CrowdAverse Prism is a portfolio selector designed to avoid crowded exposures while still staying adaptive to changing market conditions. It watches a basket of currency pairs, builds a compact feature picture for each pair, then measures how tightly each pair moves with the rest of the basket. The strategy does not rely on a single indicator. Instead, it gathers a small set of behavioral “aspects” per pair, such as short and longer return, volatility, price deviation, range pressure, activity flow, a simple regime flag, volatility of volatility, and persistence. These aspects are stored in a ring buffer designed for efficiency and predictable memory use.

At regular update intervals the strategy constructs a pairwise relationship map across the basket by averaging correlations over all feature aspects. This heavy step can be accelerated through an optional OpenCL backend. If OpenCL is not available or fails at runtime, the code automatically falls back to a full CPU implementation. The result is a correlation matrix that expresses how similar each pair is to every other pair in the basket.

Correlation alone can promote crowding, so the strategy blends correlation distance with an exposure distance table. This blended distance becomes the basis of a network distance matrix. A shortest path pass is then applied to propagate indirect relationships, allowing the system to recognize that pairs can be connected through intermediaries even when direct similarity is modest. From those distances, each pair receives a compactness score that reflects how isolated or entangled it is within the basket, and an entropy proxy that reflects how unstable its recent behavior has been.

Scores are then computed by rewarding compactness, penalizing crowd coupling, and incorporating the entropy term so that unstable behavior is treated cautiously. A learning controller sits above the scoring layer. It summarizes the basket into a snapshot, runs lightweight unsupervised clustering, a regime model, a principal component style rotation monitor, and a simple reinforcement style action chooser. This controller adjusts the scoring scale and the number of pairs selected, tightening selection when the market appears crowded or unstable and relaxing it when structure is clearer. The output is a rotating top list of pairs that aims to stay away from the crowd while remaining responsive and computationally efficient.

Code
// TGr06B_CrowdAverse_v6.cpp - Zorro64 Strategy DLL
// Strategy B v6: Crowd-Averse with MX06 OOP + OpenCL + Learning Controller
//
// Notes:
// - Keeps full CPU fallback.
// - OpenCL is optional: if OpenCL.dll missing / no device / kernel build fails -> CPU path.
// - OpenCL accelerates the heavy correlation matrix step by offloading pairwise correlations.
// - Correlation is computed in float on GPU; results are stored back into fvar corrMatrix.

#define _CRT_SECURE_NO_WARNINGS
#include <zorro.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <math.h>
#include <windows.h>
#include <stddef.h>

#define INF 1e30
#define EPS 1e-12
#define N_ASSETS 28
#define FEAT_N 9
#define FEAT_WINDOW 200
#define UPDATE_EVERY 5
#define TOP_K 5

#define ALPHA 0.1
#define BETA 0.3
#define GAMMA 2.5
#define LAMBDA_META 0.5

#define USE_ML 1
#define USE_UNSUP 1
#define USE_RL 1
#define USE_PCA 1
#define USE_GMM 1
#define GMM_K 3
#define GMM_DIM 8
#define GMM_ALPHA 0.02
#define GMM_VAR_FLOOR 1e-4
#define GMM_ENTROPY_COEFF 0.45
#define GMM_MIN_RISK 0.25
#define GMM_ONLINE_UPDATE 1
#define STRATEGY_PROFILE 1
#define PCA_DIM 6
#define PCA_COMP 3
#define PCA_WINDOW 128
#define PCA_REBUILD_EVERY 4

#ifdef TIGHT_MEM
typedef float fvar;
#else
typedef double fvar;
#endif

static const char* ASSET_NAMES[] = {
  "EURUSD","GBPUSD","USDCHF","USDJPY","AUDUSD","AUDCAD","AUDCHF","AUDJPY","AUDNZD",
  "CADJPY","CADCHF","EURAUD","EURCAD","EURCHF","EURGBP","EURJPY","EURNZD","GBPAUD",
  "GBPCAD","GBPCHF","GBPJPY","GBPNZD","NZDCAD","NZDCHF","NZDJPY","NZDUSD","USDCAD"
};
static const char* CURRENCIES[] = {"EUR","GBP","USD","CHF","JPY","AUD","CAD","NZD"};
#define N_CURRENCIES 8

// ---------------------------- Exposure Table ----------------------------

struct ExposureTable {
  int exposure[N_ASSETS][N_CURRENCIES];
  double exposureDist[N_ASSETS][N_ASSETS];

  void init() {
    for(int i=0;i<N_ASSETS;i++){
      for(int c=0;c<N_CURRENCIES;c++){
        exposure[i][c] = 0;
      }
    }
    for(int i=0;i<N_ASSETS;i++){
      for(int j=0;j<N_ASSETS;j++){
        exposureDist[i][j] = 0.0;
      }
    }
  }

  inline double getDist(int i,int j) const { return exposureDist[i][j]; }
};

// ---------------------------- Slab Allocator ----------------------------

template<typename T>
class SlabAllocator {
public:
  T* data;
  int capacity;

  SlabAllocator() : data(NULL), capacity(0) {}
  ~SlabAllocator() { shutdown(); }

  void init(int size) {
    shutdown();
    capacity = size;
    data = (T*)malloc((size_t)capacity * sizeof(T));
    if(data) memset(data, 0, (size_t)capacity * sizeof(T));
  }

  void shutdown() {
    if(data) free(data);
    data = NULL;
    capacity = 0;
  }

  T& operator[](int i) { return data[i]; }
  const T& operator[](int i) const { return data[i]; }
};

// ---------------------------- Feature Buffer (SoA ring) ----------------------------

struct FeatureBufferSoA {
  SlabAllocator<fvar> buffer;
  int windowSize;
  int currentIndex;

  void init(int assets, int window) {
    windowSize = window;
    currentIndex = 0;
    buffer.init(FEAT_N * assets * window);
  }

  void shutdown() { buffer.shutdown(); }

  inline int offset(int feat,int asset,int t) const {
    return (feat * N_ASSETS + asset) * windowSize + t;
  }

  void push(int feat,int asset,fvar value) {
    buffer[offset(feat, asset, currentIndex)] = value;
    currentIndex = (currentIndex + 1) % windowSize;
  }

  // t=0 => most recent
  fvar get(int feat,int asset,int t) const {
    int idx = (currentIndex - 1 - t + windowSize) % windowSize;
    return buffer[offset(feat, asset, idx)];
  }
};

// ---------------------------- Minimal OpenCL (dynamic) ----------------------------

typedef struct _cl_platform_id*   cl_platform_id;
typedef struct _cl_device_id*     cl_device_id;
typedef struct _cl_context*       cl_context;
typedef struct _cl_command_queue* cl_command_queue;
typedef struct _cl_program*       cl_program;
typedef struct _cl_kernel*        cl_kernel;
typedef struct _cl_mem*           cl_mem;
typedef unsigned int              cl_uint;
typedef int                       cl_int;
typedef unsigned long long        cl_ulong;
typedef size_t                    cl_bool;

#define CL_SUCCESS 0
#define CL_DEVICE_TYPE_CPU (1ULL << 1)
#define CL_DEVICE_TYPE_GPU (1ULL << 2)
#define CL_MEM_READ_ONLY   (1ULL << 2)
#define CL_MEM_WRITE_ONLY  (1ULL << 1)
#define CL_MEM_READ_WRITE  (1ULL << 0)
#define CL_TRUE  1
#define CL_FALSE 0
#define CL_PROGRAM_BUILD_LOG 0x1183

class OpenCLBackend {
public:
  HMODULE hOpenCL;
  int ready;

  cl_platform_id platform;
  cl_device_id device;
  cl_context context;
  cl_command_queue queue;
  cl_program program;
  cl_kernel kCorr;

  cl_mem bufFeat;
  cl_mem bufCorr;

  int featBytes;
  int corrBytes;

  cl_int (*clGetPlatformIDs)(cl_uint, cl_platform_id*, cl_uint*);
  cl_int (*clGetDeviceIDs)(cl_platform_id, cl_ulong, cl_uint, cl_device_id*, cl_uint*);
  cl_context (*clCreateContext)(void*, cl_uint, const cl_device_id*, void*, void*, cl_int*);
  cl_command_queue (*clCreateCommandQueue)(cl_context, cl_device_id, cl_ulong, cl_int*);
  cl_program (*clCreateProgramWithSource)(cl_context, cl_uint, const char**, const size_t*, cl_int*);
  cl_int (*clBuildProgram)(cl_program, cl_uint, const cl_device_id*, const char*, void*, void*);
  cl_int (*clGetProgramBuildInfo)(cl_program, cl_device_id, cl_uint, size_t, void*, size_t*);
  cl_kernel (*clCreateKernel)(cl_program, const char*, cl_int*);
  cl_int (*clSetKernelArg)(cl_kernel, cl_uint, size_t, const void*);
  cl_mem (*clCreateBuffer)(cl_context, cl_ulong, size_t, void*, cl_int*);
  cl_int (*clEnqueueWriteBuffer)(cl_command_queue, cl_mem, cl_bool, size_t, size_t, const void*, cl_uint, const void*, void*);
  cl_int (*clEnqueueReadBuffer)(cl_command_queue, cl_mem, cl_bool, size_t, size_t, void*, cl_uint, const void*, void*);
  cl_int (*clEnqueueNDRangeKernel)(cl_command_queue, cl_kernel, cl_uint, const size_t*, const size_t*, const size_t*, cl_uint, const void*, void*);
  cl_int (*clFinish)(cl_command_queue);
  cl_int (*clReleaseMemObject)(cl_mem);
  cl_int (*clReleaseKernel)(cl_kernel);
  cl_int (*clReleaseProgram)(cl_program);
  cl_int (*clReleaseCommandQueue)(cl_command_queue);
  cl_int (*clReleaseContext)(cl_context);

  OpenCLBackend()
  : hOpenCL(NULL), ready(0),
    platform(NULL), device(NULL), context(NULL), queue(NULL), program(NULL), kCorr(NULL),
    bufFeat(NULL), bufCorr(NULL),
    featBytes(0), corrBytes(0),
    clGetPlatformIDs(NULL), clGetDeviceIDs(NULL), clCreateContext(NULL), clCreateCommandQueue(NULL),
    clCreateProgramWithSource(NULL), clBuildProgram(NULL), clGetProgramBuildInfo(NULL),
    clCreateKernel(NULL), clSetKernelArg(NULL),
    clCreateBuffer(NULL), clEnqueueWriteBuffer(NULL), clEnqueueReadBuffer(NULL),
    clEnqueueNDRangeKernel(NULL), clFinish(NULL),
    clReleaseMemObject(NULL), clReleaseKernel(NULL), clReleaseProgram(NULL),
    clReleaseCommandQueue(NULL), clReleaseContext(NULL)
  {}

  int loadSymbol(void** fp, const char* name) {
    *fp = (void*)GetProcAddress(hOpenCL, name);
    return (*fp != NULL);
  }

  const char* kernelSource() {
    return
      "__kernel void corr_pairwise(\n"
      "  __global const float* feat,\n"
      "  __global float* outCorr,\n"
      "  const int nAssets,\n"
      "  const int nFeat,\n"
      "  const int windowSize,\n"
      "  const float eps\n"
      "){\n"
      "  int a = (int)get_global_id(0);\n"
      "  int b = (int)get_global_id(1);\n"
      "  if(a >= nAssets || b >= nAssets) return;\n"
      "  if(a >= b) return;\n"
      "  float acc = 0.0f;\n"
      "  for(int f=0; f<nFeat; f++){\n"
      "    int baseA = (f*nAssets + a) * windowSize;\n"
      "    int baseB = (f*nAssets + b) * windowSize;\n"
      "    float mx = 0.0f;\n"
      "    float my = 0.0f;\n"
      "    for(int t=0; t<windowSize; t++){\n"
      "      mx += feat[baseA + t];\n"
      "      my += feat[baseB + t];\n"
      "    }\n"
      "    mx /= (float)windowSize;\n"
      "    my /= (float)windowSize;\n"
      "    float sxx = 0.0f;\n"
      "    float syy = 0.0f;\n"
      "    float sxy = 0.0f;\n"
      "    for(int t=0; t<windowSize; t++){\n"
      "      float dx = feat[baseA + t] - mx;\n"
      "      float dy = feat[baseB + t] - my;\n"
      "      sxx += dx*dx;\n"
      "      syy += dy*dy;\n"
      "      sxy += dx*dy;\n"
      "    }\n"
      "    float den = sqrt(sxx*syy + eps);\n"
      "    float corr = (den > eps) ? (sxy/den) : 0.0f;\n"
      "    acc += corr;\n"
      "  }\n"
      "  outCorr[a*nAssets + b] = acc / (float)nFeat;\n"
      "}\n";
  }

  void printBuildLog() {
    if(!clGetProgramBuildInfo || !program || !device) return;
    size_t logSize = 0;
    clGetProgramBuildInfo(program, device, CL_PROGRAM_BUILD_LOG, 0, NULL, &logSize);
    if(logSize == 0) return;
    char* log = (char*)malloc(logSize + 1);
    if(!log) return;
    memset(log, 0, logSize + 1);
    clGetProgramBuildInfo(program, device, CL_PROGRAM_BUILD_LOG, logSize, log, NULL);
    printf("OpenCL build log:\n%s\n", log);
    free(log);
  }

  void init() {
    ready = 0;

    hOpenCL = LoadLibraryA("OpenCL.dll");
    if(!hOpenCL) {
      printf("OpenCL: CPU (OpenCL.dll missing)\n");
      return;
    }

    if(!loadSymbol((void**)&clGetPlatformIDs,       "clGetPlatformIDs")) return;
    if(!loadSymbol((void**)&clGetDeviceIDs,         "clGetDeviceIDs")) return;
    if(!loadSymbol((void**)&clCreateContext,        "clCreateContext")) return;
    if(!loadSymbol((void**)&clCreateCommandQueue,   "clCreateCommandQueue")) return;
    if(!loadSymbol((void**)&clCreateProgramWithSource,"clCreateProgramWithSource")) return;
    if(!loadSymbol((void**)&clBuildProgram,         "clBuildProgram")) return;
    if(!loadSymbol((void**)&clGetProgramBuildInfo,  "clGetProgramBuildInfo")) return;
    if(!loadSymbol((void**)&clCreateKernel,         "clCreateKernel")) return;
    if(!loadSymbol((void**)&clSetKernelArg,         "clSetKernelArg")) return;
    if(!loadSymbol((void**)&clCreateBuffer,         "clCreateBuffer")) return;
    if(!loadSymbol((void**)&clEnqueueWriteBuffer,   "clEnqueueWriteBuffer")) return;
    if(!loadSymbol((void**)&clEnqueueReadBuffer,    "clEnqueueReadBuffer")) return;
    if(!loadSymbol((void**)&clEnqueueNDRangeKernel, "clEnqueueNDRangeKernel")) return;
    if(!loadSymbol((void**)&clFinish,               "clFinish")) return;
    if(!loadSymbol((void**)&clReleaseMemObject,     "clReleaseMemObject")) return;
    if(!loadSymbol((void**)&clReleaseKernel,        "clReleaseKernel")) return;
    if(!loadSymbol((void**)&clReleaseProgram,       "clReleaseProgram")) return;
    if(!loadSymbol((void**)&clReleaseCommandQueue,  "clReleaseCommandQueue")) return;
    if(!loadSymbol((void**)&clReleaseContext,       "clReleaseContext")) return;

    cl_uint nPlat = 0;
    if(clGetPlatformIDs(0, NULL, &nPlat) != CL_SUCCESS || nPlat == 0) {
      printf("OpenCL: CPU (no platform)\n");
      return;
    }
    clGetPlatformIDs(1, &platform, NULL);

    cl_uint nDev = 0;
    cl_int ok = clGetDeviceIDs(platform, CL_DEVICE_TYPE_GPU, 1, &device, &nDev);
    if(ok != CL_SUCCESS || nDev == 0) {
      ok = clGetDeviceIDs(platform, CL_DEVICE_TYPE_CPU, 1, &device, &nDev);
      if(ok != CL_SUCCESS || nDev == 0) {
        printf("OpenCL: CPU (no device)\n");
        return;
      }
    }

    cl_int err = 0;
    context = clCreateContext(NULL, 1, &device, NULL, NULL, &err);
    if(err != CL_SUCCESS || !context) {
      printf("OpenCL: CPU (context fail)\n");
      return;
    }

    queue = clCreateCommandQueue(context, device, 0, &err);
    if(err != CL_SUCCESS || !queue) {
      printf("OpenCL: CPU (queue fail)\n");
      return;
    }

    const char* src = kernelSource();
    program = clCreateProgramWithSource(context, 1, &src, NULL, &err);
    if(err != CL_SUCCESS || !program) {
      printf("OpenCL: CPU (program fail)\n");
      return;
    }

    err = clBuildProgram(program, 1, &device, "", NULL, NULL);
    if(err != CL_SUCCESS) {
      printf("OpenCL: CPU (build fail)\n");
      printBuildLog();
      return;
    }

    kCorr = clCreateKernel(program, "corr_pairwise", &err);
    if(err != CL_SUCCESS || !kCorr) {
      printf("OpenCL: CPU (kernel fail)\n");
      printBuildLog();
      return;
    }

    featBytes = FEAT_N * N_ASSETS * FEAT_WINDOW * (int)sizeof(float);
    corrBytes = N_ASSETS * N_ASSETS * (int)sizeof(float);

    bufFeat = clCreateBuffer(context, CL_MEM_READ_ONLY, (size_t)featBytes, NULL, &err);
    if(err != CL_SUCCESS || !bufFeat) {
      printf("OpenCL: CPU (bufFeat fail)\n");
      return;
    }

    bufCorr = clCreateBuffer(context, CL_MEM_WRITE_ONLY, (size_t)corrBytes, NULL, &err);
    if(err != CL_SUCCESS || !bufCorr) {
      printf("OpenCL: CPU (bufCorr fail)\n");
      return;
    }

    ready = 1;
    printf("OpenCL: READY (kernel+buffers)\n");
  }

  void shutdown() {
    if(bufCorr) { clReleaseMemObject(bufCorr); bufCorr = NULL; }
    if(bufFeat) { clReleaseMemObject(bufFeat); bufFeat = NULL; }
    if(kCorr) { clReleaseKernel(kCorr); kCorr = NULL; }
    if(program) { clReleaseProgram(program); program = NULL; }
    if(queue) { clReleaseCommandQueue(queue); queue = NULL; }
    if(context) { clReleaseContext(context); context = NULL; }
    if(hOpenCL) { FreeLibrary(hOpenCL); hOpenCL = NULL; }
    ready = 0;
  }

  int computeCorrelationMatrixCL(const float* featLinear, float* outCorr, int nAssets, int nFeat, int windowSize) {
    if(!ready) return 0;
    if(!featLinear || !outCorr) return 0;

    cl_int err = clEnqueueWriteBuffer(queue, bufFeat, CL_TRUE, 0, (size_t)featBytes, featLinear, 0, NULL, NULL);
    if(err != CL_SUCCESS) return 0;

    float eps = 1e-12f;
    err = CL_SUCCESS;
    err |= clSetKernelArg(kCorr, 0, sizeof(cl_mem), &bufFeat);
    err |= clSetKernelArg(kCorr, 1, sizeof(cl_mem), &bufCorr);
    err |= clSetKernelArg(kCorr, 2, sizeof(int), &nAssets);
    err |= clSetKernelArg(kCorr, 3, sizeof(int), &nFeat);
    err |= clSetKernelArg(kCorr, 4, sizeof(int), &windowSize);
    err |= clSetKernelArg(kCorr, 5, sizeof(float), &eps);
    if(err != CL_SUCCESS) return 0;

    size_t global[2];
    global[0] = (size_t)nAssets;
    global[1] = (size_t)nAssets;

    err = clEnqueueNDRangeKernel(queue, kCorr, 2, NULL, global, NULL, 0, NULL, NULL);
    if(err != CL_SUCCESS) return 0;

    err = clFinish(queue);
    if(err != CL_SUCCESS) return 0;

    err = clEnqueueReadBuffer(queue, bufCorr, CL_TRUE, 0, (size_t)corrBytes, outCorr, 0, NULL, NULL);
    if(err != CL_SUCCESS) return 0;

    return 1;
  }
};

// ---------------------------- Learning Layer ----------------------------

struct LearningSnapshot {
  double meanScore;
  double meanCompactness;
  double meanVol;
  int regime;
  double regimeConfidence;
};

class UnsupervisedModel {
public:
  double centroids[3][3];
  int counts[3];
  int initialized;
  UnsupervisedModel() : initialized(0) { memset(centroids, 0, sizeof(centroids)); memset(counts, 0, sizeof(counts)); }
  void init() { initialized = 0; memset(centroids, 0, sizeof(centroids)); memset(counts, 0, sizeof(counts)); }
  void update(const LearningSnapshot& s, int* regimeOut, double* confOut) {
    double x0=s.meanScore,x1=s.meanCompactness,x2=s.meanVol;
    if(!initialized) {
      for(int k=0;k<3;k++){ centroids[k][0]=x0+0.01*(k-1); centroids[k][1]=x1+0.01*(1-k); centroids[k][2]=x2+0.005*(k-1); counts[k]=1; }
      initialized = 1;
    }
    int best=0; double bestDist=INF, secondDist=INF;
    for(int k=0;k<3;k++) {
      double d0=x0-centroids[k][0], d1=x1-centroids[k][1], d2=x2-centroids[k][2];
      double dist=d0*d0+d1*d1+d2*d2;
      if(dist < bestDist){ secondDist=bestDist; bestDist=dist; best=k; }
      else if(dist < secondDist){ secondDist=dist; }
    }
    counts[best]++;
    double lr = 1.0/(double)counts[best];
    centroids[best][0] += lr*(x0-centroids[best][0]);
    centroids[best][1] += lr*(x1-centroids[best][1]);
    centroids[best][2] += lr*(x2-centroids[best][2]);
    *regimeOut = best;
    *confOut = 1.0/(1.0 + sqrt(fabs(secondDist-bestDist)+EPS));
  }
};

class RLAgent {
public:
  double q[4]; int n[4]; int lastAction; double lastMeanScore;
  RLAgent() : lastAction(0), lastMeanScore(0) { for(int i=0;i<4;i++){q[i]=0;n[i]=0;} }
  void init(){ lastAction=0; lastMeanScore=0; for(int i=0;i<4;i++){q[i]=0;n[i]=0;} }
  int chooseAction(int updateCount){ if((updateCount%10)==0) return updateCount%4; int b=0; for(int i=1;i<4;i++) if(q[i]>q[b]) b=i; return b; }
  void updateReward(double newMeanScore){ double r=newMeanScore-lastMeanScore; n[lastAction]++; q[lastAction]+=(r-q[lastAction])/(double)n[lastAction]; lastMeanScore=newMeanScore; }
};

class PCAModel {
public:
  double hist[PCA_WINDOW][PCA_DIM];
  double mean[PCA_DIM];
  double stdev[PCA_DIM];
  double latent[PCA_COMP];
  double explainedVar[PCA_COMP];
  int writeIdx;
  int count;
  int rebuildEvery;
  int updates;
  double dom;
  double rot;
  double prevExplained0;

  PCAModel() : writeIdx(0), count(0), rebuildEvery(PCA_REBUILD_EVERY), updates(0), dom(0), rot(0), prevExplained0(0) {
    memset(hist, 0, sizeof(hist));
    memset(mean, 0, sizeof(mean));
    memset(stdev, 0, sizeof(stdev));
    memset(latent, 0, sizeof(latent));
    memset(explainedVar, 0, sizeof(explainedVar));
  }

  void init() {
    writeIdx = 0;
    count = 0;
    updates = 0;
    dom = 0;
    rot = 0;
    prevExplained0 = 0;
    memset(hist, 0, sizeof(hist));
    memset(mean, 0, sizeof(mean));
    memset(stdev, 0, sizeof(stdev));
    memset(latent, 0, sizeof(latent));
    memset(explainedVar, 0, sizeof(explainedVar));
  }

  void pushSnapshot(const double x[PCA_DIM]) {
    for(int d=0; d<PCA_DIM; d++) hist[writeIdx][d] = x[d];
    writeIdx = (writeIdx + 1) % PCA_WINDOW;
    if(count < PCA_WINDOW) count++;
  }

  void rebuildStats() {
    if(count <= 0) return;
    for(int d=0; d<PCA_DIM; d++) {
      double m = 0;
      for(int i=0; i<count; i++) m += hist[i][d];
      m /= (double)count;
      mean[d] = m;

      double v = 0;
      for(int i=0; i<count; i++) {
        double dd = hist[i][d] - m;
        v += dd * dd;
      }
      v /= (double)count;
      stdev[d] = sqrt(v + EPS);
    }
  }

  void update(const LearningSnapshot& snap, int regime, double conf) {
    double x[PCA_DIM];
    x[0] = snap.meanScore;
    x[1] = snap.meanCompactness;
    x[2] = snap.meanVol;
    x[3] = (double)regime / 2.0;
    x[4] = conf;
    x[5] = snap.meanScore - snap.meanCompactness;

    pushSnapshot(x);
    updates++;
    if((updates % rebuildEvery) == 0 || count < 4) rebuildStats();

    double z[PCA_DIM];
    for(int d=0; d<PCA_DIM; d++) z[d] = (x[d] - mean[d]) / (stdev[d] + EPS);

    latent[0] = 0.60*z[0] + 0.30*z[1] + 0.10*z[2];
    latent[1] = 0.25*z[0] - 0.45*z[1] + 0.20*z[2] + 0.10*z[4];
    latent[2] = 0.20*z[2] + 0.50*z[3] - 0.30*z[5];

    double a0 = fabs(latent[0]);
    double a1 = fabs(latent[1]);
    double a2 = fabs(latent[2]);
    double sumA = a0 + a1 + a2 + EPS;

    explainedVar[0] = a0 / sumA;
    explainedVar[1] = a1 / sumA;
    explainedVar[2] = a2 / sumA;

    dom = explainedVar[0];
    rot = fabs(explainedVar[0] - prevExplained0);
    prevExplained0 = explainedVar[0];
  }
};

class GMMRegimeModel {
public:
  double pi[GMM_K];
  double mu[GMM_K][GMM_DIM];
  double var[GMM_K][GMM_DIM];
  double p[GMM_K];
  double entropy;
  double conf;
  int bestRegime;
  int initialized;

  GMMRegimeModel() : entropy(0), conf(0), bestRegime(0), initialized(0) {
    memset(pi, 0, sizeof(pi));
    memset(mu, 0, sizeof(mu));
    memset(var, 0, sizeof(var));
    memset(p, 0, sizeof(p));
  }

  void init() {
    initialized = 0;
    entropy = 0;
    conf = 0;
    bestRegime = 0;
    for(int k=0;k<GMM_K;k++) {
      pi[k] = 1.0 / (double)GMM_K;
      for(int d=0; d<GMM_DIM; d++) {
        mu[k][d] = 0.02 * (k - 1);
        var[k][d] = 1.0;
      }
      p[k] = 1.0 / (double)GMM_K;
    }
    initialized = 1;
  }

  static double gaussianDiag(const double* x, const double* m, const double* v) {
    double logp = 0;
    for(int d=0; d<GMM_DIM; d++) {
      double vv = v[d];
      if(vv < GMM_VAR_FLOOR) vv = GMM_VAR_FLOOR;
      double z = x[d] - m[d];
      logp += -0.5 * (z*z / vv + log(vv + EPS));
    }
    if(logp < -80.0) logp = -80.0;
    return exp(logp);
  }

  void infer(const double x[GMM_DIM]) {
    if(!initialized) init();
    double sum = 0;
    for(int k=0;k<GMM_K;k++) {
      double g = gaussianDiag(x, mu[k], var[k]);
      p[k] = pi[k] * g;
      sum += p[k];
    }
    if(sum < EPS) {
      for(int k=0;k<GMM_K;k++) p[k] = 1.0 / (double)GMM_K;
    } else {
      for(int k=0;k<GMM_K;k++) p[k] /= sum;
    }

    bestRegime = 0;
    conf = p[0];
    for(int k=1;k<GMM_K;k++) {
      if(p[k] > conf) {
        conf = p[k];
        bestRegime = k;
      }
    }

    entropy = 0;
    for(int k=0;k<GMM_K;k++) entropy -= p[k] * log(p[k] + EPS);

#if GMM_ONLINE_UPDATE
    // lightweight incremental update (EM-like with forgetting)
    for(int k=0;k<GMM_K;k++) {
      double w = GMM_ALPHA * p[k];
      pi[k] = (1.0 - GMM_ALPHA) * pi[k] + w;
      for(int d=0; d<GMM_DIM; d++) {
        double diff = x[d] - mu[k][d];
        mu[k][d] += w * diff;
        var[k][d] = (1.0 - w) * var[k][d] + w * diff * diff;
        if(var[k][d] < GMM_VAR_FLOOR) var[k][d] = GMM_VAR_FLOOR;
      }
    }
#endif
  }
};

class StrategyController {
public:
  UnsupervisedModel unsup;
  RLAgent rl;
  PCAModel pca;
  GMMRegimeModel gmm;
  int dynamicTopK;
  double scoreScale;
  int regime;
  double adaptiveGamma;
  double adaptiveAlpha;
  double adaptiveBeta;
  double adaptiveLambda;
  double riskScale;

  StrategyController()
  : dynamicTopK(TOP_K), scoreScale(1.0), regime(0),
    adaptiveGamma(1.0), adaptiveAlpha(1.0), adaptiveBeta(1.0), adaptiveLambda(1.0), riskScale(1.0) {}

  static double clampRange(double x, double lo, double hi) {
    if(x < lo) return lo;
    if(x > hi) return hi;
    return x;
  }

  void init() {
    unsup.init();
    rl.init();
    pca.init();
    gmm.init();
    dynamicTopK = TOP_K;
    scoreScale = 1.0;
    regime = 0;
    adaptiveGamma = 1.0;
    adaptiveAlpha = 1.0;
    adaptiveBeta = 1.0;
    adaptiveLambda = 1.0;
    riskScale = 1.0;
  }

  void buildGMMState(const LearningSnapshot& snap, int reg, double conf, double x[GMM_DIM]) {
    x[0] = snap.meanScore;
    x[1] = snap.meanCompactness;
    x[2] = snap.meanVol;
    x[3] = pca.dom;
    x[4] = pca.rot;
    x[5] = (double)reg / 2.0;
    x[6] = conf;
    x[7] = snap.meanScore - snap.meanCompactness;
  }

  void onUpdate(const LearningSnapshot& snap, fvar* scores, int nScores, int updateCount) {
#if USE_ML
    double unsupConf = 0;
    unsup.update(snap, &regime, &unsupConf);
#if USE_PCA
    pca.update(snap, regime, unsupConf);
#else
    pca.dom = 0.5;
    pca.rot = 0.0;
#endif

#if USE_GMM
    double gx[GMM_DIM];
    buildGMMState(snap, regime, unsupConf, gx);
    gmm.infer(gx);
    // regime presets: [gamma, alpha, beta, lambda]
    const double presets[GMM_K][4] = {
      {1.05, 1.00, 0.95, 1.00},
      {0.95, 1.05, 1.05, 0.95},
      {1.00, 0.95, 1.10, 1.05}
    };
    adaptiveGamma = 0;
    adaptiveAlpha = 0;
    adaptiveBeta  = 0;
    adaptiveLambda = 0;
    for(int k=0;k<GMM_K;k++) {
      adaptiveGamma += gmm.p[k] * presets[k][0];
      adaptiveAlpha += gmm.p[k] * presets[k][1];
      adaptiveBeta  += gmm.p[k] * presets[k][2];
      adaptiveLambda += gmm.p[k] * presets[k][3];
    }
    double entNorm = gmm.entropy / log((double)GMM_K + EPS);
    riskScale = clampRange(1.0 - GMM_ENTROPY_COEFF * entNorm, GMM_MIN_RISK, 1.0);
#else
    adaptiveGamma = 1.0 + 0.35 * pca.dom - 0.25 * pca.rot;
    adaptiveAlpha = 1.0 + 0.30 * pca.dom;
    adaptiveBeta  = 1.0 + 0.25 * pca.rot;
    adaptiveLambda = 1.0 + 0.20 * pca.dom - 0.20 * pca.rot;
    riskScale = 1.0;
#endif

    adaptiveGamma = clampRange(adaptiveGamma, 0.80, 1.40);
    adaptiveAlpha = clampRange(adaptiveAlpha, 0.85, 1.35);
    adaptiveBeta  = clampRange(adaptiveBeta, 0.85, 1.35);
    adaptiveLambda = clampRange(adaptiveLambda, 0.85, 1.25);

    rl.updateReward(snap.meanScore);
    rl.lastAction = rl.chooseAction(updateCount);

    int baseTopK = TOP_K;
    if(rl.lastAction == 0) baseTopK = TOP_K - 2;
    else if(rl.lastAction == 1) baseTopK = TOP_K;
    else if(rl.lastAction == 2) baseTopK = TOP_K;
    else baseTopK = TOP_K - 1;

    double profileBias[5] = {1.00, 0.98, 0.99, 0.97, 1.02};
    scoreScale = (1.0 + 0.06 * (adaptiveGamma - 1.0) + 0.04 * (adaptiveAlpha - 1.0) - 0.04 * (adaptiveBeta - 1.0))
               * profileBias[STRATEGY_PROFILE] * riskScale;

    if(pca.dom > 0.60) baseTopK -= 1;
    if(pca.rot > 0.15) baseTopK -= 1;
#if USE_GMM
    if(gmm.bestRegime == 2) baseTopK -= 1;
#endif

    dynamicTopK = baseTopK;
    if(dynamicTopK < 1) dynamicTopK = 1;
    if(dynamicTopK > TOP_K) dynamicTopK = TOP_K;

    for(int i=0; i<nScores; i++) {
      double s = (double)scores[i] * scoreScale;
      if(s > 1.0) s = 1.0;
      if(s < 0.0) s = 0.0;
      scores[i] = (fvar)s;
    }
#else
    (void)snap; (void)scores; (void)nScores; (void)updateCount;
#endif
  }
};

// ---------------------------- Strategy ----------------------------

class CrowdAverseStrategy {
public:
  ExposureTable exposureTable;
  FeatureBufferSoA featSoA;
  OpenCLBackend openCL;

  SlabAllocator<fvar> corrMatrix;
  SlabAllocator<fvar> distMatrix;
  SlabAllocator<fvar> compactness;
  SlabAllocator<fvar> entropy;
  SlabAllocator<fvar> scores;

  SlabAllocator<float> featLinear;
  SlabAllocator<float> corrLinear;

  int barCount;
  int updateCount;
  StrategyController controller;

  CrowdAverseStrategy() : barCount(0), updateCount(0) {}

  void init() {
    printf("CrowdAverse_v6: Initializing...\n");

    exposureTable.init();
    featSoA.init(N_ASSETS, FEAT_WINDOW);

    corrMatrix.init(N_ASSETS * N_ASSETS);
    distMatrix.init(N_ASSETS * N_ASSETS);
    compactness.init(N_ASSETS);
    entropy.init(N_ASSETS);
    scores.init(N_ASSETS);

    featLinear.init(FEAT_N * N_ASSETS * FEAT_WINDOW);
    corrLinear.init(N_ASSETS * N_ASSETS);

    openCL.init();
    printf("CrowdAverse_v6: Ready (OpenCL=%d)\n", openCL.ready);
    controller.init();

    barCount = 0;
    updateCount = 0;
  }

  void shutdown() {
    printf("CrowdAverse_v6: Shutting down...\n");

    openCL.shutdown();

    featSoA.shutdown();
    corrMatrix.shutdown();
    distMatrix.shutdown();
    compactness.shutdown();
    entropy.shutdown();
    scores.shutdown();

    featLinear.shutdown();
    corrLinear.shutdown();
  }

  void computeFeatures(int assetIdx) {
    asset((char*)ASSET_NAMES[assetIdx]);

    vars C = series(priceClose(0));
    vars V = series(Volatility(C, 20));

    if(Bar < 50) return;

    fvar r1 = (fvar)log(C[0] / C[1]);
    fvar rN = (fvar)log(C[0] / C[12]);
    fvar vol = (fvar)V[0];
    fvar zscore = (fvar)((C[0] - C[50]) / (V[0] * 20.0 + EPS));
    fvar rangeP = (fvar)((C[0] - C[50]) / (C[0] + EPS));
    fvar flow = (fvar)(r1 * vol);
    fvar regime = (fvar)((vol > 0.001) ? 1.0 : 0.0);
    fvar volOfVol = (fvar)(vol * vol);
    fvar persistence = (fvar)fabs(r1);

    featSoA.push(0, assetIdx, r1);
    featSoA.push(1, assetIdx, rN);
    featSoA.push(2, assetIdx, vol);
    featSoA.push(3, assetIdx, zscore);
    featSoA.push(4, assetIdx, rangeP);
    featSoA.push(5, assetIdx, flow);
    featSoA.push(6, assetIdx, regime);
    featSoA.push(7, assetIdx, volOfVol);
    featSoA.push(8, assetIdx, persistence);
  }

  fvar computeEntropy(int assetIdx) {
    fvar mean = 0;
    for(int t=0; t<FEAT_WINDOW; t++) mean += featSoA.get(0, assetIdx, t);
    mean /= FEAT_WINDOW;
    fvar var = 0;
    for(int t=0; t<FEAT_WINDOW; t++) { fvar d = featSoA.get(0, assetIdx, t) - mean; var += d*d; }
    return (fvar)(var / FEAT_WINDOW);
  }

  void computeCorrelationMatrixCPU() {
    for(int i=0;i<N_ASSETS*N_ASSETS;i++) corrMatrix[i] = 0;

    for(int f=0; f<FEAT_N; f++){
      for(int a=0; a<N_ASSETS; a++){
        for(int b=a+1; b<N_ASSETS; b++){
          fvar mx = 0, my = 0;
          for(int t=0; t<FEAT_WINDOW; t++){
            mx += featSoA.get(f,a,t);
            my += featSoA.get(f,b,t);
          }
          mx /= (fvar)FEAT_WINDOW;
          my /= (fvar)FEAT_WINDOW;

          fvar sxx = 0, syy = 0, sxy = 0;
          for(int t=0; t<FEAT_WINDOW; t++){
            fvar dx = featSoA.get(f,a,t) - mx;
            fvar dy = featSoA.get(f,b,t) - my;
            sxx += dx*dx;
            syy += dy*dy;
            sxy += dx*dy;
          }

          fvar den = (fvar)sqrt((double)(sxx*syy + (fvar)EPS));
          fvar corr = 0;
          if(den > (fvar)EPS) corr = sxy / den;
          else corr = 0;

          int idx = a*N_ASSETS + b;
          corrMatrix[idx] += corr / (fvar)FEAT_N;
          corrMatrix[b*N_ASSETS + a] = corrMatrix[idx];
        }
      }
    }
  }

  void buildFeatLinear() {
    int idx = 0;
    for(int f=0; f<FEAT_N; f++){
      for(int a=0; a<N_ASSETS; a++){
        for(int t=0; t<FEAT_WINDOW; t++){
          featLinear[idx] = (float)featSoA.get(f, a, t);
          idx++;
        }
      }
    }
  }

  void computeCorrelationMatrix() {
    if(openCL.ready) {
      buildFeatLinear();

      for(int i=0;i<N_ASSETS*N_ASSETS;i++) corrLinear[i] = 0.0f;

      int ok = openCL.computeCorrelationMatrixCL(
        featLinear.data,
        corrLinear.data,
        N_ASSETS,
        FEAT_N,
        FEAT_WINDOW
      );

      if(ok) {
        for(int i=0;i<N_ASSETS*N_ASSETS;i++) corrMatrix[i] = (fvar)0;

        for(int a=0; a<N_ASSETS; a++){
          corrMatrix[a*N_ASSETS + a] = (fvar)1.0;
          for(int b=a+1; b<N_ASSETS; b++){
            float c = corrLinear[a*N_ASSETS + b];
            corrMatrix[a*N_ASSETS + b] = (fvar)c;
            corrMatrix[b*N_ASSETS + a] = (fvar)c;
          }
        }
        return;
      }

      printf("OpenCL: runtime fail -> CPU fallback\n");
      openCL.ready = 0;
    }

    computeCorrelationMatrixCPU();
  }

  void computeDistanceMatrix() {
    for(int i=0;i<N_ASSETS;i++){
      for(int j=0;j<N_ASSETS;j++){
        if(i == j) {
          distMatrix[i*N_ASSETS + j] = (fvar)0;
        } else {
          fvar corrDist = (fvar)1.0 - (fvar)fabs((double)corrMatrix[i*N_ASSETS + j]);
          fvar expDist  = (fvar)exposureTable.getDist(i, j);
          fvar blended = (fvar)LAMBDA_META * corrDist + (fvar)(1.0 - (double)LAMBDA_META) * expDist;
          distMatrix[i*N_ASSETS + j] = blended;
        }
      }
    }
  }

  void floydWarshall() {
    fvar d[28][28];

    for(int i=0;i<N_ASSETS;i++){
      for(int j=0;j<N_ASSETS;j++){
        d[i][j] = distMatrix[i*N_ASSETS + j];
        if(i == j) d[i][j] = (fvar)0;
        if(d[i][j] < (fvar)0) d[i][j] = (fvar)INF;
      }
    }

    for(int k=0;k<N_ASSETS;k++){
      for(int i=0;i<N_ASSETS;i++){
        for(int j=0;j<N_ASSETS;j++){
          if(d[i][k] < (fvar)INF && d[k][j] < (fvar)INF) {
            fvar nk = d[i][k] + d[k][j];
            if(nk < d[i][j]) d[i][j] = nk;
          }
        }
      }
    }

    for(int i=0;i<N_ASSETS;i++){
      fvar w = 0;
      for(int j=i+1;j<N_ASSETS;j++){
        if(d[i][j] < (fvar)INF) w += d[i][j];
      }
      if(w > (fvar)0) compactness[i] = (fvar)(1.0 / (1.0 + (double)w));
      else compactness[i] = (fvar)0;
      entropy[i] = computeEntropy(i);
    }
  }

  void computeScores() {
    for(int i=0;i<N_ASSETS;i++){
      fvar coupling = 0;
      int count = 0;

      for(int j=0;j<N_ASSETS;j++){
        if(i != j && distMatrix[i*N_ASSETS + j] < (fvar)INF) {
          coupling += compactness[j];
          count++;
        }
      }

      fvar pCouple = 0;
      if(count > 0) pCouple = coupling / (fvar)count;
      else pCouple = (fvar)0;

      fvar C_A = compactness[i];
      fvar Ent = entropy[i];

      fvar rawScore = (fvar)ALPHA * Ent + (fvar)GAMMA * C_A - (fvar)BETA * pCouple;

      if(rawScore > (fvar)30) rawScore = (fvar)30;
      if(rawScore < (fvar)-30) rawScore = (fvar)-30;

      scores[i] = (fvar)(1.0 / (1.0 + exp(-(double)rawScore)));
    }
  }

  LearningSnapshot buildSnapshot() {
    LearningSnapshot s;
    s.meanScore = 0; s.meanCompactness = 0; s.meanVol = 0;
    for(int i=0;i<N_ASSETS;i++) {
      s.meanScore += (double)scores[i];
      s.meanCompactness += (double)compactness[i];
      s.meanVol += (double)featSoA.get(2, i, 0);
    }
    s.meanScore /= (double)N_ASSETS;
    s.meanCompactness /= (double)N_ASSETS;
    s.meanVol /= (double)N_ASSETS;
    s.regime = 0;
    s.regimeConfidence = 0;
    return s;
  }

  void onBar() {
    barCount++;

    for(int i=0;i<N_ASSETS;i++) computeFeatures(i);

    if(barCount % UPDATE_EVERY == 0) {
      updateCount++;

      computeCorrelationMatrix();
      computeDistanceMatrix();
      floydWarshall();
      computeScores();
      controller.onUpdate(buildSnapshot(), scores.data, N_ASSETS, updateCount);
      printTopK();
    }
  }

  void printTopK() {
    int indices[N_ASSETS];
    for(int i=0;i<N_ASSETS;i++) indices[i] = i;

    int topN = controller.dynamicTopK;
    for(int i=0;i<topN;i++){
      for(int j=i+1;j<N_ASSETS;j++){
        if(scores[indices[j]] > scores[indices[i]]) {
          int tmp = indices[i];
          indices[i] = indices[j];
          indices[j] = tmp;
        }
      }
    }

    if(updateCount % 10 == 0) {
      printf("===CrowdAverse_v6 Top-K(update#%d,OpenCL=%d)===\n",
        updateCount, openCL.ready);

      for(int i=0;i<topN;i++){
        int idx = indices[i];
        printf(" %d.%s: score=%.4f, C=%.4f, Ent=%.6f\n", i+1, ASSET_NAMES[idx], (double)scores[idx], (double)compactness[idx], (double)entropy[idx]);
      }
    }
  }
};

// ---------------------------- Zorro DLL entry ----------------------------

static CrowdAverseStrategy* S = NULL;

DLLFUNC void run()
{
  if(is(INITRUN)) {
    BarPeriod = 60;
    LookBack = max(LookBack, FEAT_WINDOW + 50);

    asset((char*)ASSET_NAMES[0]);

    if(!S) {
      S = new CrowdAverseStrategy();
      S->init();
    }
  }

  if(is(EXITRUN)) {
    if(S) {
      S->shutdown();
      delete S;
      S = NULL;
    }
    return;
  }

  if(!S || Bar < LookBack)
    return;

  S->onBar();
}

ZorroGPT - https://bit.ly/3Gbsm4S