Le trading algorithmique

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.marché électronique;algorithme de trading;agent autonome;VWAP;TVOL;Arbitrage:marché financier; passage d'ordre automatisés

Technical analysis in the foreign exchange market

This article introduces the subject of technical analysis in the foreign exchange market, with emphasis on its importance for questions of market efficiency. Technicians view their craft, the study of price patterns, as exploiting traders’ psychological regularities. The literature on technical analysis has established that simple technical trading rules on dollar exchange rates provided 15 years of positive, risk-adjusted returns during the 1970s and 80s before those returns were extinguished. More recently, more complex and less studied rules have produced more modest returns for a similar length of time. Conventional explanations that rely on risk adjustment and/or central bank intervention are not plausible justifications for the observed excess returns from following simple technical trading rules. Psychological biases, however, could contribute to the profitability of these rules. We view the observed pattern of excess returns to technical trading rules as being consistent with an adaptive markets view of the world.Foreign exchange rates

Artificial cognitive architecture with self-learning and self-optimization capabilities. Case studies in micromachining processes

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 22-09-201

Neural Network Guided Evolution of L-system Plants

A Lindenmayer system is a parallel rewriting system that generates graphic shapes using several rules. Genetic programming (GP) is an evolutionary algorithm that evolves expressions. A convolutional neural network(CNN) is a type of neural network which is useful for image recognition and classification. The goal of this thesis will be to generate different styles of L-system based 2D images of trees from scratch using genetic programming. The system will use a convolutional neural network to evaluate the trees and produce a fitness value for genetic programming. Different architectures of CNN are explored. We analyze the performance of the system and show the capabilities of the combination of CNN and GP. We show that a variety of interesting tree images can be automatically evolved. We also found that the success of the system highly depends on CNN training, as well as the form of the GP's L-system language representation

Learning to Manipulate a Financial Benchmark

Financial benchmarks estimate market values or reference rates used in a wide variety of contexts, but are often calculated from data generated by parties who have incentives to manipulate these benchmarks. Since the London Interbank Offered Rate (LIBOR) scandal in 2011, market participants, scholars, and regulators have scrutinized financial benchmarks and the ability of traders to manipulate them. We study the impact on market welfare of manipulating transaction-based benchmarks in a simulated market environment. Our market consists of a single benchmark manipulator with external holdings dependent on the benchmark, and numerous background traders unaffected by the benchmark. We explore two types of manipulative trading strategies: zero-intelligence strategies and strategies generated by deep reinforcement learning. Background traders use zero-intelligence trading strategies. We find that the total surplus of all market participants who are trading increases with manipulation. However, the aggregated market surplus decreases for all trading agents, and the market surplus of the manipulator decreases, so the manipulator’s surplus from the benchmark significantly increases. This entails under natural assumptions that the market and any third parties invested in the opposite side of the benchmark from the manipulator are negatively impacted by this manipulation

Reinforcement Learning

Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field