Algorithmic Trading With Python - How to Backtest on 47 Assets, 10 Years Data: Diversification Strategy Using Python

Lower-drawdown way to trade a diversified portfolio. In this video I backtest a long-only, multi-asset rotation across 47 assets (tech, finance, energy, hardware, industrial, and more) on ~10 years of daily data with 1954 trades. We combine a 200/150-day Moving Average trend filter, a 90-day sliding-window volatility rank (trade the lowest-vol names), and a VIX threshold (below 30) to avoid turbulent regimes. I’ll walk through the Python code, the backtest, and the results. Trend filter: Candles above a long MA (150/200) so uptrend only (long-only). Volatility rank: Over the last ~90 days, pick the top 5 lowest-volatility assets. Market regime: Only trade when VIX below 30 for smoother conditions. Execution: Daily checks; one open position per asset; holds last several months. Risk: Exit after the planned holding window or cut if loss above 10%. ╔═╦═╦═╦═╦═╦═╦═╦═╦═╦═╦═╦═╦═╦═╦═╦═╦═╦══╦═╦═╦══╦═╦═╦══╦═╦═╦ 📘 Book available on Amazon (Algorithmic Trading Hands-On Approach Using Python): https://a.co/d/6woMBHt 💲 Algorithmic Trading Courses and Python for all levels (Udemy Sale Coupons): https://www.codetradingcafe.com/ Happy learning, happy coding ☕ ╚═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩ Python Notebook Code (Free): https://github.com/ZiadFrancis/LowRiskPortfolioStrategy/blob/main/portfolio.ipynb Enjoy Coding!