mirror of
https://github.com/chrisnov-it/quantumbotx.git
synced 2026-07-27 18:57:47 +00:00
Reynov Christian
78737b6835
- Update `.gitignore` to include `lab/` for backtesting and raw data. - Refactor `app.py` to improve error handling and logging. - Remove deprecated files: `core/bot_logic.py`, `core/bots/base_bot.py`, `core/bots/manager.py`, `core/db/database.py`, `core/routes/api_analysis.py`, `core/routes/api_bots_analysis.py`, `core/strategies/logic_ma.py`, `core/strategies/logic_rsi.py`. - Modify multiple files to enhance bot management, routing, and strategy handling. - Update JavaScript and HTML templates for better UI and functionality.
87 lines
3.6 KiB
Python
87 lines
3.6 KiB
Python
# backtester_bollinger.py
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import pandas as pd
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import pandas_ta as ta
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import matplotlib.pyplot as plt
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def get_profit_multiplier(symbol, lot_size=0.01):
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"""
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Mengembalikan multiplier yang benar untuk perhitungan profit berdasarkan
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simbol, kelas aset, dan ukuran lot.
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"""
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# 1. Untuk Indeks Saham (seperti US500, NAS100, SPX500m, dll.)
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# Pergerakan 1 poin = profit $0.01 untuk 0.01 lot.
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if "500" in symbol or "100" in symbol or "30" in symbol:
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return 1 * lot_size
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# 2. Untuk Emas (XAUUSD)
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# Pergerakan $1 = profit $1 untuk 0.01 lot.
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elif "XAU" in symbol:
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return 100 * lot_size
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# 3. Untuk Pasangan Mata Uang Forex (seperti EURUSD, USDJPY)
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# Pergerakan 1 pip = profit ~$0.10 untuk 0.01 lot.
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# Nilai 100000 adalah standar industri untuk 1 lot.
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else:
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# Periksa apakah ini pasangan JPY, karena nilainya berbeda
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if "JPY" in symbol:
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return 1000 * lot_size # Untuk pasangan JPY, 1 pip adalah 0.01
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else:
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return 100000 * lot_size # Untuk pasangan non-JPY, 1 pip adalah 0.0001
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def run_bollinger_backtest(data_path, symbol, initial_balance=10000):
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print(f"Memulai backtest BOLLINGER BANDS untuk simbol: {symbol}")
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# Kode baru yang disarankan
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LOT_SIZE = 0.01 # Definisikan ukuran lot di satu tempat
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multiplier = get_profit_multiplier(symbol, LOT_SIZE)
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df = pd.read_csv(data_path, parse_dates=['time'])
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# --- Hitung Indikator ---
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df.ta.bbands(length=20, std=2.0, append=True)
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df.dropna(inplace=True)
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# --- Siapkan Variabel Simulasi ---
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balance, position, trades, equity_curve = initial_balance, None, [], []
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print("Memulai Loop Backtest...")
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for i in range(1, len(df)):
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current = df.iloc[i]
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# --- Logika Exit (Keluar jika menyentuh MA tengah) ---
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if position:
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if position['type'] == 'BUY' and current['close'] >= current['BBM_20_2.0']:
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profit = (current['close'] - position['entry_price']) * multiplier
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balance += profit; trades.append({'profit': profit}); position = None
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elif position['type'] == 'SELL' and current['close'] <= current['BBM_20_2.0']:
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profit = (position['entry_price'] - current['close']) * multiplier
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balance += profit; trades.append({'profit': profit}); position = None
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# --- Logika Entry (Hanya jika tidak ada posisi) ---
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if not position:
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# Sinyal BELI: Harga menyentuh atau menembus band bawah
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if current['low'] <= current['BBL_20_2.0']:
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position = {'type': 'BUY', 'entry_price': current['close']}
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# Sinyal JUAL: Harga menyentuh atau menembus band atas
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elif current['high'] >= current['BBU_20_2.0']:
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position = {'type': 'SELL', 'entry_price': current['close']}
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equity_curve.append(balance)
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# --- Analisis Hasil ---
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print("\n--- Backtest Selesai ---")
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print(f"Balance Awal: ${initial_balance:.2f}")
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print(f"Balance Akhir: ${balance:.2f}")
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print(f"Total Profit/Loss: ${balance - initial_balance:.2f} ({(balance - initial_balance)/initial_balance*100:.2f}%)")
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print(f"Total Trades: {len(trades)}")
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plt.figure(figsize=(12, 6))
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plt.plot(df['time'].iloc[1:], equity_curve)
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plt.title(f'Equity Curve - Strategi BOLLINGER BANDS on {symbol}')
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plt.xlabel('Tanggal')
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plt.ylabel('Balance ($)')
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plt.grid(True)
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plt.show()
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if __name__ == '__main__':
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symbol_to_test = "XAUUSD" # Ganti dengan simbol yang ingin diuji
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file_name = "lab/XAUUSD_16385_data.csv"
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run_bollinger_backtest(file_name, symbol=symbol_to_test) |