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| import requests | |
| import yfinance as yf | |
| import pandas as pd | |
| from datetime import datetime, timedelta | |
| from typing import List, Dict, Any, Optional, Tuple | |
| from app.config import settings | |
| from app.models.fund_models import FundMeta, NAVData, FundNAVResponse, MarketIndex, MarketIndicesResponse | |
| class MutualFundDataFetcher: | |
| """Fetch mutual fund data from MFAPI.in and Yahoo Finance for indices""" | |
| def __init__(self): | |
| self.mfapi_base = settings.MFAPI_BASE_URL | |
| def get_all_schemes(self) -> List[Dict[str, Any]]: | |
| """Fetch all mutual fund schemes""" | |
| try: | |
| response = requests.get(self.mfapi_base) | |
| if response.status_code == 200: | |
| return response.json() | |
| else: | |
| return [] | |
| except Exception as e: | |
| print(f"Error fetching schemes: {e}") | |
| return [] | |
| def get_fund_nav_history(self, scheme_code: str) -> Tuple[pd.DataFrame, FundMeta]: | |
| """Fetch NAV history for a specific scheme""" | |
| try: | |
| url = f"{self.mfapi_base}/{scheme_code}" | |
| response = requests.get(url) | |
| if response.status_code == 200: | |
| data = response.json() | |
| nav_data = data.get('data', []) | |
| # Convert to DataFrame | |
| df = pd.DataFrame(nav_data) | |
| if not df.empty: | |
| df['date'] = pd.to_datetime(df['date'], format='%d-%m-%Y') | |
| df['nav'] = pd.to_numeric(df['nav']) | |
| df = df.sort_values('date') | |
| # Create FundMeta object | |
| meta_data = data.get('meta', {}) | |
| fund_meta = FundMeta( | |
| fund_house=meta_data.get('fund_house'), | |
| scheme_type=meta_data.get('scheme_type'), | |
| scheme_category=meta_data.get('scheme_category'), | |
| scheme_code=str(meta_data.get('scheme_code', scheme_code)) | |
| ) | |
| return df, fund_meta | |
| else: | |
| return pd.DataFrame(), FundMeta() | |
| except Exception as e: | |
| print(f"Error fetching NAV data: {e}") | |
| return pd.DataFrame(), FundMeta() | |
| def get_market_indices(self) -> MarketIndicesResponse: | |
| """Fetch Indian market indices using Yahoo Finance""" | |
| indices = { | |
| '^NSEI': 'Nifty 50', | |
| '^BSESN': 'BSE Sensex', | |
| '^NSEBANK': 'Nifty Bank', | |
| '^CNXIT': 'Nifty IT', | |
| '^NSEMDCP50': 'Nifty Midcap 50', | |
| 'NIFTYSMLCAP50.NS': 'Nifty Smallcap 50', | |
| '^CNXPHARMA': 'Nifty Pharma', | |
| '^CNXAUTO': 'Nifty Auto', | |
| '^CNXFMCG': 'Nifty FMCG', | |
| '^CNXENERGY': 'Nifty Energy', | |
| '^CNXREALTY': 'Nifty Realty', | |
| '^NSMIDCP': 'Nifty Next 50', | |
| } | |
| indices_data = [] | |
| for symbol, name in indices.items(): | |
| try: | |
| ticker = yf.Ticker(symbol) | |
| hist = ticker.history(period="5d") | |
| if not hist.empty: | |
| current_price = hist['Close'].iloc[-1] | |
| previous_close = hist['Close'].iloc[-2] if len(hist) > 1 else current_price | |
| change = current_price - previous_close | |
| change_pct = (change / previous_close) * 100 | |
| indices_data.append(MarketIndex( | |
| name=name, | |
| symbol=symbol, | |
| current_price=current_price, | |
| change=change, | |
| change_pct=change_pct | |
| )) | |
| except Exception as e: | |
| print(f"Could not fetch {name}: {e}") | |
| return MarketIndicesResponse( | |
| indices=indices_data, | |
| last_updated=datetime.now() | |
| ) |