on metalabeling – i.e., finding the probability of profit of your own simple basic trading strategy, and not to use it to predict the market directly. Why?

Also included is a discussion on why loss aversion is not a behavioral bias, which is opposite to what I previously believed. It stems from a profound mathematical insight that threatens to upend the economics profession.

No, the difficulty is not the lack of ideas. The difficulty is to develop a taste for which strategy is suitable for your personal circumstances and goals, and which ones look viable even before you devote the time to diligently backtest them. This taste for prospective strategies is what I will try to convey in this chapter.

Finding a trading idea is actually not the hardest part of building a quantitative trading business. There are hundreds, if not thousands, of trading ideas that are in the public sphere at any time, accessible to anyone at little or no cost. Many authors of these trading ideas will tell you their complete methodologies in addition to their backtest

For historical stock data, there is another quality that may be even more important than their frequencies: whether the data are free of survivorship bias. I will define survivorship bias in the following section. Here, we just need to know that historical stock data without survivorship bias are much more expensive than those that have such a

However, if the strategy is a long–short dollar-neutral strategy (i.e., the portfolio holds long and short positions with equal capital), then 10 percent is quite a good return, because then the benchmark of comparison is not the market index, but a riskless asset such as the yield of the three-month US Treasury bill (which at the time of this

The Sharpe ratio is actually a special case of the information ratio, suitable when we have a dollar-neutral strategy, so that the benchmark to use is always the risk-free rate. In practice, most traders use the Sharpe ratio even when they are trading a directional (long or short only) strategy, simply because it facilitates comparison across

Despite our luck with the longevity of some of the strategies I described, most arbitrage opportunities eventually fade away—the notorious alpha decay that professionals like to lament. Alpha decay can be due to competition—too many people trading the same strategy, but equally often it is due to regime shift caused by market structure or

Chapter 3: Extensive changes on MATLAB code that remove a major bug, and new commentary and codes for Python and R. Description of some new quant trading platforms. One item of particular interest: I discuss a mathematically rigorous way to decide how much backtest data and how long a paper trading period is needed. Another mathematical technique