The experimental dataset, which contains data on sharp up, sharp down, and continuous oscillation situations, was chosen to test Bitcoin in January-February, September, and November of 2022. According to the experimental results, the evolutionary strategies algorithm achieved returns of 59.18%, 25.14%, and 22.72%, respectively. The results demonstrate that deep reinforcement learning based on the evolutionary strategies outperforms Q-learning and policy gradient concerning risk resistance and return capability. The proposed approach offers a robust and adaptive solution for high-frequency trading in the digital currency market, contributing to the development of effective quantitative trading strategies.
By relying on mathematical models and statistical analysis, traders can make more objective decisions that are less influenced by market noise or psychological biases. This data-driven approach is particularly valuable in the highly volatile cryptocurrency market, where emotions can often lead to poor decision-making. Quantitative trading employs various mathematical models to forecast price movements and detect inefficiencies in the market. These models can range from simple linear regressions to complex machine learning algorithms.
This seamless connection allows strategies to send orders directly to the market, facilitating real-time execution and monitoring. Quantitative traders typically use programming languages like Python, R, and C++ to develop their models and algorithms. Specialized trading software and quantitative libraries, such as Pandas, NumPy, and TensorFlow, provide the necessary tools for data analysis, modeling, and backtesting. Although some exchanges do offer a line of credit if you have established a relationship with them over time, it would be costly and inefficient to establish such contacts with a wide range of exchanges. Luckily, however, there are service providers in the secondary market that allow you to “outsource” this relationship. Good digital asset trading platforms will include this as a core feature in order to simplify connectivity with venues and greatly reduce counterparty risk.
Data quality and accessibility issues present ongoing challenges for crypto quant trading, particularly compared to traditional financial markets. Cryptocurrency market data can be fragmented across numerous exchanges, often with inconsistent data formats, quality, and historical depth. Data cleaning, normalization, and aggregation across different sources are necessary but time-consuming and resource-intensive processes. Market manipulation, wash trading, and inaccurate volume reporting on some less regulated exchanges can further compromise data reliability. Furthermore, access to comprehensive order book data, tick-by-tick data, and historical data can be costly and limited from certain exchanges or data providers. Improving data quality and accessibility requires investing in robust data infrastructure, utilizing reputable data providers, and developing sophisticated data cleaning and validation techniques.
Traders rely on this approach to make data-driven decisions, aiming to identify profitable trading opportunities and manage risks more effectively. Evaluation metrics play a crucial role in assessing the performance of a trading strategy, as they provide quantitative measures for gauging effectiveness in different aspects. Through an analysis of these metrics, the strengths and weaknesses of the strategy can be identified, enabling us to refine and optimize the model to achieve better results in real-world trading scenarios. In the main DQN, for the final trade, the output data of the aforementioned two Preprocessing DQNs are used for learning.
