Squashed 'utils_v2/' content from commit 3be5145
git-subtree-dir: utils_v2 git-subtree-split: 3be5145c7a4cfede04d753324dfae31ace913c98
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"""
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AUTHOR:
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Khushal P Soonderji
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DATE:
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Thursday, 5th Dec., 2024
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OBJECTIVE:
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To provide a way to retrieve pre-market data from NSE. This is typically available by 9:10 AM.
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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 NSEPreMarket(AsyncNSEBase):
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# Symbol names:
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PRE_MARKET_KEY_NIFTY = "NIFTY"
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PRE_MARKET_KEY_BANK_NIFTY = "BANKNIFTY"
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PRE_MARKET_KEY_SME = "SME"
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PRE_MARKET_KEY_FO = "FO"
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PRE_MARKET_KEY_OTHERS = "OTHERS"
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PRE_MARKET_KEY_ALL = "ALL"
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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 (CECal) | ",
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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/pre-open-market-cm-and-emerge-market",
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data_url = r"https://www.nseindia.com/api/market-data-pre-open",
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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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key: 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 pre-open market trading. Useful for finding gaps and expected unusual activity in the
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trading hours.
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:param key: The type of pre-market data that you want. Choose from the class variables.
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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 = {"key": key})
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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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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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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 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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# Start by extracting basic data:
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formatted_data = {
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"scrapeTs": timestamp,
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"ts": date_time.to_timezone(
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date_time.as_if_timezone(
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date_time.parse_date_time(
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input_value = raw_json["timestamp"],
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date_formats = ["%d-%b-%Y %H:%M:%S"]
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),
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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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"advances": raw_json["advances"],
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"declines": raw_json["declines"],
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"unchanged": raw_json["unchanged"],
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"totalMarketCap": raw_json["totalmarketcap"],
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"totalTradedValue": raw_json["totalTradedValue"],
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"totalTradedVolume": raw_json["totalTradedVolume"],
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"data": []
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}
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# Now we iterate through the symbol-wise data and extract what we need:
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for raw_symbol_data in raw_json["data"]:
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raw_symbol_metadata = raw_symbol_data["metadata"]
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raw_symbol_detail = raw_symbol_data["detail"]["preOpenMarket"]
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formatted_data["data"].append({
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"symbol": raw_symbol_metadata["symbol"],
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"marketCap": raw_symbol_metadata["marketCap"],
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"trigger": raw_symbol_metadata["purpose"],
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"yearHigh": raw_symbol_metadata["yearHigh"],
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"yearLow": raw_symbol_metadata["yearLow"],
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"prevClose": raw_symbol_metadata["previousClose"],
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"premarketPrice": raw_symbol_metadata["iep"],
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"chg": raw_symbol_metadata["change"],
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"pctChg": raw_symbol_metadata["pChange"],
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"totalTradedVolume": raw_symbol_detail["totalTradedVolume"],
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"totalBuyVolume": raw_symbol_detail["totalBuyQuantity"],
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"totalSellVolume": raw_symbol_detail["totalSellQuantity"],
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})
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# Data sorting (descending order of percent change):
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formatted_data["data"] = sorted(
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formatted_data["data"],
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key = lambda x: x["pctChg"],
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reverse = True
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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 = 9.9 # ....... 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 = NSEPreMarket(http_client = test_client)
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# Get and show the data:
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api_response = await my_nse.get_data(key = my_nse.PRE_MARKET_KEY_FO, return_raw = False)
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print("SUMMARY:", api_response.to_markdown(), "\n---\n\n")
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if api_response.success: print("PRE-MARKET DATA:", json.to_string(api_response.data, default = str))
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if api_response.exception: raise api_response.exception
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asyncio.run(main())
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