179 lines
6.9 KiB
MQL5
179 lines
6.9 KiB
MQL5
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//+------------------------------------------------------------------+
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//| CFuzzyFusion.mqh |
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//| Multi-Agent Fuzzy Fusion Engine (Centaur Quant) |
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//+------------------------------------------------------------------+
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//| PURPOSE |
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//| Semua keputusan membuka posisi dihasilkan dari fusi fuzzy |
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//| kontribusi agen: struktur pasar (OB+FVG/SMC), trend (EMA), |
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//| momentum (RSI), regime volatilitas (ATR) dan AI advisory (LLM). |
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//| - Fuzzifikasi : tiap skor agen (0..100) -> derajat keanggotaan |
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//| lemah/sedang/kuat (fungsi keanggotaan trapesium/segitiga). |
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//| - Agregasi : bobot tertimbang DINAMIS per agen. |
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//| - Defuzzifikasi: centroid -> confidence fusi 0..100. |
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//| - Bobot adaptif: diperbarui online dari outcome trade (win/loss |
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//| via R-multiple) - agen yang sering benar naik bobotnya. |
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//| Anti-Veto: tidak ada agen tunggal (termasuk AI) yang memveto; |
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//| keputusan selalu kolektif (fuzzy). |
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//+------------------------------------------------------------------+
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#ifndef CFUZZYFUSION_MQH
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#define CFUZZYFUSION_MQH
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enum ENUM_FUZZY_AGENT
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{
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FZ_STRUCTURE = 0, // struktur pasar (OB+FVG) - SMC
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FZ_TREND, // trend (EMA 50/200)
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FZ_MOMENTUM, // momentum (RSI 14)
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FZ_VOLATILITY, // regime volatilitas (ATR 14/50)
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FZ_AI, // AI advisory (LLM bridge)
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FZ_AGENTS_TOTAL
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};
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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class CFuzzyFusion
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{
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private:
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double m_base[FZ_AGENTS_TOTAL]; // bobot dasar (dari input)
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double m_weight[FZ_AGENTS_TOTAL]; // bobot dinamis (ternormalisasi, sum=1)
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double m_perf[FZ_AGENTS_TOTAL]; // EWMA performa per agen [-1..1]
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double m_score[FZ_AGENTS_TOTAL]; // skor agen terakhir 0..100
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double m_alpha; // laju adaptasi bobot (0..1)
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double m_min_w; // clamp bobot bawah
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double m_max_w; // clamp bobot atas
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double Clamp(const double v, const double lo, const double hi)
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{ return (v < lo ? lo : (v > hi ? hi : v)); }
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//--- fungsi keanggotaan fuzzy (domain 0..100) ---
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double MuWeak(const double x) // trapesium [0,25,45,100)
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{ return (x <= 25.0 ? 1.0 : (x >= 45.0 ? 0.0 : (45.0 - x) / 20.0)); }
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double MuModerate(const double x) // segitiga [25,50,75]
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{ return MathMax(0.0, 1.0 - MathAbs(x - 50.0) / 25.0); }
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double MuStrong(const double x) // trapesium (0,55,75,100]
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{ return (x <= 55.0 ? 0.0 : (x >= 75.0 ? 1.0 : (x - 55.0) / 20.0)); }
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void Normalize()
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{
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double sum = 0.0;
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for(int i = 0; i < FZ_AGENTS_TOTAL; i++)
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sum += m_weight[i];
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if(sum <= 0.0)
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return;
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for(int i = 0; i < FZ_AGENTS_TOTAL; i++)
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m_weight[i] /= sum;
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}
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public:
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CFuzzyFusion()
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{
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for(int i = 0; i < FZ_AGENTS_TOTAL; i++)
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{
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m_base[i] = 1.0 / FZ_AGENTS_TOTAL;
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m_weight[i] = m_base[i];
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m_perf[i] = 0.0;
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m_score[i] = 50.0;
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}
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m_alpha = 0.10;
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m_min_w = 0.05;
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m_max_w = 0.60;
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}
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void Configure(const double &baseWeights[], const double alpha,
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const double minW, const double maxW)
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{
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m_alpha = Clamp(alpha, 0.001, 0.5);
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m_min_w = Clamp(minW, 0.01, 0.4);
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m_max_w = Clamp(maxW, m_min_w, 0.9);
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for(int i = 0; i < FZ_AGENTS_TOTAL; i++)
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{
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m_base[i] = Clamp(baseWeights[i], 0.0, 1.0);
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m_weight[i] = m_base[i];
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}
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Normalize();
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}
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void SetScore(const ENUM_FUZZY_AGENT a, const double s)
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{ if(a >= 0 && a < FZ_AGENTS_TOTAL) m_score[a] = Clamp(s, 0.0, 100.0); }
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double Score(const ENUM_FUZZY_AGENT a) const
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{ return (a >= 0 && a < FZ_AGENTS_TOTAL) ? m_score[a] : 50.0; }
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double Weight(const ENUM_FUZZY_AGENT a) const
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{ return (a >= 0 && a < FZ_AGENTS_TOTAL) ? m_weight[a] : 0.0; }
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//--- defuzzifikasi: centroid bobot-linguistik -> confidence 0..100 ---
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double Fuse()
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{
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double w = 0.0, m = 0.0, s = 0.0;
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for(int i = 0; i < FZ_AGENTS_TOTAL; i++)
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{
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const double x = m_score[i];
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const double wt = m_weight[i];
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w += wt * MuWeak(x);
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m += wt * MuModerate(x);
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s += wt * MuStrong(x);
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}
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const double denom = w + m + s;
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if(denom < 1e-9)
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return 50.0;
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return (w * 20.0 + m * 50.0 + s * 80.0) / denom;
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}
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//--- adaptasi bobot dinamis dari outcome trade ---
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void UpdateWeights(const bool won, const double &scores[])
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{
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for(int i = 0; i < FZ_AGENTS_TOTAL; i++)
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{
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const double sc = Clamp(scores[i], 0.0, 100.0);
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const double signal = ((won && sc >= 55.0) || (!won && sc < 45.0)) ? 1.0 : -1.0;
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m_perf[i] = (1.0 - m_alpha) * m_perf[i] + m_alpha * signal;
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m_weight[i] = Clamp(m_base[i] * (0.5 + m_perf[i]), m_min_w, m_max_w);
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}
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Normalize();
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}
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//--- persistensi bobot dinamis (file di MQL5\Files) ---
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bool Save(const string path)
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{
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int h = FileOpen(path, FILE_WRITE | FILE_TXT | FILE_ANSI);
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if(h == INVALID_HANDLE)
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return false;
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FileWriteString(h, StringFormat("CFZV1 alpha=%.4f min=%.2f max=%.2f\r\n", m_alpha, m_min_w, m_max_w));
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for(int i = 0; i < FZ_AGENTS_TOTAL; i++)
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FileWriteString(h, StringFormat("%d %.4f %.4f\r\n", i, m_perf[i], m_weight[i]));
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FileClose(h);
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return true;
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}
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bool Load(const string path)
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{
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int h = FileOpen(path, FILE_READ | FILE_TXT | FILE_ANSI);
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if(h == INVALID_HANDLE)
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return false;
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FileReadString(h); // header (diabaikan)
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for(int i = 0; i < FZ_AGENTS_TOTAL && !FileIsEnding(h); i++)
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{
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const string l = FileReadString(h);
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string parts[];
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const int n = StringSplit(l, ' ', parts);
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if(n >= 3)
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{
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const int idx = (int)StringToInteger(parts[0]);
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const double perf = StringToDouble(parts[1]);
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const double wgt = StringToDouble(parts[2]);
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if(idx >= 0 && idx < FZ_AGENTS_TOTAL)
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{
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m_perf[idx] = Clamp(perf, -1.0, 1.0);
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m_weight[idx] = Clamp(wgt, m_min_w, m_max_w);
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}
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}
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}
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FileClose(h);
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Normalize();
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return true;
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}
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};
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#endif
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//+------------------------------------------------------------------+
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