Generate evidence
Use synthetic markets and point-in-time data tools.
Strategy falsification & reproducible experiments
Put the strategy on trial.
A Python research platform for testing assumptions about market data, execution and strategy robustness.
Quant LabPROJECT OVERVIEW
01Define an experiment
02Challenge assumptions
03Record the evidence
Illustrated workflow · not a live application session or market feed
01 / Inside the project
Built around the work.
Challenge assumptions
Run backtests, validation and execution-cost experiments.
Keep the record
Experiment ledgers and generated reports preserve results for reproduction.
02 / Explore the workflow
Three steps to understand it.
03 / Run it yourself
Start from the source.
Python 3.12 or later. Optional extras add web, ML, tracking and storage integrations.
Clone the repository, then use the commands below. Review the README before connecting external services.
SOURCE CHECKOUT
git clone https://github.com/honeyamn10-source/Quant_Lab.git
cd Quant_Lab
python -m pip install -e ".[dev]"
quantlab data generate-synthetic --out datasets/ --seed 7 --symbols SYN
quantlab run-experiment examples/strategies/sma_crossover.yaml04 / Know the scope
Clear expectations.
Research software. Statistical checks expose some failure modes; they do not guarantee unbiased models or profitable strategies. Read the methodology before interpreting results.
See GitHub Actions for commit-specific test and build results.