Technical trading and cryptocurrencies

This paper carries out a comprehensive examination of technical trading rules in cryptocurrency markets, using data from two Bitcoin markets and three other popular cryptocurrencies. We employ almost 15,000 technical trading rules from the main five classes of technical trading rules and find significant predictability and profitability for each class of technical trading rule in each cryptocurrency. We find that the breakeven transaction costs are substantially higher than those typically found in cryptocurrency markets. To safeguard against data-snooping, we implement a number of multiple hypothesis procedures which confirms our findings that technical trading rules do offer significant predictive power and profitability to investors. We also show that the technical trading rules offer substantially higher risk-adjusted returns than the simple buy-and-hold strategy, showing protection against lengthy and severe drawdowns associated with cryptocurrency markets. However there is no predictability for Bitcoin in the out-of-sample period, although predictability remains in other cryptocurrency markets

Douleur au cou atypique: exemple d’un syndrome peu connu [Atypical neck pain: an example of a little-known syndrome]

Eagle's syndrome is an unknown disease. Its suspicion is first and foremost clinical and his symptoms are diverse. The diagnosis is confirmed by imaging. Its management is surgical: resection of the styloid process by trans-oral or trans-amygdala route. Patients often consult several specialists and there are many investigations before the right diagnosis is made

Technical trading revisited: False discoveries, persistence tests, and transaction costs

a b s t r a c t We revisit the apparent historical success of technical trading rules on daily prices of the Dow Jones Industrial Average index from 1897 to 2011, and we use the false discovery rate (FDR) as a new approach to data snooping. The advantage of the FDR over existing methods is that it selects more outperforming rules, which allows diversifying against model uncertainty. Persistence tests show that, even with the more powerful FDR technique, an investor would never have been able to select ex ante the future best-performing rules. Moreover, even in-sample, the performance is completely offset by the introduction of low transaction costs. Overall, our results seriously call into question the economic value of technical trading rules that has been reported for early periods. & 2012 Elsevier B.V. All rights reserved. Introduction Whether technical trading rules can consistently generate profits, as opposed to just being lucky every now and then, is the subject of an ongoing debate. Practitioners have devoted significant resources to technical trading, which uses past price and volume data to infer future prices. A substantial segment of the investment industry employs indicators that include moving averages, support and resistance levels, and other filter rules. Technical indicators are as ubiquitous on professional information systems as on popular finance websites and online retail brokers. In spite of its popularity among practitioners, academics have long been skeptical about the merits of technical analysis. $ We would like to thank the editor and the referee for constructive criticism and numerous suggestions that have lead to substantial improvements over previous versions of the paper. We are grateful to M. Franscini-Scaillet for helping us to get the data on fund structure costs and futures trading costs via her industry contacts. We thank L. Barras, I

A framework for optimization of pattern sets for financial time series prediction

Abstract—In this paper, a framework is introduced for generating human-interpretable structures, here called pattern sets, for short-term prediction of financial time series. The optimization is carried out using an evolutionary algorithm, which is able to modify both the structure and the parameters of the evolving pattern sets. The framework has been applied in two different modes: A tuning mode, in which the user provides a starting point in the form of loosely defined pattern set, and a discovery mode, in which the starting points consist of random pattern sets. The best results were obtained in the tuning mode, for which the top-performing pattern sets gave strongly statistically significant results in excess of one-day market returns (p−values below 0.0007 and, in many cases, even below 0.0001) over validation data (not used during optimization) for two data sets, involving stocks with large and small market capitalization, respectively. The average one-day returns ranged from 0.518 to 1.147%, with one-day Sharpe ratios ranging from 0.138 to 0.258