Create app.py

app.py

@@ -0,0 +1,103 @@
+import yfinance as yf
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import gradio as gr
+
+def get_data(symbol, start_date, end_date):
+    data = yf.download(symbol, start=start_date, end=end_date)
+    return data['Close']
+
+def monte_carlo_simulation(data, num_simulations, num_days):
+    log_returns = np.log(1 + data.pct_change())
+    mean = log_returns.mean()
+    variance = log_returns.var()
+    drift = mean - (0.5 * variance)
+    stdev = log_returns.std()
+
+    daily_returns = np.exp(drift + stdev * np.random.normal(size=(num_days, num_simulations)))
+    price_paths = np.zeros_like(daily_returns)
+    price_paths[0] = data.iloc[-1]
+
+    for t in range(1, num_days):
+        price_paths[t] = price_paths[t - 1] * daily_returns[t]
+
+    return price_paths
+
+def sigma_confidence_interval(price_paths, confidence_level=0.95):
+    final_prices = price_paths[-1]
+    lower_bound = np.percentile(final_prices, (1 - confidence_level) / 2 * 100)
+    upper_bound = np.percentile(final_prices, (1 + confidence_level) / 2 * 100)
+    return lower_bound, upper_bound
+
+def plot_simulation(price_paths, title):
+    plt.figure(figsize=(10, 5))
+    plt.plot(price_paths)
+    plt.title(title)
+    plt.xlabel('Days')
+    plt.ylabel('Price')
+    plt.grid(True)
+    plt.show()
+
+def plot_correlation(nifty_data, sensex_data):
+    correlation = nifty_data.corr(sensex_data)
+    plt.figure(figsize=(10, 5))
+    plt.scatter(nifty_data, sensex_data)
+    plt.title(f'Correlation between NIFTY and SENSEX: {correlation:.2f}')
+    plt.xlabel('NIFTY')
+    plt.ylabel('SENSEX')
+    plt.grid(True)
+    plt.show()
+
+def simulate(start_date, end_date, num_simulations, num_days):
+    nifty_data = get_data('^NSEI', start_date, end_date)
+    sensex_data = get_data('^BSESN', start_date, end_date)
+
+    nifty_paths = monte_carlo_simulation(nifty_data, num_simulations, num_days)
+    sensex_paths = monte_carlo_simulation(sensex_data, num_simulations, num_days)
+    nifty_lower_bound, nifty_upper_bound = sigma_confidence_interval(nifty_paths)
+    sensex_lower_bound, sensex_upper_bound = sigma_confidence_interval(sensex_paths)
+
+    fig, axs = plt.subplots(1, 3, figsize=(40, 10))
+
+    for path in nifty_paths.T:
+        axs[0].plot(path)
+    axs[0].set_title('Monte Carlo Simulation - NIFTY')
+    axs[0].set_xlabel('Days')
+    axs[0].set_ylabel('Price')
+    axs[0].grid(True)
+
+    for path in sensex_paths.T:
+        axs[1].plot(path)
+    axs[1].set_title('Monte Carlo Simulation - SENSEX')
+    axs[1].set_xlabel('Days')
+    axs[1].set_ylabel('Price')
+    axs[1].grid(True)
+
+    correlation = nifty_data.corr(sensex_data)
+    axs[2].scatter(nifty_data, sensex_data)
+    axs[2].set_title(f'Correlation between NIFTY and SENSEX: {correlation:.2f}')
+    axs[2].set_xlabel('NIFTY')
+    axs[2].set_ylabel('SENSEX')
+    axs[2].grid(True)
+
+    plt.tight_layout()
+    return fig, f'NIFTY Sigma Confidence Interval at 0.95: [{nifty_lower_bound:.1f} : {nifty_upper_bound:.1f}], SENSEX Sigma Confidence Interval at 0.95: [{sensex_lower_bound:.1f} : {sensex_upper_bound:.1f}]'
+
+def gradio_app(start_date, end_date, num_simulations, num_days):
+    fig, interval = simulate(start_date, end_date, num_simulations, num_days)
+    return fig, interval
+
+iface = gr.Interface(
+    fn=gradio_app,
+    inputs=[
+        gr.Textbox(placeholder="YYYY-MM-DD", label="Start Date"),
+        gr.Textbox(placeholder="YYYY-MM-DD", label="End Date"),
+        gr.Slider(minimum=100, maximum=10000, step=100, label="Number of Simulations"),
+        gr.Slider(minimum=10, maximum=365, step=10, label="Number of Days for Forecast")
+    ],
+    outputs=[gr.Plot(label="Simulation Plots and Correlation Plot"), gr.Textbox(label="Sigma Confidence Interval")],
+    title="Monte Carlo Simulation for NIFTY and SENSEX"
+)
+
+iface.launch()