ICICI Direct – Average True Range Calculation

from breeze_connect import BreezeConnect
from datetime import datetime,date,time,timedelta
import pandas as pd
import numpy as np
import pandas_ta as ta

pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)

breeze = BreezeConnect(api_key="")
breeze.generate_session(api_secret="", session_token="")

stock = breeze.get_historical_data(interval="1minute",
                                       from_date="2022-8-30T07:00:00.000Z",
                                       to_date="2022-8-30T18:00:00.000Z",
                                       stock_code="TCS",
                                       exchange_code="NSE",
                                       product_type="others")
df = pd.DataFrame(stock['Success'])
# df["RSI"] = ta.rsi(close = pd.to_numeric(df.close), length=10)
# df['ATR'] = ta.atr(pd.to_numeric(df["high"]),pd.to_numeric(df["low"]),pd.to_numeric(df["close"].shift()), length=14)

# high_low = pd.to_numeric(df["high"]) - pd.to_numeric(df["low"])
# high_close = np.abs(pd.to_numeric(df["high"]) - pd.to_numeric(df["close"].shift()))
# low_close = np.abs(pd.to_numeric(df["low"]) - pd.to_numeric(df["close"].shift()))
# ranges = pd.concat([high_low, high_close, low_close], axis=1)
# true_range = np.max(ranges, axis=1)
# df['ATR-C'] = true_range.rolling(14).sum()/14
#
# df["high_low"] = pd.to_numeric(df["high"]) - pd.to_numeric(df["low"])
# df["high_close"] = np.abs(pd.to_numeric(df["high"]) - pd.to_numeric(df["close"].shift()))
# df["low_close"] = np.abs(pd.to_numeric(df["low"]) - pd.to_numeric(df["close"].shift()))
# df["Max"] = df[['high_low','high_close','low_close']].max(axis=1)
# df["ATR-CR"] = df["Max"].rolling(14).sum()/14
# print(df)

# Function to calculate average true range
# def ATR(DF, n):
#   df = DF.copy() # making copy of the original dataframe
df['H-L'] = abs(pd.to_numeric(df['high']) - pd.to_numeric(df['low']))
df['H-PC'] = abs(pd.to_numeric(df['high']) - pd.to_numeric(df['close']).shift())# high -previous close
df['L-PC'] = abs(pd.to_numeric(df['low']) - pd.to_numeric(df['close']).shift()) #low - previous close
df['TR'] = df[['H-L','H-PC','L-PC']].max(axis =1, skipna = False) # True range
df['ATR'] = df['TR'].rolling(14).mean() # average –true range
df = df.drop(['H-L','H-PC','L-PC','TR','count'], axis =1) # dropping the unneccesary columns
  # df.dropna(inplace = True) # droping null items
df['bricks'] = round(df["ATR"], 0)
  # return df
print(df)