Open-Source Reference Architecture for Real-Time Stock Prediction

Although stock markets constantly evolve due to economic forces, new products, competition, global events, regulations, and even tweets, using historical price data to predict future prices remains a common practice. A real‑time stock analysis system must gather diverse data, respond with low latency, and be highly scalable as data volume grows.

William Markito, an enterprise application solution architect at Pivotal, published a blog post titled “Open‑Source Reference Architecture for Real‑Time Stock Prediction,” describing a reference architecture built with open‑source technologies that, while focused on financial trading, applies to other real‑time use cases.

From a top‑down perspective, the architecture consists of four parts driven by a prediction model: data storage, model training, real‑time evaluation, and action execution. Real‑time trade data is captured and stored as history, the system learns patterns from historical trends, compares incoming data with learned patterns, and finally outputs predictions and decisions.

Markito further refines each part using Spring XD (now Spring Cloud Data Flow) for data extraction and processing, Apache Geode as an in‑memory distributed database, Spark MLlib for machine‑learning, Apache HAWQ for large‑scale parallel SQL analytics, and Hadoop for batch storage.

The data flow comprises six loosely coupled, horizontally scalable steps:

Use Spring XD to read real‑time data from Yahoo! Finance API and store it in Apache Geode.

Train a model on hot data in Geode using Spark MLlib (or alternatives like Apache MADlib or R) to compare new data with historical patterns.

Deploy the trained model to the application and update Geode for real‑time prediction.

Move cold data from Geode to Apache HAWQ and ultimately to Hadoop for long‑term storage.

Periodically retrain the model on the full historical dataset, forming a closed loop that adapts to changing patterns.

For a simplified notebook‑friendly version, Markito removes the long‑term storage components (HAWQ and Hadoop).

The architecture’s components are clearly defined, extensible, and cloud‑ready. The article also discusses suitable algorithms for stock price prediction, such as Hidden Markov Models, Decision Stumps, linear regression, Support Vector Machines, Boosting, and deep neural networks, referencing external resources and case studies.