🐍 1,000,000 backtest simulations in 20 seconds
Use vectorbt walk-forward optimization to test strategies on unseen data.
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Most backtests lie to you, and you won’t know until you’ve lost real money.
Beginners usually build a trading strategy, test it on historical data, and see great results. They tweak a few settings, run it again, and the numbers look even better. But when they trade it live, the profits vanish. The problem isn’t the strategy. It’s the testing process itself.
That gap between backtest results and real-world performance has probably cost more money than any single bad trade.
The trap I fell into
Years ago I backtested a pairs trading strategy in MATLAB. The results looked fantastic, so I traded it live and lost money immediately. I adjusted the parameters, re-ran the test, saw amazing numbers again, and lost money again. I repeated this cycle more times than I’d like to admit before I realized what was happening.
Most beginners I talk to do exactly the same thing without knowing it.
The difference between traders who keep losing and traders who figure it out is one specific skill. You have to learn to test whether your strategy actually works on data it has never seen before, instead of just making it look good on old data.
What you’ll build today
In this post, you’ll use a Python library called vectorbt to run over a million backtest simulations in about 20 seconds, then apply a technique called walk-forward optimization to check whether your results hold up on fresh data.
Let’s go.
