khushalps
3a64ea7efe
git-subtree-dir: utils_v2 git-subtree-split: 8bb584e4734606740c0b42d51bc7fd458c39031a
301 lines
10 KiB
Python
301 lines
10 KiB
Python
"""
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AUTHOR:
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Khushal P Soonderji
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DATE:
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Monday, 13th Jan., 2025.
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OBJECTIVE:
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To provide data model(s) for NSE's pre-market data. This will be useful for catching gap-ups and gap-downs.
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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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# For making data behaviour_models:
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from pydantic import BaseModel, Field, field_validator, AwareDatetime, computed_field
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from typing import Optional, Literal, List
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# My utils:
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from utils_v2.string import regex
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from utils_v2.date_time import date_time
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# To work with date and time:
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import datetime
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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 NSEPreMarketSymbol(BaseModel):
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scrapeTs: AwareDatetime = Field(
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description = "The time (UTC) at which this this data was scraped.",
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frozen = True,
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default_factory = lambda: date_time.get_current_utc_date_time(as_string = False)
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)
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tz: str = Field(
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description = "The timezone in which the dates have to be interpreted.",
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frozen = True,
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default = "Asia/Kolkata"
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)
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symbol: str = Field(
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description = "The trading symbol of this listing.",
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frozen = True
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)
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ffmc: float | None = Field(
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description = "The free-floating market cap. of this symbol.",
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frozen = True
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)
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trigger: str | None = Field(
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description = "Any known trigger.",
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frozen = True,
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default = None
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)
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yearHigh: float | None = Field(
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description = "The year-high value of this symbol.",
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frozen = True
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)
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yearLow: float | None = Field(
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description = "The year-low value of this symbol.",
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frozen = True
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)
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prevClose: float | None = Field(
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description = "The previous trading session's closing price of this symbol.",
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frozen = True
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)
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preMarketPrice: float | None = Field(
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description = "The pre-market decided price of this symbol.",
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frozen = True
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)
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chg: float | None = Field(
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description = "The absolute change between last closing price and the pre-market price.",
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frozen = True
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)
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pChg: float | None = Field(
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description = "The percent change between last closing price and the pre-market price.",
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frozen = True
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)
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totalTradedVolume: int | None = Field(
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description = "The total volume that was traded in the pre-market session.",
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frozen = True
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)
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totalBuyVolume: int | None = Field(
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description = "The total buying volume that was punched-in in the pre-market session.",
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frozen = True
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)
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totalSellVolume: int | None = Field(
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description = "The total selling volume that was punched-in in the pre-market session.",
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frozen = True
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)
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# ┏┓ ┏•
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# ┃ ┏┓┏┓╋┓┏┓
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# ┗┛┗┛┛┗┛┗┗┫
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# ┛
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class Config:
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extra = "ignore"
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# ┏┓ ┏┓ ┓ ┏┓• ┓ ┓
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# ┣┫┓┏╋┏┓━━┃ ┏┓┏┳┓┏┓┓┏╋┏┓┏┫ ┣ ┓┏┓┃┏┫┏
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# ┛┗┗┻┗┗┛ ┗┛┗┛┛┗┗┣┛┗┻┗┗ ┗┻ ┻ ┗┗ ┗┗┻┛
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# ┛
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@computed_field()
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def dateIst(self) -> str:
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return date_time.to_timezone(self.scrapeTs, timezone = date_time.TIMEZONE_IST).strftime("%Y%m%d")
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# ┓┏ ┓• ┓ •
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# ┃┃┏┓┃┓┏┫┏┓╋┓┏┓┏┓┏
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# ┗┛┗┻┗┗┗┻┗┻┗┗┗┛┛┗┛
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@field_validator("scrapeTs", mode = "before")
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def parse_date_time(cls, value):
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if isinstance(value, datetime.datetime):
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value = date_time.to_timezone(value, date_time.TIMEZONE_UTC)
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return value
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# ┏┓ ┏┓
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# ┃ ┓┏┏╋┏┓┏┳┓ ┣ ┓┏┏┓┏┏
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# ┗┛┗┻┛┗┗┛┛┗┗ ┻ ┗┻┛┗┗┛
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def to_json(self) -> dict:
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model_json = self.model_dump()
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model_json["scrapeTs"] = model_json["scrapeTs"].timestamp()
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return model_json
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# ---------------------------------------------------------------------------------------------------------------------
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class NSEPreMarketData(BaseModel):
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scrapeTs: AwareDatetime = Field(
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description = "The time (UTC) at which this this data was scraped.",
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frozen = True,
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default_factory = lambda: date_time.get_current_utc_date_time(as_string = False)
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)
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ts: AwareDatetime = Field(
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description = "The time (UTC) at which this data was made available by NSE.",
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frozen = True
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)
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tz: str = Field(
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description = "The timezone in which the dates have to be interpreted.",
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frozen = True,
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default = "Asia/Kolkata"
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)
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key: str | None = Field(
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description = "The kind of list that was fetched.",
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frozen = True,
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default = None,
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examples = ["NIFTY", "BANKNIFTY", "SME", "FO", "OTHERS", "ALL"]
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)
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advances: int = Field(
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description = "The no. of symbols that will be opening positive.",
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frozen = True
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)
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declines: int = Field(
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description = "The no. of symbols that will be opening negative.",
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frozen = True
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)
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unchanged: int = Field(
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description = "The no. of symbols that will be opening flat.",
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frozen = True
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)
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totalMarketCap: float | int | None = Field(
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description = "The total market cap. of all of the listed symbols.",
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frozen = True
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)
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totalTradedValue: float | int | None = Field(
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description = "The total value in Rupees that was traded in this pre-market session.",
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frozen = True
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)
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totalTradedVolume: int | None = Field(
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description = "The total volume that was traded in the pre-market session.",
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frozen = True
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)
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symbols: List[NSEPreMarketSymbol] = Field(
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description = "The actual symbol-wise data.",
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frozen = True
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)
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# ┏┓ ┏•
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# ┃ ┏┓┏┓╋┓┏┓
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# ┗┛┗┛┛┗┛┗┗┫
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# ┛
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class Config:
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extra = "ignore"
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# ┏┓ ┏┓ ┓ ┏┓• ┓ ┓
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# ┣┫┓┏╋┏┓━━┃ ┏┓┏┳┓┏┓┓┏╋┏┓┏┫ ┣ ┓┏┓┃┏┫┏
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# ┛┗┗┻┗┗┛ ┗┛┗┛┛┗┗┣┛┗┻┗┗ ┗┻ ┻ ┗┗ ┗┗┻┛
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# ┛
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@computed_field()
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def dateIst(self) -> str:
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return date_time.to_timezone(self.ts, timezone = date_time.TIMEZONE_IST).strftime("%Y%m%d")
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# ┓┏ ┓• ┓ •
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# ┃┃┏┓┃┓┏┫┏┓╋┓┏┓┏┓┏
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# ┗┛┗┻┗┗┗┻┗┻┗┗┗┛┛┗┛
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@field_validator("scrapeTs", "ts", mode = "before")
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def parse_date_time(cls, value):
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if isinstance(value, datetime.datetime):
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value = date_time.to_timezone(value, date_time.TIMEZONE_UTC)
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return value
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# ┏┓ ┏┓
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# ┃ ┓┏┏╋┏┓┏┳┓ ┣ ┓┏┏┓┏┏
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# ┗┛┗┻┛┗┗┛┛┗┗ ┻ ┗┻┛┗┗┛
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def to_json(self) -> dict:
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model_json = self.model_dump()
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model_json["scrapeTs"] = model_json["scrapeTs"].timestamp()
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for s in model_json["symbols"]: s["scrapeTs"].timestamp()
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return model_json
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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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pass
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