No single rule can ever be relied upon completely.
● It is necessary to come up with a strategy for using rules simultaneously.
How do you identify when a strategy is not working? If it performs outside its expected historical returns, signaled when:
● drawdown exceeds what’s normal;
● its Sharpe falls;
● it otherwise goes out of the historical box, defying the rules that were initially observed.
It is important to pursue different strategies simultaneously, and to shift one’s efforts into working strategies. As a simplified example, suppose one has a theory for describing when gold prices rise. The theory works 50 % of the time (years). Suppose one has 10 such equally good theories. A combination of the theories will describe reality better than any one of them. And the best way to manage which one of them is most accurate is by observing which ones are working now.
Then comes the application of cutting losses.
When a strategy stops working, determine the belief that motivated the activity. If the belief was obviously false, you are playing dice here. Best to terminate the activity and engage in more productive ones.
For example, let’s say you hire someone to renovate your house. They promise to do the job for $50,000, but less than halfway through the job, they’ve already spent $45,000. At this point, if switching to a new builder can be done cheaply, cut the old one.
Suppose we are engaged in an activity – let’s call it X – which starts to lose money.
The activity can be anything, perhaps a trading strategy or a business. The questions to be asked are:
● Am I losing money in activity X?
● What is the loss amount? Call the loss Z.
Before starting activity X, what was the anticipated amount of the maximum loss? If Z exceeds this amount and the exit cost is not so high, cut the loss.
SUMMARY
Examine each potential action prior to embarking on it.
Determine:
● What’s the objective?
● What are the normal, expected difficulties?
Plan in advance how to get out of the strategy cheaply.
Pursue multiple strategies simultaneously.
Cut all strategies that are falling outside expectations.
PART II
Design and Evaluation
4
Alpha Design
By Scott Bender/Yongfeng He
An alpha is a method of making predictions about future asset price changes. For example, an alpha might be a computer program that predicts future returns of a particular set of stocks.
Alphas we cover in this chapter are fully systematic and can be expressed by a concrete piece of code. The alpha will typically make predictions at some periodic frequency, for example, once per day. Its predictions will then be represented by a number for each asset it intends to predict.
A simple alpha might be, for each day, assigning a prediction of +1 to all stocks that went down yesterday and –1 to all stocks that went up. This is a valid alpha for us because it systematically generates a specific prediction for a set of assets at a specific frequency.
Alphas are predictive models, but without a way to implement those predictions, there is no way to realize the potential profits that those predictions might generate. Typically, an alpha is utilized as a component of a trading strategy, which converts the alpha’s predictions into actual trading decisions. The strategy is largely driven by the combined predictions of its alphas, but it also considers practical issues such as transaction costs and portfolio risk before actually executing a trade.
CATEGORIZATION OF ALPHAS
Alphas may be categorized into three major groups according to the types of instruments traded, such as stocks, exchange-traded funds, currencies, futures, options and bonds, etc. Alphas may trade single or multiple types of instruments. They could be developed for one specific country or multiple countries combined, or even the global market.
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