6,212 research outputs found
Stochastic simulation framework for the Limit Order Book using liquidity motivated agents
In this paper we develop a new form of agent-based model for limit order
books based on heterogeneous trading agents, whose motivations are liquidity
driven. These agents are abstractions of real market participants, expressed in
a stochastic model framework. We develop an efficient way to perform
statistical calibration of the model parameters on Level 2 limit order book
data from Chi-X, based on a combination of indirect inference and
multi-objective optimisation. We then demonstrate how such an agent-based
modelling framework can be of use in testing exchange regulations, as well as
informing brokerage decisions and other trading based scenarios
Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market
We report successful results from using deep learning neural networks (DLNNs)
to learn, purely by observation, the behavior of profitable traders in an
electronic market closely modelled on the limit-order-book (LOB) market
mechanisms that are commonly found in the real-world global financial markets
for equities (stocks & shares), currencies, bonds, commodities, and
derivatives. Successful real human traders, and advanced automated algorithmic
trading systems, learn from experience and adapt over time as market conditions
change; our DLNN learns to copy this adaptive trading behavior. A novel aspect
of our work is that we do not involve the conventional approach of attempting
to predict time-series of prices of tradeable securities. Instead, we collect
large volumes of training data by observing only the quotes issued by a
successful sales-trader in the market, details of the orders that trader is
executing, and the data available on the LOB (as would usually be provided by a
centralized exchange) over the period that the trader is active. In this paper
we demonstrate that suitably configured DLNNs can learn to replicate the
trading behavior of a successful adaptive automated trader, an algorithmic
system previously demonstrated to outperform human traders. We also demonstrate
that DLNNs can learn to perform better (i.e., more profitably) than the trader
that provided the training data. We believe that this is the first ever
demonstration that DLNNs can successfully replicate a human-like, or
super-human, adaptive trader operating in a realistic emulation of a real-world
financial market. Our results can be considered as proof-of-concept that a DLNN
could, in principle, observe the actions of a human trader in a real financial
market and over time learn to trade equally as well as that human trader, and
possibly better.Comment: 8 pages, 4 figures. To be presented at IEEE Symposium on
Computational Intelligence in Financial Engineering (CIFEr), Bengaluru; Nov
18-21, 201
The Rise of Computerized High Frequency Trading: Use and Controversy
Over the last decade, there has been a dramatic shift in how securities are traded in the capital markets. Utilizing supercomputers and complex algorithms that pick up on breaking news, company/stock/economic information and price and volume movements, many institutions now make trades in a matter of microseconds, through a practice known as high frequency trading. Today, high frequency traders have virtually phased out the dinosaur floor-traders and average investors of the past. With the recent attempted robbery of one of these high frequency trading platforms from Goldman Sachs this past summer, this rise of the machines has become front page news, generating vast controversy and discourse over this largely secretive and ultra-lucrative practice. Because of this phenomenon, those of us on Main Street are faced with a variety of questions: What exactly is high frequency trading? How does it work? How long has this been going on for? Should it be banned or curtailed? What is the end-game, and how will this shape the future of securities trading and its regulation? This iBrief explores the answers to these questions
Working with OpenCL to Speed Up a Genetic Programming Financial Forecasting Algorithm: Initial Results
The genetic programming tool EDDIE has been shown to be a successful financial forecasting tool, however it has suffered from an increase in execution time as new features have been added. Speed is an important aspect in financial problems, especially in the field of algorithmic trading, where a delay in taking a decision could cost millions. To offset this performance loss, EDDIE has been modified to take advantage of multi-core CPUs and dedicated GPUs. This has been achieved by modifying the candidate solution evaluation to use an OpenCL kernel, allowing the parallel evaluation of solutions. Our computational results have shown improvements in the running time of EDDIE when the evaluation was delegated to the OpenCL kernel running on a multi-core CPU, with speed ups up to 21 times faster than the original EDDIE algorithm. While most previous works in the literature reported significantly improvements in performance when running an OpenCL kernel on a GPU device, we did not observe this in our results. Further investigation revealed that memory copying overheads and branching code in the kernel are potentially causes of the (under-)performance of the OpenCL kernel when running on the GPU device
mt5se: An Open Source Framework for Building Autonomous Traders
Autonomous trading robots have been studied in artificial intelligence area
for quite some time. Many AI techniques have been tested for building
autonomous agents able to trade financial assets. These initiatives include
traditional neural networks, fuzzy logic, reinforcement learning but also more
recent approaches like deep neural networks and deep reinforcement learning.
Many developers claim to be successful in creating robots with great
performance when simulating execution with historical price series, so called
backtesting. However, when these robots are used in real markets frequently
they present poor performance in terms of risks and return. In this paper, we
propose an open source framework, called mt5se, that helps the development,
backtesting, live testing and real operation of autonomous traders. We built
and tested several traders using mt5se. The results indicate that it may help
the development of better traders. Furthermore, we discuss the simple
architecture that is used in many studies and propose an alternative multiagent
architecture. Such architecture separates two main concerns for portfolio
manager (PM) : price prediction and capital allocation. More than achieve a
high accuracy, a PM should increase profits when it is right and reduce loss
when it is wrong. Furthermore, price prediction is highly dependent of asset's
nature and history, while capital allocation is dependent only on analyst's
prediction performance and assets' correlation. Finally, we discuss some
promising technologies in the area.Comment: This paper replaces an old version of the framework, called mt5b3,
which is now deprecate
A survey on financial applications of metaheuristics
Modern heuristics or metaheuristics are optimization algorithms that have been increasingly used during the last decades to support complex decision-making in a number of fields, such as logistics and transportation, telecommunication networks, bioinformatics, finance, and the like. The continuous increase in computing power, together with advancements in metaheuristics frameworks and parallelization strategies, are empowering these types of algorithms as one of the best alternatives to solve rich and real-life combinatorial optimization problems that arise in a number of financial and banking activities. This article reviews some of the works related to the use of metaheuristics in solving both classical and emergent problems in the finance arena. A non-exhaustive list of examples includes rich portfolio optimization, index tracking, enhanced indexation, credit risk, stock investments, financial project scheduling, option pricing, feature selection, bankruptcy and financial distress prediction, and credit risk assessment. This article also discusses some open opportunities for researchers in the field, and forecast the evolution of metaheuristics to include real-life uncertainty conditions into the optimization problems being considered.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness
(TRA2013-48180-C3-P, TRA2015-71883-REDT), FEDER, and the Universitat Jaume I mobility program
(E-2015-36)
Deep Reinforcement Learning for Active High Frequency Trading
We introduce the first end-to-end Deep Reinforcement Learning (DRL) based
framework for active high frequency trading. We train DRL agents to trade one
unit of Intel Corporation stock by employing the Proximal Policy Optimization
algorithm. The training is performed on three contiguous months of high
frequency Limit Order Book data, of which the last month constitutes the
validation data. In order to maximise the signal to noise ratio in the training
data, we compose the latter by only selecting training samples with largest
price changes. The test is then carried out on the following month of data.
Hyperparameters are tuned using the Sequential Model Based Optimization
technique. We consider three different state characterizations, which differ in
their LOB-based meta-features. Analysing the agents' performances on test data,
we argue that the agents are able to create a dynamic representation of the
underlying environment. They identify occasional regularities present in the
data and exploit them to create long-term profitable trading strategies.
Indeed, agents learn trading strategies able to produce stable positive returns
in spite of the highly stochastic and non-stationary environment.Comment: 9 pages, 4 figure
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