Library · Guides
Guides
The parts nobody warns you about until they cost you. Written for traders rather than developers.
Essentials
- How to build a trading botStart from a rule you already trade, state it precisely enough for software, and go live at a size that lets you think clearly.
- How to backtest without fooling yourselfOverfitting, look-ahead bias, survivorship bias and ignored costs — the four ways a backtest lies, and how to catch each one.
- Position sizingWhy fixed fractional beats fixed lots, what Kelly actually says, and why sizing ends more accounts than entries do.
Backtesting
- How Much History Does a Backtest Need? Sample Size, Sharpe Ratios, and Statistical SignificanceA Sharpe of 1.0 over one year has a 95% confidence interval of roughly [-1.4, 3.4]. The point estimate looks fine at every sample length; the interval is what tells you whether you have evidence. Full runnable Python code and the real output.6 min read
- Transaction Costs in Backtesting: The Fee and Slippage Line That Changes the VerdictThe same crossover loses 0.68% at zero cost and 13.18% at 50 basis points a trade. Costs are a per-trade tax, so turnover is the multiplier — here is the full runnable code and what it actually found.7 min read
- Walk-Forward Validation: The Test That Keeps a Backtest HonestA backtest that only ever sees one stretch of history will flatter whatever parameters you tuned on it. Walk-forward validation re-tunes on each window and judges only on data the strategy has never seen — here is the full runnable code and what it actually found.6 min read
- How to Backtest a Trading Strategy in Python, End to EndA backtest you can trust is a process, not a number: fixed data, one rule stated in advance, fees and slippage from the first run, and a judgement made only on data the strategy has never seen. This guide runs the whole process in Python, with the complete script.6 min read
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