HardTechnologyfinance

Investment Portfolio Tracker With Live Market Data Integration

Added Jun 2025 3 design docs

Retail investors spread holdings across brokers and asset classes, then lose the overall picture: what the portfolio is worth right now, which positions drive returns, and how allocation has drifted. This project builds a portfolio tracker that restores that picture in real time.

The intern implements a FastAPI and Python backend with PostgreSQL recording holdings, transactions, and valuation history, integrating third-party market data APIs so prices and portfolio values update live. Concurrency control keeps transaction records consistent even when updates overlap, an important discipline anywhere money is counted, and JWT authentication with role-based access protects every account. The React and TypeScript frontend, styled with Tailwind CSS, presents performance analytics and allocation breakdowns through rich interactive visualizations, alongside a complete transaction history.

The intern learns to consume and cache external financial data, design schemas for auditable transaction records, and handle correctness under concurrent writes, then finishes with a fintech-grade application that demonstrates both engineering rigor and an understanding of what investors actually need to see.

Related projects

Ridge and Lasso Regularization Tuner for Financial Modeling

Financial datasets are full of correlated, noisy features, and ordinary regression happily overfits them. Ridge and Lasso regularization are the standard defenses, but choosing between them — and tuning their strength — requires seeing how each one reshapes a model. This project builds a tool that makes those effects directly observable. The intern develops a web tool where users upload a dataset, apply feature scaling, and train Ridge and Lasso models from scikit-learn side by side, with GridSearchCV sweeping regularization strengths under cross-validation. Coefficient visualizations show the two techniques' signature behaviors — Ridge shrinking weights smoothly toward zero while Lasso zeroes some out entirely, performing implicit feature selection — alongside evaluation metrics for each configuration. The workflow is prototyped in Jupyter with pandas and NumPy, then deployed as a FastAPI microservice so tuned models become reachable over REST. The intern gains a working command of regularization, cross-validated tuning, and the reasoning behind sparse versus dense solutions, plus deployment experience packaging an analytical workflow as a service — a combination directly relevant to quantitative finance teams.

financedata-sciencefastapijupyter+5

PRD · HLD · LLD includedView

TechnologyMedium

Natural Language to SQL Query Assistant for Financial Databases

Inside most finance teams, data access has a human bottleneck: analysts who know SQL field a constant stream of requests such as revenue by region last quarter or accounts with unusual transaction volume, while the managers asking must wait days for answers that take minutes to compute. Letting business users ask questions in plain English and receive database-backed answers removes that bottleneck without teaching everyone SQL. The intern builds this assistant for financial data as a Python FastAPI service. LangChain's SQL tooling provides the core machinery: the user's natural-language question is combined with the database schema and passed to OpenAI models, which generate a candidate SQL query; the service executes it against the financial database and returns both the results and a plain-language explanation of what was computed. The intern implements the schema-awareness layer, validation that inspects generated SQL before execution to block destructive or runaway queries, error recovery when generation fails, and response formatting that presents figures clearly with the underlying query visible for audit. The project demonstrates one of the most requested applied-AI capabilities in business settings: text-to-SQL with safety rails, sound API design in FastAPI, and the engineering skepticism required when letting a language model write code that touches financial data.

financefastapigenerative-ailangchain+2

PRD · HLD · LLD includedView

TechnologyHard

Cross-Source Financial Analytics with Presto and Data Governance

Financial data never lives in one place: trading records sit in a warehouse, historical archives in object storage, and reference data in relational databases, and analysts lose days to moving data around before they can even ask a question. Federated query engines dissolve that friction by querying data where it lives, but in finance they must arrive with lineage and access control attached. The intern builds a federated analytics platform where Presto executes ad-hoc SQL across Hive tables, S3 data, and RDBMS sources in a single query. Ingestion flows through Kafka Connect and Flume into HDFS and S3, Spark handles transformation workloads in Python, and Apache Iceberg manages versioned table formats supporting time travel and safe schema evolution on the analytical datasets. Governance is a core deliverable, not an afterthought: Apache Atlas captures metadata and data lineage so analysts can trace any figure to its source, while Apache Ranger enforces fine-grained access policies determining who can query which tables and columns. The intern demonstrates mastery of federated architecture and the governance tooling that regulated industries require, showing they can design platforms where analytical freedom and institutional control coexist, precisely the profile financial data teams recruit for.

financeapache-atlasapache-rangerbig-data+8

PRD · HLD · LLD includedView