Add persistent learning data and PnL display
- Created persistent learning status API with trading statistics - Added comprehensive PnL and win rate display to AI Learning panel - Implemented trading stats tracking with win/loss ratios - Added persistent data storage for historical trading performance - Enhanced learning panel with real-time trading metrics - Fixed learning data visibility when bot is not running - Added sample trading data for demonstration
This commit is contained in:
mindesbunister
@@ -1,116 +1,163 @@
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// API route for persistent learning data that works regardless of automation status
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import { NextResponse } from 'next/server';
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import { PrismaClient } from '@prisma/client';
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import fs from 'fs/promises';
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import path from 'path';
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const prisma = new PrismaClient();
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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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export async function GET() {
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try {
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// Get persistent learning statistics from database using raw SQL
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const [
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totalDecisions,
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recentDecisions,
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totalTrades,
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successfulTrades,
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recentTrades
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] = await Promise.all([
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// Total AI decisions count
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prisma.$queryRaw`SELECT COUNT(*) as count FROM ai_learning_data`,
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// Recent decisions (last 24 hours)
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prisma.$queryRaw`SELECT COUNT(*) as count FROM ai_learning_data WHERE createdAt >= datetime('now', '-24 hours')`,
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// Total trades
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prisma.$queryRaw`SELECT COUNT(*) as count FROM trades`,
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// Successful trades (profit > 0)
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prisma.$queryRaw`SELECT COUNT(*) as count FROM trades WHERE profit > 0`,
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// Recent trades with PnL data
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prisma.$queryRaw`
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SELECT id, symbol, profit, side, confidence, marketSentiment, createdAt, closedAt, status
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FROM trades
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WHERE profit IS NOT NULL AND status = 'COMPLETED'
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ORDER BY createdAt DESC
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LIMIT 10
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`
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]);
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// Extract counts (BigInt to Number)
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const totalDecisionsCount = Number(totalDecisions[0]?.count || 0);
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const recentDecisionsCount = Number(recentDecisions[0]?.count || 0);
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const totalTradesCount = Number(totalTrades[0]?.count || 0);
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const successfulTradesCount = Number(successfulTrades[0]?.count || 0);
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// Calculate metrics
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const successRate = totalTradesCount > 0 ? (successfulTradesCount / totalTradesCount) * 100 : 0;
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const totalPnl = recentTrades.reduce((sum, trade) => sum + (Number(trade.profit) || 0), 0);
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const avgPnl = recentTrades.length > 0 ? totalPnl / recentTrades.length : 0;
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const persistentData = await loadPersistentData();
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// Get wins and losses
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const wins = recentTrades.filter(trade => Number(trade.profit) > 0).length;
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const losses = recentTrades.filter(trade => Number(trade.profit) < 0).length;
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const persistentData = {
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success: true,
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statistics: {
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totalDecisions: totalDecisionsCount,
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recentDecisions: recentDecisionsCount,
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totalTrades: totalTradesCount,
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successfulTrades: successfulTradesCount,
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successRate: Math.round(successRate * 100) / 100,
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totalPnl: Math.round(totalPnl * 100) / 100,
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avgPnl: Math.round(avgPnl * 100) / 100,
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wins,
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losses,
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winRate: wins + losses > 0 ? Math.round((wins / (wins + losses)) * 100 * 100) / 100 : 0
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},
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recentTrades: recentTrades.map(trade => ({
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id: trade.id,
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symbol: trade.symbol,
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pnl: Number(trade.profit),
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result: Number(trade.profit) > 0 ? 'WIN' : 'LOSS',
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confidence: trade.confidence,
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side: trade.side,
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sentiment: trade.marketSentiment,
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date: trade.createdAt,
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closedAt: trade.closedAt
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})),
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systemHealth: {
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dataAvailability: totalDecisionsCount > 0 ? 'Good' : 'Limited',
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lastActivity: recentTrades.length > 0 ? recentTrades[0].createdAt : null,
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databaseConnected: true,
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activeDataSources: {
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aiDecisions: totalDecisionsCount,
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completedTrades: totalTradesCount,
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recentActivity: recentDecisionsCount
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// Get current automation status if available
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let currentStatus = 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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const 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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}
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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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return NextResponse.json(persistentData);
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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 || 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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}
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}
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});
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} catch (error) {
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console.error('❌ Error fetching persistent learning data:', error);
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console.error('Error in persistent status API:', error);
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return NextResponse.json({
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success: false,
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error: error.message,
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statistics: {
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totalDecisions: 0,
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totalTrades: 0,
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successRate: 0,
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totalPnl: 0,
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wins: 0,
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losses: 0,
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winRate: 0
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},
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systemHealth: {
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dataAvailability: 'Error',
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databaseConnected: false
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}
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persistentData: defaultPersistentData
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}, { status: 500 });
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} finally {
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await prisma.$disconnect();
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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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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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} catch (error) {
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console.error('Error updating persistent data:', error);
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return NextResponse.json({
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success: false,
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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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