microalpha 0.1.1

A Python library for analyzing ultra-high-frequency tick data and discovering micro-alpha trading signals.

MicroAlpha (starter)

A minimal, vectorized Python library for tick-level microstructure signals using rolling time windows (default 100ms).

Features implemented

• Quote stuffing frequency (qs_freq): cancels per adds in window
• Spread stats: instantaneous spread and rolling volatility (spread_vol)
• Order book imbalance (obi): (bid_size - ask_size) / (bid_size + ask_size)
• Trade intensity
• Realized volatility proxy (rv)
• Optional cancel burst flags per side

Quickstart

# In a virtual environment
pip install -r requirements.txt
python -m microalpha.example

Or use your own data:

from microalpha import read_ticks, compute_features, rolling_forward_returns, join_features_and_labels

df = read_ticks("data/your_ticks.csv", ts_unit="ms")  # or "ns" / "s"
feats = compute_features(df, window="100ms")
labels = rolling_forward_returns(df, horizon="100ms")
dataset = join_features_and_labels(feats, labels)

Expected CSV schema

• timestamp, symbol, event_type, side, price, size, best_bid, best_ask, bid_size, ask_size

Notes

• Time-based rolling uses pandas offset windows (e.g. "100ms"). Ensure your index is DatetimeIndex.
• This starter uses synthetic data in example.py for a reproducible demo.

Schema Converter

Convert any CSV to MicroAlpha schema:

python -m microalpha.convert --in data/raw.csv --out data/my_ticks.csv --symbol BTCUSDT --ts-unit ms