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Add QuantGPT to Factor Analysis (#386)

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Agent-driven A-share factor research engine with 8 MCP tools
covering hypothesis design, backtesting, scoring, and anti-overfit
detection.

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
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@@ -323,6 +323,7 @@ A curated list of insanely awesome libraries, packages and resources for Quants
- [alphalens](https://github.com/quantopian/alphalens) - `Python` - Performance analysis of predictive alpha factors.
- [alphalens-reloaded](https://github.com/stefan-jansen/alphalens-reloaded) - `Python` - Performance analysis of predictive (alpha) stock factors.
- [Spectre](https://github.com/Heerozh/spectre) - `Python` - GPU-accelerated Factors analysis library and Backtester.
- [QuantGPT](https://github.com/Miasyster/QuantGPT) - `Python` - Agent-driven A-share factor research engine with 8 MCP tools covering hypothesis design, backtesting, scoring, and anti-overfit detection.
- [quant-lab-alpha](https://github.com/husainm97/quant-lab-alpha) - `Python` - Open-source investment analytics platform bridging academic research and retail finance.
- [covFactorModel](https://github.com/dppalomar/covFactorModel) - `R` - Covariance matrix estimation via factor models.
- [FactorAnalytics](https://github.com/braverock/FactorAnalytics) - `R` - The FactorAnalytics package contains fitting and analysis methods for the three main types of factor models used in conjunction with portfolio construction, optimization and risk management, namely fundamental factor models, time series factor models and statistical factor models.