Application of deep learning to algorithmic trading

New breakthroughs in AI make the headlines everyday. Far from the buzz of customer-facing businesses, the wide adoption and powerful applications of Machine Learning in Finance are less well known. About three years ago, I got involved in developing Machine Learning ML models for price predictions and algorithmic trading in Energy markets, specifically for the European market of Carbon emission certificates.

In this article, I want to share some of the learnings, approaches and insights which I have found relevant in all my ML projects since. Rather than on technical detail, my focus here is on the general considerations behind modelling choices which are discussed rarely in the classical academic textbooks or online tutorials on new techniques.

The Context. The basic idea is to put a price on pollution: each industrial installation covered in the scheme has to monitor and report its exact quantity of greenhouse gas emissions to the authorities and then offset the respective amount measured in tons by handing in allowances.

These polluters with marginal abatement costs lower than the current market price of permits eg because their specific filter requirements are cheap can then sell their excess pollution allowances on the market for a profit, to polluters facing higher marginal abatement costs.

In a perfectly efficient emissions trading market, the equilibrium price of permits would settle at the marginal abatement cost of the final unit of abatement required to meet the overall reduction target set by the cap on the supply of permits. Given the uncertainty about the actual industry-specific abatement costs, this instrument lets governments control the total amount of emissions, while the actual price of emission permits fluctuates according to demand-side market forcesnamely.

To exemplify the latter, suppose the price of natural gas per calorific unit drops below the price of brent oil. Power producers and utilities would switch over to this less carbon intense fuel, thus lowering the demand for carbon allowances. Accordingly, the price of allowances would drop as well in those periods see Figure 2. A comprehensive model needs to reflect all these factors.

While we can safely assume that patterns observed in the abundant historical market data carry over into the present and will continue into the future this is actually the sine qua non, the indispensable assumption for any analytical modellingit is obvious that this setting is too complex for any approach trying to model the market based on generic beliefs, fundamental relations or state space concepts from Econophysics.

So this is really a use case to unleash the power of Machine Learning. How to leverage it?

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Here is a typical workflow for a trading system using supervised learning:. Get the data in place. The scale of the data should at least be as fine as the scale you want to model and ultimately predict. What is your forecast horizon? Longer-term horizons will require additional input factors like market publications, policy outlooks, sentiment analysis of twitter revelations etc. If you are in for the game of short-term or even high-frequency trading based on pure market signals from tick data, you might want to include rolling averages of various lengths to provide your model with historical context and trends, especially if your learning algorithm does not have explicit memory cells like Recurrent Neural Networks or LSTMs.

Your computational capacity might be a limiting factor, especially in a context where your ML model will be up against hard-coded, fast and unique-purpose algorithms of market-making or arbitrage seekers. Deploying dedicated cloud servers or ML platforms like H2O and TensorFlow allows you to spread computation over various servers.

Clean the data how do you interpolate gaps? Supervised Model Training. Split your data into complementary sets for training, validation for parameter tuning, feature selection etc and testing. Otherwise you might waste effort tuning the model parameters on the validation set only to find that it poorly generalizes to the test set.

Early on, decide on and establish a single-number evaluation metric. Chasing too many different metrics will only lead to confusion. And it fits with the metrics you may consider for your Trading Policy. Observe the model performance on training and validation set. Examining closely the cases where the model went wrong will help to identify any potential and avoidable model bias, see Figure 4.

This baseline is very different to other ML applications like object or speech recognition which operate in a closed environment where the factors affecting the modelling target can be clearly identified the RGB channels of image pixels, the wave frequencies of sound samples.

Trading Policy. Define your trading policy: a set of rules defining the concrete trading implications of the model outputs : eg depending on a threshold for the model confidence of a given prediction, what position do you place on the market, what position size, for how long do you hold a position in the given state of the market etc.

A policy usually comes with some more free parameters which need to be optimized next step.The defined sets of instructions are based on timing, price, quantity, or any mathematical model. Using these two simple instructions, a computer program will automatically monitor the stock price and the moving average indicators and place the buy and sell orders when the defined conditions are met.

The trader no longer needs to monitor live prices and graphs or put in the orders manually. The algorithmic trading system does this automatically by correctly identifying the trading opportunity. Most algo-trading today is high-frequency trading HFTwhich attempts to capitalize on placing a large number of orders at rapid speeds across multiple markets and multiple decision parameters based on preprogrammed instructions.

Algorithmic trading provides a more systematic approach to active trading than methods based on trader intuition or instinct. Any strategy for algorithmic trading requires an identified opportunity that is profitable in terms of improved earnings or cost reduction. The following are common trading strategies used in algo-trading:. The most common algorithmic trading strategies follow trends in moving averages, channel breakouts, price level movements, and related technical indicators.

These are the easiest and simplest strategies to implement through algorithmic trading because these strategies do not involve making any predictions or price forecasts. Trades are initiated based on the occurrence of desirable trends, which are easy and straightforward to implement through algorithms without getting into the complexity of predictive analysis.

Using and day moving averages is a popular trend-following strategy. Buying a dual-listed stock at a lower price in one market and simultaneously selling it at a higher price in another market offers the price differential as risk-free profit or arbitrage. The same operation can be replicated for stocks vs. Implementing an algorithm to identify such price differentials and placing the orders efficiently allows profitable opportunities.

Index funds have defined periods of rebalancing to bring their holdings to par with their respective benchmark indices.

Machine Learning for Trading – Topic Overview

Such trades are initiated via algorithmic trading systems for timely execution and the best prices. Proven mathematical models, like the delta-neutral trading strategy, allow trading on a combination of options and the underlying security.

Mean reversion strategy is based on the concept that the high and low prices of an asset are a temporary phenomenon that revert to their mean value average value periodically.

Identifying and defining a price range and implementing an algorithm based on it allows trades to be placed automatically when the price of an asset breaks in and out of its defined range.

The aim is to execute the order close to the volume-weighted average price VWAP. Time-weighted average price strategy breaks up a large order and releases dynamically determined smaller chunks of the order to the market using evenly divided time slots between a start and end time. The aim is to execute the order close to the average price between the start and end times thereby minimizing market impact.

Until the trade order is fully filled, this algorithm continues sending partial orders according to the defined participation ratio and according to the volume traded in the markets.

I coded a stock market trading bot. This is how much it made in a week.

The implementation shortfall strategy aims at minimizing the execution cost of an order by trading off the real-time market, thereby saving on the cost of the order and benefiting from the opportunity cost of delayed execution. The strategy will increase the targeted participation rate when the stock price moves favorably and decrease it when the stock price moves adversely.

This is sometimes identified as high-tech front-running. The challenge is to transform the identified strategy into an integrated computerized process that has access to a trading account for placing orders.

The following are the requirements for algorithmic trading:. Here are a few interesting observations:. Can we explore the possibility of arbitrage trading on the Royal Dutch Shell stock listed on these two markets in two different currencies? Simple and easy!

Remember, if one investor can place an algo-generated trade, so can other market participants. In the above example, what happens if a buy trade is executed but the sell trade does not because the sell prices change by the time the order hits the market? The trader will be left with an open position making the arbitrage strategy worthless.Over the past few years, deep neural networks have become extremely popular.

This emerging field of computer science was created around the concept of biological neural networks, and deep learning has become something of a buzzword today. Deep learning scientists and engineers try to mathematically describe various patterns from biological nervous systems.

Deep learning systems have been applied to various problems: computer vision, speech recognition, natural language processing, machine translation, and more.

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It is interesting and exciting that in some tasks, deep learning has outperformed human experts. Today, we will be taking a look at deep learning in the financial sector. One of the more attractive applications of deep learning is in hedge funds. Hedge funds are investment funds, financial organizations that raise funds from investors and manage them. They usually work with time series data and try to make some predictions.

There is a special type of deep learning architecture that is suitable for time series analysis: recurrent neural networks RNNsor even more specifically, a special type of recurrent neural network: long short-term memory LSTM networks. LSTMs are capable of capturing the most important features from time series data and modeling its dependencies.

A stock price prediction model is presented as an illustrative case study on how hedge funds can use such systems. PyTorch framework, written in Python, is used to train the model, design experiments, and draw the results. We will start with some deep learning basics before moving on to real-world examples:. One of the most challenging and exciting tasks in the financial industry is predicting whether stock prices will go up or down in the future.

Today, we are aware that deep learning algorithms are very good at solving complex tasks, so it is worth trying to experiment with deep learning systems to see whether they can successfully solve the problem of predicting future prices. Nvidia helped revolutionize deep learning networks a decade ago, as it started offering very fast graphics processing units GPUs for general purpose computing in Tesla-series products. There are very few scientific papers about using deep learning in finance, but demand for deep learning experts from fintech companies is strong, as they obviously recognize its potential.

This article will help explain why deep learning in finance is becoming increasingly popular by outlining how financial data is used in constructing deep learning systems. A special type of recurrent neural network—the LSTM network —will be presented as well. We will outline how a finance-related task can be solved using recurrent neural networks.

This article also features an illustrative case study on how hedge funds can use such systems, presented through experiments. We will also consider how deep learning systems can be improved and how hedge funds can go about hiring talent to build those systems, i.

Before we proceed to the technical aspect of the problem, we need to explain what makes hedge funds unique. So, what is a hedge fund? A hedge fund is an investment fund—a financial organization which raises funds from investors and places them in short-term and long-term investments, or in different financial products.

It is typically formed as a limited partnership or a limited liability company. It is generally accepted that when more risk is taken, there is greater potential for higher returns and losses. In order to achieve good returns, hedge funds rely on various types of investment strategies, trying to make money by exploiting market inefficiencies. Due to various kinds of investment strategies that are not allowed in ordinary investment funds, hedge funds are not registered as funds, i.

Some hedge funds generate more money than the market average, but some of them lose money. Not just anyone can invest in hedge funds, though. Hedge funds are intended for a small number of wealthy investors.

Usually, the ones who want to take part in hedge funds need to be accredited.Did you know, that the Machine Learning for trading is getting more and more important?

Application of Deep Learning to Algorithmic Trading

You might be surprised to learn that Machine Learning hedge funds already significantly outperform generalized hedge funds, as well as traditional quant funds, according to a report by ValueWalk. ML and AI systems can be incredibly helpful tools for humans navigating the decision-making process involved with investments and risk assessment.

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The impact of human emotions on trading decisions is often the greatest hindrance to outperformance. Algorithms and computers make decisions and execute trades faster than any human can, and do so free from the influence of emotions. When algorithmic trading strategies were first introduced, they were wildly profitable and swiftly gained market share.

But as competition has increased, profits have declined. In this increasingly difficult environment, traders need a new tool to give them a competitive advantage and increase profits.

The good news is that tool is here now: Machine Learning. Machine Learning involves feeding an algorithm data samples, usually derived from historical prices.

application of deep learning to algorithmic trading

The data samples consist of variables called predictors, as well as a target variable, which is the expected outcome. The algorithm learns to use the predictor variables to predict the target variable.

Machine Learning offers the number of important advantages over traditional algorithmic programs. The process can accelerate the search for effective algorithmic trading strategies by automating what is often a tedious, manual process.

It also increases the number of markets an individual can monitor and respond to. Most importantly, they offer the ability to move from finding associations based on historical data to identifying and adapting to trends as they develop. If you can automate a process others are performing manually; you have a competitive advantage. And in the zero-sum world of trading, if you can adapt to changes in real time while others are standing still, your advantage will translate into profits.

application of deep learning to algorithmic trading

There are multiple strategies which use Machine Learning to optimize algorithms, including linear regressions, neural networks, deep learning, support vector machines, and naive Bayes, to name a few. And well-known funds such as Citadel, Renaissance Technologies, Bridgewater Associates and Two Sigma Investments are pursuing Machine Learning strategies as part of their investment approach.

At Sigmoidal, we have the experience and know-how to help traders incorporate ML into their own trading strategies. In one of our projects, we designed an intelligent asset allocation system that utilized Deep Learning and Modern Portfolio Theory. The task was to implement an investment strategy that could adapt to rapid changes in the market environment.

The base AI model was responsible for predicting asset returns based on historical data. This particular architecture can store information for multiple timesteps, which is made possible by a Memory Cell. This property enables the model to learn long and complicated temporal patterns in data.

In order to strengthen our predictions, we used a wealth of market data, such as currencies, indices, etc. This resulted in over features we used to make final predictions.

Of course, many of these features were correlated. This problem was mitigated by Principal Component Analysis PCAwhich reduces the dimensionality of the problem and decorrelates features. We then used the predictions of return and risk uncertainty for all the assets as inputs to a Mean-Variance Optimization algorithm, which uses a quadratic solver to minimise risk for a given return.

Contact us to learn more. It is difficult to find performance data for AI strategies given their proprietary nature, but hedge fund research firm Eurekahedge has published some informative data.

The Index tracks 23 funds in total, of which 12 continue to be live. The above data illustrate the potential in utilizing AI and Machine Learning in trading strategies. Fortunately, traders are still in the early stages of incorporating this powerful tool into their trading strategies, which means the opportunity remains relatively untapped and the potential significant. Imagine a system that can monitor stock prices in real time and predict stock price movements based on the news stream.

Below is the table that shows how it performed relative to the top 10 quantitative mutual funds in the world:.Here we are again! We already have four tutorials on financial forecasting with artificial neural networks where we compared different architectures for financial time series forecasting, realized how to do this forecasting adequately with correct data preprocessing and regularization, performed our forecasts based on multivariate time series and could produce really nice results for volatility forecasting and implemented custom loss functions.

For solving every of latter problems we used a individual model trained on particular data: we always had we had one input and one output. But we also know, that neural networks are actually computational graphs where we can pass different data in and have several outputs as well.

As always, code is available on the Github.

application of deep learning to algorithmic trading

In this tutorial we will use datasetthat contains not only multivariate time series, but also text data with daily news corresponding to trading days from Kaggle. You can check the details of the dataset on the link before, here is short summary what is inside:. Range: —06—08 to —07— Range: —08—08 to —07— I prepared a script for loading all data in useful for us form, so we will not dive into data loading in details. Workflow with time series is like in all tutorials before, some details of text preparation will be discussed later.

For those, who gonna check the code, I want to clarify variables:. In first part of our tutorial we will research multitask learning. Wikipedia says:. Multi-task learning MTL is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks.

This can result in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training the models separately. In our case I am very curious, if we want to predict, for example, volatility it worked well beforehow can we help our network to perform better adding additional information to the loss about, for example, movement direction?

Adding auxiliary loss function can help neural network to learn different representation, based not only on variability of time series, but also on movement direction. Looking on the picture above idea is more clear — we train one set of layers of a neural architecture to solve several tasks, and while backpropagation errors of all of them will be propagated through shared layers.

To understand multitask learning MTL better, I suggest you to read. Here is network for it:. And train the network with this code:. The loss function evolution during the training looks like:. And prediction on test data looks like:. Looks not bad at all, we could capture main dependencies and can predict biggest jumps. The MSE is 0. Now we are coming to multitask learning. After we need to add it to final model and set a loss in compile function.

One very important moment is, that I emphasize the attention of the model on volatility forecasting, so I set weight for binary crossentropy loss 0. Here is general loss function graph for both classification and regression loss in total :. And here is forecasting result. If we check main forecasting metrics, we have: the MSE is 0.Download the research paper.

This research paper presents a novel deep reinforcement learning DRL solution to the decision-making problem behind algorithmic trading in the stock markets: selecting the appropriate trading action buy, hold or sell shares without human intervention.

Naturally, the core objective is to achieve an appreciable profit while efficiently mitigating the trading risk. This specific task is particularly complex due to the sequential nature of the problem as well as the stochastic and adversarial aspects of the environment. Moreover, a huge amount of both quantitative and qualitative information, which is generally not available, influences the dynamics of this environment.

Until now, DRL algorithms mainly focused on well-known environment with specific properties, such as games. This research paper is a pioneer work assessing the ability of this new artificial intelligence AI approach to solve challenging real-life problems, in this case algorithmic trading. The three main contributions of this scientific article are as follows. Firstly, the research paper rigorously formalizes the particular algorithmic trading problem at hand, and makes the link with the reinforcement learning RL formalism.

Especially, the state and action spaces are discussed in detail together with the rewards and the main objective. As its name indicates, it is inspired from the famous DQN algorithm developed to play Atari games, but significantly adapted to the particular algorithmic trading problem at hand.

Thirdly, a new and more rigorous methodology is proposed to objectively assess the performance of trading strategies. This procedure is all the more important because multiple contributions in algorithmic trading tend to prioritize good results over a proper objective scientific approach.

The performance realized by the TDQN algorithm is interesting and informative. On the one hand, the DRL algorithm achieves promising results, surpassing on average the benchmark trading strategies considered. Moreover, the TDQN strategy demonstrates multiple benefits compared to more classical approaches, such as an appreciable versatility and a remarkable robustness to diverse trading costs. On the other hand, the research paper highlights the core challenges that DRL techniques still need to overcome: the management of poorly observable environments and the lack of generalization, to cite the main ones.

Download the research paper This research paper presents a novel deep reinforcement learning DRL solution to the decision-making problem behind algorithmic trading in the stock markets: selecting the appropriate trading action buy, hold or sell shares without human intervention. Follow Damien Ernst on.You will also therefore be interested to know that the bank has just released a new report on the problems of 'applying data driven learning' to algorithmic trading.

Doo Re Song also a quant research. For those who want to know how 'data driven learning' interacts with algorithmic trading, this is what the report is saying. Algorithms in finance control "micro-level" trading decisions for equities and electronic futures contracts: "They define where to trade, at what price, and what quantity.

However, algos aren't free to do as they please. JPM notes that clients, "typically transmit specific instructions with constraints and preferences to the execution broker. They might also specify that the executed basket of securities is exposed in a controlled way to certain sectors, countries or industries. When clients are placing a single order, they might want to control how the execution of the order affects the market price control market impactor to control how the order is exposed to market volatility control riskor to specify an urgency level which will balance market impact and risk.

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For example, the JPM analysts point out that a game of chess is about 40 steps long and that a game of Go is about steps long. However, even with a medium frequency electronic trading algorithm which reconsiders its options every second, there will be 3, steps per hour. Nor is this the only issue.

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When you're mapping the data in Chess and Go, it's a question of considering how to move one piece among all the eligible pieces and how they might move in response. However, an electronic trading action consists of multiple moves. It's, "a collection of child orders," say the JPM analysts.

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What's a child order? JPM points out that a single action might be, "submitting a passive buy order and an aggressive buy order. The passive child order will rest in the order book at the price specified and thus provide liquidity to other market participants. Providing liquidity might eventually be rewarded at the time of trade by locally capturing the spread: trading at a better price vs someone who makes the same trade by taking liquidity.

The aggressive child order, on the other hand, can be sent out to capture an opportunity as anticipating a price move. Both form one action. The resulting action space is massively large and increases exponentially with the number of combinations of characteristics we want to use at a moment in time.

application of deep learning to algorithmic trading

In the past, the JPM analysts note that electronic trading algos were, "a blend of scientific, quantitative models which expressed quantitative views of how the world works. Trying to encapsulate all of this is hard. Most human-compiled algos are, "tens of thousands lines of hand-written, hard to maintain and modify code. If the writing of algos can be automated and account of these constraints, life will be simpler. JPM says there are three cultural approaches to using data when you're writing a trading algorithm: the data modelling culture; the machine learning culture; and the algorithmic decision making culture.

The data modelling culture is based on a presumption that financial markets are like a black box with a simple model inside. All you need to do is to build a quantitative model that approximates the black box. Given the complexity of behaviour in the financial markets, this can be too simple.


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