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Add pit-release-gate (Factor Analysis) (#575)

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@@ -375,6 +375,7 @@ A curated list of insanely awesome libraries, packages and resources for Quants
- [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. - [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. - [quant-lab-alpha](https://github.com/husainm97/quant-lab-alpha) - `Python` - Open-source investment analytics platform bridging academic research and retail finance.
- [Perception-XAlpha Lite](https://github.com/xuxingjiankr-cpu/perception-xalpha-lite) - `Python` - Backtest-overfitting audit for factor research: CSCV probability of backtest overfitting, deflated Sharpe against the declared trial count, White's Reality Check, point-in-time universe membership and disclosure-date alignment. Ships a worked example in which 24 pure-noise series produce a 1.11 Sharpe and the audit says so. - [Perception-XAlpha Lite](https://github.com/xuxingjiankr-cpu/perception-xalpha-lite) - `Python` - Backtest-overfitting audit for factor research: CSCV probability of backtest overfitting, deflated Sharpe against the declared trial count, White's Reality Check, point-in-time universe membership and disclosure-date alignment. Ships a worked example in which 24 pure-noise series produce a 1.11 Sharpe and the audit says so.
- [pit-release-gate](https://github.com/MaxWellApexLab/pit-release-gate) - `Python` - Screens cross-sectional signals for incomplete-cross-section leakage from staggered data arrival and grades per-signal release timing; ships a known-ground-truth demo reproducing its method papers.
- [covFactorModel](https://github.com/dppalomar/covFactorModel) - `R` - Covariance matrix estimation via factor models. - [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. - [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.
- [Expected Returns](https://github.com/JustinMShea/ExpectedReturns) - `R` - Solutions for enhancing portfolio diversification and replications of seminal papers with R, most of which are discussed in one of the best investment references of the recent decade, Expected Returns: An Investors Guide to Harvesting Market Rewards by Antti Ilmanen. - [Expected Returns](https://github.com/JustinMShea/ExpectedReturns) - `R` - Solutions for enhancing portfolio diversification and replications of seminal papers with R, most of which are discussed in one of the best investment references of the recent decade, Expected Returns: An Investors Guide to Harvesting Market Rewards by Antti Ilmanen.