The Sweet Spot for Mean Reversion ETF Strategies. In his recent book, Howard Bandy discussed what he calls the "sweet spot" for developing mean reversion trading systems. Project files for Builder, which include the strategy code, are provided for each example. The basic idea behind Dr. Bandy's sweet spot is that bandy trading strategies should use a short bar size and have a fairly high accuracy with a short holding period and low drawdown. The short bar size and short holding period maximize the opportunities to compound returns while pdf high accuracy and low drawdown make it easier to recover from losses. The latter qualities also make it easier to establish the viability of the strategy and to determine when it's no longer working because typical losing streaks for high-accuracy systems tend to be relatively short. Bandy's guidelines, the following characteristics will be used in this article to define the optimal requirements for mean reversion ETF strategies:. By mean reversion, I'm referring to strategies that attempt to buy below the current average price and sell at a higher price as the price reverts to the reversion. The idea is to buy low and sell high, as opposed to trend-following systems, which typically try to buy high and sell higher. In my last newsletter article, I discussed the use of trading testing in evaluating trading strategies and its relationship to robustness and strategy over-fitting. I also mentioned that if it were incorporated into the build process, it would tend to lead to strategies that exhibited robustness. That's the approach that will be followed here. Briefly, stress testing refers to evaluating how sensitive a trading strategy is to its inputs and environment. A robust strategy -- one that is not over-fit to the market -- will be relatively insensitive to changes in its input parameter values and to other changes in its environment, such as changes to the price data. Monte Carlo analysis is the technique used to evaluate the effect of these changes. The strategy's inputs, price data, and other factors are randomly changed, and the strategy's performance is evaluated. By repeating this process many times, a distribution of results is obtained. The results from the original data represent one point on the distribution. Other points on the distribution represent the results from using slightly altered versions of the original data, which may generate results that are more or less favorable than the original data. The so-called Monte Carlo results are the values of the performance measures net profit, percent wins, profit factor, etc. When a trading strategy is developed iteratively mean successive generations of modification and test, building based on the Monte Carlo results will tend to drive the strategy to one that is robust since only a robust strategy will have good Monte Carlo results. Adaptrade Builder automates this process, including evaluating the strategy results using the Monte Carlo results of stress testing. All data were obtained from TradeStation 9. Adaptrade Builder uses a genetic programming algorithm to evolve a population of strategies over successive generations. The key to using Builder to find strategies that meet our optimal requirements is setting the so-called build metrics, shown below in Fig. The build metrics in Builder define the systems spot for the SPY strategy. The list of Build Objectives contains three general-purpose metrics, all of which are being maximized. These help guide the population of strategies towards ones that have a high net profit, correlation coefficient, and statistical significance, which are desirable for any strategy. The specific qualities we're looking for i. Notice that the condition for the number of trades is set to a range based on the number of years of in-sample data and the goal of having between 20 and 30 trades per year. The upper limit was added because strategies with an unusually high percentage of winning trades will generally fail to meet some other condition. Penalizing such strategies will help drive the population towards strategies that meet all conditions, as opposed to strategies that disproportionately meet one condition to the exclusion trading others. The same logic was used in setting a range for the profit factor. The other conditions -- correlation coefficient, statistical significance, profit factor, and Kelly fraction -- are not part of our specific requirements, but were added to improve overall results. The stress testing and Monte Carlo settings used for this example were selected on the Build Options screen, as shown below in Fig. The Monte Carlo analysis and stress testing options are selected on the Build Options tab. As shown in the figure, 99 Monte Carlo iterations were used for each analysis. This means that 99 stress tests were performed in addition reversion the evaluation of the original data. The stress tests consisted of randomizing the pdf, randomizing the strategy inputs, and randomizing the starting bar. All three randomizations were performed for each stress test. Because each strategy was evaluated times 99 stress tests plus the original data at each generation, this approach took about times as long as it would have taken had stress testing and Monte Carlo analysis not been used. For this reason, a relatively small population of only members was used in order to keep the solution time reasonable. The population was evolved over 10 generations, and an option was set to start over after 10 generations if the net profit in the out-of-sample period was negative. The equity curve plot from the top strategy systems the population after 20 generations 1 rebuild is shown below in Fig. Equity curves for each stress test for the final SPY strategy. Each curve in Fig. As can be seen, all the different equity curves generally have the same shape with positive out-of-sample results. Aside from the number of trades, which is fewer than asked for, the strategy meets the original requirements. The strategy also passes the bandy test. The strategy logic also satisfies the requirement for a mean reversion strategy: The limit entry means the market has to come down to the limit price, so the systems is buying low and selling after the market goes back up. If the stress testing is turn off and the strategy is evaluated on the original data, the equity curve is as shown below in Fig. Equity curve for the final SPY strategy on the original data. While this is only a modest return, it easily beats the buy-and-hold return of approximately 1. A Select Sector SPDR Example The second example involves building a strategy over a portfolio of ETFs consisting of the Select Sector SPDRs. The nine sectors are Consumer Discretionary symbol XLYConsumer Staples XLPEnergy XLEFinancial XLFHealth Care XLVIndustrial XLIMaterials XLBTechnology XLKand Utilities XLU. Most of the bandy settings were used to build this strategy as in the last example. However, because nine times as much price data were used in the build, I reduced the number of Monte Carlo iterations from 99 to 5. The other build options were the same as in Fig. Since not all markets were likely to be trading at the same time, this setting was chosen to provide adequate position sizes without resulting in leverage i. The equity curve plot from one of the top mean in the population after 10 generations no rebuilds is shown below in Fig. Equity curves for each stress test for the final Pdf Sector SPDR portfolio strategy. Each equity curve in Fig. Some summary Monte Carlo results are shown below. Unlike the previous example, the results are not substantially different when the Monte Carlo analysis is turned off and the results are evaluated over the original data. This strategy also passes the validation test. The equity curve for the strategy over the original data only no stress testingin which the validation period is included, is shown below in Fig. Equity curve for the final Select Sector SPDR portfolio strategy on the original data. As with the previous example, the strategy logic enters long on a limit order. Most of the exits are via a target exit, with other trades exiting based on an indicator condition or on a protective stop. The so-called sweet spot for trading strategies recommended by Dr. Bandy seems to provide effective conditions for building mean reverting trading strategies in an automated manner using a tool like Adaptrade Builder. It was possible to find strategies that met most of the requirements for both examples: Both strategies beat buy-and-hold and held up well in the validation test. For both examples, stress testing with Monte Carlo analysis was employed to increase the chances of finding trading strategies. Compared to the portfolio example, the stress test results for bandy single-market SPY strategy were substantially more conservative less favorable than the results from the original data. While some mean that may be due to the more rigorous stress testing as compared to the portfolio example, it suggests that the SPY strategy is less robust than the portfolio example. In general, where the Monte Carlo results diverge markedly from the results on the original data, it might be expected that the best estimate of future results would be somewhere in between, although that will depend on how conservative the stress testing and Reversion Carlo analysis is. It does seem reasonable that the portfolio strategy would be more robust than the single-market strategy since the portfolio strategy was built over nine different markets and was required to work reasonably well over a wider variety of price data. It was built over nine times as much data and has roughly nine times as many trades. The greater performance of the portfolio strategy may reflect the positive effect of diversification over the nine different sectors of the SPDRs. Although neither strategy met the requirement for the number of trades, it may be possible to find strategies that meet all requirements if a larger population is used or systems stringent rebuild requirements are employed, which would require more build time. Alternatively, it may be the case that such a strategy is unlikely to be found due to the conflicting trading of high accuracy, trade frequency, short trade duration, and so on. Reversion best set of build conditions is one that fully exploits the market's potential while remaining realistic. Combining a set of useful build conditions, such as those provided by Dr. Bandy, with built-in robustness features, such as stress testing and Monte Mean analysis, in an automated tool like Builder should provide a solid framework for developing effective trading strategies. This article appeared in the April issue of the Adaptrade Software newsletter. SIMULATED TRADING PROGRAMS IN GENERAL ARE ALSO SUBJECT TO THE FACT THAT THEY ARE DESIGNED WITH THE BENEFIT OF HINDSIGHT. If you'd like to be informed of new developments, news, and special offers from Adaptrade Software, please join our email list. Free email newsletter with articles on systematic trading. Download Mean Reversion Project Files: Join Our Email List Email: For Email Marketing you can trust. Adaptrade Software Newsletter Article. Daily bars 20 - 30 trades per year. Average bars in trades of between 1 and 4. Number of Closed Trades. Max Number Consecutive Wins. Max Number Consecutive Losses. Average Bars in Pdf.
NEW Trading System: Mean-Reversion (Stocks Over 50 Day MA)
NEW Trading System: Mean-Reversion (Stocks Over 50 Day MA)
5 thoughts on “Mean reversion trading systems bandy pdf”
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