Example_Monte_Carlo_Simulation_for_Stock_Price_Forecasting.ipynb

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        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Parameters for the simulation\n",
        "S0 = 100  # Initial stock price\n",
        "mu = 0.1  # Expected return\n",
        "sigma = 0.2  # Volatility\n",
        "T = 1.0  # Time period (1 year)\n",
        "N = 1000  # Number of time steps\n",
        "dt = T/N  # Time step\n",
        "M = 10  # Number of simulations\n",
        "\n",
        "# Monte Carlo Simulation\n",
        "def simulate_stock_price(S0, mu, sigma, dt, N, M):\n",
        "    price_paths = np.zeros((N + 1, M))\n",
        "    price_paths[0] = S0\n",
        "    for t in range(1, N + 1):\n",
        "        Z = np.random.standard_normal(M)\n",
        "        price_paths[t] = price_paths[t-1] * np.exp((mu - 0.5 * sigma**2) * dt + sigma * np.sqrt(dt) * Z)\n",
        "    return price_paths\n",
        "\n",
        "# Simulate and plot\n",
        "price_paths = simulate_stock_price(S0, mu, sigma, dt, N, M)\n",
        "plt.plot(price_paths)\n",
        "plt.xlabel('Time Steps')\n",
        "plt.ylabel('Stock Price')\n",
        "plt.title('Monte Carlo Simulation of Stock Prices')\n",
        "plt.show()"
      ]
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