app.py

from datetime import date

import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import streamlit as st
import yfinance as yf

# -------------------------------------------------
# Streamlit Configuration
# -------------------------------------------------
st.set_page_config(
    page_title="Financial Dashboard",
    layout="wide",
    page_icon="🤑"
)

# -------------------------------------------------
# Initialize Session State for Navigation and Carousel
# -------------------------------------------------
if "selected_tab" not in st.session_state:
    st.session_state.selected_tab = "Home"  # Default tab

if 'carousel_slide' not in st.session_state:
    st.session_state.carousel_slide = 0  # Initialize carousel slide index

# -------------------------------------------------
# Define All Possible Tabs
# -------------------------------------------------
tabs = [
    ("🏠 Home", "Home"),
    ("💱 Forex", "Forex"),
    ("📈 Stocks", "Stocks"),
    ("💰 Crypto", "Crypto"),
    ("🔧 Commodities", "Commodities"),
    ("🌐 Markets", "Markets"),
    ("📊 ETFs/Mutual Funds", "ETFs/Mutual Funds"),
    ("💹 Investments", "Investments")
]

# -------------------------------------------------
# Sidebar with Navigation
# -------------------------------------------------
st.sidebar.title("🤑 Financial Dashboard")

def sidebar_navigation(tabs):
    for display_name, internal_name in tabs:
        if st.sidebar.button(display_name):
            st.session_state.selected_tab = internal_name

sidebar_navigation(tabs)

# -------------------------------------------------
# Date Range & Timeframe Selection
# -------------------------------------------------
st.sidebar.markdown("---")
st.sidebar.markdown("#### Date Range & Timeframe")
start_date = st.sidebar.date_input("Start Date", date(2023, 1, 1))
end_date = st.sidebar.date_input("End Date", date.today())

time_increments = ["Daily", "Weekly", "Monthly"]
selected_time_increment = st.sidebar.selectbox("Time Increment", time_increments)
time_increment_mapping = {"Daily": "1d", "Weekly": "1wk", "Monthly": "1mo"}
time_increment_for_yahoo = time_increment_mapping[selected_time_increment]

# -------------------------------------------------
# Tickers Map
# -------------------------------------------------
tickers_map = {
    "Stocks": ["AAPL", "MSFT", "AMZN", "GOOGL", "NVDA", "TSLA", "META"],
    "Forex": ["EURUSD=X", "USDJPY=X", "GBPUSD=X", "AUDUSD=X", "USDCAD=X", "NZDUSD=X", "USDCHF=X"],
    "Crypto": ["BTC-USD", "ETH-USD", "USDT-USD", "BNB-USD", "XRP-USD", "ADA-USD", "DOGE-USD"],
    "Commodities": ["GLD", "SLV", "USO", "UNG", "PPLT", "COPX", "GDX"],
    "Markets": ["^GSPC", "^DJI", "^IXIC", "^FTSE", "^N225", "^HSI", "^GDAXI"],
    "ETFs/Mutual Funds": ["SPY", "VTI", "VOO", "QQQ", "ARKK", "IWM", "XLF"]
}

# -------------------------------------------------
# 1) Fetch Data (matching your snippet)
# -------------------------------------------------
@st.cache_data(ttl=3600)
def fetch_data(tickers, start_date, end_date, time_increment):
    """
    Downloads data using yfinance, then resets index and ensures
    each of Open, High, Low, Close, Volume is 1D by calling squeeze().
    """
    data = {}
    for ticker in tickers:
        try:
            df = yf.download(
                ticker,
                start=start_date,
                end=end_date,
                interval=time_increment,
                progress=False
            )
            if not df.empty:
                df = df.reset_index()
                # Flatten columns if necessary
                for col in ["Open", "High", "Low", "Close", "Volume"]:
                    if col in df.columns:
                        df[col] = df[col].squeeze()  # from your working snippet
                data[ticker] = df
            else:
                st.warning(f"No data available for {ticker}.")
        except Exception as e:
            st.error(f"Error fetching data for {ticker}: {e}")
    return data

# -------------------------------------------------
# 2) Plot Combined Data (matching your snippet)
# -------------------------------------------------
def plot_combined_data(data, title):
    combined_df = pd.DataFrame()
    # Gather all unique dates
    all_dates = pd.concat([df["Date"] for df in data.values() if not df.empty]).drop_duplicates().sort_values()
    combined_df["Date"] = all_dates
    # Align "Close" data for each ticker
    for ticker, df in data.items():
        if not df.empty:
            df = df.set_index("Date").reindex(all_dates).reset_index()
            combined_df[ticker] = df["Close"].values.flatten()
    # Melt and plot
    if not combined_df.empty:
        combined_df = combined_df.melt(id_vars=["Date"], var_name="Symbol", value_name="Price")
        fig = px.line(combined_df, x="Date", y="Price", color="Symbol", title=title)
        return fig
    else:
        st.warning("No data available for the selected symbols.")
        return None

# -------------------------------------------------
# 3) Plot Individual Data (matching your snippet)
# -------------------------------------------------
def plot_individual_data(data, ticker):
    df = data.get(ticker)
    if df is not None and not df.empty:
        close_prices = df["Close"].values.flatten()  # from your snippet
        fig = px.line(
            df,
            x="Date",
            y=close_prices,
            title=f"{ticker} Prices",
            labels={"x": "Date", "y": "Price"}
        )
        return fig
    else:
        st.warning(f"No data available for {ticker}.")
        return None

# -------------------------------------------------
# 4) Historical Data for Investments (same as before)
# -------------------------------------------------
@st.cache_data(ttl=3600)
def fetch_historical_data(df, default_start_date, default_end_date):
    historical_data = {}
    for asset in df['Asset']:
        try:
            hist_df = yf.download(asset, start=default_start_date, end=default_end_date, progress=False)
            if not hist_df.empty:
                hist_df = hist_df['Close'].rename(asset)
                historical_data[asset] = hist_df
            else:
                st.warning(f"No historical data available for {asset}.")
        except Exception as e:
            st.error(f"Error fetching historical data for {asset}: {e}")
    if historical_data:
        portfolio_df = pd.DataFrame(historical_data)
        portfolio_df.fillna(method='ffill', inplace=True)
        portfolio_df.fillna(method='bfill', inplace=True)
        return portfolio_df
    else:
        return None

# -------------------------------------------------
# 5) Display Title
# -------------------------------------------------
def display_title(title_text):
    st.markdown(
        f"<h2 style='text-align: left; color: white;'>{title_text}</h2>",
        unsafe_allow_html=True
    )

# -------------------------------------------------
# 6) Display Investments Portfolio with Carousel
# -------------------------------------------------
def display_investments_portfolio(default_start_date, default_end_date):
    uploader_col, sample_col = st.columns(2)

    with uploader_col:
        st.markdown("#### 📥 Upload Your Investment Data")
        uploaded_file = st.file_uploader("Upload your investment data (CSV)", type=["csv"])
        st.info("Ensure your CSV contains: Asset, Quantity, Purchase Price, Current Price.")

    with sample_col:
        st.markdown("#### 📄 Sample Investment Data")
        sample_data = {
            'Asset': ['AAPL', 'MSFT', 'BTC-USD', 'EURUSD=X', 'GLD'],
            'Quantity': [10, 5, 0.5, 1000, 2],
            'Purchase Price': [150, 250, 30000, 1.10, 1800],
            'Current Price': [200, 300, 45000, 1.20, 1900]
        }
        sample_df = pd.DataFrame(sample_data)
        sample_df['Total Invested'] = sample_df['Quantity'] * sample_df['Purchase Price']
        sample_df['Current Value'] = sample_df['Quantity'] * sample_df['Current Price']
        sample_df['Profit/Loss'] = sample_df['Current Value'] - sample_df['Total Invested']
        sample_df['Profit/Loss (%)'] = (sample_df['Profit/Loss'] / sample_df['Total Invested']) * 100

        st.dataframe(sample_df)

    if uploaded_file is not None:
        try:
            df = pd.read_csv(uploaded_file)
            st.subheader("Uploaded Investment Data")
            st.dataframe(df)

            required_columns = {'Asset', 'Quantity', 'Purchase Price', 'Current Price'}
            if not required_columns.issubset(df.columns):
                missing = required_columns - set(df.columns)
                st.error(f"Uploaded CSV is missing the following required columns: {', '.join(missing)}")
                return

            numeric_columns = ['Quantity', 'Purchase Price', 'Current Price']
            for col in numeric_columns:
                df[col] = pd.to_numeric(df[col], errors='coerce')

            if df[numeric_columns].isnull().any().any():
                st.error("Some entries in your CSV have invalid numerical data. Please correct them and try again.")
                return

            df['Total Invested'] = df['Quantity'] * df['Purchase Price']
            df['Current Value'] = df['Quantity'] * df['Current Price']
            df['Profit/Loss'] = df['Current Value'] - df['Total Invested']
            df['Profit/Loss (%)'] = (df['Profit/Loss'] / df['Total Invested']) * 100

            st.subheader("Investment Portfolio Summary")
            total_invested = df['Total Invested'].sum()
            total_current = df['Current Value'].sum()
            total_profit_loss = df['Profit/Loss'].sum()
            total_profit_loss_pct = (total_profit_loss / total_invested) * 100

            col1, col2, col3, col4 = st.columns(4)
            col1.metric("Total Invested", f"${total_invested:,.2f}")
            col2.metric("Current Value", f"${total_current:,.2f}")
            col3.metric("Total Profit/Loss", f"${total_profit_loss:,.2f}",
                        delta=f"{total_profit_loss_pct:.2f}%")
            col4.metric("Number of Assets", len(df))

            st.subheader("Investment Portfolio Visualizations")

            fig1 = px.pie(
                df,
                names='Asset',
                values='Current Value',
                title='Portfolio Allocation by Asset',
                hole=0.4,
                color='Asset',
                color_discrete_sequence=px.colors.qualitative.Pastel
            )
            fig1.update_traces(textposition='inside', textinfo='percent+label')

            fig2 = px.treemap(
                df,
                path=['Asset'],
                values='Current Value',
                title='Portfolio Allocation Treemap',
                color='Profit/Loss',
                color_continuous_scale='RdYlGn'
            )

            fig3 = px.scatter(
                df,
                x='Current Value',
                y='Profit/Loss',
                text='Asset',
                title='Profit/Loss vs. Current Value by Asset',
                hover_data=['Quantity', 'Purchase Price'],
                color='Profit/Loss',
                color_continuous_scale='RdYlGn'
            )
            fig3.update_traces(textposition='top center')

            fig4 = go.Figure(go.Indicator(
                mode="gauge+number",
                value=total_profit_loss_pct,
                title={'text': "Total Profit/Loss (%)"},
                gauge={
                    'axis': {'range': [-100, 100]},
                    'bar': {'color': "darkblue"},
                    'steps': [
                        {'range': [-100, 0], 'color': "red"},
                        {'range': [0, 100], 'color': "green"}
                    ],
                    'threshold': {
                        'line': {'color': "black", 'width': 4},
                        'thickness': 0.75,
                        'value': 0
                    }
                }
            ))

            fig5 = px.bar(
                df,
                x='Asset',
                y='Profit/Loss',
                color='Profit/Loss',
                title='Profit/Loss by Asset',
                color_continuous_scale='RdYlGn',
                labels={'Profit/Loss': 'Profit/Loss ($)'},
                hover_data=['Quantity', 'Purchase Price', 'Current Price']
            )
            fig5.update_layout(showlegend=False)

            # Mock portfolio growth from historical data
            historical_df = fetch_historical_data(df, default_start_date, default_end_date)
            if historical_df is not None:
                weights = df['Current Value'] / df['Current Value'].sum()
                portfolio_growth = historical_df.multiply(weights, axis=1).sum(axis=1)
                portfolio_growth_df = portfolio_growth.reset_index()
                portfolio_growth_df.columns = ['Date', 'Portfolio Value']
                fig6 = px.line(
                    portfolio_growth_df,
                    x='Date',
                    y='Portfolio Value',
                    title='Portfolio Growth Over Time',
                    labels={'Portfolio Value': 'Value ($)', 'Date': 'Date'},
                    color_discrete_sequence=['#636EFA']
                )
            else:
                fig6 = go.Figure()
                fig6.add_annotation(text="No historical data available.", x=0.5, y=0.5, showarrow=False)
                fig6.update_layout(title='Portfolio Growth Over Time')

            carousel_figs = [fig1, fig2, fig3, fig4, fig5, fig6]

            prev_col, next_col = st.columns([1, 1])
            with prev_col:
                if st.button("⏮️ Prev", key="prev"):
                    st.session_state.carousel_slide = (st.session_state.carousel_slide - 1) % len(carousel_figs)

            with next_col:
                if st.button("⏭️ Next", key="next"):
                    st.session_state.carousel_slide = (st.session_state.carousel_slide + 1) % len(carousel_figs)

            st.plotly_chart(carousel_figs[st.session_state.carousel_slide], use_container_width=True)
            st.markdown(f"**Slide {st.session_state.carousel_slide + 1} of {len(carousel_figs)}**")

            st.info(
                """
                **Navigate Through Visualizations:**  
                Use the Previous and Next buttons above to browse through different portfolio visualizations.  
                **Expand Plots:**  
                Click the full-screen icon on the top-right of each plot to view it in full screen.
                """
            )

        except Exception as e:
            st.error(f"Error processing the uploaded file: {e}")
    else:
        st.subheader("Sample Portfolio Visualizations")

        sample_data = {
            'Asset': ['AAPL', 'MSFT', 'BTC-USD', 'EURUSD=X', 'GLD'],
            'Quantity': [10, 5, 0.5, 1000, 2],
            'Purchase Price': [150, 250, 30000, 1.10, 1800],
            'Current Price': [200, 300, 45000, 1.20, 1900]
        }
        sample_df = pd.DataFrame(sample_data)
        sample_df['Total Invested'] = sample_df['Quantity'] * sample_df['Purchase Price']
        sample_df['Current Value'] = sample_df['Quantity'] * sample_df['Current Price']
        sample_df['Profit/Loss'] = sample_df['Current Value'] - sample_df['Total Invested']
        sample_df['Profit/Loss (%)'] = (sample_df['Profit/Loss'] / sample_df['Total Invested']) * 100

        fig1 = px.pie(
            sample_df,
            names='Asset',
            values='Current Value',
            title='Sample Portfolio Allocation by Asset',
            hole=0.4,
            color='Asset',
            color_discrete_sequence=px.colors.qualitative.Pastel
        )
        fig1.update_traces(textposition='inside', textinfo='percent+label')

        fig2 = px.treemap(
            sample_df,
            path=['Asset'],
            values='Current Value',
            title='Sample Portfolio Allocation Treemap',
            color='Profit/Loss',
            color_continuous_scale='RdYlGn'
        )

        fig3 = px.scatter(
            sample_df,
            x='Current Value',
            y='Profit/Loss',
            text='Asset',
            title='Sample Profit/Loss vs. Current Value by Asset',
            hover_data=['Quantity', 'Purchase Price'],
            color='Profit/Loss',
            color_continuous_scale='RdYlGn'
        )
        fig3.update_traces(textposition='top center')

        fig4 = go.Figure(go.Indicator(
            mode="gauge+number",
            value=sample_df['Profit/Loss (%)'].sum(),
            title={'text': "Sample Total Profit/Loss (%)"},
            gauge={
                'axis': {'range': [-100, 100]},
                'bar': {'color': "darkblue"},
                'steps': [
                    {'range': [-100, 0], 'color': "red"},
                    {'range': [0, 100], 'color': "green"}
                ],
                'threshold': {
                    'line': {'color': "black", 'width': 4},
                    'thickness': 0.75,
                    'value': 0
                }
            }
        ))

        fig5 = px.bar(
            sample_df,
            x='Asset',
            y='Profit/Loss',
            color='Profit/Loss',
            title='Sample Profit/Loss by Asset',
            color_continuous_scale='RdYlGn',
            labels={'Profit/Loss': 'Profit/Loss ($)'},
            hover_data=['Quantity', 'Purchase Price', 'Current Price']
        )
        fig5.update_layout(showlegend=False)

        # Mock portfolio growth data
        sample_growth = pd.DataFrame({
            'Date': pd.date_range(start=start_date, end=end_date, periods=100),
            'Portfolio Value': (sample_df['Current Value'].sum()) * (1 + 0.01 * np.arange(100))
        })
        fig6 = px.line(
            sample_growth,
            x='Date',
            y='Portfolio Value',
            title='Sample Portfolio Growth Over Time',
            labels={'Portfolio Value': 'Value ($)', 'Date': 'Date'},
            color_discrete_sequence=['#636EFA']
        )

        carousel_figs = [fig1, fig2, fig3, fig4, fig5, fig6]
        left_space, prev_col, next_col, right_space = st.columns([2.5, .5, .5, 2.5])

        with prev_col:
            if st.button("⏮️ Prev", key="prev"):
                st.session_state.carousel_slide = (
                    st.session_state.carousel_slide - 1
                ) % len(carousel_figs)

        with next_col:
            if st.button("⏭️ Next", key="next"):
                st.session_state.carousel_slide = (
                    st.session_state.carousel_slide + 1
                ) % len(carousel_figs)

        st.plotly_chart(carousel_figs[st.session_state.carousel_slide], use_container_width=True)
        st.markdown(f"**Slide {st.session_state.carousel_slide + 1} of {len(carousel_figs)}**")

        st.info(
            """
            **Navigate Through Visualizations:**  
            Use the Previous and Next buttons above to browse through different portfolio visualizations.  
            **Expand Plots:**  
            Click the full-screen icon on the top-right of each plot to view it in full screen.
            """
        )

# -------------------------------------------------
# Main Logic for Each Tab
# -------------------------------------------------
if st.session_state.selected_tab == "Home":
    display_title("Home")
    st.title("Financial Dashboard")
    st.markdown(
        """
        Welcome to the **Financial Dashboard**! This platform provides comprehensive insights into various financial instruments.

        ### 📊 Features:
        - **Stocks:** Analyze major stock performance.
        - **Forex:** Monitor currency exchange rates.
        - **Crypto:** Track cryptocurrency trends.
        - **Commodities:** Observe commodity market movements.
        - **Markets:** Overview of major market indices.
        - **ETFs/Mutual Funds:** Evaluate ETF and mutual fund performances.
        - **Investments:** Upload and track your personal investment portfolio.

        ### 🔍 How to Use:
        1. **Navigate:** Use the sidebar buttons to select a financial category.
        2. **Customize:** Choose your desired date range and time increment.
        3. **Visualize:** View combined and individual plots for selected tickers.
        4. **Investments:** Upload your investment CSV file above to track your portfolio.
        """
    )

elif st.session_state.selected_tab in tickers_map:
    display_title(st.session_state.selected_tab)

    # Let user input custom tickers
    st.sidebar.markdown("---")
    st.sidebar.markdown(f"#### Enter Custom {st.session_state.selected_tab} Symbols")
    default_symbols = ",".join(tickers_map[st.session_state.selected_tab])
    user_input = st.sidebar.text_input(
        f"Enter {st.session_state.selected_tab} symbols (comma-separated):",
        default_symbols
    )
    user_tickers = [ticker.strip().upper() for ticker in user_input.split(",") if ticker.strip()]

    if not user_tickers:
        st.warning("Please enter at least one ticker symbol.")
    else:
        data = fetch_data(user_tickers, start_date, end_date, time_increment_for_yahoo)

        if data:
            tab_names = ["Combined"] + user_tickers
            plot_tabs = st.tabs(tab_names)

            # Combined Data Tab
            with plot_tabs[0]:
                fig = plot_combined_data(data, f"{st.session_state.selected_tab} Combined Prices")
                if fig:
                    st.plotly_chart(fig, use_container_width=True)

            # Individual Data Tabs
            for i, ticker in enumerate(user_tickers):
                with plot_tabs[i + 1]:
                    fig = plot_individual_data(data, ticker)
                    if fig:
                        st.plotly_chart(fig, use_container_width=True)
        else:
            st.warning("No data fetched. Please adjust your ticker symbols or date range.")

else:
    # Investments Tab
    if st.session_state.selected_tab == "Investments":
        display_title("Investments")
        st.markdown(
            """
            ### 📥 Upload Your Investment Data

            Use the uploader above to upload a CSV file containing your investment portfolio details.

            **Required Columns:**
            - **Asset:** Name or symbol of the investment.
            - **Quantity:** Number of units held.
            - **Purchase Price:** Price at which each unit was purchased.
            - **Current Price:** Current market price of each unit.
            """
        )
        display_investments_portfolio(default_start_date=start_date, default_end_date=end_date)