Order book imbalance (OBI) - buy orders minus sell orders near the best quote - measures supply-demand imbalance that can move prices. OBI is positively correlated with returns, and some investors try to use it to improve performance. Large orders placed at once can reveal intent, invite front-running, raise volatility, and cause losses. Execution algorithms therefore split parent orders into smaller lots to limit price distortion. In principle, using OBI inside such algorithms could improve execution, but prior evidence is scarce because isolating OBI's effect in real markets is nearly impossible amid many external factors. Multi-agent simulation offers a way to study this. In an artificial market, individual actors are agents whose rules and interactions form the model. This study builds an execution algorithm that accounts for OBI, tests it across several market patterns in artificial markets, and analyzes mechanisms, comparing it with a conventional (OBI-agnostic) algorithm. Results: (i) In stable markets, the OBI strategy's performance depends on the number of order slices; outcomes vary with how the parent order is partitioned. (ii) In markets with unstable prices, the OBI-based algorithm outperforms the conventional approach. (iii) Under spoofing manipulation, the OBI strategy is not significantly worse than the conventional algorithm, indicating limited vulnerability to spoofing. Overall, OBI provides a useful signal for execution. Incorporating OBI can add value - especially in volatile conditions - while remaining reasonably robust to spoofing; in calm markets, benefits are sensitive to slicing design.

Robust utility optimization enables an investor to deal with market uncertainty in a structured way, with the goal of maximizing the worst-case outcome. In this work, we propose a generative adversarial network (GAN) approach to (approximately) solve robust utility optimization problems in general and realistic settings. In particular, we model both the investor and the market by neural networks (NN) and train them in a mini-max zero-sum game. This approach is applicable for any continuous utility function and in realistic market settings with trading costs, where only observable information of the market can be used. A large empirical study shows the versatile usability of our method. Whenever an optimal reference strategy is available, our method performs on par with it and in the (many) settings without known optimal strategy, our method outperforms all other reference strategies. Moreover, we can conclude from our study that the trained path-dependent strategies do not outperform Markovian ones. Lastly, we uncover that our generative approach for learning optimal, (non-) robust investments under trading costs generates universally applicable alternatives to well known asymptotic strategies of idealized settings.

The use of machine learning in algorithmic trading systems is increasingly common. In a typical set-up, supervised learning is used to predict the future prices of assets, and those predictions drive a simple trading and execution strategy. This is quite effective when the predictions have sufficient signal, markets are liquid, and transaction costs are low. However, those conditions often do not hold in thinly traded financial markets and markets for differentiated assets such as real estate or vehicles. In these markets, the trading strategy must consider the long-term effects of taking positions that are relatively more difficult to change. In this work, we propose a Reinforcement Learning (RL) algorithm that trades based on signals from a learned predictive model and addresses these challenges. We test our algorithm on 20+ years of equity data from Bursa Malaysia.

Optimized trade execution is to sell (or buy) a given amount of assets in a given time with the lowest possible trading cost. Recently, reinforcement learning (RL) has been applied to optimized trade execution to learn smarter policies from market data. However, we find that many existing RL methods exhibit considerable overfitting which prevents them from real deployment. In this paper, we provide an extensive study on the overfitting problem in optimized trade execution. First, we model the optimized trade execution as offline RL with dynamic context (ORDC), where the context represents market variables that cannot be influenced by the trading policy and are collected in an offline manner. Under this framework, we derive the generalization bound and find that the overfitting issue is caused by large context space and limited context samples in the offline setting. Accordingly, we propose to learn compact representations for context to address the overfitting problem, either by leveraging prior knowledge or in an end-to-end manner. To evaluate our algorithms, we also implement a carefully designed simulator based on historical limit order book (LOB) data to provide a high-fidelity benchmark for different algorithms. Our experiments on the high-fidelity simulator demonstrate that our algorithms can effectively alleviate overfitting and achieve better performance.

Asset allocation (or portfolio management) is the task of determining how to optimally allocate funds of a finite budget into a range of financial instruments/assets such as stocks. This study investigated the performance of reinforcement learning (RL) when applied to portfolio management using model-free deep RL agents. We trained several RL agents on real-world stock prices to learn how to perform asset allocation. We compared the performance of these RL agents against some baseline agents. We also compared the RL agents among themselves to understand which classes of agents performed better. From our analysis, RL agents can perform the task of portfolio management since they significantly outperformed two of the baseline agents (random allocation and uniform allocation). Four RL agents (A2C, SAC, PPO, and TRPO) outperformed the best baseline, MPT, overall. This shows the abilities of RL agents to uncover more profitable trading strategies. Furthermore, there were no significant performance differences between value-based and policy-based RL agents. Actor-critic agents performed better than other types of agents. Also, on-policy agents performed better than off-policy agents because they are better at policy evaluation and sample efficiency is not a significant problem in portfolio management. This study shows that RL agents can substantially improve asset allocation since they outperform strong baselines. On-policy, actor-critic RL agents showed the most promise based on our analysis.

Algorithmic Trading A-Z with Python, Machine Learning & AWS - Build your own truly Data-driven Day Trading Bot Learn how to create, test, implement & automate unique Strategies. Preview this Course - GET COUPON CODE Welcome to the most comprehensive Algorithmic Trading Course. In this rigorous but yet practical Course, we will leave nothing to chance, hope, vagueness, or hocus-pocus! Did you know that 75% of retail Traders lose money with Day Trading? (some sources say 95%) For me as a Data Scientist and experienced Finance Professional this is not a surprise. Day Traders typically do not know/follow the five fundamental rules of (Day) Trading.

Portfolio management via reinforcement learning is at the forefront of fintech research, which explores how to optimally reallocate a fund into different financial assets over the long term by trial-and-error. Existing methods are impractical since they usually assume each reallocation can be finished immediately and thus ignoring the price slippage as part of the trading cost. To address these issues, we propose a hierarchical reinforced stock trading system for portfolio management (HRPM). Concretely, we decompose the trading process into a hierarchy of portfolio management over trade execution and train the corresponding policies. The high-level policy gives portfolio weights at a lower frequency to maximize the long term profit and invokes the low-level policy to sell or buy the corresponding shares within a short time window at a higher frequency to minimize the trading cost. We train two levels of policies via pre-training scheme and iterative training scheme for data efficiency. Extensive experimental results in the U.S. market and the China market demonstrate that HRPM achieves significant improvement against many state-of-the-art approaches.

This scientific research paper presents an innovative approach based on deep reinforcement learning (DRL) to solve the algorithmic trading problem of determining the optimal trading position at any point in time during a trading activity in stock markets. It proposes a novel DRL trading strategy so as to maximise the resulting Sharpe ratio performance indicator on a broad range of stock markets. Denominated the Trading Deep Q-Network algorithm (TDQN), this new trading strategy is inspired from the popular DQN algorithm and significantly adapted to the specific algorithmic trading problem at hand. The training of the resulting reinforcement learning (RL) agent is entirely based on the generation of artificial trajectories from a limited set of stock market historical data. In order to objectively assess the performance of trading strategies, the research paper also proposes a novel, more rigorous performance assessment methodology. Following this new performance assessment approach, promising results are reported for the TDQN strategy.

One of my recent side projects was building an automated trading system for the crypto markets. To be fair, I probably spent more time on this than on my full-time job, so calling it a side project may not be completely accurate. The internet is full of people ready to teach you about trading. Most are trying to sell you something, and many are mistaking random chance for skill. Coming from a technical background in scientific research and software engineering, I tried to ignore anything with little scientific validity, like technical analysis, or anything that looked like marketing BS. After a lot of iterations, I managed to build and deploy a system that turned my $5k investment into around $200k of pre-tax profit over a 12-month period while staying largely market neutral, i.e. not relying on ups or downs. The best run was a 4-month period without a single losing day. I did have losses on shorter time scales, but very rarely on a daily level. In this post I want share some of the problems encountered and lessons learned. I will try to strike a balance between providing useful information while not revealing specific implementation details. A common misconception is that the market cannot be predicted and that hedge fund managers are no better than dart-throwing monkeys.

Stamford, CT, May 20, 2019 (GLOBE NEWSWIRE) -- Imperative Execution, Inc. – the financial technology company that created IntelligentCross, an AI-powered alternative trading system built to reduce implicit trading costs – today announced performance results for its first eight months of operations. The venue has matched more than 1.5 billion shares since its launch with an observed price impact following these trades of 0.13bp, which is nearly ten times less than the 1.37bp average following comparable execution on U.S. securities exchanges. At the current rate of U.S. equities daily turnover, savings of that magnitude could save investors $10B per year. IntelligentCross is the industry's first smart venue to use artificial intelligence to optimize order matching to help investment managers and brokers reduce trading costs and improve execution quality on behalf of their clients. Its design was borne out of its founders' years trading on the buy-side, where traders are obsessed with implementation shortfall (IS) costs – in other words, the difference, or "slippage," between the arrival price and the execution price for completing a trade.