Python✓ Mathematical
◆ The PatternTechnical indicators + market data in one-liners
yfinance downloads free historical market data from Yahoo Finance. pandas-ta computes 130+ technical indicators as DataFrame operations. Together, they're the fastest way to go from idea to analysis for any market strategy.
// Price data with technical indicators
# pandas-ta + yfinance — quick market analysis import yfinance as yf import pandas_ta as ta # Download data df = yf.download('AAPL', start='2023-01-01') # Add indicators as columns df.ta.sma(length=20, append=True) df.ta.rsi(length=14, append=True) df.ta.macd(append=True) df.ta.bbands(length=20, append=True) # Or run a full strategy df.ta.strategy('all') # adds 130+ indicators # Quick signal df['signal'] = (df['RSI_14'] < 30).astype(int) print(df[['Close', 'SMA_20', 'RSI_14']].tail())
| Category | Indicators | pandas-ta function |
|---|---|---|
| Trend | SMA, EMA, MACD | ta.sma(), ta.ema(), ta.macd() |
| Momentum | RSI, Stochastic, CCI | ta.rsi(), ta.stoch(), ta.cci() |
| Volatility | Bollinger, ATR, Keltner | ta.bbands(), ta.atr() |
| Volume | OBV, VWAP, MFI | ta.obv(), ta.vwap(), ta.mfi() |
Pattern bridge: These are the same indicators explored in the Markets → Indicators collection — but here you can compute them yourself in Python and feed them into ML models as features.