lisonevf Portfolio Risk
Analyzes portfolio risk and recommends position sizing and diversification.
Real Code Reference
tradinglearn/backtest/backtester.py—Backtester._calculate_performance()computes Sharpe, max drawdown, win rate, total return, CAGRtradinglearn/utils/parameter_optimizer.py—plot_optimization_results()heatmaps: return, Sharpe, drawdown, win ratetradinglearn/pytdx2/backtest.py—BacktestEnginetracks per-trade P&L for risk analysis
Risk Metrics
- VaR: historical simulation, parametric, Monte Carlo
- CVaR (Expected Shortfall): average loss beyond VaR
- Maximum Drawdown: peak-to-trough with recovery duration
- Volatility: annualized std of returns
Performance Metrics
- Sharpe Ratio: (R - Rf) / sigma
- Sortino Ratio: downside-only volatility
- Calmar Ratio: annual return / max drawdown
- Information Ratio: active return / tracking error
Portfolio Analysis
- Correlation matrix between holdings
- Beta to market benchmark
- Position concentration (Herfindahl index)
- Risk parity weights
Usage
from backtest.backtester import Backtester
bt = Backtester(initial_capital=100000.0)
bt.run_backtest(data, MyStrategy, params)
perf = bt.get_performance()
# {'total_return': 0.15, 'annual_return': 0.12, 'sharpe_ratio': 1.2,
# 'max_drawdown': -0.08, 'win_rate': 0.55, 'total_trades': 42}
portfolio = bt.get_portfolio()
# DataFrame with: portfolio_value, position, cash, returns