A Novel Trading Strategy Framework Based on Reinforcement Deep Learning for Financial Market Predictions

Abstract

The prediction of stocks is complicated by the dynamic, complex, and chaotic environment of the stock market. Investors put their money into the financial market, hoping to maximize profits by understanding market trends and designing trading strategies at the entry and exit points. Most studies propose machine learning models to predict stock prices. However, constructing trading strategies is helpful for traders to avoid making mistakes and losing money. We propose an automatic trading framework using LSTM combined with deep Q-learning to determine the trading signal and the size of the trading position. This is more sophisticated than traditional price prediction models. This study used price data from the Taiwan stock market, including daily opening price, closing price, highest price, lowest price, and trading volume. The profitability of the system was evaluated using a combination of different states of different stocks. The profitability of the proposed system was positive after a long period of testing, which means that the system performed well in predicting the rise and fall of stocks.

Enhancing renewable energy certificate transactions through reinforcement learning and smart contracts integration

“…The global transition to sustainable energy sources necessitates the development of mechanisms like green certificates (GCs) to incentivize renewable energy production. Scholars from China, Europe, America, and other regions have extensively researched and explored issues related to the market mechanisms and models of GCs [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] , technological innovations including blockchain and artificial intelligence platform technologies [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37] , policies and economic strategies and market changes 10,19,[38][39][40][41][42][43][44][45] .…”

“…The application of reinforcement learning and Q-learning in financial market forecasting 33 , learning trading rules for specific financial assets 34 , and improving financial trading decisions 35 offers a new perspective for GC trading strategies. Particularly, deep Q-learning in the algorithmic trading system for the commodity futures market 36 and the design of a supply chain carbon allowance allocation auction based on multi-agent modeling and Q-learning 37 , 57 , 58 demonstrate the potential of AI technology in energy management and GC trading.…”

A Multi-Scaling Reinforcement Learning Trading System Based on Multi-Scaling Convolutional Neural Networks

“…Recently, researchers have improved these techniques by combining reinforcement learning methods with deep learning. These methods can be divided into policy-based deep reinforcement learning [10][11][12][13][14] and value-based deep reinforcement learning [15][16][17][18][19][20][21][22][23][24]. For example, Corazza et al compared the results obtained considering different policy-based (SARSA) and off-policy-based (Q-Learning, Greedy-GQ) reinforcement learning algorithms applied to daily transactions in the Italian stock market [19].…”

Research on the Feasibility of Applying GRU and Attention Mechanism Combined with Technical Indicators in Stock Trading Strategies

“…Subsequently, more in-depth network models were applied to different trading markets and financial commodity price predictions [29][30][31][32][33][34]. In our previous research [35], we explored the effectiveness and practicality of various financial analysis technical indicators in the time series deep learning network.…”