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Algoritmos de negociação com dados de alta frequência; Algorithmic Trading with high frequency data

Uematsu, Akira Arice de Moura Galvão
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 20/03/2012 Português
Relevância na Pesquisa
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Em nosso trabalho analisamos os dados provenientes da BM&F Bovespa, a bolsa de valores de São Paulo, no período de janeiro de 2011, referentes aos índices: BOVESPA (IND), o mini índice BOVESPA (WIN) e a taxa de câmbio (DOL). Estes dados são de alta frequência e representam vários aspectos da dinâmica das negociações. No conjunto de valores encontram-se horários e datas dos negócios, preços, volumes oferecidos e outras características da negociação. A primeira etapa da tese foi extrair as informações necessárias para análises a partir de um arquivo em protocolo FIX, foi desenvolvido um programa em R com essa finalidade. Em seguida, estudamos o carácter da dependência temporal nos dados, testando as propriedades de Markov de um comprimento de memória fixa e variável. Os resultados da aplicação mostram uma grande variabilidade no caráter de dependência, o que requer uma análise mais aprofundada. Acreditamos que esse trabalho seja de muita importância em futuros estudos acadêmicos. Em particular, a parte do carácter específico do protocolo FIX utilizado pela Bovespa. Este era um obstáculo em uma série de estudos acadêmicos, o que era, obviamente, indesejável, pois a Bovespa é um dos maiores mercados comerciais do mundo financeiro moderno.; In our work we analyzed data from BM&F Bovespa...

Teoria da informação algorítmica, eficiência relativa de mercado e perda de memória em séries de retornos de alta frequência em ativos negociados na BM&F BOVESPA.; Algorithmic information theory, relative market efficiency and memory loss in high frequency asset return series traded at BM & F BOVESPA.

Ranciaro Neto, Adhemar
Fonte: Universidade Federal de Alagoas; BR; Economia; Programa de Pós-Graduação em Economia; UFAL Publicador: Universidade Federal de Alagoas; BR; Economia; Programa de Pós-Graduação em Economia; UFAL
Tipo: Dissertação Formato: application/pdf
Português
Relevância na Pesquisa
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This paper aims to apply the Kolmogorov algorithmic complexity theory using the measure proposed by Lempel and Ziv (1976) to analyze its behavior due to changes in parameters such as window size, jumps and the region of stability of high frequency financial series returns of assets traded on the BM&F BOVESPA, as well as to assess the evolution of such a measure when the intervals between the negotiations are extended and to verify the possible evidence of a relationship between the value of the complexity measure and the behavior of autocorrelation curves presented for each trading interval specified. We also discuss the criterion used to measure the relative efficiency of the market proposed by Giglio (2008).; Fundação de Amparo a Pesquisa do Estado de Alagoas; O presente trabalho tem por objetivos: 1) aplicar a teoria da complexidade de Kolmogorov utilizando a medida proposta por Lempel e Ziv (1976) para analisar o comportamento desta diante de alterações em parâmetros como tamanho de janela, salto e de região de estabilidade em séries financeiras de retornos de alta freqüência de ativos negociados na BM&F BOVESPA; 2) avaliar a evolução da medida ao se ampliarem os intervalos entre as negociações; e finalmente, 3) verificar a possibilidade de existir algum indício de relação entre o valor daquela medida e o comportamento das curvas de autocorrelação apresentadas para cada intervalo de negociação especificado. Foi também discutido o critério utilizado para a medida de eficiência relativa de mercado proposto por Giglio (2008).

Enhancing profitability through interpretability in algorithmic trading with a multiobjective evolutionary fuzzy system

Ghandar, A.; Michalewicz, Z.; Zurbrugg, R.Y.
Fonte: Springer-Verlag; Germany Publicador: Springer-Verlag; Germany
Tipo: Conference paper
Publicado em //2012 Português
Relevância na Pesquisa
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This paper examines the interaction of decision model complexity and utility in a computational intelligence system for algorithmic trading. An empirical analysis is undertaken which makes use of recent developments in multiobjective evolutionary fuzzy systems (MOEFS) to produce and evaluate a Pareto set of rulebases that balance conflicting criteria. This results in strong evidence that controlling portfolio risk and return in this and other similar methodologies by selecting for interpretability is feasible. Furthermore, while investigating these properties we contribute to a growing body of evidence that stochastic systems based on natural computing techniques can deliver results that outperform the market.; Adam Ghandar, Zbigniew Michalewicz and Ralf Zurbruegg

Modeling asset prices for algorithmic and high frequency trading

Cartea, Álvaro; Jaimungal, Sebastian
Fonte: Universidade Carlos III de Madrid Publicador: Universidade Carlos III de Madrid
Tipo: info:eu-repo/semantics/submittedVersion; info:eu-repo/semantics/workingPaper Formato: application/pdf
Publicado em 26/04/2011 Português
Relevância na Pesquisa
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Algorithmic Trading (AT) and High Frequency (HF) trading, which are responsible for over 70% of US stocks trading volume, have greatly changed the microstructure dynamics of tick-by-tick stock data. In this paper we employ a hidden Markov model to examine how the intra-day dynamics of the stock market have changed, and how to use this information to develop trading strategies at ultra-high frequencies. In particular, we show how to employ our model to submit limit-orders to profit from the bid-ask spread and we also provide evidence of how HF traders may profit from liquidity incentives (liquidity rebates). We use data from February 2001 and February 2008 to show that while in 2001 the intra-day states with shortest average durations were also the ones with very few trades, in 2008 the vast majority of trades took place in the states with shortest average durations. Moreover, in 2008 the fastest states have the smallest price impact as measured by the volatility of price innovations

Adjusting the capital asset pricing model for the short-run with liquidity proxies, while accounting for denials and deceptions in financial markets

Mooney, John J., IV
Fonte: Monterey, California: Naval Postgraduate School Publicador: Monterey, California: Naval Postgraduate School
Tipo: Tese de Doutorado
Português
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Approved for public release; distribution is unlimited.; William Sharpe's 1964 capital asset pricing model relies heavily on an accurate assessment of the asset's sensitivity to the broader market, termed _. By modifying the classic approach to incorporate liquidity of the asset, designated _', short-term return estimates may be improved. Specifically, in this research, the limit order book is used as a short-term proxy for liquidity assessments. Unfortunately, precise data were unavailable to test: however, detailed realistic examples are outlined in order to explore both rationale and critiques of the adjusted model. In light of the adjusted CAPM, modern market conditions, such as the rise in both high-frequency trading and alternative trading systems, are investigated to determine their impact on the model and asset pricing. Parallels can be drawn to appreciate these implementation obstacles under such information operation paradigms as denial, deception, and counterdeception. These topics, the protection of critical information from leakage, as well as the advancement and detection of deliberate misinformation, are increasingly critical for asset pricing. Furthermore, in response to these implementation obstacles, short-term asset pricing research is explored under both the efficient and adaptive market hypotheses. In conclusion...

Intention-Disguised Algorithmic Trading

Yuen, William; Syverson, Paul; Zhenming, Liu; Thorpe, Christopher A
Fonte: Harvard University Publicador: Harvard University
Tipo: Research Paper or Report
Português
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We propose a general model underlying the problem of designing trading strategies that leak no information to frontrunners and other exploiters. We study major scenarios in the market and design a family of algorithms that can be proven to leak no information in important scenarios. These algorithms can serve as building blocks for more challenging real-world scenarios beyond our current scope. In contrast to previous work, the strategies we propose protect trader using the existing trading infrastructure.; Engineering and Applied Sciences

Social signals and algorithmic trading of Bitcoin

Garcia, David; Schweitzer, Frank
Fonte: The Royal Society Publishing Publicador: The Royal Society Publishing
Tipo: Artigo de Revista Científica
Publicado em 23/09/2015 Português
Relevância na Pesquisa
48.532324%
The availability of data on digital traces is growing to unprecedented sizes, but inferring actionable knowledge from large-scale data is far from being trivial. This is especially important for computational finance, where digital traces of human behaviour offer a great potential to drive trading strategies. We contribute to this by providing a consistent approach that integrates various datasources in the design of algorithmic traders. This allows us to derive insights into the principles behind the profitability of our trading strategies. We illustrate our approach through the analysis of Bitcoin, a cryptocurrency known for its large price fluctuations. In our analysis, we include economic signals of volume and price of exchange for USD, adoption of the Bitcoin technology and transaction volume of Bitcoin. We add social signals related to information search, word of mouth volume, emotional valence and opinion polarization as expressed in tweets related to Bitcoin for more than 3 years. Our analysis reveals that increases in opinion polarization and exchange volume precede rising Bitcoin prices, and that emotional valence precedes opinion polarization and rising exchange volumes. We apply these insights to design algorithmic trading strategies for Bitcoin...

Optimal starting times, stopping times and risk measures for algorithmic trading: Target Close and Implementation Shortfall

Labadie, Mauricio; Lehalle, Charles-Albert
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
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We derive explicit recursive formulas for Target Close (TC) and Implementation Shortfall (IS) in the Almgren-Chriss framework. We explain how to compute the optimal starting and stopping times for IS and TC, respectively, given a minimum trading size. We also show how to add a minimum participation rate constraint (Percentage of Volume, PVol) for both TC and IS. We also study an alternative set of risk measures for the optimisation of algorithmic trading curves. We assume a self-similar process (e.g. Levy process, fractional Brownian motion or fractal process) and define a new risk measure, the p-variation, which reduces to the variance if the process is a brownian motion. We deduce the explicit formula for the TC and IS algorithms under a self-similar process. We show that there is an equivalence between selfsimilar models and a family of risk measures called p-variations: assuming a self-similar process and calibrating empirically the parameter p for the p-variation yields the same result as assuming a Brownian motion and using the p-variation as risk measure instead of the variance. We also show that p can be seen as a measure of the aggressiveness: p increases if and only if the TC algorithm starts later and executes faster. Finally...

Effective Trade Execution

Cesari, Riccardo; Marzo, Massimiliano; Zagaglia, Paolo
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 22/06/2012 Português
Relevância na Pesquisa
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This paper examines the role of algorithmic trading in modern financial markets. Additionally, order types, characteristics, and special features of algorithmic trading are described under the lens provided by the large development of high frequency trading technology. Special order types are examined together with an intuitive description of the implied dynamics of the order book conditional to special orders (iceberg and hidden). The chapter provides an analysis of the transaction costs associated with trading activity and examines the most common trading strategy employed in the market. It also examines optimal execution strategy with the description of the Efficient Trading Frontier. These concepts represent the tools needed to understand the most recent innovations in financial markets and the most recent advances in microstructures research.

Heavy-Tailed Features and Empirical Analysis of the Limit Order Book Volume Profiles in Futures Markets

Richards, Kylie-Anne; Peters, Gareth W.; Dunsmuir, William
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
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This paper poses a few fundamental questions regarding the attributes of the volume profile of a Limit Order Books stochastic structure by taking into consideration aspects of intraday and interday statistical features, the impact of different exchange features and the impact of market participants in different asset sectors. This paper aims to address the following questions: 1. Is there statistical evidence that heavy-tailed sub-exponential volume profiles occur at different levels of the Limit Order Book on the bid and ask and if so does this happen on intra or interday time scales ? 2.In futures exchanges, are heavy tail features exchange (CBOT, CME, EUREX, SGX and COMEX) or asset class (government bonds, equities and precious metals) dependent and do they happen on ultra-high (<1sec) or mid-range (1sec -10min) high frequency data? 3.Does the presence of stochastic heavy-tailed volume profile features evolve in a manner that would inform or be indicative of market participant behaviors, such as high frequency algorithmic trading, quote stuffing and price discovery intra-daily? 4. Is there statistical evidence for a need to consider dynamic behavior of the parameters of models for Limit Order Book volume profiles on an intra-daily time scale ? Progress on aspects of each question is obtained via statistically rigorous results to verify the empirical findings for an unprecedentedly large set of futures market LOB data. The data comprises several exchanges...

Le trading algorithmique

Lebreton, Victor
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
47.86009%
The algorithmic trading comes from digitalisation of the processing of trading assets on financial markets. Since 1980 the computerization of the stock market offers real time processing of financial information. This technological revolution has offered processes and mathematic methods to identify best return on transactions. Current research relates to autonomous transaction systems programmed in certain periods and some algorithms. This offers return opportunities where traders can not intervene. There are about thirty algorithms to assist the traders, the best known are the VWAP, the TWAP, TVOL. The algorithms offer the latest strategies and decision-making are the subject of much research. These advances in modeling decision-making autonomous agent can envisage a rich future for these technologies, the players already in use for more than 30% of their trading.

Optimum Liquidation Problem Associated with the Poisson Cluster Process

Sadoghi, A.; Vecer, J.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 23/07/2015 Português
Relevância na Pesquisa
37.777568%
In an illiquid market as a result of a lack of counterparties and uncertainty about asset values, trading of assets is not being secured by the actual value. In this research, we develop an algorithmic trading strategy to deal with the discrete optimal liquidation problem of large order trading with different market microstructures in an illiquid market. In this market, order flow can be viewed as a Point process with stochastic arrival intensity. Interaction between price impact and price dynamics can be modeled as a dynamic optimization problem with price impact as a linear function of the self-exciting dynamic process. We formulate the liquidation problem as a discrete-time Markov Decision Processes where the state process is a Piecewise Deterministic Markov Process (PDMP), which is a member of right continuous Markov Process family. We study the dynamics of a limit order book and its influence on the price dynamics and develop a stochastic model to retain the main statistical characteristics of limit order books in illiquid markets.; Comment: 43 pages, 9 figures

Social signals and algorithmic trading of Bitcoin

Garcia, David; Schweitzer, Frank
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
58.57739%
The availability of data on digital traces is growing to unprecedented sizes, but inferring actionable knowledge from large-scale data is far from being trivial. This is especially important for computational finance, where digital traces of human behavior offer a great potential to drive trading strategies. We contribute to this by providing a consistent approach that integrates various datasources in the design of algorithmic traders. This allows us to derive insights into the principles behind the profitability of our trading strategies. We illustrate our approach through the analysis of Bitcoin, a cryptocurrency known for its large price fluctuations. In our analysis, we include economic signals of volume and price of exchange for USD, adoption of the Bitcoin technology, and transaction volume of Bitcoin. We add social signals related to information search, word of mouth volume, emotional valence, and opinion polarization as expressed in tweets related to Bitcoin for more than 3 years. Our analysis reveals that increases in opinion polarization and exchange volume precede rising Bitcoin prices, and that emotional valence precedes opinion polarization and rising exchange volumes. We apply these insights to design algorithmic trading strategies for Bitcoin...

A Pre-Trade Algorithmic Trading Model under Given Volume Measures and Generic Price Dynamics (GVM-GPD)

Shen, Jackie Jianhong
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
47.85984%
We make several improvements to the mean-variance framework for optimal pre-trade algorithmic execution, by working with volume measures and generic price dynamics. Volume measures are the continuum analogies for discrete volume profiles commonly implemented in the execution industry. Execution then becomes an absolutely continuous measure over such a measure space, and its Radon-Nikodym derivative is commonly known as the Participation of Volume (PoV) function. The four impact cost components are all consistently built upon the PoV function. Some novel efforts are made for these linear impact models by having market signals more properly expressed. For the opportunistic cost, we are able to go beyond the conventional Brownian-type motions. By working directly with the auto-covariances of the price dynamics, we remove the Markovian restriction associated with Brownians and thus allow potential memory effects in the price dynamics. In combination, the final execution model becomes a constrained quadratic programming problem in infinite-dimensional Hilbert spaces. Important linear constraints such as participation capping are all permissible. Uniqueness and existence of optimal solutions are established via the theory of positive compact operators in Hilbert spaces. Several typical numerical examples explain both the behavior and versatility of the model.

Dynamics of Order Positions and Related Queues in a Limit Order Book

Guo, Xin; Ruan, Zhao; Zhu, Lingjiong
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
37.457795%
Order positions are key variables in algorithmic trading. This paper studies the limiting behavior of order positions and related queues in a limit order book. In addition to the fluid and diffusion limits for the processes, fluctuations of order positions and related queues around their fluid limits are analyzed. As a corollary, explicit analytical expressions for various quantities of interests in a limit order book are derived.; Comment: 42 pages, 2 figures

Evolving Chart Pattern Sensitive Neural Network Based Forex Trading Agents

Sher, Gene I.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
37.91969%
Though machine learning has been applied to the foreign exchange market for algorithmic trading for quiet some time now, and neural networks(NN) have been shown to yield positive results, in most modern approaches the NN systems are optimized through traditional methods like the backpropagation algorithm for example, and their input signals are price lists, and lists composed of other technical indicator elements. The aim of this paper is twofold: the presentation and testing of the application of topology and weight evolving artificial neural network (TWEANN) systems to automated currency trading, and to demonstrate the performance when using Forex chart images as input to geometrical regularity aware indirectly encoded neural network systems, enabling them to use the patterns & trends within, when trading. This paper presents the benchmark results of NN based automated currency trading systems evolved using TWEANNs, and compares the performance and generalization capabilities of these direct encoded NNs which use the standard sliding-window based price vector inputs, and the indirect (substrate) encoded NNs which use charts as input. The TWEANN algorithm I will use in this paper to evolve these currency trading agents is the memetic algorithm based TWEANN system called Deus Ex Neural Network (DXNN) platform.

Dynamic Mode Decomposition for Financial Trading Strategies

Mann, Jordan; Kutz, J. Nathan
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 18/08/2015 Português
Relevância na Pesquisa
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We demonstrate the application of an algorithmic trading strategy based upon the recently developed dynamic mode decomposition (DMD) on portfolios of financial data. The method is capable of characterizing complex dynamical systems, in this case financial market dynamics, in an equation-free manner by decomposing the state of the system into low-rank terms whose temporal coefficients in time are known. By extracting key temporal coherent structures (portfolios) in its sampling window, it provides a regression to a best fit linear dynamical system, allowing for a predictive assessment of the market dynamics and informing an investment strategy. The data-driven analytics capitalizes on stock market patterns, either real or perceived, to inform buy/sell/hold investment decisions. Critical to the method is an associated learning algorithm that optimizes the sampling and prediction windows of the algorithm by discovering trading hot-spots. The underlying mathematical structure of the algorithms is rooted in methods from nonlinear dynamical systems and shows that the decomposition is an effective mathematical tool for data-driven discovery of market patterns.; Comment: 18 pages, 7 figures. arXiv admin note: text overlap with arXiv:1506.00564

Realtime market microstructure analysis: online Transaction Cost Analysis

Azencott, Robert; Beri, Arjun; Gadhyan, Yutheeka; Joseph, Nicolas; Lehalle, Charles-Albert; Rowley, Matthew
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
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Motivated by the practical challenge in monitoring the performance of a large number of algorithmic trading orders, this paper provides a methodology that leads to automatic discovery of the causes that lie behind a poor trading performance. It also gives theoretical foundations to a generic framework for real-time trading analysis. Academic literature provides different ways to formalize these algorithms and show how optimal they can be from a mean-variance, a stochastic control, an impulse control or a statistical learning viewpoint. This paper is agnostic about the way the algorithm has been built and provides a theoretical formalism to identify in real-time the market conditions that influenced its efficiency or inefficiency. For a given set of characteristics describing the market context, selected by a practitioner, we first show how a set of additional derived explanatory factors, called anomaly detectors, can be created for each market order. We then will present an online methodology to quantify how this extended set of factors, at any given time, predicts which of the orders are underperforming while calculating the predictive power of this explanatory factor set. Armed with this information, which we call influence analysis...

An algorithmic information-theoretic approach to the behaviour of financial markets

Zenil, Hector; Delahaye, Jean-Paul
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
37.909902%
Using frequency distributions of daily closing price time series of several financial market indexes, we investigate whether the bias away from an equiprobable sequence distribution found in the data, predicted by algorithmic information theory, may account for some of the deviation of financial markets from log-normal, and if so for how much of said deviation and over what sequence lengths. We do so by comparing the distributions of binary sequences from actual time series of financial markets and series built up from purely algorithmic means. Our discussion is a starting point for a further investigation of the market as a rule-based system with an 'algorithmic' component, despite its apparent randomness, and the use of the theory of algorithmic probability with new tools that can be applied to the study of the market price phenomenon. The main discussion is cast in terms of assumptions common to areas of economics in agreement with an algorithmic view of the market.; Comment: Forthcoming in the Journal of Economic Surveys, special issue on Nonlinearity, Complexity and Randomness. 25 pages. 7 figures. 9 tables. Latest version fixes the glitches on the tables related to the distribution from Turing machines (using 4 states as claimed...

Study and implementation of some quantitative trading models

Sánchez López, Emiliano
Fonte: Centre de Recerca Matemàtica Publicador: Centre de Recerca Matemàtica
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em //2011 Português
Relevância na Pesquisa
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Quantitative or algorithmic trading is the automatization of investments decisions obeying a fixed or dynamic sets of rules to determine trading orders. It has increasingly made its way up to 70% of the trading volume of one of the biggest financial markets such as the New York Stock Exchange (NYSE). However, there is not a signi cant amount of academic literature devoted to it due to the private nature of investment banks and hedge funds. This projects aims to review the literature and discuss the models available in a subject that publications are scarce and infrequently. We review the basic and fundamental mathematical concepts needed for modeling financial markets such as: stochastic processes, stochastic integration and basic models for prices and spreads dynamics necessary for building quantitative strategies. We also contrast these models with real market data with minutely sampling frequency from the Dow Jones Industrial Average (DJIA). Quantitative strategies try to exploit two types of behavior: trend following or mean reversion. The former is grouped in the so-called technical models and the later in the so-called pairs trading. Technical models have been discarded by financial theoreticians but we show that they can be properly cast into a well defined scientific predictor if the signal generated by them pass the test of being a Markov time. That is...