Mode d’emploi
- Copiez la commande et examinez le paquet avant de l’installer dans votre environnement agent.
- Consultez le document original pour les outils, identifiants et environnements requis.
- Décrivez les entrées et le livrable souhaité ; comparez la sortie aux sources.
npx skills add https://github.com/agiprolabs/claude-trading-skills --skill pandas-taAvant de commencer
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Rendu depuis le dépôt source en conservant titres, exemples, code, tableaux, liens et images.
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
bash
uv pip install pandas-ta pandas httpxQuick Start
python
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:
python
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
python
# 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:
python
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
python
# 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
python
# 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
python
# 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
python
# 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)
python
# 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_spikeIntegration 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 interpretationreferences/strategy_patterns.md— Pre-built strategy combinations for scalping, day trading, and swing tradingreferences/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 summaryscripts/multi_indicator_scan.py— Run multiple strategy profiles and score current signal alignment
