Update model card with v3.1 - 33 quant modules, new regime detection, transaction costs, drawdown control, liquidity risk, data snooping guard, event study, cross-sectional factors, factor risk model

README.md

@@ -13,7 +13,7 @@ language:
 - en
 ---
 
-# AlphaForge v3.0 — Institutional-Grade Quantitative Trading System
+# AlphaForge v3.1 — Institutional-Grade Quantitative Trading System
 
 > **A research-backed, modular, institutional-grade quantitative trading framework.**
 > 
@@ -49,6 +49,11 @@ Features: real-time stock analysis, AI deep analysis via K2 Think V2, portfolio
 - Optimize portfolios with real-world constraints
 - Price options with ML (beating Black-Scholes)
 - Run **honest** backtests with walk-forward validation
+- Control drawdowns with CPPI and Kelly criterion
+- Guard against data snooping bias
+- Detect market regimes and adapt strategies
+- Measure liquidity risk and position capacity
+- Model transaction costs with market impact
 
 The system evolved through **three major versions**:
 
@@ -57,6 +62,7 @@ The system evolved through **three major versions**:
 | **v1.0** | 8 | Basic modular pipeline |
 | **v2.0** | 18 | Walk-forward validation, wavelet denoising, GP alpha mining, MTL, execution algos, risk management, microstructure, real news APIs, hyperparameter sweeps, GPU optimization |
 | **v3.0** | 25+ | RL execution, Level 2 LOB, market making, synthetic market simulation, online learning, stat arb, conformal prediction, feature stores, adversarial defense, A/B testing, DCC-GARCH regimes |
+| **v3.1** | 33 | Regime detection, transaction costs, drawdown control, liquidity risk, data snooping guard, event study, cross-sectional factors, factor risk model |
 
 ---
 
@@ -72,15 +78,30 @@ Sentiment Model (S) ───────────────┘
 Market Data ─────────► Volatility Model (σ) ───► Covariance (Σ)
 
 μ + Σ ───────────────► Portfolio Optimizer ───► Weights (w)
+                    ↓                            ↓
+            Regime Detection              Drawdown Control
+            (HMM/GMM)                    (CPPI / Kelly)
+                    ↓                            ↓
+            Regime-Conditioned              Dynamic Leverage
+            Alpha                            Position Sizing
 
 Weights + Market ───► Backtest / PnL
+         ↓
+Transaction Cost Model (market impact)
+         ↓
+Net Returns
 
 Options Model (10) ─► Derivative Signals / Hedging
+
+Event Study ───► Event-Driven Alpha
+
+Cross-Sectional Factors ───► Style Factor Exposure
+Factor Risk Model ───► Risk Decomposition
 ```
 
 ---
 
-## 📁 Module Overview (25+ Modules)
+## 📁 Module Overview (33 Modules)
 
 | Module | Purpose | Research Basis |
 |--------|---------|--------------|
@@ -112,6 +133,14 @@ Options Model (10) ─► Derivative Signals / Hedging
 | `ab_testing.py` | Sequential testing, multiple comparison correction | Johari et al. 2022 |
 | `correlation_regime.py` | DCC-GARCH dynamic correlations, Ledoit-Wolf shrinkage | Engle 2002, Ledoit & Wolf 2004 |
 | `news_data_integration.py` | NewsAPI, RSS, GDELT, Reddit/StockTwits aggregation | Alternative data best practices |
+| `regime_detection.py` | HMM/GMM market regime classifier, regime-conditioned Sharpe | Hamilton 1989, Guidolin & Timmermann 2007 |
+| `transaction_cost_model.py` | Market impact (square-root law), spread, fees, slippage | Almgren et al. 2005, Kissell & Glantz 2003 |
+| `drawdown_control.py` | CPPI, Kelly criterion, dynamic leverage, position sizing | Perold & Sharpe 1988, Thorp 2006, Grossman & Zhou 1993 |
+| `liquidity_risk.py` | Amihud illiquidity, bid-ask spread, Kyle's lambda, VPIN | Amihud 2002, Kyle 1985, Easley et al. 2012 |
+| `data_snooping_guard.py` | White's Reality Check, FDR, Bonferroni, Holm correction | White 2000, Romano & Wolf 2005, Benjamini & Hochberg 1995 |
+| `event_study.py` | Earnings drift, macro events, merger arbitrage, abnormal returns | MacKinlay 1997, Engelberg et al. 2012, Savor & Wilson 2014 |
+| `cross_sectional_factors.py` | Fama-French 5-factor, momentum, quality, low-vol, liquidity | Fama & French 2015, Carhart 1997, Asness et al. 2013 |
+| `factor_risk_model.py` | Barra-style multi-factor risk decomposition, risk budgeting | Grinold & Kahn 2000, Menchero et al. 2010 |
 
 ---
 
@@ -131,6 +160,10 @@ The system tracks and reports:
 | **Win Rate** | % of positive return days | Reported |
 | **Profit Factor** | Gross profit / Gross loss | > 1.2 |
 | **GOAT Score** | Composite 0-100 scoring system | > 70 |
+| **Regime-Conditioned Sharpe** | Sharpe in current market regime | Contextual |
+| **Transaction Cost Drag** | Annualized cost of trading | < 2% |
+| **Liquidity Score** | Amihud illiquidity ranking | Reported |
+| **Kelly Fraction** | Optimal leverage for growth | < 1.0 (practical) |
 
 ---
 
@@ -173,6 +206,21 @@ This triggered the addition of 11 elite-tier modules representing what actual qu
 
 ---
 
+## 🔥 v3.1: The Honesty & Realism Update
+
+The next tier of institutional realism added 8 critical modules that separate toy backtests from deployable systems:
+
+1. **Regime Detection** — HMM/GMM classifier for bull/bear/high-vol/mean-revert regimes with regime-conditioned Sharpe (Hamilton 1989)
+2. **Transaction Cost Model** — Square-root market impact law, spread costs, fees, optimal participation rate (Almgren et al. 2005)
+3. **Drawdown Control** — CPPI insurance, fractional Kelly criterion, dynamic leverage, volatility targeting (Perold & Sharpe 1988, Thorp 2006)
+4. **Liquidity Risk** — Amihud illiquidity, Kyle's lambda, VPIN, position capacity constraints (Amihud 2002, Kyle 1985)
+5. **Data Snooping Guard** — White's Reality Check, FDR control, Bonferroni/Holm corrections (White 2000, Benjamini & Hochberg 1995)
+6. **Event Study** — Post-earnings drift, macro event signals, merger arbitrage, abnormal returns (MacKinlay 1997, Savor & Wilson 2014)
+7. **Cross-Sectional Factors** — Fama-French 5-factor, momentum, quality, low-vol, liquidity style factors (Fama & French 2015, Carhart 1997)
+8. **Factor Risk Model** — Barra-style multi-factor decomposition, marginal risk contribution, risk parity (Grinold & Kahn 2000)
+
+---
+
 ## 🔗 K2 Think V2 Integration
 
 A dedicated Gradio Space integrates the AlphaForge quant pipeline with MBZUAI's K2 Think V2 reasoning API:
@@ -185,6 +233,7 @@ A dedicated Gradio Space integrates the AlphaForge quant pipeline with MBZUAI's
 - Portfolio optimization with efficient frontier visualization
 - AI portfolio advice (health score, concentration risk, rebalancing)
 - Direct chat with K2 Think V2 for any financial question
+- **NEW v3.1:** Regime overlay charts, position sizing recommendations, liquidity analysis, event calendar, transaction cost estimates
 
 ---
 
@@ -292,6 +341,15 @@ Every major component is backed by published research:
 | RL Execution | Buehler et al. 2019 | Deep Hedging (PPO) |
 | Market Making | Avellaneda & Stoikov 2008 | Inventory management |
 | Correlation Regimes | Engle 2002 | DCC-GARCH dynamic correlations |
+| Regime Detection | Hamilton 1989 | HMM for nonstationary time series |
+| Transaction Costs | Almgren et al. 2005 | Square-root market impact law |
+| Drawdown Control | Perold & Sharpe 1988 | CPPI dynamic asset allocation |
+| Kelly Criterion | Thorp 2006 | Fractional Kelly for practical trading |
+| Liquidity Risk | Amihud 2002 | Illiquidity premium via price impact ratio |
+| Data Snooping | White 2000 | Bootstrap reality check for multiple testing |
+| Event Studies | MacKinlay 1997 | Abnormal return methodology |
+| Fama-French Factors | Fama & French 2015 | 5-factor asset pricing model |
+| Factor Risk | Grinold & Kahn 2000 | Multi-factor risk decomposition |
 
 ---
 
@@ -320,4 +378,4 @@ MIT License — see LICENSE
 
 ---
 
-*Built by Premchan | AlphaForge v3.0 | Institutional-Grade Quantitative Trading*
+*Built by Premchan | AlphaForge v3.1 | 33 Quant Modules | Institutional-Grade Trading*