app.py

# %%
# %%
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_atr(df, window=14):
    high_low = df['High'] - df['Low']
    high_close = np.abs(df['High'] - df['Close'].shift())
    low_close = np.abs(df['Low'] - df['Close'].shift())
    tr = high_low.combine(high_close, max).combine(low_close, max)
    atr = tr.rolling(window=window).mean()
    return atr

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['ATR'] = calculate_atr(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']+3, 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'] < 15) & (df['SlowD'] < 20), 1, 0)
    df['Stochastic_Signal'] = np.where((df['SlowK'] > 80) & (df['SlowD'] > 80), -1, df['Stochastic_Signal'])
    
    df['ATR_Signal'] = np.where(df['ATR'] > df['ATR'].rolling(window=20).mean(), 1, 0)
    df['CMF_Signal'] = np.where(df['CMF'] > 0.05, 1, np.where(df['CMF'] < -0.05, -1, 0))

    
    df['CCI_Signal'] = np.where(df['CCI'] < -150, 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[['SMA_Signal', 'MACD_Signal', 'RSI_Signal', 'BB_Signal', 
                                  'Stochastic_Signal', 'ATR_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 60 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 stock_analysis(ticker, start_date, end_date):
    # Download stock data from Yahoo Finance
    df = yf.download(ticker, start=start_date, end=end_date)
    
    # Generate signals
    generate_trading_signals(df)
    
    # Last 60 days
    df_last_60 = df.tail(60)
    
    # Plot signals
    fig_signals = plot_combined_signals(df_last_60, ticker)
    
    return fig_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="2025-01-01")

    # 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")

    # Link button to function
    button.click(stock_analysis, inputs=[ticker_input, start_date_input, end_date_input], outputs=[signals_output])

# Launch the interface
demo.launch()