The theory of deep reinforcement learning is applied for stock trading strategy and investment decisions to Indian markets with three classical Deep Reinforcement Learning models Deep Q-Network, Double Deep Q -Network and Dueling Double deep Q-Netwonrk on ten Indian stock datasets. 2 PDF View 1 excerpt, cites methods Web1/5/ · Reinforcement Learning Applied To Forex Trading Pdf IM Academy Forex Trading was founded as a small business in by independent entrepreneur WebReinforcement Learning applied to Forex Trading It is already well-known that in , the computer program AlphaGo became the first Go AI to beat a world champion Go Web Reinforcement learning Reinforcement Learning is a type of machine learning technique that can enable an agent to learn in an interactive environment by trials and WebPlease cite this article as: J. Carapuço, et al., Reinforcement learning applied to Forex trading, Applied Soft Computing Journal (), ... read more

Are you sure you want to create this branch? Local Codespaces. HTTPS GitHub CLI. Sign In Required Please sign in to use Codespaces. Launching GitHub Desktop If nothing happens, download GitHub Desktop and try again. Launching Xcode If nothing happens, download Xcode and try again. Launching Visual Studio Code Your codespace will open once ready. Latest commit. Git stats 6 commits. Failed to load latest commit information. View code. py file in 'rl'. If an agent decides to take a LONG position it will initiate sequence of action such as buy- hold- hold- sell for a SHORT position vice versa e.

sell - hold -hold -buy. Only a single position can be opened per trade. Thus invalid action sequence like buy - buy will be considered buy- hold. Default transaction fee is : 0. Agent decides optimal action by observing its environment. Over a testing period of 3 years on the fifteen stocks, the actor-critic Critic DQN with GRU US, UK, and Chines Total return Wu et al. Actor-critic DDPG Stocks Sharpe ratio in 6 stocks. That is all under neglecting market frictions. with GRU D. Comparison among Different RL Approaches indicates lower returns variance during the trading horizon.

In general, we can observe that whenever an actor-critic We dedicate one subsection to gain insights on how the trading agent is compared with other methods, it either outper- different RL methods perform under QT applications. The forms or performs very well and is close to the best strategy. main reason for that is the difficulty in obtaining fair insights That is perhaps because these methods have a more stable about the suitable approach while usually each proposed policy over the learning process than both actor, which usually trading system is compared with counterpart methods using experiences high variance in their gradient, and the critic that a different dataset of the original work.

With that, one cannot has a policy that is highly sensitive to the value function emanate a clear conclusion since different data could lead updates [90]. In addition, they also can perform continuous to dissimilar results. That is especially the case under QT actions in which the agent can adequately adjust its trading since the testing data could have various resolutions, markets, volume. However, that superior performance of actor-critic volatility, and price trend depending on the market regime.

methods comes at the expense of higher computation costs, Nevertheless, it is imperative to gain insights related to the especially with deep learning for the actor and critic parts, most suitable RL method for QT applications. To this end, in where four networks are involved in the learning process. this subsection, we review papers that dedicate part of their work to compare the performance of trading systems with different RL methods over the same dataset.

We refer the VI. D IRECTIONS FOR F UTURE R ESEARCH reader to Table V for a summary of that comparison. This section proposes directions for future research predom- Moody and Safell [64] found that the learning process inantly driven by the still open challenges in implementing RL of their recurrent actor method is more stable than that of on QT applications. As we saw in our discussion of the current the Q-learning, which they found susceptible to the financial literature in sections IV and V, other than the actor approach, market noise.

They also implemented sensitivity analysis to hence they are risk-insensitive. We see that justifiable since quantify the importance of each input to the actions. Unlike the those RL algorithms are originally developed to be insensitive actor method, they found it difficult to establish a sensitivity to risk. Nonetheless, most of the time, market practitioners framework for Q-learning.

Also, the authors argued that the prefer trading strategies that are risk-aware and robust against actor method is more computationally efficient since the critic market uncertainties.

To this end, we believe improving the learning process of Q-learning agents. that safe RL, distributional RL, adaptive learning, risk-directed Most comprehensive comparison was made by Zhang et al. exploration, which we will discuss next, are active research [] where they compared all RL methods with several base- areas in the machine learning literature worth probing by line strategies, including returns momentum [66] and MACD researchers developing RL-based trading systems.

signal [], under various financial assets. Interestingly, they found out that DQN has a low turnover rate which caused it to outperform in most of the assets. Their use of experience A. Safe Reinforcement Learning replay and LSTM architecture could probably improve the Safe RL is an active research area concerned with bringing performance observed by Moody and Saffell when their Q- risk dimensions into the learning process of RL-based agents.

learning trader experienced a high turnover rate. Moreover, Zhang et al. noted that the second- while making decisions, and we see QT as one of those best performance, overall, was observed in the trading strategy applications. Following the notion of Safe RL discussed amply of the A2C algorithm synchronous version of the A3C.

by Garcia and Fernandez in [], a risk-aware agent can The comparison of Tsantekidis et al. With the former, 23 or without, rather than comparing the two RL methods, one can incorporate risk in the value function, while the latter PPO and DDQN. Nevertheless, from their results, one could can introduce risk awareness by adding a constraint to the observe that the learning process of the PPO is remarkably objective to be optimized.

Distributional RL future when it comes to the development of RL-based trading Distributional RL is about learning the distribution of the systems. Following Bellemare et al. In that sense, one endeavor to maximize denote a random variable that maps state-action pairs over the the difference between the mean and variance of the trading distribution of the returns when following policy 𝜋.

If one returns. categorical DQN, where the output of the network is softmax For efficient computation and online learning, one can consider function that represents the probability of each atom, i. Those probabilities are then updated using Bellman change in 37 through recursive computation of the first projection with gradient descent while considering the cross- and second moments using Taylor expansion.

Finally, one entropy of the Kullback—Leibler divergence as the loss. For can update the policy in the ascent direction of the objective risk-insensitive learning, one then can take greedy actions with gradient using SG methods. respect to the empirical expectation of 𝑍 𝜋 The mean-variance problem can also be approached through critic methods.

With that, []. Nevertheless, we note here that with knowing the proba- maximizing the expectation of the cumulative reward defined bility distribution of 𝑍 𝜋 , one can easily extend to distributional in 3 , which a standard critic RL does, is equivalent to risk-sensitive learning where the actions, for example, are maximizing 38 when considering the single-step reward in taken based on minimizing CVaR, as proposed by Morimura 39 and a unity discount factor 𝛾.

One then can follow tabular et al. or any function approximation algorithms we discussed in Section II to derive the optimal risk-sensitive policy under critic methods. Learning Methods 2 Risk-Constrained Agent: A risk-constrained agent aims to maximize the expectation of the returns while meeting 1 Adaptive Incremental Learning: RL is an online learning a constraint imposed on a risk measure. We saw that a system by its nature. Under that approach, one deteriorate over time due to the non-stationary behavior of can consider the following general problem the markets.

Another terminology for non-stationarity in the max E[𝑅] machine learning literature is the concept drift where it refers 40 to the evolvement of the data underlying distribution over time s.

There are established learning algorithms in the Where 𝛽 · is a risk measure, and Γ is a strictly positive literature to handle the concept drift within the data. One of constant. If one is only concerned about rare events that may those is adaptive incremental learning, where the model can incur significant losses, then 𝛽 can be represented by the be updated in real-time based on the most recent observation Conditional Value-at-Risk [42], []; otherwise, one can use or mini-batch past instances, and that all depend on the the variance of the negative returns as 𝛽.

computational resources and the nature of the application Problem 40 can be solved through actor RL with maximiz- []. With et al. barrier method []. Finally, we can recursively estimate the We nonetheless encourage researchers to further explore those policy parameters with gradient-based learning. learning approaches in QT under different RL algorithms since For solving 40 through Q-learning, Borkar [] intro- we insight their potency in extending the practicality of trading duced an online algorithm along with its convergence proof to systems performance in real-time.

an optimal risk-constrained policy under the tabular approach. Gehring and Precup [] proposed a risk- proposed an algorithm based on the Lagrange multiplier and directed exploration where it depends on the expected absolute proved its convergence to a locally optimal policy. With Q-learning, that [8] D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, controllability measure is define as M.

Lanctot, L. Sifre, D. Kumaran, T. Graepel et al. Antonoglou, T. Hubert, K. Simonyan, L. Sifre, S. Schmitt, A. Guez, E. Lockhart, D. Hassabis, T. One then can update the controllability, under tabular method, [10] R. every time a state-action pair is visited as follows —, Gao and L.

Citeseer, , pp. work of Gehring and Precup in []. Finally, the risk-oriented [13] C. Nevmyvaka, Y. Feng, and M. Where 𝜔 is a weighing parameter to emphasize on the [15] T. Hens and P. Guéant and I. Gašperov and Z. approaches, i. Jin and H. following all those methods, that is, under tabular and function [19] X. Du, J. Zhai, and K. approximation RL with different learning architectures. Under [20] Z. Jiang and J. IEEE, , pp. trading system that learns online under its interaction within [21] Z.

Jiang, D. Xu, and J. The review outcome shows that actor-critic arXiv trading agents can outperform other methods because they [22] P.

Pendharkar and P. critics, although we see that the more advanced algorithm [23] Z. Liang, H. Chen, J. Zhu, K. Jiang, and Y. However, the superior performance of actor- arXiv critic methods comes at higher computation costs, especially [24] S. Almahdi and S. Therefore, we propose Applications, vol. to research the application of Safe and distributional RL on [26] M.

García-Galicia, A. Carsteanu, and J. as measuring the controllability of state-action pairs. Finally, [27] N. dissertation, The University of Texas at Arlington, learning due to its potency in enhancing real-time trading [28] H. Park, M. Sim, and D.

Applications, vol. Xiong, X. Liu, S. Zhong, H. Yang, and A. Conegundes and A. Magill and G. Heryanto, B. Handari, and G. Conference Proceedings, vol. AIP Publishing LLC, , [3] M. Davis and A. Yang, X. Zhong, and A. Amihud and H. Kang, H. Zhou, and Y. critic reinforcement learning method for stock selection and portfolio [5] D. Bertsimas and A. Big Data Research, , pp. Sutton and A. Barto, Reinforcement Learning: An Introduction. preprint arXiv Silver, A.

Huang, C. Maddison, A. Guez, L. Sifre, G. Van [35] Y. Ye, H. Pei, B. Wang, P. Chen, Y. Zhu, J. Xiao, and B. Li, Den Driessche, J. Antonoglou, V. Lanctot et al.

ence on Artificial Intelligence, vol. Kirilenko, A. Kyle, M. Samadi, and T. Katz and D. McGraw-Hill New York, of Finance, vol. Elton and M. Meng and M. Sortino and R. Rep, pp. portfolio Management, vol. Dayan and T. Rockafellar, S. Uryasev et al. Tesauro et al. top traders. Mnih, K. Kavukcuoglu, D. Graves, I. Antonoglou, [44] C. Neely, D. Rapach, J. Tu, and G. Wierstra, and M. Abarbanell and B. Lin, Reinforcement learning for robots using neural networks. Dechow, A. Hutton, L. Meulbroek, and R.

Schaul, J. Quan, I. Antonoglou, and D. Gan, M. Lee, H. Yong, and J. management and financial innovations, no. Van Hasselt, A. Guez, and D. Humpe and P. Applied financial economics, vol. Nison, Beyond candlesticks: New Japanese charting techniques forcement learning. Lo, H. Mamaysky, and J. Hochreiter and J. computation, vol. Pring, Study guide for technical analysis explained. McGraw- [86] K. Greff, R. Srivastava, J.

Koutník, B. Steunebrink, and Hill New York, , vol. Colby, The encyclopedia of technical market indicators. neural networks and learning systems, vol. Bulkowski, Trading classic chart patterns. Person, A complete guide to technical trading tactics: how to [88] L. John Wiley ings of Neuro-Nımes, vol. Konda and J. Li, J. Wu, and H. Sutton, D. McAllester, S. Singh, Y. Mansour et al. Ican, T. Celik et al. Silver, G.

Lever, N. Heess, T. Degris, D. conference on machine learning. PMLR, , pp. Fabbri and G. Lillicrap, J. Hunt, A. Pritzel, N. Erez, Y. Tassa, The unreasonable effectiveness of recurrent neural networks. Silver, and D. Saad, D. Prokhorov, and D. Mnih, A. Badia, M. Mirza, A.

Graves, T. Lillicrap, T. Harley, of stock trend prediction using time delay, recurrent and probabilistic D. Silver, and K. Tran, A. Iosifidis, J. Kanniainen, and M. Fujimoto, H. Hoof, and D. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. monica ca, Tech. Haarnoja, A. Zhou, P. Abbeel, and S.

PMLR, , Ziebart, A. Maas, J. Bagnell, and A. Moody and M. Chicago, IL, IEEE transactions on neural Networks, vol. Mudchanatongsuk, J. Primbs, and W. Szakmary, Q. Shen, and S. Zhang and Q. Moskowitz, Y. Ooi, and L. Barmish and J. Miller, J. Muthuswamy, and R. Chong and W. Primbs and B. PapersOnLine, vol. Bradtke and A. Malekpour and B. considerations in stock trading via feedback control? Corazza and A. IEEE, , between two intelligent stochastic control approaches for financial pp.

of Economics Research [] G. Carapuço, R. Neves, and N. Korn and H. Dempster, T. Payne, Y. Romahi, and G. Chen and Q. Zhu, H. Yang, J. Jiang, and Q. IEEE, , normalization stock index trading strategy based on reinforcement pp. Lucarelli and M. Lee, J. Lee, and B. Sciences, vol. Yang, Y. Yu, and S. based trading system using gaussian inverse reinforcement learning [] A.

Tsantekidis, N. Passalis, A. After constructing the automated forex trading system , I decided to implement reinforcement learning for the trading model and acquire real-time self-adaptive ability to the forex environment. The model runs on a Windows 10 OS iK CPU with DDR4 MHz 16G RAM and NVIDIA GeForce RTX GPU. Tensorflow is used for constructing the artificial neural network ANN , and a multilayer perceptron MLP is used. The code is modified from the Frozen-Lake example of reinforcement learning using Q-Networks.

The model training process follows the Q-learning algorithm off-policy TD control , which is illustrated in Fig. Figure 1. Algorithm for Q-learning and the agent-environment interaction in a Markov decision process MDP [1].

For each step, the agent first observes the current state, feeds the state values into the MLP and outputs an action that is estimated to attain the highest reward, performs that action on the environment, and fetches the true reward for correcting its parameters.

For the 1 st generation, price values at certain time points and technical indicators are used for constructing the states. A total number of 36 inputs are connected to the MLP. There are three action values for the agent: buy, sell and do nothing. The action being taken by the agent is determined by the corresponding three outputs of the MLP, where sigmoid activation functions are used for mapping the outputs to a value range of 0 ~ 1, representing the probability of the agent taking that action.

If a buy action is taken, then the reward function is calculated by subtracting the averaged future price with the trade price; if a sell action is taken then the reward is calculated the other way around. This prevents the agent to perform actions that result in insignificant profit, which would likely lead to a loss for real trades Fig. For preliminary verification of effectiveness for the training model and methods, a noisy sine wave is generated with Brownian motion of offset and distortion in frequency.

This means at a certain time point min , the price is determined by the following equation:. where P bias is an offset value with Brownian motion, P amp is the price vibration amplitude, T is the period with fluctuating values, and P noise is the noise of the price with randomly generated values. Generally, the price seems to fluctuate randomly with no obvious highs or lows.

However, if it is viewed close-up, waves with clear highs and lows can be observed Fig. Figure 3. Price vs time of the noisy sine wave from 0 to 50, min. Figure 4.

It is already well-known that in , the computer program AlphaGo became the first Go AI to beat a world champion Go player in a five-game match. AlphaGo utilizes a combination of reinforcement learning and Monte Carlo tree search algorithm , enabling it to play against itself and for self-training.

This no doubt inspired numerous people around the world, including me. After constructing the automated forex trading system , I decided to implement reinforcement learning for the trading model and acquire real-time self-adaptive ability to the forex environment. The model runs on a Windows 10 OS iK CPU with DDR4 MHz 16G RAM and NVIDIA GeForce RTX GPU.

Tensorflow is used for constructing the artificial neural network ANN , and a multilayer perceptron MLP is used. The code is modified from the Frozen-Lake example of reinforcement learning using Q-Networks. The model training process follows the Q-learning algorithm off-policy TD control , which is illustrated in Fig.

Figure 1. Algorithm for Q-learning and the agent-environment interaction in a Markov decision process MDP [1]. For each step, the agent first observes the current state, feeds the state values into the MLP and outputs an action that is estimated to attain the highest reward, performs that action on the environment, and fetches the true reward for correcting its parameters.

For the 1 st generation, price values at certain time points and technical indicators are used for constructing the states. A total number of 36 inputs are connected to the MLP. There are three action values for the agent: buy, sell and do nothing. The action being taken by the agent is determined by the corresponding three outputs of the MLP, where sigmoid activation functions are used for mapping the outputs to a value range of 0 ~ 1, representing the probability of the agent taking that action.

If a buy action is taken, then the reward function is calculated by subtracting the averaged future price with the trade price; if a sell action is taken then the reward is calculated the other way around. This prevents the agent to perform actions that result in insignificant profit, which would likely lead to a loss for real trades Fig. For preliminary verification of effectiveness for the training model and methods, a noisy sine wave is generated with Brownian motion of offset and distortion in frequency.

This means at a certain time point min , the price is determined by the following equation:. where P bias is an offset value with Brownian motion, P amp is the price vibration amplitude, T is the period with fluctuating values, and P noise is the noise of the price with randomly generated values. Generally, the price seems to fluctuate randomly with no obvious highs or lows.

However, if it is viewed close-up, waves with clear highs and lows can be observed Fig. Figure 3. Price vs time of the noisy sine wave from 0 to 50, min. Figure 4. Price vs time of the noisy sine wave from to min.

The whole time period is 1,, min approximately days, or 2 years. Initially, a random time period is set for the environment. Otherwise, the time will move on to a random point which is around 1 ~ 2 day s in the future. This setting is expected to correspond to real conditions, where a profitable strategy can have stable earnings and can also adapt quickly to rapid changing environments.

Figure 5. Cumulative profit from trading using a noisy sine wave signal. Fundamental analysis is a tricky part in forex trading, since economic events not only correlate with each other, but also might have opposite effects on the price at different conditions.

In this project, I extracted the events that are considered significant, and contain previous, forecast and actual values for analysis. Data from 14 countries of the past 10 years are downloaded and columns with incomplete values are abandoned, making a complete table of economic events.

Because different events have different impacts on forex, the price change after the occurrence of an event is monitored, and a correlation between each event and the seven major pairs commodity pairs. Table 1 displays a portion of the correlation table for different economic events. The values are positive, which indicates the significance of an event on the currency pair.

Here, a pair is denoted by the currency other than the USD e. Table 1. Correlation table between 14 events and 5 currency pairs. Here, a pair is abbreviated as the currency other than the USD. A total of events are analyzed. However, due to the fact that a large portion of events have little influence on the price, only events that have a relatively significant impact are selected as the inputs of the MLP.

Per-minute exchange rate data of the seven currency pair is downloaded from histdata. A period from to is extracted, and blank values are filled by interpolation. This gives us a total of approximately 23 million records of price data note that weekends have no forex data records , and is deemed sufficient for model training. The data is integrated into a table, and technical indices are calculated using ta , a technical analysis library for Python built on Pandas and Numpy.

Summing the inputs from technical analysis, fundamental analysis, and pure price data, a total of inputs are fed into the MLP. Within the hidden layers , ReLU activation is used, and a sigmoid activation function is used for the output layer.

The output has a shape of 7×3, which represents the probability of the seven currency pairs and the three actions buy, sell, do nothing. An increasing spread value from 0. It can be seen that overall, the accumulative profit rises steadily. How could a profitable trading strategy be possible? Thus, the overall result is a profitable trading strategy.

Figure 7. Accumulative profit and win rate from the training procedure of 2,, steps. In conclusion, a trading model for profitable forex trading is developed using reinforcement learning. The model can automatically adapt to dynamic environments to maximize its profits.

In the future, I am planning to integrate this trading model with the automated forex trading system that I have made, and become a competitive player in this fascinating game of forex. Your email address will not be published. You may use these HTML tags and attributes:.

Save my name, email, and website in this browser for the next time I comment. Skip to content. Environment Setup The model runs on a Windows 10 OS iK CPU with DDR4 MHz 16G RAM and NVIDIA GeForce RTX GPU. Leave a Reply Cancel Reply Your email address will not be published. Looking for something? PHP Code Snippets Powered By : XYZScripts.

Webmarket to proﬁt from price ﬂuctuations with reinforcement learning and neural networks. A reinforcement learning system can be summed up by three signals: a representation Web Reinforcement learning Reinforcement Learning is a type of machine learning technique that can enable an agent to learn in an interactive environment by trials and WebReinforcement Learning Applied to Forex Trading Jo˜ao Maria B. Carapuc¸o [email protected] Instituto Superior Tecnico, Lisboa, Portugal´ June Abstract This thesis describes a system that automatically trades in the foreign exchange market to proﬁt from short-term price ﬂuctuations WebPlease cite this article as: J. Carapuço, et al., Reinforcement learning applied to Forex trading, Applied Soft Computing Journal (), WebLearning, in being able to train a model from experience rather than ground truths examples, this paper seeks to apply these techniques to produce a model that can Please cite this article as: J. Carapuço, et al., Reinforcement learning applied to Forex trading, Applied Soft Computing Journal (), blogger.com This is a ... read more

Vecerik, O. Since then, the research in On the other hand, short selling is related to borrowing the RL has been extensive and this led to diverse novel algorithms asset and selling it to a third party when predicting a decline that in turn grab the attention of other engineering field in its price. Davis and A. We next review the works searchers is the design of the financial utility. Schaul, M. in Proceedings of the thirteenth international conference on artificial intelligence and statistics.

As we saw in our discussion of the current the Q-learning, which they found susceptible to the financial literature in sections IV and V, other than the