khushalps
8bf92c04ed
git-subtree-dir: utils_v2 git-subtree-split: 41797375144632a2cdda1e4c6c4a5769e4b80306
282 lines
12 KiB
Python
282 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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Thursday, 28th Nov., 2024
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OBJECTIVE:
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To provide a way to retrieve dates of important events like financial-results, stock-splits, fund-raising, etc.
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from NSE's portal.
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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.base import AsyncNSEBase
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from utils_v2.nse.models.data.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 NSEIndexOptionChain(AsyncNSEBase):
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# Index Names:
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INDEX_NIFTY = "NIFTY"
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INDEX_BANKNIFTY = "BANKNIFTY"
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INDEX_FINNIFTY = "FINNIFTY"
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INDEX_MIDCPNIFTY = "MIDCPNIFTY"
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INDEX_NIFTYNXT50 = "NIFTYNXT50"
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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 (IdxOC) | ",
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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/option-chain",
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data_url = r"https://www.nseindia.com/api/option-chain-indices",
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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 name of the index whose option chain is needed. Use one of the options created within the
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scope of this class.
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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 = {"symbol": 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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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_chain = 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 at the expiry-level:
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expiry_dates = raw_json["records"]["expiryDates"]
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semi_formatted_chain = {e: None for e in expiry_dates}
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# Iterate through the raw records and start plugging them into the formatted records:
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for record in raw_json["records"]["data"]:
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# Extract some variables:
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expiry_date = record["expiryDate"]
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strike_price = record["strikePrice"]
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# Start building the needed structure:
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if semi_formatted_chain.get(expiry_date) is None: semi_formatted_chain[expiry_date] = {}
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if semi_formatted_chain[expiry_date].get(strike_price) is None:
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semi_formatted_chain[expiry_date][strike_price] = {
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"ts": timestamp,
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"strike": record["strikePrice"],
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"expiry": record["expiryDate"]
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}
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# Add the CE/PE data:
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for right in ["CE", "PE"]:
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right_data = record.get(right, {})
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if right_data:
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semi_formatted_chain[expiry_date][strike_price]["underlying"] = right_data["underlying"]
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semi_formatted_chain[expiry_date][strike_price]["underlyingValue"] = right_data["underlyingValue"]
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semi_formatted_chain[expiry_date][strike_price][right.lower()] = {}
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semi_formatted_chain[expiry_date][strike_price][right.lower()] = {
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"id": right_data.get("identifier"),
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"oi": right_data.get("openInterest"),
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"oiChg": right_data.get("changeinOpenInterest"),
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"oiPctChg": right_data.get("pchangeinOpenInterest"),
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"totTradedVol": right_data.get("totalTradedVolume"),
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"iv": right_data.get("impliedVolatility"),
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"ltp": right_data.get("lastPrice"),
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"change": right_data.get("change"),
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"pChange": right_data.get("pChange"),
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"totBuyQty": right_data.get("totalBuyQuantity"),
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"totSellQty": right_data.get("totalSellQuantity"),
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"bidQty": right_data.get("bidQty"),
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"bidPrice": right_data.get("bidprice"),
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"askQty": right_data.get("askQty"),
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"askPrice": right_data.get("askPrice")
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}
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# Final formatting:
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formatted_chain = []
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for expiry, _0 in semi_formatted_chain.items():
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if isinstance(_0, dict):
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for strike_price, _1 in _0.items():
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_1["ce"] = _1.pop("ce")
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_1["pe"] = _1.pop("pe")
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formatted_chain.append(_1)
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# If something goes wrong:
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except Exception as exception:
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formatted_chain = None
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if raise_exception: raise
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# Done here:
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return formatted_chain
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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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connect = 2.5, # ... Shorter connection timeout.
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read = 2.5, # ...... Like what EasyEcom gives.
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write = 10.0, # .... Time to wait for sending data.
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pool = 120.0 # ..... Time to wait for a free connection from the pool.
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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 = NSEIndexOptionChain(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 = NSEIndexOptionChain.INDEX_NIFTY,
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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("OPTION CHAIN:", json.to_string(api_response.data, default = str))
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asyncio.run(main())
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