main.py

# main.py
"""
Command-line orchestrator for the portfolio system.

This file:
- Pulls static positions from Static_Portfolio (transactions.csv)
- Computes live metrics via Market_Data (yfinance)
- Prints a human-readable summary to the console
- Optionally generates interactive matplotlib charts via visuals

Only the main() function is intended as the public entrypoint.
"""

from __future__ import annotations

from Static_Portfolio import (
    today,                # returns today's date and updates Last_Updated
    owned_stocks,         # builds current portfolio positions from files
    Portfolio_Buy_Total,  # computes total cost basis of the portfolio
    Stock_Holding,        # computes holding duration (in days) for each position
)

from Market_Data import (
    unrealized_pl,                 # per-ticker unrealized P/L using live prices
    portfolio_performance_summary, # aggregate market value & P/L summary
    Sector_Allocation,             # sector allocation by cost/market
    compare_to_sp500,              # compares portfolio return vs S&P 500
)

import visuals  # plotting functions (matplotlib)


def main() -> None:
    """
    Run a full sanity check of the portfolio system and show key charts."""
    print("========== PORTFOLIO TEST RUN ==========\n")

    # 1) Date / last updated
    current_date, last_updated = today()
    print(f"Today:        {current_date}")
    print(f"Last_Updated: {last_updated}\n")

    # 2) Static portfolio (from transactions.csv)
    df_positions, tickers = owned_stocks()
    if df_positions.empty or not tickers:
        print("No positions found. Check your data/transactions.csv file.")
        print("========== END OF TEST RUN ==========\n")
        return

    print("---- Current Positions (Static from transactions.csv) ----")
    print(df_positions.to_string(index=False))
    print()

    # 3) Total cost basis
    total_cost = Portfolio_Buy_Total()
    print("---- Total Cost Basis ----")
    print(f"Total invested (cost basis): ${total_cost:,.2f}\n")

    # 4) Holding durations
    print("---- Holding Durations (Days) ----")
    holding_df = Stock_Holding()
    if not holding_df.empty:
        print(holding_df.to_string(index=False))
    else:
        print("No holding duration data available.")
    print()

    # 5) Live unrealized P/L
    print("---- Unrealized P/L (Live Prices via yfinance) ----")
    pl_df = unrealized_pl()
    if not pl_df.empty:
        print(pl_df.to_string(index=False))
    else:
        print("No live P/L data available (perhaps no prices could be fetched).")
    print()

    # 6) Portfolio performance summary
    print("---- Portfolio Performance Summary ----")
    summary = portfolio_performance_summary()
    for k, v in summary.items():
        if isinstance(v, (float, int)):
            print(f"{k}: {v:,.2f}")
        else:
            print(f"{k}: {v}")
    print()

    # 7) Sector allocation
    print("---- Sector Allocation by Cost ----")
    sector_df = Sector_Allocation(by="cost")  # default: by cost basis
    if not sector_df.empty:
        print(sector_df.to_string(index=False))
    else:
        print("No sector allocation data available.")
    print()

    # 8) Benchmark comparison
    print("---- Benchmark vs S&P 500 ----")
    bench = compare_to_sp500(period="1y")
    for k, v in bench.items():
        if isinstance(v, (float, int)):
            print(f"{k}: {v:,.2f}")
        else:
            print(f"{k}: {v}")
    print()

    # 9) Visualizations (interactive matplotlib windows)
    print("Generating charts... (close each window to continue)\n")

    # Sector allocation bar chart
    visuals.plot_sector_allocation(by="cost")

    # Unrealized P/L by ticker
    visuals.plot_unrealized_pl_bar()

    # Portfolio value over time (1 year)
    visuals.plot_portfolio_value_over_time(period="1y")

    # Portfolio vs S&P 500 comparison
    visuals.plot_benchmark_comparison(period="1y")

    # Optional extras (useful for deeper inspection)
    visuals.plot_allocation_by_ticker(by="market")
    visuals.plot_realized_vs_unrealized()
    visuals.plot_correlation_heatmap(period="1y")

    print("========== END OF TEST RUN ==========\n")


if __name__ == "__main__":
    main()