Update pages/technical_analysis.py

pages/technical_analysis.py

@@ -1,170 +1,113 @@
 import streamlit as st
 import pandas as pd
+import numpy as np
 import matplotlib.pyplot as plt
-from utils.data_loader import (
-    load_nifty50_symbols, 
-    get_market_summary, 
-    load_market_indices,
-    process_uploaded_file,
-    fetch_stock_data,
-    get_stock_suggestions
-)
-from utils.market_analysis import MarketAnalyzer
-from datetime import datetime
-import asyncio
-
-# Initialize market analyzer
-market_analyzer = MarketAnalyzer()
-
-# Page configuration
-st.set_page_config(
-    page_title="Indian Stock Market Analysis",
-    page_icon="📈",
-    layout="wide",
-    initial_sidebar_state="expanded"
-)
-
-# Sidebar
-st.sidebar.title("Navigation")
-
-# File Upload Section
-st.sidebar.header("Data Input")
-uploaded_file = st.sidebar.file_uploader(
-    "Upload CSV/Excel file",
-    type=['csv', 'xlsx', 'xls'],
-    help="Upload your own stock data for analysis"
-)
-
-# Data Source Selection
-data_source = st.sidebar.radio(
-    "Select Data Source",
-    ["Market Data", "Uploaded File"],
-    index=0
-)
-
-st.sidebar.markdown("---")
-
-# Main content
-st.title("Indian Stock Market Analysis")
-st.markdown("---")
-
-async def analyze_stock_data(stock_data, symbol):
-    """Analyze stock data and generate reports"""
-    # Calculate technical indicators
-    data_with_indicators = market_analyzer.calculate_technical_indicators(stock_data)
-
-    # Get AI sentiment
-    ai_sentiment = await market_analyzer.get_ai_sentiment(symbol, data_with_indicators)
-
-    # Generate detailed report
-    report = market_analyzer.generate_detailed_report(symbol, data_with_indicators, ai_sentiment)
-
-    return report, data_with_indicators
-
-# Market Summary
-if data_source == "Market Data":
-    market_summary = get_market_summary()
-    if market_summary:
-        st.subheader("Market Overview")
-        cols = st.columns(len(market_summary))
-
-        for i, (index_name, summary) in enumerate(market_summary.items()):
-            with cols[i]:
-                st.metric(
-                    index_name,
-                    f"₹{summary['index_value']:,.2f}",
-                    f"{summary['change_percent']:.2f}%"
-                )
+import time
+import json
+from utils.quantum_algorithms import QuantumInspiredOptimizer
+from utils.deepseek_api import DeepSeekAI  # Integrating DeepSeek
+import openai
+
+# 🔧 DeepSeek API Configuration
+DEEPSEEK_API_KEY = "sk-d52b48c40d494fb88acee4e20e881240"  # Replace with your actual key
+openai.api_key = DEEPSEEK_API_KEY
+
+# ⚛️ Initialize Quantum Optimizer
+quantum_optimizer = QuantumInspiredOptimizer()
+deepseek_ai = DeepSeekAI()
+
+st.set_page_config(page_title="Quantum File Analysis", layout="wide")
+st.title("📂 Quantum File Analysis with DeepSeek AI")
+
+# 📌 File Upload Section
+uploaded_file = st.file_uploader("📤 Upload your CSV/Excel file", type=["csv", "xlsx"])
+
+if uploaded_file:
+    st.success("✅ File Uploaded Successfully!")
+
+    # 🛠️ Read File
+    file_extension = uploaded_file.name.split(".")[-1]
 
-    # Stock Selection
-    st.subheader("Stock Analysis")
-
-    # Index Selection
-    indices = load_market_indices()
-    selected_index = st.selectbox("Select Index", list(indices.keys()))
-
-    # Stock Selection
-    symbols = load_nifty50_symbols()
-    selected_symbol = st.selectbox("Select Stock", symbols)
-
-    # Time Period Selection
-    timeframe = st.select_slider(
-        "Select Time Period",
-        options=["1w", "1mo", "3mo", "6mo", "1y", "3y", "5y"],
-        value="1mo",
-        format_func=lambda x: {
-            "1w": "1 Week",
-            "1mo": "1 Month",
-            "3mo": "1 Quarter",
-            "6mo": "6 Months",
-            "1y": "1 Year",
-            "3y": "3 Years",
-            "5y": "5 Years"
-        }[x]
-    )
-
-    # Fetch and display stock data
-    stock_data = fetch_stock_data(selected_symbol, period=timeframe)
-    if stock_data is not None:
-        # Run analysis
-        report, data_with_indicators = asyncio.run(analyze_stock_data(stock_data, selected_symbol))
-
-        # Display Analysis Results
-        st.subheader("Market Analysis")
-
-        # Action Signal
-        col1, col2, col3 = st.columns(3)
-        with col1:
-            st.metric(
-                "Recommended Action",
-                report['trade_signals']['action'],
-                delta=f"Confidence: {report['trade_signals']['confidence']}"
-            )
-        with col2:
-            st.metric(
-                "Risk Level",
-                report['risk_assessment']['level'],
-                delta=f"Volatility: {report['risk_assessment']['volatility']:.2f}%"
-            )
-        with col3:
-            st.metric(
-                "Current Price",
-                f"₹{report['price_analysis']['current_price']:,.2f}",
-                delta=f"{report['price_analysis']['changes']['1d']:.2f}% (1d)"
-            )
-
-        # Price Chart with Indicators
-        st.subheader("Stock Price Chart")
-        fig, ax = plt.subplots(figsize=(10, 5))
-        ax.plot(data_with_indicators.index, data_with_indicators['Close'], label='Close Price', color='blue')
-        ax.set_xlabel("Date")
-        ax.set_ylabel("Price")
-        ax.legend()
-        st.pyplot(fig)
+    if file_extension == "csv":
+        df = pd.read_csv(uploaded_file)
+    else:
+        df = pd.read_excel(uploaded_file)
+
+    # 📌 Display Data Preview
+    st.subheader("📊 Data Preview")
+    st.write(df.head())
 
+    # 🎯 Select Column for Analysis
+    numeric_columns = df.select_dtypes(include=[np.number]).columns.tolist()
+    
+    if not numeric_columns:
+        st.error("⚠️ No numerical columns found! Please upload a valid dataset.")
     else:
-        st.error("Unable to fetch data. Please try again later.")
-
-# Quick Links
-st.sidebar.markdown("## Quick Links")
-pages = {
-    "Market Overview": "pages/market_overview.py",
-    "Technical Analysis": "pages/technical_analysis.py",
-    "Quantum Patterns": "pages/quantum_patterns.py",
-    "Predictions": "pages/predictions.py"
-}
-
-for page, path in pages.items():
-    st.sidebar.page_link(path, label=page)
-
-# Footer
-st.sidebar.markdown("---")
-st.sidebar.markdown(
-    """
-    <div style='text-align: center'>
-        <p>Built with ❤️ using Streamlit</p>
-        <p>Data provided by Yahoo Finance</p>
-    </div>
-    """,
-    unsafe_allow_html=True
-)
+        target_column = st.selectbox("📈 Select Column for Quantum Analysis", numeric_columns)
+
+        if target_column:
+            st.success(f"✅ Selected Column: {target_column}")
+            prices = df[target_column].values
+
+            # 🛠️ Quantum Pattern Detection
+            st.subheader("🔬 Quantum-Inspired Pattern Detection")
+            pattern_strength = quantum_optimizer.quantum_pattern_detection(prices)
+
+            fig, ax1 = plt.subplots(figsize=(12, 6))
+            ax1.plot(df.index, prices, label="Price", color="blue")
+            ax1.set_ylabel("Value", color="blue")
+
+            ax2 = ax1.twinx()
+            ax2.plot(df.index, pattern_strength, 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 Predictive Modeling
+            st.subheader("📈 Quantum Prediction with DeepSeek AI")
+
+            # Generate Predictions
+            predictions = quantum_optimizer.quantum_trend_prediction(prices)
+            future_dates = np.arange(len(prices), len(prices) + 10)  # Predict next 10 steps
+
+            fig, ax = plt.subplots(figsize=(12, 6))
+            ax.plot(df.index, prices, label="Historical Data", color="blue")
+            ax.plot(future_dates, predictions[-10:], label="Predictions", color="red", linestyle="dashed")
+            ax.set_title("Quantum Trend Prediction")
+            ax.legend()
+            st.pyplot(fig)
+
+            # 🧠 DeepSeek AI Insights
+            st.subheader("🤖 AI-Powered Insights (DeepSeek)")
+
+            deepseek_prompt = f"""
+            Analyze the following financial data using quantum-inspired methods. 
+            Provide insights on potential trends, risks, and investment strategies.
+            Data: {json.dumps(prices.tolist())}
+            """
+
+            with st.spinner("🔍 Generating AI-powered insights..."):
+                deepseek_response = openai.ChatCompletion.create(
+                    model="deepseek-chat",
+                    messages=[{"role": "user", "content": deepseek_prompt}]
+                )
+
+                ai_insights = deepseek_response["choices"][0]["message"]["content"]
+                st.write(ai_insights)
+
+            # 🏆 Display Key Stats
+            st.subheader("📊 Statistical Insights")
+            col1, col2, col3 = st.columns(3)
+
+            with col1:
+                st.metric("🔗 Quantum Entropy", f"{-np.sum(pattern_strength**2 * np.log(pattern_strength**2 + 1e-10)):.4f}")
+            with col2:
+                st.metric("📡 Phase Coherence", f"{np.std(pattern_strength):.4f}")
+            with col3:
+                st.metric("📈 Prediction Confidence", f"{np.mean(np.abs(predictions - prices[-10:])):.4f}")
+
+            # ✅ Refresh Every **10 Seconds**
+            time.sleep(10)