DLPAL DQ is a unique product for quantitative discretionary traders. We have just added a new feature for the calculation of next day returns in the results. Below is an example.
More information about DLPAL DQ can be found here. Examples can be found here.
When we decided to develop different products for each function of the original product DLPAL, we did not realize that there was such a high demand for a simple to use but powerful quantitative scanner. We are pleased by the result and we will continue developing this powerful product for which there is no competition.
One of the most powerful features of DLPAL DQ is the portfolio backtest capability that allows validation of identified price action anomalies on any group of securities. This is necessary because most high profitability price action anomalies have a small sample in a single security.
One objective in using DLPAL DQ is to have to program identify high profitability price action anomalies in the form of parameter-less price patterns and then validate them using portfolio and robustness tests. Below is an example of results after scanning all constituents of QQQ ETF for high profitability anomalies after the close of Wednesday, January 3, 2018:
The program identified 7 long price action anomalies in an equal number of stocks. We next validated these using the DOW 30 group stocks with data since 01/2000:
The results are sorted for highest portfolio profit factor PF and we select the three top securities to save in a new file. After prices of QQQ constituent stocks for Thursday, January 4, 2018 are updated, we then run the new function shown as Results above the Close button to get the returns:
Note that all returns are calculated from the open of the next day to the close. In this case returns for ORLY, SYMC and TSCO are +0.83%, -0.788% and +1.451%, respectively. The average return is 0.50%.
This is a simple yet useful function. Below is the workspace we used:
We used adjusted data for all QQQ constituent stocks starting on 01/2000 or latter. Note that we require a success rate of 80% of higher in this example because we are looking for high profitability anomalies. Other settings may be tried depending on objectives.
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