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mql5/Experts/Advisors/DualEA/Include/StrategySelector.mqh
Chinedu Emmanuel Chukwu 08b817cae2 DualEA: P6 latency + per-minute dedup + Prewarm; stage include deps and docs 
- LiveEA.mq5: add per-strategy per-minute dedup via Metadata p6_last_bucket
- Add p6_latency telemetry at selector, correlation prune, underperformance gates
- Call PrewarmIndicators() in OnInit() and before timer evaluation; cast timeframe explicitly
- Update docs (README, LiveEA_README)
- Stage include deps (PositionManager, InstrumentProfile, ML folder) and PaperEA build script

Excluded binaries (*.ex5) and runtime logs/profiles from commit
2025-08-27 12:44:21 -04:00

426 lignes
19 Kio
MQL5
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#include <Files/File.mqh>
class CStrategySelector
{
private:
// Slice metrics
string m_strat[];
string m_sym[];
int m_tf[];
int m_cnt[];
double m_wr[];
double m_avgR[];
double m_pf[];
double m_dd[];
// Data presence flag (for cold-start pass-through)
bool m_has_data;
// Selector strictness: when true, apply hard thresholds inside Score(); otherwise gate in Insights_Allow only
bool strict_thresholds;
// Thresholds
int th_min_trades;
double th_min_wr;
double th_min_exp;
double th_min_pf;
double th_max_dd;
// Weights
double w_pf, w_exp, w_wr, w_dd;
// Recency weighting
bool use_recency; int recent_days; double rec_alpha;
// Recent overlays (parallel arrays to main slices)
int m_rc_cnt[]; double m_rc_wr[]; double m_rc_avgR[]; double m_rc_pf[];
// Correlation cache (per symbol+timeframe, across strategies)
// Stores pairwise Pearson correlations of recent daily-aggregated r_multiple between strategies
string m_corr_sym_key; int m_corr_tf_key; datetime m_corr_built_ts;
string m_corr_a[]; string m_corr_b[]; double m_corr_val[]; int m_corr_n[];
bool m_corr_ready;
string TrimCopy(string s){ StringTrimLeft(s); StringTrimRight(s); return s; }
public:
CStrategySelector()
{
th_min_trades = 20; th_min_wr = 0.50; th_min_exp = 0.0; th_min_pf = 2.0; th_max_dd = 6.0;
w_pf = 1.0; w_exp = 1.0; w_wr = 0.5; w_dd = 0.3;
use_recency=false; recent_days=14; rec_alpha=0.5;
m_has_data=false;
strict_thresholds=false;
m_corr_sym_key=""; m_corr_tf_key=0; m_corr_built_ts=0; m_corr_ready=false;
}
void ConfigureThresholds(const int min_trades, const double min_wr, const double min_exp, const double min_pf, const double max_dd)
{ 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; }
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; }
void ConfigureRecency(const bool useRecency, const int lookbackDays, const double alpha)
{ use_recency=useRecency; recent_days=lookbackDays; rec_alpha=alpha; }
// Toggle whether hard thresholds are enforced inside Score() vs deferred to EA-level gating
void SetStrictThresholds(const bool strict)
{
strict_thresholds = strict;
}
bool Load()
{
int h = FileOpen("DualEA\\insights.json", FILE_READ|FILE_TXT|FILE_SHARE_READ|FILE_SHARE_WRITE|FILE_COMMON|FILE_ANSI);
if(h==INVALID_HANDLE) return false;
ArrayResize(m_strat,0); ArrayResize(m_sym,0); ArrayResize(m_tf,0);
ArrayResize(m_cnt,0); ArrayResize(m_wr,0); ArrayResize(m_avgR,0); ArrayResize(m_pf,0); ArrayResize(m_dd,0);
while(!FileIsEnding(h))
{
string line = FileReadString(h);
if(line=="" && FileIsEnding(h)) break;
if(StringFind(line, "\"strategy\"", 0) < 0) continue;
// parse simple JSON line for slice fields
string sname="", yname=""; int tfv=0, cnt=0; double wr=0, avgR=0, pf=0, dd=0;
int p=0;
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, "\"", ""); } }
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, "\"", ""); } }
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);} }
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);} }
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);} }
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);} }
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);} }
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);} }
if(sname!="" && yname!="" && tfv>0)
{
int n = ArraySize(m_strat);
ArrayResize(m_strat,n+1); ArrayResize(m_sym,n+1); ArrayResize(m_tf,n+1);
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);
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);
m_has_data = (n>0);
ArrayResize(m_rc_cnt,n); ArrayInitialize(m_rc_cnt,0);
ArrayResize(m_rc_wr,n); ArrayInitialize(m_rc_wr,0.0);
ArrayResize(m_rc_avgR,n);ArrayInitialize(m_rc_avgR,0.0);
ArrayResize(m_rc_pf,n); ArrayInitialize(m_rc_pf,0.0);
return ArraySize(m_strat)>0;
}
bool LoadRecent()
{
if(!use_recency) return false;
int h = FileOpen("DualEA\\features.csv", FILE_READ|FILE_TXT|FILE_SHARE_READ|FILE_SHARE_WRITE|FILE_COMMON|FILE_ANSI);
if(h==INVALID_HANDLE) return false;
datetime now = TimeCurrent();
datetime cutoff = now - recent_days*24*60*60;
// Build accumulators per slice index
int n=ArraySize(m_strat);
double pos_sum[]; double neg_sum[]; double total_trades[]; double wins[]; double sumR[];
ArrayResize(pos_sum,n); ArrayInitialize(pos_sum,0.0);
ArrayResize(neg_sum,n); ArrayInitialize(neg_sum,0.0);
ArrayResize(total_trades,n); ArrayInitialize(total_trades,0.0);
ArrayResize(wins,n); ArrayInitialize(wins,0.0);
ArrayResize(sumR,n); ArrayInitialize(sumR,0.0);
while(!FileIsEnding(h))
{
string line = FileReadString(h);
if(line=="" && FileIsEnding(h)) break;
// Expect CSV: ts,symbol,strategy,key,value
int p0 = StringFind(line, ",", 0); if(p0<0) continue; string ts = StringSubstr(line,0,p0);
int p1 = StringFind(line, ",", p0+1); if(p1<0) continue; string sym = StringSubstr(line,p0+1,p1-p0-1);
int p2 = StringFind(line, ",", p1+1); if(p2<0) continue; string strat = StringSubstr(line,p1+1,p2-p1-1);
int p3 = StringFind(line, ",", p2+1); if(p3<0) continue; string key = StringSubstr(line,p2+1,p3-p2-1);
string val = StringSubstr(line,p3+1);
StringTrimLeft(sym); StringTrimRight(sym); StringTrimLeft(strat); StringTrimRight(strat); StringTrimLeft(key); StringTrimRight(key);
datetime t = (datetime)StringToTime(ts);
if(t<cutoff) continue;
if(StringCompare(key,"r_multiple")!=0) continue;
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
for(int i=0;i<n;++i)
{
if(m_sym[i]==sym && m_strat[i]==strat)
{
total_trades[i] += 1.0;
sumR[i] += r;
if(r>0) pos_sum[i]+=r; else if(r<0) neg_sum[i]+=r;
if(r>0) wins[i]+=1.0;
}
}
}
FileClose(h);
for(int i=0;i<n;++i)
{
if(total_trades[i]<=0) continue;
double rc_wr = wins[i]/total_trades[i];
double rc_avgR = sumR[i]/total_trades[i];
double rc_pf = (pos_sum[i]>0 && neg_sum[i]<0? pos_sum[i]/MathAbs(neg_sum[i]) : 0.0);
m_rc_cnt[i] = (int)total_trades[i];
m_rc_wr[i] = rc_wr;
m_rc_avgR[i] = rc_avgR;
m_rc_pf[i] = rc_pf;
}
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
if(ArraySize(m_strat)==0 || !m_has_data)
{
return 1.0; // neutral positive score; no thresholds applied
}
for(int i=0;i<ArraySize(m_strat);++i)
{
if(m_strat[i]==strategy && m_sym[i]==symbol && m_tf[i]==timeframe)
{
if(strict_thresholds)
{
if(m_cnt[i] < th_min_trades) return 0.0;
if(m_wr[i] < th_min_wr) return 0.0;
if(m_avgR[i]< th_min_exp) return 0.0;
if(m_pf[i] < th_min_pf) return 0.0;
if(m_dd[i] > th_max_dd) return 0.0;
}
double pf = m_pf[i];
double avgR = m_avgR[i];
double wr = m_wr[i];
if(use_recency && i<ArraySize(m_rc_pf))
{
if(m_rc_cnt[i]>0)
{
pf = (1.0-rec_alpha)*pf + rec_alpha*m_rc_pf[i];
avgR = (1.0-rec_alpha)*avgR + rec_alpha*m_rc_avgR[i];
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];
if(!strict_thresholds && m_cnt[i] < th_min_trades)
{
double scale = (double)m_cnt[i] / (double)MathMax(1, th_min_trades);
s *= scale;
}
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[];
for(int j=0;j<ArraySize(m_strat);++j)
if(m_strat[j]==strategy && m_sym[j]==symbol)
{
total += m_cnt[j];
sumR += m_avgR[j]*m_cnt[j];
wins += m_wr[j]*m_cnt[j];
if(m_pf[j]>0 && m_pf[j]<9999){ // approximate gp/gl from pf and avgR if possible
// Not exact; use pf as weighting only in final score. For thresholds use aggregated avg/wr only.
}
int k = ArraySize(dd_series); ArrayResize(dd_series,k+1); dd_series[k]=m_avgR[j]; // proxy for series; conservative
}
if(total>0)
{
double wr = wins/total;
double avgR = sumR/total;
double pf = 0.0; // unknown reliably from aggregates; approximate via weighted mean of pf
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++; } }
if(pf_cnt>0) pf = pf_sum/pf_cnt;
double dd = 0.0; // drawdown proxy not reliable without series; use worst of slices
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;
if(wr < th_min_wr) return 0.0;
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;
}
// 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;
for(int j=0;j<ArraySize(m_strat);++j)
if(m_strat[j]==strategy)
{
total += m_cnt[j];
sumR += m_avgR[j]*m_cnt[j];
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;
if(wr < th_min_wr) return 0.0;
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;
ArrayResize(out_scores, ArraySize(strategies));
for(int i=0;i<ArraySize(strategies);++i)
{
double s = Score(symbol, timeframe, strategies[i]);
out_scores[i]=s;
if(s>bestS){ bestS=s; best=i; }
}
return best;
}
string NormalizeSymbolLocal(string s)
{
string lowers = s; StringToLower(lowers);
string suf[] = { "_otc", "_pro", "_ecn", "_mini", "_micro", ".r", ".i", ".pro", ".ecn", ".m" };
for(int i=0;i<ArraySize(suf);++i)
{
int p = StringFind(lowers, suf[i], StringLen(lowers)-StringLen(suf[i]));
if(p>=0 && p==StringLen(lowers)-StringLen(suf[i]))
{
s = StringSubstr(s, 0, StringLen(s)-StringLen(suf[i]));
break;
}
}
return s;
}
// Return exact slice index for strategy+symbol+timeframe; -1 if missing
int FindIndex(const string symbol, const int timeframe, const string strategy)
{
string symN = NormalizeSymbolLocal(symbol);
for(int i=0;i<ArraySize(m_strat);++i)
if(m_strat[i]==strategy && NormalizeSymbolLocal(m_sym[i])==symN && m_tf[i]==timeframe)
return i;
return -1;
}
// Get win rate for a slice, optionally blended with recency overlay.
// Fallbacks: (1) aggregate by strategy+symbol across TFs; (2) aggregate by strategy across all.
// Returns -1.0 when no data exists.
double GetWinRate(const string symbol, const int timeframe, const string strategy, const bool blended=true)
{
// No insights loaded
if(ArraySize(m_strat)==0 || !m_has_data)
return -1.0;
int i = FindIndex(symbol, timeframe, strategy);
if(i>=0)
{
double wr = m_wr[i];
if(blended && use_recency && i<ArraySize(m_rc_wr) && m_rc_cnt[i]>0)
wr = (1.0-rec_alpha)*wr + rec_alpha*m_rc_wr[i];
return wr;
}
// Fallback 1: aggregate by strategy+symbol across all timeframes
int total=0; double wins=0.0; string symN = NormalizeSymbolLocal(symbol);
for(int j=0;j<ArraySize(m_strat);++j)
if(m_strat[j]==strategy && NormalizeSymbolLocal(m_sym[j])==symN)
{
total += m_cnt[j];
wins += m_wr[j]*m_cnt[j];
}
if(total>0)
return wins/(double)total;
// Fallback 2: aggregate by strategy across all symbols/timeframes
total=0; wins=0.0;
for(int j=0;j<ArraySize(m_strat);++j)
if(m_strat[j]==strategy)
{ total += m_cnt[j]; wins += m_wr[j]*m_cnt[j]; }
if(total>0)
return wins/(double)total;
return -1.0;
}
// Ensure recent overlays are loaded for a given lookback window.
// Returns true if overlays are available after the call (may still be empty for some slices).
bool EnsureRecentLoaded(const int lookbackDays)
{
// enable recency and set window if different
use_recency = true;
recent_days = lookbackDays;
return LoadRecent();
}
// Get recent overlay metrics for an exact slice. Returns true when overlay exists (m_rc_cnt>0).
bool GetRecentMetrics(const string symbol, const int timeframe, const string strategy,
int &out_cnt, double &out_wr, double &out_avgR, double &out_pf)
{
out_cnt = 0; out_wr = 0.0; out_avgR = 0.0; out_pf = 0.0;
if(ArraySize(m_strat)==0 || !m_has_data)
return false;
int i = FindIndex(symbol, timeframe, strategy);
if(i<0) return false;
if(i>=ArraySize(m_rc_cnt)) return false;
if(m_rc_cnt[i]<=0) return false;
out_cnt = m_rc_cnt[i];
out_wr = m_rc_wr[i];
out_avgR = m_rc_avgR[i];
out_pf = m_rc_pf[i];
return true;
}
// Get baseline (insights slice) metrics for an exact slice. Returns true if exact slice found.
bool GetBaselineMetrics(const string symbol, const int timeframe, const string strategy,
int &out_cnt, double &out_wr, double &out_avgR, double &out_pf)
{
out_cnt = 0; out_wr = 0.0; out_avgR = 0.0; out_pf = 0.0;
if(ArraySize(m_strat)==0 || !m_has_data)
return false;
int i = FindIndex(symbol, timeframe, strategy);
if(i<0) return false;
out_cnt = m_cnt[i];
out_wr = m_wr[i];
out_avgR = m_avgR[i];
out_pf = m_pf[i];
return true;
}
// Underperformance check: true when recent overlay fails any threshold; falls back to baseline when overlay missing.
bool IsUnderperforming(const string symbol, const int timeframe, const string strategy,
const double minPF, const double minWR, const double minExpR)
{
int cnt=0; double wr=0.0, avgR=0.0, pf=0.0;
bool have_recent = GetRecentMetrics(symbol, timeframe, strategy, cnt, wr, avgR, pf);
if(!have_recent)
{
// fallback to baseline slice metrics; if missing entirely, do not block
if(!GetBaselineMetrics(symbol, timeframe, strategy, cnt, wr, avgR, pf))
return false;
}
// treat missing/zero PF as 0.0
double pf_eff = (pf>0? pf : 0.0);
if(pf_eff < minPF) return true;
if(wr < minWR) return true;
if(avgR < minExpR) return true;
return false;
}
};
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