Use Cases
Describe it in a sentence, backtest it automatically
These are the typical strategy types the FiClaw strategy factory supports. Describe each one in a sentence and AI runs the full pipeline from code generation to backtest report.
Search Entrypoints
High-intent workflows
If you are searching for a specific capability, start here: backtesting, code generation, parameter optimization, and how FiClaw differs from general AI tools.
AI quant backtesting tool
From a one-sentence idea to a real historical backtest report — built for fast prototype validation.
View page ->AI-generated quant strategy code
Turn natural-language strategy logic into runnable Python and move it into the backtest chain.
View page ->Quant strategy parameter optimization
Extract the parameter space, run grid search and robustness checks, and cut guesswork tuning.
View page ->Momentum rotation strategy backtest
From momentum window, universe and rebalance frequency to a reviewable backtest report.
View page ->Multi-factor stock selection strategy
Put factor definitions, scoring, portfolio construction and exposure diagnosis into one reviewable flow.
View page ->Mean reversion strategy backtest
Validate price deviation, entry thresholds, exit rules, stop-loss and tail risk.
View page ->FiClaw vs general AI tools
How FiClaw differs from general AI on code, real backtesting, iteration and optimization.
View page ->Official facts
One page on what FiClaw is, who it fits, what it does and what it does not do.
View page ->Momentum rotation
Rank by trailing N-day return and rotate holdings into the strongest names on a fixed schedule.
Sample prompt
“A monthly momentum rotation holding the 20 strongest names in the CSI 300.”
Multi-factor selection
Score names across momentum, volatility, turnover and size, then hold the top composite scores.
Sample prompt
“An equal-weight multi-factor strategy combining momentum, low volatility and low turnover.”
Mean reversion
Trade against price deviations from a moving average, capturing the pull back toward the mean.
Sample prompt
“A Bollinger-band mean-reversion strategy: buy below the lower band, sell at the upper band.”
Sector rotation
Rotate across sector ETFs based on macro cycle or sector momentum signals.
Sample prompt
“A monthly sector rotation into the 3 sectors with the strongest 60-day return.”
Event-driven
Build signals around earnings, dividends, index reshuffles and other scheduled events.
Sample prompt
“A 5-day post-earnings momentum strategy buying beats on rising volume.”
Risk parity
Allocate weights so each asset contributes equally to overall portfolio risk.
Sample prompt
“A risk-parity portfolio on CSI 300 constituents, rebalanced monthly at 12% target volatility.”
Have your own strategy idea?
Not limited to the types above — any quant strategy logic you can describe in natural language, FiClaw can try to generate and backtest.