pandas-ta
pandas-ta extends pandas with 130+ technical indicators callable via `df.ta` on OHLCV DataFrames. It covers trend, momentum, volatility, volume, and overlap categories, with built-in strategies for scalping, mean reversion, and trend following. Designed for crypto markets with guidance on 24/7 volatility adjustments, low-liquidity tokens, and timeframe-specific indicator selection.
pandas-ta computes 130+ technical indicators across trend, momentum, volatility, and volume categories on OHLCV crypto data.
AI-generated summary based on this skill's SKILL.md
Install
agiprolabs/claude-trading-skills/pandas-ta · repository language: Python
git clone https://github.com/agiprolabs/claude-trading-skills
cp -r claude-trading-skills/skills/pandas-ta ~/.claude/skills/pandas-taFrequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
What technical analysis indicators for crypto does pandas-ta provide?
pandas-ta extends pandas with 130+ technical indicators callable via `df.ta` on OHLCV DataFrames. It covers trend (EMA, ADX, SuperTrend), momentum (RSI, MACD, Stochastic RSI), volatility (Bollinger Bands, ATR, Keltner Channels), volume (OBV, CMF), and overlap categories. pandas-ta is designed for crypto markets with guidance on 24/7 volatility adjustments, low-liquidity tokens, and timeframe-specific indicator selection.
How do I compute 130+ indicators on OHLCV crypto data with pandas-ta?
pandas-ta computes 130+ technical indicators on OHLCV DataFrames through a simple `df.ta` accessor. Load your crypto OHLCV data into a pandas DataFrame with columns for Open, High, Low, Close, and Volume, then call indicator methods like `df.ta.rsi()`, `df.ta.macd()`, or `df.ta.bbands()`. Each returns a Series or DataFrame of computed values appended to your original data for further analysis.
Can pandas-ta build and backtest multi-indicator trading strategies?
Yes, pandas-ta includes a built-in strategy class for backtesting multi-indicator approaches. It supports scalping, mean reversion, and trend-following strategies that combine indicators like EMA crossovers, ADX confirmation, and Bollinger Bands signals. You define entry/exit logic using trend, momentum, and volatility indicators, then backtest on historical OHLCV data to evaluate performance before live trading.
Which pandas-ta indicators generate buy/sell signals for crypto trading?
pandas-ta generates buy/sell signals from trend (SuperTrend, ADX, EMA crossovers), momentum (RSI, MACD, Stochastic RSI), and volatility (Bollinger Bands squeeze, Keltner Channels breakouts) indicators. Common setups include RSI oversold/overbought levels, MACD crossovers, mean reversion at Bollinger Bands extremes, and trend confirmation via ADX. Combine multiple signals to reduce false positives in crypto's 24/7 volatile markets.
How does pandas-ta handle position sizing and ATR for crypto scalping?
pandas-ta computes ATR (Average True Range) to measure volatility and set dynamic stop-losses and position sizes. For crypto scalping on 1m or 5m timeframes, use ATR to scale position size inversely with volatility—smaller positions during high ATR spikes, larger during low volatility. Combine ATR with SuperTrend or Keltner Channels for breakout detection and risk management tailored to crypto's rapid price swings.
What crypto-specific tuning does pandas-ta offer for indicators and timeframes?
pandas-ta provides guidance on crypto-specific adjustments: 24/7 market volatility requires higher ATR multipliers and wider Bollinger Bands; low-liquidity tokens need larger position sizing buffers; timeframe selection (1m scalping vs. 4h trend following) changes optimal indicator periods. Use shorter RSI/MACD periods for scalping, longer for swing trading. Adjust Stochastic RSI and volume indicators (OBV, CMF) based on your target timeframe and asset liquidity.
SKILL.md
rendered from the published skill — quoted content, verbatim
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:
```python import pandas as pd
df = pd.DataFrame({ "open": [...],
(truncated - see the full file via the links below)
Read as markdown · JSON record · Browse the source repository
File tree — 6 files
skills/pandas-ta/SKILL.md
skills/pandas-ta/references/common_pitfalls.md
skills/pandas-ta/references/indicator_guide.md
skills/pandas-ta/references/strategy_patterns.md
skills/pandas-ta/scripts/compute_indicators.py
skills/pandas-ta/scripts/multi_indicator_scan.py