// Simple Moving Average Crossover Strategy _SECTION_BEGIN("MA_Crossover_635"); // User Inputs fastPeriod = Param("Fast MA Period", 20, 5, 100, 1); slowPeriod = Param("Slow MA Period", 50, 10, 200, 1); atrPeriod = Param("ATR Period", 14, 3, 30, 1); atrMult = Param("ATR Stop Multiplier", 2.5, 1, 5, 0.1); // Calculate Indicators fastMA = MA(Close, fastPeriod); slowMA = MA(Close, slowPeriod); myATR = ATR(atrPeriod); // Define Buy and Sell Rules Buy = Cross(fastMA, slowMA); Sell = Cross(slowMA, fastMA); // Define Short and Cover Rules (For Two-Way Trading) Short = Cross(slowMA, fastMA); Cover = Cross(fastMA, slowMA); // Price Plotting for Charting Window Plot(Close, "Price", colorDefault, styleCandle); Plot(fastMA, "Fast MA (" + fastPeriod + ")", colorGreen, styleLine | styleThick); Plot(slowMA, "Slow MA (" + slowPeriod + ")", colorRed, styleLine | styleThick); // Backtest Execution Parameters PositionSize = -10; // Allocate 10% of equity per trade SetStopMode(1, 2, atrMult * myATR, 1); // Apply dynamic ATR Trailing Stop _SECTION_END(); Use code with caution. Key Array Functions in Version 6.35
┌─────────────────────────┐ │ AmiBroker 6.35 │ └────────────┬────────────┘ │ ┌──────────────────────────┼──────────────────────────┐ ▼ ▼ ▼ ┌───────────────┐ ┌───────────────┐ ┌───────────────┐ │ UI & Theme │ │ Batch Engine │ │ AFL & Math │ │ Redesign │ │ Enhancements │ │ Expansions │ └───────────────┘ └───────────────┘ └───────────────┘ • Black Theme • Clipboard Tool • SafeDivide() • 5x Faster Lists • Working Dir • inverf() & erf() • Color Parity • Format Definition • Code Warnings 1. UI Redesign and "Black Theme" Implementation amibroker 6.35
– AmiBroker includes a Walk-Forward Analysis tool that splits data into multiple in-sample (optimization) and out-of-sample (testing) periods, providing more realistic performance estimates. – Always split historical data into an optimization
– Always split historical data into an optimization (in-sample) period and a testing (out-of-sample) period. After finding optimal parameters for the training data, validate them on unseen test data. validate them on unseen test data.