Versatile Enterprise Risk Gateway Entry System for equities, options and futures, FIX based, pre-trade
A scalable, extensible, and cloud-ready QuickFIX-based risk management system built in Python. Designed to handle equities, options, and futures trading with robust pre-trade and post-trade risk checks. The system integrates with PostgreSQL for data persistence and Polygon.io for market data.
Named after a character in "Much Ado About Nothing" by William Shakespeare, Verges always appears alongside his boss Dogberry. They are both constables who lead the night watch. They are well-intentioned but bumbling and they often make mistakes.
- Features
- System Architecture
- Getting Started
- Configuration
- Running the Application
- Project Structure
- Database Schemas
- Risk Management
- Market Data Integration
- Deployment
- Contributing
- License
- Acknowledgments
- Contact
- Multi-Asset Support: Handles equities, options, and futures trading.
- Risk Management: Implements pre-trade risk checks for margin requirements, notional limits, volume limits, and more.
- Extensible Plugin Architecture: Easily add new risk checks and features using a plugin system.
- Real-Time Market Data: Integrates with Polygon.io for up-to-date market prices.
- Database Integration: Uses PostgreSQL for data storage and retrieval.
- Scalable and Cloud-Ready: Designed to run efficiently in the cloud using Docker.
- User Management: Supports user authentication and authorization with role-based access control.
- Audit Logging: Tracks changes and actions for compliance and auditing.
Note: Include a system architecture diagram in the docs directory.
The system consists of the following key components:
- FIX Engine Module: Manages FIX protocol sessions using QuickFIX for Python.
- Risk Management Module: Performs risk checks before orders are accepted.
- Order Management System (OMS): Tracks orders, executions, and positions.
- Market Data Module: Fetches real-time market data from Polygon.io.
- Data Management Module: Handles data persistence using PostgreSQL.
- Configuration Management: Manages system configurations dynamically.
- Plugin System: Allows dynamic loading and reloading of risk management plugins.
- Python 3.8 or higher
- PostgreSQL 13 or higher
- Docker (optional, for containerization)
- Polygon.io API Key: Sign up at Polygon.io to get a free API key.
-
Clone the Repository
git clone https://github.com/yourusername/trading_system.git cd trading_system -
Create a Virtual Environment
python -m venv venv source venv/bin/activate # On Windows use `venv\Scripts\activate`
-
Install Dependencies
pip install -r requirements.txt
-
Set Up PostgreSQL Database
-
Create the Database: Create a PostgreSQL database named
trading_system. -
Update Database Configuration: Edit the
config/database.inifile with your database credentials.# config/database.ini [postgresql] host=localhost port=5432 database=trading_system user=your_username password=your_password
-
-
Initialize the Database
-
Run the initialization script to create tables and set up the database schema.
python scripts/initialize_db.py
-
Configure the Application
-
Update Configurations: Edit the
config/config.ymlfile to update application settings. -
Set API Key: Replace
YOUR_POLYGON_IO_API_KEYwith your actual Polygon.io API key.# config/config.yml market_data: api_key: YOUR_POLYGON_IO_API_KEY
-
The QuickFIX settings are defined in config/quickfix.cfg. Adjust the settings according to your environment and requirements.
# config/quickfix.cfg
[DEFAULT]
ConnectionType=acceptor
BeginString=FIX.4.4
SenderCompID=YOUR_SENDER_COMP_ID
FileStorePath=store
FileLogPath=logs
HeartBtInt=30
UseDataDictionary=Y
DataDictionary=path/to/FIX44.xml
[SESSION]
# Additional session settingsRisk management plugins are specified in config/config.yml under the risk_management section.
risk_management:
plugins:
- credit_limit
- margin_check
- notional_limit
- volume_limit
- trading_modeActivate your virtual environment and run the application.
source venv/bin/activate # On Windows use `venv\Scripts\activate`
python src/application.pydocker build -t trading_system .docker run -d -p 9876:9876 --name trading_system trading_systemIf you prefer to run the application along with PostgreSQL using Docker Compose:
Ensure you have the docker-compose.yml file in the root directory.
version: '3.8'
services:
db:
image: postgres:13
environment:
POSTGRES_USER: your_username
POSTGRES_PASSWORD: your_password
POSTGRES_DB: trading_system
volumes:
- db_data:/var/lib/postgresql/data
ports:
- "5432:5432"
app:
build: .
depends_on:
- db
ports:
- "9876:9876"
volumes:
- .:/app
environment:
- DATABASE_HOST=db
- DATABASE_PORT=5432
- DATABASE_USER=your_username
- DATABASE_PASSWORD=your_password
- DATABASE_NAME=trading_system
volumes:
db_data:
docker-compose up -dtrading_system/
├── config/
│ ├── config.yml
│ ├── database.ini
│ └── quickfix.cfg
├── data/
├── docs/
│ └── system_architecture.png
├── logs/
├── risk_plugins/
│ ├── __init__.py
│ ├── base.py
│ ├── credit_limit.py
│ ├── margin_check.py
│ ├── notional_limit.py
│ ├── trading_mode.py
│ └── volume_limit.py
├── market_data/
│ ├── __init__.py
│ └── polygon_io.py
├── scripts/
│ ├── initialize_db.py
│ ├── start.sh
│ ├── restart.sh
│ └── daily_tasks.sh
├── src/
│ ├── __init__.py
│ ├── application.py
│ ├── fix_engine.py
│ ├── risk_management.py
│ ├── order_manager.py
│ ├── database.py
│ └── utils.py
├── Dockerfile
├── docker-compose.yml
├── requirements.txt
└── VERGES_README.mdThe database schemas are defined in scripts/initialize_db.py and include tables for:
- Users and Roles
- Clients and Accounts
- Orders and Executions
- Positions
- Risk Limits and Parameters
- Instruments and Market Data
- Audit Logs
To set up the database, run:
python scripts/initialize_db.pyBelow are the SQL scripts to create the necessary tables.
-- Users Table
CREATE TABLE users (
user_id SERIAL PRIMARY KEY,
username VARCHAR(50) UNIQUE NOT NULL,
password_hash VARCHAR(255) NOT NULL,
full_name VARCHAR(100),
email VARCHAR(100),
is_active BOOLEAN DEFAULT TRUE,
last_login TIMESTAMP,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);
-- Roles Table
CREATE TABLE roles (
role_id SERIAL PRIMARY KEY,
role_name VARCHAR(50) UNIQUE NOT NULL
);
-- User Roles Association Table
CREATE TABLE user_roles (
user_id INTEGER REFERENCES users(user_id) ON DELETE CASCADE,
role_id INTEGER REFERENCES roles(role_id) ON DELETE CASCADE,
PRIMARY KEY (user_id, role_id)
);
-- Clients Table
CREATE TABLE clients (
client_id SERIAL PRIMARY KEY,
client_name VARCHAR(100) NOT NULL,
status VARCHAR(20) CHECK (status IN ('ACTIVE', 'INACTIVE')),
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);
-- Accounts Table
CREATE TABLE accounts (
account_id SERIAL PRIMARY KEY,
client_id INTEGER REFERENCES clients(client_id) ON DELETE CASCADE,
account_number VARCHAR(50) UNIQUE NOT NULL,
account_type VARCHAR(20) CHECK (account_type IN ('CASH', 'MARGIN', 'PORTFOLIO_MARGIN')),
status VARCHAR(20) CHECK (status IN ('ACTIVE', 'INACTIVE')),
cash_balance DECIMAL(20,5) DEFAULT 0.0,
margin_balance DECIMAL(20,5) DEFAULT 0.0,
equity DECIMAL(20,5) DEFAULT 0.0,
available_funds DECIMAL(20,5) DEFAULT 0.0,
maintenance_margin DECIMAL(20,5) DEFAULT 0.0,
trading_mode VARCHAR(20) DEFAULT 'NORMAL' CHECK (trading_mode IN ('NORMAL', 'CLOSING_ONLY', 'RISK_REDUCE')),
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);
-- Risk Limits Table
CREATE TABLE risk_limits (
limit_id SERIAL PRIMARY KEY,
account_id INTEGER REFERENCES accounts(account_id),
max_position_value DECIMAL(20,5),
max_order_value DECIMAL(20,5),
max_daily_volume INTEGER,
max_order_volume INTEGER,
last_updated TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
updated_by VARCHAR(50)
);
-- Risk Parameters Table
CREATE TABLE risk_parameters (
ticker VARCHAR(10) PRIMARY KEY,
margin_requirement DECIMAL(5,2),
volatility DECIMAL(10,5),
beta DECIMAL(10,5),
var DECIMAL(10,5),
is_margin_overridden BOOLEAN DEFAULT FALSE,
overridden_margin DECIMAL(5,2),
is_volatility_overridden BOOLEAN DEFAULT FALSE,
overridden_volatility DECIMAL(10,5),
last_updated TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
updated_by VARCHAR(50)
);
-- Margin Requirements Table
CREATE TABLE margin_requirements (
ticker VARCHAR(10) PRIMARY KEY,
initial_margin_rate DECIMAL(5,4),
maintenance_margin_rate DECIMAL(5,4)
);
-- Borrow Status Table
CREATE TABLE borrow_status (
ticker VARCHAR(10) PRIMARY KEY,
borrow_status VARCHAR(3) CHECK (borrow_status IN ('ETB', 'HTB')),
last_updated TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
updated_by VARCHAR(50)
);
-- Tradable Tickers Table
CREATE TABLE tradable_tickers (
ticker VARCHAR(10) PRIMARY KEY,
is_tradable BOOLEAN DEFAULT TRUE,
reason TEXT,
last_updated TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
updated_by VARCHAR(50)
);
-- Instruments Table
CREATE TABLE instruments (
ticker VARCHAR(10) PRIMARY KEY,
instrument_type VARCHAR(20) CHECK (instrument_type IN ('EQUITY', 'OPTION', 'FUTURE')),
underlying_ticker VARCHAR(10),
expiration_date DATE,
strike_price DECIMAL(15,5),
option_type VARCHAR(4) CHECK (option_type IN ('CALL', 'PUT')),
contract_size INTEGER,
exchange VARCHAR(50),
currency VARCHAR(10) DEFAULT 'USD'
);
-- Positions Table
CREATE TABLE positions (
position_id SERIAL PRIMARY KEY,
account_id INTEGER REFERENCES accounts(account_id),
ticker VARCHAR(10) NOT NULL,
quantity INTEGER NOT NULL,
average_price DECIMAL(15,5),
market_value DECIMAL(20,5),
initial_margin_requirement DECIMAL(20,5),
maintenance_margin_requirement DECIMAL(20,5),
realized_pnl DECIMAL(20,5) DEFAULT 0,
unrealized_pnl DECIMAL(20,5) DEFAULT 0,
last_updated TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);
-- Orders Table
CREATE TABLE orders (
order_id SERIAL PRIMARY KEY,
client_order_id VARCHAR(50) UNIQUE NOT NULL,
account_id INTEGER REFERENCES accounts(account_id),
ticker VARCHAR(10) NOT NULL,
side VARCHAR(4) CHECK (side IN ('BUY', 'SELL')),
order_type VARCHAR(20) CHECK (order_type IN ('MARKET', 'LIMIT', 'STOP', 'STOP_LIMIT', 'SPREAD', 'COMPLEX')),
quantity INTEGER NOT NULL CHECK (quantity > 0),
price DECIMAL(15,5),
stop_price DECIMAL(15,5),
status VARCHAR(20) CHECK (status IN ('NEW', 'PARTIALLY_FILLED', 'FILLED', 'CANCELED', 'REJECTED')),
asset_class VARCHAR(20) CHECK (asset_class IN ('EQUITY', 'OPTION', 'FUTURE')),
received_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
last_updated TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
updated_by VARCHAR(50)
);
-- Executions Table
CREATE TABLE executions (
execution_id SERIAL PRIMARY KEY,
order_id INTEGER REFERENCES orders(order_id) ON DELETE CASCADE,
execution_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
quantity INTEGER NOT NULL CHECK (quantity > 0),
price DECIMAL(15,5) NOT NULL,
execution_type VARCHAR(20) CHECK (execution_type IN ('FILL', 'PARTIAL_FILL')),
exchange VARCHAR(50),
trade_id VARCHAR(50),
commission DECIMAL(10,5),
net_amount DECIMAL(15,5),
updated_by VARCHAR(50)
);
-- Audit Logs Table
CREATE TABLE audit_logs (
audit_id SERIAL PRIMARY KEY,
user_id INTEGER REFERENCES users(user_id),
action VARCHAR(50) NOT NULL,
table_name VARCHAR(50) NOT NULL,
record_id VARCHAR(50),
changed_data JSONB,
timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);
-- Configurations Table
CREATE TABLE configurations (
config_key VARCHAR(50) PRIMARY KEY,
config_value TEXT,
last_updated TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
updated_by VARCHAR(50)
);The risk management system uses a plugin architecture, allowing for easy addition and modification of risk checks. Current plugins include:
- Credit Limit Check
- Margin Requirement Check
- Notional Value Limit Check
- Volume Limit Check
- Trading Mode Check
Plugins are located in the risk_plugins directory. Each plugin inherits from the RiskPlugin base class and implements the check method.
-
Create a New Plugin File
touch risk_plugins/new_risk_check.py
-
Implement the Plugin
# risk_plugins/new_risk_check.py
from .base import RiskPlugin
class NewRiskCheck(RiskPlugin):
def check(self, order, account):
# Implement the new risk check logic
return True, ""- Add the Plugin to Configuration
# config/config.yml
risk_management:
plugins:
- new_risk_check- Reload Plugins
The system monitors plugins and reloads them automatically. Alternatively, restart the application.
Thought for 22 seconds
Certainly! Here's the markdown for the entire Risk Management section. You can copy and paste this into your .md file.
markdown Copy code
The risk management system uses a plugin architecture, allowing for easy addition and modification of risk checks. Current plugins include:
- Credit Limit Check
- Margin Requirement Check
- Notional Value Limit Check
- Volume Limit Check
- Trading Mode Check
Plugins are located in the risk_plugins directory. Each plugin inherits from the RiskPlugin base class and implements the check method.
-
Create a New Plugin File
touch risk_plugins/new_risk_check.py
-
Implement the Plugin
# risk_plugins/new_risk_check.py
from .base import RiskPlugin
class NewRiskCheck(RiskPlugin):
def check(self, order, account):
# Implement the new risk check logic
return True, ""- Add the Plugin to Configuration
Copy code
# config/config.yml
risk_management:
plugins:
- new_risk_check- Reload Plugins
The system monitors plugins and reloads them automatically. Alternatively, restart the application.
Risk Plugin Structure
Each risk plugin should inherit from the RiskPlugin base class and implement the check method, which takes an order and an account as parameters and returns a tuple (result, message).
result: A boolean indicating whether the order passes the risk check.message: An optional string providing information about the risk check result.
Example base class:
# risk_plugins/base.py
class RiskPlugin:
def __init__(self, database):
self.database = database
def check(self, order, account):
raise NotImplementedError("Risk plugins must implement the 'check' method.")Ensures that the order does not exceed the account's credit limits.
# risk_plugins/credit_limit.py
from .base import RiskPlugin
class CreditLimitCheck(RiskPlugin):
def check(self, order, account):
# Implement credit limit logic
return True, ""Verifies that the account has sufficient margin for the order.
# risk_plugins/margin_check.py
from .base import RiskPlugin
class MarginCheck(RiskPlugin):
def check(self, order, account):
# Implement margin requirement checks
return True, ""Ensures the order quantity does not exceed volume limits.
# risk_plugins/volume_limit.py
from .base import RiskPlugin
class VolumeLimitCheck(RiskPlugin):
def check(self, order, account):
# Implement volume limit checks
return True, ""Checks if the account's trading mode allows the order.
# risk_plugins/trading_mode.py
from .base import RiskPlugin
class TradingModeCheck(RiskPlugin):
def check(self, order, account):
# Implement trading mode checks
return True, ""The risk management module loads and manages the risk plugins.
# src/risk_management.py
import importlib
import threading
import logging
class RiskManagement:
def __init__(self, config, database):
self.config = config
self.database = database
self.plugins = {}
self.load_plugins()
threading.Thread(target=self.monitor_plugins).start()
def load_plugins(self):
plugin_names = self.config['plugins']
for name in plugin_names:
module = importlib.import_module(f"risk_plugins.{name}")
plugin_class = getattr(module, ''.join([part.title() for part in name.split('_')]) + "Check")
self.plugins[name] = plugin_class(self.database)
logging.info(f"Risk plugins loaded: {list(self.plugins.keys())}")
def reload_plugins(self):
self.load_plugins()
logging.info("Risk plugins reloaded.")
def check_order(self, order, account):
for plugin in self.plugins.values():
result, message = plugin.check(order, account)
if not result:
return False, message
return True, ""
def monitor_plugins(self):
import time
while True:
# Implement logic to detect changes in plugin files
time.sleep(60) # Check every minuteThought for 22 seconds
Certainly! Here's the markdown for the entire Risk Management section. You can copy and paste this into your .md file.
markdown Copy code
The risk management system uses a plugin architecture, allowing for easy addition and modification of risk checks. Current plugins include:
- Credit Limit Check
- Margin Requirement Check
- Notional Value Limit Check
- Volume Limit Check
- Trading Mode Check
Plugins are located in the risk_plugins directory. Each plugin inherits from the RiskPlugin base class and implements the check method.
-
Create a New Plugin File
touch risk_plugins/new_risk_check.py
# risk_plugins/new_risk_check.py
from .base import RiskPlugin
class NewRiskCheck(RiskPlugin):
def check(self, order, account):
# Implement the new risk check logic
return True, ""# config/config.yml
risk_management:
plugins:
- new_risk_checkThe system monitors plugins and reloads them automatically. Alternatively, restart the application.
Each risk plugin should inherit from the RiskPlugin base class and implement the check method, which takes an order and an account as parameters and returns a tuple (result, message).
result: A boolean indicating whether the order passes the risk check. message: An optional string providing information about the risk check result. Example base class:
# risk_plugins/base.py
class RiskPlugin:
def __init__(self, database):
self.database = database
def check(self, order, account):
raise NotImplementedError("Risk plugins must implement the 'check' method.")Ensures that the order does not exceed the account's credit limits.
# risk_plugins/credit_limit.py
from .base import RiskPlugin
class CreditLimitCheck(RiskPlugin):
def check(self, order, account):
# Implement credit limit logic
return True, ""Verifies that the account has sufficient margin for the order.
# risk_plugins/margin_check.py
from .base import RiskPlugin
class MarginCheck(RiskPlugin):
def check(self, order, account):
# Implement margin requirement checks
return True, ""Checks if the notional value of the order exceeds predefined limits.
# risk_plugins/notional_limit.py
from .base import RiskPlugin
class NotionalLimitCheck(RiskPlugin):
def check(self, order, account):
# Implement notional value checks
return True, ""Ensures the order quantity does not exceed volume limits.
# risk_plugins/volume_limit.py
from .base import RiskPlugin
class VolumeLimitCheck(RiskPlugin):
def check(self, order, account):
# Implement volume limit checks
return True, ""Checks if the account's trading mode allows the order.
# risk_plugins/trading_mode.py
from .base import RiskPlugin
class TradingModeCheck(RiskPlugin):
def check(self, order, account):
# Implement trading mode checks
return True, ""The risk management module loads and manages the risk plugins.
# src/risk_management.py
import importlib
import threading
import logging
class RiskManagement:
def __init__(self, config, database):
self.config = config
self.database = database
self.plugins = {}
self.load_plugins()
threading.Thread(target=self.monitor_plugins).start()
def load_plugins(self):
plugin_names = self.config['plugins']
for name in plugin_names:
module = importlib.import_module(f"risk_plugins.{name}")
plugin_class = getattr(module, ''.join([part.title() for part in name.split('_')]) + "Check")
self.plugins[name] = plugin_class(self.database)
logging.info(f"Risk plugins loaded: {list(self.plugins.keys())}")
def reload_plugins(self):
self.load_plugins()
logging.info("Risk plugins reloaded.")
def check_order(self, order, account):
for plugin in self.plugins.values():
result, message = plugin.check(order, account)
if not result:
return False, message
return True, ""
def monitor_plugins(self):
import time
while True:
# Implement logic to detect changes in plugin files
time.sleep(60) # Check every minuteThe main application integrates the risk management module to perform risk checks before processing orders.
The market_data module integrates with Polygon.io to fetch real-time market data.
- API Key: Obtain an API key from Polygon.io.
- Usage: The
PolygonIOclass inmarket_data/polygon_io.pyprovides methods to fetch market prices.
# market_data/polygon_io.py
import requests
import logging
class PolygonIO:
def __init__(self, api_key):
self.api_key = api_key
self.base_url = 'https://api.polygon.io'
def get_last_trade(self, ticker):
url = f"{self.base_url}/v2/last/trade/{ticker}"
params = {'apiKey': self.api_key}
response = requests.get(url, params=params)
if response.status_code == 200:
data = response.json()
return data['results']['price']
else:
logging.error(f"Polygon.io API error: {response.text}")
return None
# Additional methods to fetch market data...In the main application, you can use the PolygonIO class to fetch market data for risk checks and order processing.
# src/application.py
# ... [other imports]
from market_data.polygon_io import PolygonIO
class TradingApplication:
def __init__(self, config_file):
# ... [other initializations]
self.market_data = PolygonIO(self.config['market_data']['api_key'])
# ... [rest of the code]
# Example method using market data
def get_market_price(self, ticker):
price = self.market_data.get_last_trade(ticker)
return price
# ... [rest of the code]- Error Handling: Ensure to handle exceptions and API errors when fetching market data.
- Rate Limits: Be mindful of API rate limits imposed by Polygon.io.
- Data Caching: Implement caching mechanisms if necessary to reduce API calls.
You can test the market data module independently.
# test_market_data.py
from market_data.polygon_io import PolygonIO
def test_get_last_trade():
api_key = 'YOUR_POLYGON_IO_API_KEY'
polygon = PolygonIO(api_key)
ticker = 'AAPL'
price = polygon.get_last_trade(ticker)
if price:
print(f"Last trade price for {ticker}: {price}")
else:
print("Failed to fetch market data.")
if __name__ == '__main__':
test_get_last_trade()The application is cloud-ready and can be deployed using Docker containers.
- AWS: Use ECS or EKS to deploy the Docker container.
- Azure: Use Container Instances or AKS.
- Google Cloud: Use GKE or Cloud Run.
-
Build the Docker Image
docker build -t trading_system . -
Push the Docker Image to a Registry
-
Docker Hub:
docker tag trading_system your_dockerhub_username/trading_system:latest docker push your_dockerhub_username/trading_system:latest
-
AWS ECR:
aws ecr create-repository --repository-name trading_system aws ecr get-login-password --region your_region | docker login --username AWS --password-stdin your_aws_account_id.dkr.ecr.your_region.amazonaws.com docker tag trading_system your_aws_account_id.dkr.ecr.your_region.amazonaws.com/trading_system:latest docker push your_aws_account_id.dkr.ecr.your_region.amazonaws.com/trading_system:latest
-
-
Set Up Cloud Environment
-
AWS ECS:
- Create an ECS Cluster: Use the AWS Management Console or AWS CLI.
- Define a Task Definition: Specify your Docker image and configure container settings.
- Create a Service: Link the task definition to the cluster and configure scaling.
- Configure Networking: Set up load balancers, security groups, and VPC settings.
-
Azure Container Instances:
-
Deploy Using Azure CLI:
az container create \ --resource-group your_resource_group \ --name trading-system \ --image your_dockerhub_username/trading_system:latest \ --ports 9876 \ --environment-variables DATABASE_HOST=db_host DATABASE_USER=db_user DATABASE_PASSWORD=db_pass \ --cpu 2 \ --memory 4
-
-
Google Cloud Run:
-
Deploy Using gcloud CLI:
gcloud run deploy trading-system \ --image your_dockerhub_username/trading_system:latest \ --platform managed \ --port 9876 \ --allow-unauthenticated \ --set-env-vars DATABASE_HOST=db_host,DATABASE_USER=db_user,DATABASE_PASSWORD=db_pass \ --memory 2Gi
-
-
-
Environment Variables: Use environment variables or secrets management services to handle sensitive information like database credentials and API keys.
-
Secure Network Communication: Ensure all communications, especially FIX protocol connections, are secured using SSL/TLS encryption.
-
Access Control: Implement proper authentication and authorization mechanisms. Use cloud provider security features like IAM roles, security groups, and network policies.
-
Firewall Settings: Configure firewalls to restrict access to necessary ports only.
-
Horizontal Scaling: Use container orchestration platforms to scale out instances based on load metrics.
- AWS: Use ECS Service Auto Scaling.
- Azure: Use Virtual Machine Scale Sets or AKS scaling features.
- Google Cloud: Use GKE cluster autoscaler.
-
Load Balancers: Set up load balancers to distribute incoming traffic evenly across instances.
- AWS: Use Application Load Balancer (ALB) or Network Load Balancer (NLB).
- Azure: Use Azure Load Balancer or Application Gateway.
- Google Cloud: Use Cloud Load Balancing.
-
Monitoring Tools:
- AWS CloudWatch: Monitor resources and applications.
- Azure Monitor: Collect, analyze, and act on telemetry data.
- Google Cloud Monitoring: Gain insights into your applications and infrastructure.
-
Logging: Centralize logs using services like:
- AWS CloudWatch Logs
- Azure Log Analytics
- Google Cloud Logging
-
Alerting: Set up alerts for critical metrics and events.
-
Database Backups:
- Schedule regular backups of your PostgreSQL database.
- Use cloud-native database services with automated backup features (e.g., AWS RDS, Azure Database for PostgreSQL).
-
Disaster Recovery Plan:
- Define Recovery Time Objectives (RTO) and Recovery Point Objectives (RPO).
- Implement multi-region deployments if necessary.
- Regularly test your disaster recovery procedures.
-
Set Up CI/CD Pipelines:
- GitHub Actions: Automate build, test, and deployment workflows.
- Jenkins: Use for complex CI/CD pipelines.
- AWS CodePipeline, Azure DevOps, Google Cloud Build: Cloud-native CI/CD services.
-
Automate Testing:
- Implement unit tests, integration tests, and end-to-end tests.
- Use testing frameworks like pytest for Python.
-
Automate Deployment:
- Deploy to staging environments before production.
- Use infrastructure as code tools like Terraform or CloudFormation.
-
Compliance Standards:
- Ensure the system complies with relevant industry standards (e.g., PCI DSS, GDPR, FINRA).
- Use cloud services that are compliant with these standards.
-
Audit Trails:
- Maintain detailed logs for all transactions and system changes.
- Use immutable storage for logs to prevent tampering.
-
Access Auditing:
- Monitor and audit user access to systems and data.
- Use cloud provider tools for identity and access management.
-
Resource Optimization:
- Use appropriate instance sizes.
- Implement auto-scaling to adjust resources based on demand.
-
Cost Monitoring:
- Use cloud provider cost management tools.
- Set up budgets and alerts for cost thresholds.
-
Documentation:
- Maintain up-to-date documentation for deployment processes.
- Document infrastructure configurations and code repositories.
-
Support Plans:
- Consider support plans from cloud providers for production environments.
- AWS Documentation: AWS Documentation
- Azure Documentation: Azure Docs
- Google Cloud Documentation: Google Cloud Docs
Contributions are welcome! Please follow these steps to contribute to the project:
-
Fork the Repository
Click the "Fork" button at the top right corner of the repository page to create a copy of the repository in your GitHub account.
-
Clone Your Fork
git clone https://github.com/yourusername/trading_system.git cd trading_system -
Create a Feature Branch
git checkout -b feature/your-feature-name
-
Make Your Changes
- Implement your feature or fix.
- Ensure your code follows the project's coding standards.
- Write unit tests for your code if applicable.
- Update documentation if necessary.
-
Commit Your Changes
git add . git commit -m "Add feature: your feature name"
-
Push to Your Fork
git push origin feature/your-feature-name
-
Open a Pull Request
- Go to the original repository on GitHub.
- Click on the "Pull Requests" tab.
- Click the "New Pull Request" button.
- Select your feature branch and create the pull request.
- Provide a clear and descriptive title and description for your pull request.
- PEP 8 Compliance: Ensure your code follows PEP 8 style guidelines.
- Comments and Documentation: Write clear comments and update documentation as needed.
- Testing: Include unit tests for new functionalities using frameworks like
unittestorpytest. - Commit Messages: Use descriptive commit messages that explain the changes you have made.
If you encounter any problems or have suggestions for improvements, please open an issue on GitHub:
-
Go to the Issues Tab
- Navigate to the repository's "Issues" tab.
-
Click "New Issue"
- Provide a descriptive title and detailed description of the issue.
-
Label the Issue
- Use appropriate labels to categorize the issue (e.g., bug, enhancement, question).
- Be respectful and considerate in your communications.
- Provide constructive feedback and be open to feedback on your contributions.
- Follow the Contributor Covenant Code of Conduct.
This project is licensed under the MIT License. See the LICENSE file for details.
- QuickFIX: quickfixengine.org
- Polygon.io: polygon.io
- SQLAlchemy: sqlalchemy.org
- Docker: docker.com
For any inquiries or support, please contact:
- Name: George Kledaras
- Email: george.kledaras@limnitrading.com
- GitHub: github.com/limnit