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Academic Journal of Computing & Information Science, 2022, 5(9); doi: 10.25236/AJCIS.2022.050915.

The Quantization Algorithm Based on DRL


Can Pan

Corresponding Author:
Can Pan

School of Agricultural University of Hunan, Changsha, 410000, China


Maximizing returns is always people’s investment goal. Gold and Bitcoin have some hedging ability, and their prices fluctuate greatly, making them popular varieties for investors. However, the market is risky, and different economic, political and environmental factors will impact the market. As a result, Bitcoin and gold prices fluctuate sharply, leading to uncertain investment and uncertain returns. To maximize returns, we model time and price, analyze and model the price trends of gold and bitcoin in problems, give the best trading model, and analyze the transaction cost sensitivity of transaction risk and trading strategies. Predictive techniques are used to move the future and to build some heuristic-based robots to make decisions. Use (DRL) deep reinforcement learning to simulate stock trading based on the Markov decision-making process of avoiding risk aversion, reducing trading costs, maintaining liquidity, and assuming that the stock market is not affected by enhanced trading agents. The second step is to use the risk analysis of the Sharp ratio to compare with the portfolio-managed Markowitz effective boundary model to select a portfolio with medium risk and high yield in the third step, to effectively calculate the sensitivity of the transaction strategy to the transaction cost, we used the time series to calculate the change difference between the final result and the standard value after the transaction cost change. A system of equations based on a number of yield values can be established to meet the optimization requirements, introduce computational historical data, and replace the relevant transaction costs with the above model to clarify the change curve of the final return results. Later, ac- cording to the needs of the trading strategy, complete the sensitivity analysis of the transaction strategy on the trading cost (calculate the impact of variable factors on profit), determine the sensitivity anal- ysis mode, and evaluate the advantages and disadvantages of the model combined with the above results. Ultimately, our strategy and models are passed on to traders as memos. 


Maximizing returns, deep reinforcement learning, Markowitz’ efficient frontier, Markov decision-making process, sensitivity analysis mode

Cite This Paper

Can Pan. The Quantization Algorithm Based on DRL. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 9: 98-100. https://doi.org/10.25236/AJCIS.2022.050915.


[1] Shahriar Shafiee, Erkan Topal, An overview of global gold market and gold price forecasting, Resources Policy, Volume 35, Issue 3,2010,Pages 178-189, ISSN 0301- 4207, https://doi.org/10.1016/ j.resourpol.2010.05.004.

[2] Letteri, I., Della Penna, G., De Gasperis, G., and Dyoub, A.. “A Stock Trading System for a Medium Volatile Asset using Multi Layer Perceptron”, 2022.

[3] M. Nabipour, P. Nayyeri, H. Jabani, S. S. and A. Mosavi. "Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis," in IEEE Access, vol. 8, pp. 150199-150212, 2020, doi: 10.1109/ACCESS.2020.3015966.

[4] C. Zheng and J. Zhu. "Research on stock price forecast based on gray relational analysis and ARMAX model," 2017 International Conference on Grey Systems and Intelligent Services (GSIS), 2017, pp. 145-148, doi: 10.1109/GSIS.2017.8077689.

[5] S. Selvin, R. Vinayakumar, E. A. Gopalakrishnan, V. K. Menon and K. P. Soman. "Stock price prediction using LSTM, RNN and CNN-sliding window model," 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017, pp. 1643-1647, doi: 10.1109/ICACCI.2017.8126078.