Python: Technical Analysis

Lesson Content

Technical analysis is a cornerstone of algorithmic trading, enabling traders to make data-driven decisions using historical price data. In Python, libraries like TA-Lib, Pandas, and Matplotlib provide powerful tools for technical analysis. Let's explore how to implement key technical indicators using the Zerodha Kite API for Indian market data. For example, to calculate the 50-day Simple Moving Average (SMA) for Nifty 50: import kiteconnect import pandas as pd kite = KiteConnect(api_key='your_api_key') data = kite.historical_data('NSE:NIFTY50', '2023-01-01', '2023-06-30', '5minute') df = pd.DataFrame(data) df['SMA_50'] = df['close'].rolling(window=50).mean() Relative Strength Index (RSI) is another crucial momentum indicator. You can implement it as follows: def calculate_rsi(prices, period=14): delta = prices.diff() gain = (delta.where(delta > 0, 0)).rolling(window=period).mean() loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean() rs = gain / loss return 100 - (100 / (1 + rs)) df['RSI'] = calculate_rsi(df['close']) Bollinger Bands, which help identify overbought or oversold conditions, can be calculated using: df['SMA_20'] = df['close'].rolling(window=20).mean() df['std'] = df['close'].rolling(window=20).std() df['Upper_Band'] = df['SMA_20'] + (df['std'] * 2) df['Lower_Band'] = df['SMA_20'] - (df['std'] * 2) These indicators form the foundation of many algorithmic trading strategies for the Indian markets.