Category
Testing a strategy on past data, reading the report, and avoiding results that won't hold up live.
Slippage is only one of nine reasons a backtest and a live strategy drift apart. Here is the full list, roughly in order of damage, and a four-step method to reconcile live trades against the test.
Running a backtest takes a click. Running one you can trust takes a method. Here is the workflow professionals use, applied step by step to an example NIFTY strategy on Tradetron.
The five problems a Live Offline run catches that a backtest misses, how many trades you really need before judging a strategy, and a checklist for going live without surprises.
Optimization is where most algo traders accidentally ruin a good strategy. Here is how to improve return on capital for real: what to optimize, in what order, and how to tell an improvement from a curve fit.
An option strategy tester replays your options rules on real historical prices. Here is what a good one must do, how payoff tools differ from backtests, and how the main Indian platforms compare.
Backtesting is the cheapest way to find out a strategy doesn't work. It is also easy to fool yourself with. Here are the ten mistakes that make backtests lie, and how to avoid each one.
Slippage is the gap between the price you expected and the price you got. It is small per trade and large per month. Here is why it happens, how to measure it and how to reduce it.
Five settings decide whether a Tradetron backtest is accurate: date range, candle frequency, trade price, type and expiry. Here is what each one does and how to pick it for your strategy.
The Sharpe ratio tells you how much return a strategy earned for each unit of volatility. Here is the formula, how to annualise it, worked examples, and the blind spots every algo trader should know.
Total profit is the least useful number in a strategy report. Here are the metrics that actually tell you whether an algo trading strategy is worth running, with a worked example and a checklist.
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