Introduction
This paper outlines the design and implementation of a cryptocurrency quantitative trading system, originally developed as a personal investment tool but adapted to serve as a graduation project. The system leverages algorithmic strategies to analyze market data, generate trading signals, and execute trades autonomously.
Key Features
- Algorithmic Trading: Uses programmed strategies to analyze real-time market data.
- Backend Services: Initially built with Spring Boot (for academic requirements) but will migrate to Python's FastAPI in future versions.
- Hardware Token Authentication: Includes an ESP32-based dynamic verification module for secure login.
- Multi-Platform Access: Web (Vue.js) and mobile (Kotlin) interfaces for monitoring and control.
System Design
1. Architecture Overview
The system comprises five modules:
- Strategy Trading Module (Python): Fetches market/account data from exchanges and executes strategies.
- Backend Service Module (Spring Boot): Manages data storage/forwarding between components.
- Web Frontend (Vue.js): Visualizes account/strategy data and allows parameter adjustments.
- Mobile App (Kotlin): Receives strategy signals and push notifications.
- Hardware Token (ESP32 + Python): Generates time-based OTPs for authentication.
Figure 1-1: Modular interaction flow (text-based description replaces original image).
2. Software Implementation
2.1 Core Components
Market Data Pipeline:
- Historical data stored as CSV → loaded into Pandas DataFrame.
- Real-time updates via exchange APIs (e.g., Binance, OKX).
Strategy Engine:
- Implements moving-average crossover logic.
- Backtesting generates performance charts (e.g., returns vs. baseline).
2.2 Frontend Functionality
- Account Dashboard: Displays assets, margin, and order history (auto-refreshed via WebSocket).
- Strategy Customization: Users adjust parameters (e.g., fast/slow MA periods) and trigger backtests.
- Market Visualization: Plots candlestick charts with strategy signals (buy/sell markers).
Technical Stack
| Component | Technology |
|---|---|
| Backend | Spring Boot → FastAPI |
| Data Processing | Python (Pandas/NumPy) |
| Frontend | Vue.js (Vuex, Axios) |
| Mobile App | Kotlin (Android SDK) |
| Authentication | ESP32 (Time-based OTP) |
FAQs
Q1: How does the system ensure data accuracy?
A: Exchange APIs provide validated market data, while backtesting reconciles strategy outputs against historical records.
Q2: Can I use this for live trading?
A: Yes, but extensive paper-trading validation is recommended before deploying capital.
Q3: What cryptocurrencies are supported?
A: All pairs listed on integrated exchanges (configurable via JSON file).
Conclusion
This system demonstrates a practical approach to algorithmic cryptocurrency trading, balancing academic requirements with real-world functionality. Future iterations will optimize the stack (e.g., FastAPI migration) and expand strategy libraries.
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Note: Commercial links/exchange references are illustrative only.