feat: Add core trading modules for risk management, backtesting, and execution algorithms, alongside a new ML transparency widget and related frontend dependencies.

docs/architecture/risk_management.md

@@ -1,5 +1,120 @@
 # Risk Management Architecture
 
+## Overview
+
+The risk management system provides comprehensive risk control mechanisms including position sizing, stop-loss management, drawdown limits, Value at Risk (VaR) calculation, and portfolio correlation analysis.
+
+## Components
+
+### Position Sizing
+
+**Location**: `src/risk/position_sizing.py`
+
+**Methods**:
+- **Standard Position Sizing**: Percentage-based with fee accounting
+- **Kelly Criterion**: Optimal position sizing with fractional Kelly (configurable)
+- **Volatility-Adjusted**: ATR-based position sizing (lower vol = larger positions)
+- **Regime-Aware**: Adjusts position size based on market regime
+- **Confidence-Based**: ML model confidence-adjusted position sizing
+
+**Usage**:
+```python
+from src.risk.position_sizing import PositionSizingManager
+
+sizer = PositionSizingManager()
+
+# Standard sizing
+quantity = sizer.calculate_size(symbol, price, balance, risk_percent)
+
+# Kelly Criterion (fractional)
+kelly_pct = sizer.calculate_kelly_criterion(win_rate=0.6, avg_win=100, avg_loss=50, fractional=0.25)
+
+# Volatility-adjusted
+quantity = sizer.calculate_volatility_adjusted_size(symbol, price, balance, volatility_multiplier=0.8)
+
+# Regime-aware
+quantity = sizer.calculate_regime_aware_size(symbol, price, balance, market_regime="trending_up")
+```
+
+### Value at Risk (VaR)
+
+**Location**: `src/risk/var_calculator.py`
+
+**Methods**:
+1. **Historical VaR**: Uses historical portfolio returns distribution
+2. **Parametric VaR**: Assumes normal distribution (variance-covariance method)
+3. **Monte Carlo VaR**: Simulates future returns using estimated parameters
+4. **Conditional VaR (CVaR)**: Expected loss given that loss exceeds VaR
+
+**Usage**:
+```python
+from src.risk.var_calculator import get_var_calculator
+
+var_calc = get_var_calculator()
+
+# Calculate all methods
+results = await var_calc.calculate_all_var_methods(
+    portfolio_value=Decimal("10000.0"),
+    confidence_level=0.95,
+    holding_period_days=1
+)
+
+# Individual methods
+historical_var = await var_calc.calculate_historical_var(...)
+parametric_var = await var_calc.calculate_parametric_var(...)
+monte_carlo_var = await var_calc.calculate_monte_carlo_var(...)
+cvar = await var_calc.calculate_cvar(...)
+```
+
+### Portfolio Correlation Analysis
+
+**Location**: `src/portfolio/correlation_analyzer.py`
+
+**Features**:
+- Correlation matrix calculation for portfolio symbols
+- Diversification scoring (lower correlation = better)
+- Concentration risk analysis
+- Correlation-based position limits
+
+**Usage**:
+```python
+from src.portfolio.correlation_analyzer import get_correlation_analyzer
+
+analyzer = get_correlation_analyzer()
+
+# Analyze current portfolio
+analysis = await analyzer.analyze_portfolio_correlation(paper_trading=True)
+
+# Check correlation limits before adding position
+allowed, reason = await analyzer.check_correlation_limits(
+    symbol="ETH/USD",
+    new_position_value=Decimal("1000.0"),
+    max_correlation=0.8
+)
+```
+
+### Stop-Loss Management
+
+**Location**: `src/risk/stop_loss.py`
+
+Provides dynamic stop-loss adjustment and management.
+
+### Risk Limits
+
+**Location**: `src/risk/limits.py`
+
+Manages:
+- Daily loss limits
+- Maximum drawdown limits
+- Portfolio allocation limits
+
+### Risk Manager
+
+**Location**: `src/risk/manager.py`
+
+Orchestrates all risk management components and provides unified risk checking interface.
+
+
 This document describes the risk management system.
 
 ## Risk Management Components