pandas ta — Technical Analysis for Crypto Markets pandas ta is a Python library that extends pandas DataFrames with 130+ technical analysis indicators accessible via df.ta . It covers trend, momentum, volatility, volume, and overlap indicator categories — all callable with a single method on any OHLCV DataFrame. Installation Quick Start OHLCV DataFrame Format pandas ta expects a DataFrame with lowercase column names: Important : Set the index to a DatetimeIndex for time aware indicators like VWAP. Column names must be lowercase ( close , not Close ). Handling Missing Data Core Indicator Categories Trend Indicators Identify market direction and trend strength. Indicator Call Key Signal SMA df.ta.sma(length=20) Price above = bullish EMA df.ta.ema(length=20) Faster than SMA, less lag SuperTrend df.ta.supertrend(length=10, multiplier=3) Direction column: 1=bull, 1=bear Ichimoku df.ta.ichimoku() Returns tuple of (span, lines) DataFrames VWMA df.ta.vwma(length=20) Volume weighted price trend HMA df.ta.hma(length=20) Minimal lag, smooth trend ADX df.ta.adx(length=14) 25 = trending, <20 = ranging Momentum Indicators Measure speed and magnitude of price changes. Indicator Call Key Signal RSI df.ta.rsi(length=14) 70 overbought, <30 oversold MACD df.ta.macd(fast=12, slow=26, signal=9) Histogram crossover = entry Stochastic df.ta.stoch(k=14, d=3, smooth k=3) 80 overbought, <20 oversold CCI df.ta.cci(length=20) 100 overbought, < 100 oversold Williams %R df.ta.willr(length=14) 20 overbought, < 80 oversold ROC df.ta.roc(length=10) Positive = upward momentum MFI df.ta.mfi(length=14) Money flow version of RSI Volatility Indicators Measure price dispersion and expected range. Indicator Call Key Signal Bollinger Bands df.ta.bbands(length=20, std=2) Squeeze = breakout pending ATR df.ta.atr(length=14) Position sizing, stop placement Keltner Channels df.ta.kc(length=20, scalar=1.5) BB inside KC = squeeze Donchian Channels df.ta.donchian(lower length=20, upper length=20) Breakout detection Volume Indicators Confirm price moves with volume analysis. Indicator Call Key Signal OBV df.ta.obv() Divergence from price = reversal VWAP df.ta.vwap() Intraday fair value (needs DatetimeIndex) CMF df.ta.cmf(length=20) 0 accumulation, <0 distribution AD df.ta.ad() Accumulation/Distribution line Strategy Class Run multiple indicators in a single call using ta.Strategy : Named Strategy Patterns Crypto Specific Considerations 24/7 Markets No session gaps — indicators that rely on open/close of sessions behave differently VWAP resets at midnight UTC by default; consider anchored VWAP for custom periods Weekend data is continuous — no Monday gap effects High Volatility Adjustments Bollinger Bands : Use 2.5 3x standard deviation instead of the default 2x RSI periods : Shorter periods (7 10) capture faster crypto cycles ATR : Use for dynamic stop losses; crypto ATR is typically 2 5x equity ATR SuperTrend multiplier : 3 4x for crypto vs 2 3x for equities Low Cap Token Considerations Volume indicators (OBV, CMF, MFI) are unreliable with thin order books Prefer price based indicators (RSI, BBands, SuperTrend) for low liquidity tokens ATR based position sizing is critical — wide spreads amplify losses Wash trading inflates volume; cross reference with on chain data Timeframe Selection Timeframe Use Case Recommended Indicators 1m 5m Scalping, PumpFun RSI(5 7), EMA(5,13), ATR(5) 15m 1h Day trading MACD, RSI(14), BBands, EMA(20,50) 4h 1d Swing trading SuperTrend, ADX, EMA(50,200) 1w Position trading SMA(20,50), RSI(14), monthly VWAP Common Indicator Combinations Trend Following Mean Reversion Momentum Confirmation Volatility Breakout (BB Squeeze) Integration with Other Skills birdeye api : Fetch OHLCV data → feed into pandas ta for indicator computation vectorbt : Use pandas ta indicators as signal inputs for backtesting trading visualization : Plot indicator overlays on price charts slippage modeling : Combine ATR with slippage estimates for realistic execution modeling position sizing : Use ATR based sizing from pandas ta output Files References references/indicator guide.md — Top 20 crypto indicators with syntax, parameters, and interpretation references/strategy patterns.md — Pre built strategy combinations for scalping, day trading, and swing trading references/common pitfalls.md — Common mistakes with technical indicators in crypto markets Scripts scripts/compute indicators.py — Fetch OHLCV data and compute standard indicator set with signal summary scripts/multi indicator scan.py — Run multiple strategy profiles and score current signal alignment

pandas-ta