Turning ideas into evidence: data quality, sample size, forward testing and review loops.
Backtesting turns a hunch into evidence. Here's why skipping it is the single biggest reason retail traders fail.
Bar-by-bar replay or coded simulation? Each method has strengths, weaknesses, and a place in your process.
Garbage in, garbage out. Bad data, lookahead bias, and survivorship bias can make a losing strategy look profitable.
20 trades tell you almost nothing. Here's how many you actually need before trusting a backtest's numbers.
The more you optimize a strategy to fit the past perfectly, the less likely it is to work in the future.
The bridge between backtest and live trading: proving a strategy on data it has never seen before.
Win rate alone tells you almost nothing. Here are the metrics that actually reveal whether a strategy is good.
A journal is only useful if it captures the right fields. Here's what to log for every backtest and live trade.
A structured weekly review turns your journal into feedback that actually changes your trading.
A validated backtest doesn't mean you should trade full size on day one. Here's the ladder to climb.