Decoupled Java/Spring Boot Market Data Analysis Engine

Decoupled Java/Spring Boot Market Data Analysis EngineDecoupled Java/Spring Boot Market Data Analysis Engine

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Chudi Weekes

• Oct 22

Decoupled Market Data & Analysis Engine (Java / Spring Boot)

Description: Designed and built a modular algorithmic trading infrastructure demonstrating senior-level backend architecture.

Core Architecture: Utilizes Hexagonal Architecture to completely isolate the core IngestionService and AnalysisService from external dependencies. Implements the Adapter, Repository, Builder, and Dependency Injection patterns. Data Engineering: Implements a dual-database design. PostgreSQL handles instrument symbology and run-audit logging, while QuestDB manages high-throughput OHLCV time-series data using monthly partitioning and deduplication keys (idempotent upserts). Pipeline: Autonomously fetches live market data (via REST), parses raw JSON into canonical domain objects, computes statistical indicators (SMA, Z-Score), and stores the analytical results for downstream signal generation.

Completed work

Software Architecture Software Development Java SQL Spring Boot

Mahammadali Mammadov

• Nov 7

End-to-end retail analytics pipeline that turns raw, messy CSV exports into business decisions, built in Python and SQL and runnable with one command.

What it does:

Cleans and validates 6 relational tables, fixes 585 data-quality issues and logs every action

Loads a SQLite warehouse with keys, constraints and window-function SQL

Customer analytics: RFM segmentation, cohort retention, CAC / LTV by channel, product Pareto, market-basket analysis

Churn prediction model validated on future data (ROC-AUC 0.71, no data leakage)

6-month revenue forecast, best of 3 models by backtest (4.2% MAPE)

Auto-generated Excel report (live formulas, native charts) and an interactive Streamlit dashboard

18 automated tests

Data Analysis Data Visualization Statistics Matplotlib pandas Python

Eros C.

• Nov 7

Logging every cleaning action is the part I like most: when a number in the report looks strange, you can follow it back to the fix that changed it. With 585 issues, how did you decide which ones the script fixes alone and which ones it only reports?