Stock Market prediction on High frequency data using Long-Short Term Memory

Zineb Lanbouri · Saïd Achchab

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Forecasting Techniques and Applications

Abstract

High Frequency Trading (HFT) is part of algorithmic trading, and one of the biggest changes that happened in the last 15 years. HFT or nanotrading represents the ability, for a trader, to take orders within very short delays. This paper presents a model based on technical indicators with Long Short Term Memory in order to forecast the price of a stock one-minute, five-minutes and ten-minutes ahead. First, we get the S&P500 intraday trading data from Kaggle, then we calculate technical indicators and finally, we train the regression Long-Short Term Memory model. Based on the price history, alongside technical analysis indicators and strategies, this model is executed, and the results are analyzed based on performance metrics and profitability. Experiment results show that the proposed method is effective as well as suitable for prediction a few minutes before.

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References

2017Deep learning with long short-term memory networks for financial market predictions 2000Data Cleaning: Problems and Current Approaches.2017Stock price prediction using LSTM, RNN and CNN-sliding window model 2017Stock market's price movement prediction with LSTM neural networks 2012High Frequency Trading and the New-Market Makers 2018NSE Stock Market Prediction Using Deep-Learning Models 2011Technical analysis: the complete resource for financial market technicians 2017Classification-based financial markets prediction using deep neural networks

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2020Improving Stock Prediction Accuracy Using CNN and LSTM 2021DPP: Deep predictor for price movement from candlestick charts

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