{"enrichment":{"faq":[{"a":"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.","q":"What technical analysis indicators for crypto does pandas-ta provide?"},{"a":"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.","q":"How do I compute 130+ indicators on OHLCV crypto data with pandas-ta?"},{"a":"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.","q":"Can pandas-ta build and backtest multi-indicator trading strategies?"},{"a":"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.","q":"Which pandas-ta indicators generate buy/sell signals for crypto trading?"},{"a":"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\u2014smaller 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.","q":"How does pandas-ta handle position sizing and ATR for crypto scalping?"},{"a":"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.","q":"What crypto-specific tuning does pandas-ta offer for indicators and timeframes?"}],"shadow_tags":["indicator-library","signal-generation","backtesting-framework","price-action","order-flow","risk-management","strategy-templates","data-preparation","chart-overlay","algo-trading"],"summary_rewrite":"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."},"files":[{"bytes":9162,"path":"skills/pandas-ta/SKILL.md","sha256":"692a50c3ceb9d69315ab1bad55029ffaf59a58a524599ed4aa74023f42b2fc5d","url":"https://skillfed.io/files/agiprolabs/claude-trading-skills/pandas-ta/8b323470/SKILL.md"}],"id":"agiprolabs/claude-trading-skills/pandas-ta","links":{"html":"https://skillfed.io/agiprolabs/claude-trading-skills/pandas-ta","md":"https://skillfed.io/agiprolabs/claude-trading-skills/pandas-ta.md","repo":"https://github.com/agiprolabs/claude-trading-skills"},"meta":{"agents_supported":[],"first_seen":"2026-07-28","forks":52,"language":"Python","last_updated":"2026-06-24","license":"MIT","name":"pandas-ta","publisher":"agiprolabs","stars":248},"relations":{"similar":[{"id":"marketcalls/openalgo-indicator-skills/indicator-chart"},{"id":"Starchild-ai-agent/official-skills/taapi"},{"id":"marketcalls/vectorbt-backtesting-skills/vectorbt-expert"},{"id":"marketcalls/openalgo-indicator-skills/indicator-expert"},{"id":"0xrikt/crypto-skills/crypto-backtest"},{"id":"agiprolabs/claude-trading-skills/ta-lib"},{"id":"marketcalls/vectorbt-backtesting-skills/backtest"},{"id":"besoeasy/open-skills/trading-indicators-from-price-data"},{"id":"agiprolabs/claude-trading-skills/exit-strategies"},{"id":"staskh/trading_skills/technical-analysis"}]},"slug":{"owner":"agiprolabs","repo":"claude-trading-skills","skill":"pandas-ta"},"version":"8b323470"}