diff options
author | Igor Radovanovic <74266147+IgorWounds@users.noreply.github.com> | 2024-02-14 15:33:22 +0100 |
---|---|---|
committer | GitHub <noreply@github.com> | 2024-02-14 14:33:22 +0000 |
commit | 28cd6979b4b8ba7c48e955685d2794dd86f59bc9 (patch) | |
tree | e8643685f4aa416b5c7ac3e06c248a6a84f3a634 | |
parent | 2516cfbc72d23a7656715314ea94cc34d4c994ee (diff) |
Fix QA Examples (#6072)
-rw-r--r-- | openbb_platform/extensions/quantitative/openbb_quantitative/quantitative_router.py | 20 |
1 files changed, 10 insertions, 10 deletions
diff --git a/openbb_platform/extensions/quantitative/openbb_quantitative/quantitative_router.py b/openbb_platform/extensions/quantitative/openbb_quantitative/quantitative_router.py index 42c17bac503..6f37b987f33 100644 --- a/openbb_platform/extensions/quantitative/openbb_quantitative/quantitative_router.py +++ b/openbb_platform/extensions/quantitative/openbb_quantitative/quantitative_router.py @@ -55,7 +55,7 @@ def normality(data: List[Data], target: str) -> OBBject[NormalityModel]: Examples -------- >>> from openbb import obb - >>> stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df() >>> obb.quantitative.normality(data=stock_data, target="close") """ from scipy import stats # pylint: disable=import-outside-toplevel @@ -103,7 +103,7 @@ def capm(data: List[Data], target: str) -> OBBject[CAPMModel]: Examples -------- >>> from openbb import obb - >>> stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df() >>> obb.quantitative.capm(data=stock_data, target="close") """ import statsmodels.api as sm # pylint: disable=import-outside-toplevel # type: ignore @@ -169,7 +169,7 @@ def omega_ratio( Examples -------- >>> from openbb import obb - >>> stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df() >>> obb.quantitative.omega_ratio(data=stock_data, target="close") """ df = basemodel_to_df(data) @@ -223,7 +223,7 @@ def kurtosis(data: List[Data], target: str, window: PositiveInt) -> OBBject[List Examples -------- >>> from openbb import obb - >>> stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df() >>> obb.quantitative.kurtosis(data=stock_data, target="close", window=252) """ import pandas_ta as ta # pylint: disable=import-outside-toplevel # type: ignore @@ -272,7 +272,7 @@ def unitroot_test( Examples -------- >>> from openbb import obb - >>> stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df() >>> obb.quantitative.unitroot_test(data=stock_data, target="close") >>> obb.quantitative.unitroot_test(data=stock_data, target="close", fuller_reg="ct", kpss_reg="ct") """ @@ -334,7 +334,7 @@ def sharpe_ratio( Examples -------- >>> from openbb import obb - >>> stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df() >>> obb.quantitative.sharpe_ratio(data=stock_data, target="close") >>> obb.quantitative.sharpe_ratio(data=stock_data, target="close", rfr=0.01, window=126) """ @@ -393,7 +393,7 @@ def sortino_ratio( Examples -------- >>> from openbb import obb - >>> stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df() >>> obb.quantitative.sortino_ratio(data=stock_data, target="close") >>> obb.quantitative.sortino_ratio(data=stock_data, target="close", target_return=0.01, window=126, adjusted=True) """ @@ -441,7 +441,7 @@ def skewness(data: List[Data], target: str, window: PositiveInt) -> OBBject[List Examples -------- >>> from openbb import obb - >>> stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df() >>> obb.quantitative.skewness(data=stock_data, target="close", window=252) """ import pandas_ta as ta # pylint: disable=import-outside-toplevel # type: ignore @@ -488,7 +488,7 @@ def quantile( Examples -------- >>> from openbb import obb - >>> stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df() >>> obb.quantitative.quantile(data=stock_data, target="close", window=252, quantile_pct=0.25) >>> obb.quantitative.quantile(data=stock_data, target="close", window=252, quantile_pct=0.75) """ @@ -532,7 +532,7 @@ def summary(data: List[Data], target: str) -> OBBject[SummaryModel]: Examples -------- >>> from openbb import obb - >>> stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp") + >>> stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df() >>> obb.quantitative.summary(data=stock_data, target="close") """ df = basemodel_to_df(data) |