Algorithmic trading for a buy-sell platform: study and comparison (original) (raw)

Algorithmic Trading Stock Price Model

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Stock market price prediction is a difficult undertaking that generally requires a lot of human-computer interaction. Traditional batch processing methods cannot be used effectively for stock market analysis due to the linked nature of stock prices. This project presents an online learning technique that employs a recurrent neural network of some sort (RNN) called Long Short-Term Memory (LSTM), which uses stochastic gradient descent to update the weights for individual data points. When compared to existing stock price prediction systems, this will yield more accurate results. With varying sizes of data, the network is trained and evaluated for accuracy, and the results are tallied. A comparison with respect to accuracy is then performed against an Artificial Neural Network. Traditional approaches to securities market analysis and stock value prediction embrace basic analysis, that appearance at a stock's past performance and therefore the general believability of the corporate itself, and applied mathematics analysis, that is exclusively involved with computation and distinguishing patterns available value variation.

FORECASTING EQUITY PRICES USING LSTM ALGORITHM Based on the concepts of Machine Learning

2021

This research paper specifically targets the Indian Stock Market, The National Stock Exchange. Stock Market Prediction refers to understanding various aspects of the stock market that can influence the price of the stocks and based on these potential factors we built a Website/Application to predict the stock’s price. We’ll be using Python as a programming language to predict the prices, and in this paper, we propose a Machine Learning (ML) approach that will be trained from the available top 50 stocks of the NSE and gain intelligence and then use the acquired knowledge for an accurate prediction. This application with help the user to speculate the stock price trend and help him decide whether to buy or short the stock price to maximize their profit. You can expect to have a decent level of understanding of all the phenomenon and processes of stock market predictions and the associated technologies. This article will cover up topics such as how we created our predictive model and t...

Stock Price Prediction using LSTM: An advanced review

Elsevier - SSRN, 2022

This paper presents a brief study of some existing methods by which a retail investor can predict the stock price. Either the price to go down or up depending upon the quarterly result, financial news inflow, technical behavior, or market sentiment due to global scenario, in the past few days. The authors discussed methodologies for anticipating the movement of stock price and accuracy proposed by respective authors of their proposed methods in their papers. The methods are based on LSTM (Long Short-Term Memory) with other features.

STOCK MARKET PREDICTION USING NEURAL NETWORKS

IRJET, 2022

This paper provides an overview of a Financial Modelling technique for predicting the closing prices of Stocks. The paper describes how LSTM models are designed and implemented. The paper also shows that future stock prices can be predicted using Machine Learning and training the Neural Network with the previous years' 'stock closing price' data.

Stock Market Prediction Using LSTM Technique

One of the most intricate machine learning problems is the share value prediction. Stock market prediction is an activity in which investors need fast and accurate information to make effective decisions. Moreover, the behavior of stock prices is uncertain and hard to predict. For these reasons, stock price prediction is an important process and a challenging one. This leads to the research of finding the most effective prediction model that generates the most accurate prediction with the lowest error percentage. Prices of stocks are depicted by time series data and neural networks are trained to learn the patterns from trends in the existing data. This system employed algorithm using LSTM to improve the accuracy of stock price prediction.

STOCK MARKET PRICE PREDICTION USING LONG-SHORT TERM MEMORY (LSTM)

IAEME PUBLICATION, 2020

Stock index prices predicting is a tough task and, because of various reasons relating to many technological and non-tech reasons, share price knowledge is an extremely difficult, unpredictable and dynamic environment. In parallel to deep learning techniques, a variety of academic experiments from different disciplines to resolve this topic and machine learning techniques are one of the many technologies used. Many machine learning techniques in this field were able to produce acceptable outcomes while it was used in this type of predictions. This paper studies stock market price prediction using LSTM model which is applied on Stock index prices historical data along with indications analysis which will be used to achieve more accurate results. In this study, data sets of historical prices of common stock of Agilent Technology, and American Airlines Group Common Stock were gathered to achieve this objective, and several tests were carried out using LSTM, the findings were evaluated using RMSE and RMSPE values that guarantee better performance for the LSTM method used.

Algorithmic Trading Using Long Short-Term Memory Network and Portfolio Optimization

2020

Investors typically rely on a mix of experience, intuition, knowledge of economic fundamentals and real-time information to make informed choices and try to get as high a rate of return as possible. Their decisions are customarily more instinct-driven than methodical. Propelled by the need for numerically inspired judgments, ever stronger within the financial community, in recent years the usage of computational and mathematical tools has been taking root. In this work we used a Long Short-Term Memory (LSTM) Network trained on historical prices to predict future daily closing prices of several stocks listed on the Standard & Poor 500 (S&P500) index. We compared the predictions of our LSTM network with those produced by another state-of-the-art approach, the Hidden Markov Model (HMM), in order to validate our findings. We then fed our forecasts into aMarkowitz Portfolio Optimization (PO) procedure to identify the best trading strategy. The purpose of PO, which allows for simultaneous...

Stock Market Prediction Using LSTM Recurrent Neural Network

Procedia Computer Science, 2020

It has never been easy to invest in a set of assets, the abnormally of financial market does not allow simple models to predict future asset values with higher accuracy. Machine learning, which consist of making computers perform tasks that normally requiring human intelligence is currently the dominant trend in scientific research. This article aims to build a model using Recurrent Neural Networks (RNN) and especially Long-Short Term Memory model (LSTM) to predict future stock market values. The main objective of this paper is to see in which precision a Machine learning algorithm can predict and how much the epochs can improve our model.

Prediction of Stock Price using RNN's LSTM-Based Deep Learning Model

IJRASET, 2021

Stock Market is referred to as a trading platform where trading of listed companies share price is exchanged. It is a place where individuals can buy or sell shares of the publicly listed companies. The prediction of stock market that how it will perform, its movement is one of the challenging tasks to do. Stock market prediction involves determining the future movement of the stock value of a financial exchange. In this paper the prediction of the stock prices using deep learning's LSTM (Long Short-Term Memory) which is the extension of Recurrent Neural Network is done. The previous two years historical dataset from 31/7/2019 to 13/8/2021 is taken for the prediction purpose. The prediction is based on the time series analysis of data, since it can help us to get an idea of the stock price pattern and also it is considered to be the best tool for understanding the pattern of the previously observed values and make the predictions based on it. For a greater accuracy of the predictions, we should consider past happenings or events as the past affects the future. Since for stock market prediction the data will be in time series and LSTM performs well when the information or the data is of the past and the prediction is to be made for the future then we can say that LSTMs are quite capable of doing the prediction for the stock market values.