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https://github.com/OpenBB-finance/OpenBB.git
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4b5c465c7d
* Ignore nft of the day test * Refactor common-ta-overlap * Refactor trend indicators * Refactor volatility * Volume refactored * Custom functions * Final momentum refactor * Update crypto menu with same format * Address review, update poetry * Fix export path
80 lines
1.7 KiB
Python
80 lines
1.7 KiB
Python
"""Trend Indicators Technical Analysis Model"""
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__docformat__ = "numpy"
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import pandas as pd
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import pandas_ta as ta
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def adx(s_interval: str, df_stock: pd.DataFrame, length: int, scalar: int, drift: int):
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"""ADX technical indicator
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Parameters
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----------
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s_interval : str
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Interval of stock data
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df_stock : pd.DataFrame
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Dataframe with prices
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length: int
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Length of window
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scalar: int
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Scalar variable
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droft: int
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Drift variable
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Returns
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-------
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df_ta: pd.DataFrame
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DataFrame with adx indicator
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"""
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# Daily
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if s_interval == "1440min":
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df_ta = ta.adx(
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high=df_stock["High"],
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low=df_stock["Low"],
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close=df_stock["Adj Close"],
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length=length,
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scalar=scalar,
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drift=drift,
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).dropna()
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# Intraday
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else:
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df_ta = ta.adx(
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high=df_stock["High"],
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low=df_stock["Low"],
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close=df_stock["Close"],
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length=length,
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scalar=scalar,
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drift=drift,
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).dropna()
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return df_ta
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def aroon(df_stock: pd.DataFrame, length: int, scalar: int) -> pd.DataFrame:
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"""Aroon technical indicator
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Parameters
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----------
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df_stock : pd.DataFrame
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Dataframe of stock prices
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length : int
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Length of window
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scalar : int
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Scalar variable
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Returns
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-------
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df_ta: pd.DataFrame
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DataFrame with aroon indicator
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"""
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df_ta = ta.aroon(
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high=df_stock["High"],
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low=df_stock["Low"],
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length=length,
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scalar=scalar,
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).dropna()
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return pd.DataFrame(df_ta)
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