quantum_patterns.py

import streamlit as st
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import time
from utils.data_loader import load_nifty50_symbols, fetch_stock_data
from utils.quantum_algorithms import QuantumInspiredOptimizer

st.set_page_config(page_title="Quantum Pattern Analysis", layout="wide")
st.title("⚛️ Quantum Pattern Analysis")

# 🎯 Initialize quantum optimizer
quantum_optimizer = QuantumInspiredOptimizer()

# 📌 Stock selection
symbol = st.selectbox("🔍 Select Stock", load_nifty50_symbols())
timeframe = st.selectbox("📅 Select Timeframe", ["1mo", "3mo", "6mo", "1y"])

# 📊 Fetch data
data_placeholder = st.empty()
tabs = st.tabs(["🔍 Quantum Pattern Detection", "⚡ Quantum Momentum", "📡 Quantum State Analysis"])

# 🔄 Live Data Streaming
while True:
    try:
        data = fetch_stock_data(symbol, period=timeframe)
        if data is None or data.empty:
            st.error("⚠️ Unable to fetch data. Please try again later.")
            time.sleep(10)
            continue
        
        prices = data['Close'].values
        index_vals = data.index
        
        # 🛠️ Pattern Detection
        with tabs[0]:
            st.subheader("🔬 Quantum-Inspired Pattern Detection")

            pattern_strength = quantum_optimizer.quantum_pattern_detection(prices)
            fig, ax1 = plt.subplots(figsize=(12, 6))

            ax1.plot(index_vals, prices, label="Price", color="blue")
            ax1.set_ylabel("Stock Price (₹)", color="blue")

            ax2 = ax1.twinx()
            ax2.plot(index_vals, pattern_strength[:len(data)], label="Pattern Strength", color="orange", linestyle="dashed")
            ax2.set_ylabel("Pattern Strength", color="orange")

            ax1.set_title("Quantum Pattern Detection")
            ax1.legend(loc="upper left")
            ax2.legend(loc="upper right")
            st.pyplot(fig)

        # ⚡ Quantum Momentum
        with tabs[1]:
            st.subheader("⚡ Quantum Momentum Indicator")

            momentum = quantum_optimizer.quantum_momentum_indicator(prices)
            fig, ax1 = plt.subplots(figsize=(12, 6))

            ax1.plot(index_vals, prices, label="Price", color="blue")
            ax1.set_ylabel("Stock Price (₹)", color="blue")

            ax2 = ax1.twinx()
            ax2.plot(index_vals, momentum, label="Quantum Momentum", color="red", linestyle="dashed")
            ax2.set_ylabel("Momentum", color="red")

            ax1.set_title("Quantum Momentum Analysis")
            ax1.legend(loc="upper left")
            ax2.legend(loc="upper right")
            st.pyplot(fig)

        # 📡 Quantum State Analysis
        with tabs[2]:
            st.subheader("📡 Quantum State Analysis")

            encoded_data = quantum_optimizer.quantum_inspired_encoding(prices)
            amplitudes = np.abs(encoded_data)
            phases = np.angle(encoded_data)

            fig, ax1 = plt.subplots(figsize=(12, 6))

            ax1.plot(index_vals, amplitudes.flatten(), label="Amplitude", color="green")
            ax1.set_ylabel("Amplitude", color="green")

            ax2 = ax1.twinx()
            ax2.plot(index_vals, phases.flatten(), label="Phase", color="purple", linestyle="dashed")
            ax2.set_ylabel("Phase", color="purple")

            ax1.set_title("Quantum State Representation")
            ax1.legend(loc="upper left")
            ax2.legend(loc="upper right")
            st.pyplot(fig)

            # 📌 Display quantum state statistics
            col1, col2, col3 = st.columns(3)
            with col1:
                st.metric("📊 Average Amplitude", f"{np.mean(amplitudes):.4f}")
            with col2:
                st.metric("📡 Phase Coherence", f"{np.std(phases):.4f}")
            with col3:
                st.metric("🔗 Quantum Entropy", f"{-np.sum(amplitudes**2 * np.log(amplitudes**2 + 1e-10)):.4f}")

        # ✅ Refresh Every **10 Seconds**
        time.sleep(10)

    except Exception as e:
        st.error(f"⚠️ Unexpected error: {e}")
        time.sleep(10)  # Prevent app from breaking and wait before retrying