C-optimized technical analysis with 150+ functions and 61 candlestick pattern recognition functions via TA-Lib

ta lib — C Optimized Technical Analysis TA Lib (Technical Analysis Library) is a C library with a Python wrapper providing 150+ technical analysis functions and 61 candlestick pattern recognition functions. It is the industry standard for performance critical indicator computation, used in production trading systems where pandas ta or pure Python alternatives are too slow. What TA Lib Is TA Lib was originally written in C for financial market data analysis. The Python wrapper ( TA Lib on PyPI, imported as talib ) provides: 150+ indicator functions across overlap, momentum, volume, volatility, cycle, and math categories 61 candlestick pattern recognition functions — the most comprehensive pattern library available C speed computation — 10 100x faster than pure Python equivalents on large datasets Two APIs : a function API (pass arrays directly) and an abstract API (pass dict of arrays) NumPy native — all inputs and outputs are NumPy arrays Installation TA Lib requires the underlying C library to be installed first: If the C library is not installed, import talib will fail with an ImportError . The scripts in this skill include fallback logic for environments without TA Lib installed. When to Use TA Lib vs pandas ta Criterion TA Lib pandas ta Speed C optimized, 10 100x faster Pure Python, slower on large data Candlestick patterns 61 built in patterns Limited pattern support Installation Requires C library pip install only API style NumPy arrays DataFrame .ta accessor Indicator count 150+ 130+ Streaming Single value update possible Recompute entire series Dependencies C lib + numpy pandas only Use TA Lib when: Processing millions of bars or running backtests at scale You need candlestick pattern recognition (TA Lib is unmatched here) You are building a production pipeline where latency matters You need cycle indicators (Hilbert Transform family) Use pandas ta when: You want DataFrame native convenience Installation simplicity matters (no C dependency) You need indicators not in TA Lib (pandas ta has some extras) Quick Start Function API vs Abstract API Function API (Recommended) Call functions directly with NumPy arrays: Abstract API Pass a dictionary of arrays and get results by name: The abstract API is useful for dynamic indicator selection (e.g., looping over a list of indicator names). Function Groups TA Lib organizes functions into these groups: Overlap Studies Moving averages and envelope indicators that overlay price charts. Momentum Indicators Oscillators and trend strength measures. Volume Indicators Volume based analysis functions. Volatility Indicators Measures of price variability. Pattern Recognition (Candlestick) 61 functions that detect candlestick patterns. All return integer arrays: +100 = bullish pattern detected 100 = bearish pattern detected 0 = no pattern See references/candlestick patterns.md for the full list of 61 patterns with reliability ratings and crypto relevance. Math Transform & Math Operators Mathematical functions (sin, cos, ln, etc.) and operators (add, sub, mult, div) on arrays. Rarely used directly but available. Crypto Considerations 24/7 Markets Candlestick patterns designed for traditional markets with opening/closing gaps may behave differently on crypto's continuous markets Gap based patterns (morning star, evening star) are less reliable without session gaps Body ratio patterns (doji, hammer, engulfing) still work well on any timeframe Timeframe Selection 1m 5m : Patterns are noisy; combine with volume confirmation 15m 1h : Good for intraday signals on high cap tokens 4h 1d : Most reliable for pattern recognition Tip : Higher timeframes produce fewer but more reliable pattern signals NaN Handling TA Lib returns NaN for the initial lookback period of each indicator. Always account for this: Solana Token Data When using TA Lib with Solana token OHLCV data: Ensure arrays are float64 dtype — TA Lib requires this Sort by timestamp ascending before passing to TA Lib Handle gaps in low liquidity token data before computing indicators Integration with Other Skills With pandas ta pandas ta can use TA Lib as a backend when installed, getting C speed through the pandas ta API: With vectorbt vectorbt integrates with TA Lib for fast backtesting: With Birdeye/DexScreener Data Fetch OHLCV data from API skills, then process with TA Lib: Listing Available Functions Files File Description references/function reference.md Most useful functions by category with syntax and parameters references/candlestick patterns.md All 61 candlestick patterns grouped by type with reliability ratings scripts/compute indicators.py Computes common indicators with TA Lib/fallback comparison scripts/pattern scanner.py Scans OHLCV data for all 61 candlestick patterns

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