Code Editor for app.py

import streamlit as st
import yfinance as yf
import pandas as pd
import plotly.graph_objects as go
from datetime import datetime, timedelta
import numpy as np
import ta

# Page config
st.set_page_config(page_title="Stock Trading Dashboard", layout="wide")

# Title
st.title("📈 Stock Trading Dashboard")

# Sidebar
st.sidebar.header("Dashboard Settings")

# Stock symbol input
symbol = st.sidebar.text_input("Enter Stock Symbol (e.g., AAPL)", value="AAPL")

# Date range selection
end_date = datetime.now()
start_date = end_date - timedelta(days=365)
date_range = st.sidebar.date_input(
    "Select Date Range",
    value=(start_date, end_date),
    max_value=end_date
)

# Technical indicators selection
st.sidebar.subheader("Technical Indicators")
show_sma = st.sidebar.checkbox("Show SMA", value=True)
show_ema = st.sidebar.checkbox("Show EMA", value=True)
show_bollinger = st.sidebar.checkbox("Show Bollinger Bands", value=True)
show_rsi = st.sidebar.checkbox("Show RSI", value=True)

# Fetch data
@st.cache_data(ttl=3600)
def fetch_stock_data(symbol, start_date, end_date):
    try:
        stock = yf.Ticker(symbol)
        df = stock.history(start=start_date, end=end_date)
        return df
    except Exception as e:
        st.error(f"Error fetching data: {e}")
        return None

# Main content
col1, col2 = st.columns([2, 1])

with col1:
    df = fetch_stock_data(symbol, date_range[0], date_range[1])
    
    if df is not None and not df.empty:
        # Create candlestick chart
        fig = go.Figure(data=[go.Candlestick(x=df.index,
                                            open=df['Open'],
                                            high=df['High'],
                                            low=df['Low'],
                                            close=df['Close'])])
        
        # Add technical indicators
        if show_sma:
            sma_20 = ta.trend.sma_indicator(df['Close'], window=20)
            sma_50 = ta.trend.sma_indicator(df['Close'], window=50)
            fig.add_trace(go.Scatter(x=df.index, y=sma_20, name='SMA 20', line=dict(color='orange')))
            fig.add_trace(go.Scatter(x=df.index, y=sma_50, name='SMA 50', line=dict(color='blue')))
            
        if show_ema:
            ema_20 = ta.trend.ema_indicator(df['Close'], window=20)
            fig.add_trace(go.Scatter(x=df.index, y=ema_20, name='EMA 20', line=dict(color='purple')))
            
        if show_bollinger:
            bollinger = ta.volatility.BollingerBands(df['Close'])
            fig.add_trace(go.Scatter(x=df.index, y=bollinger.bollinger_hband(), name='Bollinger High',
                                   line=dict(color='gray', dash='dash')))
            fig.add_trace(go.Scatter(x=df.index, y=bollinger.bollinger_lband(), name='Bollinger Low',
                                   line=dict(color='gray', dash='dash')))

        fig.update_layout(title=f'{symbol} Stock Price',
                         yaxis_title='Price',
                         xaxis_title='Date',
                         template='plotly_white')
        
        st.plotly_chart(fig, use_container_width=True)
        
        if show_rsi:
            rsi = ta.momentum.RSIIndicator(df['Close']).rsi()
            fig_rsi = go.Figure()
            fig_rsi.add_trace(go.Scatter(x=df.index, y=rsi, name='RSI'))
            fig_rsi.add_hline(y=70, line_color='red', line_dash='dash')
            fig_rsi.add_hline(y=30, line_color='green', line_dash='dash')
            fig_rsi.update_layout(title='RSI Indicator',
                                yaxis_title='RSI',
                                template='plotly_white')
            st.plotly_chart(fig_rsi, use_container_width=True)

with col2:
    if df is not None and not df.empty:
        # Summary statistics
        st.subheader("Summary Statistics")
        
        # Current price and daily change
        current_price = df['Close'].iloc[-1]
        previous_price = df['Close'].iloc[-2]
        price_change = current_price - previous_price
        price_change_pct = (price_change / previous_price) * 100
        
        st.metric(
            "Current Price",
            f"${current_price:.2f}",
            f"{price_change_pct:.2f}%"
        )
        
        # Trading volume
        st.metric(
            "Trading Volume",
            f"{df['Volume'].iloc[-1]:,.0f}",
            f"{((df['Volume'].iloc[-1] / df['Volume'].iloc[-2]) - 1) * 100:.2f}%"
        )
        
        # Basic statistics
        st.write("Price Statistics:")
        stats_df = pd.DataFrame({
            'Metric': ['High', 'Low', 'Average', 'Std Dev'],
            'Value': [
                f"${df['High'].max():.2f}",
                f"${df['Low'].min():.2f}",
                f"${df['Close'].mean():.2f}",
                f"${df['Close'].std():.2f}"
            ]
        })
        st.table(stats_df)
        
        # Recent trades
        st.subheader("Recent Price Data")
        recent_data = df.tail().reset_index()
        recent_data['Date'] = recent_data['Date'].dt.date
        st.dataframe(recent_data[['Date', 'Open', 'High', 'Low', 'Close', 'Volume']])