The Doe method is a popular trading strategy that has gained attention in the financial community. This approach involves defining a parameter space for various factors, such as interest rates, stock prices, and time periods, to optimize trading performance. Here's an analysis of using the Doe method in trading.

Defining the Parameter Space

To define the parameter space, we choose minimums and maximums for the first four factors: 1. Interest Rates (i.e., short-term interest rates), 2. Stock Prices (i.e., prices of individual stocks or indices like S&P 500), 3. Time Periods (e.g., daily, weekly, monthly), and 4. Discrete Integers that represent the number of days or trading hours.

The fifth factor is a discrete integer that will be defined to take on the values of 2, 3, 4, 5, and 6. The first factor is also a discrete integer, but its large interval allows us to treat it as continuous. Any value we find will be truncated to an integer without serious effect to the investigation.

Generating Parameter Settings

Using the Gosset computer program described by Hardin and Sloane (see "Further reading"), we generate parameter settings for 56 trial runs. Daily data for the SPY (S&P 500) and Vix (a measure of the volatility of the S&P 500 index) from Jan. 29, 1993, through Oct. 17, 2003, were selected for the trial runs.

A computer program written in the GAUSS programming language (www.aptech.com) was developed for the simulation of the trading system given the parameters. To produce a more realistic result, each simulation started with an account of $500,000, paid a $20 transaction cost per trade, and a bid/ask spread was also included to simulate slippage.

Forward Testing

To gain degrees of freedom and a better fit to the observations, we add a center point to the parameter space. This is done by finding the sweet spots with large error deviations from our initial run. We then forward test each of these sweet spots in another data set, the SPY and Vix from Oct. 17, 2003, through Oct. 12, 2009.

Results

The results for each value of Factor 5 are shown in "By a factor of 5." Rather than rely on backtesting for our choice of sweet spot, we will forward test each of them in another data set, the SPY and Vix from Oct. 17, 2003, through Oct. 12, 2009.

Forward Test Parameters

The forward test parameters are as follows:

Annualized Sharpe Maximum Profit/Loss Return Ratio: -20434.68 Drawdown: 28329.22 1-Year Maximum Return: 3.5% 2-Year Maximum Return: 4.5%

Forward Test Results

The forward test results are shown in "Forward thinker." On average, the Doe method finds settings that produce from 3.5-1 to 4.5-1 greater profit, and more than three times the annualized return compared to the original input values recommended by the trading system authors.

Practical Implementation

To implement the Doe method in trading, we need to consider timing considerations and entry/exit strategies. A key aspect is to use a stop-loss strategy to limit potential losses. Additionally, we should monitor our trades closely to adjust the parameters as needed.

Conclusion

The Doe method provides a framework for defining a parameter space that can be used to optimize trading performance. By considering multiple factors and using forward testing to validate our results, we can find settings that produce more profitable outcomes. However, it is essential to remember that this approach requires careful consideration of timing and risk management.