RiskLabAI
RiskLabAI is a Python library for quantitative finance, financial machine learning, and causal factor analysis. It provides research-oriented implementations of methods associated with Marcos López de Prado's Advances in Financial Machine Learning, Machine Learning for Asset Managers, and Causal Factor Investing.
Version 3.1.0 preserved the clean 57-name causal-factor-analysis namespace released in 3.0.0 and added 30 paper-derived names, producing an 87-name causal API. The companion RiskLabAI.jl package independently implements the same concepts. This parity statement applies to the causal API, not to every Python module.
The 3.2.0 candidate adds Python methods for sampling, optimization, market features, causal evaluation, synthetic data, and neural financial models. See the release notes for scope and limitations. Julia implementations of these new additions are deferred.
What is included
- Causal factor analysis - constrained minimum-variance allocation, factor-mirage and allocation-misspecification diagnostics, graphical identification and factor roles, deterministic structural-model evaluation, treatment-effect formulas, search-adjusted false discovery, specification experiments, and evidence records for the seven-stage causal-factor protocol
- Financial data structures - tick, volume, dollar, imbalance, run, and time bars
- Labeling and sample weights - triple-barrier, meta-labeling, trend-scanning, uniqueness, and time-decay methods
- Fractional differentiation and denoising - fixed-width fractional differentiation, Marcenko-Pastur denoising, and targeted shrinkage
- Validation and feature importance - purged and combinatorial cross-validation, walk-forward validation, MDI, MDA, SFI, and clustered variants
- Portfolio and backtest analytics - HRP, NCO, hedging, PSR/DSR, PBO, and strategy-risk tools
- Market features - microstructure, entropy, structural-break, and technical-indicator utilities
- Optional capabilities - plotting, optimization, simulation, symbolic analysis, changepoint detection, acceleration, synthetic-control tools, and a Deep-BSDE PDE solver
Compatibility
RiskLabAI 3.2.0 supports CPython 3.12,
3.13, and 3.14 with NumPy >=2.2,<3. The complete tested policy and
feature-specific limitations are in
docs/compatibility.md.
Installation
Install the base package from PyPI:
python -m pip install RiskLabAI
Optional dependency groups can be installed individually or together:
| Extra | Enables |
|---|---|
speed |
Numba acceleration |
pde |
PyTorch support for Deep-BSDE, neural SDEs, TimeGAN, and deep hedging |
synth |
synthetic-control utilities using QuantEcon |
hpo |
hyperparameter tuning using Optuna |
plot |
Matplotlib, Seaborn, and Plotly helpers |
symbolic |
symbolic analysis using SymPy |
profile |
memory profiling |
simulation |
simulation progress support |
changepoints |
changepoint detection on Python 3.12-3.13 |
test |
the supported pytest test runner |
For example:
python -m pip install "RiskLabAI[plot,speed]"
There is intentionally no all extra. See
INSTALLATION.md for environment setup, all tested optional
groups, and development instructions.
Causal-factor quick start
import numpy as np
from RiskLabAI.causal_factor_analysis import (
CausalDAG,
average_treatment_effect,
check_backdoor_adjustment_set,
minimum_variance_factor_weights,
)
covariance = np.diag([1.0, 2.0, 4.0])
factor_exposures = np.array([[1.0, 0.0], [0.0, 1.0], [1.0, 1.0]])
target_exposures = np.array([0.0, 1.0])
weights = minimum_variance_factor_weights(
covariance,
factor_exposures,
target_exposures,
)
np.testing.assert_allclose(weights, [-2.0 / 7.0, 5.0 / 7.0, 2.0 / 7.0])
effect = average_treatment_effect(3.5, 1.25)
assert effect == 2.25
dag = CausalDAG(
nodes=("T", "U", "Y"),
directed_edges=(("U", "T"), ("U", "Y"), ("T", "Y")),
observed_nodes=("T", "U", "Y"),
)
assert check_backdoor_adjustment_set(dag, "T", "Y", ("U",)).admissible
The complete deterministic example is
examples/causal_factor_analysis_quickstart.py.
The causal API and its limits are documented in
docs/causal_factor_analysis.md.
Financial-data quick start
from RiskLabAI.data.structures.standard_bars import StandardBars
from RiskLabAI.utils.constants import CUMULATIVE_DOLLAR
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)
Extending the library
The RiskLabAI.core registries provide discovery and construction points for
bars, cross-validators, feature-importance models, labelers, bet sizers, and
portfolio optimizers. For example:
import pandas as pd
from RiskLabAI.core import CROSS_VALIDATORS, list_components
catalogue = list_components()
start_times = pd.to_datetime(["2024-01-02", "2024-01-03", "2024-01-04"])
event_times = pd.Series(
pd.to_datetime(["2024-01-03", "2024-01-04", "2024-01-05"]),
index=start_times,
)
cross_validator = CROSS_VALIDATORS.create(
"purgedkfold",
n_splits=5,
times=event_times,
)
See EXTENDING.md
for the extension interfaces and examples.
Development
python -m pip install -e ".[test]" "black==26.5.1" "ruff==0.15.17"
python -m pytest -q
black --check src/RiskLabAI/causal_factor_analysis test/causal_factor_analysis
ruff check src/RiskLabAI/causal_factor_analysis test/causal_factor_analysis
Please branch from main, keep changes focused, and include tests for behavior
changes.
Scope
RiskLabAI is research software, not investment advice. Graph routines evaluate criteria on a caller-supplied directed acyclic graph; they do not discover or certify that graph. Protocol records validate declared evidence structures; they do not prove that empirical assumptions are true.
License
RiskLabAI is distributed under the BSD 3-Clause License.
1 maintainer
Metadata
1 maintainer
Release files for RiskLabAI 3.2.0
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