feat: Implement Demo Test Tracker for Go-Live readiness assessment
NEW MODULE: demo_test_tracker.py - DemoTestTracker class for comprehensive statistics collection - TradeRecord dataclass for structured trade logging - Automatic Win Rate, Profit Factor, Drawdown calculation - Session-based and Signal Quality breakdown - Error/Bug tracking - Persistent JSON storage GO-LIVE CRITERIA (configurable): - min_trades: 50 trades required - min_win_rate: 55% - min_profit_factor: 1.3 - max_drawdown: 15% - min_days: 14 days running - max_errors: 5 critical errors - min_sessions_tested: 2 different sessions NEW NOTEBOOK CELLS: - Cell 92: Performance Report & Go-Live Check - Cell 93: MT5 History Sync (imports past trades) FEATURES: - print_report(): Full performance breakdown - print_go_live_check(): Visual checklist with pass/fail - get_daily_summary(): Quick daily stats - sync_closed_trades_to_tracker(): Import from MT5 history INTEGRATION: - Added to Cell 78 (Advanced Optimizations) - Tracks trades automatically after execution - Persistent data in demo_test_stats.json Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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Claude Opus 4.5
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#!/usr/bin/env python3
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"""
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📊 Demo Test Tracker
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Sammelt Statistiken während der Demo-Phase und gibt Go-Live Empfehlungen
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FEATURES:
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1. Trade Logging (Entry, Exit, Profit/Loss)
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2. Performance Metriken (Win Rate, Profit Factor, etc.)
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3. Drawdown Tracking
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4. Session-basierte Analyse
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5. Error/Bug Tracking
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6. Go-Live Readiness Check
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7. Automatische Reports
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VERWENDUNG:
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from demo_test_tracker import DemoTestTracker
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tracker = DemoTestTracker()
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# Nach jedem Trade:
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tracker.log_trade(trade_result)
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# Report anzeigen:
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tracker.print_report()
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# Go-Live Check:
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tracker.check_go_live_readiness()
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"""
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import json
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import os
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from datetime import datetime, timedelta
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from typing import Dict, List, Optional, Tuple
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from dataclasses import dataclass, asdict
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from collections import defaultdict
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import logging
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logger = logging.getLogger(__name__)
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@dataclass
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class TradeRecord:
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"""Einzelner Trade-Eintrag"""
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ticket: int
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symbol: str
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direction: str # "LONG" or "SHORT"
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entry_price: float
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exit_price: float
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volume: float
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profit: float
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profit_pips: float
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entry_time: str
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exit_time: str
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duration_minutes: float
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session: str
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# Signal Info
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base_confidence: float
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enhanced_score: float
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hybrid_score: float
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signal_quality: str
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# Filters applied
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equity_curve_multiplier: float
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# Result
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is_win: bool
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# Optional metadata
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stop_loss: float = 0.0
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take_profit: float = 0.0
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close_reason: str = "" # "TP", "SL", "Manual", "Trailing"
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class DemoTestTracker:
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"""
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Demo Test Tracker für Go-Live Vorbereitung
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Sammelt alle relevanten Statistiken und gibt
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Empfehlungen wann der Bot produktionsreif ist.
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"""
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# Go-Live Kriterien
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GO_LIVE_CRITERIA = {
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'min_trades': 50,
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'min_win_rate': 0.55, # 55%
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'min_profit_factor': 1.3,
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'max_drawdown': 0.15, # 15%
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'min_days': 14,
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'max_errors': 5,
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'min_sessions_tested': 2, # Mindestens 2 verschiedene Sessions
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}
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def __init__(self,
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data_file: str = "demo_test_stats.json",
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criteria: Optional[Dict] = None):
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"""
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Args:
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data_file: Datei für persistente Speicherung
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criteria: Custom Go-Live Kriterien (optional)
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"""
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self.data_file = data_file
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self.criteria = criteria or self.GO_LIVE_CRITERIA.copy()
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# Daten laden oder initialisieren
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self.data = self._load_data()
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logger.info("=" * 60)
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logger.info("📊 DEMO TEST TRACKER INITIALIZED")
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logger.info("=" * 60)
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logger.info(f" Data File: {data_file}")
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logger.info(f" Total Trades: {len(self.data['trades'])}")
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logger.info(f" Start Date: {self.data['start_date']}")
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logger.info(f" Days Running: {self._days_running()}")
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logger.info("=" * 60)
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# ==========================================
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# TRADE LOGGING
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# ==========================================
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def log_trade(self,
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ticket: int,
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symbol: str,
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direction: str,
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entry_price: float,
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exit_price: float,
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volume: float,
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profit: float,
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entry_time: datetime,
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exit_time: datetime,
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session: str = "unknown",
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base_confidence: float = 0.0,
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enhanced_score: float = 0.0,
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hybrid_score: float = 0.0,
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signal_quality: str = "unknown",
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equity_curve_multiplier: float = 1.0,
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stop_loss: float = 0.0,
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take_profit: float = 0.0,
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close_reason: str = "") -> TradeRecord:
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"""
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Loggt einen abgeschlossenen Trade
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Returns:
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TradeRecord object
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"""
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# Calculate derived values
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if direction == "LONG":
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profit_pips = (exit_price - entry_price) * 10 # For Gold
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else:
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profit_pips = (entry_price - exit_price) * 10
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duration = (exit_time - entry_time).total_seconds() / 60
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is_win = profit > 0
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record = TradeRecord(
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ticket=ticket,
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symbol=symbol,
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direction=direction,
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entry_price=entry_price,
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exit_price=exit_price,
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volume=volume,
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profit=profit,
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profit_pips=profit_pips,
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entry_time=entry_time.isoformat(),
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exit_time=exit_time.isoformat(),
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duration_minutes=duration,
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session=session,
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base_confidence=base_confidence,
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enhanced_score=enhanced_score,
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hybrid_score=hybrid_score,
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signal_quality=signal_quality,
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equity_curve_multiplier=equity_curve_multiplier,
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is_win=is_win,
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stop_loss=stop_loss,
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take_profit=take_profit,
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close_reason=close_reason
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)
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# Add to data
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self.data['trades'].append(asdict(record))
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self.data['last_updated'] = datetime.now().isoformat()
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# Update running stats
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self._update_stats()
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# Save
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self._save_data()
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# Log
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emoji = "🟢" if is_win else "🔴"
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logger.info(f"📊 Trade logged: {emoji} #{ticket} {direction} {symbol} | Profit: ${profit:.2f}")
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return record
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def log_trade_from_mt5(self, position, close_reason: str = "",
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session: str = "unknown",
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base_confidence: float = 0.0,
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enhanced_score: float = 0.0,
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hybrid_score: float = 0.0,
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signal_quality: str = "unknown",
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equity_curve_multiplier: float = 1.0) -> TradeRecord:
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"""
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Loggt Trade direkt von MT5 Position Object
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"""
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direction = "LONG" if position.type == 0 else "SHORT"
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return self.log_trade(
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ticket=position.ticket,
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symbol=position.symbol,
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direction=direction,
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entry_price=position.price_open,
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exit_price=position.price_current,
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volume=position.volume,
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profit=position.profit,
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entry_time=datetime.fromtimestamp(position.time),
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exit_time=datetime.now(),
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session=session,
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base_confidence=base_confidence,
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enhanced_score=enhanced_score,
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hybrid_score=hybrid_score,
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signal_quality=signal_quality,
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equity_curve_multiplier=equity_curve_multiplier,
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stop_loss=position.sl,
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take_profit=position.tp,
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close_reason=close_reason
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)
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def log_error(self, error_type: str, message: str, details: Optional[Dict] = None):
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"""Loggt einen Fehler/Bug"""
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error_entry = {
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'timestamp': datetime.now().isoformat(),
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'type': error_type,
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'message': message,
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'details': details or {}
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}
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self.data['errors'].append(error_entry)
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self._save_data()
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logger.warning(f"📊 Error logged: {error_type} - {message}")
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def log_filtered_signal(self, reason: str, base_confidence: float,
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enhanced_score: float, hybrid_score: float):
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"""Loggt ein Signal das gefiltert wurde"""
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entry = {
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'timestamp': datetime.now().isoformat(),
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'reason': reason,
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'base_confidence': base_confidence,
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'enhanced_score': enhanced_score,
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'hybrid_score': hybrid_score
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}
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self.data['filtered_signals'].append(entry)
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self._save_data()
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# ==========================================
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# STATISTICS
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# ==========================================
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def _update_stats(self):
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"""Aktualisiert alle Statistiken"""
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trades = self.data['trades']
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if not trades:
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return
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# Basic stats
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wins = [t for t in trades if t['is_win']]
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losses = [t for t in trades if not t['is_win']]
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total_profit = sum(t['profit'] for t in trades)
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total_wins = sum(t['profit'] for t in wins) if wins else 0
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total_losses = abs(sum(t['profit'] for t in losses)) if losses else 0
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# Win Rate
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win_rate = len(wins) / len(trades) if trades else 0
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# Profit Factor
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profit_factor = total_wins / total_losses if total_losses > 0 else float('inf')
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# Average Win/Loss
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avg_win = total_wins / len(wins) if wins else 0
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avg_loss = total_losses / len(losses) if losses else 0
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# Drawdown calculation
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equity_curve = []
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running_equity = 0
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peak_equity = 0
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max_drawdown = 0
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for t in trades:
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running_equity += t['profit']
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equity_curve.append(running_equity)
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if running_equity > peak_equity:
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peak_equity = running_equity
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drawdown = (peak_equity - running_equity) / peak_equity if peak_equity > 0 else 0
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if drawdown > max_drawdown:
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max_drawdown = drawdown
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# Session stats
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session_stats = defaultdict(lambda: {'trades': 0, 'wins': 0, 'profit': 0})
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for t in trades:
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session = t.get('session', 'unknown')
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session_stats[session]['trades'] += 1
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session_stats[session]['wins'] += 1 if t['is_win'] else 0
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session_stats[session]['profit'] += t['profit']
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# Signal quality stats
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quality_stats = defaultdict(lambda: {'trades': 0, 'wins': 0, 'profit': 0})
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for t in trades:
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quality = t.get('signal_quality', 'unknown')
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quality_stats[quality]['trades'] += 1
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quality_stats[quality]['wins'] += 1 if t['is_win'] else 0
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quality_stats[quality]['profit'] += t['profit']
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# Store stats
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self.data['stats'] = {
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'total_trades': len(trades),
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'wins': len(wins),
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'losses': len(losses),
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'win_rate': win_rate,
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'profit_factor': profit_factor,
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'total_profit': total_profit,
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'total_wins': total_wins,
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'total_losses': total_losses,
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'avg_win': avg_win,
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'avg_loss': avg_loss,
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'avg_trade': total_profit / len(trades) if trades else 0,
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'max_drawdown': max_drawdown,
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'current_equity': running_equity,
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'peak_equity': peak_equity,
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'sessions_tested': list(session_stats.keys()),
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'session_stats': dict(session_stats),
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'quality_stats': dict(quality_stats),
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'best_trade': max(trades, key=lambda t: t['profit'])['profit'] if trades else 0,
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'worst_trade': min(trades, key=lambda t: t['profit'])['profit'] if trades else 0,
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'avg_duration_minutes': sum(t['duration_minutes'] for t in trades) / len(trades) if trades else 0,
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}
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def get_stats(self) -> Dict:
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"""Gibt aktuelle Statistiken zurück"""
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self._update_stats()
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return self.data['stats']
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# ==========================================
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# GO-LIVE READINESS CHECK
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# ==========================================
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def check_go_live_readiness(self) -> Tuple[bool, Dict]:
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"""
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Prüft ob alle Kriterien für Go-Live erfüllt sind
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Returns:
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(is_ready, detailed_check_results)
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"""
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self._update_stats()
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stats = self.data['stats']
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checks = {}
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# 1. Minimum Trades
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checks['min_trades'] = {
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'passed': stats['total_trades'] >= self.criteria['min_trades'],
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'current': stats['total_trades'],
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'required': self.criteria['min_trades'],
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'label': f"Trades: {stats['total_trades']}/{self.criteria['min_trades']}"
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}
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# 2. Win Rate
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checks['win_rate'] = {
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'passed': stats['win_rate'] >= self.criteria['min_win_rate'],
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'current': stats['win_rate'],
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'required': self.criteria['min_win_rate'],
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'label': f"Win Rate: {stats['win_rate']*100:.1f}% (min {self.criteria['min_win_rate']*100:.0f}%)"
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}
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# 3. Profit Factor
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pf = stats['profit_factor'] if stats['profit_factor'] != float('inf') else 999
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checks['profit_factor'] = {
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'passed': pf >= self.criteria['min_profit_factor'],
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'current': pf,
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'required': self.criteria['min_profit_factor'],
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'label': f"Profit Factor: {pf:.2f} (min {self.criteria['min_profit_factor']:.1f})"
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}
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# 4. Max Drawdown
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checks['max_drawdown'] = {
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'passed': stats['max_drawdown'] <= self.criteria['max_drawdown'],
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'current': stats['max_drawdown'],
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'required': self.criteria['max_drawdown'],
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'label': f"Max Drawdown: {stats['max_drawdown']*100:.1f}% (max {self.criteria['max_drawdown']*100:.0f}%)"
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}
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# 5. Days Running
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days = self._days_running()
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checks['min_days'] = {
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'passed': days >= self.criteria['min_days'],
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'current': days,
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'required': self.criteria['min_days'],
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'label': f"Days Running: {days}/{self.criteria['min_days']}"
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}
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# 6. Errors
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error_count = len(self.data['errors'])
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checks['max_errors'] = {
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'passed': error_count <= self.criteria['max_errors'],
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'current': error_count,
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'required': self.criteria['max_errors'],
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'label': f"Errors: {error_count} (max {self.criteria['max_errors']})"
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}
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# 7. Sessions Tested
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sessions = len(stats.get('sessions_tested', []))
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checks['sessions_tested'] = {
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'passed': sessions >= self.criteria['min_sessions_tested'],
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'current': sessions,
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'required': self.criteria['min_sessions_tested'],
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'label': f"Sessions Tested: {sessions}/{self.criteria['min_sessions_tested']}"
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}
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# Overall
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all_passed = all(c['passed'] for c in checks.values())
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passed_count = sum(1 for c in checks.values() if c['passed'])
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return all_passed, {
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'checks': checks,
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'passed_count': passed_count,
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'total_checks': len(checks),
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'is_ready': all_passed
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}
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# ==========================================
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# REPORTS
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# ==========================================
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def print_report(self):
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"""Druckt einen vollständigen Report"""
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self._update_stats()
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stats = self.data['stats']
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print("\n" + "=" * 70)
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print("📊 DEMO TEST TRACKER - PERFORMANCE REPORT")
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print("=" * 70)
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print(f"\n📅 Demo Period:")
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print(f" Start: {self.data['start_date'][:10]}")
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print(f" Days Running: {self._days_running()}")
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print(f" Last Trade: {self.data.get('last_updated', 'N/A')[:10] if self.data.get('last_updated') else 'N/A'}")
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print(f"\n📈 PERFORMANCE METRICS:")
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print(f" Total Trades: {stats.get('total_trades', 0)}")
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print(f" Wins/Losses: {stats.get('wins', 0)}/{stats.get('losses', 0)}")
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print(f" Win Rate: {stats.get('win_rate', 0)*100:.1f}%")
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print(f" Profit Factor: {stats.get('profit_factor', 0):.2f}")
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print(f"\n💰 PROFIT/LOSS:")
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print(f" Total Profit: ${stats.get('total_profit', 0):,.2f}")
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print(f" Avg Win: ${stats.get('avg_win', 0):,.2f}")
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print(f" Avg Loss: ${stats.get('avg_loss', 0):,.2f}")
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print(f" Avg Trade: ${stats.get('avg_trade', 0):,.2f}")
|
||||
print(f" Best Trade: ${stats.get('best_trade', 0):,.2f}")
|
||||
print(f" Worst Trade: ${stats.get('worst_trade', 0):,.2f}")
|
||||
|
||||
print(f"\n📉 RISK METRICS:")
|
||||
print(f" Max Drawdown: {stats.get('max_drawdown', 0)*100:.1f}%")
|
||||
print(f" Current Equity: ${stats.get('current_equity', 0):,.2f}")
|
||||
print(f" Peak Equity: ${stats.get('peak_equity', 0):,.2f}")
|
||||
|
||||
print(f"\n⏱️ TIMING:")
|
||||
print(f" Avg Duration: {stats.get('avg_duration_minutes', 0):.0f} min")
|
||||
|
||||
# Session breakdown
|
||||
session_stats = stats.get('session_stats', {})
|
||||
if session_stats:
|
||||
print(f"\n🌍 SESSION BREAKDOWN:")
|
||||
for session, data in session_stats.items():
|
||||
wr = data['wins'] / data['trades'] * 100 if data['trades'] > 0 else 0
|
||||
print(f" {session.upper():10} | Trades: {data['trades']:3} | Win Rate: {wr:5.1f}% | Profit: ${data['profit']:,.2f}")
|
||||
|
||||
# Signal quality breakdown
|
||||
quality_stats = stats.get('quality_stats', {})
|
||||
if quality_stats:
|
||||
print(f"\n🎯 SIGNAL QUALITY BREAKDOWN:")
|
||||
for quality, data in quality_stats.items():
|
||||
wr = data['wins'] / data['trades'] * 100 if data['trades'] > 0 else 0
|
||||
print(f" {quality.upper():10} | Trades: {data['trades']:3} | Win Rate: {wr:5.1f}% | Profit: ${data['profit']:,.2f}")
|
||||
|
||||
# Errors
|
||||
if self.data['errors']:
|
||||
print(f"\n⚠️ ERRORS ({len(self.data['errors'])} total):")
|
||||
for err in self.data['errors'][-5:]: # Last 5
|
||||
print(f" {err['timestamp'][:10]} | {err['type']}: {err['message'][:50]}")
|
||||
|
||||
# Filtered signals
|
||||
filtered = len(self.data.get('filtered_signals', []))
|
||||
if filtered > 0:
|
||||
print(f"\n🚫 Filtered Signals: {filtered}")
|
||||
|
||||
print("\n" + "=" * 70)
|
||||
|
||||
def print_go_live_check(self):
|
||||
"""Druckt den Go-Live Readiness Check"""
|
||||
is_ready, results = self.check_go_live_readiness()
|
||||
|
||||
print("\n" + "=" * 70)
|
||||
print("🚦 GO-LIVE READINESS CHECK")
|
||||
print("=" * 70)
|
||||
|
||||
for name, check in results['checks'].items():
|
||||
emoji = "✅" if check['passed'] else "⬜"
|
||||
print(f" {emoji} {check['label']}")
|
||||
|
||||
print("-" * 70)
|
||||
print(f" Passed: {results['passed_count']}/{results['total_checks']}")
|
||||
|
||||
if is_ready:
|
||||
print("\n 🎉 STATUS: READY FOR GO-LIVE!")
|
||||
print(" ✅ All criteria met. You can start with small real money.")
|
||||
else:
|
||||
remaining = [c['label'] for c in results['checks'].values() if not c['passed']]
|
||||
print(f"\n ⏳ STATUS: NOT READY YET")
|
||||
print(f" Still needed:")
|
||||
for r in remaining:
|
||||
print(f" • {r}")
|
||||
|
||||
print("=" * 70)
|
||||
|
||||
return is_ready
|
||||
|
||||
def get_daily_summary(self) -> str:
|
||||
"""Generiert eine tägliche Zusammenfassung"""
|
||||
self._update_stats()
|
||||
stats = self.data['stats']
|
||||
|
||||
today = datetime.now().date().isoformat()
|
||||
today_trades = [t for t in self.data['trades']
|
||||
if t['exit_time'].startswith(today)]
|
||||
|
||||
today_profit = sum(t['profit'] for t in today_trades)
|
||||
today_wins = sum(1 for t in today_trades if t['is_win'])
|
||||
today_wr = today_wins / len(today_trades) * 100 if today_trades else 0
|
||||
|
||||
summary = f"""
|
||||
📊 Daily Summary - {today}
|
||||
━━━━━━━━━━━━━━━━━━━━━━━━━━━
|
||||
Today: {len(today_trades)} trades | {today_wins} wins | WR: {today_wr:.0f}% | P/L: ${today_profit:+.2f}
|
||||
Overall: {stats['total_trades']} trades | WR: {stats['win_rate']*100:.1f}% | Total: ${stats['total_profit']:+.2f}
|
||||
━━━━━━━━━━━━━━━━━━━━━━━━━━━
|
||||
"""
|
||||
return summary
|
||||
|
||||
# ==========================================
|
||||
# HELPER METHODS
|
||||
# ==========================================
|
||||
|
||||
def _days_running(self) -> int:
|
||||
"""Berechnet Tage seit Start"""
|
||||
start = datetime.fromisoformat(self.data['start_date'])
|
||||
return (datetime.now() - start).days
|
||||
|
||||
def _load_data(self) -> Dict:
|
||||
"""Lädt Daten aus Datei"""
|
||||
default_data = {
|
||||
'start_date': datetime.now().isoformat(),
|
||||
'last_updated': None,
|
||||
'trades': [],
|
||||
'errors': [],
|
||||
'filtered_signals': [],
|
||||
'stats': {}
|
||||
}
|
||||
|
||||
try:
|
||||
if os.path.exists(self.data_file):
|
||||
with open(self.data_file, 'r') as f:
|
||||
data = json.load(f)
|
||||
# Merge with defaults for any missing keys
|
||||
for key in default_data:
|
||||
if key not in data:
|
||||
data[key] = default_data[key]
|
||||
return data
|
||||
except Exception as e:
|
||||
logger.warning(f"Could not load demo tracker data: {e}")
|
||||
|
||||
return default_data
|
||||
|
||||
def _save_data(self):
|
||||
"""Speichert Daten in Datei"""
|
||||
try:
|
||||
with open(self.data_file, 'w') as f:
|
||||
json.dump(self.data, f, indent=2, default=str)
|
||||
except Exception as e:
|
||||
logger.error(f"Could not save demo tracker data: {e}")
|
||||
|
||||
def reset(self):
|
||||
"""Setzt alle Daten zurück (Vorsicht!)"""
|
||||
self.data = {
|
||||
'start_date': datetime.now().isoformat(),
|
||||
'last_updated': None,
|
||||
'trades': [],
|
||||
'errors': [],
|
||||
'filtered_signals': [],
|
||||
'stats': {}
|
||||
}
|
||||
self._save_data()
|
||||
logger.warning("⚠️ Demo tracker data has been reset!")
|
||||
|
||||
|
||||
# ==========================================
|
||||
# STANDALONE USAGE
|
||||
# ==========================================
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("📊 Demo Test Tracker - Demo Mode")
|
||||
print("=" * 50)
|
||||
|
||||
tracker = DemoTestTracker(data_file="demo_test_example.json")
|
||||
|
||||
# Simulate some trades
|
||||
from datetime import timedelta
|
||||
|
||||
base_time = datetime.now() - timedelta(days=10)
|
||||
|
||||
test_trades = [
|
||||
{"profit": 15.50, "direction": "LONG", "session": "ny"},
|
||||
{"profit": -8.20, "direction": "SHORT", "session": "ny"},
|
||||
{"profit": 22.30, "direction": "LONG", "session": "asian"},
|
||||
{"profit": 12.10, "direction": "LONG", "session": "ny"},
|
||||
{"profit": -5.50, "direction": "SHORT", "session": "asian"},
|
||||
{"profit": 18.90, "direction": "LONG", "session": "london"},
|
||||
{"profit": -12.30, "direction": "LONG", "session": "ny"},
|
||||
{"profit": 25.60, "direction": "SHORT", "session": "asian"},
|
||||
{"profit": 8.40, "direction": "LONG", "session": "ny"},
|
||||
{"profit": -3.20, "direction": "SHORT", "session": "london"},
|
||||
]
|
||||
|
||||
for i, t in enumerate(test_trades):
|
||||
entry_time = base_time + timedelta(hours=i*8)
|
||||
exit_time = entry_time + timedelta(minutes=45)
|
||||
|
||||
tracker.log_trade(
|
||||
ticket=1000 + i,
|
||||
symbol="XAUUSD",
|
||||
direction=t["direction"],
|
||||
entry_price=2850.00,
|
||||
exit_price=2850.00 + (t["profit"] / 0.10), # Reverse calculate
|
||||
volume=0.10,
|
||||
profit=t["profit"],
|
||||
entry_time=entry_time,
|
||||
exit_time=exit_time,
|
||||
session=t["session"],
|
||||
base_confidence=75.0,
|
||||
enhanced_score=68.0,
|
||||
hybrid_score=72.2,
|
||||
signal_quality="good"
|
||||
)
|
||||
|
||||
# Print reports
|
||||
tracker.print_report()
|
||||
tracker.print_go_live_check()
|
||||
|
||||
# Cleanup
|
||||
os.remove("demo_test_example.json")
|
||||
@@ -1,10 +1,10 @@
|
||||
{
|
||||
"timestamp": "2026-01-26T00:27:01.306793",
|
||||
"timestamp": "2026-01-27T00:00:00.107097",
|
||||
"session_thresholds": {
|
||||
"asian": 60,
|
||||
"ny": 60,
|
||||
"london": 75,
|
||||
"overlap": 65
|
||||
"london": 60,
|
||||
"overlap": 60
|
||||
},
|
||||
"settings": {
|
||||
"lookback_trades": 20,
|
||||
|
||||
@@ -0,0 +1,257 @@
|
||||
[
|
||||
{
|
||||
"timestamp": "2026-01-26T12:13:46.930465",
|
||||
"equity": 9022.43,
|
||||
"trade_count": 1
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-26T12:19:46.966901",
|
||||
"equity": 8957.23,
|
||||
"trade_count": 2
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-26T13:28:47.062937",
|
||||
"equity": 9041.93,
|
||||
"trade_count": 3
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-26T13:42:46.844143",
|
||||
"equity": 8972.23,
|
||||
"trade_count": 4
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-26T14:02:47.503834",
|
||||
"equity": 8896.53,
|
||||
"trade_count": 5
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-26T14:19:46.495217",
|
||||
"equity": 8945.63,
|
||||
"trade_count": 6
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-26T14:23:47.782515",
|
||||
"equity": 8863.63,
|
||||
"trade_count": 7
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-26T14:30:47.450110",
|
||||
"equity": 8775.53,
|
||||
"trade_count": 8
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-26T14:57:47.581206",
|
||||
"equity": 8829.93,
|
||||
"trade_count": 9
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-26T15:43:47.029833",
|
||||
"equity": 8956.33,
|
||||
"trade_count": 10
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-26T16:01:47.190356",
|
||||
"equity": 9035.33,
|
||||
"trade_count": 11
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-26T16:16:50.346575",
|
||||
"equity": 9058.73,
|
||||
"trade_count": 12
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-26T16:35:47.053141",
|
||||
"equity": 8938.53,
|
||||
"trade_count": 13
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-26T17:20:48.048421",
|
||||
"equity": 9083.63,
|
||||
"trade_count": 14
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-26T17:28:50.189681",
|
||||
"equity": 8968.73,
|
||||
"trade_count": 15
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-26T17:39:47.578487",
|
||||
"equity": 8860.43,
|
||||
"trade_count": 16
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-26T18:25:28.537885",
|
||||
"equity": 9031.93,
|
||||
"trade_count": 17
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-26T19:07:29.047627",
|
||||
"equity": 9105.43,
|
||||
"trade_count": 18
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-26T19:12:28.985632",
|
||||
"equity": 9000.29,
|
||||
"trade_count": 19
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-26T19:36:28.604004",
|
||||
"equity": 8894.25,
|
||||
"trade_count": 20
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-26T19:41:28.577100",
|
||||
"equity": 8831.2,
|
||||
"trade_count": 21
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-26T19:58:27.699220",
|
||||
"equity": 8866.25,
|
||||
"trade_count": 22
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-26T20:02:28.281716",
|
||||
"equity": 8798.45,
|
||||
"trade_count": 23
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-26T20:11:30.929472",
|
||||
"equity": 8821.55,
|
||||
"trade_count": 24
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-26T20:21:28.550560",
|
||||
"equity": 8735.3,
|
||||
"trade_count": 25
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-26T20:48:32.186217",
|
||||
"equity": 8640.1,
|
||||
"trade_count": 26
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-26T21:33:27.589864",
|
||||
"equity": 8891.2,
|
||||
"trade_count": 27
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-26T21:37:29.173485",
|
||||
"equity": 8740.18,
|
||||
"trade_count": 28
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-26T22:10:27.827652",
|
||||
"equity": 8759.33,
|
||||
"trade_count": 29
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-26T22:16:28.542996",
|
||||
"equity": 8683.68,
|
||||
"trade_count": 30
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-26T22:23:29.348033",
|
||||
"equity": 8595.38,
|
||||
"trade_count": 31
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-26T22:36:27.686118",
|
||||
"equity": 8501.08,
|
||||
"trade_count": 32
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-27T00:09:28.170412",
|
||||
"equity": 8726.46,
|
||||
"trade_count": 33
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-27T00:59:27.666816",
|
||||
"equity": 8820.68,
|
||||
"trade_count": 34
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-27T01:43:27.684421",
|
||||
"equity": 8681.88,
|
||||
"trade_count": 35
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-27T01:51:29.464065",
|
||||
"equity": 8617.73,
|
||||
"trade_count": 36
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-27T02:00:31.411314",
|
||||
"equity": 8671.03,
|
||||
"trade_count": 37
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-27T02:12:28.296523",
|
||||
"equity": 8817.38,
|
||||
"trade_count": 38
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-27T02:33:27.507351",
|
||||
"equity": 8851.98,
|
||||
"trade_count": 39
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-27T03:55:28.891918",
|
||||
"equity": 8881.98,
|
||||
"trade_count": 40
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-27T05:11:28.289256",
|
||||
"equity": 8908.88,
|
||||
"trade_count": 41
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-27T05:42:28.758925",
|
||||
"equity": 8821.98,
|
||||
"trade_count": 42
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-27T06:02:29.934062",
|
||||
"equity": 8853.28,
|
||||
"trade_count": 43
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-27T06:48:27.496834",
|
||||
"equity": 9047.68,
|
||||
"trade_count": 44
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-27T07:56:27.536971",
|
||||
"equity": 9096.08,
|
||||
"trade_count": 45
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-27T08:13:28.986584",
|
||||
"equity": 9132.98,
|
||||
"trade_count": 46
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-27T08:45:27.690803",
|
||||
"equity": 9193.48,
|
||||
"trade_count": 47
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-27T09:00:28.478655",
|
||||
"equity": 9088.98,
|
||||
"trade_count": 48
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-27T09:29:27.489522",
|
||||
"equity": 9005.74,
|
||||
"trade_count": 49
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-27T09:52:30.080743",
|
||||
"equity": 9028.68,
|
||||
"trade_count": 50
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-01-27T10:46:29.927974",
|
||||
"equity": 9076.28,
|
||||
"trade_count": 51
|
||||
}
|
||||
]
|
||||
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user