| #include <Files/File.mqh>
|
|
|
| class CStrategySelector
|
| {
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| private:
|
| // Slice metrics
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| string m_strat[];
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| string m_sym[];
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| int m_tf[];
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| int m_cnt[];
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| double m_wr[];
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| double m_avgR[];
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| double m_pf[];
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| double m_dd[];
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|
|
| // Data presence flag (for cold-start pass-through)
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| bool m_has_data;
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|
|
| // Selector strictness: when true, apply hard thresholds inside Score(); otherwise gate in Insights_Allow only
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| bool strict_thresholds;
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|
|
| // Thresholds
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| int th_min_trades;
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| double th_min_wr;
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| double th_min_exp;
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| double th_min_pf;
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| double th_max_dd;
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|
|
| // Weights
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| double w_pf, w_exp, w_wr, w_dd;
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|
|
| // Recency weighting
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| bool use_recency; int recent_days; double rec_alpha;
|
| // Recent overlays (parallel arrays to main slices)
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| int m_rc_cnt[]; double m_rc_wr[]; double m_rc_avgR[]; double m_rc_pf[];
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|
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| string TrimCopy(string s){ StringTrimLeft(s); StringTrimRight(s); return s; }
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|
|
| public:
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| CStrategySelector()
|
| {
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| th_min_trades = 20; th_min_wr = 0.50; th_min_exp = 0.0; th_min_pf = 2.0; th_max_dd = 6.0;
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| w_pf = 1.0; w_exp = 1.0; w_wr = 0.5; w_dd = 0.3;
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| use_recency=false; recent_days=14; rec_alpha=0.5;
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| m_has_data=false;
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| strict_thresholds=false;
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| }
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|
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| void ConfigureThresholds(const int min_trades, const double min_wr, const double min_exp, const double min_pf, const double max_dd)
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| { th_min_trades=min_trades; th_min_wr=min_wr; th_min_exp=min_exp; th_min_pf=min_pf; th_max_dd=max_dd; }
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| void ConfigureWeights(const double wp, const double we, const double ww, const double wd)
|
| { w_pf=wp; w_exp=we; w_wr=ww; w_dd=wd; }
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| void ConfigureRecency(const bool useRecency, const int lookbackDays, const double alpha)
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| { use_recency=useRecency; recent_days=lookbackDays; rec_alpha=alpha; }
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|
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| bool Load()
|
| {
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| int h = FileOpen("DualEA\\insights.json", FILE_READ|FILE_TXT|FILE_COMMON|FILE_ANSI);
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| if(h==INVALID_HANDLE) return false;
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| ArrayResize(m_strat,0); ArrayResize(m_sym,0); ArrayResize(m_tf,0);
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| ArrayResize(m_cnt,0); ArrayResize(m_wr,0); ArrayResize(m_avgR,0); ArrayResize(m_pf,0); ArrayResize(m_dd,0);
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| while(!FileIsEnding(h))
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| {
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| string line = FileReadString(h);
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| if(line=="" && FileIsEnding(h)) break;
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| if(StringFind(line, "\"strategy\"", 0) < 0) continue;
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| // parse simple JSON line for slice fields
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| string sname="", yname=""; int tfv=0, cnt=0; double wr=0, avgR=0, pf=0, dd=0;
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| int p=0;
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| p = StringFind(line, "\"strategy\":", 0); if(p>=0){ int q=StringFind(line, ",", p+1); string seg=(q>p? StringSubstr(line,p,q-p):StringSubstr(line,p)); int c=StringFind(seg, ":", 0); if(c>=0){ sname=TrimCopy(StringSubstr(seg, c+1)); StringReplace(sname, "\"", ""); } }
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| p = StringFind(line, "\"symbol\":", 0); if(p>=0){ int q=StringFind(line, ",", p+1); string seg=(q>p? StringSubstr(line,p,q-p):StringSubstr(line,p)); int c=StringFind(seg, ":", 0); if(c>=0){ yname=TrimCopy(StringSubstr(seg, c+1)); StringReplace(yname, "\"", ""); } }
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| p = StringFind(line, "\"timeframe\":",0); if(p>=0){ int q=StringFind(line, ",", p+1); string seg=(q>p? StringSubstr(line,p,q-p):StringSubstr(line,p)); int c=StringFind(seg, ":", 0); if(c>=0){ string num=TrimCopy(StringSubstr(seg,c+1)); tfv=(int)StringToInteger(num);} }
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| p = StringFind(line, "\"trade_count\":",0);if(p>=0){ int q=StringFind(line, ",", p+1); string seg=(q>p? StringSubstr(line,p,q-p):StringSubstr(line,p)); int c=StringFind(seg, ":", 0); if(c>=0){ string num=TrimCopy(StringSubstr(seg,c+1)); cnt=(int)StringToInteger(num);} }
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| p = StringFind(line, "\"win_rate\":",0); if(p>=0){ int q=StringFind(line, ",", p+1); string seg=(q>p? StringSubstr(line,p,q-p):StringSubstr(line,p)); int c=StringFind(seg, ":", 0); if(c>=0){ string num=TrimCopy(StringSubstr(seg,c+1)); wr=StringToDouble(num);} }
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| p = StringFind(line, "\"avg_R\":",0); if(p>=0){ int q=StringFind(line, ",", p+1); string seg=(q>p? StringSubstr(line,p,q-p):StringSubstr(line,p)); int c=StringFind(seg, ":", 0); if(c>=0){ string num=TrimCopy(StringSubstr(seg,c+1)); avgR=StringToDouble(num);} }
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| p = StringFind(line, "\"profit_factor\":",0);if(p>=0){int q=StringFind(line, ",", p+1); string seg=(q>p? StringSubstr(line,p,q-p):StringSubstr(line,p)); int c=StringFind(seg, ":", 0); if(c>=0){ string num=TrimCopy(StringSubstr(seg,c+1)); pf=StringToDouble(num);} }
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| p = StringFind(line, "\"max_drawdown_R\":",0);if(p>=0){int q=StringFind(line, ",", p+1); string seg=(q>p? StringSubstr(line,p,q-p):StringSubstr(line,p)); int c=StringFind(seg, ":", 0); if(c>=0){ string num=TrimCopy(StringSubstr(seg,c+1)); dd=StringToDouble(num);} }
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| if(sname!="" && yname!="" && tfv>0)
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| {
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| int n = ArraySize(m_strat);
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| ArrayResize(m_strat,n+1); ArrayResize(m_sym,n+1); ArrayResize(m_tf,n+1);
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| ArrayResize(m_cnt,n+1); ArrayResize(m_wr,n+1); ArrayResize(m_avgR,n+1); ArrayResize(m_pf,n+1); ArrayResize(m_dd,n+1);
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| m_strat[n]=sname; m_sym[n]=yname; m_tf[n]=tfv; m_cnt[n]=cnt; m_wr[n]=wr; m_avgR[n]=avgR; m_pf[n]=pf; m_dd[n]=dd;
|
| }
|
| }
|
| FileClose(h);
|
| // initialize recent arrays to defaults
|
| int n=ArraySize(m_strat);
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| m_has_data = (n>0);
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| ArrayResize(m_rc_cnt,n); ArrayInitialize(m_rc_cnt,0);
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| ArrayResize(m_rc_wr,n); ArrayInitialize(m_rc_wr,0.0);
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| ArrayResize(m_rc_avgR,n);ArrayInitialize(m_rc_avgR,0.0);
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| ArrayResize(m_rc_pf,n); ArrayInitialize(m_rc_pf,0.0);
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| return ArraySize(m_strat)>0;
|
| }
|
|
|
| bool LoadRecent()
|
| {
|
| if(!use_recency) return false;
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| int h = FileOpen("DualEA\\features.csv", FILE_READ|FILE_TXT|FILE_COMMON|FILE_ANSI);
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| if(h==INVALID_HANDLE) return false;
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| datetime now = TimeCurrent();
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| datetime cutoff = now - recent_days*24*60*60;
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| // Build accumulators per slice index
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| int n=ArraySize(m_strat);
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| double pos_sum[]; double neg_sum[]; double total_trades[]; double wins[]; double sumR[];
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| ArrayResize(pos_sum,n); ArrayInitialize(pos_sum,0.0);
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| ArrayResize(neg_sum,n); ArrayInitialize(neg_sum,0.0);
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| ArrayResize(total_trades,n); ArrayInitialize(total_trades,0.0);
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| ArrayResize(wins,n); ArrayInitialize(wins,0.0);
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| ArrayResize(sumR,n); ArrayInitialize(sumR,0.0);
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|
|
| while(!FileIsEnding(h))
|
| {
|
| string line = FileReadString(h);
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| if(line=="" && FileIsEnding(h)) break;
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| // Expect CSV: ts,symbol,strategy,key,value
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| int p0 = StringFind(line, ",", 0); if(p0<0) continue; string ts = StringSubstr(line,0,p0);
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| int p1 = StringFind(line, ",", p0+1); if(p1<0) continue; string sym = StringSubstr(line,p0+1,p1-p0-1);
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| int p2 = StringFind(line, ",", p1+1); if(p2<0) continue; string strat = StringSubstr(line,p1+1,p2-p1-1);
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| int p3 = StringFind(line, ",", p2+1); if(p3<0) continue; string key = StringSubstr(line,p2+1,p3-p2-1);
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| string val = StringSubstr(line,p3+1);
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| StringTrimLeft(sym); StringTrimRight(sym); StringTrimLeft(strat); StringTrimRight(strat); StringTrimLeft(key); StringTrimRight(key);
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| datetime t = (datetime)StringToTime(ts);
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| if(t<cutoff) continue;
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| if(StringCompare(key,"r_multiple")!=0) continue;
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| double r = StringToDouble(val);
|
| // find index of slice by symbol/strategy and timeframe is unknown in features.csv; approximate by current timeframe of EA symbol
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| for(int i=0;i<n;++i)
|
| {
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| if(m_sym[i]==sym && m_strat[i]==strat)
|
| {
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| total_trades[i] += 1.0;
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| sumR[i] += r;
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| if(r>0) pos_sum[i]+=r; else if(r<0) neg_sum[i]+=r;
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| if(r>0) wins[i]+=1.0;
|
| }
|
| }
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| }
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| FileClose(h);
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| for(int i=0;i<n;++i)
|
| {
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| if(total_trades[i]<=0) continue;
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| double rc_wr = wins[i]/total_trades[i];
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| double rc_avgR = sumR[i]/total_trades[i];
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| double rc_pf = (pos_sum[i]>0 && neg_sum[i]<0? pos_sum[i]/MathAbs(neg_sum[i]) : 0.0);
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| m_rc_cnt[i] = (int)total_trades[i];
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| m_rc_wr[i] = rc_wr;
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| m_rc_avgR[i] = rc_avgR;
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| m_rc_pf[i] = rc_pf;
|
| }
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| return true;
|
| }
|
|
|
| double Score(const string symbol, const int timeframe, const string strategy)
|
| {
|
| // Cold-start: if no insights slices loaded, allow pass-through to generate data
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| if(ArraySize(m_strat)==0 || !m_has_data)
|
| {
|
| return 1.0; // neutral positive score; no thresholds applied
|
| }
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| for(int i=0;i<ArraySize(m_strat);++i)
|
| {
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| if(m_strat[i]==strategy && m_sym[i]==symbol && m_tf[i]==timeframe)
|
| {
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| if(strict_thresholds)
|
| {
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| if(m_cnt[i] < th_min_trades) return 0.0;
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| if(m_wr[i] < th_min_wr) return 0.0;
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| if(m_avgR[i]< th_min_exp) return 0.0;
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| if(m_pf[i] < th_min_pf) return 0.0;
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| if(m_dd[i] > th_max_dd) return 0.0;
|
| }
|
| double pf = m_pf[i];
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| double avgR = m_avgR[i];
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| double wr = m_wr[i];
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| if(use_recency && i<ArraySize(m_rc_pf))
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| {
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| if(m_rc_cnt[i]>0)
|
| {
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| pf = (1.0-rec_alpha)*pf + rec_alpha*m_rc_pf[i];
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| avgR = (1.0-rec_alpha)*avgR + rec_alpha*m_rc_avgR[i];
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| wr = (1.0-rec_alpha)*wr + rec_alpha*m_rc_wr[i];
|
| }
|
| }
|
| double s = w_pf*pf + w_exp*avgR + w_wr*wr - w_dd*m_dd[i];
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| if(!strict_thresholds && m_cnt[i] < th_min_trades)
|
| {
|
| double scale = (double)m_cnt[i] / (double)MathMax(1, th_min_trades);
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| s *= scale;
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| }
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| return s;
|
| }
|
| }
|
| // Fallback 1: aggregate by strategy+symbol across all timeframes
|
| int total=0; double sumR=0.0, wins=0.0, gp=0.0, gl=0.0; double dd_series[];
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| for(int j=0;j<ArraySize(m_strat);++j)
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| if(m_strat[j]==strategy && m_sym[j]==symbol)
|
| {
|
| total += m_cnt[j];
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| sumR += m_avgR[j]*m_cnt[j];
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| wins += m_wr[j]*m_cnt[j];
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| if(m_pf[j]>0 && m_pf[j]<9999){ // approximate gp/gl from pf and avgR if possible
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| // Not exact; use pf as weighting only in final score. For thresholds use aggregated avg/wr only.
|
| }
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| int k = ArraySize(dd_series); ArrayResize(dd_series,k+1); dd_series[k]=m_avgR[j]; // proxy for series; conservative
|
| }
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| if(total>0)
|
| {
|
| double wr = wins/total;
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| double avgR = sumR/total;
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| double pf = 0.0; // unknown reliably from aggregates; approximate via weighted mean of pf
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| double pf_sum=0.0; int pf_cnt=0; for(int q=0;q<ArraySize(m_strat);++q){ if(m_strat[q]==strategy && m_sym[q]==symbol){ pf_sum+=m_pf[q]; pf_cnt++; } }
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| if(pf_cnt>0) pf = pf_sum/pf_cnt;
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| double dd = 0.0; // drawdown proxy not reliable without series; use worst of slices
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| for(int q=0;q<ArraySize(m_strat);++q){ if(m_strat[q]==strategy && m_sym[q]==symbol && m_dd[q]>dd) dd=m_dd[q]; }
|
| if(strict_thresholds)
|
| {
|
| if(total < th_min_trades) return 0.0;
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| if(wr < th_min_wr) return 0.0;
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| if(avgR< th_min_exp) return 0.0;
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| if(pf < th_min_pf) return 0.0;
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| if(dd > th_max_dd) return 0.0;
|
| }
|
| double s = w_pf*pf + w_exp*avgR + w_wr*wr - w_dd*dd;
|
| if(!strict_thresholds && total < th_min_trades)
|
| {
|
| double scale = (double)total / (double)MathMax(1, th_min_trades);
|
| s *= scale;
|
| }
|
| return s;
|
| }
|
|
|
| // Fallback 2: aggregate by strategy across all symbols/timeframes
|
| total=0; sumR=0.0; wins=0.0; double pf_sum2=0.0; int pf_cnt2=0; double worst_dd=0.0;
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| for(int j=0;j<ArraySize(m_strat);++j)
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| if(m_strat[j]==strategy)
|
| {
|
| total += m_cnt[j];
|
| sumR += m_avgR[j]*m_cnt[j];
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| wins += m_wr[j]*m_cnt[j];
|
| pf_sum2 += m_pf[j]; pf_cnt2++;
|
| if(m_dd[j]>worst_dd) worst_dd=m_dd[j];
|
| }
|
| if(total>0)
|
| {
|
| double wr = wins/total;
|
| double avgR = sumR/total;
|
| double pf = (pf_cnt2>0? pf_sum2/pf_cnt2 : 0.0);
|
| double dd = worst_dd;
|
| if(strict_thresholds)
|
| {
|
| if(total < th_min_trades) return 0.0;
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| if(wr < th_min_wr) return 0.0;
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| if(avgR< th_min_exp) return 0.0;
|
| if(pf < th_min_pf) return 0.0;
|
| if(dd > th_max_dd) return 0.0;
|
| }
|
| double s = w_pf*pf + w_exp*avgR + w_wr*wr - w_dd*dd;
|
| if(!strict_thresholds && total < th_min_trades)
|
| {
|
| double scale = (double)total / (double)MathMax(1, th_min_trades);
|
| s *= scale;
|
| }
|
| return s;
|
| }
|
|
|
| return 0.0; // unknown slice
|
| }
|
|
|
| int PickBest(const string symbol, const int timeframe, string &strategies[], double &out_scores[])
|
| {
|
| int best=-1; double bestS=-1e9;
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| ArrayResize(out_scores, ArraySize(strategies));
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| for(int i=0;i<ArraySize(strategies);++i)
|
| {
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| double s = Score(symbol, timeframe, strategies[i]);
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| out_scores[i]=s;
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| if(s>bestS){ bestS=s; best=i; }
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| }
|
| return best;
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| }
|
| };
|
