Lstm high frequency trading

d3 heatmap cryptocurrency market-data d3js order Feb 2, 2022 · Order flow imbalance represents the changes in supply and demand. In this context, High-Frequency Trading (HFT) helps traders hold positions for short periods of time and earn their profits by accumulating tiny gains on a large number of transactions (Huang, Huan, Xu, Zheng, & Zou, 2019). Predict trading volume through a relatively low-complexity model. , the original idea of the LSTM model is to create self-loops to remain the gradient for a longer duration. Togeneralize the May 15, 2024 · In order to maintain the consistency of the frequency multiplicity difference between low-frequency data and high-frequency data, this paper treats the monthly trading days of the high-frequency data as 20 days and randomly removes the excess trading day data in the months that are more than 20 days; the trading days that are less than 20 days Sep 15, 2022 · HAN: A hybrid attention network for predicting stock trends based on sequences of recent related news items by imitating the learning process of human beings. For example, in the Chinese A-share market, the unit of change is always 0. The model first uses convolution to extract the deep features of the original stock data, and then uses the Long Short-Term Memory networks to mine the long-term time series features. Specifically, intraday volatility forecasts may support traders in assessing the likelihood of price changes and therefore better understanding the risk involved in certain automated trading strategies (see Bates (2019)). State-of-the-art models such as Random models, Logistic Regressions, LSTMs, LSTMs equipped with an Attention mask, CNN-LSTMs and MLPs are reviewed and compared on the same tasks, feature space and dataset, and then clustered according to pairwise similarity and performance Feb 5, 2021 · High-frequency input has important results for learning financial data. Published 2 January 2012. So far, we have seen numerous achievements that LSTM has made since its invention, but these achievements are concentrated in areas such as speech recognition and pattern recognition. lstm high-frequency-trading Resources. This model includes news-level attention and temporal attention mechanisms, which are used to focus on key information in the news ( Hu et al. Qu and Zhang (2016), assuming that high-frequency returns periodically trigger momentum and reversal, designed a new SVM kernel method2 for forecast-ing high-frequency market directions and Sep 14, 2023 · I decided to do High Frequency Trading - traded a single stock. When traders do technical analysis they inspect different time periods Nov 19, 2021 · Therefore, the motivation that we propose the machine learning strategy in this study is to explore the statistical arbitrage opportunities as much as possible for the purpose of obtaining the highest low-risk profits and exceeding the traditional cointegration strategy in high-frequency trading. 2. However, difficulties still remain to make RNNs more successful in a cluttered stock market. Results indicate that the HFformer achieves a higher cumulative PnL than the LSTM when trading with multiple signals during backtesting, and possible high-frequency trading strategies for use with the HFformer model are discussed. 3 watching Forks. According to Goodfellow et al. 7(a) and 7(b) clearly indicates superior modelling of daily volatility and therefore can be used for intra-day modelling for volatility, specifically in high frequency trading environments. They have been around for more than 70 years. Numerous studies on algorithmic trading models using deep learning have been conducted to perform trading forecasting and analysis. 2 Long Short-Term Memory (LSTM) LSTM was designed to capture long-term dependencies in sequential data according High Frequency Trading Framework with Machine/Deep Learning In this project, we provide a framework/pipeline for high frequency trading using machine/deep learning techniques. • Attention module for integrating multiple frequency and currency data. 2) 5-minute high-frequency data for the FTSE 100 of UK and the S&P 500 of US from January 1, 2019 to May 14, 2020 are obtained from the Github platform to examine the applicability of NR-LSTM to other stock markets. - hon-gyu/meta-labeling-and-lstm . Explore financial data, address anomalies, normalize via LSTM, implement high-frequency trading, design flexible LSTM model, create versatile data loader, predict prices, execute trading logic, evaluate with EMA, extend to multi-stock prediction. Agents execute trades by placing market orders that match existing limit orders in the order book, or by posting buy or sell limit orders at the desired price level. May 16, 2023 · In the computer era, high frequency trading is one of the hottest topics in the financial investment market. They are able to utilize several effective algorithms to analyse the market data and identify the key points required to carry out a successful trading operation. Manag. Expand. g. sentiment on bitcoin return and high-frequency volatility. Keywords: High-Frequency Trading, Deep Learning, LSTM Neural Networks, Ensemble Models JEL Classi cation: C45, C53, C55, G17. Feb 18, 2024 · Many existing statistical models could better describe and forecast the characteristics of volatility, whereas they do not simultaneously account for the long-term memory of volatility, the nonlinear characteristics of high-frequency data, and technical index information during the modeling phase. 6. This program implements such a solution on data from NYSE OpenBook history which allows to recreate the limit order book for any Due to the higher stochasticity of financial time series, we will build up two models in LSTM and compare their performances: one single Layer LSTM memory model, and one Stacked-LSTM model. [25] Rundo, F. Deep neural network can reveal complex patterns and hidden relationships, analyze different indicators and their interactions, so as to predict market Jul 12, 2020 · The present work addresses theoretical and practical questions in the domain of Deep Learning for High Frequency Trading. Oct 9, 2019 · Changes in intraday trading volume are integral to any algorithmic trading strategy. It can be seen that on the minute scale, there was a difference between the exchange markets in the two fiat currencies, with larger volatility in CNY minute prices Aug 11, 2023 · The portfolio investing each ETF by the weight from the model records a Sharpe ratio of 0. 629 to 1. e. Keywords: High Frequency Quantitative Trading, LSTM Model, Grid Trading Strategies. IEEE. AU - Tsiamas, Ioannis. T1 - An ensemble of LSTM neural networks for high-frequency stock market classification. Best bid or size at the best bid increase -> increase in demand. Jan 8, 2024 · By maintaining a high level of trading investment strategy model based on LSTM. Liquidity-taking strategy: A strategy that takes liquidity by crossing the spread with aggressive or marketable orders. March 2019; Journal of Forecasting 38(1) of a stock, as high liquidity ensures low trading costs. The high-frequency trading framework for the price trend prediction model and trading strategy has been a popular Dec 3, 2022 · The proposed novel high frequency algorithmic trading strategy built over an LSTM based short-term price forecasting is used for Bitcoin and Ethereum. Abbink. Resources Mar 15, 2024 · The peculiarity of the work is the very high frequency trading strategy, which executes 2852 trades during the backtesting, (ours just 4). High-frequency strategy: A strategy characterized by a large number of trades. A new LSTM-based model called DLSTM is built and new architecture for the Transformer-based model is designed to adapt for financial prediction. Dec 5, 2021 · LSTM is a variant of RNN Bi, G. LSTM Model. The experiment result reflects High Frequency Trading (HFT) is one of the extreme forms of electronic trading. , 2015) into the LSTM architecture. We will try to take advantage and we will feed time series data of several currency pairs and do correlation analysis on them. Major world currencies often correlate and affect each other. Data source and relevant characteristics. Keep in mind that a lot of work is needed for this to work in all market conditions. 7 An ANN can be described as a non-linear fitting algorithm whether the fit is performed by adjusting the weights of information propagation between stacked layers of elementary functions, called neurons, in analogy with the simplest On the other hand, LSTM illustrated in Figs. Recommend designing platforms, monitoring risk, and forming price predictions. The high-frequency data provide useful information for learning investment trading and describe market volatility in detail. Based on the models, high-frequency trading strategies were proposed. Convolutional neural network (CNN) and long short-term memory (LSTM) neural network were selected to build up and down classification models, respectively. This module offers flexibility in adjusting input dimensions, the number of units, and the number of layers. This was a short exploratory project. This paper introduces a new method to forecast the log change in trading volume, leveraging the power of Long Short Term Memory (LSTM) networks in conjunction with Support Vector Regression (SVR Jun 25, 2018 · We propose an ensemble of Long-Short Term Memory (LSTM) Neural Networks for intraday stock predictions, using a large variety of Technical Analysis indicators as network inputs. Y1 - 2019. The contributions of this paper are twofold. Introduction The long-lasting debate on predictability of nancial markets has led to volumes of research on this Here, deep learning algorithms were applied to high-frequency trading. This project used light-GBM and LSTM and other models building a high-frequency trading strategy. In order to develop a high-frequency trading strategy model with specific validity for different types of stocks, this paper proposes a LM jump detection-grid trading model based on LSTM prediction to construct a trading strategy for three types of stocks: blue chips, volatile stocks The availability of high-frequency trade data has made it possible for the intraday forecast of price patterns. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects. 41% and of 0. returns, the goal of this project is to develop 1-Dimensional CNN and LSTM prediction models for high-frequency automated algorithmic trading. based on LSTM, which was claimed to be more accurate and e cient in voice search. High-frequency trading is a method of intervention on the financial markets that uses sophisticated software tools, and sometimes also hardware, with which to implement high-frequency negotiations, guided by mathematical algorithms, that act on markets for shares, options, bonds, derivative instruments, commodities, and so on. The sample data studied in this paper is the 5 min Footnote 1 high-frequency data from 2015-01-01 to 2020-04-28 of 50 stocks Footnote 2 screened from the main board of Shanghai and Shenzhen Stock Exchange, including opening price, closing price and trading volume (excluding holidays, a total of 1296 trading days). Compared with low-frequency data at daytime intervals or longer intervals, high-frequency data mainly refers to the data collected in hours and minutes, usually referring to the type of data such as transaction price and trading volume collected during the trading intraday (Aït-Sahalia & Jacod, 2014). 39% with respect to LSTM and random forests, respectively. NVIDIA also submitted a throughput-optimized configuration on the same hardware for the Sumaco Suite in FP16 precision (NVDA221118a): LSTM_A: 1. 24 stars Watchers. Feb 27, 2021 · This paper aims to find a superior strategy for the daily trading on a portfolio of stocks for which traditional trading strategies perform poorly due to the low frequency of new information. With the help of technical indicators, recent studies have shown that LSTM based deep learning models are able to predict price directions (a binary classification problem) with performance better than a random guess. Readme Activity. More advanced feature engineering (with depth trade and quote data) and models (such as pre-trained models) can be applied in this framework. The experimental work is divided into a set of traditional trading strategies and a set of long short-term memory networks. TLDR. P. The special challenges for ML presented by HFT can be considered two fold : (i) Microsec-ond sensitive live trading - As the complexity of the model increases, it gets more computationally expensive to keep up with the speed of live trading and actually use the informa- May 16, 2023 · In order to develop a high-frequency trading strategy model with specific validity for different types of stocks, this paper proposes a LM jump detection-grid trading model based on LSTM Mar 1, 2019 · An ensemble of LSTM neural networks for high‐frequency stock market classification. 1% fees, the transactions reported sum to a 258% loss due to the fees plus the earning or loss for the trades. Abstract. Feb 1, 2022 · In a quantitative trading system, price is always one of the most important factors. Stars. To generalize the LSTM to the case of multiple mismatch ratios, we adopt the unrestricted Mixed DAta Sampling (U-MIDAS) scheme (Foroni et al. This repo contains the work done by me during my assignment of EE782 (Advanced Machine Learning). Then the data of bitumen futures contract was Mar 1, 2024 · Introduction. The special challenges for ML presented by HFT can be considered two fold : (i) Microsec-ond sensitive live trading - As the complexity of the model increases, it gets more computationally expensive to keep up with the speed of live trading and actually use the informa- The proposed novel high frequency algorithmic trading strategy built over an LSTM based short-term price forecasting is used for Bitcoin and Ethereum. Introduction. Data Loader I created a flexible data loader tailored for training the LSTM model, especially when dealing In this paper, we develop an online time series forecasting method for high-frequency trading (HFT) by integrating three neural network deep learning models, i. With the aid of technology, traders and trading establishments use trading platforms to perform various transactions. An implementation that applies meta-labeling to minute-frequency stock data, utilizing LSTM as the primary model for price direction prediction, which forms the basis for a trading strategy augmented by a secondary meta-labeling layer to filter false positives and improve risk-return metrics. Currently, family of recurrent neural networks (RNNs) have been widely used for stock prediction with many successes. Jul 1, 2022 · Whether the change trend of futures price can be accurately analyzed and predicted is the key to the success or failure of futures trading. There’s no public consensus on what this means A tag already exists with the provided branch name. , 2018 ). W e. , 2021). LOBs offer many details, but at the same time, they are very noisy. In: 4th Int. Innov. Therefore, the proposed HW_LSTM_RL structure is suitable for investment transactions in highly volatile markets, e. In this project we try to use recurrent neural network with long short term memory to predict prices in high frequency stock exchange. 707 M 9 inferences per second at a power consumption of 949 watts; LSTM_B: exceeded190 K 10 inferences per second at a power consumption Jun 25, 2018 · The proposed model is found to perform better than the benchmark models or equally weighted ensembles. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. High Frequency Trading Price Prediction using LSTM Recursive Neural Networks. Starting with a data set of 130 anonymous intra-day market features and trade returns, the goal of this project is to develop 1-Dimensional CNN and (2014) also demonstrated that intraday measures are useful for market makers, high-frequency trading, and option traders. We first present how theconventional LSTM model can be adapted to the time series observed at mixed frequencies when thesame mismatch ratio is applied for all pairs of low-frequency output and higher-frequency variable. Ultimately, the performance of the HFformer and Long Short-Term Memory models are assessed and results indicate that the HFformer achieves a higher cumulative Jan 10, 2018 · To associate your repository with the high-frequency topic, visit your repo's landing page and select "manage topics. Extracting effective information from Limit Order Books (LOBs) is a key point in high-frequency trading based on stock-movement forecasting. For high-frequency trading, the last price of our tick data is not accurate enough, because the data often has a huge spread. PY - 2019. State-of-the-art models such as Random models, Logistic Regressions, LSTMs, LSTMs equipped with an Attention mask, CNN-LSTMs and MLPs are reviewed and com-pared on the same tasks, feature space, and dataset and clustered according to pairwise similarity and performance metrics. These self-loops are weighted by a gating A novel dynamic parameter optimization algorithm based on reinforcement learning for stock prediction and trading, and a reward-enhanced upper confidence bound selection algorithm is proposed to automatically optimize the parameters of the trading logic in real-time trading. Some existing work has considered this issue and proposed various methods to perform high-frequency forecasting, such as predicting prices or identifying uptrends or downtrends in the next few minutes (Zhang et al. • Simultaneous multi-cryptocurrency forecasting to help hybrid investments. This simple, yet effective trading algorithm uses the network’s price forecasts to make buy and short selling decisions for cryptocurrency based on certain set criteria. on Econ. We have computed the high frequency returns on a minute scale and a half-an-hour scale for reference, which are shown in Figure 5. 1. October 2021. , Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Transformer; and we abbreviate the new method to online LGT or O-LGT. Dec 27, 2021 · Algorithmic trading is one of the most concerned directions in financial applications. Sep 29, 2021 · same mismatch ratio is applied for all pairs of low-frequency output and higher-frequency variable. May 17, 2022 · Abstract: High-frequency trading is a method of intervention on the financial markets that uses sophisticated software tools, and sometimes also hardware, with which to implement high-frequency negotiations, guided by mathematical algorithms, that act on markets for shares, options, bonds, derivative instruments, commodities, and so on. Mar 1, 2019 · 1. Jan 3, 2024 · This study introduces an augmented Long-Short Term Memory (LSTM) neural network architecture, integrating Symbolic Genetic Programming (SGP), with the objective of forecasting cross-sectional HFT-LSTM. , et al. Oct 1, 2019 · A known risk minimization strategy is a high frequency trading regime, taking benefit from minor price variations, and achieving small profits multiple times a day. Artificial Neural Networks (ANN) are a class of computational models used in Machine Learning (ML). This experiment is aimed at testing the multivariate LSTM model. A high-frequency trading model using Interactive Brokers API with pairs and mean-reversion in Python Python 2 MIT 9 0 0 Updated Jul 24, 2019 ECR-Pattern-Recognition-for-Forex-Trading Public Forked from ernestcr/ECR-Pattern-Recognition-for-Forex-Trading May 15, 2024 · Wu, Li, et al. Introduction High frequency trading refers to computerized trading that seeks to profit from extremely short- Mar 1, 2024 · Therefore, a high-frequency price prediction method and trading strategy are required in the cryptocurrency market. AU - Borovkova, Svetlana. 1. Mar 1, 2024 · Therefore, we make use of the individual share to test the stability of NR-LSTM. , 2021) is a high-frequency trading simulator developed by Microsoft Research Asia in 2022, which provides a Feb 1, 2024 · A novel labeling method to reduce the number of high-frequency trading. Long Short-Term Memory, as a variant of RNN, can obtain hidden dependencies in data and has shown a significant performance in processing time series data, and is applied to predict the price movement of a short-term and test it by an experiment indicates that LSTM can be used in the trend Oct 6, 2021 · How to Use Deep Order Flow Imbalance. This simple, yet effective trading algorithm uses the network's price forecasts to make buy and short selling decisions for cryptocurrency based on certain set criteria. " GitHub is where people build software. This method performs ensemble empirical mode decomposition (EEMD) on commodity futures prices, and incorporates the components obtained by EEMD decomposition and the adaptive fractal Hurst index calculated by using intraday high-frequency data as new features into the LSTM model to decompose its correlation with the external market to detect Long-Short Term Memory (LSTM) was first introduced by Hochreiter and Schmidhuber in 1997 . Sep 21, 2023 · To answer this question, various LSTM-based and Transformer-based models are compared on multiple financial prediction tasks based on high-frequency limit order book data. The limit order book contains buy and sell orders at different price levels. 01. 1 Keywords: Random forest, LSTM, Forecasting, Statistical Arbitrage, Machine learning, Intraday trading 1. Finally, the XGBoost model is adopted for fine-tuning. The proposed novel high frequency algorithmic trading strategy built over an LSTM based short-term price forecasting is used for Bitcoin and Ethereum. LSTM is a variant of RNN in which a gating mechanism is provided. We assess via both Mar 1, 2021 · We first decompose the carbon price series into different frequency sub-modes through variational modal decomposition (VMD) of Dragomiretskiy and Zosso [27], then develops Generalized autoregressive conditional heteroskedasticity (GARCH) and Long Short-Term Memory Model (LSTM) for high- and low-frequency sub-modes respectively, finally combines Feb 20, 2023 · Furthermore, possible high-frequency trading strategies for use with the HFformer model are discussed, including trade sizing, trading signal aggregation, and minimal trading threshold. One of the most sought after forms of electronic trading is high-frequency trading (HFT), typically known for microsecond sensitive changes, which results in a tremendous amount of data. With each row one of the price or size at the best bid or ask changes which corresponds to change in the supply or demand, even at a high frequency level, of Bitcoin. Therefore, an increasing amount of investors are dedicated to finding and making the most of reasonable models In high-frequency trading, assets are usually traded in a limit order book on an electronic exchange. John B. Accordingly, forecasting the change in trading volume is paramount to better understanding the financial markets. 2. Aug 1, 2018 · High-Frequency Stock Trend Forecast Using LSTM Model. Symp. : Discovery of jump breaks in joint volatility for volume and price of high-frequency trading data in China. I wrote a PyTorch module for defining an LSTM model. 19. Computer Science. Order book information is crucial for traders, but it can be complex. The goal of this project is to develop 1-Dimensional CNN and LSTM prediction models for high-frequency automated algorithmic trading and the models are compared using both machine learning accuracy measures and investment risk and return metrics. Specifically, RNNs lack power to retrieve discerning features from a clutter of signals in Jan 15, 2024 · The proposed novel high frequency algorithmic trading strategy built over an LSTM based short-term price forecasting is used for Bitcoin and Ethereum. Sep 5, 2018 · Financial markets have both long term and short term signals and thus a good predictive model in financial trading should be able to incorporate them together. 1210-1217). In high-frequency trading, though the model displayed competency in specific scenar-ios, researchers pinpointed potential limitations when grappling with certain volatile market dynamics, suggesting the need for adaptive [17]. In Jul 27, 2023 · hidden information, we propose a DRL based stock trading system using cascaded LSTM (CLSTM-PPO Model), which first uses LSTM to extract the time-series features from daily stock data, and then the features extracted are fed to the agent for training, while the strategy functions in reinforcement learning also use another LSTM for training. Dec 25, 2018 · The data ranged from 25 July 2014–29 March 2017. optional arguments: -h, --help show this help message and exit--data DATA location of the market data --model MODEL type of recurrent net (RNN_TANH, RNN_RELU, LSTM, GRU) --symbol SYMBOL symbol of asset (a, b) --nhid NHID number of hidden units per layer --nlayers NLAYERS number of layers --lr LR initial learning rate —-decay DECAY learning rate dacay --clip CLIP gradient clipping --epochs The proposed novel high frequency algorithmic trading strategy built over an LSTM based short-term price forecasting is used for Bitcoin and Ethereum. Read online. Hist (Xu et al. 65 while that of the market portfolio is 0. Feb 16, 2024 · Rule-based strategy: A strategy that’s based on trading rules instead of model-based alphas. I performed high frequency trading on stocks of AMZN and AAPL both independently and combined to make better predictions. Apr 6, 2022 · The pre-training model is the Attention-based CNN-LSTM model based on sequence-to-sequence framework. N2 - We propose an ensemble of long–short-term memory (LSTM) neural networks for intraday stock predictions, using a large variety of technical analysis indicators as network inputs. The proposed ensemble operates in an online way, weighting the individual models proportionally to their recent performance, which allows us to deal with possible non Sep 28, 2021 · This paper demonstrates the potentials of the long short-term memory (LSTM) when applyingwith macroeconomic time series data sampled at different frequencies. In this Jul 4, 2023 · Predicting stock prices has long been the holy grail for providing guidance to investors. Given any stock portfolio, they should be able to separate the risky stocks from the less risky and rich ones before making any investment decisions. 3 forks High Frequency Trading. Multidimensional LSTM for High-Frequency Time Series Topics. About. Jun 1, 2023 · Use neural networks for high-frequency spot trading volume predictions. (2019). We can try to estimate the loss by supposing that no compounding is applied and 0. 2 Test 2 - Multivariate Input LSTM Testing. 5. This paper proposes a differential transformer neural network model, dubbed DTNN, to predict OrderBook Heatmap visualizes the limit order book, compares resting limit orders and shows a time & sales log with live market data streamed directly from the Binance WS API. high frequency trading machine learning market making order flow. However, in the meantime, some classical financial characteristics have striking similarities. Two novelties are introduced, first, rather than trying to predict the exact value of the return for a given trading opportunity, the problem is framed as a binary classification with Sep 15, 2022 · Lanbouri and Achchab used the LSTM model for the high-frequency trading perspective in which their goal was to use the S&P 500 stock trading data to predict the stock price in the next 1, 5, and 10 minutes (Lanbouri & Achchab, 2020). With the numbers of stocks listed in stock exchanges, it is impossible to track all the available information for the human mind. Feb 2, 2023 · High-throughput optimized results for the Sumaco Suite. Time-weighted LSTM model with redefined labeling for stock trend prediction. Dev. Best bid or size at the best bid decreases Jul 5, 2021 · The world of trading and market has evolved greatly. (2021) designs a new framework based on CNN and LSTM to predict the direction of the stock market by aggregating multiple variables, automatically extracting features through CNN, and feeding them into LSTM. We expected the Stacked-LSTM model can capture more stochasticity within the stock market due to its more complex structure. daily returns of 0. The article will test if Long Short Term Memory (LSTM) neural networks are suitable for high frequency foreign exchange (forex) trading. High-frequency trading (HFT) platforms are capable of such Under high-frequency trading settings, traders should be able to quickly, efficiently, and profitably detach and disseminate information from complex sets. Compared with traditional trading strategies, algorithmic trading applications perform forecasting and arbitrage with higher efficiency and more stable performance. This paper constructs a new deep ensemble learning framework combining signal decomposition and exogenous variable feature mining for high-frequency futures price prediction, which consists of depth feature extraction (DFE), long short-term memory Jul 1, 2020 · These have benefited the automation of algorithmic trades in financial instruments at very high speeds. Dec 19, 2019 · The high-frequency trading framework for the price trend prediction model and trading strategy has been a popular approach for T+0 trading in the stock market. •. High Frequency Trading (HFT) is one of the extreme forms of electronic trading. In 2017 IEEE 29th international conference on tools with artificial intelligence (ICTAI) (p. Introduction In the last decade, machine learning methods have exhibited distinguished development in financial time series prediction. Jan 2, 2012 · High-Frequency Trading. Dec 24, 2022 · Stock price prediction is crucial but also challenging in any trading system in stock markets. First distant futures trading series does not help improve spot trading predictions. The networks incorporate general and specific trading patterns, where the former Abstract: High-frequency trading is a method of intervention on the financial markets that uses Deep Learning (LSTM) and RL Based Trading Systems: Literature Review In [6], the authors Dec 5, 2022 · Using high-frequency data can get a large data set, and using deep neural network can overcome the problems of data snooping and over fitting in the process of using data (Bengio Citation 2007). , financial market Recently, the global market returns are characterized by year-long episodes of high volatility, leading market traders to struggle to maximize their total return with high-frequency trading. The model portfolio outperformed the market by over 3 times. Deep LSTM with reinforcement learning layer for financial trend prediction in FX high frequency trading systems. hc kg un fg zq cg mv se cu ra