Скачать или смотреть How to Backtest a Portfolio Optimization Strategy Using Python

Backtesting a portfolio optimization strategy in Python involves simulating how different asset allocations would have performed historically to identify the most efficient way to invest capital. This process begins with gathering price data (real or synthetic) for a diverse set of assets, calculating returns and covariances, and applying optimization techniques such as maximizing the Sharpe ratio or minimizing volatility. Optimization helps balance return targets with risk exposure, often using constraints like weight limits or trend filters. Sharpe optimization prioritizes risk-adjusted return, while minimum variance focuses on reducing portfolio fluctuations. Historical trend-based filters can refine asset selection by choosing only those with recent positive momentum. Transaction costs and cash balances are factored in during rebalancing to simulate realistic execution. The strategy is validated by comparing metrics like total return, annualized volatility, and Sharpe ratio against a benchmark (e.g., S&P 500). Python libraries like yfinance, scipy.optimize, and plotly are commonly used to download data, perform calculations, and visualize results. Ultimately, the process supports dynamic, rules-based investing that can adapt to market conditions while managing risk. However, caution is required since past performance does not guarantee future results.