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Shop the journey🌀
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Tuesday 21 July 2026 22:19:26 GMT
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➡️ Quantitative Finance From Scratch, Part 20: Backtesting. A backtest tests a trading strategy on historical data, as if you had used it back then, to see whether it would have made money and how much risk it took along the way. Portfolio managers, analysts and quant researchers run backtests before real money goes into an idea. The key idea: split the past in two. You build the rule on the first part (in sample) and then test it on later data it has never seen (out of sample). The out of sample result is the fairer check. Why it matters: if you search the past hard enough, you will find patterns, even in pure noise. Trying rule after rule until one looks good is called data mining, and many of those patterns are just statistical chance. In this video we test 498 simple rules (hold after the price rose or fell over 2 to 250 days) on 10 simulated years of a market with no edge at all. The best rule in the first five years makes +11% in sample. On the next five years, data it has never seen, the same rule loses 28% and drops to rank 460 of 498. The ten best rules in sample lose 18.5% on average out of sample. Even a properly backtested result only describes the past. Markets change, and a pattern that worked for years can stop working. Classic mistakes: look-ahead bias (using information you could not have known at that time), survivorship bias (testing only companies that still exist today) and leaving out trading costs. All numbers come from one simulated run with assumed numbers (seed 20, daily returns with zero average and a 1% daily swing). No real market data, no strategy recommendation. Education only, not financial advice. #quantitativefinance #quant #backtesting #finance #trading
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