By Ernie Chan
"Algorithmic buying and selling is an insightful booklet on quantitative buying and selling written by way of a professional practitioner. What units this booklet except many others within the house is the emphasis on genuine examples instead of simply concept. recommendations usually are not simply defined, they're dropped at existence with real buying and selling concepts, which provide the reader perception into how and why each one method was once constructed, the way it was once applied, or even the way it used to be coded. This e-book is a necessary source for somebody seeking to create their very own systematic buying and selling techniques and people focused on supervisor choice, the place the data contained during this e-book will bring about a extra proficient and nuanced dialog with managers."
—DAREN SMITH, CFA, CAIA, FSA, dealing with Director, supervisor choice & Portfolio development, collage of Toronto Asset Management
"Using a great number of suggest reversion and momentum options, Ernie explains the reason in the back of every one, exhibits how you can try it, tips to enhance it, and discusses implementation concerns. His ebook is a cautious, specific exposition of the clinical procedure utilized to technique improvement. For severe retail investors, i do know of no different publication that offers this diversity of examples and point of element. His discussions of the way regime alterations have an effect on options, and of hazard administration, are worthy bonuses."
—Roger Hunter, Mathematician and Algorithmic Trader
Read or Download Algorithmic Trading: Winning Strategies and Their Rationale (Wiley Trading) PDF
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Additional resources for Algorithmic Trading: Winning Strategies and Their Rationale (Wiley Trading)
We suppose that the true average daily return based on an infinite data set is actually zero. This supposition is called the null hypothesis. 3. We suppose that the probability distribution of daily returns is known. This probability distribution has a zero mean, based on the null hypothesis. We describe later how we determine this probability distribution. 4. Based on this null hypothesis probability distribution, we compute the probability p that the average daily returns will be at least as large as the observed value in the backtest (or, for a general test statistic, as extreme, allowing for the possibility of a negative test statistic).
Some people may decide to roll over 10 days before the current front contract expires; others may decide to roll over when there is an “open interest crossover”; that is, when the open interest of the next contract exceeds that of the current front contract. No matter how you decide your rollover date, it is quite an extra bother to have to incorporate that in your 13 BACKTESTING AND AUTOMATED EXECUTION trading strategy, as this buying and selling is independent of the strategy and should result in minimal additional return or profit and loss (P&L).
Its average holding period is 50 seconds. Can we really backtest a high-frequency trading strategy? The performance of a high-frequency trading strategy depends on the order types used and the execution method in general. Furthermore, it depends crucially on the market microstructure. Even if we have historical data of the entire order book, the profit from a high-frequency strategy is still very dependent on the reactions of other market participants. One has to question whether there is a “Heisenberg uncertainty principle” at work: The act of placing or executing an order might alter the behavior of the other market participants.
Algorithmic Trading: Winning Strategies and Their Rationale (Wiley Trading) by Ernie Chan