ta-patterns 1.1.0

Pure-numpy vectorised library of 300 candlestick and chart pattern detectors with no look-forward bias.

ta_patterns

Pure-NumPy, fully vectorised library of 300 technical-analysis pattern detectors — 106 candlestick patterns and 194 chart patterns (short-bar, double/multi, classic-geometric, harmonic, volume, busted). No TA-Lib dependency.

• Version: 1.1.0
• Python: ≥ 3.10
• Dependencies: numpy (required); pandas (optional, for the DataFrame helpers)

Every detector returns a signed int8 array (+1 bullish / -1 bearish / 0 none) and is point-in-time safe — the value at bar i depends only on bars 0..i.

Project layout

.
├── pyproject.toml          # build metadata, deps, pytest config
├── README.md
├── mkdocs.yml              # docs site config
├── run_tests.py            # zero-dependency test runner (pytest fallback)
├── src/
│   └── ta_patterns/        # the package (src layout)
│       ├── __init__.py     # public candlestick API + re-exports
│       ├── _core.py        # shared numeric helpers
│       ├── scanner.py      # scan_all_patterns, net_score_all, batch_all, ...
│       ├── single.py two_bar.py three_bar.py multi_bar.py   # 106 candlesticks
│       └── chart_patterns/ # 194 chart patterns (own pivot engine + scanner)
│           ├── _core.py scanner.py
│           └── short.py double_multi.py classic.py harmonic.py
│               volume.py busted.py
├── tests/                  # pytest suite (also runnable via run_tests.py)
└── docs/                   # full documentation (mkdocs)

Why a chart_patterns/ sub-package? Candlestick detectors work bar-by-bar; chart detectors need a pivot/swing engine, their own scanner, and a separate _core. Keeping them as a sub-package isolates that machinery (and avoids two _core/scanner modules colliding). The two families are still scanned together via scan_all_patterns.

Install

pip install .                  # or: pip install -e ".[test,docs]"

Quickstart

import ta_patterns as tap

sig     = tap.hammer(o, h, l, c)                 # one pattern -> int8 array
signals = tap.scan_all_patterns(o, h, l, c, v=v) # all 300 -> {name: array}
score   = tap.net_score_all(o, h, l, c, v=v)     # net directional score
table   = tap.batch_all(o, h, l, c, v=v)         # wide pandas DataFrame

See docs/ for the full guide:

• Installation
• Quickstart
• Core concepts
• API reference
• Pattern catalog
• Examples

Tests

pytest               # preferred
python run_tests.py  # zero-dependency fallback

The suite enforces the library's guarantees across the whole catalog: output dtype/length/range, sign-vs-classification, no look-ahead, and textbook firing cases for every pattern family.

Design guarantees

• No look-forward bias — bar i uses only bars 0..i; pivots use delayed confirmation.
• Point-in-time at the close — a flag is set on the bar that completes the pattern.
• Signed int8 output — +1 / -1 / 0, always the input length.
• NumPy / pandas friendly — Series inputs are accepted; the index is stripped automatically.

Notes

• Elliott Wave patterns are intentionally not implemented — they require subjective multi-level wave labelling that does not reduce to a deterministic OHLC rule.
• Five chart patterns are volume-based and run only when a volume array is supplied (295 from OHLC alone, 300 with volume).