The automatic cryptocurrency trading system using a scalping strategy
DOI:
https://doi.org/10.20535/2786-8729.5.2024.316563Keywords:
automated trading, scalping, cryptocurrency, Binance API, algorithmic tradingAbstract
The study focuses on the development and implementation of an automated system for scalping strategies in cryptocurrency markets. Scalping, a high-frequency trading strategy, aims to generate profits from small price fluctuations. The primary goal of the research is to create an automated trading bot that addresses critical issues such as latency, risk management, scalability, and reliability in
real-world market conditions. To achieve this, the following objectives were defined: develop a novel scalping method, implement a software solution to integrate the method into an automated trading system, and evaluate its effectiveness through experimental testing.
The research methodology utilized technical indicators, including the Exponential Moving Average (EMA) and Volume Weighted Average Price (VWAP). Pseudocode was created to illustrate the decision-making process, incorporating key parameters such as smoothing factors, time periods, and thresholds for trade execution. The software architecture consists of modules: Binance exchange integration, data collection and management, strategy analysis, trade execution, and historical data storage. Technologies such as PostgreSQL, Redis, WebSocket, and Python libraries (Pandas, NumPy, TA-Lib) were employed to ensure the robustness and efficiency of the system.
Experiments were conducted using the BTC/USDT trading pair, known for its high liquidity and volatility. The system was tested on hardware featuring an Intel Core i7-10700K processor, 32 GB of RAM, and a 1 Gbps network connection. A comparative analysis between the scalping strategy and a trend-following strategy demonstrated the advantages of scalping in volatile markets. The scalping bot executed 15 trades (13 successful) within two hours, achieving a total profit of 120 USDT.
Performance metrics, including latency (15–50 ms), signal processing time, CPU utilization
(5–55%), and memory usage (120–2100 MB), were measured. The results confirmed the system's modular architecture and its ability to scale linearly with increasing trading volumes.
The findings validate the effectiveness of the proposed method and the reliability of the developed system in real-world conditions. Future research may focus on optimizing algorithms to reduce resource consumption and integrating advanced risk management techniques to enhance performance.
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