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How to backtest a factor idea with a hidden out-of-sample test with Finathink

Turn a strategy hypothesis into a controlled factor proposal and test it on training, validation and hidden OOS data.

Short answer: Create a venv and run python3 -m pip install -e '.[dev]'; then start the local app with python3 scripts/run_local_app.py --sample --port 8765; describe your hypothesis in the factor workbench so it maps to a fixed DSL template. All steps.

  • Money
  • Advanced
  • 1 hour
  • From the README
  • Claude Code
  • Codex

Who it is forA quant learner who wants to test a factor idea without fooling themselves with overfitting.

The problemBacktests that reuse the same data for tuning and testing look great and fail live.

The outcomeIC, ICIR, turnover and decay metrics with a one-time hidden OOS test, logged with fingerprints in an offline HTML report.

What you need first

  • Python 3.11 or later
  • A local copy of the repo (inferred: git clone https://github.com/ceyyy427/finathink.git)
  • No broker, live trading account or real vendor credentials are needed for the offline sample run

Steps

  1. Create a venv and run python3 -m pip install -e '.[dev]'
  2. Start the local app with python3 scripts/run_local_app.py --sample --port 8765
  3. Describe your hypothesis in the factor workbench so it maps to a fixed DSL template
  4. Run training, validation and the one-time hidden OOS test
  5. Read IC, ICIR, turnover and decay, and keep the proposal only if OOS holds up

Prompt for your agent

Using ceyyy427/finathink with the sample data, turn the hypothesis <idea> into a factor proposal, run the backtest with the hidden OOS test, and summarize IC, ICIR, turnover and decay. Paper trade only.

Caveat. Not financial advice: paper trade first, and remember you can lose money. The README is in Chinese.

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