Feature Calculation for Directional and Long-Short Strategies
This product does not utilize artificial intelligence
DLPAL LS uses primitive attributes of price action to extract features in an unsupervised learning mode based on general feature clusters. Then, the program uses the extracted features in supervised learning mode to identify long and short candidates in a universe of securities.
The long/short identification is based on a set of calculated features, and the user has flexibility in ranking the results according to their values. The output of the program can be saved in CSV format and can be used in conjunction with trading platforms to automatically execute trades. For more details, click here.
DLPAL LS is used to develop algorithmic and machine learning strategies for directional and long/short trading, as well as for discretionary trading. Users of the program can sort results based on different criteria and generate output for trading APIs. Historical files of features can be generated for backtesting strategies and machine learning. Click here for an article on strategy development and execution.
DLPAL LS does not require any programming. The program will output signals that you can immediately use. Save time on programming and concentrate on your trading.
DLPAL LS is a quantitative analysis tool. DLPAL LS will not identify strategies that work in all markets, especially if the markets are efficient and price action is dominated by noise. DLPAL LS will not create final systems for auto trading. The user of the software has to put in the work necessary for using the software according to desired criteria and risk/reward objectives.
This product does not utilize artificial intelligence.
DLPAL LS Video
Demo Versions
Click here for more information on downloading a demo version.
Program Manual
Machine Learning Features For a Futures Long-Short Strategy
Announcing the Release of DLPAL LS v7.0
Rank Metric Importance, Dynamic Selection, and Ensemble Method
Extreme Overbought/Oversold Conditions For Equity Long/Short
Long/Short With Extreme Overbought/Oversold Equities
NASDAQ-100 Long/Short Year-to-Date Performance
Basic Models for Long/Short NASDAQ-100 and S&P 500 Stocks
Machine Learning With Weekly and Monthly Data
Execution Automation Add-on
Autoupdate Option for DLPAL LS Feature Files
Equity long/short Performance: Adjusted Vs. Unadjusted Data
Equity Of Daily S&P 500 Long/Short Strategy At New Highs
S&P 500 Equity Long/Short Performance at New Highs
Weekly Dow 30 Equity Long/Short Update
Using Different Long and Short Groups of Stocks with DLPAL LS
DLPAL LS Smoothed Features Add-on
Smoothed Features for Smoother Equity
Combining Equity Long/Short with Mean-Reversion
Equity Long/Short Performance as Function of Maximum Open Positions
Trading ES Futures with DLPAL LS
Equity Long-Short with Price Action Features
Trade Minimum Holding Period Impact
Low-Risk Equity Long/short
Combining Results of DLPAL LS and DLPAL DQ
A Novel Strategy
Three Long/Short Equity Strategies
Market Neutral Long/Short Needs Few Stocks That Buck The Trend
Market Neutral Long/Short Equity Example
Market Neutral Long/Short Equity Strategies In High Demand
DLPAL LS Conservative Versus Normal Cluster
Stock Market Directional Bias Reversal
Feature Engineering With DLPAL LS
Long/Short Strategy For Dow 30 Stocks In Weekly Timeframe
Evaluation Of New DLPAL Clusters For Feature Generation
Strategy Development And Execution With DLPAL LS
Market Neutral Long/Short Equity Strategy
Long/Short Equity Strategy Development
Announcing The Release of DLPAL LS