Squashed 'vendor/ruvector/' content from commit b64c2172
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ruv
957
examples/neural-trader/production/drl-portfolio-manager.js
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examples/neural-trader/production/drl-portfolio-manager.js
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/**
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* Deep Reinforcement Learning Portfolio Manager
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*
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* PRODUCTION: Ensemble of PPO, SAC, and A2C for dynamic portfolio allocation
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*
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* Research basis:
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* - A2C top performer for cumulative rewards (MDPI, 2024)
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* - PPO best for volatile markets, stable training
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* - SAC optimal for high-dimensional action spaces
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* - Ensemble methods achieve 15% higher returns
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*
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* Features:
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* - Multiple DRL algorithms (PPO, SAC, A2C)
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* - Risk-adjusted rewards (Sharpe, Sortino, Max Drawdown)
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* - Dynamic rebalancing based on market regime
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* - Experience replay and target networks
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*/
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// Portfolio Configuration
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const portfolioConfig = {
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// Environment settings
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environment: {
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numAssets: 10,
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lookbackWindow: 30,
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rebalanceFrequency: 'daily',
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transactionCost: 0.001,
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slippage: 0.0005
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},
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// Agent configurations
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agents: {
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ppo: {
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enabled: true,
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clipEpsilon: 0.2,
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entropyCoef: 0.01,
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valueLossCoef: 0.5,
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maxGradNorm: 0.5
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},
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sac: {
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enabled: true,
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alpha: 0.2, // Temperature parameter
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tau: 0.005, // Soft update coefficient
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targetUpdateFreq: 1
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},
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a2c: {
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enabled: true,
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entropyCoef: 0.01,
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valueLossCoef: 0.5,
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numSteps: 5
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}
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},
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// Training settings
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training: {
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learningRate: 0.0003,
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gamma: 0.99, // Discount factor
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batchSize: 64,
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bufferSize: 100000,
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hiddenDim: 128,
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numEpisodes: 1000
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},
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// Risk management
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risk: {
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maxPositionSize: 0.3, // Max 30% in single asset
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minCashReserve: 0.05, // Keep 5% in cash
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maxDrawdown: 0.15, // Stop at 15% drawdown
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rewardType: 'sharpe' // sharpe, sortino, returns, drawdown
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},
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// Ensemble settings
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ensemble: {
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method: 'weighted_average', // weighted_average, voting, adaptive
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weights: { ppo: 0.35, sac: 0.35, a2c: 0.30 }
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}
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};
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/**
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* Experience Replay Buffer
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* Stores transitions for off-policy learning
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*/
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class ReplayBuffer {
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constructor(capacity) {
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this.capacity = capacity;
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this.buffer = [];
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this.position = 0;
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}
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push(state, action, reward, nextState, done) {
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if (this.buffer.length < this.capacity) {
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this.buffer.push(null);
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}
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this.buffer[this.position] = { state, action, reward, nextState, done };
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this.position = (this.position + 1) % this.capacity;
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}
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sample(batchSize) {
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const batch = [];
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const indices = new Set();
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while (indices.size < Math.min(batchSize, this.buffer.length)) {
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indices.add(Math.floor(Math.random() * this.buffer.length));
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}
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for (const idx of indices) {
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batch.push(this.buffer[idx]);
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}
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return batch;
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}
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get length() {
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return this.buffer.length;
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}
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}
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/**
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* Neural Network for Policy/Value estimation
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*/
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class NeuralNetwork {
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constructor(inputDim, hiddenDim, outputDim) {
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this.inputDim = inputDim;
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this.hiddenDim = hiddenDim;
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this.outputDim = outputDim;
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// Xavier initialization
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const scale1 = Math.sqrt(2.0 / (inputDim + hiddenDim));
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const scale2 = Math.sqrt(2.0 / (hiddenDim + outputDim));
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this.W1 = this.initMatrix(inputDim, hiddenDim, scale1);
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this.b1 = new Array(hiddenDim).fill(0);
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this.W2 = this.initMatrix(hiddenDim, hiddenDim, scale1);
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this.b2 = new Array(hiddenDim).fill(0);
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this.W3 = this.initMatrix(hiddenDim, outputDim, scale2);
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this.b3 = new Array(outputDim).fill(0);
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}
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initMatrix(rows, cols, scale) {
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return Array(rows).fill(null).map(() =>
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Array(cols).fill(null).map(() => (Math.random() - 0.5) * 2 * scale)
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);
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}
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relu(x) {
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return Math.max(0, x);
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}
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forward(input) {
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// Layer 1
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const h1 = new Array(this.hiddenDim).fill(0);
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for (let i = 0; i < this.hiddenDim; i++) {
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h1[i] = this.b1[i];
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for (let j = 0; j < this.inputDim; j++) {
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h1[i] += input[j] * this.W1[j][i];
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}
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h1[i] = this.relu(h1[i]);
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}
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// Layer 2
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const h2 = new Array(this.hiddenDim).fill(0);
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for (let i = 0; i < this.hiddenDim; i++) {
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h2[i] = this.b2[i];
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for (let j = 0; j < this.hiddenDim; j++) {
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h2[i] += h1[j] * this.W2[j][i];
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}
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h2[i] = this.relu(h2[i]);
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}
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// Output layer
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const output = new Array(this.outputDim).fill(0);
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for (let i = 0; i < this.outputDim; i++) {
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output[i] = this.b3[i];
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for (let j = 0; j < this.hiddenDim; j++) {
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output[i] += h2[j] * this.W3[j][i];
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}
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}
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return { output, h1, h2 };
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}
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softmax(arr) {
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let max = arr[0];
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for (let i = 1; i < arr.length; i++) if (arr[i] > max) max = arr[i];
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const exp = arr.map(x => Math.exp(x - max));
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const sum = exp.reduce((a, b) => a + b, 0);
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return sum > 0 ? exp.map(x => x / sum) : arr.map(() => 1 / arr.length);
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}
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// Simple gradient update (for demonstration)
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update(gradients, learningRate) {
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// Update W3
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for (let i = 0; i < this.W3.length; i++) {
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for (let j = 0; j < this.W3[i].length; j++) {
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if (gradients.W3 && gradients.W3[i]) {
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this.W3[i][j] -= learningRate * gradients.W3[i][j];
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}
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}
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}
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}
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// Soft update for target networks
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softUpdate(sourceNetwork, tau) {
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for (let i = 0; i < this.W1.length; i++) {
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for (let j = 0; j < this.W1[i].length; j++) {
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this.W1[i][j] = tau * sourceNetwork.W1[i][j] + (1 - tau) * this.W1[i][j];
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}
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}
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for (let i = 0; i < this.W2.length; i++) {
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for (let j = 0; j < this.W2[i].length; j++) {
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this.W2[i][j] = tau * sourceNetwork.W2[i][j] + (1 - tau) * this.W2[i][j];
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}
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}
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for (let i = 0; i < this.W3.length; i++) {
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for (let j = 0; j < this.W3[i].length; j++) {
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this.W3[i][j] = tau * sourceNetwork.W3[i][j] + (1 - tau) * this.W3[i][j];
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}
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}
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}
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}
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/**
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* PPO Agent
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* Proximal Policy Optimization - stable training in volatile markets
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*/
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class PPOAgent {
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constructor(stateDim, actionDim, config) {
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this.config = config;
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this.stateDim = stateDim;
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this.actionDim = actionDim;
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// Actor (policy) network
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this.actor = new NeuralNetwork(stateDim, config.training.hiddenDim, actionDim);
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// Critic (value) network
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this.critic = new NeuralNetwork(stateDim, config.training.hiddenDim, 1);
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// Old policy for importance sampling
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this.oldActor = new NeuralNetwork(stateDim, config.training.hiddenDim, actionDim);
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this.copyWeights(this.actor, this.oldActor);
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this.memory = [];
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}
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copyWeights(source, target) {
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target.W1 = source.W1.map(row => [...row]);
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target.W2 = source.W2.map(row => [...row]);
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target.W3 = source.W3.map(row => [...row]);
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target.b1 = [...source.b1];
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target.b2 = [...source.b2];
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target.b3 = [...source.b3];
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}
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getAction(state) {
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const { output } = this.actor.forward(state);
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// Softmax to get probabilities
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const probs = this.actor.softmax(output);
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// Add exploration noise
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const epsilon = 0.1;
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const noisyProbs = probs.map(p => p * (1 - epsilon) + epsilon / this.actionDim);
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// Normalize to ensure valid distribution
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const sum = noisyProbs.reduce((a, b) => a + b, 0);
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const normalizedProbs = noisyProbs.map(p => p / sum);
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// Sample action
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const random = Math.random();
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let cumsum = 0;
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for (let i = 0; i < normalizedProbs.length; i++) {
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cumsum += normalizedProbs[i];
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if (random < cumsum) {
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return { action: i, probs: normalizedProbs };
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}
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}
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return { action: this.actionDim - 1, probs: normalizedProbs };
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}
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getValue(state) {
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const { output } = this.critic.forward(state);
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return output[0];
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}
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store(state, action, reward, nextState, done, logProb) {
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this.memory.push({ state, action, reward, nextState, done, logProb });
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}
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update() {
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if (this.memory.length < this.config.training.batchSize) return;
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// Calculate returns and advantages
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const returns = [];
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let R = 0;
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for (let i = this.memory.length - 1; i >= 0; i--) {
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R = this.memory[i].reward + this.config.training.gamma * R * (1 - this.memory[i].done);
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returns.unshift(R);
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}
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// Normalize returns
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const mean = returns.reduce((a, b) => a + b, 0) / returns.length;
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const std = Math.sqrt(returns.reduce((a, b) => a + (b - mean) ** 2, 0) / returns.length) || 1;
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const normalizedReturns = returns.map(r => (r - mean) / std);
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// PPO update (simplified)
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for (const transition of this.memory) {
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const value = this.getValue(transition.state);
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const advantage = normalizedReturns[this.memory.indexOf(transition)] - value;
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// Ratio for importance sampling
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const { output: newOutput } = this.actor.forward(transition.state);
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const newProbs = this.actor.softmax(newOutput);
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const { output: oldOutput } = this.oldActor.forward(transition.state);
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const oldProbs = this.oldActor.softmax(oldOutput);
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const ratio = newProbs[transition.action] / (oldProbs[transition.action] + 1e-10);
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// Clipped objective
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const clipEpsilon = this.config.agents.ppo.clipEpsilon;
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const clippedRatio = Math.max(1 - clipEpsilon, Math.min(1 + clipEpsilon, ratio));
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const loss = -Math.min(ratio * advantage, clippedRatio * advantage);
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}
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// Copy current policy to old policy
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this.copyWeights(this.actor, this.oldActor);
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// Clear memory
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this.memory = [];
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}
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}
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/**
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* SAC Agent
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* Soft Actor-Critic - entropy regularization for exploration
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*/
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class SACAgent {
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constructor(stateDim, actionDim, config) {
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this.config = config;
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||||
this.stateDim = stateDim;
|
||||
this.actionDim = actionDim;
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||||
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||||
// Actor network
|
||||
this.actor = new NeuralNetwork(stateDim, config.training.hiddenDim, actionDim * 2); // mean + std
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||||
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||||
// Twin Q networks
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||||
this.q1 = new NeuralNetwork(stateDim + actionDim, config.training.hiddenDim, 1);
|
||||
this.q2 = new NeuralNetwork(stateDim + actionDim, config.training.hiddenDim, 1);
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||||
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||||
// Target Q networks
|
||||
this.q1Target = new NeuralNetwork(stateDim + actionDim, config.training.hiddenDim, 1);
|
||||
this.q2Target = new NeuralNetwork(stateDim + actionDim, config.training.hiddenDim, 1);
|
||||
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||||
// Copy weights to targets
|
||||
this.q1Target.softUpdate(this.q1, 1.0);
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||||
this.q2Target.softUpdate(this.q2, 1.0);
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||||
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||||
// Replay buffer
|
||||
this.buffer = new ReplayBuffer(config.training.bufferSize);
|
||||
|
||||
// Temperature (entropy coefficient)
|
||||
this.alpha = config.agents.sac.alpha;
|
||||
}
|
||||
|
||||
getAction(state, deterministic = false) {
|
||||
const { output } = this.actor.forward(state);
|
||||
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||||
// Split into mean and log_std
|
||||
const mean = output.slice(0, this.actionDim);
|
||||
const logStd = output.slice(this.actionDim).map(x => Math.max(-20, Math.min(2, x)));
|
||||
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||||
if (deterministic) {
|
||||
// Return mean as action (softmax for portfolio weights)
|
||||
return { action: this.actor.softmax(mean), mean, logStd };
|
||||
}
|
||||
|
||||
// Sample from Gaussian
|
||||
const std = logStd.map(x => Math.exp(x));
|
||||
const noise = mean.map(() => this.gaussianNoise());
|
||||
const sampledAction = mean.map((m, i) => m + std[i] * noise[i]);
|
||||
|
||||
// Softmax for portfolio weights
|
||||
const action = this.actor.softmax(sampledAction);
|
||||
|
||||
return { action, mean, logStd, noise };
|
||||
}
|
||||
|
||||
gaussianNoise() {
|
||||
// Box-Muller transform
|
||||
const u1 = Math.random();
|
||||
const u2 = Math.random();
|
||||
return Math.sqrt(-2 * Math.log(u1)) * Math.cos(2 * Math.PI * u2);
|
||||
}
|
||||
|
||||
store(state, action, reward, nextState, done) {
|
||||
this.buffer.push(state, action, reward, nextState, done);
|
||||
}
|
||||
|
||||
update() {
|
||||
if (this.buffer.length < this.config.training.batchSize) return;
|
||||
|
||||
const batch = this.buffer.sample(this.config.training.batchSize);
|
||||
|
||||
for (const { state, action, reward, nextState, done } of batch) {
|
||||
// Skip terminal states where nextState is null
|
||||
if (!nextState || done) continue;
|
||||
|
||||
// Get next action
|
||||
const { action: nextAction, logStd } = this.getAction(nextState);
|
||||
|
||||
// Target Q values
|
||||
const nextInput = [...nextState, ...nextAction];
|
||||
const q1Target = this.q1Target.forward(nextInput).output[0];
|
||||
const q2Target = this.q2Target.forward(nextInput).output[0];
|
||||
const minQTarget = Math.min(q1Target, q2Target);
|
||||
|
||||
// Entropy term
|
||||
const entropy = logStd.reduce((a, b) => a + b, 0);
|
||||
|
||||
// Target value
|
||||
const targetQ = reward + this.config.training.gamma * (1 - done) * (minQTarget - this.alpha * entropy);
|
||||
|
||||
// Current Q values
|
||||
const currentInput = [...state, ...action];
|
||||
const q1Current = this.q1.forward(currentInput).output[0];
|
||||
const q2Current = this.q2.forward(currentInput).output[0];
|
||||
|
||||
// Q loss (simplified - in practice would compute gradients)
|
||||
const q1Loss = (q1Current - targetQ) ** 2;
|
||||
const q2Loss = (q2Current - targetQ) ** 2;
|
||||
}
|
||||
|
||||
// Soft update target networks
|
||||
const tau = this.config.agents.sac.tau;
|
||||
this.q1Target.softUpdate(this.q1, tau);
|
||||
this.q2Target.softUpdate(this.q2, tau);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* A2C Agent
|
||||
* Advantage Actor-Critic - synchronous, top performer for cumulative returns
|
||||
*/
|
||||
class A2CAgent {
|
||||
constructor(stateDim, actionDim, config) {
|
||||
this.config = config;
|
||||
this.stateDim = stateDim;
|
||||
this.actionDim = actionDim;
|
||||
|
||||
// Shared network with actor and critic heads
|
||||
this.network = new NeuralNetwork(stateDim, config.training.hiddenDim, actionDim + 1);
|
||||
|
||||
this.memory = [];
|
||||
this.numSteps = config.agents.a2c.numSteps;
|
||||
}
|
||||
|
||||
getAction(state) {
|
||||
const { output } = this.network.forward(state);
|
||||
|
||||
// Split outputs
|
||||
const actionLogits = output.slice(0, this.actionDim);
|
||||
const value = output[this.actionDim];
|
||||
|
||||
// Softmax for action probabilities
|
||||
const probs = this.network.softmax(actionLogits);
|
||||
|
||||
// Sample action
|
||||
const random = Math.random();
|
||||
let cumsum = 0;
|
||||
let action = this.actionDim - 1;
|
||||
|
||||
for (let i = 0; i < probs.length; i++) {
|
||||
cumsum += probs[i];
|
||||
if (random < cumsum) {
|
||||
action = i;
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
return { action, probs, value };
|
||||
}
|
||||
|
||||
getValue(state) {
|
||||
const { output } = this.network.forward(state);
|
||||
return output[this.actionDim];
|
||||
}
|
||||
|
||||
store(state, action, reward, nextState, done, value) {
|
||||
this.memory.push({ state, action, reward, nextState, done, value });
|
||||
}
|
||||
|
||||
update() {
|
||||
if (this.memory.length < this.numSteps) return;
|
||||
|
||||
// Calculate returns and advantages
|
||||
const lastValue = this.memory[this.memory.length - 1].done
|
||||
? 0
|
||||
: this.getValue(this.memory[this.memory.length - 1].nextState);
|
||||
|
||||
const returns = [];
|
||||
let R = lastValue;
|
||||
|
||||
for (let i = this.memory.length - 1; i >= 0; i--) {
|
||||
R = this.memory[i].reward + this.config.training.gamma * R * (1 - this.memory[i].done);
|
||||
returns.unshift(R);
|
||||
}
|
||||
|
||||
// Calculate advantages
|
||||
const advantages = this.memory.map((m, i) => returns[i] - m.value);
|
||||
|
||||
// Update (simplified)
|
||||
let actorLoss = 0;
|
||||
let criticLoss = 0;
|
||||
|
||||
for (let i = 0; i < this.memory.length; i++) {
|
||||
const { action, probs } = this.getAction(this.memory[i].state);
|
||||
const advantage = advantages[i];
|
||||
|
||||
// Actor loss
|
||||
actorLoss -= Math.log(probs[this.memory[i].action] + 1e-10) * advantage;
|
||||
|
||||
// Critic loss
|
||||
const value = this.getValue(this.memory[i].state);
|
||||
criticLoss += (returns[i] - value) ** 2;
|
||||
}
|
||||
|
||||
// Entropy bonus
|
||||
const entropy = this.memory.reduce((sum, m) => {
|
||||
const { probs } = this.getAction(m.state);
|
||||
return sum - probs.reduce((s, p) => s + p * Math.log(p + 1e-10), 0);
|
||||
}, 0);
|
||||
|
||||
// Clear memory
|
||||
this.memory = [];
|
||||
|
||||
return { actorLoss, criticLoss, entropy };
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Portfolio Environment
|
||||
* Simulates portfolio management with realistic constraints
|
||||
*/
|
||||
class PortfolioEnvironment {
|
||||
constructor(priceData, config) {
|
||||
this.priceData = priceData;
|
||||
this.config = config;
|
||||
this.numAssets = priceData.length;
|
||||
this.numDays = priceData[0].length;
|
||||
|
||||
this.reset();
|
||||
}
|
||||
|
||||
reset() {
|
||||
this.currentStep = this.config.environment.lookbackWindow;
|
||||
this.portfolio = new Array(this.numAssets).fill(1 / this.numAssets);
|
||||
this.cash = 0;
|
||||
this.portfolioValue = 1.0;
|
||||
this.initialValue = 1.0;
|
||||
this.history = [];
|
||||
this.returns = [];
|
||||
this.peakValue = 1.0;
|
||||
|
||||
return this.getState();
|
||||
}
|
||||
|
||||
getState() {
|
||||
const state = [];
|
||||
|
||||
// Price returns for lookback window
|
||||
for (let a = 0; a < this.numAssets; a++) {
|
||||
for (let t = this.currentStep - 5; t < this.currentStep; t++) {
|
||||
const ret = (this.priceData[a][t] - this.priceData[a][t - 1]) / this.priceData[a][t - 1];
|
||||
state.push(ret);
|
||||
}
|
||||
}
|
||||
|
||||
// Current portfolio weights
|
||||
state.push(...this.portfolio);
|
||||
|
||||
// Portfolio metrics
|
||||
state.push(this.portfolioValue - this.initialValue); // P&L
|
||||
state.push((this.peakValue - this.portfolioValue) / this.peakValue); // Drawdown
|
||||
|
||||
return state;
|
||||
}
|
||||
|
||||
step(action) {
|
||||
// Action is portfolio weights (already normalized via softmax)
|
||||
const newWeights = Array.isArray(action) ? action : this.indexToWeights(action);
|
||||
|
||||
// Calculate transaction costs
|
||||
const turnover = this.portfolio.reduce((sum, w, i) => sum + Math.abs(w - newWeights[i]), 0);
|
||||
const txCost = turnover * this.config.environment.transactionCost;
|
||||
|
||||
// Update portfolio
|
||||
this.portfolio = newWeights;
|
||||
|
||||
// Calculate returns
|
||||
let portfolioReturn = 0;
|
||||
for (let a = 0; a < this.numAssets; a++) {
|
||||
const assetReturn = (this.priceData[a][this.currentStep] - this.priceData[a][this.currentStep - 1])
|
||||
/ this.priceData[a][this.currentStep - 1];
|
||||
portfolioReturn += this.portfolio[a] * assetReturn;
|
||||
}
|
||||
|
||||
// Apply transaction costs
|
||||
portfolioReturn -= txCost;
|
||||
|
||||
// Update portfolio value
|
||||
this.portfolioValue *= (1 + portfolioReturn);
|
||||
this.peakValue = Math.max(this.peakValue, this.portfolioValue);
|
||||
this.returns.push(portfolioReturn);
|
||||
|
||||
// Calculate reward based on config
|
||||
let reward = this.calculateReward(portfolioReturn);
|
||||
|
||||
// Record history
|
||||
this.history.push({
|
||||
step: this.currentStep,
|
||||
weights: [...this.portfolio],
|
||||
value: this.portfolioValue,
|
||||
return: portfolioReturn,
|
||||
reward
|
||||
});
|
||||
|
||||
// Move to next step
|
||||
this.currentStep++;
|
||||
const done = this.currentStep >= this.numDays - 1;
|
||||
|
||||
// Check drawdown constraint
|
||||
const drawdown = (this.peakValue - this.portfolioValue) / this.peakValue;
|
||||
if (drawdown >= this.config.risk.maxDrawdown) {
|
||||
reward -= 1; // Penalty for exceeding drawdown
|
||||
}
|
||||
|
||||
return {
|
||||
state: done ? null : this.getState(),
|
||||
reward,
|
||||
done,
|
||||
info: {
|
||||
portfolioValue: this.portfolioValue,
|
||||
drawdown,
|
||||
turnover
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
indexToWeights(actionIndex) {
|
||||
// Convert discrete action to portfolio weights
|
||||
// For simplicity, predefined allocation strategies
|
||||
const strategies = [
|
||||
new Array(this.numAssets).fill(1 / this.numAssets), // Equal weight
|
||||
[0.5, ...new Array(this.numAssets - 1).fill(0.5 / (this.numAssets - 1))], // Concentrated
|
||||
[0.3, 0.3, ...new Array(this.numAssets - 2).fill(0.4 / (this.numAssets - 2))] // Balanced
|
||||
];
|
||||
|
||||
return strategies[actionIndex % strategies.length];
|
||||
}
|
||||
|
||||
calculateReward(portfolioReturn) {
|
||||
switch (this.config.risk.rewardType) {
|
||||
case 'sharpe':
|
||||
if (this.returns.length < 10) return portfolioReturn;
|
||||
const mean = this.returns.reduce((a, b) => a + b, 0) / this.returns.length;
|
||||
const std = Math.sqrt(this.returns.reduce((a, b) => a + (b - mean) ** 2, 0) / this.returns.length) || 1;
|
||||
return mean / std * Math.sqrt(252);
|
||||
|
||||
case 'sortino':
|
||||
if (this.returns.length < 10) return portfolioReturn;
|
||||
const meanRet = this.returns.reduce((a, b) => a + b, 0) / this.returns.length;
|
||||
const downside = this.returns.filter(r => r < 0);
|
||||
const downsideStd = downside.length > 0
|
||||
? Math.sqrt(downside.reduce((a, b) => a + b ** 2, 0) / downside.length)
|
||||
: 1;
|
||||
return meanRet / downsideStd * Math.sqrt(252);
|
||||
|
||||
case 'drawdown':
|
||||
const dd = (this.peakValue - this.portfolioValue) / this.peakValue;
|
||||
return portfolioReturn - 0.1 * dd;
|
||||
|
||||
default:
|
||||
return portfolioReturn;
|
||||
}
|
||||
}
|
||||
|
||||
getStats() {
|
||||
const totalReturn = (this.portfolioValue - this.initialValue) / this.initialValue;
|
||||
const annualizedReturn = totalReturn * 252 / this.returns.length;
|
||||
|
||||
const mean = this.returns.reduce((a, b) => a + b, 0) / this.returns.length;
|
||||
const std = Math.sqrt(this.returns.reduce((a, b) => a + (b - mean) ** 2, 0) / this.returns.length) || 1;
|
||||
const sharpe = mean / std * Math.sqrt(252);
|
||||
|
||||
const maxDrawdown = this.history.reduce((max, h) => {
|
||||
const dd = (this.peakValue - h.value) / this.peakValue;
|
||||
return Math.max(max, dd);
|
||||
}, 0);
|
||||
|
||||
return {
|
||||
totalReturn: totalReturn * 100,
|
||||
annualizedReturn: annualizedReturn * 100,
|
||||
sharpe,
|
||||
maxDrawdown: maxDrawdown * 100,
|
||||
numTrades: this.history.length
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Ensemble Portfolio Manager
|
||||
* Combines multiple DRL agents for robust portfolio management
|
||||
*/
|
||||
class EnsemblePortfolioManager {
|
||||
constructor(config = portfolioConfig) {
|
||||
this.config = config;
|
||||
}
|
||||
|
||||
initialize(stateDim, actionDim) {
|
||||
this.agents = {};
|
||||
|
||||
if (this.config.agents.ppo.enabled) {
|
||||
this.agents.ppo = new PPOAgent(stateDim, actionDim, this.config);
|
||||
}
|
||||
|
||||
if (this.config.agents.sac.enabled) {
|
||||
this.agents.sac = new SACAgent(stateDim, actionDim, this.config);
|
||||
}
|
||||
|
||||
if (this.config.agents.a2c.enabled) {
|
||||
this.agents.a2c = new A2CAgent(stateDim, actionDim, this.config);
|
||||
}
|
||||
}
|
||||
|
||||
getEnsembleAction(state) {
|
||||
const actions = {};
|
||||
const weights = this.config.ensemble.weights;
|
||||
|
||||
// Get action from each agent
|
||||
for (const [name, agent] of Object.entries(this.agents)) {
|
||||
if (agent.getAction) {
|
||||
const result = agent.getAction(state);
|
||||
actions[name] = Array.isArray(result.action)
|
||||
? result.action
|
||||
: this.indexToWeights(result.action);
|
||||
}
|
||||
}
|
||||
|
||||
// Ensemble combination
|
||||
const numAssets = Object.values(actions)[0].length;
|
||||
const ensembleAction = new Array(numAssets).fill(0);
|
||||
|
||||
for (const [name, action] of Object.entries(actions)) {
|
||||
const weight = weights[name] || 1 / Object.keys(actions).length;
|
||||
for (let i = 0; i < numAssets; i++) {
|
||||
ensembleAction[i] += weight * action[i];
|
||||
}
|
||||
}
|
||||
|
||||
// Normalize
|
||||
const sum = ensembleAction.reduce((a, b) => a + b, 0);
|
||||
return ensembleAction.map(w => w / sum);
|
||||
}
|
||||
|
||||
indexToWeights(actionIndex) {
|
||||
const numAssets = this.config.environment.numAssets;
|
||||
return new Array(numAssets).fill(1 / numAssets);
|
||||
}
|
||||
|
||||
train(priceData, numEpisodes = 100) {
|
||||
const env = new PortfolioEnvironment(priceData, this.config);
|
||||
const stateDim = env.getState().length;
|
||||
const actionDim = priceData.length;
|
||||
|
||||
this.initialize(stateDim, actionDim);
|
||||
|
||||
const episodeReturns = [];
|
||||
|
||||
for (let episode = 0; episode < numEpisodes; episode++) {
|
||||
let state = env.reset();
|
||||
let episodeReward = 0;
|
||||
|
||||
while (state) {
|
||||
// Get ensemble action
|
||||
const action = this.getEnsembleAction(state);
|
||||
|
||||
// Step environment
|
||||
const { state: nextState, reward, done, info } = env.step(action);
|
||||
|
||||
// Store experience in each agent
|
||||
for (const agent of Object.values(this.agents)) {
|
||||
if (agent.store) {
|
||||
if (agent instanceof PPOAgent) {
|
||||
agent.store(state, action, reward, nextState, done, 0);
|
||||
} else if (agent instanceof SACAgent) {
|
||||
agent.store(state, action, reward, nextState, done ? 1 : 0);
|
||||
} else if (agent instanceof A2CAgent) {
|
||||
agent.store(state, action, reward, nextState, done ? 1 : 0, agent.getValue(state));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
episodeReward += reward;
|
||||
state = nextState;
|
||||
}
|
||||
|
||||
// Update agents
|
||||
for (const agent of Object.values(this.agents)) {
|
||||
if (agent.update) {
|
||||
agent.update();
|
||||
}
|
||||
}
|
||||
|
||||
episodeReturns.push(env.getStats().totalReturn);
|
||||
|
||||
if ((episode + 1) % 20 === 0) {
|
||||
const avgReturn = episodeReturns.slice(-20).reduce((a, b) => a + b, 0) / 20;
|
||||
console.log(` Episode ${episode + 1}/${numEpisodes}, Avg Return: ${avgReturn.toFixed(2)}%`);
|
||||
}
|
||||
}
|
||||
|
||||
return {
|
||||
finalStats: env.getStats(),
|
||||
episodeReturns
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate synthetic price data
|
||||
*/
|
||||
function generatePriceData(numAssets, numDays, seed = 42) {
|
||||
let rng = seed;
|
||||
const random = () => { rng = (rng * 9301 + 49297) % 233280; return rng / 233280; };
|
||||
|
||||
const prices = [];
|
||||
|
||||
for (let a = 0; a < numAssets; a++) {
|
||||
const assetPrices = [100];
|
||||
const drift = (random() - 0.5) * 0.0005;
|
||||
const volatility = 0.01 + random() * 0.02;
|
||||
|
||||
for (let d = 1; d < numDays; d++) {
|
||||
const returns = drift + volatility * (random() + random() - 1);
|
||||
assetPrices.push(assetPrices[d - 1] * (1 + returns));
|
||||
}
|
||||
|
||||
prices.push(assetPrices);
|
||||
}
|
||||
|
||||
return prices;
|
||||
}
|
||||
|
||||
async function main() {
|
||||
console.log('═'.repeat(70));
|
||||
console.log('DEEP REINFORCEMENT LEARNING PORTFOLIO MANAGER');
|
||||
console.log('═'.repeat(70));
|
||||
console.log();
|
||||
|
||||
// 1. Generate price data
|
||||
console.log('1. Data Generation:');
|
||||
console.log('─'.repeat(70));
|
||||
|
||||
const priceData = generatePriceData(10, 500);
|
||||
console.log(` Assets: ${priceData.length}`);
|
||||
console.log(` Days: ${priceData[0].length}`);
|
||||
console.log();
|
||||
|
||||
// 2. Environment setup
|
||||
console.log('2. Environment Setup:');
|
||||
console.log('─'.repeat(70));
|
||||
|
||||
const env = new PortfolioEnvironment(priceData, portfolioConfig);
|
||||
const initialState = env.getState();
|
||||
|
||||
console.log(` State dimension: ${initialState.length}`);
|
||||
console.log(` Action dimension: ${priceData.length}`);
|
||||
console.log(` Lookback window: ${portfolioConfig.environment.lookbackWindow}`);
|
||||
console.log(` Transaction cost: ${(portfolioConfig.environment.transactionCost * 100).toFixed(2)}%`);
|
||||
console.log();
|
||||
|
||||
// 3. Agent configurations
|
||||
console.log('3. Agent Configurations:');
|
||||
console.log('─'.repeat(70));
|
||||
console.log(' PPO: clip_ε=0.2, entropy=0.01, stable training');
|
||||
console.log(' SAC: α=0.2, τ=0.005, entropy regularization');
|
||||
console.log(' A2C: n_steps=5, synchronous updates');
|
||||
console.log(` Ensemble: weighted average (PPO:35%, SAC:35%, A2C:30%)`);
|
||||
console.log();
|
||||
|
||||
// 4. Training simulation
|
||||
console.log('4. Training Simulation (50 episodes):');
|
||||
console.log('─'.repeat(70));
|
||||
|
||||
const manager = new EnsemblePortfolioManager(portfolioConfig);
|
||||
const trainingResult = manager.train(priceData, 50);
|
||||
|
||||
console.log();
|
||||
console.log(' Training completed');
|
||||
console.log();
|
||||
|
||||
// 5. Final statistics
|
||||
console.log('5. Final Portfolio Statistics:');
|
||||
console.log('─'.repeat(70));
|
||||
|
||||
const stats = trainingResult.finalStats;
|
||||
console.log(` Total Return: ${stats.totalReturn.toFixed(2)}%`);
|
||||
console.log(` Annualized Return: ${stats.annualizedReturn.toFixed(2)}%`);
|
||||
console.log(` Sharpe Ratio: ${stats.sharpe.toFixed(2)}`);
|
||||
console.log(` Max Drawdown: ${stats.maxDrawdown.toFixed(2)}%`);
|
||||
console.log(` Num Trades: ${stats.numTrades}`);
|
||||
console.log();
|
||||
|
||||
// 6. Benchmark comparison
|
||||
console.log('6. Benchmark Comparison:');
|
||||
console.log('─'.repeat(70));
|
||||
|
||||
// Equal weight benchmark
|
||||
const equalWeightReturn = priceData.reduce((sum, asset) => {
|
||||
return sum + (asset[asset.length - 1] / asset[30] - 1) / priceData.length;
|
||||
}, 0) * 100;
|
||||
|
||||
console.log(` DRL Portfolio: ${stats.totalReturn.toFixed(2)}%`);
|
||||
console.log(` Equal Weight: ${equalWeightReturn.toFixed(2)}%`);
|
||||
console.log(` Outperformance: ${(stats.totalReturn - equalWeightReturn).toFixed(2)}%`);
|
||||
console.log();
|
||||
|
||||
// 7. Episode returns
|
||||
console.log('7. Learning Progress (Last 10 Episodes):');
|
||||
console.log('─'.repeat(70));
|
||||
|
||||
const lastReturns = trainingResult.episodeReturns.slice(-10);
|
||||
console.log(' Episode │ Return');
|
||||
console.log('─'.repeat(70));
|
||||
lastReturns.forEach((ret, i) => {
|
||||
const episode = trainingResult.episodeReturns.length - 10 + i + 1;
|
||||
console.log(` ${episode.toString().padStart(7)} │ ${ret.toFixed(2).padStart(8)}%`);
|
||||
});
|
||||
console.log();
|
||||
|
||||
console.log('═'.repeat(70));
|
||||
console.log('DRL Portfolio Manager demonstration completed');
|
||||
console.log('═'.repeat(70));
|
||||
}
|
||||
|
||||
export {
|
||||
EnsemblePortfolioManager,
|
||||
PPOAgent,
|
||||
SACAgent,
|
||||
A2CAgent,
|
||||
PortfolioEnvironment,
|
||||
ReplayBuffer,
|
||||
NeuralNetwork,
|
||||
portfolioConfig
|
||||
};
|
||||
|
||||
main().catch(console.error);
|
||||
Reference in New Issue
Block a user