Why Backtesting with 1-Minute Data is Dangerous

Your backtest shows a 180% CAGR. Sharpe 3.4. Near-zero drawdown. You deploy ₹5L. In three weeks, the strategy is down 28%.

This is not bad luck. This is what 1-minute data does to intelligent people who trust it too much. Let's break down exactly why — and what actually works.

The Core Problem: You're Fitting to Noise

A 1-minute OHLC bar doesn't represent the market. It represents one snapshot of a noisy, microstructure-contaminated stream of prices. Every 1-minute close is polluted by bid-ask bounce — prices oscillating between ₹100.05 and ₹99.95 not because of any real movement, but because of the mechanical alternation between buyers hitting the ask and sellers hitting the bid.

Add to this the fact that a 5-year backtest on 1-minute data gives you ~625,000 data points. When you're optimising even 4–5 parameters across that, you will find combinations that look like genius. That's not edge — that's the multiple testing problem. If you run 1,000 random parameter combos, roughly 50 will show a Sharpe above 2.0 purely by chance.

"With four parameters I can fit an elephant, and with five I can make him wiggle his trunk."

John von Neumann said that about curve fitting in general. In quant trading on 1-minute data, you've got 20 parameters and a very cooperative elephant.

What the Research Actually Says

Quantopian studied 888 live strategies. The finding: Sharpe ratios from 1-minute backtests had near-zero predictive power for live performance. The more a strategy was optimised, the worse it performed. Bailey & López de Prado demonstrated mathematically that after enough trials on the same dataset, finding a "profitable" strategy by pure chance is essentially guaranteed.

The academic consensus on sampling frequency is also telling. Researchers working on volatility estimation found that as bar frequency increases beyond a certain point, microstructure noise begins to dominate the true price signal. The optimal sampling frequency for most equity strategies is between 5 and 30 minutes — not 1 minute.

The Execution Reality Check

Even if your signal were valid, 1-minute backtests almost never model execution honestly. Your backtest assumes you bought at the 1-minute close. In reality, your market order moved the price, you waited for order routing latency, and you filled ₹0.35 above the close. Do this 200 times a month and the "alpha" you thought you had is entirely eaten by execution costs.

Walk-forward optimisation is the standard fix for curve-fitting — optimise on a 2-year window, test on the next 6 months, roll forward, repeat. Most retail traders never do this because their backtesting tool doesn't support it. They run one optimisation on 5 years of data and call it validated.

What You Should Actually Do

Test on tick-level data, not OHLC bars. Use at minimum 8–10 years of data to capture multiple market regimes — bull, bear, crash, sideways. Model slippage realistically based on your typical order size vs. average traded volume. Run Monte Carlo simulation to stress-test the strategy across synthetic price paths, not just the one historical path you happened to test on. And always hold out a completely unseen out-of-sample period that you never touch during development.

These aren't nice-to-haves. They're the difference between a strategy that works and one that only worked on paper.