📈 ohlcpattern

ohlcpattern is a modern, high-performance Python library for detecting candlestick patterns in financial data. Built with pandas and designed for speed and ease of use, it helps traders and quantitative analysts model and identify over 40+ different market signals.

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✨ Key Features

• 🚀 Fast Pattern Detection: Optimized with pandas for handling large historical datasets.
• 🧩 Comprehensive Coverage: Support for 40+ single, double, and triple candlestick patterns.
• 🛠️ Modern Package Structure: Uses src layout and pyproject.toml for standard installation.
• 🧪 Test-Driven Development: High reliability with pytest integration.
• 🧹 Clean Code: Linted and formatted with ruff for maximum readability.

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📦 Installation

To install the library with pip:

pip install ohlcpattern

To install the library with uv:

uv sync

For development, install with optional dependencies using pip:

git clone https://github.com/yourusername/ohlcpattern.git
cd ohlcpattern
pip install -e ".[dev]"

Or use uv:

git clone https://github.com/yourusername/ohlcpattern.git
cd ohlcpattern
uv sync --extra dev

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🚀 Quick Start

Ensure your pandas DataFrame contains the standard Open, High, Low, and Close columns.

import pandas as pd
from ohlcpattern.candlestick import CandlestickPatterns

# 1. Load your dataset
df = pd.read_csv('market_data.csv', index_col='Date', parse_dates=True)

# 2. Initialize pattern detector
csp = CandlestickPatterns(df)

# 3. Add detection categories (reversal, continue, or full)
csp._add('reversal')

# 4. Generate modeling result
modeling_data = csp.pattern_modeling()

# 5. Filter for detected patterns
detected = modeling_data[modeling_data.model != '']
print(detected[['Open', 'High', 'Low', 'Close', 'model']])

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🖥️ CLI

The package exposes a command-line interface named ohlcpattern.

ohlcpattern --help
ohlcpattern --version
ohlcpattern extract market_data.csv --output patterns.csv
ohlcpattern extract --help

If you prefer running it through uv:

uv run ohlcpattern --help
uv run ohlcpattern --version
uv run ohlcpattern extract market_data.csv --output patterns.csv
uv run ohlcpattern extract --help

The extract command expects a CSV file with Open, High, Low, and Close columns. If --output is omitted, it prints a preview of detected patterns to the terminal.

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🕯️ Supported Patterns

We support a wide variety of patterns across multiple categories:

Reversal Patterns

Bullish	Bearish
Hammer	Shooting Star
Inverted Hammer	Hanging Man
Bullish Engulfing	Bearish Engulfing
Morning Star	Evening Star
Piercing Pattern	Dark Cloud Cover
Bullish Harami	Bearish Harami
Tweezers Bottom	Tweezers Top

Continuation Patterns

• Gaps: Bullish Gap, Bearish Gap
• Advanced: Rising/Falling Three Methods, Fair Value Gaps (FVG)
• Lines: Separating Lines, Neck Patterns

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🛠️ Development

We use ruff and pytest for quality insurance.

Running Tests

uv run pytest src tests

Linting & Formatting

uv run ruff check src
uv run ruff format src

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📄 License

This project is licensed under the MIT License. See the LICENSE file for more details.

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