GA-MSSR: Genetic Algorithm Maximizing Sharpe and Sterling Ratio Method for RoboTrading

Zhang, Zezheng; Khushi, Matloob

doi:10.1109/ijcnn48605.2020.9206647

Cited by 21 publications

(15 citation statements)

References 22 publications

(27 reference statements)

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“…Zhang, Z. proposed a genetic algorithm using a crossover of technique indicators as input. It successfully outperformed some of the traditional trading strategies [91].…”

Section: Other Deep Learning Methodsmentioning

confidence: 97%

“…Table 20 shows the details of the papers that used the Sharpe ratio as a performance metric. In this table, papers [14,18] used a CNN-based model; papers [71,75] used a DNN-based model; papers [77] and [82] used a reinforcement learning-based model; and papers [84,88,90,91] used other deep learning methods. It could be found that papers [75,84,91] performed best, which used a DNN model, and Sharpe ratio was in the range of 5-10.…”

Section: Analysis Based On Sharpe Ratiomentioning

confidence: 99%

“…In this table, papers [14,18] used a CNN-based model; papers [71,75] used a DNN-based model; papers [77] and [82] used a reinforcement learning-based model; and papers [84,88,90,91] used other deep learning methods. It could be found that papers [75,84,91] performed best, which used a DNN model, and Sharpe ratio was in the range of 5-10. Paper [77] also had a great performance, but papers [14,18,82,88,90] did not have high Sharpe ratios.…”

Section: Analysis Based On Sharpe Ratiomentioning

confidence: 99%

“…Table 21 shows the details of the papers' return rates. In this table, papers [14,16,18,21] used the CNN-based model; paper [33] used the LSTM-based model; papers [70,71] used the DNN-based model; papers [78,79] used the reinforcement learning based model; and papers [84,[87][88][89][90][91] used other deep learning methods. It could be found that paper [78] performed the best, which used the reinforcement learning model, and the paper's return rate was in the range of 1.4-1.8.…”

Section: Analysis Based On the Return Ratementioning

confidence: 99%

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A Survey of Forex and Stock Price Prediction Using Deep Learning

Zhao

Khushi

2021

ASI

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194

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Predictions of stock and foreign exchange (Forex) have always been a hot and profitable area of study. Deep learning applications have been proven to yield better accuracy and return in the field of financial prediction and forecasting. In this survey, we selected papers from the Digital Bibliography & Library Project (DBLP) database for comparison and analysis. We classified papers according to different deep learning methods, which included Convolutional neural network (CNN); Long Short-Term Memory (LSTM); Deep neural network (DNN); Recurrent Neural Network (RNN); Reinforcement Learning; and other deep learning methods such as Hybrid Attention Networks (HAN), self-paced learning mechanism (NLP), and Wavenet. Furthermore, this paper reviews the dataset, variable, model, and results of each article. The survey used presents the results through the most used performance metrics: Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Square Error (MSE), accuracy, Sharpe ratio, and return rate. We identified that recent models combining LSTM with other methods, for example, DNN, are widely researched. Reinforcement learning and other deep learning methods yielded great returns and performances. We conclude that, in recent years, the trend of using deep-learning-based methods for financial modeling is rising exponentially.

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“…Zhang, Z. proposed a genetic algorithm using a crossover of technique indicators as input. It successfully outperformed some of the traditional trading strategies [91].…”

Section: Other Deep Learning Methodsmentioning

confidence: 97%

Section: Analysis Based On Sharpe Ratiomentioning

confidence: 99%

Section: Analysis Based On Sharpe Ratiomentioning

confidence: 99%

Section: Analysis Based On the Return Ratementioning

confidence: 99%

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A Survey of Forex and Stock Price Prediction Using Deep Learning

Zhao

Khushi

2021

ASI

Self Cite

194

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“…Trillions of dollars are traded every day on financial markets [1,2]. The stock market is often deemed volatile and unpredictable, making it a challenge to maximise profit and hedging losses.…”

Section: Introductionmentioning

confidence: 99%

Text Mining of Stocktwits Data for Predicting Stock Prices

Jaggi

Mandal

Narang

et al. 2021

ASI

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Stock price prediction can be made more efficient by considering the price fluctuations and understanding people’s sentiments. A limited number of models understand financial jargon or have labelled datasets concerning stock price change. To overcome this challenge, we introduced FinALBERT, an ALBERT based model trained to handle financial domain text classification tasks by labelling Stocktwits text data based on stock price change. We collected Stocktwits data for over ten years for 25 different companies, including the major five FAANG (Facebook, Amazon, Apple, Netflix, Google). These datasets were labelled with three labelling techniques based on stock price changes. Our proposed model FinALBERT is fine-tuned with these labels to achieve optimal results. We experimented with the labelled dataset by training it on traditional machine learning, BERT, and FinBERT models, which helped us understand how these labels behaved with different model architectures. Our labelling method’s competitive advantage is that it can help analyse the historical data effectively, and the mathematical function can be easily customised to predict stock movement.

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