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Hotfix/rvol model (#4371)

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* makes trading_periods inputs Optional and prints override message when variable presides

* linter type

* typo fix
This commit is contained in:
Danglewood
2023-03-02 05:58:43 -08:00
committed by GitHub
parent 602cfa2c8c
commit a6951bd577
1 changed files with 50 additions and 24 deletions
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74
openbb_terminal/common/technical_analysis/volatility_model.py
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@@ -2,6 +2,7 @@
__docformat__ = "numpy"
import logging
from typing import Optional
import numpy as np
import pandas as pd
@@ -183,7 +184,7 @@ def atr(
def standard_deviation(
data: pd.DataFrame,
window: int = 30,
trading_periods: int = 252,
trading_periods: Optional[int] = None,
is_crypto: bool = False,
clean: bool = True,
) -> pd.DataFrame:
@@ -195,8 +196,8 @@ def standard_deviation(
data : pd.DataFrame
Dataframe of OHLC prices.
window : int [default: 30]
Length of window to calculate standard deviation.
trading_periods : int [default: 252]
Length of window to calculate over.
trading_periods : Optional[int] [default: 252]
Number of trading periods in a year.
is_crypto : bool [default: False]
If true, trading_periods is defined as 365.
@@ -218,7 +219,11 @@ def standard_deviation(
print("Error: Window must be at least 2, defaulting to 30.")
window = 30
trading_periods = 365 if is_crypto else 252
if trading_periods and is_crypto:
print("is_crypto is overridden by trading_periods.")
if not trading_periods:
trading_periods = 365 if is_crypto else 252
log_return = (data["Close"] / data["Close"].shift(1)).apply(np.log)
@@ -236,7 +241,7 @@ def standard_deviation(
def parkinson(
data: pd.DataFrame,
window: int = 30,
trading_periods: int = 252,
trading_periods: Optional[int] = None,
is_crypto: bool = False,
clean=True,
) -> pd.DataFrame:
@@ -248,8 +253,8 @@ def parkinson(
data : pd.DataFrame
Dataframe of OHLC prices.
window : int [default: 30]
Length of window to calculate standard deviation.
trading_periods : int [default: 252]
Length of window to calculate over.
trading_periods : Optional[int] [default: 252]
Number of trading periods in a year.
is_crypto : bool [default: False]
If true, trading_periods is defined as 365.
@@ -271,7 +276,11 @@ def parkinson(
print("Error: Window must be at least 1, defaulting to 30.")
window = 30
trading_periods = 365 if is_crypto else 252
if trading_periods and is_crypto:
print("is_crypto is overridden by trading_periods.")
if not trading_periods:
trading_periods = 365 if is_crypto else 252
rs = (1.0 / (4.0 * np.log(2.0))) * (
(data["High"] / data["Low"]).apply(np.log)
@@ -292,7 +301,7 @@ def parkinson(
def garman_klass(
data: pd.DataFrame,
window: int = 30,
trading_periods: int = 252,
trading_periods: Optional[int] = None,
is_crypto: bool = False,
clean=True,
) -> pd.DataFrame:
@@ -305,8 +314,8 @@ def garman_klass(
data : pd.DataFrame
Dataframe of OHLC prices.
window : int [default: 30]
Length of window to calculate standard deviation.
trading_periods : int [default: 252]
Length of window to calculate overn.
trading_periods : Optional[int] [default: 252]
Number of trading periods in a year.
is_crypto : bool [default: False]
If true, trading_periods is defined as 365.
@@ -328,7 +337,11 @@ def garman_klass(
print("Error: Window must be at least 1, defaulting to 30.")
window = 30
trading_periods = 365 if is_crypto else 252
if trading_periods and is_crypto:
print("is_crypto is overridden by trading_periods.")
if not trading_periods:
trading_periods = 365 if is_crypto else 252
log_hl = (data["High"] / data["Low"]).apply(np.log)
log_co = (data["Close"] / data["Open"]).apply(np.log)
@@ -350,7 +363,7 @@ def garman_klass(
def hodges_tompkins(
data: pd.DataFrame,
window: int = 30,
trading_periods: int = 252,
trading_periods: Optional[int] = None,
is_crypto: bool = False,
clean=True,
) -> pd.DataFrame:
@@ -362,8 +375,8 @@ def hodges_tompkins(
data : pd.DataFrame
Dataframe of OHLC prices.
window : int [default: 30]
Length of window to calculate standard deviation.
trading_periods : int [default: 252]
Length of window to calculate over.
trading_periods : Optional[int] [default: 252]
Number of trading periods in a year.
is_crypto : bool [default: False]
If true, trading_periods is defined as 365.
@@ -385,7 +398,11 @@ def hodges_tompkins(
print("Error: Window must be at least 2, defaulting to 30.")
window = 30
trading_periods = 365 if is_crypto else 252
if trading_periods and is_crypto:
print("is_crypto is overridden by trading_periods.")
if not trading_periods:
trading_periods = 365 if is_crypto else 252
log_return = (data["Close"] / data["Close"].shift(1)).apply(np.log)
@@ -410,7 +427,7 @@ def hodges_tompkins(
def rogers_satchell(
data: pd.DataFrame,
window: int = 30,
trading_periods: int = 252,
trading_periods: Optional[int] = None,
is_crypto: bool = False,
clean=True,
) -> pd.Series:
@@ -423,8 +440,8 @@ def rogers_satchell(
data : pd.DataFrame
Dataframe of OHLC prices.
window : int [default: 30]
Length of window to calculate standard deviation.
trading_periods : int [default: 252]
Length of window to calculate over.
trading_periods : Optional[int] [default: 252]
Number of trading periods in a year.
is_crypto : bool [default: False]
If true, trading_periods is defined as 365.
@@ -446,7 +463,11 @@ def rogers_satchell(
print("Error: Window must be at least 1, defaulting to 30.")
window = 30
trading_periods = 365 if is_crypto else 252
if trading_periods and is_crypto:
print("is_crypto is overridden by trading_periods.")
if not trading_periods:
trading_periods = 365 if is_crypto else 252
log_ho = (data["High"] / data["Open"]).apply(np.log)
log_lo = (data["Low"] / data["Open"]).apply(np.log)
@@ -468,8 +489,8 @@ def rogers_satchell(
def yang_zhang(
data: pd.DataFrame,
window: int = 30,
trading_periods: int = 252,
is_crypto: bool = True,
trading_periods: Optional[int] = None,
is_crypto: bool = False,
clean=True,
) -> pd.DataFrame:
"""Yang-Zhang volatility is the combination of the overnight (close-to-open volatility).
@@ -481,7 +502,7 @@ def yang_zhang(
Dataframe of OHLC prices.
window : int [default: 30]
Length of window to calculate standard deviation.
trading_periods : int [default: 252]
trading_periods : Optional[int] [default: 252]
Number of trading periods in a year.
is_crypto : bool [default: False]
If true, trading_periods is defined as 365.
@@ -502,7 +523,12 @@ def yang_zhang(
if window < 2:
print("Error: Window must be at least 2, defaulting to 30.")
window = 30
trading_periods = 365 if is_crypto else 252
if trading_periods and is_crypto:
print("is_crypto is overridden by trading_periods.")
if not trading_periods:
trading_periods = 365 if is_crypto else 252
log_ho = (data["High"] / data["Open"]).apply(np.log)
log_lo = (data["Low"] / data["Open"]).apply(np.log)