Add fundamentals overlay — valuation, quality, growth metrics from yfinance info

Add options flow analysis with put/call ratio, IV skew, unusual volume, max pain from real yfinance chains

Add earnings model with calendar detection, implied move, post-earnings drift analysis

Add event risk guard — pre-trade checks for earnings/FOMC/macro with dynamic size reduction

Add macro overlay with VIX, DXY, treasury yields, Fed calendar, CPI tracking

Add news intelligence with FinBERT sentiment + event detection

Add multi-factor scoring engine with 7 weighted factors

Update model card with multi-market support documentation

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

Add liquidity risk module - Amihud illiquidity, bid-ask bounce, price impact modeling

Add event study module - earnings, macro events, event-driven alpha with abnormal returns

Add data snooping guard - White's Reality Check, FDR, Bonferroni for multiple testing

Add drawdown control - CPPI, Kelly criterion, dynamic position sizing

Add regime detection - HMM/GMM market regime classifier with regime-switching strategy

Add cross-sectional factors - Fama-French, momentum, quality, low-vol style factors

Add transaction cost model - market impact, square-root law, realistic slippage

Add Barra-style multi-factor risk model with factor covariance and specific risk

Fix YAML frontmatter - remove badge links causing flow indicator errors

Commit

Complete end-to-end README.md for AlphaForge quant trading platform

Upload requirements.txt

Upload app.py

Upload pipeline.py

Add v3.0 Elite Tier README: Jane Street / quant hedge fund level architecture

Add DCC-GARCH and dynamic copula correlation regime modeling for multi-asset covariance estimation

Add A/B testing framework for strategy comparison with statistical significance testing

Add adversarial robustness: gradient-based attacks, model stealing detection, adversarial training

Add real-time feature store with drift detection for streaming market data

Add conformal prediction for uncertainty quantification: prediction intervals with guaranteed coverage

Add cross-asset statistical arbitrage: cointegration, pairs trading, PCA mean-reversion

Add online learning: per-symbol adaptive models with meta-learning, concept drift adaptation

Add synthetic market simulation with agent-based modeling for self-play training

Add market making engine with Avellaneda-Stoikov quoting, inventory management, adverse selection detection

Add Level 2 LOB reconstruction with full order book, queue position, depth profile, spread dynamics

Add RL execution engine: PPO-based Deep Hedging, self-play training, RL vs TWAP comparison

Done few changes

Update README with v2.0 architecture, all components, and usage guide

Update main.py with full integration of all 10/10 components

Add GPU optimization: flash attention, mixed precision, kernel-based acceleration

Add real news API integration: NewsAPI, RSS feeds, GDELT, social media

Add hyperparameter sweep infrastructure: grid, random, Latin Hypercube search

Add market microstructure features: Kyle's lambda, VPIN, Roll measure, OFI, Amihud

Add risk management engine: VaR/CVaR, stress testing, compliance monitor

Add execution algorithms: TWAP, VWAP, Smart Order Router with market impact model

Add multi-task learning: joint alpha + volatility + portfolio optimization

Add automated alpha mining with genetic programming + LLM-driven factor discovery

Add wavelet denoising engine with adaptive parameter selection

Add walk-forward validation engine with purged CV and combinatorial CPCV

Upload ALPHA_FORGE_GUIDE.md

Upload goat_strategy.py