Stock Data Built for Neural Net Training.

Create stock prediction models with a single click. Run your models through rigorous backtesters. Get stock predictions from your models.

In the world of quantitative trading, D.AT simplifies the process of machine learning model development by providing standard data engineering and significance testing. D.AT provides an integrated solution for segmenting time-series data to isolate significant trends, cleaning datasets to enhance reliability, aggregating varied data streams for a comprehensive view, engineering features to pinpoint key influencers, crafting strategy-oriented labels, and strategically splitting data to avoid common biases. Equipped with functionalities that streamline these critical stages, D.AT empowers you to create robust and accurate predictive models, positioning you a step ahead in the stock market game.
Building a stock prediction model with D.AT involves three key stages: Data Engineering, Modeling, and Backtesting/Significance. Data Engineering refines raw stock data into tailored datasets. This groundwork is streamlined by D.AT, allowing users to focus on the exciting Modeling phase, where expertise shines as they craft prediction models. Lastly, in the Backtesting/Significance phase, models are tested against random simulations to measure their true predictive power. While users bring their expertise in model creation, D.AT simplifies the essential yet tedious tasks of data preparation and backtesting.

Stock Data Engineering

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