1. Introduction
   1. Constraints
   2. Database
      1. Schema Model
2. Test
   1. Listing trades
   2. Single trade
   3. Searching trades
   4. Advanced filtering
   5. Bonus points
3. Submission
4. Resources

At SteelEye we are using FastAPI for our primary client-facing API layer. FastAPI's usage of Pydantic for expressing data types aligns quite well with how we model our own Schema models (using Pydantic).

In this exercise, you will be building a REST API with endpoints serving Trade data from a mocked database.

You are expected to write the API in Python using the FastAPI framework. You are not expected to have any previous FastAPI experience and unfamiliarty with FastAPI will not be held against you when reviewing your submission. However, it is expected that you will be able to produce a functional API.

At SteelEye we use Elasticsearch as our primary data storage technology. Setting up an instance of Elasticsearch with the necessary mappings and data required for the functionality in this test is outside of the scope of submission.

We do expect you to mock a database interaction layer in any way you see fit and generate data (which can be randomized) for the purposes of this test. The implementation of this database layer is left up to you.

We have provided a Pydantic model representing a single Trade below:

import datetime as dt

from typing import Optional
from pydantic import BaseModel, Field

class TradeDetails(BaseModel):
    buySellIndicator: str = Field(description="A value of BUY for buys, SELL for sells.")

    price: float = Field(description="The price of the Trade.")

    quantity: int = Field(description="The amount of units traded.")


class Trade(BaseModel):
    asset_class: Optional[str] = Field(alias="assetClass", default=None, description="The asset class of the instrument traded. E.g. Bond, Equity, FX...etc")

    counterparty: Optional[str] = Field(default=None, description="The counterparty the trade was executed with. May not always be available")

    instrument_id: str = Field(alias="instrumentId", description="The ISIN/ID of the instrument traded. E.g. TSLA, AAPL, AMZN...etc")

    instrument_name: str = Field(alias="instrumentName", description="The name of the instrument traded.")

    trade_date_time: dt.datetime = Field(alias="tradeDateTime", description="The date-time the Trade was executed")

    trade_details: TradeDetails = Field(alias="tradeDetails", description="The details of the trade, i.e. price, quantity")

    trade_id: str = Field(alias="tradeId", default=None, description="The unique ID of the trade")

    trader: str = Field(description="The name of the Trader")

This tests represents a common request when building an API. You need to provide a set of endpoints for retrieving a list of Trades, retrieving a single Trade by ID, searching against Trades, and filtering Trades

Please provide an endpoint to fetch a list of trades.

Users would like to be able to retrieve a single trade from the API. Please provide an endpoint to fetch a trade by id.

Users would now like to be able to search across the trades using the API. Your endpoint for fetching a list of trades will need to support searching for trades through the following fields:

• counterparty
• instrumentId
• instrumentName
• trader

If a user was to call your endpoint and provide a ?search=bob%20smith query parameter, your endpoint will return any trades where the text bob smith exists in any of the fields listed above.

The users would now like the ability to filter trades. Your endpoint for fetching a list of trades will need to support filtering using the following optional query parameters:

All maximum and minimum fields are inclusive (e.g. minPrice=2&maxPrice=10 will return 2 <= tradeDetails.price <= 10).

Implement support for pagination and sorting on the list of trades.

You can return your solution via email, or provide access to a Git repo. Please include a document describing your solution and the reasoning behind your approach. Incomplete solutions will not be considered.

• FastAPI: https://fastapi.tiangolo.com/
• Pydantic: https://pydantic-docs.helpmanual.io/

1. Python
2. FastAPI
3. Uvicorn
4. ElasticSearch
5. Render

1. Go to main.py file
2. Run the following command :



uvicorn main:app --reload  

            or

python -m uvicorn main:app --reload