Trading Signal Controller: An Solution for Managing Trading Signals

Table of Contents

1. Introduction
2. Prerequisites
3. Quick Start
   • Conventional Installation
   • Docker Setup
4. Built With
5. Project Structure
6. Core Features
   • Balancer for Assignment
7. How to Contribute
8. Licensing

Introduction

Trading Signal Controller is an application for managing trading signals. Built on Django, the system aims to streamline the handling of trading signals across both development and production settings.

Prerequisites

• Python: Version 3.11
• Django: Version 4.1.7
• Database: PostgreSQL

Quick Start

Conventional Installation

1. Clone the Git Repo

   git clone <repository_link>

2. Install Dependencies

   poetry install

3. Migrate the Database

   poetry run python manage.py migrate

4. Start the Dev Server

   poetry run python manage.py runserver

Docker Setup

Navigate to the root folder of the project:

1. Create Docker Image

   docker build -t trading_signal_controller .

2. Initiate Docker Container

   docker run -p 8000:8000 trading_signal_controller

Built With

• Web Framework: Django
• RESTful API: Django REST Framework

Project Structure

• pyproject.toml: Manages package metadata and dependencies.
• Dockerfile: Sets up the Python runtime, handles dependencies, and starts the app with Gunicorn.
• nginx.conf: Configures Nginx as a reverse proxy and serves static files.
• docker-compose.yaml: Manages multi-container Docker applications.
• wsgi.py & asgi.py: Set up WSGI and ASGI servers for deployment.
• models.py: Houses data models like MasterTrader.

Core Features

Balancer for Assignment

Introduction

The function balance_runner_assignment in src/api/models.py balances the allocation of MasterTrader instances across active CrawlRunner instances to prevent any one from being overloaded.

Implementation Steps

1. Fetch Active Instances: Enumerates all active CrawlRunner and MasterTrader instances.
2. Calculate Metrics: Finds total and average MasterTrader instances for each CrawlRunner.
3. Early Exit Check: Exits the function if no active instances are found.
4. Average Computation: Evaluates the average MasterTrader instances for each CrawlRunner.
5. Identify Pending Assignments: Determines any remaining MasterTrader instances that need to be assigned.

How to Contribute

For a detailed guide on contributing to this project, please see CONTRIBUTING.md.

Licensing

The software is distributed under the MIT License. For additional details, refer to LICENSE.md.