khushalps
9e8db857f4
git-subtree-dir: utils_v2 git-subtree-split: 3be5145c7a4cfede04d753324dfae31ace913c98
302 lines
12 KiB
Python
302 lines
12 KiB
Python
"""
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AUTHOR:
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Khushal P Soonderji
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DATE:
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Monday, 16th Dec., 2024
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OBJECTIVE:
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This file aims to fetch the current details about the constituents of various indices. This gives you not only
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the constituent stocks of the selected index, but also that stock's current activity in the market.
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https://www.nseindia.com/market-data/live-equity-market?symbol=NIFTY%2050
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NOTE: This method involves web scraping. It is good for proof-of-concept development, but it is recommended that
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more professional data-sources be used when the product starts becoming mature.
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REFERENCES:
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N/A
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DOWNLOADS:
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N/A
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"""
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# *****************************************************************************************************************
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# ***** ****
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# *** IMPORT ***
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# ***** ****
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# *****************************************************************************************************************
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# To make sibling directories accessible for imports:
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import sys
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sys.path.append(".")
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sys.path.append("..")
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# System-level activities:
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import io
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# My utils:
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from utils_v2.string import json
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from utils_v2.date_time import date_time
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# NSE-related utils:
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from utils_v2.nse.controllers.base import AsyncNSEBase
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from utils_v2.nse.models.api_call import NSEApiResponse
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# To make REST-ful API calls:
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import httpx
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# To work with date and time:
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import datetime
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# To work with datatypes:
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from typing import Any, List
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# For asynchronous activities:
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import asyncio
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# *****************************************************************************************************************
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# ***** ****
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# *** MACROS / ONE-TIME INIT ***
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# ***** ****
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# *****************************************************************************************************************
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# --- Nothing Yet
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# *****************************************************************************************************************
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# ***** ****
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# *** VARIABLES ***
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# ***** ****
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# *****************************************************************************************************************
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# --- Nothing Yet
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# *****************************************************************************************************************
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# ***** ****
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# *** FUNCTIONS ***
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# ***** ****
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# *****************************************************************************************************************
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class NSEIndexConstituents(AsyncNSEBase):
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# Broad Market Indices:
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INDEX_NIFTY_50 = "NIFTY 50"
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INDEX_NIFTY_NEXT_50 = "NIFTY NEXT 50"
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INDEX_NIFTY_MIDCAP_50 = "NIFTY MIDCAP 50"
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INDEX_NIFTY_MIDCAP_100 = "NIFTY MIDCAP 100"
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INDEX_NIFTY_MIDCAP_150 = "NIFTY MIDCAP 150"
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INDEX_NIFTY_SMALLCAP_50 = "NIFTY SMALLCAP 50"
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INDEX_NIFTY_SMALLCAP_100 = "NIFTY SMALLCAP 100"
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INDEX_NIFTY_SMALLCAP_250 = "NIFTY SMALLCAP 250"
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INDEX_NIFTY_MIDSMALLCAP_400 = "NIFTY MIDSMALLCAP 400"
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INDEX_NIFTY_100 = "NIFTY 100"
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INDEX_NIFTY_200 = "NIFTY 200"
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INDEX_NIFTY_500_MULTICAP_50_25_25 = "NIFTY500 MULTICAP 50:25:25"
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INDEX_NIFTY_LARGEMIDCAP_250 = "NIFTY LARGEMIDCAP 250"
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INDEX_NIFTY_MIDCAP_SELECT = "NIFTY MIDCAP SELECT"
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INDEX_NIFTY_TOTAL_MARKET = "NIFTY TOTAL MARKET"
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INDEX_NIFTY_MICROCAP_250 = "NIFTY MICROCAP 250"
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INDEX_NIFTY_500 = "NIFTY 500"
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INDEX_NIFTY_500_LARGEMIDSMALL_EQUAL_CAP_WEIGHTED = "NIFTY500 LARGEMIDSMALL EQUAL-CAP WEIGHTED"
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# Sectoral Indices:
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INDEX_NIFTY_AUTO = "NIFTY AUTO"
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INDEX_NIFTY_BANK = "NIFTY BANK"
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INDEX_NIFTY_ENERGY = "NIFTY ENERGY"
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INDEX_NIFTY_FINANCIAL_SERVICES = "NIFTY FINANCIAL SERVICES"
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INDEX_NIFTY_FINANCIAL_SERVICES_25_50 = "NIFTY FINANCIAL SERVICES 25/50"
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INDEX_NIFTY_FMCG = "NIFTY FMCG"
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INDEX_NIFTY_IT = "NIFTY IT"
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INDEX_NIFTY_MEDIA = "NIFTY MEDIA"
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INDEX_NIFTY_METAL = "NIFTY METAL"
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INDEX_NIFTY_PHARMA = "NIFTY PHARMA"
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INDEX_NIFTY_PSU_BANK = "NIFTY PSU BANK"
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INDEX_NIFTY_REALTY = "NIFTY REALTY"
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INDEX_NIFTY_PRIVATE_BANK = "NIFTY PRIVATE BANK"
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INDEX_NIFTY_HEALTHCARE_INDEX = "NIFTY HEALTHCARE INDEX"
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INDEX_NIFTY_CONSUMER_DURABLES = "NIFTY CONSUMER DURABLES"
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INDEX_NIFTY_OIL_GAS = "NIFTY OIL & GAS"
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INDEX_NIFTY_MIDSMALL_HEALTHCARE = "NIFTY MIDSMALL HEALTHCARE"
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INDEX_NIFTY_FINANCIAL_SERVICES_EX_BANK = "NIFTY FINANCIAL SERVICES EX-BANK"
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INDEX_NIFTY_MIDSMALL_FINANCIAL_SERVICES = "NIFTY MIDSMALL FINANCIAL SERVICES"
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INDEX_NIFTY_MIDSMALL_IT_TELECOM = "NIFTY MIDSMALL IT & TELECOM"
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def __init__(
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self,
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http_client: httpx.AsyncClient,
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debug = True,
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debug_prefix = "NSE (IdxCons) | ",
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debug_only_errors = True
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):
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# Pass on the initialization to the parent:
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super().__init__(
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base_url = r"https://www.nseindia.com/market-data/live-equity-market",
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data_url = r"https://www.nseindia.com/api/equity-stockIndices",
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http_client = http_client,
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debug = debug,
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debug_prefix = debug_prefix,
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debug_only_errors = debug_only_errors
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)
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async def get_data(
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self,
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index_name: str,
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return_raw: bool = False,
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) -> NSEApiResponse:
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"""
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To get the data of the corporate event calendar.
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:param index_name: The value held in the 'indexName' field of the formatted output of the Index Master.
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:param return_raw: Whether you want the raw JSON from NSE or you want it formatted.
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:return: The raw or formatted event calendar data in the 'data' field of the response model.
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"""
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# Make the API call:
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api_response = await self.get(params = {"index": index_name})
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# If the API call was successful:
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if api_response.httpCode in [200]:
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api_response.success = True
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if return_raw: api_response.data = await api_response.get_json()
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else:
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try: api_response.data = self.format_data(
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raw_json = await api_response.get_json(),
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index_name = index_name,
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timestamp = date_time.get_current_utc_date_time(as_string = True),
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raise_exception = True
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)
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except Exception as exception:
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api_response.exception = exception
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api_response.success = False
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# Done here:
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return api_response
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@staticmethod
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def format_data(
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raw_json: dict,
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index_name: str,
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timestamp: datetime.datetime = None,
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raise_exception: bool = False
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) -> List[dict] | None:
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"""
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We format the data here to be able to retrieve it properly later.
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:param raw_json: The raw data as scraped from NSE.
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:param index_name: The value that you had used to fetch the raw data in the first place.
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:param timestamp: The timestamp at which the data was scraped. This shall be useful for data retrieval from the
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database, later.
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:param raise_exception: If set to True, any exception will be propagated. If set to False, any exception will be
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suppressed internally.
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:return: The formatted data if successful, else None.
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"""
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# Can't do anything if the chain itself is null:
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if raw_json is None: return raw_json
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# Start by assuming failure:
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formatted_data = None
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# Ensure that we've got a proper timestamp:
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if timestamp is None: timestamp = date_time.get_current_utc_date_time(as_string = False)
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try:
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# Format the data:
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formatted_data = [
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{
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"scrapeTs": timestamp,
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"ts": date_time.to_timezone(
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date_time.parse_date_time(
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input_value = symbol["lastUpdateTime"],
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date_formats = ["%d-%b-%Y %H:%M:%S"],
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timezone = date_time.TIMEZONE_IST
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),
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timezone = date_time.TIMEZONE_UTC
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),
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"indexName": index_name,
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"symbol": symbol["symbol"],
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"name": symbol["meta"]["companyName"],
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"industry": symbol["meta"]["industry"],
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"isFNOSec": symbol["meta"]["isFNOSec"],
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"isSuspended": symbol["meta"]["isSuspended"],
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"isin": symbol["meta"]["isin"],
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"open": symbol["open"],
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"high": symbol["dayHigh"],
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"low": symbol["dayLow"],
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"close": symbol["lastPrice"],
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"totTradedVol": symbol["totalTradedVolume"],
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"totTradedVal": symbol["totalTradedValue"],
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"prevClose": symbol["previousClose"],
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"change": symbol["change"],
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"pctChange": symbol["pChange"],
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"yearHigh": symbol["yearHigh"],
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"yearLow": symbol["yearLow"],
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"pctChange30d": symbol["perChange30d"],
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"pctChange365d": symbol["perChange365d"],
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"ffmc": symbol["ffmc"]
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} for symbol in raw_json["data"] if symbol["priority"] == 0
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]
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# If something goes wrong:
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except Exception as exception:
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formatted_data = None
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if raise_exception: raise
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# Done here:
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return formatted_data
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# *****************************************************************************************************************
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# ***** ****
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# *** MAIN PROGRAM ***
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# ***** ****
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# *****************************************************************************************************************
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if __name__ == "__main__":
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async def main():
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# Create an HTTP client:
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test_client = httpx.AsyncClient(
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limits = httpx.Limits(
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max_connections = 100, # ............ Maximum number of connections allowed in the pool.
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max_keepalive_connections = 50, # ... Maximum number of connections that can be kept alive.
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),
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timeout = httpx.Timeout(
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pool = 120.0, # .... Time to wait for a free connection from the pool.
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connect = 2.5, # ... Time to wait for establishing a connection to the server.
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write = 10.0, # .... Time to wait for sending data.
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read = 2.5 # ....... Time to wait for receiving data.
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)
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)
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# Create an instance of the scraper, and refresh its cookies:
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my_nse = NSEIndexConstituents(http_client = test_client)
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# Get and show the data:
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api_response = await my_nse.get_data(
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index_name = NSEIndexConstituents.INDEX_NIFTY_50,
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return_raw = False
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)
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print("SUMMARY:", api_response.to_markdown(), "\n---\n\n")
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if api_response.success: print("INDEX CONSTITUENTS:", json.to_string(api_response.data, default = str))
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asyncio.run(main())
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