fix: replace fake learning data with real database performance metrics
This commit is contained in:
mindesbunister
@@ -1,158 +1,172 @@
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import { NextResponse } from 'next/server';
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import fs from 'fs/promises';
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import path from 'path';
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import { PrismaClient } from '@prisma/client';
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const PERSISTENT_DATA_FILE = path.join(process.cwd(), 'data', 'learning-persistent.json');
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// Default persistent data structure
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const defaultPersistentData = {
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totalTrades: 0,
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winningTrades: 0,
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losingTrades: 0,
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totalPnL: 0,
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winRate: 0,
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avgWinAmount: 0,
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avgLossAmount: 0,
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bestTrade: 0,
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worstTrade: 0,
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learningDecisions: 0,
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aiEnhancements: 0,
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riskThresholds: {
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emergency: 1,
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risk: 2,
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mediumRisk: 5
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},
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lastUpdated: new Date().toISOString(),
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systemStatus: 'learning',
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dataCollected: true
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};
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async function ensureDataDirectory() {
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const dataDir = path.join(process.cwd(), 'data');
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try {
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await fs.access(dataDir);
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} catch {
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await fs.mkdir(dataDir, { recursive: true });
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}
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}
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async function loadPersistentData() {
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try {
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await ensureDataDirectory();
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const data = await fs.readFile(PERSISTENT_DATA_FILE, 'utf8');
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return JSON.parse(data);
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} catch (error) {
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// File doesn't exist or is invalid, return default data
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return defaultPersistentData;
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}
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}
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async function savePersistentData(data) {
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try {
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await ensureDataDirectory();
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await fs.writeFile(PERSISTENT_DATA_FILE, JSON.stringify(data, null, 2));
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return true;
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} catch (error) {
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console.error('Error saving persistent data:', error);
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return false;
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}
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}
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const prisma = new PrismaClient();
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export async function GET() {
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try {
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const persistentData = await loadPersistentData();
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// Get current automation status if available
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let currentStatus = null;
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let learningStatus = null;
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try {
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const { getAutomationInstance } = await import('../../../../lib/automation-singleton.js');
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const automation = await getAutomationInstance();
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if (automation) {
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currentStatus = automation.getStatus();
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// If automation has learning status, get it too
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if (typeof automation.getLearningStatus === 'function') {
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learningStatus = await automation.getLearningStatus();
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if (learningStatus && learningStatus.report) {
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// Update some data from current learning status
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persistentData.lastUpdated = new Date().toISOString();
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persistentData.systemStatus = learningStatus.enabled ? 'active' : 'standby';
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}
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}
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// Get real trading data from database
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const trades = await prisma.trades.findMany({
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orderBy: { createdAt: 'desc' },
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take: 50, // Last 50 trades for meaningful statistics
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select: {
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id: true,
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symbol: true,
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side: true,
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amount: true,
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entryPrice: true,
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exitPrice: true,
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profit: true,
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leverage: true,
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confidence: true,
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createdAt: true,
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outcome: true
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}
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} catch (error) {
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console.warn('Could not get current automation status:', error.message);
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}
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});
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return NextResponse.json({
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success: true,
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persistentData: {
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...persistentData,
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isLive: currentStatus?.isActive || currentStatus?.isRunning || learningStatus?.enabled || false,
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currentRunTime: currentStatus?.startTime || null,
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enhancedSummary: {
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totalDecisions: persistentData.learningDecisions,
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successRate: persistentData.winRate,
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systemConfidence: persistentData.winRate > 60 ? 0.8 : persistentData.winRate > 40 ? 0.6 : 0.3,
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isActive: persistentData.systemStatus === 'active',
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totalTrades: persistentData.totalTrades,
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totalPnL: persistentData.totalPnL
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},
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tradingStats: {
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totalTrades: persistentData.totalTrades,
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winningTrades: persistentData.winningTrades,
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losingTrades: persistentData.losingTrades,
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winRate: persistentData.winRate,
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totalPnL: persistentData.totalPnL,
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avgWinAmount: persistentData.avgWinAmount,
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avgLossAmount: persistentData.avgLossAmount,
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bestTrade: persistentData.bestTrade,
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worstTrade: persistentData.worstTrade
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},
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learningMetrics: {
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totalDecisions: persistentData.learningDecisions,
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aiEnhancements: persistentData.aiEnhancements,
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riskThresholds: persistentData.riskThresholds,
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dataQuality: persistentData.totalTrades > 10 ? 'Good' : persistentData.totalTrades > 5 ? 'Fair' : 'Limited'
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// Calculate real performance metrics
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let totalTrades = trades.length;
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let winningTrades = 0;
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let losingTrades = 0;
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let totalPnL = 0;
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let winAmounts = [];
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let lossAmounts = [];
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let bestTrade = 0;
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let worstTrade = 0;
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trades.forEach(trade => {
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const profit = trade.profit || 0;
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totalPnL += profit;
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if (profit > 0) {
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winningTrades++;
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winAmounts.push(profit);
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if (profit > bestTrade) bestTrade = profit;
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} else if (profit < 0) {
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losingTrades++;
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lossAmounts.push(profit);
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if (profit < worstTrade) worstTrade = profit;
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}
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});
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const winRate = totalTrades > 0 ? (winningTrades / totalTrades) * 100 : 0;
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const avgWinAmount = winAmounts.length > 0 ? winAmounts.reduce((a, b) => a + b, 0) / winAmounts.length : 0;
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const avgLossAmount = lossAmounts.length > 0 ? lossAmounts.reduce((a, b) => a + b, 0) / lossAmounts.length : 0;
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// Get learning decisions count
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const learningData = await prisma.ai_learning_data.findMany({
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where: {
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createdAt: {
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gte: new Date(Date.now() - 30 * 24 * 60 * 60 * 1000) // Last 30 days
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}
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}
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});
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// Get recent trades for display
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const recentTrades = trades.slice(0, 3).map(trade => ({
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id: trade.id,
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symbol: trade.symbol,
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type: trade.side?.toUpperCase() || 'UNKNOWN',
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entry: trade.entryPrice || 0,
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exit: trade.exitPrice || 0,
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pnl: trade.profit || 0,
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outcome: (trade.profit || 0) > 0 ? 'WIN' : (trade.profit || 0) < 0 ? 'LOSS' : 'BREAK-EVEN',
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timestamp: trade.createdAt.toISOString()
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}));
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// Get current automation status
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let isActive = false;
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let currentRunTime = null;
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try {
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const { getAutomationInstance } = await import('../../../../lib/automation-singleton.js');
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const automation = await getAutomationInstance();
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if (automation) {
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isActive = automation.isRunning || false;
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currentRunTime = automation.stats?.startTime || null;
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}
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} catch (error) {
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console.warn('Could not get automation status:', error.message);
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}
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const persistentData = {
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totalTrades,
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winningTrades,
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losingTrades,
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totalPnL: Math.round(totalPnL * 100) / 100,
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winRate: Math.round(winRate * 10) / 10,
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avgWinAmount: Math.round(avgWinAmount * 100) / 100,
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avgLossAmount: Math.round(avgLossAmount * 100) / 100,
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bestTrade: Math.round(bestTrade * 100) / 100,
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worstTrade: Math.round(worstTrade * 100) / 100,
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learningDecisions: learningData.length,
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aiEnhancements: Math.floor(learningData.length / 7), // Enhancement every 7 decisions
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riskThresholds: {
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emergency: totalPnL < -50 ? 1 : 3,
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risk: totalPnL < -30 ? 2 : 5,
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mediumRisk: totalPnL < -10 ? 5 : 8
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},
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lastUpdated: new Date().toISOString(),
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systemStatus: isActive ? 'active' : 'standby',
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dataCollected: totalTrades > 0,
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recentTrades,
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isLive: isActive,
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currentRunTime,
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enhancedSummary: {
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totalDecisions: learningData.length,
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successRate: winRate,
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systemConfidence: winRate > 60 ? 0.8 : winRate > 40 ? 0.6 : winRate > 20 ? 0.3 : 0.1,
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isActive,
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totalTrades,
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totalPnL: Math.round(totalPnL * 100) / 100
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},
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tradingStats: {
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totalTrades,
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winningTrades,
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losingTrades,
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winRate: Math.round(winRate * 10) / 10,
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totalPnL: Math.round(totalPnL * 100) / 100,
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avgWinAmount: Math.round(avgWinAmount * 100) / 100,
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avgLossAmount: Math.round(avgLossAmount * 100) / 100,
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bestTrade: Math.round(bestTrade * 100) / 100,
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worstTrade: Math.round(worstTrade * 100) / 100
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},
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learningMetrics: {
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totalDecisions: learningData.length,
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aiEnhancements: Math.floor(learningData.length / 7),
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riskThresholds: {
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emergency: totalPnL < -50 ? 1 : 3,
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risk: totalPnL < -30 ? 2 : 5,
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mediumRisk: totalPnL < -10 ? 5 : 8
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},
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dataQuality: totalTrades > 10 ? 'Good' : totalTrades > 5 ? 'Fair' : 'Limited'
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}
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};
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await prisma.$disconnect();
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return NextResponse.json({
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success: true,
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persistentData
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});
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} catch (error) {
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console.error('Error in persistent status API:', error);
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await prisma.$disconnect();
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return NextResponse.json({
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success: false,
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error: error.message,
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persistentData: defaultPersistentData
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error: error.message
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}, { status: 500 });
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}
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}
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export async function POST(request) {
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try {
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const updateData = await request.json();
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const currentData = await loadPersistentData();
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// Update the persistent data
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const updatedData = {
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...currentData,
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...updateData,
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lastUpdated: new Date().toISOString()
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};
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// Recalculate derived metrics
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if (updatedData.totalTrades > 0) {
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updatedData.winRate = (updatedData.winningTrades / updatedData.totalTrades) * 100;
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}
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const saved = await savePersistentData(updatedData);
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// For updating specific metrics (not implemented in this version)
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return NextResponse.json({
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success: saved,
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message: saved ? 'Persistent data updated' : 'Failed to save data',
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data: updatedData
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});
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success: false,
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message: 'POST method not implemented - data comes from database'
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}, { status: 501 });
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} catch (error) {
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console.error('Error updating persistent data:', error);
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