Create app.py

app.py

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+import gradio as gr
+import yfinance as yf
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error, r2_score
+import mplfinance as mpf
+import matplotlib.pyplot as plt
+
+
+def get_stock_data(symbol, timeframe):
+    """Fetches stock data from Yahoo Finance."""
+    ticker = yf.Ticker(symbol)
+
+    # Calculate period based on timeframe
+    if timeframe in ['1m', '5m', '15m', '30m']:
+        period = "1d"
+    elif timeframe in ['1h']:
+        period = "5d"
+    else:
+        period = "60d"
+    
+    data = ticker.history(period=period, interval=timeframe)
+    return data
+
+
+def calculate_indicators(data):
+    """Calculates technical indicators."""
+    data['SMA20'] = data['Close'].rolling(window=20).mean()  # Simple Moving Average (20 days)
+    data['EMA20'] = data['Close'].ewm(span=20, adjust=False).mean()  # Exponential Moving Average (20 days)
+    data['RSI'] = calculate_rsi(data['Close'])  # Relative Strength Index
+    data['MACD'], data['MACD_Signal'], _ = calculate_macd(data['Close'])  # Moving Average Convergence Divergence
+    data['Stochastic_K'], data['Stochastic_D'] = calculate_stochastic(data['High'], data['Low'], data['Close'])  # Stochastic Oscillator
+    return data
+
+
+
+def calculate_rsi(close_prices, period=14):
+    """Calculates the Relative Strength Index (RSI)."""
+    delta = close_prices.diff()
+    gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
+    loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
+    rs = gain / loss
+    rsi = 100 - (100 / (1 + rs))
+    return rsi
+
+def calculate_macd(close_prices, fast_period=12, slow_period=26, signal_period=9):
+    """Calculates the Moving Average Convergence Divergence (MACD)."""
+    fast_ema = close_prices.ewm(span=fast_period, adjust=False).mean()
+    slow_ema = close_prices.ewm(span=slow_period, adjust=False).mean()
+    macd = fast_ema - slow_ema
+    macd_signal = macd.ewm(span=signal_period, adjust=False).mean()
+    macd_histogram = macd - macd_signal
+    return macd, macd_signal, macd_histogram
+
+def calculate_stochastic(high_prices, low_prices, close_prices, period=14):
+    """Calculates the Stochastic Oscillator."""
+    lowest_low = low_prices.rolling(window=period).min()
+    highest_high = high_prices.rolling(window=period).max()
+    k = ((close_prices - lowest_low) / (highest_high - lowest_low)) * 100
+    d = k.rolling(window=3).mean()
+    return k, d
+
+def predict_next_day(symbol, timeframe):
+    """Predicts the next day's closing price."""
+    data = get_stock_data(symbol, timeframe)
+    data = calculate_indicators(data)
+
+    # Prepare data for training
+    data = data.dropna()
+    X = data[['SMA20', 'EMA20', 'RSI', 'MACD', 'MACD_Signal', 'Stochastic_K', 'Stochastic_D']]
+    y = data['Close']
+
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    # Scale the data
+    scaler = MinMaxScaler()
+    # Create a linear regression model
+    model = LinearRegression()
+    model.fit(X_train, y_train)
+
+    # Predict next day's closing price
+    last_data_point = data.iloc[-1]
+    last_data_point = last_data_point[['SMA20', 'EMA20', 'RSI', 'MACD', 'MACD_Signal', 'Stochastic_K', 'Stochastic_D']]
+    predicted_price = model.predict([last_data_point.values])[0]
+
+    # Calculate model evaluation metrics
+    y_pred = model.predict(X_test)
+    mse = mean_squared_error(y_test, y_pred)
+    rmse = np.sqrt(mse)
+    r2 = r2_score(y_test, y_pred)
+
+    print(f"Mean Squared Error: {mse:.2f}")
+    print(f"Root Mean Squared Error: {rmse:.2f}")
+    print(f"R-squared: {r2:.2f}")
+
+    return predicted_price
+
+def plot_candlestick(data, symbol, timeframe, predicted_price=None):
+    """Plots the candlestick chart with technical indicators."""
+    fig, ax = plt.subplots(figsize=(12, 6))
+    mpf.plot(data, type='candle', style='charles', ax=ax, volume=True, show_nontrading=True)
+
+    # Add moving averages
+    ax.plot(data.index, data['SMA20'], label='SMA20', color='blue', alpha=0.7)
+    ax.plot(data.index, data['EMA20'], label='EMA20', color='red', alpha=0.7)
+
+    # Add prediction
+    if predicted_price is not None:
+        last_timestamp = data.index[-1] + pd.Timedelta(timeframe)
+        ax.scatter(last_timestamp, predicted_price, color='green', marker='*', s=100, label='Prediction')
+
+    ax.legend()
+    ax.set_title(f"{symbol} - {timeframe}")
+    ax.tick_params(axis='x', rotation=45)
+    fig.tight_layout()
+    return fig
+
+def main():
+    """Gradio Interface."""
+
+    symbol = gr.inputs.Textbox(label="Symbol", default="AAPL")
+    timeframe = gr.inputs.Dropdown(label="Timeframe", choices=["1m", "5m", "15m", "30m", "1h", "1d"], default="1d")
+
+    with gr.Blocks() as interface:
+        gr.Markdown("## Real-time Stock Market Analysis")
+        with gr.Row():
+            symbol_input = gr.Textbox(label="Symbol", default="AAPL", interactive=True)
+            timeframe_input = gr.Dropdown(label="Timeframe", choices=["1m", "5m", "15m", "30m", "1h", "1d"], value="1d", interactive=True)
+
+        with gr.Row():
+            predict_button = gr.Button(value="Predict")
+            predicted_price = gr.Textbox(label="Predicted Price")
+
+        with gr.Row():
+            output_plot = gr.Plot(label="Candlestick Chart")
+
+        predict_button.click(fn=predict_next_day, inputs=[symbol_input, timeframe_input], outputs=predicted_price)
+
+        predicted_price.change(fn=plot_candlestick, inputs=[symbol_input, timeframe_input, predicted_price], outputs=output_plot)
+
+    interface.launch()
+
+if _name_ == "_main_":
+    main()