We have discussed Walk Forward analysis and its importance in the past. The first part of this tutorial series introduced the concept. The second part demonstrated how to use Walk Forward in TradersStudio. Finally, the third part described how to use Walk Forward on a portfolio. One of the biggest issues with system development is that many systems might test well using historical results, but they do not perform well in the future. There are several reasons for this. The most common one is that the system is not based on a valid premise. Other reasons include:

• Lack of robustness in the system due to improper parameters. A system is considered robust if it runs well in any market condition.
• Inconsistent rules and improper testing of the system using “out-of-sample” and “in-sample” data.

As described through our other tutorials on this site, Walk Forward Analysis does optimizations on a training set, then tests on a period after the set and keeps rolling it forward repeatedly. Thus we have multiple out-of-sample periods and we can look at these results combined. Originally discussed by Robert Pardo, this type of analysis can keep a trading system model a step ahead. The reason is that you are not over-fitting the training set but rather you are fitting a portion of it, and then testing it on the out-of-sample data to ensure that it performs well outside of the training set.

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Using Intermarket Analysis to Trade DOW tracking ETF’s DIA and DOG.

Earlier we showed a system which traded the Dow index using the dollar index futures as the other market. In both tutorials we have used DIA, and ETF which has existed since 1998, that tracks the Dow on the long side. We also use DOG, which is an inverse ETF that started in 2006. The ETFs are different than the cash index so we reoptimized these systems separately. One important fact is that either long or short we are buying the ETF and use a buy command. If we want to go long, we buy DIA. If we want to go short we buy DOG. This article show the power of intermarket analysis to trade the index based ETF’s; DIA and DOG.

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Developing ETF and stock trading systems

TradersStudio Turbo has been designed to build, test, and trade systems for ETFs and stocks. Let’s look at an example of a relative strength ETF based trading system. First, TradersStudio takes care of the issues with splits and dividends for trading ETFs. Even though splits are not as important for ETFs as stocks but it’s important for negatively correlated ETFs. For example, TBT which is 2X inverse ten year as well as leveraged inverse stock index ETFs.

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The best set of parameters on a one lot of each market for a trend following portfolio system is often not the best set of parameters when it comes to trading a balanced risk portfolio, for example your are trading one Natural gas and 5 corn because that give you the same dollar risk based on volatility. Doing this analysis is almost impossible in other backtesting and trading software but it’s very easy in TradersStudio. TradersStudio understands that when trading real money it’s important to optimize your trend following system with your money management rules included.

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TradersStudio has many powerful features which allow system developers to test advance trading system concepts easily. One of these concepts equity curve feedback uses the two sets of “virtual backtesters” is built in TradersStudio. Let’s look at the following trivial system.

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In our recent post on a function unique to TradersStudio which makes it easy to Disabling Signals after being stopped out. In that last article we focused on the DisableAfterExit function. However there are other “on/off” or disable functions that can be valuable in implementing systems. For example, if you’re developing an equity curve feedback or a Donchian breakout system, you may want to disable entry for a few bars after a large winner. This post will briefly cover a few functions that are included in TradersStudio for just that.

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TradersStudio was designed primarily for system developers and as such it is focused on making developing systems easy and intuitive. When I designed TradersStudio I added many features which allowed simple but hard to code concepts in trading systems development to be coded with only a few lines of code. DisableSignalAfterExit is one of those functions designed for Disabling signals after being stopped out.

Essentially what this function does is it disables a buy-sell signal until a certain criteria is met. Indeed, this powerful tool is very difficult – if not impossible – to duplicate in other trading systems. Even when it can be implemented, this feature is almost always a bar late because flags need to be set. Yet within TradersStudio it is one single line and done on the same bar.

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In the first part of this series on TradersStudio Walk Forward testing and analysis, we analyzed why walk forward testing is so important. In the second part, we showed how we could do walk forward testing on the Natural Gas market.

One of the awesome features of TradersStudio is  Walk Forward Testing on a Portfolio of markets. Let’s create a session using a basket of markets and run the walk-forward analysis like the single market session. TradersStudio runs the walk-forward analysis across the basket of markets.

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Walk forward testing within TradersStudio is an extremely valuable tool, as evidenced by the first part of this series. The concept for walk-forward testing is similar to using ‘in-sample’ and ‘out-of-sample’ testing periods. Instead of optimizing on twenty years of data and using the last four years of data for testing, the optimization is done across ten years and the system is tested on the eleventh. Once this test is completed, move the whole time window forward one year and run the test run on the next year. Find the optimum set of parameters for each of the 10-year windows and use that set of parameters to trade for the next year. Move the time window forward one year and run the test on the next year until all of the years in the data series have been tested.

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Many traders develop trading systems using a process of testing and optimizing different sets of trading rules and parameters on the same data. Systems developed using this repetitive process tend to fall apart quickly when they are traded moving forward in real-time if the developer is not careful. The primary problem with this type of system development is what mathematicians call ‘too few degrees of freedom.’ In trading parlance, it is known as ‘curve fitting.’ Walk forward testing aims to solve this issue.

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