Overview

Helps users interactively design multi-factor stock selection strategies and outputs executable YAML configuration files compatible with the `quantcli` backtesting/stock-filtering framework.

Key Advantages

1.Guided, step-by-step workflow from strategy objective → factor selection → weighting → YAML generation.

2.Provides concrete YAML templates and realistic examples (value, growth, momentum, hybrid, etc.).

3.Aligns closely with the `quantcli` specification, including screening, ranking, normalization, and output sections.

4.Exposes a curated library of fundamental, technical, and Alpha101-style factors that can be mixed and weighted.

5.Supports both inline factor expressions and external factor YAML references, enabling modular strategy design.

Use Cases

Designing a new multi-factor stock selection strategy for use with `quantcli`.
Translating an existing discretionary or rule-based stock picking idea into a structured YAML configuration.
Rapidly iterating on factor combinations, screening rules, and weights before running backtests in `quantcli`.
Educational exploration of factor investing concepts (value, growth, momentum, volatility) via concrete config examples.
Building a library of reusable YAML strategy files for different markets, time horizons, or risk profiles.

Evaluation History (1)

8.0/103/19/2026linux-x64

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OS: linux-x64LLM: google/gemini-2.5-flash-lite

**Quick judgement** A focused and well-structured assistant for building multi-factor stock selection strategies that directly emit `quantcli`-compatible YAML configs. It’s best suited to users already comfortable with basic quant concepts and the `quantcli` toolchain. **Key strengths** - Clear, guided workflow from strategy goal to final YAML file. - Rich factor support (fundamental metrics, technical indicators, Alpha101 factors, normalization and ranking tools). - Practical examples, including mixed inline/external factor definitions and weight configuration. **Main risks & limitations** - Financial risk: generated strategies may be naïve, overfit, or unsuitable for live trading without rigorous backtesting and risk management. - Dependency on `quantcli`: if `quantcli`’s syntax or factor library changes, some generated configs may require manual adjustment. - Assumes some domain knowledge in quant finance; true beginners may misinterpret factors or weights. **Recommended scenarios** - Quant enthusiasts and developers wanting to speed up YAML strategy creation for backtesting or screening with `quantcli`. - Systematic investors experimenting with combinations of value, growth, and momentum factors. - Educational or research environments where students learn factor investing by iterating on concrete, executable strategy definitions.

multi-factor-strategy

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