| import yfinance as yf |
| import pandas as pd |
| import streamlit as st |
| import requests |
| import json |
| from datetime import datetime, timedelta |
|
|
| |
| DEEPSEEK_API_KEY = "sk-d52b48c40d494fb88acee4e20e881240" |
| DEEPSEEK_API_URL = "https://api.deepseek.com/v1/analyze" |
|
|
| |
| @st.cache_data(ttl=600) |
| def load_nifty50_symbols(): |
| """Load Nifty 50 and additional stocks categorized by sectors""" |
| symbols = { |
| "IT": ["TCS.NS", "INFY.NS", "HCLTECH.NS", "TECHM.NS", "WIPRO.NS", "MPHASIS.NS", "LTIM.NS"], |
| "Banking & Finance": [ |
| "HDFCBANK.NS", "ICICIBANK.NS", "SBIN.NS", "AXISBANK.NS", "KOTAKBANK.NS", |
| "BAJFINANCE.NS", "BAJAJFINSV.NS", "HDFC.NS", "INDUSINDBK.NS", "IDFCFIRSTB.NS", "PNB.NS" |
| ], |
| "Energy & Oil": ["RELIANCE.NS", "ONGC.NS", "POWERGRID.NS", "NTPC.NS", "BPCL.NS", "GAIL.NS", "IOC.NS"], |
| "Automobile": ["TATAMOTORS.NS", "M&M.NS", "MARUTI.NS", "HEROMOTOCO.NS", "EICHERMOT.NS", "TVSMOTOR.NS"], |
| "Consumer Goods": ["HINDUNILVR.NS", "ITC.NS", "NESTLEIND.NS", "BRITANNIA.NS", "TITAN.NS", "DABUR.NS"], |
| "Metals & Mining": ["TATASTEEL.NS", "HINDALCO.NS", "JSWSTEEL.NS", "COALINDIA.NS", "NMDC.NS"], |
| "Pharmaceuticals": ["SUNPHARMA.NS", "DRREDDY.NS", "CIPLA.NS", "DIVISLAB.NS", "LUPIN.NS"], |
| "Infrastructure & Realty": ["LT.NS", "ADANIPORTS.NS", "ULTRACEMCO.NS", "SHREECEM.NS", "DLF.NS", "IRCTC.NS"], |
| "Telecom": ["BHARTIARTL.NS", "IDEA.NS", "RELIANCE.NS"], |
| "Others": ["ASIANPAINT.NS", "HDFCLIFE.NS", "SBILIFE.NS", "UPL.NS", "ADANIENT.NS", "DMART.NS"] |
| } |
| return symbols |
|
|
| @st.cache_data(ttl=600) |
| def fetch_stock_data(symbol, period='1y', interval="1d"): |
| """Fetch stock data from Yahoo Finance""" |
| try: |
| stock = yf.Ticker(symbol) |
| data = stock.history(period=period, interval=interval) |
| return data |
| except Exception as e: |
| st.error(f"⚠️ Error fetching data for {symbol}: {str(e)}") |
| return None |
|
|
| @st.cache_data(ttl=600) |
| def get_stock_info(symbol): |
| """Get stock information""" |
| try: |
| stock = yf.Ticker(symbol) |
| info = stock.info |
| return { |
| 'name': info.get('longName', symbol), |
| 'sector': info.get('sector', 'N/A'), |
| 'market_cap': info.get('marketCap', 0), |
| 'pe_ratio': info.get('trailingPE', 0), |
| 'volume': info.get('volume', 0), |
| 'recommendation': info.get('recommendationKey', 'N/A'), |
| 'target_price': info.get('targetMeanPrice', 0) |
| } |
| except Exception as e: |
| st.error(f"⚠️ Error fetching info for {symbol}: {str(e)}") |
| return None |
|
|
| def analyze_with_deepseek(stock_symbol, data): |
| """Send stock data to Deepseek AI for analysis and prediction""" |
| try: |
| if data is None or data.empty: |
| return "⚠️ No data available for analysis." |
| |
| |
| payload = { |
| "api_key": DEEPSEEK_API_KEY, |
| "stock_symbol": stock_symbol, |
| "historical_data": data.to_dict(orient="records"), |
| } |
|
|
| headers = { |
| "Content-Type": "application/json" |
| } |
|
|
| response = requests.post(DEEPSEEK_API_URL, headers=headers, data=json.dumps(payload)) |
| response_data = response.json() |
|
|
| if response.status_code == 200: |
| return response_data.get("prediction", "No prediction available.") |
| else: |
| return f"⚠️ Error: {response_data.get('error', 'Unknown error occurred')}" |
| |
| except Exception as e: |
| return f"⚠️ Deepseek API Error: {str(e)}" |
|
|
| def process_uploaded_file(uploaded_file): |
| """Process uploaded CSV/Excel file""" |
| try: |
| if uploaded_file.name.endswith('.csv'): |
| df = pd.read_csv(uploaded_file) |
| elif uploaded_file.name.endswith(('.xls', '.xlsx')): |
| df = pd.read_excel(uploaded_file) |
| else: |
| raise ValueError("⚠️ Unsupported file format") |
|
|
| |
| required_columns = ['Date', 'Open', 'High', 'Low', 'Close', 'Volume'] |
| if not all(col in df.columns for col in required_columns): |
| raise ValueError("⚠️ Missing required columns") |
|
|
| |
| df['Date'] = pd.to_datetime(df['Date']) |
| return df |
| except Exception as e: |
| st.error(f"⚠️ Error processing file: {str(e)}") |
| return None |
|
|
| def get_stock_suggestions(data): |
| """Generate stock suggestions based on technical indicators and Deepseek AI""" |
| try: |
| |
| data['SMA20'] = data['Close'].rolling(window=20).mean() |
| data['SMA50'] = data['Close'].rolling(window=50).mean() |
|
|
| |
| current_price = data['Close'].iloc[-1] |
| sma20 = data['SMA20'].iloc[-1] |
| sma50 = data['SMA50'].iloc[-1] |
|
|
| |
| if sma20 > sma50: |
| trend = "BULLISH" |
| suggestion = "📈 Consider buying. Price is above moving averages." |
| elif sma20 < sma50: |
| trend = "BEARISH" |
| suggestion = "📉 Consider selling. Price is below moving averages." |
| else: |
| trend = "NEUTRAL" |
| suggestion = "➡️ Market is sideways. Wait for a clear trend." |
|
|
| deepseek_analysis = analyze_with_deepseek("Custom Data", data) |
|
|
| return { |
| 'trend': trend, |
| 'suggestion': suggestion, |
| 'current_price': current_price, |
| 'sma20': sma20, |
| 'sma50': sma50, |
| 'deepseek_analysis': deepseek_analysis |
| } |
| except Exception as e: |
| st.error(f"⚠️ Error generating suggestions: {str(e)}") |
| return None |
|
|
