Risk System Backend

Overview

This backend project powers a Risk Management System designed to fetch stock data, manage client positions, and monitor margin requirements in real-time. Built using FastAPI for rapid API development, it utilizes Tortoise ORM with PostgreSQL as the database, and integrates Yahoo Finance to fetch live stock prices. Additionally, the system uses OAuth2 authentication for secure access to the APIs.

Key Features

OAuth2 Authentication: Secure login and token-based authentication for clients, ensuring that only authorized users can access the system’s data.
Stock Data Fetching: Real-time stock data retrieval using Yahoo Finance API, storing and updating market prices regularly.
Client Position Management: Allows users to view and manage client stock positions, including the symbol, quantity, and cost basis.
Margin Requirement Monitoring: Calculates and monitors margin requirements for each client based on their portfolio, highlighting any margin shortfalls or triggering margin calls.

Setup Instructions

Step 1. Create virtual env

python -m venv venv

Step 2. Activate then install Dependencies

For macOS/Linux:

source venv/bin/activate

For Windows (Command Prompt):

venv\Scripts\Activate.ps1

Step 3. Install Dependencies

pip install -r requirements.txt

Step 4. Setup Database

Modify the DATABASE_URL in config.py to point to your PostgreSQL instance.

Step 5. Run main.py in virtual env

python3 main.py

Step 6. Run insert_data.py to create some clients data

python3 insert_data.py

API Endpoints

Fetch Real-time Stock Data

GET /stocks/{symbol}

Description: Retrieves the latest stock price from Yahoo Finance and stores it in the database.
Parameters:
- symbol (string): Stock ticker symbol (e.g., "AAPL").
Response:

{
  "symbol": "AAPL",
  "timestamp": "2024-03-28T10:30:00Z",
  "current_price": 175.50
}

Retrieve All Stored Stock Data

GET /stocks

Description: Fetches all stock data stored in the database.
Response: List of stock records.

Get Client Positions

GET /positions/{clientId}

Description: Retrieves all stock positions for a given client.
Parameters:
- clientId (integer): Unique identifier of the client.
Response:

{
  "clientId": 1,
  "positions": [
    {"symbol": "AAPL", "quantity": 100, "cost_basis": 150.0}
  ]
}

Get Margin Status for a Client

GET /margin/{clientId}

Description: Computes and retrieves margin status for a client.
Parameters:
- clientId (integer): Unique identifier of the client.
Response:

{
  "timestamp": "2024-03-28T10:30:00Z",
  "clientId": 1,
  "portfolio_value": 20000.0,
  "loan_amount": 10000.0,
  "net_equity": 10000.0,
  "margin_requirement": 5000.0,
  "margin_shortfall": 0.0,
  "margin_call_triggered": false
}

Database Models

1. Client

Stores client details.

class Client(Model):
    id = fields.IntField(pk=True)
    name = fields.CharField(max_length=255)

2. Position

Stores client stock holdings.

class Position(Model):
    id = fields.IntField(pk=True)
    symbol = fields.CharField(max_length=10)
    quantity = fields.IntField(null=True)
    cost_basis = fields.FloatField()
    client = fields.ForeignKeyField("models.Client", related_name="positions")

3. MarketData

Stores stock prices fetched from real-time market data API.

class MarketData(Model):
    id = fields.IntField(pk=True)
    symbol = fields.CharField(max_length=50)
    current_price = fields.FloatField()
    timestamp = fields.DatetimeField()

4. Margin

Stores margin requirements for clients. If we store all the historical margin requirement for clients, it is a big burden for database. So I choose to calculate portfolio values on the fly.

class Margin(Model):
    id = fields.IntField(pk=True)  # Auto-increment primary key
    client = fields.ForeignKeyField("models.Client", related_name="margins", on_delete=fields.CASCADE)
    margin_requirement = fields.FloatField()
    loan = fields.FloatField()
    timestamp = fields.DatetimeField(default=datetime.datetime.now)

Tech Stack

Overview of the tools that we use

Component	Description	URL
Backend Framework: FastAPI	A modern, fast (high-performance) web framework for building APIs with Python 3.7+. It's easy to use, based on standard Python type hints.	FastAPI Documentation
Realtime Trading API: Yahoo Finance	Provides access to stock market data, historical data, and financial news. It is used for fetching real-time market prices and historical stock information.	Yahoo Finance
Middleware: Tortoise ORM	An easy-to-use asyncio ORM (Object Relational Mapper) built for Python with support for asynchronous programming, commonly used with databases like PostgreSQL.	Tortoise ORM GitHub
Database: PostgreSQL 14.17	A powerful, open-source object-relational database system, known for its scalability, reliability, and extensibility.	PostgreSQL Official Site
Dependencies: Python 3.10.9 and `requirements.txt`	The version of Python used for the application along with a `requirements.txt` file to specify all necessary dependencies.	Python Official Site

Comparision of backend frameworks

Feature	FastAPI	Django	Flask	Tornado	Pyramid
Type	Asynchronous (ASGI)	Synchronous (WSGI)	Synchronous (WSGI)	Asynchronous (ASGI)	Synchronous (WSGI)
Performance	High	Moderate	Moderate	High	Moderate
Ease of Use	Easy to learn	Moderate (Full-Stack)	Very easy (Minimal)	Moderate (Async-based)	Moderate
Built-in Features	Minimal, Pydantic validation	Full-stack (ORM, Admin, Auth)	Minimal	WebSockets, Async I/O	Minimal
Asynchronous Support	Yes, built-in	Limited (Django 3+)	Limited (via extensions)	Yes, fully async	Limited
Use Case	APIs, Microservices	Full-stack applications	Lightweight applications	Real-time applications	Customizable web apps
ORM Support	Tortoise ORM, SQLAlchemy	Django ORM	SQLAlchemy, Flask-SQLAlchemy	Custom	SQLAlchemy, ZODB
WebSockets	Yes, built-in	No (via Channels)	No (via Flask-SocketIO)	Yes, built-in	No
Documentation	Excellent	Extensive	Good	Moderate	Moderate
Community Support	Growing	Very Large	Large	Small	Moderate

Code structure

risk-system-backend/
├── main.py                # Core application logic and API endpoints
├── auth.py                # Authentication-related APIs and utilities
├── models.py              # Database models
├── utils/
│   ├── logging/           # Logging utilities
│   ├── yfinance/          # Yahoo Finance utilities
├── config.py              # Configuration settings (e.g., DATABASE_URL, SECRET_KEY)
├── requirements.txt       # Python dependencies
└── README.md              # Documentation

Comparision of database

Feature	Oracle Database	Microsoft SQL Server	PostgreSQL	MySQL	MongoDB	Redis
Primary Use Case	Enterprise-level transactions and analytics	Enterprise-level transactions	Data warehousing, financial analysis	Transactional systems, analytics	NoSQL document-based storage	Caching, high-speed transactions
Data Model	Relational	Relational	Relational	Relational	Document-oriented	Key-value store
ACID Compliance	Yes	Yes	Yes	Yes	No	No
SQL Support	Full SQL support	Full SQL support	Full SQL support	Full SQL support	Limited SQL support	No
Transactions (ACID)	Yes	Yes	Yes	Yes	No	No
Horizontal Scaling	Yes (with Oracle RAC)	Yes (with Always On Availability Groups)	Yes (with Citus extension)	Yes (with Galera Cluster)	Yes (with sharding)	Yes (with clustering)
Vertical Scaling	Yes	Yes	Yes	Yes	No (sharding)	Yes (in-memory)
Query Performance	High (optimized for large-scale enterprise queries)	High (optimized for transactional workloads)	High (supports complex queries)	High (well-suited for web apps)	Low to moderate (document search)	Extremely fast for small, simple queries
Data Integrity	High (strong consistency)	High (strong consistency)	High (strong consistency)	High (strong consistency)	Low (eventual consistency)	Low (eventual consistency)
Data Replication	Yes (advanced replication options)	Yes (Always On)	Yes (synchronous and asynchronous)	Yes (master-slave replication)	Yes (replica sets)	Yes (master-slave replication)
Concurrency Control	Multi-version concurrency control (MVCC)	Lock-based concurrency	MVCC	Lock-based concurrency	Eventual consistency	Single-threaded
Licensing Model	Paid (Enterprise licenses)	Paid (Enterprise licenses)	Open-source, Paid options	Open-source, Paid options	Open-source, Paid options	Open-source, Paid options
Cost	High	High	Free	Free	Free	Free (paid for enterprise versions)
Cloud Deployment	Yes (Oracle Cloud, AWS, Azure, GCP)	Yes (Azure)	Yes (AWS, Azure, GCP)	Yes (AWS, Azure, GCP)	Yes (AWS, Azure, GCP)	Yes (AWS, Azure, GCP)
Industry Adoption	Widely adopted in banking, investment firms, insurance	Widely adopted in banking, corporate finance	Widely used in analytics, fintech, startups	Widely used in fintech, startups, web applications	Increasing in fintech for unstructured data	Popular for caching in high-performance environments

Comparision of real-time market data API

The pros and cons of popular market data APIs including Twelve Data, IEX Cloud, Alpha Vantage with a real-time plan, Yahoo Finance, Moomoo and IBKR

Tried IBKR, real time data is too expensive, charges by usage
Tried Moomoo, real time data for US stock is charged, but HK stock is free
Will try Alpha Vantage soon.

Feature/ Provider	Twelve Data	IEX Cloud	Alpha Vantage	Yahoo Finance	Moomoo	Interactive Brokers
Real-time Data	Yes	Yes (delayed)	Yes	Yes (delayed)	Yes	Yes
Granularity	1-minute	1-minute	1-minute	15-min delay	1-minute	1-minute
Data Coverage	Global	US only	Global	US only	US only	Global
API Rate Limit	5/min (free)	50,000/ month	5/min (free)	N/A	N/A	Varies
Historical Data	Yes	Yes (limited)	Yes	Yes	Yes	Yes
Premium Plan Cost	$29/ month	$9/month	$29.99/ month	Free	Free	Depends on equity and subsctiption fee
Additional Features	Crypto, Forex	Financials, News	Technical Indicators	Basic Data	Charting, Trading	Advanced Trading Tools

Comparision of authorization framework

OAuth2 → Best for API security (but lacks authentication).
OpenID Connect (OIDC) → Adds authentication to OAuth2 (good for logins).
SAML → Used for enterprise SSO (corporate environments).
JWT → Lightweight, stateless token-based auth (good for microservices & APIs).

Feature	OAuth2.0	OpenID Connect (OIDC)	SAML	JWT
Type	Authorization	Authentication + Authorization	Authentication + Authorization	Token-based Authorization
Token Type	Access Token	ID Token + Access Token	SAML Assertion	JWT (HS256/RS256)
Stateful?	Optional	Optional	Usually Stateful	Stateless
Use Case	API Security, Delegated Auth	SSO, API Security	Enterprise SSO	API Auth, Microservices
Third-Party Auth	✅ Yes	✅ Yes	✅ Yes	❌ No (Self-contained)
Requires Identity Provider?	✅ Yes	✅ Yes	✅ Yes	❌ No
Common Usage	Google Login, GitHub OAuth	Google Sign-In, Azure AD, Okta	Enterprise SSO (corporate logins)	API security, microservices
Best For	API authorization	Authentication + API Access	Enterprise SSO (large orgs)	Stateless API access

Contributing

Feel free to fork this repository, submit issues, or create pull requests. If you would like to contribute to the project, please follow the steps below:

License

This project is licensed under the MIT License - see the LICENSE file for details.

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Repository files navigation

Risk System Backend

Overview

Key Features

Setup Instructions

Step 1. Create virtual env

Step 2. Activate then install Dependencies

Step 3. Install Dependencies

Step 4. Setup Database

Step 5. Run main.py in virtual env

Step 6. Run insert_data.py to create some clients data

API Endpoints

Fetch Real-time Stock Data

Retrieve All Stored Stock Data

Get Client Positions

Get Margin Status for a Client

Database Models

1. Client

2. Position

3. MarketData

4. Margin

Tech Stack

Overview of the tools that we use

Comparision of backend frameworks

Code structure

Comparision of database

Comparision of real-time market data API

Comparision of authorization framework

Contributing

License

About

Releases

Packages

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Name		Name	Last commit message	Last commit date
Latest commit History 16 Commits
images		images
utils		utils
.gitignore		.gitignore
README.md		README.md
__init__.py		__init__.py
auth.py		auth.py
config.py		config.py
db_config.py		db_config.py
insert_data.py		insert_data.py
main.py		main.py
models.py		models.py
print_secret_key.py		print_secret_key.py
requirements.txt		requirements.txt

WideSu/risk-system-backend

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Risk System Backend

Overview

Key Features

Setup Instructions

Step 1. Create virtual env

Step 2. Activate then install Dependencies

Step 3. Install Dependencies

Step 4. Setup Database

Step 5. Run main.py in virtual env

Step 6. Run insert_data.py to create some clients data

API Endpoints

Fetch Real-time Stock Data

Retrieve All Stored Stock Data

Get Client Positions

Get Margin Status for a Client

Database Models

1. Client

2. Position

3. MarketData

4. Margin

Tech Stack

Overview of the tools that we use

Comparision of backend frameworks

Code structure

Comparision of database

Comparision of real-time market data API

Comparision of authorization framework

Contributing

License

About

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