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Crypto Trading Bot Platform

A simplified crypto trading bot platform for strategy testing and development using real OKX market data and virtual trading simulation.

Overview

This platform enables rapid strategy development with a monolithic architecture that supports multiple concurrent trading bots, real-time monitoring, and performance tracking.

Key Features

  • Multi-Bot Management: Run multiple trading bots simultaneously with different strategies.
  • Real-time Monitoring: Live OHLCV charts with bot trading signals overlay.
  • Modular Chart System: Advanced technical analysis with 26+ indicators and strategy presets.
  • Virtual Trading: Simulation-first approach with realistic fee modeling.
  • JSON Configuration: Easy strategy parameter testing without code changes.
  • Backtesting Engine: Test strategies on historical market data (planned).
  • Crash Recovery: Automatic bot restart and state restoration.

Tech Stack

  • Framework: Python 3.10+ with Dash
  • Database: PostgreSQL
  • Real-time Messaging: Redis
  • Package Management: UV
  • Containerization: Docker

Quick Start

For detailed instructions on setting up and running the project, please refer to the main documentation.

➡️ Go to the Full Documentation

# Quick setup for development
git clone <repository>
cd TCPDashboard
uv sync
cp env.template .env
docker-compose up -d
uv run python main.py

Documentation

All project documentation is located in the docs/ directory. The best place to start is the main documentation index.

  • [Main Documentation (docs/README.md)] - The central hub for all project documentation, including setup guides, architecture, and module details.
  • [Setup Guide (docs/guides/setup.md)] - Complete setup instructions for new machines.
  • [Project Context (CONTEXT.md)] - The single source of truth for the project's current state.

Contributing

We welcome contributions! Please review the [Contributing Guidelines (docs/CONTRIBUTING.md)] and the [Project Context (CONTEXT.md)] before getting started.

Description
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Readme 5.5 MiB
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Python 98.5%
PLpgSQL 1.4%