1. What is DataMine Machine Learning Service?
The DataMine Machine Learning Service is an Automated Machine Learning platform that uses CME Group data and other public data to build machine learning models for financial instruments such as equities, indices, commodities, currencies, and cryptoassets. You can access the service with this link.
DataMine Machine Learning Service
2. Why do I need DataMine Machine Learning Service?
The goal with DataMine Machine Learning Service is to enable anyone to derive meaningful data-driven insights for procurement, trading and risk management. The platform comes pre-loaded with a set of example models, and users can also build their own models; no coding or data science expertise required.
3. What is Automated Machine Learning?
Automated Machine Learning is a technology that streamlines and accelerates the processes and workflow typically required to utilize machine learning. This involves the automation of data pre-processing, model creation, parameter adaptation, and model updates. Historically this process requires a team of Data Scientists and takes weeks or even months to implement. With Machine Learning Service, non-technical users can build and leverage machine learning models.
4. How does DataMine Machine Learning Service work?
DataMine Machine Learning Service is a machine learning tool that allows users to build their own machine learning models. Once built, data is ingested automatically every day to update the models and generate daily signals for different timeframes specified by the user. Model signals are converted into strategies and then backtested and evaluated across a variety of performance metrics, allowing users to evaluate and compare different data, models and strategies.
5. How is DataMine Machine Learning Service hosted?
The DataMine Machine Learning Service is hosted as a managed cloud solution.
6. Who can use DataMine Machine Learning Service?
DataMine Machine Learning Service is designed to make machine learning accessible to non-technical users. With Machine Learning Service you don’t have to be a Data Scientist to utilize machine learning. Look at the user guide to learn more:
7. Is DataMine Machine Learning Service also helpful for data scientists and other more advanced users?
Yes, DataMine Machine Learning Service streamlines and automates many of the steps in machine learning workflows, freeing up more advanced users to work on higher-value tasks such as signals processing and strategy implementation.
8. How much does DataMine Machine Learning Service cost?
The DataMine Machine Learning Service is charged on a per-model basis. The cost has two components: an initial cost to build the model and an ongoing maintenance cost to update the models daily.
9. Can I get trial access to the DataMine Machine Learning Service?
Yes, there is a one-week trial available for all new users. You must opt-out of the trial to avoid being charged. Contact a sales agent at cmedatasales.com for more details.
10. How many users can DataMine Machine Learning Service support?
The DataMine Machine Learning Service can support any number of users. Licenses are sold on an individual basis.
11. What financial instruments can DataMine Machine Learning Service build models for?
Please contact us for a complete list of instruments available for model building.
12. How far into the future can DataMine Machine Learning Service predict?
Machine Learning Service can build models for any timeframe. Different timeframes generally benefit from different data inputs.
13. How often are the models updated?
Models are updated every day. New data is ingested overnight, and the models update first thing in the morning to be available on the US market open.
14. How many models can I build using the DataMine Machine Learning Service?
There is no limit to the number of models a user can build.
15. How is model performance measured?
The DataMine Machine Learning Service is a tool. All models are measured via performance metrics and backtesting in order for users to make their own evaluation of the model performance and establish confidence in the tool. <see model backtesting>
16. How many machine learning algorithms does DataMine Machine Learning Service use?
DataMine Machine Learning Service currently offers a library of algorithms as options when building a machine learning model. These include Gradient Boosting Regressors, XGB, LGBM, CAT boost, Extra trees, KNN, SVM, SVR, Linear Regression, Ridge Regression, Lasso Regression, Huber Regressors, and Neural Networks.
17. What is the Markets Page?
The Markets Page is a high-level view of all available forecasts across categories and sectors. The primary goal of the Markets Page is to give users a directional indication of different markets. Additionally, users can toggle on or off labels for Signals and Accuracy, to see the respective signal strength and accuracy of forecasts at different time intervals.
18. What is the Forecast Page?
The Forecast Page is a medium-level view of forecasts for specific target variables. The purpose of this page is to provide users with a more detailed illustration of the forecast shape (Prediction Curve), behavior and performance at different time intervals, as well as a historical record of the forecast backtesting over time.
19. What is the Strategy Simulator Page?
The Strategy Simulator page is a tool that uses signal processing to turn model signals of various timeframes into actionable insights. It does this by combining signals from multiple models using different parameters and filters, so only the best model signals are used. Users are provided with default strategy parameters, but can further optimize strategies to their own individual goals, strategy preferences, and risk profile. Examples of strategy preferences:
- Select which model signals are used in the strategy.
- Optimize for different performance metrics such as NetPosition (Equity Curve), ROI, Sharpe Ratio, Information Ratio, Max Drawdown, etc.
- Filter out model signals below certain performance thresholds.
- Trade Long/Short, Long-Cash, or Short-Cash.
- Trade only signals above or below certain thresholds.
20. What is the Data Explorer Page?
The Data Explorer is a tool designed to aid users in exploring and navigating CME data available for modeling. The dashboard provides a classification Search function as well as helpful visual elements to help the user find what they are looking for, and see the structure and categorization of the available data.Data
21. Is the input data included in the license cost?
Yes, input data is included in the license cost. Users are only charged for building models.
22. What data is available through The Machine Learning Service?
For a complete list of available data, click here.
23. How are the CME Group Rolling Futures Indices calculated?
The CME Rolling Futures Indices are designed to provide market participants with an indicator for investment performance in a single CME Market.
CME Rolling Futures Indices are calculated on 28 futures products across five different asset classes. The CME Rolling Futures Indices will be calculated for each Business Day, in accordance with the CME Globex Trading Schedule and follow a next month rolling schedule.
To maintain a continuity or pricing and account for contract expiry and movements in liquidity, a roll period is required. The roll period is designed to gradually transfer the weighting of the inputs from the Near Futures Contract to the Second Futures Contract prior to expiry. The weighting of the Near Futures Contract is 100% up until seven days to expiry. After this point 20% weighting is transferred to the Second Futures Contract on a daily basis. When the Near Futures Contract is 2 days to expiry, it will have a weight of 0% and the Second Futures Contract will have a weight of 100%.CME Rolling Futures Indices are calculated for the following products: Jeff Jensen - is it possible to scroll back up to the new table I put in the general section that outlines products?
24. What frequency of data can/do the models use?
Machine Learning Service is capable of using data of different frequencies. While the models themselves update daily, the input data frequency ranges from daily, to weekly, monthly, quarterly, or annual data resolution.
25. Can I use my own data?
Currently the solution does not support users ingesting their own data.Model Building
26. Can I build my own models?
Yes, users can build and maintain their own machine learning models.
27. How are models trained?
Models are trained on 5 years of historical data preceding the model Start Date (assuming the data is available). For example, if a model is built from Jan 1st, 2022, the model will be trained on historical data from Jan 1st, 2017. If historical data isn’t available going back that far, the model will train on what it is provided.
28. How are the models backtested?
Model metrics are displayed in the dashboard to facilitate straightforward analysis and evaluation of model performance.
29. How do I assess model performance?
The Machine Learning Service provides a list of performance metrics that are updated daily as a part of the model building/update process.
30. Are my models saved?
With Machine Learning Service, all user created models are stored on the cloud. Models are backed up to hard storage periodically in case of system failure.
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