technical-indicators

name	technical-indicators
description	Calculate technical analysis indicators for stock market analysis

Technical Indicators Calculator

Quick guide for calculating technical indicators using Python and pandas-ta.

Quick Start

import yfinance as yf
import pandas_ta as ta

df = yf.download('AAPL', period='1y')
df['RSI'] = ta.rsi(df['Close'], length=14)
print(df[['Close', 'RSI']].tail())

Key Indicators

1. RSI (Relative Strength Index)

df['RSI'] = ta.rsi(df['Close'], length=14)
# RSI > 70: overbought | RSI < 30: oversold

2. SMA (Simple Moving Average)

df['SMA_50'] = ta.sma(df['Close'], length=50)
# Golden Cross: SMA_50 > SMA_200
# Death Cross: SMA_50 < SMA_200

3. EMA (Exponential Moving Average)

df['EMA_12'] = ta.ema(df['Close'], length=12)

4. MACD

macd = ta.macd(df['Close'])
# MACD > Signal: bullish | MACD < Signal: bearish

5. Bollinger Bands

bbands = ta.bbands(df['Close'], length=20)
# Price > Upper: overbought | Price < Lower: oversold

6. ADX (Trend Strength)

adx = ta.adx(df['High'], df['Low'], df['Close'], length=14)
# ADX > 25: strong trend | ADX < 20: weak trend

7. ATR (Volatility)

df['ATR'] = ta.atr(df['High'], df['Low'], df['Close'], length=14)

8. Stochastic Oscillator

stoch = ta.stoch(df['High'], df['Low'], df['Close'], length=14)

9. OBV (On-Balance Volume)

df['OBV'] = ta.obv(df['Close'], df['Volume'])

Complete Example

import yfinance as yf
import pandas as pd
import pandas_ta as ta

# Fetch data
ticker = 'AAPL'
df = yf.download(ticker, period='2y')

# Calculate multiple indicators
df['RSI'] = ta.rsi(df['Close'], length=14)
df['SMA_20'] = ta.sma(df['Close'], length=20)
df['SMA_50'] = ta.sma(df['Close'], length=50)
df['EMA_12'] = ta.ema(df['Close'], length=12)

# MACD
macd = ta.macd(df['Close'])
df = pd.concat([df, macd], axis=1)

# Bollinger Bands
bbands = ta.bbands(df['Close'], length=20)
df = pd.concat([df, bbands], axis=1)

# ATR
df['ATR'] = ta.atr(df['High'], df['Low'], df['Close'], length=14)

print(df[['Close', 'RSI', 'SMA_20', 'SMA_50', 'ATR']].tail())

Resources

• pandas-ta: https://pandas-ta.readthedocs.io/
• yfinance: https://github.com/ranaroussi/yfinance
• TA-Lib: https://ta-lib.org/