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Backtesting Engine & Strategy Lab

A testing protocol designed first to avoid false positive results.

PythonpandasNumPyBacktest
MOTEUR BACKTEST CRYPTO
01

Context

The goal was to test strategies on historical data rigorously.

02

Problem

Look-ahead, overfitting and missing baselines can make backtests misleading.

03

Solution

Strict data splits, out-of-sample validation and comparison against a random baseline.

04

Key lesson

A robust negative result is more valuable than an attractive biased one.

Under the hood

How the project actually works

Architecture, constraints, deliverables and real impact.

01 Python
02 pandas
03 NumPy
04 Backtest
05

Impact

A robust negative result is more valuable than an attractive biased one.

Designed for real-world use
06

What this project demonstrates

I start from the problem and business flow, then aim for an observable, testable and maintainable solution rather than a demo effect.