Descriptions:
Algovibes presents a Python Jupyter notebook built around a single quantitative trading rule: comparing month-end asset prices to their 12-month simple moving average, with signals applied one month forward to eliminate look-ahead bias. Rather than seeking an optimal strategy, the video is explicitly structured as a sandbox for understanding how one transparent rule behaves differently across distinct markets.
The same notebook — with fixed parameters and identical execution logic — is applied to two ETFs: an emerging markets fund (EM) and the S&P 500 (SPY via the SPDR ETF). For the EM ETF, the trend filter cuts maximum drawdown from over 60% to roughly 24%, with the strategy invested about 65% of the time. For SPY, drawdown drops from approximately 50% to the mid-teens, but the equity curve underperforms buy-and-hold over the long run because delayed re-entries after recoveries compound into permanent missed upside.
The video emphasizes methodology over results: cash return is set at a conservative 3% when uninvested, monthly resampling reduces noise from daily price swings, and any observed differences between markets are attributed to the underlying series rather than parameter tuning. The notebook is available to Algovibes tier-3 members and is designed to be modified with custom tickers and assumptions.
📺 Source: Algovibes · Published January 04, 2026
🏷️ Format: Benchmark Test
