68 lines
2.8 KiB
MQL5
68 lines
2.8 KiB
MQL5
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//+------------------------------------------------------------------+
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//| Taylor_Effect_Visualization.mq5 |
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//| Copyright 2025, MetaQuotes Ltd. |
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//| https://www.mql5.com |
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//+------------------------------------------------------------------+
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#property copyright "Copyright 2025, MetaQuotes Ltd."
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#property link "https://www.mql5.com"
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#property version "1.00"
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#property script_show_inputs
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#include"Arch\univariate\acf.mqh"
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//--- input parameters
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input datetime StartDate = D'2025.01.01'; //--- Historical capture anchor stop date
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input ulong HistoryLen = 5000; //--- Total historical data bars to request
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//---
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ulong Max_Lags = 100;
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double Powers_Start = 0.5;
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double Powers_Stop = 2.0;
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double Powers_Step = 0.5;
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//+------------------------------------------------------------------+
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//| Script program start function |
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//+------------------------------------------------------------------+
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void OnStart()
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{
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//---
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vector prices;
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//--- --- Historical Data Fetch ---
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//--- Pull close prices directly into an array using native vector operations
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if(!prices.CopyRates(NULL,PERIOD_CURRENT, COPY_RATES_CLOSE, StartDate, HistoryLen))
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{
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Print(" failed to get close prices for ", _Symbol, ". Error ", GetLastError());
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return;
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}
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//--- --- Transform Prices to Returns ---
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//--- Map closing prices to logarithmic space
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prices = log(prices);
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//--- Compute log returns: r_t = ln(P_t) - ln(P_{t-1})
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vector returns = np::diff(prices) * 100.0;
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//--- Demean the data and
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vector demeaned_returns = returns - returns.Mean();
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vector abs_returns = fabs(demeaned_returns);
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vector powers = np::arange(Powers_Start,Powers_Stop+Powers_Step,Powers_Step);
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//--- Compute the Autocorrelation at different powers of absolute returns
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//--- This is the data plotted on the y-axis of the graph
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vector t_series;
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ACFResult acf_result;
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vector acf_results[];
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ArrayResize(acf_results,(int)powers.Size());
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for(ulong i = 0; i<powers.Size(); ++i)
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{
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t_series = pow(abs_returns,powers[i]);
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acf_result = acf(t_series,Max_Lags,0.05,true,true,false,true);
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acf_results[i] = np::sliceVector(acf_result.acf,1);
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}
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//--- The data on x-axis of plot
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vector lags = np::arange(Max_Lags,1.0,1.0);
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//--- Plot the graph
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//--- Prepare curve labels
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string ylabels[];
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ArrayResize(ylabels,(int)powers.Size());
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for(uint i = 0; i<ylabels.Size(); ++i)
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ylabels[i] = "Pow("+string(powers[i])+")";
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//--- Show the graphic
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np::plotxys(lags,acf_results,ylabels,"Autocorrelation Drop off","Lag","ACF",false,0,0,0,0,750,500,true,3,CURVE_LINES,30);
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//---
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return;
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}
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//+------------------------------------------------------------------+
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