Delete app1.py

app1.py

@@ -1,287 +0,0 @@
-# %%
-# %%
-import gradio as gr
-import pandas as pd
-import yfinance as yf
-from datetime import datetime
-import plotly.graph_objects as go
-import numpy as np
-
-# Functions for calculating indicators (SMA, RSI, etc.) and generating trading signals
-
-def calculate_sma(df, window):
-    return df['Close'].rolling(window=window).mean()
-
-def calculate_ema(df, window):
-    return df['Close'].ewm(span=window, adjust=False).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 calculate_cmf(df, window=20):
-    mfv = ((df['Close'] - df['Low']) - (df['High'] - df['Close'])) / (df['High'] - df['Low']) * df['Volume']
-    cmf = mfv.rolling(window=window).sum() / df['Volume'].rolling(window=window).sum()
-    return cmf
-
-def calculate_cci(df, window=20):
-    """Calculate Commodity Channel Index (CCI)."""
-    typical_price = (df['High'] + df['Low'] + df['Close']) / 3
-    sma = typical_price.rolling(window=window).mean()
-    mean_deviation = (typical_price - sma).abs().rolling(window=window).mean()
-    cci = (typical_price - sma) / (0.015 * mean_deviation)
-    return cci
-
-
-
-def generate_trading_signals(df):
-    # Calculate various indicators
-    df['SMA_30'] = calculate_sma(df, 30)
-    df['SMA_100'] = calculate_sma(df, 100)
-    df['EMA_12'] = calculate_ema(df, 12)
-    df['EMA_26'] = calculate_ema(df, 26)
-    df['RSI'] = calculate_rsi(df)
-    df['MiddleBB'], df['UpperBB'], df['LowerBB'] = calculate_bollinger_bands(df)
-    df['SlowK'], df['SlowD'] = calculate_stochastic_oscillator(df)
-    df['CMF'] = calculate_cmf(df)
-    df['CCI'] = calculate_cci(df)
-
-    
-
-    # Generate trading signals
-    df['SMA_Signal'] = np.where(df['SMA_30'] > df['SMA_100'], 1, 0)
-    
-    macd, signal = calculate_macd(df)
-    df['MACD_Signal'] = np.select([(macd > signal) & (macd.shift(1) <= signal.shift(1)),
-                                    (macd < signal) & (macd.shift(1) >= signal.shift(1))],[1, -1], default=0)
-
-
-    
-    df['RSI_Signal'] = np.where(df['RSI'] < 20, 1, 0)
-    df['RSI_Signal'] = np.where(df['RSI'] > 90, -1, df['RSI_Signal'])
-    
-    df['BB_Signal'] = np.where(df['Close'] < df['LowerBB'], 0, 0)
-    df['BB_Signal'] = np.where(df['Close'] > df['UpperBB'], -1, df['BB_Signal'])
-    
-    df['Stochastic_Signal'] = np.where((df['SlowK'] < 10) & (df['SlowD'] < 15), 1, 0)
-    df['Stochastic_Signal'] = np.where((df['SlowK'] > 90) & (df['SlowD'] > 85), -1, df['Stochastic_Signal'])
-    
-
-    df['CMF_Signal'] = np.where(df['CMF'] > 0.3, -1, np.where(df['CMF'] < -0.3, 1, 0))
-
-    
-    df['CCI_Signal'] = np.where(df['CCI'] < -180, 1, 0)
-    df['CCI_Signal'] = np.where(df['CCI'] > 150, -1, df['CCI_Signal'])
-    
-
-
-    # Combined signal for stronger buy/sell points
-    df['Combined_Signal'] = df[['RSI_Signal', 'BB_Signal', 
-                                  'Stochastic_Signal', 'CMF_Signal', 
-                                  'CCI_Signal']].sum(axis=1)
-
-    return df
-
-
-# %%
-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'] >= 3]
-    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'] <= -3]
-    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'
-    ))
-
-    # 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
-    fig.update_layout(
-        title=f'{ticker}: Stock Price and Combined Trading Signal (Last 120 Days)',
-        xaxis=dict(title='Date'),
-        yaxis=dict(title='Price', side='left'),
-        yaxis2=dict(title='Combined Signal', overlaying='y', side='right', showgrid=False),
-        plot_bgcolor='black',
-        paper_bgcolor='black',
-        font=dict(color='white')
-    )
-
-    return fig
-
-
-
-
-# %%
-def plot_individual_signals(df, ticker):
-    # Create a figure
-    fig = go.Figure()
-    fig.add_trace(go.Scatter(
-        x=df.index, y=df['Close'], 
-        mode='lines', 
-        name='Closing Price', 
-        line=dict(color='lightcoral', width=2)
-    ))
-
-    # Add buy/sell signals for each indicator
-    signal_names = ['RSI_Signal', 'BB_Signal', 
-                    'Stochastic_Signal', 'CMF_Signal', 
-                    'CCI_Signal']
-
-    for signal in signal_names:
-        buy_signals = df[df[signal] == 1]
-        sell_signals = df[df[signal] == -1]
-        
-        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=f'{signal} Buy Signal'
-        ))
-
-        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=f'{signal} Sell Signal'
-        ))
-
-    fig.update_layout(
-        title=f'{ticker}: Individual Trading Signals',
-        xaxis=dict(title='Date'),
-        yaxis=dict(title='Price', side='left'),
-        plot_bgcolor='black',
-        paper_bgcolor='black',
-        font=dict(color='white')
-    )
-
-    return fig
-
-
-def display_signals(df):
-    # Create a signals DataFrame
-    signals_df = df[['Close', 'SMA_Signal', 'MACD_Signal', 'RSI_Signal', 
-                      'BB_Signal', 'Stochastic_Signal', 
-                      'CMF_Signal', 'CCI_Signal']].copy()
-
-    # The Date is the index, so we don't need to add it as a column
-    signals_df.index.name = 'Date'  # Name the index for better readability
-
-    # Replace signal values with 'Buy', 'Sell', or 'Hold'
-    for column in signals_df.columns:
-        signals_df[column] = signals_df[column].replace(
-            {1: 'Buy', -1: 'Sell', 0: 'Hold'}
-        )
-
-    return signals_df
-
-def stock_analysis(ticker, start_date, end_date):
-    # Download stock data from Yahoo Finance
-    df = yf.download(ticker, start=start_date, end=end_date)
-
-    # If the DataFrame has a MultiIndex for columns, drop the 'Ticker' level
-    if isinstance(df.columns, pd.MultiIndex):
-        df.columns = df.columns.droplevel(level=1)  # Drop the 'Ticker' level
-
-    # Explicitly set column names (optional)
-    df.columns = ['Close', 'High', 'Low', 'Open', 'Volume']
-
-    # Generate signals
-    df = generate_trading_signals(df)
-    
-    # Last 60 days for plotting
-    df_last_60 = df.tail(120)
-
-    # Plot combined signals
-    fig_signals = plot_combined_signals(df_last_60, ticker)
-
-    # Plot individual signals
-    fig_individual_signals = plot_individual_signals(df_last_60, ticker)
-
-    # Display signals DataFrame
-    signals_df = df_last_60[['Close', 'SMA_Signal', 'MACD_Signal', 'RSI_Signal', 'BB_Signal', 
-                              'Stochastic_Signal','CMF_Signal', 
-                              'CCI_Signal']]
-
-    return fig_signals, fig_individual_signals
-
-
-
-# %%
-# 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="2026-01-01")
-
-    # Create a submit button that runs the stock analysis function
-    button = gr.Button("Analyze Stock")
-    
-    # Outputs: Display results, charts
-    combined_signals_output = gr.Plot(label="Combined Trading Signals")
-    individual_signals_output = gr.Plot(label="Individual Trading Signals")
-    #signals_df_output = gr.Dataframe(label="Buy/Sell Signals")
-
-    # Link button to function
-    button.click(stock_analysis, inputs=[ticker_input, start_date_input, end_date_input], 
-                  outputs=[combined_signals_output, individual_signals_output])
-
-# Launch the interface
-demo.launch()