This work is inspired by Robert Carver's books. Robert is the author of "Systematic Trading", "Smart Porfolios" and "Leveraged Trading".
The Sharpe Ratio of a given strategy will be proportional to the square root of the number of independent bets made per year.
- How much do you like this trade? Heart
- How much can you afford to lose? Gut
- How risky is it? Brain
- https://twitter.com/AllThatIsSolid/status/1192107498809044992?s=20 EM strats!!!!!!!
- I'm assuming = holding period for all the strategies. Fix this.
- beware of instruments experiencing an artificial low volatility period through external factors (e.g. CHF whilst the peg to USD was in place). When the risk returns to normal it's gonna do it very sharply.
- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3459866 There are three fundamental ways of testing the validity of an investment algorithm against historical evidence: a) the walk-forward method; b) the resampling method; and c) the Monte Carlo method. By far the most common approach followed among academics and practitioners is the walk-forward method. Implicit in that choice is the assumption that a given investment algorithm should be deployed throughout all market regimes. We denote such assumption the “all-weather” hypothesis, and the algorithms based on that hypothesis “strategic investment algorithms” (or “investment strategies”). The all-weather hypothesis is not necessarily true, as demonstrated by the fact that many investment strategies have floundered in a zero-rate environment. This motivates the problem of identifying investment algorithms that are optimal for specific market regimes, denoted “tactical investment algorithms.” This paper argues that backtesting against synthetic datasets should be the preferred approach for developing tactical investment algorithms. A new organizational structure for asset managers is proposed, as a tactical algorithmic factory, consistent with the Monte Carlo backtesting paradigm.