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Basics of Market Microstructure

A strategy with a slightly lower return but significantly lower volatility is preferably over a highly volatile but only slightly more profitable strategy. We may also want to take into account something like Maximum Drawdown , described above. One can image a wide range of complex reward function that trade-off between profit and risk. Developing trading strategies using RL looks something like this. Much simpler, and more principled than the approach we saw in the previous section.

In the traditional strategy development approach we must go through several steps, a pipeline, before we get to the metric we actually care about.

Reinforcement Learning allows for end-to-end optimization and maximizes potentially delayed rewards. Of course, we can combine drawdown with many other metrics you care about. This is not only easier, but also a much more powerful model.

Instead of needing to hand-code a rule-based policy, Reinforcement Learning directly learns a policy. And because the policy can be parameterized by a complex model, such as a Deep Neural network, we can learn policies that are more complex and powerful than any rules a human trader could possibly come up with.

We needed separate backtesting and parameter optimization steps because it was difficult for our strategies to take into account environmental factors, such as order book liquidity, fee structures, latencies, and others, when using a supervised approach. It is not uncommon to come up with a strategy, only to find out much later that it does not work, perhaps because the latencies are too high and the market is moving too quickly so that you cannot get the trades you expected to get.

Getting around environmental limitations is part of the optimization process. For example, if we simulate the latency in the Reinforcement Learning environment, and this results in the agent making a mistake, the agent will get a negative reward, forcing it to learn to work around the latencies.

We could take this a step further and simulate the response of the other agents in the same environment, to model impact of our own orders, for example. Typically, simulators ignore this and assume that orders do not have market impact. However, by learning a model of the environment and performing rollouts using techniques like a Monte Carlo Tree Search MCTS , we could take into account potential reactions of the market other agents.

By being smart about the data we collect from the live environment, we can continuously improve our model. Do we act optimally in the live environment to generate profits, or do we act suboptimally to gather interesting information that we can use to improve the model of our environment and other agents? By building an increasingly complex simulation environment that models the real world you can train very sophisticated agents that learn to take environment constraints into account.

Intuitively, certain strategies and policies will work better in some market environments than others. For example, a strategy may work well in a bearish environment, but lose money in a bullish environment.

Partly, this is due to the simplistic nature of the policy, which does not have a parameterization powerful enough to learn to adapt to changing market conditions.

Because RL agents are learning powerful policies parameterized by Neural Networks, they can also learn to adapt to various market conditions by seeing them in historical data, given that they are trained over a long time horizon and have sufficient memory. This allows them to be much more robust to changing markets. In facts, we can directly optimize them to become robust to changes in market conditions, by putting appropriate penalties into your reward function. A unique ability of Reinforcement Learning is that we can explicitly take into account other agents.

However, if we explicitly modeled the other agents in the environment, our agent could learn to exploit their strategies. This is much more similar to what we are doing in multiplayer games, like DotA. My goal with this post is not only to give an introduction to Reinforcement Learning for Trading, but also to convince more researchers to take a look at the problem. When training Reinforcement Learning agents, it is often difficult or expensive to deploy them in the real world and get feedback.

For example, if you trained an agent to play Starcraft 2, how would you let it play against a larger number of human players?

Same for Chess, Poker, or any other game that is popular in the RL community. You would probably need to somehow enter a tournament and let your agent play there. Trading agents have characteristics very similar to those for multiplayer games. But you can easily test them live! You can deploy your agent on an exchange through their API and immediately get real-world market feedback. If your agent does not generalize and loses money you know that you have probably overfit to the training data.

In other words, the iteration cycle can be extremely fast. The trading environment is essentially a multiplayer game with thousands of agents acting simultaneously. This is an active research area. We are now making progress at multiplayer games such as Poker, Dota2, and others, and many of the same techniques will apply here. In fact, the trading problem is a much more difficult one due to the sheer number of simultaneous agents who can leave or join the game at any time.

Understanding how to build models of other agents is only one possible direction one can go into. As mentioned earlier, one could choose to perform actions in a live environment with the goal maximizing the information gain with respect to kind policies the other agents may be following. Closely related is the question of whether we can learn to exploit other agents acting in the environment.

For example, if we knew exactly what algorithms were running in the market we can trick them into taking actions they should not take and profit from their mistakes. This also applies to human traders, who typically act based on a combination of well-known market signals, such as exponential moving averages or order book pressures. Do comply with all applicable laws in your jurisdiction. And finally, past performance is no guarantee of future results. Trading agents typically receive sparse rewards from the market.

Most of the time you will do nothing. Buy and sell actions typically account for a tiny fraction of all actions you take. This opens up the possibility for new algorithms and techniques, especially model-based ones, that can efficiently deal with sparse rewards.

A similar argument can be made for exploration. However, in the trading case, most states in the environment are bad, and there are only a few good ones. A naive random approach to exploration will almost never stumble upon those good state-actions pairs. A new approach is necessary here. Similar to how self-play is applied to two-player games such as Chess or Go, one could apply self-play techniques to a multiplayer environment. For example, you could imagine simultaneously training a large number of competing agents, and investigate whether the resulting market dynamic somehow resembles the dynamics found in the real world.

You could also mix the types of agents you are training, from different RL algorithms, the evolution-based ones, and deterministic ones.

Because markets change on micro- to milliseconds times scales, the trading domain is a good approximation of a continuous time domain. However, you could imagine making this part of the agent training. Thus, the agent would not only decide what actions to take, but also when to take an action. Again, this is an active research area useful for many other domains, including robotics.

The trading environment is inherently nonstationary. Market conditions change and other agent join, leave, and constantly change their strategies. For example, can an agent successfully transition from a bear to a bull market and then back to a bear market, without needing to be re-trained?

Can an agent adjust to other agent joining and learning to exploit them automatically? There are many ways to speed up the training of Reinforcement Learning agents, including transfer learning, and using auxiliary tasks. The goal was to give an introduction to Reinforcement Learning based trading agents, make an argument for why they are superior to current trading strategy development models, and make an argument for why I believe more researcher should be working on this.

I hope I achieved some this in this post. Please let me know in the comments what you think, and feel free to get in touch to ask questions. The year is coming to an end. I did not write nearly as much as I had planned to.

And what better way to start than with a summary of all the amazing things that happened in ? Looking back through my Twitter history and the WildML newsletter , the following topics repeatedly came up.

Due to its extremely large search space, Go was thought to be out of reach of Machine Learning techniques for a couple more years. What a nice surprise! The first version of AlphaGo was bootstrapped using training data from human experts and further improved through self-play and an adaptation of Monte-Carlo Tree Search. Soon after, AlphaGo Zero Nature Paper took it a step further and learned to play Go from scratch, without human training data whatsoever, using a technique simultaneously published in the Thinking Fast and Slow with Deep Learning and Tree Search paper.

It also handily beat the first version of AlphaGo. Towards the end of the year, we saw yet another generalization of the AlphaGo Zero algorithm, called AlphaZero , which not only mastered Go, but also Chess and Shogi, using the exact same techniques. Interestingly, these programs made moves that surprised even the most experienced Go players, motivating players to learn from AlphaGo and adjusting their own play style accordingly.

A little earlier, DeepStack , a system developed by researchers from Charles University, The Czech Technical University, and the University of Alberta, became the first to beat professional poker players. Note that both of these systems played Heads-up poker, which is played between two players and a significantly easier problem than playing at a table of multiple players.

The latter will most likely see additional progress in The next frontiers for Reinforcement Learning seem to be more complex multi-player games, including multi-player Poker. DeepMind is actively working on Starcraft 2, releasing a research environment , and OpenAI demonstrated initial success in 1v1 Dota 2 , with the goal of competing in the the full 5v5 game in the near future.

For supervised learning, gradient-based approaches using the back-propagation algorithm have been working extremely well. Because the data typically is not iid independent and identically distributed , error signals are sparser, and because there is a need for exploration, algorithms that do not rely on gradients can work quite well.

In addition, evolutionary algorithms can scale linearly to thousands of machines enabling extremely fast parallel training. They do not require expensive GPUs, but can be trained on a large number typically hundreds to thousands of cheap CPUs. Earlier in the year, researchers from OpenAI demonstrated that Evolution Strategies can achieve performance comparable to standard Reinforcement Learning algorithms such as Deep Q-Learning. Towards the end of the year, a team from Uber released a blog post and a set of five research papers , further demonstrating the potential of Genetic Algorithms and novelty search.

Using an extremely simple Genetic Algorithm, and no gradient information whatsoever, their algorithm learns to play difficult Atari Games.

WaveNet had previously been applied to Machine Translation as well, resulting in faster training times that recurrent architectures. The move away from expensive recurrent architectures that take long to train seems to be larger trend in Machine Learning subfields. In Attention is All you Need , researchers get rid of recurrence and convolutions entirely, and use a more sophisticated attention mechanism to achieve state of the art results at a fraction of the training costs.

If I had to summarize in one sentence, it would be the year of frameworks. Facebook made a big splash with PyTorch. Due to its dynamic graph construction similar to what Chainer offers, PyTorch received much love from researchers in Natural Language Processing, who regularly have to deal with dynamic and recurrent structures that hard to declare in a static graph frameworks such as Tensorflow. Tensorflow had quite a run in Currently, Tensorflow is at version 1.

In addition to Google and Facebook, many other companies jumped on the Machine Learning framework bandwagon:. And because the number of framework is getting out of hand, Facebook and Microsoft announced the ONNX open format to share deep learning models across frameworks.

For example, you may train your model in one framework, but then serve it in production in another one. In addition to general-purpose Deep Learning frameworks, we saw a large number of Reinforcement Learning frameworks being released, including:.

But at least one very popular framework died. In an announcement on the Theano mailing list, the developers decided that 1. With Deep Learning and Reinforcement Learning gaining popularity, an increasing number of lectures, bootcamps, and events have been recorded and published online in The following are some of my favorites:. Several academic conferences continued the new tradition of publishing conference talks online.

Researchers also started publishing easily accessible tutorial and survey papers on arXiv. Here are some of my favorites from this year:. There was a lot of hype, and understanding true breakthroughs is anything but easy for someone not coming from a medical background.

I will briefly highlight some developments here. Among the top news this year was a Stanford team releasing details about a Deep learning algorithm that does as well as dermatologists in identifying skin cancer.

You can read the Nature article here. Another team at Stanford developed a model which can diagnose irregular heart rhythms, also known as arrhythmias, from single-lead ECG signals better than a cardiologist. But this year was not without blunders. The NIH released a chest x-ray dataset to the scientific community, but upon closer inspection it was found that it is not really suitable for training diagnostic AI models.

Another application that started to gain more traction this year is generative modeling for images, music, sketches, and videos. Using the released dataset you may even teach machines to finish your drawings for you. Will GANs become the new paintbrush? Uber started out the year with a few setbacks as their self-driving cars missed several red lights in San Francisco due to software error, not human error as had been reported previously.

Later on, Uber shared details about its car visualization platform used internally. Waymo also published details about their testing and simulation technology. Lyft announced that it is building its own autonomous driving hard- and software. Its first pilot in Boston is now underway.

Tim Cook confirmed that Apple is working on software for self-driving cars, and researchers from Apple published a mapping-related paper on arXiv. However, here are a couple the stood out during the year:. Neural Networks used for supervised learning are notoriously data hungry. The following are a few datasets that stood out this year:.

Throughout the year, several researchers raised concerns about the reproducibility of academic paper results. Deep Learning models often rely on a huge number of hyperparameters which must to be optimized in order to achieve results that are good enough to publish.

This optimization can become so expensive that only companies such as Google and Facebook can afford it. Researchers do not always release their code, forget to put important details into the finished paper, use slightly different evaluation procedures, or overfit to the dataset by repeatedly optimizing hyperparameters on the same splits.

This makes reproducibility a big issue. In Reinforcement Learning That Matters , researchers showed that the same algorithms taken from different code bases achieve vastly different results with high variance:. A Large-Scale Study , researchers showed that a well-tuned GAN using expensive hyperparameter search can beat more sophisticated approaches that claim to be superior.

Similarly, in On the State of the Art of Evaluation in Neural Language Models , researchers showed that simple LSTM architectures, when properly regularized and tuned, can outperform more recent models.

Yann LeCun took it as an insult and promptly responded the next day. With United States immigration policies tightening, it seems that companies are increasingly opening offices overseas, with Canada being a prime destination. China is another destination that is receiving a lot of attention. With a lot of capital, a large talent pool, and government data readily available, it is competing head to head with the United States in terms of AI developments and production deployments.

Google also announced that it will soon open a new lab in Beijing. It comes in gold color, by the way. But competition is increasing. Competition may also come from China , where hardware makers specializing in Bitcoin mining want to enter the Artificial Intelligence focused GPU space. With great hype comes great responsibility. What the mainstream media reports almost never corresponds to what actually happened in a research lab or production system.

IBM Watson is the poster-child over overhyped marketing and failed to deliver corresponding results. This year, everyone was hating on IBM Watson , which is not surprising after its repeated failures in healthcare. It has already done enough damage and you can google it. What happened was researchers stopping a standard experiment that did not seem to give good results. Researchers also overstepped boundaries with titles and abstracts that do not reflect the actual experiment results, such as in this natural language generation paper , or this Machine Learning for markets paper.

The trend of Academia losing scientists to the industry also continued , with university labs complaining that they cannot compete with the salaries offered by the industry giants. Just like the year before, the AI startup ecosystem was booming with several high-profile acquisitions:. See the Hacker News Discussion for additional context.

Update August 17th, OpenAI has published a blog post with more details about the bot. Almost everything of the post below still holds true, however. See this tweetstorm by smerity for a good analysis. For one, I am a big eSports fan. I have never played DotA 2, but I regularly watch other eSports competitions on Twitch and even played semi-professionally when I was in high school.

But more importantly, multiplayer online battle arena MOBA games like DotA and real-time strategy RTS games like Starcraft 2, are seen as being way beyond the capabilities of current Artificial Intelligence techniques. DeepMind has been working on Starcraft 2 for a while and just recently released their research environment.

So far no researchers have managed to make significant breakthroughs. It is thought that we are at least years away from beating good human players at Starcraft 2. How can this be true? There is a real danger of overhyping Artificial Intelligence progress, nicely captured by misleading tweets like these:. OpenAI first ever to defeat world's best players in competitive eSports. Nobody likes being regulated, but everything cars, planes, food, drugs, etc that's a danger to the public is regulated.

AI should be too. Let me start out by saying that none of the hype or incorrect assumptions is the fault of OpenAI researchers. OpenAI has traditionally been very straightforward and explicit about the limitations of their research contributions. I am sure it will be the same in this case. OpenAI has not yet published technical details of their solution, so it is easy to jump to wrong conclusions for people not in the field. How does it compare to something like AlphaGo?

Given that 1v1 is mostly a game of mechanical skill, it is not surprising that a bot beats human players. And given the severely restricted environment, the artificially restricted set of possible actions, and that there was little to no need for long-term planning or coordination, I come to the conclusion that this problem was actually significantly easier than beating a human champion in the game of Go.

We did not make sudden progress in AI because our algorithms are so smart — it worked because our researchers are smart about setting up the problem in just the right way to work around the limitations of current techniques.

The training time for the bot, said to be around 2 weeks, suggests the same. Now, enough with the criticism.

The work may be a little overhyped by the press, but there are in fact some extremely cool and surprising things about it. And clearly, a large amount of challenging engineering work and partnership building must have gone into making this happen.

Thanks to smerity for useful feedback, suggestions, and DotA knowledge. Skip all the talk and go directly to the Github Repo with code and exercises. Over the past few years amazing results like learning to play Atari Games from raw pixels and Mastering the Game of Go have gotten a lot of attention, but RL is also widely used in Robotics, Image Processing and Natural Language Processing.

Combining Reinforcement Learning and Deep Learning techniques works extremely well. Both fields heavily influence each other. On the Reinforcement Learning side Deep Neural Networks are used as function approximators to learn good representations, e. In the other direction, RL techniques are making their way into supervised problems usually tackled by Deep Learning.

For example, RL techniques are used to implement attention mechanisms in image processing, or to optimize long-term rewards in conversational interfaces and neural translation systems. Finally, as Reinforcement Learning is concerned with making optimal decisions it has some extremely interesting parallels to human Psychology and Neuroscience and many other fields. And what could be more fun than teaching machines to play Starcraft and Doom? There are many excellent Reinforcement Learning resources out there.

Two I recommend the most are:. The course is based on the book so the two work quite well together. In fact, these two cover almost everything you need to know to understand most of the recent research papers. The prerequisites are basic Math and some knowledge of Machine Learning.

That covers the theory. But what about practical resources? I separated them into chapters with brief summaries and exercises and solutions so that you can use them to supplement the theoretical material above. All of this is in the Github repository. Some of the more time-intensive algorithms are still work in progress, so feel free to contribute.

Dynamic Programming Policy Evaluation. Dynamic Programming Policy Iteration. With that using an RNN should be as easy as calling a function, right? The post comes with a Github repository that contains Jupyter notebooks with minimal examples for:.

The Code and data for this tutorial is on Github. A bit more formally, the input to a retrieval-based model is a context the conversation up to this point and a potential response. The model outputs is a score for the response. To find a good response you would calculate the score for multiple responses and choose the one with the highest score. Chatbots, also called Conversational Agents or Dialog Systems, are a hot topic. There is a new wave of startups trying to change how consumers interact with services by building consumer apps like Operator or x.

Microsoft recently released their own bot developer framework. Many companies are hoping to develop bots to have natural conversations indistinguishable from human ones, and many are claiming to be using NLP and Deep Learning techniques to make this possible.

A recent trend in Deep Learning are Attention Mechanisms. In an interview , Ilya Sutskever, now the research director of OpenAI, mentioned that Attention Mechanisms are one of the most exciting advancements, and that they are here to stay.

But what are Attention Mechanisms? Attention Mechanisms in Neural Networks are very loosely based on the visual attention mechanism found in humans. The full code is available on Github. The model presented in the paper achieves good classification performance across a range of text classification tasks like Sentiment Analysis and has since become a standard baseline for new text classification architectures.

Skip to content Thanks a lot to aerinykim , suzatweet and hardmaru for the useful feedback! You would go to this page and see something like this: Price chart Middle The current price is the price of the most recent trade. Trade History Right The right side shows a history of all recent trades.

Order Book Left The left side shows the order book, which contains information about who is willing to buy and sell at what price. Data The main reasons I am using cryptocurrencies in this post is because data is public, free, and easy to obtain. Trade A new Trade has happened. A few Trading Strategy Metrics When developing trading algorithms, what do you optimize for? Net PnL Net Profit and Loss Simply how much money an algorithm makes positive or loses negative over some period of time, minus the trading fees.

Alpha and Beta Alpha defines how much better, in terms of profit, your strategy is when compared to an alternative, relatively risk-free, investment, like a government bond. Sharpe Ratio The Sharpe Ratio measures the excess return per unit of risk you are taking. Maximum Drawdown The Maximum Drawdown is the maximum difference between a local maximum and the subsequent local minimum, another measure of risk.

Value at Risk VaR Value at Risk is a risk metric that quantifies how much capital you may lose over a given time frame with some probability, assuming normal market conditions.

Most likely we will not be able to get all our 1. We may be forced to buy 0. On GDAX, we also pay a 0. We place the sell order. Because the market moves very fast, by the time the order is delivered over the network the price has slipped already.

Similar to above, we most likely cannot sell all of your 1 BTC at that price. Perhaps we are forced to sell 0. It looks something like this: You perform exploratory data analysis to find trading opportunities. You may look at various charts, calculate data statistics, and so on.

If necessary, you may train one or more supervised learning models to predict quantities of interest that are necessary for the strategy to work. For example, price prediction, quantity prediction, etc.

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