backtesting
Paper Trading vs Backtesting: What Each One Can and Cannot Prove
Both look like validation. Neither is sufficient alone. Here's exactly what each test can and cannot prove — and why confusing them costs traders real money.
strategy validation
metrics
Sharpe Ratio vs Sortino Ratio vs Calmar Ratio: Which Risk-Adjusted Metric Should You Actually Optimize For?
Three metrics, three different definitions of risk — and optimizing for the wrong one can make a losing strategy look brilliant. Here's which one actually matters for your strategy.
backtesting
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.
statistics
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.
backtesting
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
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
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.
prop firms
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.
overfitting
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.