yatmesh
671 lines
21 KiB
Python
671 lines
21 KiB
Python
"""
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AUTHOR:
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Khushal P Soonderji
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DATE:
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Tuesday, 24th Dec., 2024.
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OBJECTIVE:
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To provide a structure to represent trading tick updates.
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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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import utils_v2.cache.async_redis_cache_v2
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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, PastDatetime, model_validator, AwareDatetime, computed_field
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from typing import Optional, Literal, Union, List
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# My utils:
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from utils_v2.string import json
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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 OneMarketDepth(BaseModel):
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price: float = Field(
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description = "a price at which trader(s) are willing to trade this instrument",
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frozen = True,
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alias = "price"
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)
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qty: int = Field(
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description = "the no. of shares available at the above price",
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frozen = True,
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alias = "quantity"
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)
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orders: int = Field(
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description = "how many orders have contributed to the above quantity",
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frozen = True,
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alias = "orders"
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)
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@computed_field
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def lqdty(self) -> float:
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return self.price * self.qty
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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 = "forbid"
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# ---------------------------------------------------------------------------------------------------------------------
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class MarketDepth(BaseModel):
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buy: List[OneMarketDepth] = Field(
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description = "the buying side market depth",
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frozen = True
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)
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sell: List[OneMarketDepth] = Field(
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description = "the selling side market depth",
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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 = "forbid"
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# ┓┏ ┓• ┓ •
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# ┃┃┏┓┃┓┏┫┏┓╋┓┏┓┏┓
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# ┗┛┗┻┗┗┗┻┗┻┗┗┗┛┛┗
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@field_validator("buy", mode = "after")
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def sort_buying_depth(cls, value):
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value.sort(key = lambda x: x.price, reverse = True)
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return value
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@field_validator("sell", mode = "after")
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def sort_selling_depth(cls, value):
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value.sort(key = lambda x: x.price, reverse = False)
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return value
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# ---------------------------------------------------------------------------------------------------------------------
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class TradingTick(BaseModel):
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symbol: str = Field(
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description = "the symbol of the instrument"
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)
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name: str = Field(
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description = "the name of the co./underlying"
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)
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exchange: Literal["NSE", "NFO", "BSE", "BFO", "MCX", "CDS", "BCD"] = Field(
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description = "the exchange on which this instrument is traded",
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frozen = True
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)
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exchangeToken: str | int = Field(
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description = "the code by which the exchange identifies this instrument",
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frozen = True
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)
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broker: Literal["zerodhaKite", "iciciBreeze"] = Field(
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description = "the broker that gave you the details of this instrument",
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frozen = True
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)
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brokerToken: str | int = Field(
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description = "the code by which the broker identifies this instrument",
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frozen = True
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)
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tradeable: bool = Field(
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description = "whether, or not, this instrument is tradeable",
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frozen = True
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)
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segment: str = Field(
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description = "the segment which this asset represents",
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frozen = True
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)
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type: str = Field(
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description = "the type of the instrument in the segment",
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frozen = True
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)
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strike: int | float | None = Field(
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description = "the strike price of the instrument if it is a derivative",
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default = None,
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frozen = True
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)
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expiryTs: AwareDatetime | None = Field(
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description = "the expiry (utc) of this instrument if it is a derivative",
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default = None,
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frozen = True
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)
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expiryTz: str | None = Field(
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description = "the timezone in which the expiry timestamp my be interpreted; should be compatible with pytz",
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frozen = True,
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examples = ["UTC", "Asia/Kolkata"]
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)
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prevClose: float = Field(
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description = "the closing price of this instrument on the previous day",
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frozen = True
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)
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ltp: float = Field(
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description = "the last price of this instrument at the time of requesting the symbol list",
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frozen = True
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)
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qty: int | None = Field(
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description = "how many units were traded in this tick"
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)
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chg: float = Field(
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description = "the absolute change since the previous close",
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frozen=True
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)
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pChg: float = Field(
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description = "the percentage change since last close",
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frozen = True
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)
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o: float = Field(
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description = "this session's open price",
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frozen = True
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)
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h: float = Field(
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description = "this session's highest price",
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frozen = True
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)
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l: float = Field(
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description = "this session's lowest price",
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frozen = True
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)
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c: float = Field(
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description = "this session's close price; typically the same as the ltp",
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frozen = True
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)
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totVol: int | None = Field(
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description = "the total volume of this instrument that has been traded in this session",
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frozen = True
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)
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vwap: float | None = Field(
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description = "the volume weighted average price in this session",
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frozen = True
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)
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totBuyQty: int | None = Field(
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description = "the total open buy qty. on the exchange for this symbol",
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frozen = True
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)
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totSellQty: int | None = Field(
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description = "the total open sell qty. on the exchange for this symbol",
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frozen = True
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)
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oi: int | None = Field(
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description = "the total open interest of this instrument (if derivative)",
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frozen = True
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)
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oiDayHigh: int | None = Field(
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description = "this session's highest open interest of this instrument (if derivative)",
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frozen = True
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)
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oiDayLow: int | None = Field(
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description = "this session's lowest open interest of this instrument (if derivative)",
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frozen = True
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)
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rcvdTs: AwareDatetime | None = Field(
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description = "the time (utc) at which this update was received",
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frozen = True,
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default = None,
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validate_default = True
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)
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tradeTs: AwareDatetime | None = Field(
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description = "the last trade time (utc) of this instrument",
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frozen = True
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)
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tradeTz: str | None = Field(
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description = "the timezone in which the last trade time should be interpreted; should be compatible with pytz",
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frozen = True,
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examples = ["UTC", "Asia/Kolkata"]
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)
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exchgTs: AwareDatetime | None = Field(
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description = "the time (utc) at which this update was received from the exchange",
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frozen = True
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)
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exchgTz: str | None = Field(
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description = "the timezone in which the exchange's time should be interpreted; should be compatible with pytz",
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frozen = True,
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examples = ["UTC", "Asia/Kolkata"]
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)
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depth: MarketDepth | None = Field(
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description = "the market depth data for this instrument at the time of this update"
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)
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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 tickCashflow(self) -> float | None:
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if self.qty is not None and self.ltp is not None:
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return self.qty * self.ltp
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@computed_field
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def totCashflow(self) -> float | None:
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if self.totVol is not None and self.vwap is not None:
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return self.totVol * self.vwap
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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 = "forbid"
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# ┏┓ ┏┓
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# ┃ ┓┏┏╋┏┓┏┳┓ ┣ ┓┏┏┓┏┏
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# ┗┛┗┻┛┗┗┛┛┗┗ ┻ ┗┻┛┗┗┛
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@property
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def summary(self):
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highest_bid = None
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lowest_ask = None
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if self.depth:
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highest_bid = self.depth.buy[0] if self.depth.buy else None
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lowest_ask = self.depth.sell[0] if self.depth.sell else None
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return {
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"broker": self.broker,
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"brokerToken": self.brokerToken,
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"exchange": self.exchange,
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"exchangeToken": self.exchangeToken,
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"segment": self.segment,
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"type": self.type,
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"symbol": self.symbol,
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"name": self.name,
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"expiry": date_time.to_timezone(
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self.expiryTs,
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timezone = self.expiryTz
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).strftime("%Y-%m-%d") if self.expiryTs is not None else None,
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"strike": self.strike,
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"bidQty": highest_bid.qty if highest_bid else None,
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"bidRate": highest_bid.price if highest_bid else None,
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"askQty": lowest_ask.qty if lowest_ask else None,
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"askRate": lowest_ask.price if lowest_ask else None,
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"o": self.o,
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"h": self.h,
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"l": self.l,
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"ltp": self.ltp,
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"qty": self.qty,
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"chg": self.chg,
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"pChg": self.pChg,
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"vwap": self.vwap,
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"totVol": self.totVol,
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"rcvdTs": self.rcvdTs.timestamp(),
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"tradeTs": None if self.tradeTs is None else self.tradeTs.timestamp(),
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"tradeTz": self.tradeTz,
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"exchgTs": self.exchgTs.timestamp(),
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"exchgTz": self.exchgTz
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}
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@staticmethod
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def from_zerodha_kite(
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ticks: dict | List[dict],
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instrument_lookup: dict,
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received_ts: datetime.datetime = None
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) -> list:
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# Ensure that we are working with a list:
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if not isinstance(ticks, list): ticks = [ticks]
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# Iterate through the ticks and fit them into the model:
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modelled_ticks = []
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for tick in ticks:
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# Stash frequently needed vars:
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broker_token = tick["instrument_token"]
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tick_lookup = instrument_lookup[broker_token]
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last_price = tick["last_price"]
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prev_close = tick["ohlc"]["close"]
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change = last_price - prev_close
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p_change = tick["change"]
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# Model the currently picked tick:
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modelled_ticks.append(
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TradingTick(
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symbol = tick_lookup["symbol"],
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name = tick_lookup["name"],
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exchange = tick_lookup["exchange"],
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exchangeToken = tick_lookup["exchangeToken"],
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broker = "zerodhaKite",
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brokerToken = broker_token,
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tradeable = tick["tradable"],
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segment = tick_lookup["segment"],
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type = tick_lookup["type"],
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strike = tick_lookup.get("strike"),
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expiryTs = tick_lookup.get("expiryTs"),
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expiryTz = tick_lookup.get("expiryTz"),
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prevClose = prev_close,
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ltp = last_price,
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qty = tick.get("last_traded_quantity"),
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chg = change,
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pChg = p_change,
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o = tick["ohlc"]["open"],
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h = tick["ohlc"]["high"],
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l = tick["ohlc"]["low"],
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c = last_price,
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totVol = tick.get("volume_traded"),
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vwap = tick.get("average_traded_price"),
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totBuyQty = tick.get("total_buy_quantity"),
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totSellQty = tick.get("total_sell_quantity"),
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oi = tick.get("oi"),
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oiDayHigh = tick.get("oi_day_high"),
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oiDayLow = tick.get("oi_day_low"),
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rcvdTs = received_ts,
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tradeTs = tick.get("last_trade_time"),
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tradeTz = "Asia/Kolkata",
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exchgTs = tick.get("exchange_timestamp"),
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exchgTz = "Asia/Kolkata",
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depth = tick.get("depth")
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)
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)
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# Done here:
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return modelled_ticks
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@staticmethod
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def time_setter(item):
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if not item.get("expiryTs"):
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return item
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# Try parsing the expiry timestamp
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expiry = date_time.parse_date_time(
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item["expiryTs"],
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timezone=date_time.TIMEZONE_UTC
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)
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# Handle case where parsing fails
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if expiry is None:
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return item # or log an error / raise exception
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# Convert to the timezone of the exchange:
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expiry = date_time.to_timezone(
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expiry,
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timezone=item["expiryTz"]
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)
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# Format for MariaDB:
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item["expiryTs"] = expiry.strftime("%Y-%m-%d")
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return item
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@staticmethod
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def redis_market_key(
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exchange=None,
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symbol=None,
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segment=None,
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expiry_date=None,
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strike=None
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):
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# variable for each -
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exchange = TradingTick.exchange if exchange is None else exchange
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symbol = TradingTick.symbol if symbol is None else symbol
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segment = TradingTick.segment if segment is None else segment
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expiry_date = TradingTick.time_setter(TradingTick.exchgTs) if expiry_date is None else expiry_date
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strike = TradingTick.strike if strike is None else strike
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redis_key = {"exchange":exchange, "symbol":symbol, "segment":segment, "expiryDate":expiry_date, "strike":strike}
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return redis_key
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# ┓┏ ┓• ┓ •
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# ┃┃┏┓┃┓┏┫┏┓╋┓┏┓┏┓
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# ┗┛┗┻┗┗┗┻┗┻┗┗┗┛┛┗
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@field_validator(
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"expiryTs",
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"tradeTs", "exchgTs",
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mode = "before"
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)
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def parse_date_time(cls, value):
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# If the input is null,
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# we can't do anything:
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if not value: value = None
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# When the input is a string, we try to parse it:
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elif isinstance(value, str):
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value = value.strip()
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value = date_time.parse_date_time(
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input_value = value,
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timezone = date_time.TIMEZONE_UTC,
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date_formats = [
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"%Y-%m-%d",
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"%Y-%m-%d %H:%M:%S",
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]
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)
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# When the input is a datetime obj.,
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# we only work on the timezone:
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elif isinstance(value, datetime.datetime):
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value = date_time.to_timezone(
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value,
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timezone = date_time.TIMEZONE_UTC
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)
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# Done here:
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return value
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@field_validator("rcvdTs", mode = "before")
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def validate_received_timestamp(cls, value):
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if value is None: value = date_time.get_current_utc_date_time(as_string = False)
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return value
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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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trade = TradingTick
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trade.redis_market_key_maker(
|
|
exchange="TATA",
|
|
segment="EQ"
|
|
)
|
|
|
|
|
|
# zerodha_tick_a = {
|
|
# "tradable": True,
|
|
# "mode": "full",
|
|
# "instrument_token": 408065,
|
|
# "last_price": 1928.25,
|
|
# "last_traded_quantity": 157,
|
|
# "average_traded_price": 1942.5,
|
|
# "volume_traded": 5596931,
|
|
# "total_buy_quantity": 217126,
|
|
# "total_sell_quantity": 380092,
|
|
# "ohlc": {
|
|
# "open": 1975.15,
|
|
# "high": 1979.95,
|
|
# "low": 1911.25,
|
|
# "close": 1946.2
|
|
# },
|
|
# "change": -0.9223101428424646,
|
|
# "last_trade_time": "2024-12-20 14:46:14",
|
|
# "oi": 0,
|
|
# "oi_day_high": 0,
|
|
# "oi_day_low": 0,
|
|
# "exchange_timestamp": "2024-12-20 14:46:15",
|
|
# "depth": {
|
|
# "buy": [
|
|
# {
|
|
# "quantity": 3,
|
|
# "price": 1928.15,
|
|
# "orders": 2
|
|
# },
|
|
# {
|
|
# "quantity": 6,
|
|
# "price": 1927.95,
|
|
# "orders": 2
|
|
# },
|
|
# {
|
|
# "quantity": 66,
|
|
# "price": 1927.7,
|
|
# "orders": 2
|
|
# },
|
|
# {
|
|
# "quantity": 25,
|
|
# "price": 1927.65,
|
|
# "orders": 1
|
|
# },
|
|
# {
|
|
# "quantity": 194,
|
|
# "price": 1927.6,
|
|
# "orders": 6
|
|
# }
|
|
# ],
|
|
# "sell": [
|
|
# {
|
|
# "quantity": 134,
|
|
# "price": 1928.25,
|
|
# "orders": 5
|
|
# },
|
|
# {
|
|
# "quantity": 3,
|
|
# "price": 1928.3,
|
|
# "orders": 1
|
|
# },
|
|
# {
|
|
# "quantity": 560,
|
|
# "price": 1928.35,
|
|
# "orders": 2
|
|
# },
|
|
# {
|
|
# "quantity": 64,
|
|
# "price": 1928.4,
|
|
# "orders": 2
|
|
# },
|
|
# {
|
|
# "quantity": 400,
|
|
# "price": 1928.45,
|
|
# "orders": 1
|
|
# }
|
|
# ]
|
|
# }
|
|
# }
|
|
# zerodha_tick_b = {
|
|
#
|
|
# }
|
|
# zerodha_lookup = {
|
|
# 408065: {
|
|
# "symbol": "MYSTOCK",
|
|
# "exchange": "NSE",
|
|
# "exchangeToken": 12345678,
|
|
# "segment": "NFO-OPT",
|
|
# "type": "CE",
|
|
# "expiryTs": "2024-12-20",
|
|
# "expiryTz": "Asia/Kolkata"
|
|
# }
|
|
# }
|
|
#
|
|
# my_ticks = TradingTick.from_zerodha_kite(
|
|
# ticks = [zerodha_tick_a],
|
|
# instrument_lookup = zerodha_lookup
|
|
# )
|
|
#
|
|
# print(json.to_string(my_ticks[0].model_dump(), default = str))
|
|
# print(json.to_string(my_ticks[0].summary, default = str))
|