""" Récupère l'historique complet de trading Hyperliquid """ import sys sys.stdout.reconfigure(encoding='utf-8') from hyperliquid_client import HyperliquidClient from datetime import datetime, timedelta import json def get_complete_trading_history(): """Récupère l'historique complet de trading""" print("=== RÉCUPÉRATION HISTORIQUE COMPLET HYPERLIQUID ===\n") try: client = HyperliquidClient() # Récupérer tous les fills all_fills = client.get_fill_info(limit=1000) if not all_fills: print("Aucun fill trouvé") return [] print(f"Total fills récupérés: {len(all_fills)}") # Organiser par symbole et analyser trades_by_symbol = {} total_volume = 0 total_fees = 0 for fill in all_fills: try: symbol = fill.get('coin', '').upper() if not symbol: continue if symbol not in trades_by_symbol: trades_by_symbol[symbol] = { 'trades': [], 'volume': 0, 'fees': 0, 'pnl': 0, 'open_positions': {}, 'closed_trades': [] } # Données du fill fill_time = datetime.fromtimestamp(int(fill.get('time', 0)) / 1000) direction = fill.get('dir', '') price = float(fill.get('px', 0)) size = abs(float(fill.get('sz', 0))) fee = float(fill.get('fee', 0)) side = fill.get('side', '') trade_info = { 'time': fill_time, 'direction': direction, 'price': price, 'size': size, 'fee': fee, 'side': side, 'value': price * size } trades_by_symbol[symbol]['trades'].append(trade_info) trades_by_symbol[symbol]['volume'] += price * size trades_by_symbol[symbol]['fees'] += fee total_volume += price * size total_fees += fee except Exception as e: print(f"Erreur traitement fill: {e}") continue # Trier les trades par date pour chaque symbole for symbol in trades_by_symbol: trades_by_symbol[symbol]['trades'].sort(key=lambda x: x['time']) # Afficher le résumé print("\n=== RÉSUMÉ PAR SYMBOLE ===") print("-" * 80) # Trier par volume sorted_symbols = sorted(trades_by_symbol.items(), key=lambda x: x[1]['volume'], reverse=True) for symbol, data in sorted_symbols: trades = data['trades'] if not trades: continue first_trade = trades[0]['time'] last_trade = trades[-1]['time'] print(f"\n{symbol}:") print(f" Nombre de trades: {len(trades)}") print(f" Volume total: ${data['volume']:,.2f}") print(f" Frais totaux: ${data['fees']:.2f}") print(f" Première transaction: {first_trade.strftime('%Y-%m-%d %H:%M')}") print(f" Dernière transaction: {last_trade.strftime('%Y-%m-%d %H:%M')}") # Calculer le PnL approximatif long_positions = [] short_positions = [] realized_pnl = 0 for trade in trades: if 'Open Long' in trade['direction']: long_positions.append(trade) elif 'Close Long' in trade['direction'] and long_positions: # Matching FIFO entry = long_positions.pop(0) if long_positions else None if entry: # Utiliser la méthode centralisée de calcul PnL pnl_data = client.calculate_position_pnl( entry_price=entry['price'], current_price=trade['price'], size=trade['size'], side='LONG' ) pnl = pnl_data['pnl_dollar'] # Déduire les frais d'ouverture ET de fermeture total_fees = entry['fee'] + trade['fee'] pnl_net = pnl - total_fees realized_pnl += pnl_net elif 'Open Short' in trade['direction']: short_positions.append(trade) elif 'Close Short' in trade['direction'] and short_positions: entry = short_positions.pop(0) if short_positions else None if entry: # Utiliser la méthode centralisée de calcul PnL pnl_data = client.calculate_position_pnl( entry_price=entry['price'], current_price=trade['price'], size=trade['size'], side='SHORT' ) pnl = pnl_data['pnl_dollar'] # Déduire les frais d'ouverture ET de fermeture total_fees = entry['fee'] + trade['fee'] pnl_net = pnl - total_fees realized_pnl += pnl_net print(f" PnL réalisé estimé: ${realized_pnl:.2f}") # Positions encore ouvertes if long_positions: print(f" Positions LONG ouvertes: {len(long_positions)}") if short_positions: print(f" Positions SHORT ouvertes: {len(short_positions)}") print("\n" + "=" * 80) print("TOTAUX GLOBAUX:") print(f" Volume total: ${total_volume:,.2f}") print(f" Frais totaux: ${total_fees:.2f}") print(f" Nombre de symboles tradés: {len(trades_by_symbol)}") # Sauvegarder dans un fichier JSON output_data = { 'generated_at': datetime.now().isoformat(), 'total_fills': len(all_fills), 'total_volume': total_volume, 'total_fees': total_fees, 'symbols': {} } for symbol, data in trades_by_symbol.items(): output_data['symbols'][symbol] = { 'trades_count': len(data['trades']), 'volume': data['volume'], 'fees': data['fees'], 'trades': [ { 'time': t['time'].isoformat(), 'direction': t['direction'], 'price': t['price'], 'size': t['size'], 'fee': t['fee'], 'value': t['value'] } for t in data['trades'] ] } with open('hyperliquid_trading_history.json', 'w') as f: json.dump(output_data, f, indent=2) print(f"\nHistorique sauvegardé: hyperliquid_trading_history.json") # Analyser les périodes d'activité print("\n=== PÉRIODES D'ACTIVITÉ ===") all_trades = [] for symbol_data in trades_by_symbol.values(): all_trades.extend(symbol_data['trades']) all_trades.sort(key=lambda x: x['time']) if all_trades: # Grouper par jour trades_by_day = {} for trade in all_trades: day = trade['time'].date() if day not in trades_by_day: trades_by_day[day] = [] trades_by_day[day].append(trade) print(f"\nActivité par jour (derniers 7 jours):") sorted_days = sorted(trades_by_day.keys(), reverse=True)[:7] for day in sorted_days: day_trades = trades_by_day[day] day_volume = sum(t['value'] for t in day_trades) print(f" {day}: {len(day_trades)} trades, Volume: ${day_volume:,.2f}") return trades_by_symbol except Exception as e: print(f"Erreur: {e}") return {} if __name__ == "__main__": history = get_complete_trading_history() # Analyser spécifiquement BNB si présent if 'BNB' in history: print("\n=== FOCUS SUR BNB ===") bnb_data = history['BNB'] print(f"Trades BNB: {len(bnb_data['trades'])}") # Afficher les 10 derniers trades BNB print("\nDerniers trades BNB:") for trade in bnb_data['trades'][-10:]: print(f" {trade['time'].strftime('%Y-%m-%d %H:%M')} | {trade['direction']:<15} | " f"${trade['price']:.2f} x {trade['size']:.3f} | Fee: ${trade['fee']:.2f}")