---name: pandas-tadescription: 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 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

# 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)

breakout_setup = squeeze & vol_spike

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