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Afl Code: Amibroker

This AFL code example demonstrates how to create a simple moving average crossover strategy. This strategy will plot two moving averages and generate buy/sell signals.

// Parameters
lengthFast = Param("Fast MA Length", 10, 2, 100, 1);
lengthSlow = Param("Slow MA Length", 30, 2, 100, 1);
// Input arrays
Close = C;
// Calculate moving averages
FastMA = EMA(Close, lengthFast);
SlowMA = EMA(Close, lengthSlow);
// Plot moving averages
Plot(FastMA, "Fast MA", colorRed);
Plot(SlowMA, "Slow MA", colorGreen);
// Conditions for buy and sell signals
BuySignal = Cross(FastMA, SlowMA);
SellSignal = Cross(SlowMA, FastMA);
// Plot buy and sell signals
PlotShapes(BuySignal * shapeLabelUp + SellSignal * shapeLabelDown, 
           BuySignal ? ColorGreen : ColorRed, 
           shapeLabel, "", 0, 0, -8);
// Alert conditions
Alert(BuySignal, "Buy Signal", "Sound ON", 1);
Alert(SellSignal, "Sell Signal", "Sound ON", 2);
// Exploration
AddColumn(BuySignal, "Buy", 1);
AddColumn(SellSignal, "Sell", 1);

This example provides a foundational understanding of writing AFL code for Amibroker. You can expand on this by adding more complex indicators, strategies, or integrating functions according to your trading needs.

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AmiBroker Formula Language (AFL) is a high-performance programming language used to build custom indicators, scan for market opportunities, and backtest complex trading systems within the AmiBroker platform. Designed with a syntax similar to C and JScript, AFL is optimized for speed by minimizing the need for manual loops, making it an essential tool for quantitative traders. Core Functions of AFL Code

Traders use AFL to automate almost every aspect of their technical analysis: This AFL code example demonstrates how to create

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AFL (Analysis Formula Language) is the proprietary scripting language of AmiBroker, a popular technical analysis and trading system development platform. AFL allows users to: AFL (Analysis Formula Language) is the proprietary scripting

Unlike many other platforms, AFL is array-based (vectorized), meaning operations are applied to entire arrays of price data (Open, High, Low, Close, Volume) rather than single values.