portfolio.py

import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import yfinance as yf
from utils.quantum_algorithms import QuantumInspiredOptimizer
from utils.deepseek_api import DeepSeekAI
from datetime import datetime

# ⚛️ Initialize Quantum Optimizer & DeepSeek AI
quantum_optimizer = QuantumInspiredOptimizer()
deepseek_ai = DeepSeekAI()

# 🎯 Page Config
st.set_page_config(page_title="Investor Portfolio", layout="wide")
st.title("📊 Investor Portfolio Management")

# 📤 Upload Portfolio File
uploaded_file = st.file_uploader("📂 Upload Portfolio CSV/Excel file", type=["csv", "xlsx"])

if uploaded_file:
    st.success("✅ Portfolio Uploaded Successfully!")

    # 📌 Read File
    file_extension = uploaded_file.name.split(".")[-1]
    if file_extension == "csv":
        df = pd.read_csv(uploaded_file)
    else:
        df = pd.read_excel(uploaded_file)

    # 📊 Display Portfolio Preview
    st.subheader("📊 Portfolio Overview")
    st.write(df.head())

    # Ensure required columns exist
    required_columns = ["Stock Symbol", "Quantity", "Purchase Price"]
    missing_columns = [col for col in required_columns if col not in df.columns]
    if missing_columns:
        st.error(f"⚠️ Missing required columns: {missing_columns}")
    else:
        st.success("✅ Valid Portfolio File Detected")

        # 🎯 **Fetch Live Market Data**
        st.subheader("📈 Live Market Tracking")
        df["Current Price"] = df["Stock Symbol"].apply(lambda x: yf.Ticker(x).history(period="1d")["Close"].iloc[-1] if not yf.Ticker(x).history(period="1d").empty else np.nan)
        df["Market Value"] = df["Quantity"] * df["Current Price"]
        df["Unrealized Gain/Loss (%)"] = ((df["Current Price"] - df["Purchase Price"]) / df["Purchase Price"]) * 100

        # 📊 Show Updated Portfolio
        st.dataframe(df)

        # 📉 **Portfolio Performance Visualization**
        st.subheader("📉 Portfolio Performance")
        fig, ax = plt.subplots(figsize=(12, 6))
        ax.bar(df["Stock Symbol"], df["Unrealized Gain/Loss (%)"], color=["green" if x > 0 else "red" for x in df["Unrealized Gain/Loss (%)"]])
        ax.set_ylabel("Unrealized Gain/Loss (%)")
        ax.set_title("Portfolio Stock Performance")
        st.pyplot(fig)

        # ⚛️ **Quantum Analysis on Portfolio**
        st.subheader("⚛️ Quantum Portfolio Insights")
        portfolio_prices = df["Current Price"].dropna().values
        quantum_patterns = quantum_optimizer.quantum_pattern_detection(portfolio_prices)

        # Plot Quantum Patterns
        fig, ax1 = plt.subplots(figsize=(12, 6))
        ax1.plot(df["Stock Symbol"], portfolio_prices, label="Current Price", color="blue")
        ax2 = ax1.twinx()
        ax2.plot(df["Stock Symbol"], quantum_patterns, label="Quantum Pattern Strength", color="orange", linestyle="dashed")
        ax1.set_title("Quantum Stock Pattern Detection")
        ax1.set_ylabel("Price")
        ax2.set_ylabel("Pattern Strength")
        ax1.legend(loc="upper left")
        ax2.legend(loc="upper right")
        st.pyplot(fig)

        # 🔮 **DeepSeek AI Investment Insights**
        st.subheader("🤖 AI-Powered Investment Insights")
        portfolio_summary = df[["Stock Symbol", "Unrealized Gain/Loss (%)"]].to_dict(orient="records")

        ai_prompt = f"""
        Analyze the following investor portfolio using AI and quantum methods. 
        Provide risk assessment, investment insights, and potential improvements.

        Portfolio Data: {portfolio_summary}
        """

        with st.spinner("🔍 Generating AI Insights..."):
            deepseek_response = deepseek_ai.analyze_portfolio(ai_prompt)
            st.write(deepseek_response)

        # 🏆 **Portfolio Metrics**
        st.subheader("📊 Key Portfolio Metrics")
        col1, col2, col3 = st.columns(3)
        with col1:
            st.metric("📈 Total Market Value", f"₹{df['Market Value'].sum():,.2f}")
        with col2:
            st.metric("📊 Average Unrealized Gain/Loss (%)", f"{df['Unrealized Gain/Loss (%)'].mean():.2f}%")
        with col3:
            st.metric("⚠️ Risk Level", "Moderate" if df['Unrealized Gain/Loss (%)'].mean() > 0 else "High Risk")