first commit

.gitignore

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+bin/*
+include/*
+lib/*
+lib64/*
+share/*
+.env
+.git
+pyvenv.cfg

bot.py

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+import ccxt
+import time
+import pandas as pd
+import numpy as np
+import ta
+import matplotlib.pyplot as plt
+import os
+from dotenv import load_dotenv
+load_dotenv()
+
+# Connect to Binance exchange
+exchange = ccxt.binance({
+    'apiKey': os.getenv("API_KEY"),
+    'secret': os.getenv("API_SECRET"),
+})
+
+# Fetch historical data with pagination
+def fetch_full_ohlcv(symbol, timeframe, since):
+    all_data = []
+    while True:
+        try:
+            data = exchange.fetch_ohlcv(symbol, timeframe, since=since, limit=1000)
+            print(f"Fetched {len(data)} records from {exchange.iso8601(since)}")
+            if not data:
+                break
+            all_data.extend(data)
+            since = data[-1][0] + 1
+            time.sleep(0.2)
+        except Exception as e:
+            print(f"Error fetching data: {e}")
+            break
+    return all_data
+
+# Fetch historical data
+timeframe = '1m'
+symbol = 'BTC/USDC'
+since = exchange.parse8601('2025-04-01T00:00:00Z')
+historical_data = fetch_full_ohlcv(symbol, timeframe, since)
+
+
+
+
+
+# Load historical data into DataFrame
+df = pd.DataFrame(historical_data, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
+df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
+
+virtual_balance = 50
+open_trades = []
+trade_log = []
+
+def calculate_indicators(df):
+    df['rsi'] = ta.momentum.RSIIndicator(df['close'], window=14).rsi()
+    macd = ta.trend.MACD(df['close'])
+    df['macd'] = macd.macd()
+    df['signal'] = macd.macd_signal()
+    return df
+
+df = calculate_indicators(df)
+
+RISK_PER_TRADE = 0.3
+STOP_LOSS = 0.02 # 100%
+TAKE_PROFIT = 0.03 # 5%
+
+
+# Open new trades with investment tracking
+for index, row in df.iterrows():
+    current_price = row['close']
+    
+    # Close existing trades
+    for trade in open_trades[:]:
+        if current_price >= trade['entry_price'] * (1 + TAKE_PROFIT) or \
+           current_price <= trade['entry_price'] * (1 - STOP_LOSS):
+            open_trades.remove(trade)
+            profit_pct = (current_price / trade['entry_price'] - 1)
+            trade_log.append({
+                'entry_time': trade['entry_time'],
+                'exit_time': row['timestamp'],
+                'profit': profit_pct * 100,
+                'success': profit_pct > 0,
+                'investment_usd': trade['size']
+            })
+            virtual_balance += trade['size'] * profit_pct
+
+    # Calculate available capital for new trades
+    allocated_capital = sum(trade['size'] for trade in open_trades)
+    available_capital = max(0, virtual_balance - allocated_capital)
+    
+    # Open new trades with capital check
+    if available_capital > 0 and len(open_trades) < 15:
+        if row['rsi'] < 30 and row['macd'] > row['signal']:
+            position_size = min(
+                RISK_PER_TRADE * virtual_balance,  # Normal risk amount
+                available_capital  # Never exceed remaining balance
+            )
+            open_trades.append({
+                'entry_time': row['timestamp'],
+                'entry_price': current_price,
+                'size': position_size,
+                'investment_usd': position_size  # Track investment in USD
+            })
+
+
+print(open_trades)
+# Calculate Performance Metrics
+total_trades = len(trade_log)
+winning_trades = sum(1 for trade in trade_log if trade['success'])
+win_rate = (winning_trades / total_trades) * 100 if total_trades > 0 else 0
+total_return = ((virtual_balance / 50) - 1) * 100
+returns = [trade['profit'] for trade in trade_log]
+sharpe_ratio = np.mean(returns) / np.std(returns) if len(returns) > 1 else 0
+
+# Print Results
+print(f"""
+Backtest Results:
+- Starting Capital: $50 USDC
+- Ending Balance: ${virtual_balance:.2f} USDC
+- Total Return: {total_return:.2f}%
+- Win Rate: {win_rate:.2f}%
+- Trades Executed: {total_trades}
+- Sharpe Ratio: {sharpe_ratio:.2f}
+""")
+
+
+
+# Visualization
+plt.figure(figsize=(16, 12))
+
+# Price Chart
+ax1 = plt.subplot(3, 1, 1)
+plt.plot(df['timestamp'], df['close'], label='Price', color='black')
+for trade in trade_log:
+    entry_idx = df[df['timestamp'] == trade['entry_time']].index[0]
+    exit_idx = df[df['timestamp'] == trade['exit_time']].index[0]
+    plt.plot([trade['entry_time'], trade['exit_time']],
+             [df.loc[entry_idx, 'close'], df.loc[exit_idx, 'close']],
+             color='green' if trade['success'] else 'red', alpha=0.3)
+    plt.scatter(trade['entry_time'], df.loc[entry_idx, 'close'],
+                marker='^', color='green', s=100)
+    plt.scatter(trade['exit_time'], df.loc[exit_idx, 'close'],
+                marker='v', color='red', s=100)
+    
+    # Annotate investment amount
+    plt.text(trade['entry_time'], df.loc[entry_idx, 'close'],
+             f"${trade['investment_usd']:.2f}", fontsize=8, color='blue')
+
+plt.title('Trading Strategy Performance')
+plt.ylabel('Price')
+
+# Plot open trades with orange and annotate investments
+for trade in open_trades:
+    entry_idx = df[df['timestamp'] == trade['entry_time']].index[0]
+    plt.plot([trade['entry_time'], df['timestamp'].iloc[-1]],
+             [df.loc[entry_idx, 'close'], df.iloc[-1]['close']],
+             color='orange', alpha=0.3)
+    plt.scatter(trade['entry_time'], df.loc[entry_idx, 'close'],
+                marker='^', color='orange', s=100)
+    
+    # Annotate investment amount for open trades
+    plt.text(trade['entry_time'], df.loc[entry_idx, 'close'],
+             f"${trade['investment_usd']:.2f}", fontsize=8, color='blue')
+
+# Indicators
+plt.subplot(3, 1, 2, sharex=ax1)
+plt.plot(df['timestamp'], df['rsi'], label='RSI', color='purple')
+plt.axhline(30, linestyle='--', color='green')
+plt.axhline(70, linestyle='--', color='red')
+plt.ylabel('RSI')
+
+plt.subplot(3, 1, 3, sharex=ax1)
+plt.plot(df['timestamp'], df['macd'], label='MACD', color='blue')
+plt.plot(df['timestamp'], df['signal'], label='Signal', color='orange')
+plt.ylabel('MACD')
+
+plt.tight_layout()
+plt.show()