Create calculator.py

utils/calculator.py

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+def simulate_trading(signals_data, initial_capital=1000, investment_per_buy=100):
+    """
+    Simulates trading based on buy and sell signals.
+
+    Parameters:
+    - signals_data (pd.DataFrame): DataFrame with 'Close', 'Buy_Signal', and 'Sell_Signal'.
+    - initial_capital (float): The initial capital at the start of the trading period.
+    - investment_per_buy (float): The fixed amount in dollars to invest at each buy signal.
+
+    Returns:
+    - float: The value of the stock currently held at the end of the period.
+    - float: The amount of cash remaining if any.
+    """
+    cash = initial_capital
+    holdings = 0
+
+    for _, row in signals_data.iterrows():
+        # Buy
+        if row['Buy_Signal'] and cash >= investment_per_buy:
+            shares_bought = investment_per_buy / row['Close']
+            holdings += shares_bought
+            cash -= investment_per_buy
+
+        # Sell
+        if row['Sell_Signal'] and holdings > 0:
+            shares_sold = holdings * 0.25
+            holdings -= shares_sold
+            cash += shares_sold * row['Close']
+
+    # Calculate the value of the remaining stock holdings at the last known price
+    final_stock_value = holdings * signals_data.iloc[-1]['Close']
+
+    return final_stock_value, cash
+
+# Example usage
+if __name__ == "__main__":
+    # Assuming signals_data DataFrame exists with 'Close', 'Buy_Signal', 'Sell_Signal'
+    signals_data = pd.DataFrame({
+        'Close': [100, 105, 103, 108, 107],  # Example close prices
+        'Buy_Signal': [True, False, True, False, False],
+        'Sell_Signal': [False, True, False, True, False]
+    }, index=pd.date_range(start='2020-01-01', periods=5, freq='D'))
+
+    final_stock_value, remaining_cash = simulate_trading(signals_data)
+    print(f"Final Value of Stock Holdings: ${final_stock_value:.2f}")
+    print(f"Remaining Cash: ${remaining_cash:.2f}")