IB Analytics MCP Server

#!/usr/bin/env python3 """ Monthly Performance Tracking and Analysis Visualizes 2025 trading performance by month with trends """ import csv from datetime import datetime from collections import defaultdict from typing import Dict, List, Any def parse_data(filepath: str) -> Dict[str, List[Dict]]: """Parse CSV sections""" sections = {} current_section = None headers = [] with open(filepath, 'r', encoding='utf-8') as f: reader = csv.reader(f) for row in reader: if not row or not row[0]: continue if row[0] == 'ClientAccountID': headers = row if 'TradeID' in headers: current_section = 'trades' elif 'StartingCash' in headers and 'LevelOfDetail' in headers: current_section = 'cash_summary' if current_section: sections[current_section] = [] elif current_section and headers: row_dict = dict(zip(headers, row)) sections[current_section].append(row_dict) return sections def analyze_monthly_performance(trades: List[Dict], cash_summary: List[Dict]) -> Dict[str, Any]: """Analyze performance by month""" # Group trades by month monthly_data = defaultdict(lambda: { 'trades': [], 'buy_count': 0, 'sell_count': 0, 'realized_pnl': 0, 'commissions': 0, 'volume': 0, 'symbols': set() }) for trade in trades: trade_date = trade.get('TradeDate', '') if not trade_date: continue try: date_obj = datetime.strptime(trade_date, '%Y%m%d') month_key = date_obj.strftime('%Y-%m') qty = float(trade.get('Quantity', 0)) trade_money = abs(float(trade.get('TradeMoney', 0))) pnl = float(trade.get('FifoPnlRealized', 0)) commission = abs(float(trade.get('IBCommission', 0))) symbol = trade.get('Symbol', '') monthly_data[month_key]['trades'].append(trade) if qty > 0: monthly_data[month_key]['buy_count'] += 1 else: monthly_data[month_key]['sell_count'] += 1 monthly_data[month_key]['realized_pnl'] += pnl monthly_data[month_key]['commissions'] += commission monthly_data[month_key]['volume'] += trade_money monthly_data[month_key]['symbols'].add(symbol) except ValueError: continue # Calculate cumulative metrics sorted_months = sorted(monthly_data.keys()) cumulative_pnl = 0 cumulative_commissions = 0 for month in sorted_months: cumulative_pnl += monthly_data[month]['realized_pnl'] cumulative_commissions += monthly_data[month]['commissions'] monthly_data[month]['cumulative_pnl'] = cumulative_pnl monthly_data[month]['cumulative_commissions'] = cumulative_commissions monthly_data[month]['net_pnl'] = monthly_data[month]['realized_pnl'] - monthly_data[month]['commissions'] # Get period info from cash summary period_start = None period_end = None starting_cash = 0 ending_cash = 0 if cash_summary: cs = cash_summary[0] period_start = cs.get('FromDate', '') period_end = cs.get('ToDate', '') starting_cash = float(cs.get('StartingCash', 0)) ending_cash = float(cs.get('EndingCash', 0)) return { 'monthly_data': dict(monthly_data), 'sorted_months': sorted_months, 'period_start': period_start, 'period_end': period_end, 'starting_cash': starting_cash, 'ending_cash': ending_cash, 'total_months': len(sorted_months) } def generate_monthly_report(analysis: Dict): """Generate monthly performance report with visualizations""" print("=" * 80) print("MONTHLY PERFORMANCE TRACKING - 2025") print("=" * 80) print() period_start = analysis['period_start'] period_end = analysis['period_end'] if period_start and period_end: start_date = datetime.strptime(period_start, '%Y%m%d') end_date = datetime.strptime(period_end, '%Y%m%d') print(f"Analysis Period: {start_date.strftime('%B %d, %Y')} - {end_date.strftime('%B %d, %Y')}") print(f"Total Months: {analysis['total_months']}") print() monthly_data = analysis['monthly_data'] sorted_months = analysis['sorted_months'] # Calculate overall statistics total_trades = sum(len(data['trades']) for data in monthly_data.values()) total_pnl = sum(data['realized_pnl'] for data in monthly_data.values()) total_commissions = sum(data['commissions'] for data in monthly_data.values()) total_volume = sum(data['volume'] for data in monthly_data.values()) print("OVERALL SUMMARY") print("-" * 80) print(f"Total Trades: {total_trades}") print(f"Total Realized P&L: ${total_pnl:,.2f}") print(f"Total Commissions: ${total_commissions:,.2f}") print(f"Net P&L: ${total_pnl - total_commissions:,.2f}") print(f"Total Trading Volume: ${total_volume:,.2f}") print(f"Average P&L/Month: ${total_pnl / len(sorted_months):,.2f}") print() print("=" * 80) print("MONTH-BY-MONTH BREAKDOWN") print("=" * 80) print() print(f"{'Month':<12} {'Trades':<8} {'Buy':<6} {'Sell':<6} {'P&L':<12} " f"{'Comm.':<10} {'Net P&L':<12} {'Symbols':<15}") print("-" * 80) for month in sorted_months: data = monthly_data[month] month_obj = datetime.strptime(month, '%Y-%m') month_str = month_obj.strftime('%b %Y') trade_count = len(data['trades']) buy_count = data['buy_count'] sell_count = data['sell_count'] pnl = data['realized_pnl'] comm = data['commissions'] net_pnl = data['net_pnl'] symbols = ', '.join(sorted(data['symbols'])) print(f"{month_str:<12} {trade_count:<8} {buy_count:<6} {sell_count:<6} " f"${pnl:<11,.2f} ${comm:<9,.2f} ${net_pnl:<11,.2f} {symbols:<15}") print("-" * 80) print(f"{'TOTAL':<12} {total_trades:<8} {'':<6} {'':<6} " f"${total_pnl:<11,.2f} ${total_commissions:<9,.2f} ${total_pnl - total_commissions:<11,.2f}") print() print("=" * 80) print("CUMULATIVE PERFORMANCE CHART") print("=" * 80) print() # ASCII chart for cumulative P&L print(f"{'Month':<12} {'Cumulative P&L':<20} {'Chart'}") print("-" * 80) max_pnl = max(data['cumulative_pnl'] for data in monthly_data.values()) for month in sorted_months: data = monthly_data[month] month_obj = datetime.strptime(month, '%Y-%m') month_str = month_obj.strftime('%b %Y') cum_pnl = data['cumulative_pnl'] # Create bar chart bar_length = int((cum_pnl / max_pnl) * 40) if max_pnl > 0 else 0 bar = '█' * bar_length print(f"{month_str:<12} ${cum_pnl:<19,.2f} {bar}") print() print("=" * 80) print("MONTHLY P&L VISUALIZATION") print("=" * 80) print() # ASCII chart for monthly P&L print(f"{'Month':<12} {'Monthly P&L':<15} {'Chart'}") print("-" * 80) max_monthly_pnl = max(abs(data['net_pnl']) for data in monthly_data.values()) for month in sorted_months: data = monthly_data[month] month_obj = datetime.strptime(month, '%Y-%m') month_str = month_obj.strftime('%b %Y') net_pnl = data['net_pnl'] # Create bar chart (positive/negative) if net_pnl >= 0: bar_length = int((net_pnl / max_monthly_pnl) * 30) if max_monthly_pnl > 0 else 0 bar = '█' * bar_length chart = f"{'':>30}{bar}" else: bar_length = int((abs(net_pnl) / max_monthly_pnl) * 30) if max_monthly_pnl > 0 else 0 bar = '█' * bar_length chart = f"{bar[::-1]:>30}" print(f"{month_str:<12} ${net_pnl:<14,.2f} {chart}") print() print("=" * 80) print("TRADING ACTIVITY HEATMAP") print("=" * 80) print() print(f"{'Month':<12} {'Activity Level':<20} {'Intensity'}") print("-" * 80) max_trades = max(len(data['trades']) for data in monthly_data.values()) for month in sorted_months: data = monthly_data[month] month_obj = datetime.strptime(month, '%Y-%m') month_str = month_obj.strftime('%b %Y') trade_count = len(data['trades']) # Activity level if trade_count >= 10: level = "Very High" intensity = '🔥' * 5 elif trade_count >= 7: level = "High" intensity = '🔥' * 4 elif trade_count >= 4: level = "Moderate" intensity = '🔥' * 3 elif trade_count >= 2: level = "Low" intensity = '🔥' * 2 else: level = "Minimal" intensity = '🔥' * 1 print(f"{month_str:<12} {level:<20} {intensity}") print() print("=" * 80) print("PERFORMANCE TRENDS & INSIGHTS") print("=" * 80) print() # Identify best and worst months best_month = max(sorted_months, key=lambda m: monthly_data[m]['net_pnl']) worst_month = min(sorted_months, key=lambda m: monthly_data[m]['net_pnl']) most_active_month = max(sorted_months, key=lambda m: len(monthly_data[m]['trades'])) best_month_obj = datetime.strptime(best_month, '%Y-%m') worst_month_obj = datetime.strptime(worst_month, '%Y-%m') most_active_obj = datetime.strptime(most_active_month, '%Y-%m') print("KEY METRICS") print("-" * 80) print(f"Best Month: {best_month_obj.strftime('%B %Y')} (${monthly_data[best_month]['net_pnl']:,.2f})") print(f"Worst Month: {worst_month_obj.strftime('%B %Y')} (${monthly_data[worst_month]['net_pnl']:,.2f})") print(f"Most Active Month: {most_active_obj.strftime('%B %Y')} ({len(monthly_data[most_active_month]['trades'])} trades)") print() # Calculate consistency profitable_months = sum(1 for data in monthly_data.values() if data['net_pnl'] > 0) consistency_rate = profitable_months / len(sorted_months) * 100 print(f"Profitable Months: {profitable_months}/{len(sorted_months)} ({consistency_rate:.1f}%)") print() # Calculate growth rate if len(sorted_months) >= 2: first_month_pnl = monthly_data[sorted_months[0]]['net_pnl'] last_month_pnl = monthly_data[sorted_months[-1]]['net_pnl'] print("TREND ANALYSIS") print("-" * 80) if last_month_pnl > first_month_pnl: print("📈 POSITIVE TREND: Performance improving over time") elif last_month_pnl < first_month_pnl: print("📉 DECLINING TREND: Performance decreasing over time") else: print("➡️ FLAT TREND: Consistent performance") print() # Trading frequency analysis avg_trades_per_month = total_trades / len(sorted_months) print("TRADING FREQUENCY") print("-" * 80) print(f"Average Trades/Month: {avg_trades_per_month:.1f}") if avg_trades_per_month > 10: print("Assessment: High frequency - Active trader") elif avg_trades_per_month > 5: print("Assessment: Moderate frequency - Regular trader") elif avg_trades_per_month > 2: print("Assessment: Low frequency - Selective trader") else: print("Assessment: Minimal frequency - Conservative trader") print() print("=" * 80) print("SEASONALITY ANALYSIS") print("=" * 80) print() # Group by calendar month (across years if applicable) calendar_month_data = defaultdict(lambda: {'pnl': 0, 'count': 0, 'trades': 0}) for month in sorted_months: month_obj = datetime.strptime(month, '%Y-%m') calendar_month = month_obj.strftime('%B') calendar_month_data[calendar_month]['pnl'] += monthly_data[month]['net_pnl'] calendar_month_data[calendar_month]['count'] += 1 calendar_month_data[calendar_month]['trades'] += len(monthly_data[month]['trades']) print(f"{'Month':<12} {'Avg P&L':<15} {'Total Trades':<15} {'Occurrences'}") print("-" * 80) month_order = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'] for month_name in month_order: if month_name in calendar_month_data: data = calendar_month_data[month_name] avg_pnl = data['pnl'] / data['count'] total_trades = data['trades'] occurrences = data['count'] print(f"{month_name:<12} ${avg_pnl:<14,.2f} {total_trades:<15} {occurrences}") print() print("=" * 80) print("INSIGHTS & RECOMMENDATIONS") print("=" * 80) print() insights = [] # Consistency insight if consistency_rate >= 75: insights.append("✓ EXCELLENT consistency: 75%+ profitable months") elif consistency_rate >= 50: insights.append("✓ GOOD consistency: 50-74% profitable months") else: insights.append("⚠ INCONSISTENT: Less than 50% profitable months") # Best month insight best_pnl = monthly_data[best_month]['net_pnl'] if best_pnl > total_pnl * 0.7: insights.append(f"⚠ CONCENTRATION: {best_month_obj.strftime('%B')} accounts for {best_pnl/total_pnl*100:.0f}% of total profits") # Trading frequency insight if avg_trades_per_month < 2: insights.append("✓ DISCIPLINED: Low trading frequency suggests selective approach") elif avg_trades_per_month > 15: insights.append("⚠ HIGH FREQUENCY: Consider if all trades add value") # Trend insight if len(sorted_months) >= 3: recent_months = sorted_months[-3:] recent_avg = sum(monthly_data[m]['net_pnl'] for m in recent_months) / len(recent_months) overall_avg = total_pnl / len(sorted_months) if recent_avg > overall_avg * 1.2: insights.append("📈 IMPROVING: Recent performance exceeds overall average") elif recent_avg < overall_avg * 0.8: insights.append("📉 DECLINING: Recent performance below overall average") for insight in insights: print(f" {insight}") print() print("RECOMMENDATIONS") print("-" * 80) recommendations = [] if consistency_rate < 75: recommendations.append("• Focus on improving consistency across all months") recommendations.append("• Review unsuccessful months for common patterns") if best_pnl > total_pnl * 0.5: recommendations.append("• Reduce reliance on single exceptional month") recommendations.append("• Develop more consistent monthly returns") if avg_trades_per_month > 10: recommendations.append("• Consider reducing trading frequency") recommendations.append("• Focus on higher quality setups") recommendations.append("• Continue tracking monthly performance for long-term trends") recommendations.append("• Set monthly performance targets and review regularly") for i, rec in enumerate(recommendations, 1): print(f"{i}. {rec}") print() print("=" * 80) def main(): filepath = '/Users/ken/Developer/private/ib-sec/trades_2025_full.csv' # Parse data sections = parse_data(filepath) # Analyze monthly performance analysis = analyze_monthly_performance( sections.get('trades', []), sections.get('cash_summary', []) ) # Generate report generate_monthly_report(analysis) if __name__ == '__main__': main()