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General FAQ

Is DLPAL DQ  a neural net program?

DLPAL DQ is not a neural net program. It uses a deep learning algorithm for supervised and unsupervised learning developed by Michael Harris.

If everyone uses DLPAL DQ will that affect the strategy performance?

There are so many different markets, so many different strategies and so many different ways traders can approach strategy development  (in terms of profit targets, stop-loss, profitability, risk management, etc.) that makes this question a philosophical one rather than practical. Of course, trading very liquid markets reduces the risk of being part of a hypothetical “herd” using the same strategy.

Are the strategies discovered by DLPAL DQ particular to a certain market?

Although with DLPAL DQ you can discover strategies particular to a certain stock or futures contract, it turns out that many of those strategies work for a group of stocks or even future contracts.

Are the strategies found by DLPAL DQ preprogrammed in a database?

There are no hard coded strategies in DLPAL DQ. Instead, DLPAL DQ uses deep learning principles guided by major cluster types. DLPAL DQ does not look for traditional chart strategies but for price action strategies. Some of the strategies the program finds may look similar to traditional chart formations. Strategies have up to 6 price bars lookback period. The program finds strategies dynamically as it goes through a data file of historical prices and they are specific to the data used although it may turn out that some of the strategies work for a group of securities.

Can DLPAL DQ consider volume or other indicators in the scan for strategies?

DLPAL DQ discovers strategies that fulfill user-defined risk/reward and performance criteria. The strategies do not consider volume information, only the open, high, low and close of price bars. There is a way of modifying the input data to identify volume strategies.

DLPAL DQ cannot find the file when I try to use the Backtest or Test Strategies tools

The name of the file used with the back-test or Test Strategies tools must be identical to that shown on the results workspace. Otherwise the program will generate an error message that the file was not found. The same error message is generated if the file name, excluding the path and the .txt extension, is greater than 26 characters. Important: Always double-click on a directory to select it. Only the directory structure is shown, not the files.

How to implement DLPAL DQ strategies in NinjaTrader?

As soon as you find a strategy you like in DLPAL DQ you can generate code for the strategies in NT script and then you can implement the extra code you desire (with money management, position sizing, etc). Then the strategy generates the orders through the broker you use with NT when the signals are generated.

Why DLPAL DQ does not have an option for exits based on ATR?

According to the system development philosophy of DLPAL DQ any exit that “adapts” to market conditions may produce fitted systems. Specifically, by adjusting exits to adapt to short-term volatility, which is what ATR accomplishes, the entry part of a strategy tends to become less significant. By selecting exits that fit any signals to the data random strategies may be developed. Instead, the philosophy of DLPAL DQ is that signals should perform well for small constant exit thresholds and if a security shows strictly increasing volatility as a function of time then it should not be used for creating automated strategies not because it cannot be traded but because adapting to the volatility during the design phase will create fitted strategies. Fortunately, most popular securities and markets exhibit volatility cycles. The profit-target and stop-loss can be calculated as the average of actual changes or percentage:

daily changes: abs(close – close of n bars ago)
percentage changes: 100 x abs[(close/close of n bars ago )-1]

where abs stands for the absolute value and n is set to the expected average trade duration, which must be less or equal to maximum strategy length of 6 bars in DLPAL DQ. It has been determined in the finance literature than memory in price series is slowly lost after 4 to 5 bars and as a result any type of exits that close positions after many bars may contribute to the creation of fitted strategies.

Can DLPAL DQ use multiple CPU cores in parallel?

DLPAL DQ is not a multithreaded application because a substantial investment is required for rewriting the code to use multiple cores in an effective way and not in some pseudo manner done by other applications. With that price would have to increase beyond levels that the average trader can afford. However, we offer an alternative solution for those users that have already determined that DLPAL DQ suits their needs in the form of an upgrade that allows multiple instances to run on the same machine.

Why I do not get any results?

There are several reasons for not getting any results. Below are a few recommendations:

(1) The markets and timeframes tried may have become too efficient for the parameters specified, including the target and stop levels. Usually futures and forex markets, especially in intraday timeframes, provide very few tradable strategies.
(2) The stop-loss must be set outside the 1-bar volatility range. Stops are checked immediately before profit targets to produce conservative backtests and if the stop-loss is too low no significant strategies will be found.
(3) Properly back-adjusted continuous data for futures contracts must be used.
(4) Appropriate values for the profit-target and stop-loss must be used. In the case of futures point values should be used in the T/S file. More details can be found under “A short note on using targets and stops” in “Creating a T/S File” in the program manual.
(6) Workspaces must be created by first selecting the exit type and then creating a scan line. It is always a good idea to check the scan lines to see if the proper parameters are selected before running the workspace.