ML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine.34 The application of ML to business problems is known as predictive analytics. Aspiring quantitative traders and analysts, data scientists interested in finance, and researchers or students studying quantitative finance, financial engineering, or related fields. In my next article, I will explain the implementation of these indicators into a Machine Learning model and dive deeper into creating and carefully back testing the strategy.
Momentum Trading Strategy
Most of the dimensionality reduction techniques can be considered as either feature elimination or extraction. One of the popular methods of dimensionality reduction is principal component analysis (PCA). Our findings suggest that patterns claimed by chart analysts are insufficient to provide a reliable prediction and are more likely to happen randomly. Therefore, the most promising approach for stock price prediction involves integrating fundamental analysis tools, including financial and political news, annual reports, companies’ product lifecycles, or their financial horizon. The LSTM model predicts stock prices corresponding to the trading sessions in the test set. The test set data length includes observations from January 1, 2021 to April 1, 2021.
Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves “rules” to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilisation of a set of relational rules that collectively represent the knowledge captured by the system. Taking into account the limitations that have been observed in LSTM methods, we propose a structure that addresses the shortcomings of LSTM models.
Explainable AI for Financial Forecasting
Support-vector machines (SVMs), also known as support-vector networks, are a set of related supervised learning methods used for classification and regression. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximise some notion of cumulative reward. Due to its generality, the field is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimisation, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In reinforcement learning, the environment is typically represented as a Markov decision process (MDP).
The results presented in this paper are based on training and testing conducted on 12 stocks from the Tehran Stock Exchange (TSE). Detailed information about these stocks, including their respective dates, is provided in Appendix 1. Average True Range is a common technical indicator used to measure volatility in the market, measured as a moving average of True Ranges. ATR however is primarily used in identifying when to exit or enter a trade rather than the direction in which to trade the stock. Even though this article does not argue for or against use of Technical analysis, the technical indicators below can be used to perform various back-tests and come up with an opinion on their prediction power.
In this study, we investigate the feasibility of using deep learning for stock market prediction and technical analysis. We explore the dynamics of the stock market and prominent classical methods and deep learning-based approaches that are used to forecast prices and market trends. Subsequently, we evaluate prior research applicability for stock markets and their efficacy in real-world applications. Our analysis reveals that the most prominent studies regarding LSTMs and DNNs predictors for stock market forecasting create a false positive.
It appears that there is a strong correlation between the closing prices from one day to the next, which is evident from the linear pattern on the graph. This suggests that the stock price tends to follow a trend and may be non-stationary, as the price movement does not fluctuate around a constant average, but rather follows a continuous pattern. Test combinations of up to 54 technical indicators and 4 machine learning models to compare and determine the best model to apply to a chosen stock for algorithmic trading purposes. Dimensionality reduction is a process of reducing the number of random variables under consideration by obtaining a set of principal variables.57 In other words, it is a process of reducing the dimension of the feature set, also called the “number of features”.
The Neural Networks model performed the best in terms of prediction accuracy, with an improvement of 15% compared to the traditional method. These findings have important implications for investors and financial professionals in data-driven decision making. This study contributes to the development of more effective stock price prediction methods by combining analytical and technological approaches. However, LSTM model is more appropriate in short-term stock price forecasting in the field of machine learning. Furthermore, Vietnam’s stock market was established in 2000, but it has developed rapidly and has a market capitalization of 82.15% of GDP.
Implementing Convolutional Neural Networks in TensorFlow
Hence, once we have the 10 stocks, we will wait for 3rd January 2019 and buy at the Open price, hold for 7 days, and sell on the 7th trading day end Closing price. The main assumption here is that we can trade at the Open price and sell at the Close price. This is not too unrealistic given we know the timings of the market and can code to execute 1 minute after Opening and 1 minute before Closing of the day. Inductive logic programming (ILP) is an approach to rule learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative machine learning technical analysis examples. Inductive programming is a related field that considers any kind of programming language for representing hypotheses (and not only logic programming), such as functional programs.
Integration of Technical Analysis and Machine Learning to Improve Stock Price Prediction Accuracy
By providing a more adaptive and data-driven model, this research is expected to make a significant contribution to the field of stock market analysis and investment decision-making 15. In addition, the findings of this study are expected to motivate further research and practical applications in the financial industry, as well as provide new insights into the development of more effective investment strategies 16. Thus, this research offers the development of stock market analysis theory for smarter investment practices that are responsive to market dynamics. The stock market indices are determined based on their market impact and subsequent capitalization.
- Our findings suggest that patterns claimed by chart analysts are insufficient to provide a reliable prediction and are more likely to happen randomly.
- Whether you’re an experienced trader looking to automate your trading strategies or a beginner interested in learning quantitative trading, this book will be a valuable resource.
- Using the process described above, below chart lays out the cumulative returns offered by following the strategy from January 2019 to November 2020.
Note that for each data of different stocks, the accuracy of the LSTM model’s forecast will have a difference, a specific comparison chart between the forecast price and the actual price of all stocks. Predicting the future direction of stock prices has been an interest sector of researchers and investors. There is quite a bit of research that seeks to address that challenge, offering a variety of approaches to achieving the goal (Appel, 2005; Brown et al., 1998; El-Nagar et al., 2022; and Fromlet, 2001). Until the widespread of algorithmic trading, technical indicators were primarily used by traders who would look up at these indicators on their trading screen to make a buy/sell decision.
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Descriptive statistics, including mean, median, and standard deviation of the dataset, are summarized in Table 6. The purpose of the Project is to create a streamlit dashboard; which given a specific stock, performs a comparative analysis of machine-learning model design in a testing environment. Thus, below is the table with the probability of prediction, the actual movement after 7 days and the percentage change that took place in next 7 days. Python offers a very convenient way of saving function files using the pickle package. The idea would be to fit the model on the data and save this fitted model into pickle files for each cluster. Hence, when predicting for a particular company, we will use the model in the corresponding cluster’s pickle file and make our prediction.
- The LSTM architecture features a unique cell structure that includes mechanisms to regulate the flow of information through the network.
- An elbow curve helps to determine the approximate point at which the marginal decrease in sum of squares is small.
- Before moving on to the next indicator, I would like to mention another type of smoothening or moving average that is commonly used with other indicators.
It may not be obvious, but by choosing a smaller time window for the stock, the lagging effect will be more apparent as demonstrated in Fig. It can be seen that the price forecast from the LSTM model tends to be very similar to the variation trend of the actual price on the data of the test set. In addition, the difference between the forecast price and the actual price is not significant.
It is particularly useful in scenarios where outputs are interdependent or share underlying patterns, such as predicting multiple economic indicators or reconstructing images,93 which are inherently multi-dimensional. An ANN is a model based on a collection of connected units or nodes called “artificial neurons”, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a “signal”, from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. Artificial neurons and edges typically have a weight that adjusts as learning proceeds.
Most of the indicators are created using Close rather than Adjusted Close in this article. – There is another strategy which says when the band squeezes (stock price is less volatile) or we can say the Standard Deviation of 20-day price reduces there is a possible breakout in either direction coming up. Traders make BUY or SELL decisions immediately after UP or DOWN breakout respectively. These statistics provide a comprehensive overview of the dataset’s characteristics, which can be useful for further analysis or modeling. Moving Average Convergence Divergence (MACD) is an indicator that measures the difference between two moving averages to capture market momentum. The above is a simplistic back-test assuming no transaction costs, and perfect execution of trades.