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An automated fx trading system using adaptive reinforcement learning

I recently came across the word of "Recurrent Reinforcement Learning". I understand what "Recurrent Neural Network" is and what "Reinforcement Learning" is, but couldn't find much information about what a "Recurrent Reinforcement Learning" is.

Can someone explain to me what is a "Recurrent Reinforcement learning" and what is the difference between "Recurrent Reinforcement learning" and normal "Reinforcement learning" like Q-Learning algorithm.

2 Answers

what is a "Recurrent Reinforcement learning"

Recurrent reinforcement learning (RRL) was first introduced for training neural network trading systems in 1996 ( recurrent means that previous output is fed into the model as a part of input. ) and was soon extended to trading in a FX market.

The RRL technique has been found to be a successful machine learning technique for building financial trading systems.

what is the difference between "Recurrent Reinforcement learning" and normal "Reinforcement learning" like Q-Learning algorithm.

The RRL approach differs clearly from dynamic programming and reinforcement algorithms such as TD-learning and Q-learning, which attempt to estimate a value function for the control problem.

The RRL framework allows to create the simple and elegant problem representation, avoids Bellman's curse of dimensionality and offers compelling advantages in efficiency:

RRL produces real valued actions (portfolio weights) naturally without resorting to the discretization method in the Q-learning.

RRL has more stable performance compared to the Q-learning when exposed to noisy datasets. Q-learning algorithm is more sensitive to the value function selection (perhaps) due to the recursive property of dynamic optimization, while RRL algorithm is more flexible in choosing objective function and saving computational time.

With RRL, trading systems can be optimized by maximizing performance functions, U( ), such as profit (return after transaction costs), wealth , utility functions of wealth or risk-adjusted performance ratios like the Sharpe ratio .

An automated FX trading system using adaptive reinforcement learning

Abstract

This paper introduces adaptive reinforcement learning (ARL) as the basis for a fully automated trading system application. The system is designed to trade foreign exchange (FX) markets and relies on a layered structure consisting of a machine learning algorithm, a risk management overlay and a dynamic utility optimization layer. An existing machine-learning method called recurrent reinforcement learning (RRL) was chosen as the underlying algorithm for ARL. One of the strengths of our approach is that the dynamic optimization layer makes a fixed choice of model tuning parameters unnecessary. It also allows for a risk-return trade-off to be made by the user within the system. The trading system is able to make consistent gains out-of-sample while avoiding large draw-downs.

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An automated fx trading system using adaptive reinforcement learning

I believe reinforcement learning has a lot of potential in trading.

We had a great meetup on Reinforcement Learning at qplum office last week. Check out the video here :

What is reinforcement learning?

“Like a human, our agents learn for themselves to achieve successful strategies that lead to the greatest long-term rewards. This paradigm of learning by trial-and-error, solely from rewards or punishments, is known as reinforcement learning (RL).”

What is representation learning?

“The performance of machine learning algorithms depends heavily on the representation of the data they are given.”

What is deep learning?

Deep Learning is a subset of machine learning where we spend much more time learning the core features of the data before we start trying to fit the objective function. This leads to much lower overfitting.

What is deep reinforcement learning?

Deep reinforcement learning is actually very similar to how humans learn. We learn high level abstractions of data and throw out much of the rest. At this level, we are able to identify patterns much faster and learn from trial and error (RL) without fear of overfitting.

It is clear that financial markets change suddenly or can change suddenly. Even small periods of possibly “irrational” participants can make the market find trends that last much longer than an efficient market would. Hence the need to understand the higher level paradigm and to learn from it is very acute in financial markets.

In this video we talk about three sources of returns. I believe deep-reinforcement-learning (DRL) will be more relevant to the Stat-Arb part of the trade, or the medium-term trade.

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Introduction

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Paper

Git & Code

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Introduction

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Mechanical Forex

Trading in the FX market using mechanical trading strategies

Reinforcement learning and trading: Approaching the markets like a game

Those of you who are familiarized with my blog and work know that I am always striving to find new ways to profit from the markets and build novel and adaptive trading strategies. Through the past several years I have been focused on automated system mining using price action and supervised machine learning techniques but today I want to talk about another area of machine learning that has the potential to yield very powerful trading strategies — reinforcement learning. In today’s post I want to talk about how we might approach the trading problem from a reinforcement learning perspective, what this type of approach can do in practice and how we can improve on it to arrive at practical trading applications. This post will serve as an introduction to a new journey in trading system development, a journey that seeks to find out whether or not we can approach trading as a game and find suitable solutions.

Reinforcement learning is an area of machine learning focused on solving constantly-evolving problems for which an agent cannot possibly have all information but where agents can make decisions and gather information about their environment. This type of problem can usually be described as a “game”, it is a situation where there is an environment that rewards/punishes you and gives you information such that you can take actions that influence future rewards/punishments. If you learn how the environment reacts to your actions and how these actions can lead to different outcomes you can certainly take actions that maximize your potential for rewards within the game. We can look at trading as this sort of problem, you have access to past and current market information – which sets your environment – and based on this you must make decisions that maximize the rewards (profits) you get from the market.

In the simplest incarnations of reinforcement learning we can assume that the market is a finite state machine – there are only a given fixed number of environments possible – and we can try to learn from these possibilities and the rewards that they entail. One of the simplest ways to do this is to use Q-learning, where we have a table with numerical values – which we call Q-values – and these values are constantly updated as we experience the game. We then take actions according to the action that has the highest predicted Q-value within our present game environment. As the game progresses we gain more experience and we should become much better at getting rewards.

If we approach the market using Q-learning we need to wonder about how we define the environment or game “states”. To implement a very simple Q-learning approach on the daily charts I defined states as binary numbers using the bullish/bearish quality of the past 8 bars and used this for reinforcement learning, initializing all Q-values as zero at the beginning of the game. Since a player can only have three instances within the game – long exposure, short exposure or no exposure – I defined the reward for each action as the gain/loss that would have been collected for being short, long or neutral going forward one bar. In this example an agent will always look at the past 8 bars and to define its state – so for example if the last two bars were bearish and the other 6 were bullish the state would be defined as 11000000 (192) – and when the bar finishes it will look at its reward and update its Q-table so that it reflects its experience. Luckily in trading we do not need to take all actions to see their effect since we immediately know the effect if we had been long, short or neutral when a bar finishes and we can therefore update all Q-values for any given state even if we didn’t take all actions but only one.

The first image above shows you how powerful Q-learning can be (this are all GBPUSD 1D tests). If you run a back-test one time on the data the agent will perform poorly since it does not have any knowledge about the environment but after playing just one game the agent has become extremely skilled at playing it, since it has already seen the outcomes. Play a few more times and the system actually reinforces its learning and gains an even better understanding of how to play this game extremely well. The agent converges really fast and reaches a state where it does not improve further running on the same back-testing data, it has already learned all it can from the states within the environment. The second image above shows you how the historical balance curves change as we perform Q-learning. The first curve is losing but the agent quickly learns and effectively trades this with the efficacy you would expect from the hindsight acquired within the learning process. The agent is learning to play the game we’re showing it very well, but we should wonder about whether or not it’s learning relevant information or not.

The above shows you why reinforcement learning is very dangerous in trading. Reinforcement learning provides a very easy mechanism for N-degree curve fitting exercises and can easily generate beautiful back-testing curves with almost no computational effort. While it would take you a lot of trying to be able to find a specific algorithm that was able to generate extremely highly linear historical testing curves, Q-learning does this in a few minutes with extraordinarily high efficiency. You can do this on any trading symbol on any timeframe, you will always find an embodiment of Q-learning that will create extremely enticing equity curves. The point however is that Q-learning applied blindly can be expected to fail extremely rapidly as it can easily provide no insights into market inefficiencies but simply insights into noise within the historical data. This is a specific problem related to the application of learning algorithms on at least partially stochastic problems.

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