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An Unofficial Library Containing JSON Schema and Data Class / Structs / Class Object Code For Serializing And Deserializing The Kite Connect Trading Apis JSON Responses Into The Language Of User Choice.

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KiteConnect Data Structures

An Unofficial Library Containing JSON Schema and Data Class / Structs / Class Object Code For Serializing And Deserializing The Kite Connect Trading Apis JSON Responses Into The Language Of User Choice.

Why Unofficial ?

The Pull Request wasn't merged due to scope mismatch and it was advised by kiteconnect-mocks team to maintain this library as a seperate repository.

What Is The Purpose Of This Library ?

Having a JSON schema from kiteconnect-mocks sample .json files, is a standard way to generate Data Class / Structs / Class Object code for serializing and deserializing the kiteconnect responses .json into the language of user choice.

In Context of the same, have generated and added the JSON schema from kiteconnect-mocks sample .json files and subsequently from the Json Schema, generated, Some Languages Data Class / Structs / Class Object code for serializing and deserializing the kiteconnect responses .json.

Example for Python (gist)

# To use this code, make sure you
#
#     import json
#
# and then, to convert JSON from a string, do
#
#     result = ltp_from_dict(json.loads(json_string))

from dataclasses import dataclass
from typing import Optional, Any, Dict, TypeVar, Callable, Type, cast


T = TypeVar("T")


def from_int(x: Any) -> int:
    assert isinstance(x, int) and not isinstance(x, bool)
    return x


def from_none(x: Any) -> Any:
    assert x is None
    return x


def from_union(fs, x):
    for f in fs:
        try:
            return f(x)
        except:
            pass
    assert False


def from_float(x: Any) -> float:
    assert isinstance(x, (float, int)) and not isinstance(x, bool)
    return float(x)


def to_float(x: Any) -> float:
    assert isinstance(x, float)
    return x


def from_dict(f: Callable[[Any], T], x: Any) -> Dict[str, T]:
    assert isinstance(x, dict)
    return { k: f(v) for (k, v) in x.items() }


def from_str(x: Any) -> str:
    assert isinstance(x, str)
    return x


def to_class(c: Type[T], x: Any) -> dict:
    assert isinstance(x, c)
    return cast(Any, x).to_dict()


@dataclass(slots=True)
class Datum:
    instrument_token: Optional[int] = None
    last_price: Optional[float] = None

    @staticmethod
    def from_dict(obj: Any) -> 'Datum':
        assert isinstance(obj, dict)
        instrument_token = from_union([from_int, from_none], obj.get("instrument_token"))
        last_price = from_union([from_float, from_none], obj.get("last_price"))
        return Datum(instrument_token, last_price)

    def to_dict(self) -> dict:
        result: dict = {}
        result["instrument_token"] = from_union([from_int, from_none], self.instrument_token)
        result["last_price"] = from_union([to_float, from_none], self.last_price)
        return result


@dataclass(slots=True)
class Ltp:
    data: Optional[Dict[str, Datum]] = None
    status: Optional[str] = None

    @staticmethod
    def from_dict(obj: Any) -> 'Ltp':
        assert isinstance(obj, dict)
        data = from_union([lambda x: from_dict(Datum.from_dict, x), from_none], obj.get("data"))
        status = from_union([from_str, from_none], obj.get("status"))
        return Ltp(data, status)

    def to_dict(self) -> dict:
        result: dict = {}
        result["data"] = from_union([lambda x: from_dict(lambda x: to_class(Datum, x), x), from_none], self.data)
        result["status"] = from_union([from_str, from_none], self.status)
        return result


def ltp_from_dict(s: Any) -> Ltp:
    return Ltp.from_dict(s)


def ltp_to_dict(x: Ltp) -> Any:
    return to_class(Ltp, x)

Example for Rust:

// Example code that deserializes and serializes the model.
// extern crate serde;
// #[macro_use]
// extern crate serde_derive;
// extern crate serde_json;
//
// use generated_module::[object Object];
//
// fn main() {
//     let json = r#"{"answer": 42}"#;
//     let model: [object Object] = serde_json::from_str(&json).unwrap();
// }

extern crate serde_derive;

#[derive(Debug, Serialize, Deserialize)]
pub struct Profile {
    pub data: Option<Data>,
    pub status: Option<String>,
}

#[derive(Debug, Serialize, Deserialize)]
pub struct Data {
    pub avatar_url: Option<serde_json::Value>,
    pub broker: Option<String>,
    pub email: Option<String>,
    pub exchanges: Option<Vec<String>>,
    pub meta: Option<Meta>,
    pub order_types: Option<Vec<String>>,
    pub products: Option<Vec<String>>,
    pub user_id: Option<String>,
    pub user_name: Option<String>,
    pub user_shortname: Option<String>,
    pub user_type: Option<String>,
}

#[derive(Debug, Serialize, Deserialize)]
pub struct Meta {
    pub demat_consent: Option<String>,
}

Visualizing Kite Connect API Responses JSON Data As Graphs

Graph of profile.json profile

Grpah of generate.session.json generate_session

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An Unofficial Library Containing JSON Schema and Data Class / Structs / Class Object Code For Serializing And Deserializing The Kite Connect Trading Apis JSON Responses Into The Language Of User Choice.

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