/pandas-ta

name: pandas-ta
description: Technical analysis with 130+ indicators using pandas-ta for crypto market data

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.

| 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`:

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 eq