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RiskLabAI.py

PyPI version CI

A Python library for quantitative finance and financial machine learning, implementing core methods from Marcos López de Prado's Advances in Financial Machine Learning and Machine Learning for Asset Managers.

The library provides implementations for:

  • Financial data structures — tick, volume, dollar, imbalance, and run bars
  • Labeling — the triple-barrier method, meta-labeling, trend-scanning
  • Fractional differentiation — standard and fixed-width window (FFD)
  • Sample weights, denoising (Marčenko–Pastur), distance metrics
  • Cross-validation — Purged K-Fold, Combinatorial Purged CV (+ adaptive/bagged), walk-forward
  • Feature importance — MDI, MDA, SFI, and clustered variants
  • Portfolio optimization — HRP, NCO, hedging
  • Backtest statistics — PSR/DSR, PBO, strategy risk
  • Microstructure & entropy features, structural breaks, and a Deep-BSDE PDE solver

There is a companion Julia package, RiskLabAI.jl, which mirrors this API.

Installation

pip install RiskLabAI

The base install is lightweight. Heavier, optional capabilities are available as extras:

Extra Installs Enables
RiskLabAI[pde] torch the Deep-BSDE PDE solver (RiskLabAI.pde)
RiskLabAI[plot] matplotlib, seaborn, plotly plotting helpers
RiskLabAI[synth] quantecon synthetic-data utilities
RiskLabAI[all] all of the above everything 
pip install "RiskLabAI[all]"

For development (editable install + tests), see INSTALLATION.md.

Quickstart

Sample dollar/volume/tick bars from raw ticks:

from RiskLabAI.data.structures.standard_bars import StandardBars
from RiskLabAI.utils.constants import CUMULATIVE_DOLLAR

# ticks: an iterable of (datetime, price, volume)
ticks = [
    ("2020-01-01 10:00:00", 100.0, 10),
    ("2020-01-01 10:00:01", 101.0, 5),
    ("2020-01-01 10:00:02", 100.0, 20),
]

bars = StandardBars(bar_type=CUMULATIVE_DOLLAR, threshold=3000)
bar_list = bars.construct_bars_from_data(ticks)
# each bar: [date_time, idx, open, high, low, close, volume,
#            buy_volume, sell_volume, ticks, dollar, threshold]

Discover and construct components by name through the extension registry:

import RiskLabAI.core as core

core.list_components()                       # {family: [available keys]}
cv = core.CROSS_VALIDATORS.create("purgedkfold", n_splits=5, times=event_times)

Logging

RiskLabAI logs under the "RiskLabAI" logger and is silent by default. To see progress and diagnostics, configure logging in your application:

import logging
logging.basicConfig(level=logging.INFO)

Extending the library

RiskLabAI is built to be extended with new models. The RiskLabAI.core layer provides a component registry and base interfaces so a new bar type, labeler, cross-validator, etc. can be registered and discovered without editing central code. See EXTENDING.md for a step-by-step guide with worked examples.

Contributing

Contributions are welcome. The project uses pytest for tests and black + ruff for formatting/linting (run before opening a PR):

pip install -e ".[all]" pytest black ruff
pytest -q --ignore=test/pde
black RiskLabAI test
ruff check RiskLabAI test

Please branch from main, keep changes focused, and update CHANGELOG.md.

License

See LICENSE.txt.