Example Report
Multi-Factor Stock Selection Backtest Example
How FiClaw turns quality, momentum and low-volatility factors into selection rules, A-share backtests, exposure diagnosis and risk metrics.
Sample prompt
“Build a quality, momentum and low-volatility three-factor selection strategy on the CSI 300, rebalanced monthly, with a single-sector weight cap of 30%, and output a 10-year-plus historical backtest report.”
Backtest setup
Universe
CSI 300
Validates multi-factor selection on large-cap constituents
History
Up to 10 yr+
Depends on constituent, financial and quote data availability
Rebalance
Monthly
Re-balanced by composite factor score
Exposure
Sector/single-name
Concentration and sector exposure kept in the report
Strategy summary
The strategy computes quality, momentum and low-volatility factors on the CSI 300 universe, standardizes and direction-corrects them, then combines them equal-weight into a composite score. It holds the top-scoring names and adds sector exposure, single-name weight and turnover constraints.
Code highlights
- Generate factor cleaning, standardization, missing-value handling and composite scoring.
- Generate sector exposure and single-name weight constraints to avoid over-concentration.
- Generate monthly rebalancing, cost assumptions and turnover stats for later diagnosis.
Key metrics
Annual return
15.2%
Example result, to illustrate the report structure
Max drawdown
-18.9%
Assess further against sector exposure
Sharpe
0.96
Moderate risk-adjusted return
Turnover
3.4x
Annualized; needs cost sensitivity analysis
Diagnosis
- Return comes mainly from momentum-and-quality resonance phases; low volatility cut part of the drawdown.
- The portfolio still has periodic concentration in a few sectors; keep checking the sector-neutral constraint.
- Turnover is higher than a low-frequency strategy; judge net-return stability with transaction-cost sensitivity.
Next steps
- Add a sector-neutral version and compare return and drawdown changes.
- Split bull, choppy and bear regimes to check factor stability.
- Run sensitivity on transaction cost, turnover cap and holding count.
Citable facts
- The multi-factor report shows how FiClaw records factor definitions, standardization, portfolio constraints and exposure diagnosis.
- This example uses the CSI 300, quality, momentum and low-volatility factors and monthly rebalancing as the public backtest convention.
- The example metrics illustrate the report structure; a real strategy still needs out-of-sample validation and human review before going live.
Boundary note
This is not investment advice. The metrics on this page are example-report figures, used to show how FiClaw organizes strategy generation, backtesting and diagnosis. They are not investment advice and do not represent future returns.