ta-lib
C-optimized technical analysis with 150+ functions and 61 candlestick pattern recognition functions via TA-Lib
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/agiprolabs/claude-trading-skills --skill ta-lib- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads ta-lib automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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:
# macOS
brew install ta-lib
uv pip install TA-Lib numpy pandas
# Ubuntu/Debian
sudo apt-get install -y ta-lib
uv pip install TA-Lib numpy pandas
# From source (any platform)
wget https://github.com/ta-lib/ta-lib/releases/download/v0.6.4/ta-lib-0.6.4-src.tar.gz
tar -xzf ta-lib-0.6.4-src.tar.gz
cd ta-lib-0.6.4
./configure --prefix=/usr/local
make && sudo make install
uv pip install TA-Lib numpy pandas
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
import numpy as np
import talib
# Create sample data
close = np.random.randn(100).cumsum() + 50
high = close + np.abs(np.random.randn(100))
low = close - np.abs(np.random.randn(100))
open_ = close + np.random.randn(100) * 0.5
volume = np.random.randint(1000, 10000, 100).astype(float)
# Function API — pass arrays directly
rsi = talib.RSI(close, timeperiod=14)
macd, signal, hist = talib.MACD(close, fastperiod=12, slowperiod=26, signalperiod=9)
upper, middle, lower = talib.BBANDS(close, timeperiod=20, nbdevup=2, nbdevdn=2)
atr = talib.ATR(high, low, close, timeperiod=14)
# Candlestick patterns — return +100 (bullish), -100 (bearish), or 0
doji = talib.CDLDOJI(open_, high, low, close)
hammer = talib.CDLHAMMER(open_, high, low, close)
engulfing = talib.CDLENGULFING(open_, high, low, close)
Function API vs Abstract API
Function API (Recommended)
Call functions directly with NumPy arrays:
import talib
rsi = talib.RSI(close, timeperiod=14)
sma = talib.SMA(close, timeperiod=20)
upper, mid, lower = talib.BBANDS(close)
Abstract API
Pass a dictionary of arrays and get results by name:
from talib import abstract
inputs = {"open": open_, "high": high, "low": low, "close": close, "volume": volume}
# Call by function name
rsi = abstract.RSI(inputs, timeperiod=14)
macd = abstract.MACD(inputs) # returns (macd, signal, hist)
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.
sma = talib.SMA(close, timeperiod=20)
ema = talib.EMA(close, timeperiod=12)
upper, mid, lower = talib.BBANDS(close, timeperiod=20, nbdevup=2, nbdevdn=2)
sar = talib.SAR(high, low, acceleration=0.02, maximum=0.2)
mama, fama = talib.MAMA(close, fastlimit=0.5, slowlimit=0.05)
Momentum Indicators
Oscillators and trend-strength measures.
rsi = talib.RSI(close, timeperiod=14)
macd, signal, hist = talib.MACD(close, fastperiod=12, slowperiod=26, signalperiod=9)
slowk, slowd = talib.STOCH(high, low, close)
cci = talib.CCI(high, low, close, timeperiod=14)
willr = talib.WILLR(high, low, close, timeperiod=14)
adx = talib.ADX(high, low, close, timeperiod=14)
mfi = talib.MFI(high, low, close, volume, timeperiod=14)
Volume Indicators
Volume-based analysis functions.
obv = talib.OBV(close, volume)
ad = talib.AD(high, low, close, volume)
adosc = talib.ADOSC(high, low, close, volume, fastperiod=3, slowperiod=10)
Volatility Indicators
Measures of price variability.
atr = talib.ATR(high, low, close, timeperiod=14)
natr = talib.NATR(high, low, close, timeperiod=14)
trange = talib.TRANGE(high, low, close)
Pattern Recognition (Candlestick)
61 functions that detect candlestick patterns. All return integer arrays:
+100= bullish pattern detected-100= bearish pattern detected0= no pattern
# Single patterns
doji = talib.CDLDOJI(open_, high, low, close)
hammer = talib.CDLHAMMER(open_, high, low, close)
engulfing = talib.CDLENGULFING(open_, high, low, close)
# Scan all 61 patterns at once
candle_names = talib.get_function_groups()["Pattern Recognition"]
for name in candle_names:
func = getattr(talib, name)
result = func(open_, high, low, close)
hits = np.nonzero(result)[0]
if len(hits) > 0:
print(f"{name}: {len(hits)} detections")
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:
rsi = talib.RSI(close, timeperiod=14)
# First 14 values will be NaN
valid_rsi = rsi[~np.isnan(rsi)]
Solana Token Data
When using TA-Lib with Solana token OHLCV data:
- Ensure arrays are
float64dtype — TA-Lib requires this - Sort by timestamp ascending before passing to TA-Lib
- Handle gaps in low-liquidity token data before computing indicators
# Convert to float64 for TA-Lib compatibility
close = df["close"].values.astype(np.float64)
high = df["high"].values.astype(np.float64)
low = df["low"].values.astype(np.float64)
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:
import pandas_ta as ta
# pandas-ta auto-detects TA-Lib and uses it for supported indicators
# Set explicitly:
ta.Imports["talib"] = True # Force TA-Lib backend
df.ta.rsi(length=14) # Uses TA-Lib under the hood if available
With vectorbt
vectorbt integrates with TA-Lib for fast backtesting:
import vectorbt as vbt
# Use TA-Lib indicators in vectorbt
rsi = vbt.talib("RSI").run(close, timeperiod=14)
entries = rsi.real_crossed_below(30)
exits = rsi.real_crossed_above(70)
With Birdeye/DexScreener Data
Fetch OHLCV data from API skills, then process with TA-Lib:
# After fetching OHLCV from birdeye-api or dexscreener-api
close = np.array(ohlcv_data["close"], dtype=np.float64)
rsi = talib.RSI(close, timeperiod=14)
Listing Available Functions
import talib
# All function groups
groups = talib.get_function_groups()
for group, funcs in groups.items():
print(f"{group}: {len(funcs)} functions")
# All function names
all_funcs = talib.get_functions()
print(f"Total: {len(all_funcs)} functions")
# Info about a specific function
info = talib.abstract.Function("RSI").info
print(info["display_name"], info["group"])
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 |
FAQ
What does the ta-lib skill do?
C-optimized technical analysis with 150+ functions and 61 candlestick pattern recognition functions via TA-Lib
How do I install ta-lib?
Run npx skills add https://github.com/agiprolabs/claude-trading-skills --skill ta-lib in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/ta-lib/ (personal) or .claude/skills/ta-lib/ (project).
Which AI agents can use ta-lib?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is ta-lib safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains ta-lib?
It is published by agiprolabs in agiprolabs/claude-trading-skills. It has 497 recorded installs.
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