How to turn a trading idea into unambiguous rules, test it on clean data with honest costs, read the results without fooling yourself, avoid overfitting, and carry a system from spreadsheet to live account. Worked examples for stocks, futures, forex and crypto.
Module 1: What a backtest can and cannot tell you
Start from a hypothesis, keep data you have not looked at, and understand why a backtest is evidence rather than a track record.
Module 2: Data
Where price data comes from in each market, the biases baked into it, adjustments, continuous futures, sessions and bad ticks.
Module 3: Rules a machine could follow
Write entries, exits, sizing and filters so precisely that two people testing the same idea would get the same trades.
Module 4: Costs and realism
Commissions, spread, slippage models, what a limit or stop order really fills at, latency, capacity, borrow and funding.
Module 5: Reading results
Expectancy, profit factor, Sharpe and Sortino without the marketing, drawdown, R distributions, equity curve shapes, sample size and Monte Carlo.
Module 6: Overfitting and how to avoid it
Parameter sensitivity, degrees of freedom, walk-forward analysis, multiple testing, the deflated Sharpe idea, and why a single bull market proves nothing.
Module 7: Tools
Bar-by-bar replay, a spreadsheet template you can copy, the TradingView strategy tester and its traps, Python libraries, and desktop platforms.
Module 8: From backtest to live
Paper trading, incubation, sizing from backtest drawdown, monitoring live versus expected, knowing when to stop, and a go-live checklist.
Educational content, not financial advice.