mirror of
https://github.com/chrisnov-it/quantumbotx.git
synced 2026-07-28 11:17:44 +00:00
Reynov Christian
243bdda80c
``` The commit introduces significant improvements to the backtesting functionality: 1. Added dynamic position sizing based on risk percentage 2. Implemented ATR-based stop loss and take profit calculations 3. Enhanced backtest state management with detailed trade tracking 4. Updated database schema to store additional backtest metrics 5. Modified UI components to display new backtest results 6. Added logging and error handling improvements 7. Removed the London Breakout strategy from the codebase These changes improve the depth and accuracy of backtesting while providing more comprehensive performance metrics.
180 lines
7.1 KiB
Python
180 lines
7.1 KiB
Python
# core/backtesting/engine.py
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import math # Import modul math
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import logging # Import modul logging
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from core.strategies.strategy_map import STRATEGY_MAP
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logger = logging.getLogger(__name__)
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def run_backtest(strategy_id, params, historical_data_df):
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"""
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Menjalankan simulasi backtesting dengan position sizing dinamis.
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"""
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strategy_class = STRATEGY_MAP.get(strategy_id)
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if not strategy_class:
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return {"error": "Strategi tidak ditemukan"}
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# --- LANGKAH 1: Pra-perhitungan Indikator & ATR ---
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class MockBot:
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def __init__(self):
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# Dapatkan nama simbol dari data historis
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self.market_for_mt5 = historical_data_df.columns[0].split('_')[0]
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self.timeframe = "H1"
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self.tf_map = {}
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strategy_instance = strategy_class(bot_instance=MockBot(), params=params)
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df = historical_data_df.copy()
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df_with_signals = strategy_instance.analyze_df(df)
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df_with_signals.ta.atr(length=14, append=True)
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df_with_signals.dropna(inplace=True)
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df_with_signals.reset_index(inplace=True)
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if df_with_signals.empty:
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return {"error": "Data tidak cukup untuk analisa."}
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# --- LANGKAH 2: Inisialisasi state & parameter ---
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trades = []
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in_position = False
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initial_capital = 10000.0
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capital = initial_capital
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equity_curve = [initial_capital]
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peak_equity = initial_capital
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max_drawdown = 0.0
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position_type = None
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entry_price = 0.0
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sl_price = 0.0
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tp_price = 0.0
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lot_size = 0.0
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entry_time = None # Inisialisasi entry_time
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risk_percent = float(params.get('lot_size', 1.0))
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sl_atr_multiplier = float(params.get('sl_pips', 2.0))
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tp_atr_multiplier = float(params.get('tp_pips', 4.0))
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# --- LANGKAH 3: Loop melalui data ---
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for i in range(1, len(df_with_signals)):
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current_bar = df_with_signals.iloc[i]
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# Hentikan backtest jika modal habis
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if capital <= 0:
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break
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if in_position:
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exit_price = None
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if position_type == 'BUY' and current_bar['low'] <= sl_price: exit_price = sl_price
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elif position_type == 'BUY' and current_bar['high'] >= tp_price: exit_price = tp_price
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elif position_type == 'SELL' and current_bar['high'] >= sl_price: exit_price = sl_price
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elif position_type == 'SELL' and current_bar['low'] <= tp_price: exit_price = tp_price
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if exit_price is not None:
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# Tentukan ukuran kontrak berdasarkan simbol
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contract_size = 100 if 'XAU' in strategy_instance.bot.market_for_mt5.upper() else 100000
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# Profit calculation needs to account for scaled prices in commodities
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symbol = strategy_instance.bot.market_for_mt5.upper()
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if 'XAU' in symbol or 'XAG' in symbol:
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point_value = 0.01
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profit_multiplier = lot_size * contract_size * point_value
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else:
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profit_multiplier = lot_size * contract_size
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if position_type == 'BUY':
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profit = (exit_price - entry_price) * profit_multiplier
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else: # SELL
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profit = (entry_price - exit_price) * profit_multiplier
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# Pastikan profit adalah angka yang valid
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if not math.isfinite(profit):
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profit = 0.0
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capital += profit
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trades.append({
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'entry_time': str(entry_time),
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'exit_time': str(current_bar['time']),
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'entry': entry_price,
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'exit': exit_price,
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'profit': profit,
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'reason': 'SL/TP', # Default reason
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'position_type': position_type
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})
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equity_curve.append(capital)
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peak_equity = max(peak_equity, capital)
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drawdown = (peak_equity - capital) / peak_equity if peak_equity > 0 else 0
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max_drawdown = max(max_drawdown, drawdown)
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in_position = False
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if not in_position:
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signal = current_bar.get("signal", "HOLD")
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if signal in ['BUY', 'SELL']:
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entry_price = current_bar['close']
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entry_time = current_bar['time'] # Tambahkan baris ini
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atr_value = current_bar['ATRr_14']
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if atr_value <= 0:
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continue
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sl_distance = atr_value * sl_atr_multiplier
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tp_distance = atr_value * tp_atr_multiplier
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if signal == 'BUY':
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sl_price = entry_price - sl_distance
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tp_price = entry_price + tp_distance
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else:
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sl_price = entry_price + sl_distance
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tp_price = entry_price - tp_distance
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# Kalkulasi Lot Size
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amount_to_risk = capital * (risk_percent / 100.0)
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contract_size = 100 if 'XAU' in strategy_instance.bot.market_for_mt5.upper() else 100000
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symbol = strategy_instance.bot.market_for_mt5.upper()
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# Risk calculation needs to account for scaled prices in commodities
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if 'XAU' in symbol or 'XAG' in symbol:
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point_value = 0.01
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risk_in_currency_per_lot = sl_distance * contract_size * point_value
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else:
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risk_in_currency_per_lot = sl_distance * contract_size
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if risk_in_currency_per_lot <= 0:
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continue
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calculated_lot_size = amount_to_risk / risk_in_currency_per_lot
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# Terapkan batasan lot size minimum dan maksimum
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if calculated_lot_size < 0.00001:
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continue
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if calculated_lot_size > 10.0:
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continue
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# Round lot size to a reasonable precision (e.g., 2 decimal places for most brokers)
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# Jika calculated_lot_size sangat kecil tapi positif, gunakan lot minimum broker
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if calculated_lot_size > 0 and calculated_lot_size < 0.01:
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lot_size = 0.01 # Gunakan lot minimum broker
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else:
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lot_size = round(calculated_lot_size, 2)
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# Pastikan lot_size tidak nol setelah pembulatan
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if lot_size <= 0:
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continue
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in_position = True
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position_type = signal
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# --- LANGKAH 4: Hitung hasil akhir ---
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total_profit = capital - initial_capital
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wins = len([t for t in trades if t['profit'] > 0])
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losses = len(trades) - wins
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win_rate = (wins / len(trades) * 100) if trades else 0
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return {
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"strategy_name": strategy_class.name,
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"total_trades": len(trades),
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"final_capital": round(capital, 2),
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"total_profit_usd": round(total_profit, 2),
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"win_rate_percent": round(win_rate, 2),
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"wins": wins,
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"losses": losses,
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"max_drawdown_percent": round(max_drawdown * 100, 2),
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"equity_curve": equity_curve,
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"trades": trades[-20:]
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}
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