Replicating a Trading Strategy by Means of LSTM for Financial Industry Applications
By Luigi Troiano, Elena Mejuto Villa, Vincenzo Loia. 2018
81 citations recorded by the source corpus.
This paper investigates the possibility of learning a trading rule looking at the relationship between market indicators and decisions undertaken regarding entering or quitting a position. As means to achieve this objective, we employ a long short-term memory machine, due its capability to relate past and recent events. Our solution is a first step in the direction of building a model-free robot, based on deep learning, able to identify the logic that links the market mood given by technical indicators to the undertaken investment decisions. Although preliminary, experimental results show that the proposed solution is viable and promising.
Subfields: Economics and Econometrics, Finance, Management Science and Operations Research, Economics & Operations Research, Time-Series & Forecasting
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Citations in the corpus
- Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “In [195], the crossover and Moving Average Convergence and Divergence (MACD) signals were used to predict the trend of the Dow 30”
- Deep Learning for Financial Applications : A Survey (2020, methodology): “[54] Dow30 stocks 2012-2016 Close data and”