Pandas Ta

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

uv pip install pandas-ta pandas httpx

Quick Start

import pandas as pd
import pandas_ta as ta

# Assume df is a DataFrame with columns: open, high, low, close, volume
# All lowercase column names required

# Single indicator
df["rsi"] = df.ta.rsi(length=14)
df["atr"] = df.ta.atr(length=14)

# Multiple indicators via strategy
df.ta.strategy(ta.Strategy(
    name="Quick Check",
    ta=[
        {"kind": "rsi", "length": 14},
        {"kind": "macd", "fast": 12, "slow": 26, "signal": 9},
        {"kind": "bbands", "length": 20, "std": 2.0},
    ]
))

OHLCV DataFrame Format

pandas-ta expects a DataFrame with lowercase column names:

import pandas as pd

df = pd.DataFrame({
    "open": [...],
    "high": [...],
    "low": [...],
    "close": [...],
    "volume": [...]
}, index=pd.DatetimeIndex([...]))

Important: Set the index to a DatetimeIndex for time-aware indicators like VWAP. Column names must be lowercase (close, not Close).

Handling Missing Data

# Drop rows with NaN in OHLCV columns
df = df.dropna(subset=["open", "high", "low", "close", "volume"])

# Forward-fill small gaps (1-2 bars max)
df = df.ffill(limit=2)

# Verify no zero-volume bars for volume indicators
df = df[df["volume"] > 0]

Core Indicator Categories

Trend Indicators

Identify market direction and trend strength.

Momentum Indicators

Measure speed and magnitude of price changes.

Volatility Indicators

Measure price dispersion and expected range.

Volume Indicators

Confirm price moves with volume analysis.

Strategy Class

Run multiple indicators in a single call using ta.Strategy:

import pandas_ta as ta

# Built-in "All" strategy runs every indicator
df.ta.strategy(ta.AllStrategy)

# Custom strategy
my_strategy = ta.Strategy(
    name="Crypto Scalp",
    description="Fast indicators for crypto scalping",
    ta=[
        {"kind": "ema", "length": 9},
        {"kind": "ema", "length": 21},
        {"kind": "rsi", "length": 7},
        {"kind": "stoch", "k": 5, "d": 3, "smooth_k": 3},
        {"kind": "atr", "length": 7},
        {"kind": "bbands", "length": 10, "std": 2.0},
        {"kind": "obv"},
    ]
)
df.ta.strategy(my_strategy)

Named Strategy Patterns

# Trend following
trend_strategy = ta.Strategy(
    name="Trend",
    ta=[
        {"kind": "ema", "length": 20},
        {"kind": "ema", "length": 50},
        {"kind": "adx", "length": 14},
        {"kind": "supertrend", "length": 10, "multiplier": 3},
        {"kind": "atr", "length": 14},
    ]
)

# Mean reversion
reversion_strategy = ta.Strategy(
    name="Mean Reversion",
    ta=[
        {"kind": "rsi", "length": 14},
        {"kind": "bbands", "length": 20, "std": 2.0},
        {"kind": "stoch", "k": 14, "d": 3, "smooth_k": 3},
        {"kind": "cci", "length": 20},
    ]
)

# Momentum
momentum_strategy = ta.Strategy(
    name="Momentum",
    ta=[
        {"kind": "macd", "fast": 12, "slow": 26, "signal": 9},
        {"kind": "rsi", "length": 14},
        {"kind": "obv"},
        {"kind": "roc", "length": 10},
        {"kind": "mfi", "length": 14},
    ]
)

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

Common Indicator Combinations

Trend Following

# EMA crossover + ADX confirmation + SuperTrend direction
ema_fast = df.ta.ema(length=20)
ema_slow = df.ta.ema(length=50)
adx_df = df.ta.adx(length=14)
st_df = df.ta.supertrend(length=10, multiplier=3)

bullish = (
    (ema_fast > ema_slow) &
    (adx_df["ADX_14"] > 25) &
    (st_df["SUPERTd_10_3.0"] == 1)
)

Mean Reversion

# RSI oversold + price at lower BB + Stochastic oversold
rsi = df.ta.rsi(length=14)
bb = df.ta.bbands(length=20, std=2.5)
stoch = df.ta.stoch(k=14, d=3, smooth_k=3)

buy_signal = (
    (rsi < 30) &
    (df["close"] <= bb["BBL_20_2.5"]) &
    (stoch["STOCHk_14_3_3"] < 20)
)

Momentum Confirmation

# MACD histogram positive + RSI above 50 + OBV rising
macd = df.ta.macd(fast=12, slow=26, signal=9)
rsi = df.ta.rsi(length=14)
obv = df.ta.obv()

momentum_bull = (
    (macd["MACDh_12_26_9"] > 0) &
    (rsi > 50) &
    (obv > obv.shift(1))
)

Volatility Breakout (BB Squeeze)

# Bollinger Band width contracting + volume spike
bb = df.ta.bbands(length=20, std=2.0)
atr = df.ta.atr(length=14)
vol_sma = df["volume"].rolling(20).mean()

bb_width = (bb["BBU_20_2.0"] - bb["BBL_20_2.0"]) / bb["BBM_20_2.0"]
squeeze = bb_width < bb_width.rolling(120).quantile(0.1)
vol_spike = df["volume"] > (vol_sma * 2.0)