Mugrel Trafing, indeed, we confirmed their cointegration in Chapter 3. In practical trading, the constant drift in price, if any, tends to be of a much smaller magnitude than the daily fluctuations in price. The important difference between ensemble average and time average has been raised in this paper by Ole Peters and Murray Gell-Mann another Nobel laureate like Kahneman. Quantitative Trading Finally, we run the strategy on these simulated prices and calculate the average return of the strategy. He can be reached at john jryle. With the proliferation of ETFs tracking more or less the same sector, pair-trading opportunities are steadily increasing.
|Published (Last):||6 October 2013|
|PDF File Size:||6.6 Mb|
|ePub File Size:||8.59 Mb|
|Price:||Free* [*Free Regsitration Required]|
While institutional traders continue to implement this highly effective approach, many independent traders--with limited resources and less computing power--have wondered if they can still challenge powerful industry professionals at their own game? The answer is "yes," and in Quantitative Trading, author Dr.
Ernest Chan, a respected independent trader and consultant, will show you how. Back cover copy Praise for Quantitative Trading "As technology has evolved, so has the ease in developing trading strategies.
Ernest Chan does all traders, current and prospective, a real service by succinctly outlining the tremendous benefits, but also some of the pitfalls, in utilizing many of the recently implemented quantitative trading techniques. Ernest Chan provides an optimal framework for strategy development, back-testing, risk management, programming knowledge, and real-time system implementation to develop and run an algorithmic trading business step by step in Quantitative Trading.
In this honest and practical guide, Dr. Chan highlights the essential cornerstones of a successful automated trading operation and shares lessons he learned the hard way while offering clear direction to steer readers away from common traps that both individual and institutional traders often succumb to.
In most instances, the authors have no real knowledge of the subject matter, or do have something important to say but are unwilling to do so because of fears of having trade secrets stolen.
Ernie subscribes to a different credo: Share meaningful information and have meaningful interactions with the quantitative community at large. Ernie successfully distills a large amount of detailed and difficult subject matter down to a very clear and comprehensive resource for novice and pro alike. The Business Case for Quantitative Trading.
Demand on Time. The Nonnecessity of Marketing. The Way Forward. Chapter 2: Fishing for Ideas. Your Working Hours. Your Programming Skills. Your Trading Capital.
Your Goal. How Deep and Long is the Drawdown? Does the Data Suffer from Survivorship Bias? Chapter 3: Backtesting. Common Backtesting Platforms. High-End Backtesting Platforms. Finding and Using Historical Databases. Are the Data Split- and Dividend-Adjusted? Are the Data Survivorship Bias Free?
Performance Measurement. Common Backtesting Pitfalls to Avoid. Look-Ahead Bias. Data-Snooping Bias. Transaction Costs. Strategy Refinement. Chapter 4: Setting up Your Business. Business Structure: Retail or Proprietary? Choosing a Brokerage or Proprietary Trading Firm. Physical Infrastructure. Chapter 5: Execution Systems. Building a Semiautomated Trading System.
Building a Fully Automated Trading System. Minimizing Transaction Costs. Testing Your System by Paper Trading. Chapter 6: Money and Risk Management. Optimal Capital Allocation and Leverage. Risk Management. Psychological Preparedness. Chapter 7: Special Topics in Quantitative Trading. Mean-Reverting versus Momentum Strategies. Regime Switching. Stationarity and Cointegration. Factor Models. What Is Your Exit Strategy? Seasonal Trading Strategies. High-Frequency Trading Strategies.
Next Steps. About the Author. Chan, PhD, is a quantitative trader and consultant who advises clients on how to implement automated statistical trading strategies. Chan earned a PhD in physics from Cornell University.
Quantitative Trading : How to Build Your Own Algorithmic Trading Business
Starting with ML for Trading Question 1: What is trading all about and how can it be done profitably? Dr Ernest Chan: If you check the stock exchanges of your regions, you will find that the stock prices are always dynamic, ie they either move up or down on a daily basis. In fact, some have wild swings in an hour too. We call this volatility. Now, the stock price is a result of a variety of factors, such as revenues, profits or losses in a financial year of the company, or even the industry they are in matters. Now, trading is about understanding the influence of these factors as well as studying the divergence in the market and predicting the outcome to make a profit.
E-mail Books Dr. Ernest Chan does all traders, current and prospective, a real service by succinctly outlining the tremendous benefits, but also some of the pitfalls, in utilizing many of the recently implemented quantitative trading techniques. It delves into the reasons certain markets display either mean reversion or momentum, and describes the common techniques that can exploit these profit opportunities. Numerous strategy examples are drawn from stocks, ETFs, futures, and currencies. What sets this book apart from many others in the space is the emphasis on real examples as opposed to just theory.