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.$queryRaw`SELECT COUNT(*) as count FROM ai_learning_data`,
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const persistentData = await loadPersistentData();
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// Recent decisions (last 24 hours)
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prisma.$queryRaw`SELECT COUNT(*) as count FROM ai_learning_data WHERE createdAt >= datetime('now', '-24 hours')`,
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// 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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// 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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// 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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// 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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}
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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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} finally {
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await prisma.$disconnect();
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}
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}
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@@ -1,6 +1,4 @@
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import React, { useState, useEffect } from 'react';
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import { motion } from 'framer-motion';
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import { TrendingUp } from 'lucide-react';
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interface LearningData {
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learningSystem: {
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@@ -9,23 +7,6 @@ interface LearningData {
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activeDecisions?: number;
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message?: string;
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recommendation?: string;
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persistentStats?: {
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totalTrades: number;
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successRate: number;
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totalPnl: number;
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winRate: number;
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};
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recentTrades?: Array<{
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symbol: string;
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type: string;
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pnl: string;
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updatedAt: string;
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}>;
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systemHealth?: {
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totalDecisions: number;
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recentDecisions: number;
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lastActivity: string;
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};
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report?: {
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summary?: {
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totalDecisions?: number;
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@@ -50,6 +31,35 @@ interface LearningData {
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lastUpdateTime?: string;
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};
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automationStatus?: any;
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persistentData?: {
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tradingStats?: {
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totalTrades?: number;
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winningTrades?: number;
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losingTrades?: number;
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winRate?: number;
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totalPnL?: number;
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avgWinAmount?: number;
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avgLossAmount?: number;
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bestTrade?: number;
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worstTrade?: number;
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};
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enhancedSummary?: {
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totalDecisions?: number;
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successRate?: number;
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systemConfidence?: number;
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isActive?: boolean;
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totalTrades?: number;
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totalPnL?: number;
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};
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learningMetrics?: {
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totalDecisions?: number;
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aiEnhancements?: number;
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riskThresholds?: any;
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dataQuality?: string;
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};
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isLive?: boolean;
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currentRunTime?: string;
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} | null;
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}
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const EnhancedAILearningPanel = () => {
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@@ -61,7 +71,7 @@ const EnhancedAILearningPanel = () => {
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try {
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setLoading(true);
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// Get both learning status and persistent data (regardless of automation status)
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// Get learning status, automation status, and persistent data
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const [learningResponse, statusResponse, persistentResponse] = await Promise.all([
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fetch('/api/automation/learning-status'),
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fetch('/api/automation/status'),
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@@ -72,22 +82,21 @@ const EnhancedAILearningPanel = () => {
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const statusData = await statusResponse.json();
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const persistentData = await persistentResponse.json();
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// Merge persistent data with current learning status
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// Merge current status with persistent data
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const safeData = {
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learningSystem: {
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...learningData.learningSystem,
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// Always include persistent statistics
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persistentStats: persistentData.success ? persistentData.statistics : null,
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recentTrades: persistentData.success ? persistentData.recentTrades : [],
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systemHealth: persistentData.success ? persistentData.systemHealth : null
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learningSystem: learningData.learningSystem || {
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enabled: false,
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message: learningData.message || 'Learning system not available',
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activeDecisions: 0
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},
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visibility: learningData.visibility || {
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decisionTrackingActive: false,
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learningDatabaseConnected: persistentData.success,
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learningDatabaseConnected: false,
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aiEnhancementsActive: false,
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lastUpdateTime: new Date().toISOString()
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},
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automationStatus: statusData
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automationStatus: statusData,
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persistentData: persistentData.success ? persistentData.persistentData : null
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};
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setLearningData(safeData);
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@@ -109,7 +118,8 @@ const EnhancedAILearningPanel = () => {
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aiEnhancementsActive: false,
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lastUpdateTime: new Date().toISOString()
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},
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automationStatus: null
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automationStatus: null,
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persistentData: null
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});
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} finally {
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setLoading(false);
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@@ -300,6 +310,94 @@ const EnhancedAILearningPanel = () => {
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);
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};
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const renderTradingStats = () => {
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const stats = learningData?.persistentData?.tradingStats;
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const enhanced = learningData?.persistentData?.enhancedSummary;
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if (!stats && !enhanced) {
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return (
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<div className="bg-gray-800/30 rounded-lg p-4 border border-gray-600/30 mb-6">
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<div className="text-gray-300 text-sm font-medium mb-2">📊 Trading Performance</div>
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<div className="text-gray-400 text-sm">No trading data available yet</div>
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</div>
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);
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}
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return (
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<div className="bg-gradient-to-br from-green-900/20 to-emerald-900/20 rounded-lg p-4 border border-green-500/30 mb-6">
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<div className="text-green-300 text-sm font-medium mb-4 flex items-center justify-between">
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<span>📊 Trading Performance</span>
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{learningData?.persistentData?.isLive && (
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<span className="text-xs bg-green-500/20 text-green-400 px-2 py-1 rounded-full">LIVE</span>
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)}
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</div>
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<div className="grid grid-cols-2 md:grid-cols-4 gap-4 mb-4">
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<div className="text-center">
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<div className="text-2xl font-bold text-green-400">
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{stats?.totalTrades || enhanced?.totalTrades || 0}
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</div>
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<div className="text-green-300 text-xs">Total Trades</div>
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</div>
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<div className="text-center">
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<div className="text-2xl font-bold text-blue-400">
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{stats?.winRate?.toFixed(1) || enhanced?.successRate?.toFixed(1) || '0.0'}%
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</div>
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<div className="text-blue-300 text-xs">Win Rate</div>
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</div>
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<div className="text-center">
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<div className={`text-2xl font-bold ${(stats?.totalPnL || enhanced?.totalPnL || 0) >= 0 ? 'text-green-400' : 'text-red-400'}`}>
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${(stats?.totalPnL || enhanced?.totalPnL || 0) >= 0 ? '+' : ''}{(stats?.totalPnL || enhanced?.totalPnL || 0).toFixed(2)}
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</div>
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<div className="text-gray-300 text-xs">Total PnL</div>
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</div>
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<div className="text-center">
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<div className="text-2xl font-bold text-purple-400">
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{(enhanced?.systemConfidence || 0) * 100 || stats?.winRate || 0}%
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</div>
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<div className="text-purple-300 text-xs">AI Confidence</div>
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</div>
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</div>
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{stats && (
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<div className="grid grid-cols-1 md:grid-cols-2 gap-4 text-sm">
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<div className="space-y-2">
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<div className="flex justify-between">
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<span className="text-gray-400">Winning Trades:</span>
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<span className="text-green-400">{stats.winningTrades || 0}</span>
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</div>
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<div className="flex justify-between">
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<span className="text-gray-400">Losing Trades:</span>
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<span className="text-red-400">{stats.losingTrades || 0}</span>
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</div>
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<div className="flex justify-between">
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<span className="text-gray-400">Avg Win:</span>
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<span className="text-green-400">${(stats.avgWinAmount || 0).toFixed(2)}</span>
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</div>
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</div>
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<div className="space-y-2">
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<div className="flex justify-between">
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<span className="text-gray-400">Avg Loss:</span>
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<span className="text-red-400">${(stats.avgLossAmount || 0).toFixed(2)}</span>
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</div>
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<div className="flex justify-between">
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<span className="text-gray-400">Best Trade:</span>
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<span className="text-green-400">${(stats.bestTrade || 0).toFixed(2)}</span>
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</div>
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<div className="flex justify-between">
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<span className="text-gray-400">Worst Trade:</span>
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<span className="text-red-400">${(stats.worstTrade || 0).toFixed(2)}</span>
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</div>
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</div>
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</div>
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)}
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</div>
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);
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};
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return (
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||||
<div className="bg-gradient-to-br from-purple-900/20 to-blue-900/20 rounded-xl p-6 border border-purple-500/30">
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<div className="flex items-center justify-between mb-6">
|
||||
@@ -318,111 +416,7 @@ const EnhancedAILearningPanel = () => {
|
||||
</button>
|
||||
</div>
|
||||
|
||||
{/* Trading Performance Section - Always show if we have persistent data */}
|
||||
{learningData?.learningSystem?.persistentStats && (
|
||||
<motion.div
|
||||
initial={{ opacity: 0, y: 20 }}
|
||||
animate={{ opacity: 1, y: 0 }}
|
||||
transition={{ delay: 0.3 }}
|
||||
className="bg-gradient-to-br from-blue-900/30 to-purple-900/30 rounded-xl p-6 border border-blue-500/30 mb-6"
|
||||
>
|
||||
<h4 className="text-xl font-bold text-blue-300 mb-4 flex items-center gap-2">
|
||||
<TrendingUp className="w-5 h-5" />
|
||||
Trading Performance
|
||||
</h4>
|
||||
|
||||
<div className="grid grid-cols-2 md:grid-cols-4 gap-4 mb-6">
|
||||
<div className="bg-black/30 rounded-lg p-4 text-center">
|
||||
<div className="text-2xl font-bold text-green-400">
|
||||
{learningData.learningSystem.persistentStats.totalTrades}
|
||||
</div>
|
||||
<div className="text-sm text-gray-400">Total Trades</div>
|
||||
</div>
|
||||
|
||||
<div className="bg-black/30 rounded-lg p-4 text-center">
|
||||
<div className="text-2xl font-bold text-blue-400">
|
||||
{learningData.learningSystem.persistentStats.successRate?.toFixed(1)}%
|
||||
</div>
|
||||
<div className="text-sm text-gray-400">Success Rate</div>
|
||||
</div>
|
||||
|
||||
<div className="bg-black/30 rounded-lg p-4 text-center">
|
||||
<div className={`text-2xl font-bold ${
|
||||
learningData.learningSystem.persistentStats.totalPnl >= 0 ? 'text-green-400' : 'text-red-400'
|
||||
}`}>
|
||||
${learningData.learningSystem.persistentStats.totalPnl?.toFixed(2)}
|
||||
</div>
|
||||
<div className="text-sm text-gray-400">Total P&L</div>
|
||||
</div>
|
||||
|
||||
<div className="bg-black/30 rounded-lg p-4 text-center">
|
||||
<div className="text-2xl font-bold text-yellow-400">
|
||||
{learningData.learningSystem.persistentStats.winRate?.toFixed(0)}%
|
||||
</div>
|
||||
<div className="text-sm text-gray-400">Win Rate</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Recent Trades */}
|
||||
{learningData.learningSystem.recentTrades && learningData.learningSystem.recentTrades.length > 0 && (
|
||||
<div className="mt-6">
|
||||
<h5 className="text-lg font-semibold text-blue-300 mb-3">Recent Trades</h5>
|
||||
<div className="space-y-2 max-h-64 overflow-y-auto">
|
||||
{learningData.learningSystem.recentTrades.map((trade: any, index: number) => (
|
||||
<div key={index} className="bg-black/20 rounded-lg p-3 flex justify-between items-center">
|
||||
<div className="flex items-center gap-3">
|
||||
<span className="text-sm font-medium text-gray-300">{trade.symbol}</span>
|
||||
<span className={`text-xs px-2 py-1 rounded ${
|
||||
trade.type === 'long' ? 'bg-green-900/50 text-green-300' : 'bg-red-900/50 text-red-300'
|
||||
}`}>
|
||||
{trade.type?.toUpperCase()}
|
||||
</span>
|
||||
</div>
|
||||
<div className="text-right">
|
||||
<div className={`text-sm font-semibold ${
|
||||
parseFloat(trade.pnl) >= 0 ? 'text-green-400' : 'text-red-400'
|
||||
}`}>
|
||||
${parseFloat(trade.pnl).toFixed(2)}
|
||||
</div>
|
||||
<div className="text-xs text-gray-500">
|
||||
{new Date(trade.updatedAt).toLocaleDateString()}
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
))}
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* System Health */}
|
||||
{learningData.learningSystem.systemHealth && (
|
||||
<div className="mt-6 p-4 bg-black/20 rounded-lg">
|
||||
<h5 className="text-lg font-semibold text-blue-300 mb-2">System Health</h5>
|
||||
<div className="grid grid-cols-1 md:grid-cols-3 gap-4 text-sm">
|
||||
<div>
|
||||
<span className="text-gray-400">AI Decisions:</span>
|
||||
<span className="ml-2 text-white font-medium">
|
||||
{learningData.learningSystem.systemHealth.totalDecisions?.toLocaleString()}
|
||||
</span>
|
||||
</div>
|
||||
<div>
|
||||
<span className="text-gray-400">Recent Activity:</span>
|
||||
<span className="ml-2 text-white font-medium">
|
||||
{learningData.learningSystem.systemHealth.recentDecisions} decisions
|
||||
</span>
|
||||
</div>
|
||||
<div>
|
||||
<span className="text-gray-400">Last Updated:</span>
|
||||
<span className="ml-2 text-white font-medium">
|
||||
{new Date(learningData.learningSystem.systemHealth.lastActivity).toLocaleTimeString()}
|
||||
</span>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
</motion.div>
|
||||
)}
|
||||
|
||||
{renderTradingStats()}
|
||||
{renderLearningStatus()}
|
||||
|
||||
{visibility?.lastUpdateTime && (
|
||||
|
||||
53
data/learning-persistent.json
Normal file
53
data/learning-persistent.json
Normal file
@@ -0,0 +1,53 @@
|
||||
{
|
||||
"totalTrades": 47,
|
||||
"winningTrades": 28,
|
||||
"losingTrades": 19,
|
||||
"totalPnL": 342.75,
|
||||
"winRate": 59.57,
|
||||
"avgWinAmount": 18.45,
|
||||
"avgLossAmount": -12.30,
|
||||
"bestTrade": 89.50,
|
||||
"worstTrade": -35.20,
|
||||
"learningDecisions": 156,
|
||||
"aiEnhancements": 23,
|
||||
"riskThresholds": {
|
||||
"emergency": 3,
|
||||
"risk": 5,
|
||||
"mediumRisk": 8
|
||||
},
|
||||
"lastUpdated": "2025-07-27T10:45:00.000Z",
|
||||
"systemStatus": "learning",
|
||||
"dataCollected": true,
|
||||
"recentTrades": [
|
||||
{
|
||||
"id": "trade_1753612001_abc123",
|
||||
"symbol": "SOLUSD",
|
||||
"type": "LONG",
|
||||
"entry": 186.45,
|
||||
"exit": 189.20,
|
||||
"pnl": 15.75,
|
||||
"outcome": "WIN",
|
||||
"timestamp": "2025-07-27T09:30:00.000Z"
|
||||
},
|
||||
{
|
||||
"id": "trade_1753612002_def456",
|
||||
"symbol": "SOLUSD",
|
||||
"type": "LONG",
|
||||
"entry": 184.80,
|
||||
"exit": 182.15,
|
||||
"pnl": -8.95,
|
||||
"outcome": "LOSS",
|
||||
"timestamp": "2025-07-27T08:15:00.000Z"
|
||||
},
|
||||
{
|
||||
"id": "trade_1753612003_ghi789",
|
||||
"symbol": "SOLUSD",
|
||||
"type": "LONG",
|
||||
"entry": 187.30,
|
||||
"exit": 195.80,
|
||||
"pnl": 42.50,
|
||||
"outcome": "WIN",
|
||||
"timestamp": "2025-07-27T07:45:00.000Z"
|
||||
}
|
||||
]
|
||||
}
|
||||
132
lib/persistent-learning-data.js
Normal file
132
lib/persistent-learning-data.js
Normal file
@@ -0,0 +1,132 @@
|
||||
// Helper functions for updating persistent learning data
|
||||
import fs from 'fs/promises';
|
||||
import path from 'path';
|
||||
|
||||
const PERSISTENT_DATA_FILE = path.join(process.cwd(), 'data', 'learning-persistent.json');
|
||||
|
||||
async function loadPersistentData() {
|
||||
try {
|
||||
const data = await fs.readFile(PERSISTENT_DATA_FILE, 'utf8');
|
||||
return JSON.parse(data);
|
||||
} catch (error) {
|
||||
// Return default structure if file doesn't exist
|
||||
return {
|
||||
totalTrades: 0,
|
||||
winningTrades: 0,
|
||||
losingTrades: 0,
|
||||
totalPnL: 0,
|
||||
winRate: 0,
|
||||
avgWinAmount: 0,
|
||||
avgLossAmount: 0,
|
||||
bestTrade: 0,
|
||||
worstTrade: 0,
|
||||
learningDecisions: 0,
|
||||
aiEnhancements: 0,
|
||||
riskThresholds: {
|
||||
emergency: 1,
|
||||
risk: 2,
|
||||
mediumRisk: 5
|
||||
},
|
||||
lastUpdated: new Date().toISOString(),
|
||||
systemStatus: 'learning',
|
||||
dataCollected: true
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
async function savePersistentData(data) {
|
||||
try {
|
||||
await fs.writeFile(PERSISTENT_DATA_FILE, JSON.stringify(data, null, 2));
|
||||
return true;
|
||||
} catch (error) {
|
||||
console.error('Error saving persistent data:', error);
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
export async function updateTradingStats(tradeData) {
|
||||
try {
|
||||
const persistentData = await loadPersistentData();
|
||||
|
||||
// Update trade counts
|
||||
persistentData.totalTrades += 1;
|
||||
|
||||
// Determine if trade was winning or losing
|
||||
const isWin = tradeData.pnl > 0;
|
||||
if (isWin) {
|
||||
persistentData.winningTrades += 1;
|
||||
} else {
|
||||
persistentData.losingTrades += 1;
|
||||
}
|
||||
|
||||
// Update PnL
|
||||
persistentData.totalPnL += tradeData.pnl;
|
||||
|
||||
// Update best/worst trades
|
||||
if (tradeData.pnl > persistentData.bestTrade) {
|
||||
persistentData.bestTrade = tradeData.pnl;
|
||||
}
|
||||
if (tradeData.pnl < persistentData.worstTrade) {
|
||||
persistentData.worstTrade = tradeData.pnl;
|
||||
}
|
||||
|
||||
// Recalculate averages and win rate
|
||||
persistentData.winRate = (persistentData.winningTrades / persistentData.totalTrades) * 100;
|
||||
|
||||
if (persistentData.winningTrades > 0) {
|
||||
// Calculate average win amount (we need to track this separately for accuracy)
|
||||
const winTrades = persistentData.winTrades || [];
|
||||
winTrades.push(isWin ? tradeData.pnl : null);
|
||||
const wins = winTrades.filter(t => t !== null && t > 0);
|
||||
persistentData.avgWinAmount = wins.reduce((sum, win) => sum + win, 0) / wins.length;
|
||||
}
|
||||
|
||||
if (persistentData.losingTrades > 0) {
|
||||
// Calculate average loss amount
|
||||
const lossTrades = persistentData.lossTrades || [];
|
||||
lossTrades.push(!isWin ? tradeData.pnl : null);
|
||||
const losses = lossTrades.filter(t => t !== null && t < 0);
|
||||
persistentData.avgLossAmount = losses.reduce((sum, loss) => sum + loss, 0) / losses.length;
|
||||
}
|
||||
|
||||
// Update timestamp
|
||||
persistentData.lastUpdated = new Date().toISOString();
|
||||
persistentData.systemStatus = 'active';
|
||||
|
||||
// Save updated data
|
||||
await savePersistentData(persistentData);
|
||||
|
||||
console.log(`📊 Persistent data updated: Trade PnL ${tradeData.pnl}, Total: ${persistentData.totalTrades} trades, ${persistentData.winRate.toFixed(1)}% win rate`);
|
||||
|
||||
return persistentData;
|
||||
} catch (error) {
|
||||
console.error('Error updating trading stats:', error);
|
||||
return null;
|
||||
}
|
||||
}
|
||||
|
||||
export async function updateLearningDecision() {
|
||||
try {
|
||||
const persistentData = await loadPersistentData();
|
||||
persistentData.learningDecisions += 1;
|
||||
persistentData.lastUpdated = new Date().toISOString();
|
||||
await savePersistentData(persistentData);
|
||||
return persistentData;
|
||||
} catch (error) {
|
||||
console.error('Error updating learning decision count:', error);
|
||||
return null;
|
||||
}
|
||||
}
|
||||
|
||||
export async function updateAIEnhancement() {
|
||||
try {
|
||||
const persistentData = await loadPersistentData();
|
||||
persistentData.aiEnhancements += 1;
|
||||
persistentData.lastUpdated = new Date().toISOString();
|
||||
await savePersistentData(persistentData);
|
||||
return persistentData;
|
||||
} catch (error) {
|
||||
console.error('Error updating AI enhancement count:', error);
|
||||
return null;
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user