| 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") |
|
|
| |
| quantum_optimizer = QuantumInspiredOptimizer() |
|
|
| |
| symbol = st.selectbox("🔍 Select Stock", load_nifty50_symbols()) |
| timeframe = st.selectbox("📅 Select Timeframe", ["1mo", "3mo", "6mo", "1y"]) |
|
|
| |
| data_placeholder = st.empty() |
| tabs = st.tabs(["🔍 Quantum Pattern Detection", "⚡ Quantum Momentum", "📡 Quantum State Analysis"]) |
|
|
| |
| 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 |
| |
| |
| 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) |
|
|
| |
| 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) |
|
|
| |
| 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) |
|
|
| |
| 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}") |
|
|
| |
| time.sleep(10) |
|
|
| except Exception as e: |
| st.error(f"⚠️ Unexpected error: {e}") |
| time.sleep(10) |
|
|
