FiClaw
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Financial Agent Platform
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Multi-Factor Stock Selection

Multi-Factor Stock Selection: from a factor combination to a real backtest

The hard part of a multi-factor strategy is not writing formulas but factor direction, standardization, missing-value handling, weighting, sector exposure and turnover control. FiClaw organizes these constraints into an executable spec, then generates strategy code and submits a backtest.

Search intent

The user is looking for how to build a multi-factor selection strategy, AI-generated code and a real backtest validation flow.

Sample prompts

“An equal-weight multi-factor selection combining momentum, low volatility and low turnover.”

“A quality-and-momentum two-factor strategy on the CSI 300 with a single-sector exposure cap.”

“Compare equal-weight vs weighted multi-factor across return, drawdown and turnover.”

Reviewable metrics

Factor count

2-5

Start with a few core factors

Construction

Score-based

Hold by composite ranking

Diagnosis

Exposure + turnover

Avoid hidden risk concentration

State the factor combination clearly

A multi-factor strategy needs a clear direction, processing method and combination for each factor, or the backtest is hard to review.

  • Factor definitions: momentum, low volatility, low turnover, value, quality
  • Standardization, winsorization, missing-value handling and sector-neutral constraints
  • Equal-weight, weighted or layered scoring

Read factor quality back from the metrics

FiClaw looks at portfolio return, drawdown, win rate, turnover and exposure diagnosis together to judge whether the combination is worth continuing.

  • Watch whether the combination only works in a few regimes
  • Check concentration, sector exposure and turnover cost
  • Keep the spec, code and report for team review

Workflow

How FiClaw handles this

1

Describe the factors

Enter factor names, directions and universe constraints.

2

Generate the scoring

AI generates standardization, ranking, weighting and portfolio construction code.

3

Run the backtest

Output return, drawdown, Sharpe, win rate and turnover.

4

Diagnose exposure

Check sector, concentration, turnover and factor stability.

FAQ

Frequently asked questions

Can FiClaw judge factor effectiveness automatically?

FiClaw can output backtest metrics and diagnostic leads to help judge whether a combination is worth further research, but factor effectiveness still needs the researcher to confirm with out-of-sample tests and business assumptions.

What human review does multi-factor selection need?

You need to review factor definitions, data conventions, sector exposure, transaction cost, turnover and out-of-sample behavior, not just a single backtest return.

Boundaries

Where it applies

A financial AI tool needs clear boundaries. FiClaw is here to speed up strategy research; its output still needs team review.

  • Multi-factor backtests can be affected by data conventions, survivorship bias and overfitting.
  • Factor combinations need out-of-sample validation and multi-regime review.
  • FiClaw does not provide investment advice or promise strategy returns.

Put the idea into a real backtest loop

If you are evaluating how AI fits into quant strategy research, start by running your first reviewable backtest report with FiClaw.

Download FiClawSee quant capabilities