Why Backtesting Returns Lie: 5 Overfitting Traps

Your 47% Annual Return Probably Doesn't Exist

I've seen dozens of backtests claim double-digit monthly returns. Most vanish within weeks of going live. The problem isn't bad luck — it's that backtesting is designed to lie to you.

The uncomfortable truth: every feature you add, every parameter you tune, every threshold you test is another chance to fit noise instead of signal. Your 47% backtest return? It's probably measuring how well you memorized historical randomness.

Let me show you the five traps that turn promising strategies into expensive lessons.

Trap 1: Look-Ahead Bias (The Time Travel Bug)

This one's subtle and deadly. You're using information that wouldn't have been available at the time you claim to be trading.

Classic example: using today's close to generate today's signal. In backtest land, you have the close price at 9:30 AM. In reality, you don't get it until 4:00 PM. By then, the opportunity is gone.

Here's what this looks like in code:

python
import pandas as pd
import numpy as np

# WRONG: This leaks future data

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*Continue reading the full article on [TildAlice](https://tildalice.io/backtesting-overfitting-traps/)*