Using Techniques from Technical Analysis, an Intelligent Lightweight Stock Trading System Using Deep Learning Models is Designed

Pavillion Delix^*
*Correspondence: Pavillion Delix, Department of Big Data Analytics, Kyung Hee University, Seoul 02447, Republic of Korea, Email:

Introduction

In the realm of financial markets, the ability to predict stock price movements accurately is a highly sought-after skill. Traders and investors constantly seek innovative ways to gain an edge in this complex and dynamic environment. Traditional methods such as technical analysis have long been employed to forecast price trends based on historical data patterns. However, with the advent of deep learning technologies, there is a growing interest in harnessing the power of Artificial Intelligence (AI) to enhance trading strategies. In this article, we explore the design and implementation of an intelligent lightweight stock trading system that integrates deep learning models with techniques from technical analysis [1].

Description

Technical analysis involves the study of historical market data, primarily price and volume, to forecast future price movements. It is based on the premise that market trends tend to repeat themselves due to human behavior patterns. Key tools and techniques in technical analysis include: Moving averages smooth out price data to identify trends over specific time periods. Common types include Simple Moving Averages (SMA) and Exponential Moving Averages (EMA). RSI measures the magnitude of recent price changes to determine overbought or oversold conditions, indicating potential trend reversals. Bollinger Bands consist of a simple moving average and upper and lower bands that represent volatility around the average price. They are used to identify potential breakout or reversal points. Support and resistance levels are price levels where a stock tends to find barriers in its movement. Support is the price level below which a stock rarely falls, while resistance is the price level above which it rarely rises. Deep learning, a subset of AI, has gained popularity for its ability to learn complex patterns from large datasets. In stock trading, deep learning models can be trained on historical price and volume data to identify intricate patterns that may not be discernible to human analysts. Common deep learning architectures used in stock prediction include: Recurrent Neural Networks (RNNs) are well-suited for sequential data such as time-series stock prices. They can capture temporal dependencies and long-range dependencies in the data [2].

Long Short-Term Memory (LSTM) Networks are a type of RNN designed to overcome the vanishing gradient problem, making them effective for capturing long-term dependencies in time-series data. Convolutional Neural Networks (CNNs) are typically used for image recognition tasks but can also be applied to stock trading by treating historical price data as a two-dimensional image. To build an intelligent lightweight stock trading system, we combine the principles of technical analysis with deep learning models. Historical price and volume data for various stocks are collected from financial databases or APIs. Additional features such as moving averages, RSI, and Bollinger Bands are computed from the raw data. Relevant features are selected or engineered from the raw data to feed into the deep learning models. This may include lagged price data, technical indicators, and market sentiment features [3].

Deep learning models such as LSTM networks are trained on the historical data to learn patterns and relationships between input features and stock price movements. The training process involves optimizing model parameters using techniques like gradient descent. Once the models are trained, they are used to make predictions on unseen data. The predicted price movements are compared with actual movements to assess the model's accuracy. Based on the model predictions, a trading strategy is devised to generate buy or sell signals. This strategy may incorporate rules based on technical indicators, such as crossing moving average lines or RSI thresholds. Risk management techniques are implemented to mitigate potential losses. This may include setting stop-loss orders, position sizing based on portfolio risk, and diversification across multiple assets. Backtesting and Optimization: The trading system is backtested on historical data to evaluate its performance and fine-tune parameters. Optimization techniques such as genetic algorithms or grid search may be employed to improve the system's profitability [4,5].

Conclusion

In conclusion, the integration of deep learning models with techniques from technical analysis offers a promising approach to designing intelligent stock trading systems. By leveraging the power of AI to analyze vast amounts of data and extract meaningful patterns, traders can make more informed decisions and gain a competitive edge in the financial markets. However, it is essential to exercise caution and thoroughly validate the performance of such systems through rigorous testing and evaluation. With continuous refinement and adaptation, intelligent trading systems have the potential to revolutionize the way we approach stock trading in the digital age.

Acknowledgement

None.

Conflict of Interest

None.

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