ta-lib

Name: ta-lib
Author: agiprolabs

235stars53forks

How to install ta-lib?

agiprolabs/claude-trading-skills/ta-lib

$npx -y skills add agiprolabs/claude-trading-skills --skill ta-lib

Installs into the current project.

Summary

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

Files · 1

View on GitHub

SKILL.md

1	# ta-lib — C-Optimized Technical Analysis
2
3	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.
4
5	## What TA-Lib Is
6
7	TA-Lib was originally written in C for financial market data analysis. The Python wrapper (`TA-Lib` on PyPI, imported as `talib`) provides:
8
9	- 150+ indicator functions across overlap, momentum, volume, volatility, cycle, and math categories
10	- 61 candlestick pattern recognition functions — the most comprehensive pattern library available
11	- C-speed computation — 10-100x faster than pure-Python equivalents on large datasets
12	- Two APIs: a function API (pass arrays directly) and an abstract API (pass dict of arrays)
13	- NumPy native — all inputs and outputs are NumPy arrays
14
15	## Installation
16
17	TA-Lib requires the underlying C library to be installed first:
18
19	```bash
20	# macOS
21	brew install ta-lib
22	uv pip install TA-Lib numpy pandas
23
24	# Ubuntu/Debian
25	sudo apt-get install -y ta-lib
26	uv pip install TA-Lib numpy pandas
27
28	# From source (any platform)
29	wget https://github.com/ta-lib/ta-lib/releases/download/v0.6.4/ta-lib-0.6.4-src.tar.gz
30	tar -xzf ta-lib-0.6.4-src.tar.gz
31	cd ta-lib-0.6.4
32	./configure --prefix=/usr/local
33	make && sudo make install
34	uv pip install TA-Lib numpy pandas
35	```
36
37	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.
38
39	## When to Use TA-Lib vs pandas-ta
40
41	\| Criterion \| TA-Lib \| pandas-ta \|
42	\|---\|---\|---\|
43	\| Speed \| C-optimized, 10-100x faster \| Pure Python, slower on large data \|
44	\| Candlestick patterns \| 61 built-in patterns \| Limited pattern support \|
45	\| Installation \| Requires C library \| `pip install` only \|
46	\| API style \| NumPy arrays \| DataFrame `.ta` accessor \|
47	\| Indicator count \| 150+ \| 130+ \|
48	\| Streaming \| Single-value update possible \| Recompute entire series \|
49	\| Dependencies \| C lib + numpy \| pandas only \|
50
51	Use TA-Lib when:
52	- Processing millions of bars or running backtests at scale
53	- You need candlestick pattern recognition (TA-Lib is unmatched here)
54	- You are building a production pipeline where latency matters
55	- You need cycle indicators (Hilbert Transform family)
56
57	Use pandas-ta when:
58	- You want DataFrame-native convenience
59	- Installation simplicity matters (no C dependency)
60	- You need indicators not in TA-Lib (pandas-ta has some extras)
61
62	## Quick Start
63
64	```python
65	import numpy as np
66	import talib
67
68	# Create sample data
69	close = np.random.randn(100).cumsum() + 50
70	high = close + np.abs(np.random.randn(100))
71	low = close - np.abs(np.random.randn(100))
72	open_ = close + np.random.randn(100) * 0.5
73	volume = np.random.randint(1000, 10000, 100).astype(float)
74
75	# Function API — pass arrays directly
76	rsi = talib.RSI(close, timeperiod=14)
77	macd, signal, hist = talib.MACD(close, fastperiod=12, slowperiod=26, signalperiod=9)
78	upper, middle, lower = talib.BBANDS(close, timeperiod=20, nbdevup=2, nbdevdn=2)
79	atr = talib.ATR(high, low, close, timeperiod=14)
80
81	# Candlestick patterns — return +100 (bullish), -100 (bearish), or 0
82	doji = talib.CDLDOJI(open_, high, low, close)
83	hammer = talib.CDLHAMMER(open_, high, low, close)
84	engulfing = talib.CDLENGULFING(open_, high, low, close)
85	```
86
87	## Function API vs Abstract API
88
89	### Function API (Recommended)
90
91	Call functions directly with NumPy arrays:
92
93	```python
94	import talib
95
96	rsi = talib.RSI(close, timeperiod=14)
97	sma = talib.SMA(close, timeperiod=20)
98	upper, mid, lower = talib.BBANDS(close)
99	```
100
101	### Abstract API
102
103	Pass a dictionary of arrays and get results by name:
104
105	```python
106	from talib import abstract
107
108	inputs = {"open": open_, "high": high, "low": low, "close": close, "volume": volume}
109
110	# Call by function name
111	rsi = abstract.RSI(inputs, timeperiod=14)
112	macd = abstract.MACD(inputs) # returns (macd, signal, hist)
113	```
114
115	The abstract API is useful for dynamic indicator selection (e.g., looping over a list of indicator names).
116
117	## Function Groups
118
119	TA-Lib organizes functions into these groups:
120
121	### Overlap Studies
122	Moving averages and envelope indicators that overlay price charts.
123
124	```python
125	sma = talib.SMA(close, timeperiod=20)
126	ema = talib.EMA(close, timeperiod=12)
127	upper, mid, lower = talib.BBANDS(close, timeperiod=20, nbdevup=2, nbdevdn=2)
128	sar = talib.SAR(high, low, acceleration=0.02, maximum=0.2)
129	mama, fama = talib.MAMA(close, fastlimit=0.5, slowlimit=0.05)
130	```
131
132	### Momentum Indicators
133	Oscillators and trend-strength measures.
134
135	```python
136	rsi = talib.RSI(close, timeperiod=14)
137	macd, signal, hist = talib.MACD(close, fastperiod=12, slowperiod=26, signalperiod=9)
138	slowk, slowd = talib.STOCH(high, low, close)
139	cci = talib.CCI(high,

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ta-lib

235stars53forks

View on GitHub

How to install ta-lib?

agiprolabs/claude-trading-skills/ta-lib

$npx -y skills add agiprolabs/claude-trading-skills --skill ta-lib

Installs into the current project.

Summary

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

Files · 1

View on GitHub

SKILL.md

1	# ta-lib — C-Optimized Technical Analysis
2
3	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.
4
5	## What TA-Lib Is
6
7	TA-Lib was originally written in C for financial market data analysis. The Python wrapper (`TA-Lib` on PyPI, imported as `talib`) provides:
8
9	- 150+ indicator functions across overlap, momentum, volume, volatility, cycle, and math categories
10	- 61 candlestick pattern recognition functions — the most comprehensive pattern library available
11	- C-speed computation — 10-100x faster than pure-Python equivalents on large datasets
12	- Two APIs: a function API (pass arrays directly) and an abstract API (pass dict of arrays)
13	- NumPy native — all inputs and outputs are NumPy arrays
14
15	## Installation
16
17	TA-Lib requires the underlying C library to be installed first:
18
19	```bash
20	# macOS
21	brew install ta-lib
22	uv pip install TA-Lib numpy pandas
23
24	# Ubuntu/Debian
25	sudo apt-get install -y ta-lib
26	uv pip install TA-Lib numpy pandas
27
28	# From source (any platform)
29	wget https://github.com/ta-lib/ta-lib/releases/download/v0.6.4/ta-lib-0.6.4-src.tar.gz
30	tar -xzf ta-lib-0.6.4-src.tar.gz
31	cd ta-lib-0.6.4
32	./configure --prefix=/usr/local
33	make && sudo make install
34	uv pip install TA-Lib numpy pandas
35	```
36
37	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.
38
39	## When to Use TA-Lib vs pandas-ta
40
41	\| Criterion \| TA-Lib \| pandas-ta \|
42	\|---\|---\|---\|
43	\| Speed \| C-optimized, 10-100x faster \| Pure Python, slower on large data \|
44	\| Candlestick patterns \| 61 built-in patterns \| Limited pattern support \|
45	\| Installation \| Requires C library \| `pip install` only \|
46	\| API style \| NumPy arrays \| DataFrame `.ta` accessor \|
47	\| Indicator count \| 150+ \| 130+ \|
48	\| Streaming \| Single-value update possible \| Recompute entire series \|
49	\| Dependencies \| C lib + numpy \| pandas only \|
50
51	Use TA-Lib when:
52	- Processing millions of bars or running backtests at scale
53	- You need candlestick pattern recognition (TA-Lib is unmatched here)
54	- You are building a production pipeline where latency matters
55	- You need cycle indicators (Hilbert Transform family)
56
57	Use pandas-ta when:
58	- You want DataFrame-native convenience
59	- Installation simplicity matters (no C dependency)
60	- You need indicators not in TA-Lib (pandas-ta has some extras)
61
62	## Quick Start
63
64	```python
65	import numpy as np
66	import talib
67
68	# Create sample data
69	close = np.random.randn(100).cumsum() + 50
70	high = close + np.abs(np.random.randn(100))
71	low = close - np.abs(np.random.randn(100))
72	open_ = close + np.random.randn(100) * 0.5
73	volume = np.random.randint(1000, 10000, 100).astype(float)
74
75	# Function API — pass arrays directly
76	rsi = talib.RSI(close, timeperiod=14)
77	macd, signal, hist = talib.MACD(close, fastperiod=12, slowperiod=26, signalperiod=9)
78	upper, middle, lower = talib.BBANDS(close, timeperiod=20, nbdevup=2, nbdevdn=2)
79	atr = talib.ATR(high, low, close, timeperiod=14)
80
81	# Candlestick patterns — return +100 (bullish), -100 (bearish), or 0
82	doji = talib.CDLDOJI(open_, high, low, close)
83	hammer = talib.CDLHAMMER(open_, high, low, close)
84	engulfing = talib.CDLENGULFING(open_, high, low, close)
85	```
86
87	## Function API vs Abstract API
88
89	### Function API (Recommended)
90
91	Call functions directly with NumPy arrays:
92
93	```python
94	import talib
95
96	rsi = talib.RSI(close, timeperiod=14)
97	sma = talib.SMA(close, timeperiod=20)
98	upper, mid, lower = talib.BBANDS(close)
99	```
100
101	### Abstract API
102
103	Pass a dictionary of arrays and get results by name:
104
105	```python
106	from talib import abstract
107
108	inputs = {"open": open_, "high": high, "low": low, "close": close, "volume": volume}
109
110	# Call by function name
111	rsi = abstract.RSI(inputs, timeperiod=14)
112	macd = abstract.MACD(inputs) # returns (macd, signal, hist)
113	```
114
115	The abstract API is useful for dynamic indicator selection (e.g., looping over a list of indicator names).
116
117	## Function Groups
118
119	TA-Lib organizes functions into these groups:
120
121	### Overlap Studies
122	Moving averages and envelope indicators that overlay price charts.
123
124	```python
125	sma = talib.SMA(close, timeperiod=20)
126	ema = talib.EMA(close, timeperiod=12)
127	upper, mid, lower = talib.BBANDS(close, timeperiod=20, nbdevup=2, nbdevdn=2)
128	sar = talib.SAR(high, low, acceleration=0.02, maximum=0.2)
129	mama, fama = talib.MAMA(close, fastlimit=0.5, slowlimit=0.05)
130	```
131
132	### Momentum Indicators
133	Oscillators and trend-strength measures.
134
135	```python
136	rsi = talib.RSI(close, timeperiod=14)
137	macd, signal, hist = talib.MACD(close, fastperiod=12, slowperiod=26, signalperiod=9)
138	slowk, slowd = talib.STOCH(high, low, close)
139	cci = talib.CCI(high,

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