
|
||||
by=["date", "expiration"]
|
||||
).reset_index(drop=True)
|
||||
flattened_data.loc[:, "date"] = flattened_data["date"].dt.strftime("%Y-%m-%d")
|
||||
flattened_data["date"] = flattened_data["date"].dt.strftime("%Y-%m-%d")
|
||||
|
||||
return flattened_data
|
||||
|
||||
@@ -544,21 +544,21 @@ def yf_download( # pylint: disable=too-many-positional-arguments
|
||||
threads=False,
|
||||
**kwargs,
|
||||
)
|
||||
if hasattr(data.index, "tz") and data.index.tz is not None:
|
||||
data = data.tz_convert(None)
|
||||
if hasattr(data.index, "tz") and data.index.tz is not None: # type: ignore
|
||||
data = data.tz_convert(None) # type: ignore
|
||||
|
||||
except ValueError as exc:
|
||||
raise EmptyDataError() from exc
|
||||
|
||||
tickers = symbol.split(",")
|
||||
if len(tickers) == 1:
|
||||
data = data.get(symbol, DataFrame())
|
||||
data = data.get(symbol, DataFrame()) # type: ignore
|
||||
elif len(tickers) > 1:
|
||||
_data = DataFrame()
|
||||
for ticker in tickers:
|
||||
temp = data[ticker].copy().dropna(how="all")
|
||||
temp = data[ticker].copy().dropna(how="all") # type: ignore
|
||||
if len(temp) > 0:
|
||||
temp.loc[:, "symbol"] = ticker
|
||||
temp["symbol"] = ticker
|
||||
temp = temp.reset_index().rename(
|
||||
columns={"Date": "date", "Datetime": "date", "index": "date"}
|
||||
)
|
||||
@@ -568,10 +568,10 @@ def yf_download( # pylint: disable=too-many-positional-arguments
|
||||
_data = _data.set_index(index_keys).sort_index()
|
||||
data = _data
|
||||
|
||||
if data.empty:
|
||||
if data.empty: # type: ignore
|
||||
raise EmptyDataError()
|
||||
|
||||
data = data.reset_index()
|
||||
data = data.reset_index() # type: ignore
|
||||
data = data.rename(columns={"Date": "date", "Datetime": "date"})
|
||||
data["date"] = data["date"].apply(to_datetime)
|
||||
data = data[data["Open"] > 0]
|
||||
|
||||
42
openbb_platform/providers/yfinance/poetry.lock
generated
vendored
42
openbb_platform/providers/yfinance/poetry.lock
generated
vendored
@@ -1,4 +1,4 @@
|
||||
# This file is automatically @generated by Poetry 2.3.0 and should not be changed by hand.
|
||||
# This file is automatically @generated by Poetry 2.3.1 and should not be changed by hand.
|
||||
|
||||
[[package]]
|
||||
name = "aiohappyeyeballs"
|
||||
@@ -525,24 +525,22 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "curl-cffi"
|
||||
version = "0.14.0"
|
||||
version = "0.13.0"
|
||||
description = "libcurl ffi bindings for Python, with impersonation support."
|
||||
optional = false
|
||||
python-versions = ">=3.10"
|
||||
python-versions = ">=3.9"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "curl_cffi-0.14.0-cp39-abi3-macosx_14_0_arm64.whl", hash = "sha256:e35e89c6a69872f9749d6d5fda642ed4fc159619329e99d577d0104c9aad5893"},
|
||||
{file = "curl_cffi-0.14.0-cp39-abi3-macosx_15_0_x86_64.whl", hash = "sha256:5945478cd28ad7dfb5c54473bcfb6743ee1d66554d57951fdf8fc0e7d8cf4e45"},
|
||||
{file = "curl_cffi-0.14.0-cp39-abi3-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c42e8fa3c667db9ccd2e696ee47adcd3cd5b0838d7282f3fc45f6c0ef3cfdfa7"},
|
||||
{file = "curl_cffi-0.14.0-cp39-abi3-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:060fe2c99c41d3cb7f894de318ddf4b0301b08dca70453d769bd4e74b36b8483"},
|
||||
{file = "curl_cffi-0.14.0-cp39-abi3-manylinux_2_28_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:b158c41a25388690dd0d40b5bc38d1e0f512135f17fdb8029868cbc1993d2e5b"},
|
||||
{file = "curl_cffi-0.14.0-cp39-abi3-manylinux_2_28_i686.whl", hash = "sha256:1439fbef3500fb723333c826adf0efb0e2e5065a703fb5eccce637a2250db34a"},
|
||||
{file = "curl_cffi-0.14.0-cp39-abi3-manylinux_2_34_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:e7176f2c2d22b542e3cf261072a81deb018cfa7688930f95dddef215caddb469"},
|
||||
{file = "curl_cffi-0.14.0-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:03f21ade2d72978c2bb8670e9b6de5260e2755092b02d94b70b906813662998d"},
|
||||
{file = "curl_cffi-0.14.0-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:58ebf02de64ee5c95613209ddacb014c2d2f86298d7080c0a1c12ed876ee0690"},
|
||||
{file = "curl_cffi-0.14.0-cp39-abi3-win_amd64.whl", hash = "sha256:6e503f9a103f6ae7acfb3890c843b53ec030785a22ae7682a22cc43afb94123e"},
|
||||
{file = "curl_cffi-0.14.0-cp39-abi3-win_arm64.whl", hash = "sha256:2eed50a969201605c863c4c31269dfc3e0da52916086ac54553cfa353022425c"},
|
||||
{file = "curl_cffi-0.14.0.tar.gz", hash = "sha256:5ffbc82e59f05008ec08ea432f0e535418823cda44178ee518906a54f27a5f0f"},
|
||||
{file = "curl_cffi-0.13.0-cp39-abi3-macosx_10_9_x86_64.whl", hash = "sha256:434cadbe8df2f08b2fc2c16dff2779fb40b984af99c06aa700af898e185bb9db"},
|
||||
{file = "curl_cffi-0.13.0-cp39-abi3-macosx_11_0_arm64.whl", hash = "sha256:59afa877a9ae09efa04646a7d068eeea48915a95d9add0a29854e7781679fcd7"},
|
||||
{file = "curl_cffi-0.13.0-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d06ed389e45a7ca97b17c275dbedd3d6524560270e675c720e93a2018a766076"},
|
||||
{file = "curl_cffi-0.13.0-cp39-abi3-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b4e0de45ab3b7a835c72bd53640c2347415111b43421b5c7a1a0b18deae2e541"},
|
||||
{file = "curl_cffi-0.13.0-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8eb4083371bbb94e9470d782de235fb5268bf43520de020c9e5e6be8f395443f"},
|
||||
{file = "curl_cffi-0.13.0-cp39-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:28911b526e8cd4aa0e5e38401bfe6887e8093907272f1f67ca22e6beb2933a51"},
|
||||
{file = "curl_cffi-0.13.0-cp39-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:6d433ffcb455ab01dd0d7bde47109083aa38b59863aa183d29c668ae4c96bf8e"},
|
||||
{file = "curl_cffi-0.13.0-cp39-abi3-win_amd64.whl", hash = "sha256:66a6b75ce971de9af64f1b6812e275f60b88880577bac47ef1fa19694fa21cd3"},
|
||||
{file = "curl_cffi-0.13.0-cp39-abi3-win_arm64.whl", hash = "sha256:d438a3b45244e874794bc4081dc1e356d2bb926dcc7021e5a8fef2e2105ef1d8"},
|
||||
{file = "curl_cffi-0.13.0.tar.gz", hash = "sha256:62ecd90a382bd5023750e3606e0aa7cb1a3a8ba41c14270b8e5e149ebf72c5ca"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -551,9 +549,9 @@ cffi = ">=1.12.0"
|
||||
|
||||
[package.extras]
|
||||
build = ["cibuildwheel", "wheel"]
|
||||
dev = ["charset_normalizer (>=3.3.2,<4.0)", "coverage (>=6.4.1,<7.0)", "cryptography (>=42.0.5,<43.0)", "httpx (==0.23.1)", "mypy (>=1.9.0,<2.0)", "pytest (>=8.1.1,<9.0)", "pytest-asyncio (>=0.23.6,<1.0)", "pytest-trio (>=0.8.0,<1.0)", "ruff (>=0.3.5,<1.0)", "trio (>=0.25.0,<1.0)", "trustme (>=1.1.0,<2.0)", "typing_extensions", "uvicorn (>=0.29.0,<1.0)", "websockets (>=14.0)"]
|
||||
dev = ["charset_normalizer (>=3.3.2,<4.0)", "coverage (>=6.4.1,<7.0)", "cryptography (>=42.0.5,<43.0)", "httpx (==0.23.1)", "mypy (>=1.9.0,<2.0)", "pytest (>=8.1.1,<9.0)", "pytest-asyncio (>=0.23.6,<1.0)", "pytest-trio (>=0.8.0,<1.0)", "ruff (>=0.3.5,<1.0)", "trio (>=0.25.0,<1.0)", "trustme (>=1.1.0,<2.0)", "typing_extensions", "uvicorn (>=0.29.0,<1.0)", "websockets (>=12.0,<13.0)"]
|
||||
extra = ["lxml_html_clean", "markdownify (>=1.1.0)", "readability-lxml (>=0.8.1)"]
|
||||
test = ["charset_normalizer (>=3.3.2,<4.0)", "cryptography (>=42.0.5,<43.0)", "fastapi (>=0.110.0,<1.0)", "httpx (==0.23.1)", "proxy.py (>=2.4.3,<3.0)", "pytest (>=8.1.1,<9.0)", "pytest-asyncio (>=0.23.6,<1.0)", "pytest-trio (>=0.8.0,<1.0)", "python-multipart (>=0.0.9,<1.0)", "trio (>=0.25.0,<1.0)", "trustme (>=1.1.0,<2.0)", "typing_extensions", "uvicorn (>=0.29.0,<1.0)", "websockets (>=14.0)"]
|
||||
test = ["charset_normalizer (>=3.3.2,<4.0)", "cryptography (>=42.0.5,<43.0)", "fastapi (==0.110.0)", "httpx (==0.23.1)", "proxy.py (>=2.4.3,<3.0)", "pytest (>=8.1.1,<9.0)", "pytest-asyncio (>=0.23.6,<1.0)", "pytest-trio (>=0.8.0,<1.0)", "python-multipart (>=0.0.9,<1.0)", "trio (>=0.25.0,<1.0)", "trustme (>=1.1.0,<2.0)", "typing_extensions", "uvicorn (>=0.29.0,<1.0)", "websockets (>=12.0,<13.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "exceptiongroup"
|
||||
@@ -2206,19 +2204,19 @@ propcache = ">=0.2.1"
|
||||
|
||||
[[package]]
|
||||
name = "yfinance"
|
||||
version = "0.2.66"
|
||||
version = "1.1.0"
|
||||
description = "Download market data from Yahoo! Finance API"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "yfinance-0.2.66-py2.py3-none-any.whl", hash = "sha256:511a1a40a687f277aae3a02543009a8aeaa292fce5509671f58915078aebb5c7"},
|
||||
{file = "yfinance-0.2.66.tar.gz", hash = "sha256:fae354cc1649109444b2c84194724afcc52c2a7799551ce44c739424ded6af9c"},
|
||||
{file = "yfinance-1.1.0-py2.py3-none-any.whl", hash = "sha256:e610fec1d2b052e3b8f2cf44bdcff014bcf15458a9b072d5a3e02507e20d69d2"},
|
||||
{file = "yfinance-1.1.0.tar.gz", hash = "sha256:1e852ce10a5d6679200efa2b09ed3f3b01dbe84505e2c1c139be7d2b3a597b10"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
beautifulsoup4 = ">=4.11.1"
|
||||
curl_cffi = ">=0.7"
|
||||
curl_cffi = ">=0.7,<0.14"
|
||||
frozendict = ">=2.3.4"
|
||||
multitasking = ">=0.0.7"
|
||||
numpy = ">=1.16.5"
|
||||
@@ -2257,4 +2255,4 @@ type = ["pytest-mypy"]
|
||||
[metadata]
|
||||
lock-version = "2.1"
|
||||
python-versions = ">=3.10,<3.14"
|
||||
content-hash = "151203e6c62e409eb6b3a853b8855a797ec3a86174e49c4c293763b376cbc9ef"
|
||||
content-hash = "a84f95432f8265f69669a2dc7f0f07b5f69435d6dd75113ea9a08e0483fabb4a"
|
||||
|
||||
@@ -9,7 +9,7 @@ packages = [{ include = "openbb_yfinance" }]
|
||||
|
||||
[tool.poetry.dependencies]
|
||||
python = ">=3.10,<3.14"
|
||||
yfinance = "^0.2.66"
|
||||
yfinance = "^1.1.0"
|
||||
openbb-core = "^1.5.8"
|
||||
|
||||
[build-system]
|
||||
|
||||
Reference in New Issue
Block a user