FAQ: safe-f and CAR25

In response to a request for clarification of the metrics safe-f and CAR25, and their use, asked by a reader —

Greetings —

Use CAR25 as an objective function while developing trading systems, and also as an objective function while managing the trading of those systems.

CAR25 is a metric that estimates future profit potential for any set of trades. It is a dominant metric. Each of the alternatives being evaluated and ranked have the same probability of loss of capital. If this was done correctly, the trader / investor should be impartial to choice among them and willing to accept any of the alternatives. CAR25 ranks the alternatives. When measuring and managing daily — mark to market daily — rank each alternative daily and have a signal for the next day’s action for each day. Each day, focus on the top-ranked of the alternatives. Follow its signal for the next one-day action. Using funds to take signals and trades from any of the alternatives that have lower CAR25 is a suboptimal use of funds.

All of that being said, we are looking for the best return from a portfolio of risk-normalized alternatives. These will be trading systems, each of which trades a single issue long/flat or short/flat. CAR25 ranks them daily. The rotation is among a portfolio of systems, rather than a portfolio of issues. Include among the systems a (nearly) risk-free alternative — such as certificates of deposit or money market funds — which can also have its risk and CAR25 computed. When the risk-free alternative is top-ranked, stay risk-free.

The risk evaluation has four steps. Revisit the video — The Four Faces of Risk

Follow the steps in this order:

1. Risk assessment begins with a personal statement of risk tolerance. All alternatives will be normalized to give the same probability of this amount of risk.

2. Each series of prices has risk associated with it, even before a trading model is applied. This is the risk the “data prospector” assesses. We are looking for tradable data series that have both enough variation to offer profit, but not so much that there will be excessive risk no matter which model is applied.

3. Given a series with those “goldilocks” features of volatility, try to develop a model that identifies profitable trades. Use the scientific method — fit in-sample data and validate using out-of-sample data. When a promising model has been fit, analyze the out-of-sample trades, computing safe-f and CAR25. If the development was done correctly, the out-of-sample results are the best estimate of future performance.

4. Financial data is not stationary. As the data changes, the fit between the model and the data will change, and the distribution of trades will change. Regularly update the set of trades used to calculate safe-f and CAR25. When the model and data lose synchronization, safe-f will drop and CAR25 will drop.

Continue monitoring CAR25 day by day, taking signals from the top-ranked system.

Best regards, Howard

The Papers page has new content

A new paper, entitled “Assessing Trading System Health,” has been added to the Papers page.  The paper describes a technique for validating that the system developed in-sample does have predictive value and will probably be profitable in future trading.  It continues on with further techniques for assessing the ongoing health of the system and adjusting position size to reduce the risk of a drawdown that exceeds the trader’s tolerance.  Complete details, including code, is presented.

This is a link to the Papers page