marketapp.py

app.py

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+# %%
+# Install necessary packages if not already installed
+# pip install gradio yfinance prophet plotly matplotlib
+
+import gradio as gr
+import pandas as pd
+import yfinance as yf
+from datetime import datetime
+from prophet import Prophet
+import plotly.express as px
+import plotly.graph_objects as go
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Functions for calculating indicators (SMA, RSI, etc.) and generating trading signals
+# (Reuse the code you've already written for technical indicators and forecasting)
+
+def calculate_sma(df, window):
+    return df['Close'].rolling(window=window).mean()
+
+def calculate_macd(df):
+    short_ema = df['Close'].ewm(span=12, adjust=False).mean()
+    long_ema = df['Close'].ewm(span=26, adjust=False).mean()
+    macd = short_ema - long_ema
+    signal = macd.ewm(span=9, adjust=False).mean()
+    return macd, signal
+
+def calculate_rsi(df):
+    delta = df['Close'].diff()
+    gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
+    loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
+    rs = gain / loss
+    rsi = 100 - (100 / (1 + rs))
+    return rsi
+
+def calculate_bollinger_bands(df):
+    middle_bb = df['Close'].rolling(window=20).mean()
+    upper_bb = middle_bb + 2 * df['Close'].rolling(window=20).std()
+    lower_bb = middle_bb - 2 * df['Close'].rolling(window=20).std()
+    return middle_bb, upper_bb, lower_bb
+
+def calculate_stochastic_oscillator(df):
+    lowest_low = df['Low'].rolling(window=14).min()
+    highest_high = df['High'].rolling(window=14).max()
+    slowk = ((df['Close'] - lowest_low) / (highest_high - lowest_low)) * 100
+    slowd = slowk.rolling(window=3).mean()
+    return slowk, slowd
+
+def generate_trading_signals(df):
+
+    # Calculate Simple Moving Averages (SMA)
+    df['SMA_50'] = calculate_sma(df, 50)
+    df['SMA_200'] = calculate_sma(df, 200)
+
+    # Calculate other technical indicators
+    df['RSI'] = calculate_rsi(df)
+    df['MiddleBB'], df['UpperBB'], df['LowerBB'] = calculate_bollinger_bands(df)
+    df['SlowK'], df['SlowD'] = calculate_stochastic_oscillator(df)
+
+    # Generate trading signals
+    df['SMA_Signal'] = np.where(df['SMA_50'] > df['SMA_200'], 1, 0)
+    
+    macd, signal = calculate_macd(df)
+    df['MACD_Signal'] = np.where((macd > signal.shift(1)) & (macd.shift(1) < signal), 1, 0)
+    
+    df['RSI_Signal'] = np.where(df['RSI'] < 30, 1, 0)
+    df['RSI_Signal'] = np.where(df['RSI'] > 70, -1, df['RSI_Signal'])
+    
+    df['BB_Signal'] = np.where(df['Close'] < df['LowerBB'], 1, 0)
+    df['BB_Signal'] = np.where(df['Close'] > df['UpperBB'], -1, df['BB_Signal'])
+    
+    df['Stochastic_Signal'] = np.where((df['SlowK'] < 20) & (df['SlowD'] < 20), 1, 0)
+    df['Stochastic_Signal'] = np.where((df['SlowK'] > 80) & (df['SlowD'] > 80), -1, df['Stochastic_Signal'])
+
+    # Summing the values of each individual signal column
+    df['Combined_Signal'] = df[['SMA_Signal', 'MACD_Signal', 'RSI_Signal', 'BB_Signal', 'Stochastic_Signal']].sum(axis=1)
+
+
+
+
+
+
+# %%
+import plotly.graph_objects as go
+
+def plot_combined_signals(df, ticker):
+    # Create a figure
+    fig = go.Figure()
+
+    # Add closing price trace
+    fig.add_trace(go.Scatter(
+        x=df.index, y=df['Close'], 
+        mode='lines', 
+        name='Closing Price', 
+        line=dict(color='lightcoral', width=2)
+    ))
+
+    # Add buy signals
+    buy_signals = df[df['Combined_Signal'] >= 2]
+    fig.add_trace(go.Scatter(
+        x=buy_signals.index, y=buy_signals['Close'], 
+        mode='markers', 
+        marker=dict(symbol='triangle-up', size=10, color='lightgreen'), 
+        name='Buy Signal'
+    ))
+
+    # Add sell signals
+    sell_signals = df[df['Combined_Signal'] <= -2]
+    fig.add_trace(go.Scatter(
+        x=sell_signals.index, y=sell_signals['Close'], 
+        mode='markers', 
+        marker=dict(symbol='triangle-down', size=10, color='lightsalmon'), 
+        name='Sell Signal'
+    ))
+
+    # Add combined signal trace
+    fig.add_trace(go.Scatter(
+        x=df.index, y=df['Combined_Signal'], 
+        mode='lines', 
+        name='Combined Signal', 
+        line=dict(color='deepskyblue', width=2), 
+        yaxis='y2'
+    ))
+
+    # Update layout for secondary y-axis
+    fig.update_layout(
+        title=f'{ticker}: Stock Price and Combined Trading Signal (Last 60 Days)',
+        xaxis=dict(title='Date', gridcolor='gray', gridwidth=0.5),
+        yaxis=dict(title='Price', side='left', gridcolor='gray', gridwidth=0.5),
+        yaxis2=dict(title='Combined Signal', overlaying='y', side='right', showgrid=False),
+        plot_bgcolor='black',
+        paper_bgcolor='black',
+        font=dict(color='white'),
+        legend=dict(x=0.01, y=0.99, bgcolor='rgba(0,0,0,0)'),
+        hovermode='x unified'
+    )
+
+    return fig
+
+
+# %%
+def stock_analysis(ticker, start_date, end_date):
+    # Download stock data from Yahoo Finance
+    df = yf.download(ticker, start=start_date, end=end_date)
+    
+    # Run your existing trading signals and indicators here
+    generate_trading_signals(df)
+    
+    # Last 60 days
+    df_last_60 = df.tail(60)
+    
+    # Plot trading signals using the improved function
+    fig_signals = plot_combined_signals(df_last_60, ticker)
+    
+    # Prophet-based stock price forecasting
+    df_plot = df.reset_index()[['Date', 'Close']].rename(columns={'Date': 'ds', 'Close': 'y'})
+    m = Prophet()
+    m.fit(df_plot)
+    future = m.make_future_dataframe(periods=30)
+    forecast = m.predict(future)
+    #fig_forecast = px.line(forecast.tail(40), x="ds", y=['yhat', 'yhat_lower', 'yhat_upper'], title=f'{ticker} - 30 Days Forecast')
+    fig_forecast = m.plot_components(forecast)
+    
+    # Combine the figures into HTML output
+    return fig_signals, fig_forecast
+
+
+# %%
+# Define Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("## Stock Market Analysis App")
+
+    ticker_input = gr.Textbox(label="Enter Stock Ticker (e.g., AAPL, NVDA)", value="NVDA")
+    start_date_input = gr.Textbox(label="Start Date (YYYY-MM-DD)", value="2022-01-01")
+    end_date_input = gr.Textbox(label="End Date (YYYY-MM-DD)", value=str(datetime.now().date()))
+
+    # Create a submit button that runs the stock analysis function
+    button = gr.Button("Analyze Stock")
+    
+    # Outputs: Display results, charts
+    signals_output = gr.Plot(label="Trading Signals")
+    forecast_output = gr.Plot(label="Stock Price Forecast")
+
+    # Link button to function
+    button.click(stock_analysis, inputs=[ticker_input, start_date_input, end_date_input], outputs=[signals_output, forecast_output])
+
+# Launch the interface
+demo.launch()
+
+