Navigating Edinburgh’s Forex And Mining Scene: Tips For Success

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Navigating Edinburgh’s Forex And Mining Scene: Tips For Success

Navigating Edinburgh's Forex And Mining Scene: Tips For Success

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By Jakub Michańków Jakub Michańków Scilit Google Scholar 1, † , Paweł Sakowski Paweł Sakowski Scilit Google Scholar 2, † and Robert Ślepaczuk Robert Ślepaczuk Scilit Google Scholar 2, *

Quantitative Finance Research Group, Department of Quantitative Finance, Faculty of Economic Sciences, University of Warsaw, ul. Długa 44/50, 00-241 Warsaw, Poland

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Received: 7 January 2022 / Revised: 18 January 2022 / Accepted: 18 January 2022 / Published: 25 January 2022

(This article belongs to Special Issue Complex Data Processing Systems and Computing Algorithms: New Concepts and Applications)

We use LSTM network to predict the value of BTC and S&P500 index, using data from 2013 to the end of 2020, with the following frequencies: daily, 1h and 15 min data. We introduce our innovative loss function, which improves the utility of the forecasting ability of the LSTM model in algorithmic investment strategies. Based on the forecasts from the LSTM model, we generate buy and sell investment signals, use them in algorithmic investment strategies and create equity lines for our investment. For this purpose, we use various combinations of LSTM models, optimized on the in-sample period and tested on the out-of-sample period, using a rolling window approach. We place special emphasis on data preprocessing in the input layer, to avoid overfitting in the estimation and optimization process, and ensure correct selection of hyperparameters at the beginning of our tests. The next step is devoted to the merging of signals from different frequencies into one ensemble model, and the selection of the best combinations for the period outside the sampling, through optimization of the given criterion in a similar way as in the portfolio analysis. Finally, we perform a sensitivity analysis of the main parameters and hyperparameters of the model.

Navigating Edinburgh's Forex And Mining Scene: Tips For Success

Machine learning; recurrent neural networks; long-term memory model; neural network; algorithmic investment strategies; systematic trading systems; loss function; walk-forward optimization

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The main objective of this paper is to explore deep learning opportunities in time series forecasting using buy/sell signals generated by LSTM (Long Short-Term Memory) type recurrent neural networks for algorithmic investment strategies, tested on different frequencies of BTC (Bitcoin) and the S&P500 index . We focus exclusively on LSTM networks, and compare the performance on different datasets, frequencies, selected hyperparameters and ensemble models, created by combining the aforementioned variables.

The main advantages and novelty of our work can be divided into five important points, listed below. First, the use of the latest Machine Learning (ML) methods (LSTM model) in algorithmic investment strategies (AIS) used for cryptocurrency (BTC) and traditional stock index markets (S&P500 index). Second, the indication of frequently encountered drawbacks encountered in paper testing various algorithmic strategies. Third, designing the proper architecture (initial hyperparameter setting) of the LSTM model and testing the performance of AIS compared with the traditional Buy&Hold (B&H) model. Fourth, use of different frequencies from daily to 15 min data in algorithmic investment strategies. Finally, the construction of an ensemble model, based on the combination of algorithmic investment strategies, on different frequencies used for BTC and S&P500 index for separate and combined frequencies.

The idea for this article arises from endless attempts to understand and beat the market, through the construction of algorithmic investment strategies that generate abnormal returns, i.e. characterized by risk-adjusted returns significantly higher than the benchmark or other existing strategies. Moreover, none of the previous works covered the topic of performance analysis of signals from the LSTM model in algorithmic investment strategies, with a simultaneous focus on building a correct architecture of LSTM networks, testing at different frequencies and different asset classes with a rolling window approach. , enhanced with additional sensitivity analysis at the end. Although every year researchers publish several articles devoted to testing a variety of alternative approaches used in AIS, the results of these studies include a number of drawbacks and errors, which in practice make it impossible to use in real trading. Therefore, the search for an effective algorithmic investment strategy continues.

(H1). The signals from the LSTM model used in AIS are more efficient than the Buy&Hold approach, regardless of the asset class tested.

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(H2). The signals from the LSTM model used in AIS are more efficient than the Buy&Hold approach, regardless of the tested data frequency.

(H3). The signals from the LSTM model used in AIS are more effective in case of BTC than in case of S&P500 index.

(H5). The ensemble model constructed as a combination of models with different frequencies and assets can provide better risk-adjusted returns than individual models.

Navigating Edinburgh's Forex And Mining Scene: Tips For Success

With reference to software, libraries, hardware and the time of calculation, we can say that the results for the LSTM model were obtained using R 4.1.0 together with Python 3.7.10. Deep learning libraries used for designing, training and testing the network are Keras 2.4.0 and TensorFlow 2.5.0. The rest of the calculations, as well as graphs and tables, were made with only the R and RStudio environment. The computer specification used in this research was as follows: AMD Ryzen 7 3700X 3.6 GHz, 16 GB RAM, NVIDIA GeForce RTX 2060 Super with 270 tensor cores. One full training session (number of rolling windows × 40 epochs) lasted around 20 min at 1d frequency, 60 min at 1h frequency, 180 min at 15 min frequency for S&P500 data, 80/240/720 min for BTC data.

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The structure of this paper includes a brief introduction with motivation and hypotheses in Section 1 and literature review in Section 2. Methodology and data are covered in Section 3. The main results are presented in Section 4. Then Section 5 covers the sensitivity analysis and Section 6 combined strategies, and Section 7 concludes the research.

Although literature review is very broad on this topic, the main problem is that most of the papers testing algorithmic investment strategies do not maintain proper test structure, which is why their results cannot be treated as valid and robust. Before we go to the empirical main part of this research, it is important to list and describe the most common disadvantages of articles that test different AIS, i.e.:

Papers describing different approaches to LSTM can be dived into those that refer to the theoretical aspects of the LSTM model and those that mainly focus on LSTM and various ML models’ empirical properties, tested on different sets of data.

The first introduction of LSTM was presented in the paper written by Hochreiter and Schmidhuber (1997) [13]. By introducing Constant Error Carousel (CEC) devices, LSTM can handle the exploding and vanishing gradient problems. The first version of the LSTM block included cells, input and output gates. LSTM real feature was the ability to preserve information through the chain of iterations during training. The next theoretical advance was introduced by Gers (1999) [14] who introduced the forget gate (also called “keep gate”) in the LSTM architecture, which enabled the network to reset its own state. Subsequently, Gers et al. (2000) [15] added peephole connections, which are connections from the cell to the gates. In addition, the output activation function was omitted. Recent advances cover putting forward a simplified variant called the Gated Recurrent Unit (GRU) by Chung et al. (2014) [16].

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Another part of the literature focuses on empirical research. Some studies either provide a general review of ML applications in financial time series forecasting (Heaton et al. (2016) [17], Tsantekidis et. al. (2017) [18], Rechentin (2014) [19]) or report performance of specific non-LSTM tools in this area (Tay and Cao (2002) [20], Sun et. al. (2017) [21], Van Gestel et. al. (2001) [22], Qu and Zhang (2016 ) ) [23]).

We can also find a number of studies that present results of using the LSTM model, mostly in predicting stock prices.

Chen et al. (2015) [24] implemented LSTM on the stock market in China. They collected data from stocks and divided the percentage returns of the prices into seven groups: (−∞, −1.5], (−1.5, −0.5], (−0.5, 0.4], (0.4, 1.4], (1.4, 2.5],

Navigating Edinburgh's Forex And Mining Scene: Tips For Success

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