adx.py

import yfinance as yf
import numpy as np

tickers = ["AMZN","GOOG","MSFT"]
ohlcv_data = {}


for ticker in tickers:
    temp = yf.download(ticker,period='1mo',interval='5m')
    temp.dropna(how="any",inplace=True)
    ohlcv_data[ticker] = temp
    
def ATR(DF, n=14):
    "function to calculate True Range and Average True Range"
    df = DF.copy()
    df["H-L"] = df["High"] - df["Low"]
    df["H-PC"] = abs(df["High"] - df["Adj Close"].shift(1))
    df["L-PC"] = abs(df["Low"] - df["Adj Close"].shift(1))
    df["TR"] = df[["H-L","H-PC","L-PC"]].max(axis=1, skipna=False)
    df["ATR"] = df["TR"].ewm(com=n, min_periods=n).mean()
    return df["ATR"]

def ADX(DF, n=20):
    "function to calculate ADX"
    df = DF.copy()
    df["ATR"] = ATR(DF, n)
    df["upmove"] = df["High"] - df["High"].shift(1)
    df["downmove"] = df["Low"].shift(1) - df["Low"]
    df["+dm"] = np.where((df["upmove"]>df["downmove"]) & (df["upmove"] >0), df["upmove"], 0)
    df["-dm"] = np.where((df["downmove"]>df["upmove"]) & (df["downmove"] >0), df["downmove"], 0)
    df["+di"] = 100 * (df["+dm"]/df["ATR"]).ewm(alpha=1/n, min_periods=n).mean()
    df["-di"] = 100 * (df["-dm"]/df["ATR"]).ewm(alpha=1/n, min_periods=n).mean()
    df["ADX"] = 100* abs((df["+di"] - df["-di"])/(df["+di"] + df["-di"])).ewm(alpha=1/n, min_periods=n).mean()
    return df["ADX"]

for ticker in ohlcv_data:
    ohlcv_data[ticker]["ADX"] = ADX(ohlcv_data[ticker],20)