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Backtesting guides
Plain answers to the questions traders ask about backtesting: data, costs, robustness, optimization, prop firm rules and the platforms. Each guide ends with how the Orion Quant Technologies Engine handles it.
What is backtesting? How to backtest a trading strategy
Backtesting runs a trading strategy over historical data to see how it would have done. What it measures, the steps, and the mistakes that fake a result.
Tick data backtesting: OHLC vs every tick with bid and ask
Tick data backtesting replays every recorded quote with bid and ask. When OHLC bars are enough, when they mislead, and how to backtest a strategy with ticks.
Spread, commission, swap and slippage in a backtest
Realistic backtest costs: how to charge spread, commission per lot and side, swap with the triple Wednesday, and slippage, with worked EURUSD examples.
Forex backtesting software: how to backtest a forex strategy
Forex backtesting software tests a currency strategy on historical rates. What the best forex backtester needs, how to backtest EURUSD, and forex traps.
Overfitting and lookahead bias: how to avoid a backtest that lies
Overfitting tunes a strategy to past noise, lookahead bias lets it see the future. How to spot curve fitting, the lookahead sources and how to avoid both.
Trading strategy optimization: grid search and genetic algorithms
Strategy optimization searches for the parameters that work best. Grid search and genetic algorithms compared, what to optimize for, and how to stay honest.
Walk forward analysis: how to test a strategy out of sample
Walk forward analysis optimizes a strategy on one window of history and tests it on the next unseen one. Anchored vs rolling windows and how to read the result.
Monte Carlo simulation in trading: drawdown and risk of ruin
A Monte Carlo simulation reshuffles a backtest's trades thousands of times to show the drawdowns and risk of ruin the strategy could face. Methods and examples.
How to backtest a prop firm challenge before you pay for it
How to backtest a prop firm challenge: the profit target, daily loss limit, trailing drawdown and consistency rule, with worked examples and cost per attempt.
AI trading strategies: how to build and backtest one with ChatGPT or Claude
How to turn an idea into an AI trading strategy with ChatGPT or Claude, catch the errors AI code makes, and backtest it honestly, also over MCP from Claude.
How to backtest a trading strategy in Python
How to backtest a trading strategy in Python: write the rules as a class, validate them, run on tick data with real costs, then optimize and test robustness.
How to backtest Pine Script, on TradingView and outside it
How to backtest a Pine Script strategy: what the TradingView strategy tester does, how to run Pine v5 and v6 on tick data, and convert it to MQL5 or a cBot.
An alternative to the MT5 strategy tester, and how to use both
What the MetaTrader 5 strategy tester does well, where a second engine helps, and how to backtest a strategy and export it as an Expert Advisor in .mq5.
cTrader backtesting: how to backtest a cBot, and where to test it next
How cTrader backtests a cBot on tick, M1 or H1 data, what cTrader Automate has built in, and how to run the same cBot or export a new one in another engine.
Research and testing only. A backtest shows how rules behaved in the past, not what they will earn. Nothing here is investment advice.