Pivot point is a leading indicator used to assess directional movement of an asset and potential support and resistance level .

• it is calculated using the previous day’s high , low and close prices .
• traders use pivot points to predict where price may reverse or breakout .
• pivot point work well in intraday trading because they give a quick view of market sentiment .

How to calculate pivot points.

pivot point(p) =(high+low+close)%3

then we calculate supports and resistance levels.

First resistance = (2*p)-low
second resistance = p + (high-low)
third resistance = high + 2* (p-low)

First support = (2*p) - high
second support = p- (high-low)
third support = low - 2*(high-p)  

How to trade using pivot points

1. Range trading strategy

• if price approaches support s1 or s2 , look for bullish candlesticks signals (pin bar , hammer) , go for buy .
• if price approaches first resistance or second resistance , look for bearish candlestick signals (shooting star ) , go for sell .

2.Breakout strategy

• if price breaks above resistance1 with strong volume , it may continue to second resistance and third resistance , go for long .
• if price breaks below first support with strong volume , it may continue to second support and third support , go for short .

3. How to Implement pivot point using python

from kiteconnect import KiteConnect
import pandas as pd
import datetime as dt
import os
import numpy as np

cwd = os.chdir("D:\GenAi\python\python basic\Strategy")

#generate trading session
access_token = open("access_token.txt",'r').read()
key_secret = open("api_key.txt",'r').read().split()
kite = KiteConnect(api_key=key_secret[0])
kite.set_access_token(access_token)

#get dump of all NSE instruments
instrument_dump = kite.instruments("NSE")
instrument_df = pd.DataFrame(instrument_dump)

def instrumentLookup(instrument_df,symbol):
    try:
        return instrument_df[instrument_df.tradingsymbol==symbol].instrument_token.values[0]
    except:
        return -1

def fetchOHLC(ticker,interval,duration):
    instrument = instrumentLookup(instrument_df,ticker)
    data = pd.DataFrame(kite.historical_data(instrument,dt.date.today()-dt.timedelta(duration), dt.date.today(),interval))
    data.set_index("date",inplace=True)
    return data

def levels(df):    
    high = round(df["high"][-1],2)
    low = round(df["low"][-1],2)
    close = round(df["close"][-1],2)
    pivot = round((high + low + close)/3,2)
    r1 = round((2*pivot - low),2)
    r2 = round((pivot + (high - low)),2)
    r3 = round((high + 2*(pivot - low)),2)
    s1 = round((2*pivot - high),2)
    s2 = round((pivot - (high - low)),2)
    s3 = round((low - 2*(high - pivot)),2)
    return (pivot,r1,r2,r3,s1,s2,s3)

df= fetchOHLC("INFY","day",30)
pp_levels = levels(df.iloc[:-1,:])

Explanation of above code

def levels(df):
    high = round(df["high"][-1],2)

• Defines a function called levels which takes ohlc_day as input.
• df is expected to be a dataset (like a dictionary or DataFrame) containing "high", "low", and "close" prices.
• The docstring tells us the purpose: it returns pivot point and support/resistance levels.
• Gets the last high price ([-1] means last value in the list/series).
• Rounds it to 2 decimal places.

low = round(ohlc_day["low"][-1],2)
close = round(ohlc_day["close"][-1],2)

• Gets the last low price, rounded to 2 decimals.
• Gets the last closing price, rounded to 2 decimals.

pivot = round((high + low + close)/3,2)

• Calculates the pivot point.
• Formula: (High + Low + Close) / 3
• Pivot is the central reference price for next day’s trading.

r1 = round((2*pivot - low),2)

• Resistance 1 (R1) level.
• Formula: 2*Pivot - Low
• First level where price might face resistance if it goes up.

r2 = round((pivot + (high - low)),2)

• Resistance 2 (R2) level.
• Formula: Pivot + (High - Low)
• Second, stronger resistance level.

r3 = round((high + 2*(pivot - low)),2)

• Resistance 3 (R3) level.
• Formula: High + 2*(Pivot - Low)
• Strongest resistance level.

s1 = round((2*pivot - high),2)

• Support 1 (S1) level.
• Formula: 2*Pivot - High
• First support level (price may stop falling here).

s2 = round((pivot - (high - low)),2)

• Support 2 (S2) level.
• Formula: Pivot - (High - Low)
• Second support level.

s3 = round((low - 2*(high - pivot)),2)

• Support 3 (S3) level.
• Formula: Low - 2*(High - Pivot)
• Strongest support level.

return (pivot,r1,r2,r3,s1,s2,s3)

Returns all calculated levels as a tuple.

Output looks like:
(Pivot, R1, R2, R3, S1, S2, S3)