Four pain points in quant research,
where a structured research workflow helps

Manual coding is too slow

Pain: Going from a strategy idea to runnable code takes days, most of it spent on data handling, API plumbing and debugging, with the actual strategy logic only a small part.

Approach: The strategy factory generates complete Python strategy code from a natural-language description, including data preprocessing, signal computation, portfolio construction and risk logic.

Outcome: Code generation drops from days to 30 seconds, so effort goes into the strategy logic itself.

Tuning by guesswork

Pain: When a backtest falls short there is no systematic diagnosis, so you tweak parameters by intuition, which is slow and easy to miss key issues.

Approach: AI diagnoses the backtest (overfitting, factor decay, position concentration), edits the code and re-validates; parameter optimization extracts the space and runs a grid search.

Outcome: From blind tuning to systematic diagnose-and-iterate, with data behind the optimization.

Backtest setup is hard

Pain: Setting up a backtest environment means wiring quote data, handling adjustments, and configuring fee and slippage models — a high bar that is easy to get wrong.

Approach: FiClaw provides out-of-the-box backtesting via QuantAPI: real A-share history, a standardized fee model and professional performance metrics in one step.

Outcome: Zero-config backtest submission, focused on strategy logic rather than infrastructure.

Results are not reproducible

Pain: Strategy code, parameters, data versions and backtest configs live in separate places, so results cannot be reproduced after a while.

Approach: The strategy factory saves the full context of each run (spec, code, parameters, results), supporting resume and historical comparison.

Outcome: Every strategy version is traceable, reproducible and comparable.

Download, install, start

The macOS Apple Silicon desktop runs on install — no environment setup, no server to build — and the strategy factory is ready on first launch.

Enter an idea, test feasibility

Describe a strategy idea in one sentence and see a full backtest report in 2-3 minutes. Validate several ideas fast and pick the ones worth pursuing.

Iterate into a strategy library

Run parameter optimization and robustness checks on the promising ones and gradually build a set of validated strategy assets.