Bootstrap

Today, Monte Carlo (MC) methods are widely used in finance to price derivative securities. In this approach, the value of the option is expressed in terms of an integral of very high dimensionality. Monte Carlo methods are used to estimate the value of this integral by brute force. These calculations consume a significant portion of the run-time and energy of financial data centers. Therefore, we present a hardware accelerator that computes the price of a European call option via MC. In our approach, after some initial setup, the entire MC simulation is performed by the FPGA. We demonstrate performance in excess of 250× that of a modern 3 GHz multi-core processor. By Nathan Woods, XtremeData, Inc.

This is an extended version of the Tech Forum that appeared in the Q1 2008 edition of Automated Trader. It includes an additional interviewee and expanded answers from all interviewees on the latest techniques for hardware and networking infrastructures.

Statistical Arbitrage: Algorithmic Trading Insights and Techniques Chapter 3 Structural Models

Statistical Arbitrage: Algorithmic Trading Insights and Techniques Chapter 2 Statistical Arbitrage

Statistical Arbitrage: Algorithmic Trading Insights and Techniques Chapter 1 Monte Carlo or Bust

The following excerpt is from Chapter 6 of David Aronson's recently published book "Evidence-Based Technical Analysis". Together with Chapters 4 and 5 of the book it addresses aspects of statistics that are particularly relevant to evidence-based (as opposed to subjective) technical analysis.

The following excerpt is from Chapter 5 of David Aronson's recently published book "Evidence-Based Technical Analysis". Together with Chapters 4 and 6 of the book it addresses aspects of statistics that are particularly relevant to evidence-based (as opposed to subjective) technical analysis.

The following excerpt is from Chapter 4 of David Aronson's recently published book "Evidence-Based Technical Analysis". Together with Chapters 5 and 6 of the book (which will be available as excerpts later) it addresses aspects of statistics that are particularly relevant to evidence-based (as opposed to subjective) technical analysis.

Dave Ackerman, the narrator of Naked Option, is a brilliant trader but one day, recklessly trying to one-up his firm's superstar, he goes naked on an option trade and loses $112 million in two hours. His career is over. Then he hears about an auditing job at an investment bank. He knows within minutes that something is very wrong, but he's so desperate, he takes the job.

Genetic Programming (GP) is an appealing machine-learning technique for tackling financial engineering problems: it belongs to the family of evolutionary algorithms that have proven to be remarkably successful at handling complex optimization problems, and possesses the unique feature of producing solutions under a symbolic form that can be understood and analyzed by humans. Over the last decade, GP has been applied to generate financial trading strategies, forecast stocks and options prices, or grasp some insight into the dynamics of the markets and the behavior of the agents. In this paper, we first provide a brief survey of the existing studies, then highlight fields of investigations that, we believe, should lead to enhance the applicability and efficiency of GP in the financial domain. By Nicolas NAVET and Shu-Heng CHEN

The entropy rate of a dynamic process measures the uncertainty that remains in the next information produced by the process given complete knowledge of the past. It is thus a natural measure of the difficulty to predict the evolution of the process. The first question investigated here is whether stock price time series exhibit temporal dependencies that can be measured through entropy estimates. Then we study the extent to which the return of financial trading rules is correlated with the entropy rates of the price time series. Experiments are conducted on EOD data of the stocks composing the NYSE US 100 index during period 2000-2006, with the use of genetic programming to induce the trading rules. By Nicolas NAVET and Shu-Heng CHEN

We apply large deviation theory to assess the probability that a trading system performs below or above a certain threshold. Our technique does not require that the distribution of the performance criterion obeys a closed-form equation, and can accept as input empirical distributions given under the form of frequency histograms obtained by backtesting or from prior use of the trading system. A nice property of the technique is that it can be easily automated and integrated into a trading platform. Furthermore, the approach is not limited to a single trading system but can be applied on portfolio of trading systems. By Nicolas NAVET and René SCHOTT

An excerpt from Kendall Kim's forthcoming book "Electronic and Algorithmic Trading Technology: The Complete Guide" Chapter 10: Transaction Cost Research

Part 2 of Pretests for genetic-programming evolved trading programs: “zero-intelligence” strategies and lottery trading bootstrap paper. By Shu-Heng Chen and Nicolas Navet

In this paper, we discuss a series of pretests, based on several variants of random search, aiming at giving more clearcut answers as to whether a GP scheme, or any other machine-learning technique, can be effective with the training data at hand. Precisely, pretesting allows us to distinguish between a failure due to the market being efficient of due to GP being inefficient. The analysis is illustrated with GP-evolved strategies for three stock exchanges exhibiting different trends.