Technology
Our investment strategies are driven by the applications of machine learning disciplines and data science technologies.
Why?
Generating actionable investment insights from data poses a challenge. Why? Because an individual dataset is not enough. Validating and extracting the specific value from data is a complex process requiring advanced technology and the expertise to apply it.
How?
Neuravest has developed a set of powerful artificial intelligence-based analytic solutions to validate quantitative investment strategies. These tools identify predictive qualities from datasets and then build market-ready investment portfolios.
![](https://www.neuravest.net/wp-content/uploads/2021/04/Untitled-1-5-e1634675350548.png)
DAS
Our Data Science Platform
Neuravest’s Data Analytics Suite (DAS) uses machine learning to analyze data. It then validates the application of the data for investment decisions.
![](https://www.neuravest.net/wp-content/uploads/2021/04/Untitled-1-6-e1634675390334.png)
QuantDesk
Our Model Portfolio Manager
Neuravest analysts can evaluate multiple alternative data sources, discover predictive signals, evaluate new theories, backtest and deploy investment strategies.
Discover More
![](https://www.neuravest.net/wp-content/uploads/2021/05/Regression-e1626272407326.png)
Regression Analysis
Applying multinomial regression algorithms for a fit/prediction of financial instruments.
![](https://www.neuravest.net/wp-content/uploads/2021/05/Deasoning-e1626274908748.png)
Classification Analysis
A data analysis task contained within data mining, which identifies and classifies a collected set of data so that it can be analyzed with greater accuracy.
![](https://www.neuravest.net/wp-content/uploads/2021/05/Unsupervised-e1626274619130.png)
Unsupervised Learning
A method of discovering patterns from untagged data.
![](https://www.neuravest.net/wp-content/uploads/2021/05/Featured-e1626270675452.png)
Feature Engineering
Deriving meaningful features from raw data. Applying one of our specialized algorithms to determine the optimal feature subset for model building.
![](https://www.neuravest.net/wp-content/uploads/2021/05/Target-e1634675447950.png)
Target Engineering
Deriving meaningful target values for pre-trained machine learning models. Target functions are engineered to reduce noise to the greatest extent possible, thereby improving the learning process.
![](https://www.neuravest.net/wp-content/uploads/2021/04/Untitled-1-e1634584437544.png)
Model Strength Analysis
Analyzing model performance with metrics such as confusion matrices, ROC curves, Sharpe ratio, and max drawdown.
![](https://www.neuravest.net/wp-content/uploads/2021/05/Computer-Vision-e1626274954324.png)
Computer Vision
Applying specialized computer vision approaches such as CNNs and Capsule Networks to extract predictive temporal patterns from time series data.
![](https://www.neuravest.net/wp-content/uploads/2021/05/Natural-Language-e1626270960231.png)
Natural Language Processing (NLP)
Deriving meaningful features from unstructured text data to create machine learning models.