Slippage Is Quietly Killing Your Backtest's Edge: How to Model It Honestly

Your backtest looks great — until you actually trade it. Slippage is the silent tax that turns winning strategies into losers, and most backtests ignore it almost entirely.

I Tested the Golden Cross on Every Major Asset Class. Here Is What the Data Actually Shows.

The golden cross is one of the most cited signals in retail trading. We ran the numbers — commission-adjusted, out-of-sample, across four asset classes — so you don't have to take anyone's word for it.

Probability of Backtest Overfitting: How to Quantify Whether Your Strategy Was Lucky

Suspecting your backtest is overfit is not the same as knowing. The PBO score turns that suspicion into a number — here's how it works and what to do with it.

The Deflated Sharpe Ratio: Why Your Strategy's Sharpe of 2.1 Is Probably Noise

A Sharpe of 2.1 sounds bulletproof. The Deflated Sharpe Ratio shows it's probably not. Here's the math that exposes why.

I Tested the RSI(2) Mean-Reversion Strategy. Here's the Honest Verdict.

RSI(2) is one of the most cited mean-reversion setups in retail trading. We ran it through rigorous, multiple-testing-corrected validation to find out whether any real edge survives.

P-Hacking Your Backtest: How Traders Fool Themselves with Statistics

Run enough backtests and you'll find a 'winning' strategy by pure chance. That's not edge — that's p-hacking, and it's more common than you think.

Walk-Forward Analysis: The Right Way to Validate a Trading Strategy

A single in-sample/out-of-sample split feels rigorous — it isn't. Walk-forward analysis is the only test that mimics how a strategy actually ages in the market.

Monte Carlo Simulation for Trading Strategies: What It Can and Cannot Tell You

Your backtest shows one equity curve. Reality will hand you thousands of possible ones. Monte Carlo simulation maps that entire landscape — but most traders misread the map.

Your Backtest Only Sees the Survivors

Test a strategy on today's top coins or stocks and you've already rigged the result. Survivorship bias quietly inflates every backtest that ignores the assets that died.

A 90% Win Rate That Loses Money: The Win-Rate Trap

Win rate is the most cited and most misleading number in trading. Here's how a strategy that wins 90% of the time can still bleed you dry — and what to track instead.

Your Backtest Peeked at the Future. That's Why It Looks Perfect.

Lookahead bias is the quietest backtest killer: a single accidental glance at future data turns random noise into a flawless equity curve. Here's how it sneaks in — and how to catch it.

Will Your Trading Strategy Pass an FTMO Challenge? The Statistics Prop Firms Don't Tell You

A high Sharpe ratio and a smooth equity curve aren't enough to pass an FTMO challenge. Here's how to map your backtest statistics to prop-firm rules before you risk the fee.

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The Curve-Fitting Trap: Why Your Optimized Strategy Dies the Moment It Goes Live

You ran the optimizer, found the best settings, and the backtest looks great. That's exactly the problem. Here's why optimized strategies die in live trading — and the two tests that catch it first.

backtesting

I Generated 1,500 Trading Strategies. 114 "Passed" on Bitcoin. None Were Real.

A hands-on demonstration of why in-sample / out-of-sample / holdout testing isn't enough to catch overfitting — and the statistics that actually are.