[//]: # SSoT-OK
opendeviationbar-py
High-performance open deviation bar construction for quantitative trading, with Python bindings via PyO3/maturin.
| Resource | URL |
|---|---|
| PyPI | https://pypi.org/project/opendeviationbar/ |
| Repository | https://github.com/terrylica/opendeviationbar-py |
| Performance Dashboard | https://terrylica.github.io/opendeviationbar-py/ |
| API Reference | docs/api/INDEX.md |
| Issues | https://github.com/terrylica/opendeviationbar-py/issues |
Installation
pip install opendeviationbar
Pre-built wheels: Linux (x86_64), macOS (ARM64), Python 3.14. Source build requires Rust toolchain and maturin.
Quick Start
from opendeviationbar import get_open_deviation_bars
# Fetch data and generate open deviation bars in one call
df = get_open_deviation_bars("BTCUSDT", "2024-01-01", "2024-06-30")
# Use with backtesting.py
from backtesting import Backtest, Strategy
bt = Backtest(df, MyStrategy, cash=10000, commission=0.0002)
stats = bt.run()
Output: pandas DataFrame with DatetimeIndex and OHLCV columns, compatible with backtesting.py.
API Overview
| Function | Use Case |
|---|---|
get_open_deviation_bars() |
Date-bounded, auto-fetch |
get_n_open_deviation_bars() |
Exact N bars (ML training) |
process_trades_polars() |
Polars DataFrames (2-3x faster) |
process_trades_chunked() |
Large datasets (>10M trades) |
populate_cache_resumable() |
Long ranges (>30 days) |
run_sidecar() |
Real-time streaming sidecar |
# Count-bounded (ML training)
from opendeviationbar import get_n_open_deviation_bars
df = get_n_open_deviation_bars("BTCUSDT", n_bars=10000)
# Polars (2-3x faster)
import polars as pl
from opendeviationbar import process_trades_polars
bars = process_trades_polars(pl.scan_parquet("trades.parquet"), threshold_decimal_bps=250)
# With microstructure features (57 columns: OFI, Kyle lambda, Hurst, etc.)
df = get_open_deviation_bars("BTCUSDT", "2024-01-01", "2024-06-30", include_microstructure=True)
# Real-time streaming sidecar
from opendeviationbar import run_sidecar, SidecarConfig
config = SidecarConfig(symbol="BTCUSDT", threshold_decimal_bps=250)
run_sidecar(config)
Designed for Claude Code
This repository uses a CLAUDE.md network that provides comprehensive project context for AI-assisted development via Anthropic's Claude Code CLI.
npm install -g @anthropic-ai/claude-code
cd opendeviationbar-py
claude
Claude Code reads the CLAUDE.md files automatically and understands the full architecture, API, build system, and development workflow.
Development
git clone https://github.com/terrylica/opendeviationbar-py.git
cd opendeviationbar-py
mise install # Setup tools (Rust, Python, zig)
mise run build # maturin develop
mise run test # Rust tests
mise run test-py # Python tests
Requirements
Runtime: Python >= 3.14, pandas >= 2.0, numpy >= 1.24, polars >= 1.0
Build: Rust toolchain, maturin >= 1.7
License
MIT License. See LICENSE.
Citation
@software{opendeviationbar-py,
title = {opendeviationbar-py: High-performance open deviation bar construction for quantitative trading},
author = {Terry Li},
url = {https://github.com/terrylica/opendeviationbar-py}
}
1 maintainer
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