Back to Project Library
In Progress
Public-safe proof

Python + SQLite + Streamlit

Institutional Market Data Engine

Private Python/SQLite/Streamlit workspace for market-data ingestion, local storage, dashboard review, risk summaries, and validation-oriented market-data experiments.

PythonSQLiteStreamlitPandasRisk MetricsValidation

Institutional Market Data Engine

Python / SQLite / Streamlit / market-data validation

What this project is

Institutional Market Data Engine is the public-facing proof surface for the market-data analytics lane. It consolidates the older "Python Market Data Dashboard" wording into one clearer public-facing project: a private Python, SQLite, and Streamlit workspace for ingestion, storage, dashboard review, risk summaries, and validation-oriented market-data experiments.

The implementation remains private. This page explains the architecture and evidence boundary without exposing credentials, token files, local databases, broker/account data, webhook payloads, Discord/n8n/TradingView state, or production experiments.

What it demonstrates

  • Python ingestion and normalization patterns for authorized market-data sources.
  • Duplicate-aware SQLite storage using repeatable relational tables.
  • Streamlit and Plotly dashboarding for human review of market conditions.
  • Risk and volatility summaries such as drawdown, VaR-style views, correlation, implied-volatility notes, and expected-move framing.
  • Source-governance discipline around what is public, private, experimental, and production-only.
  • Quant-adjacent research maturity without claiming a live trading system or professional quant production platform.

Architecture

  1. Authorized or public source access feeds Python ingestion scripts.
  2. Python normalizes symbols, date ranges, price fields, and metadata.
  3. SQLite stores market-data snapshots and derived review tables locally.
  4. Pandas and NumPy prepare feature sets, summary views, and validation checks.
  5. Streamlit and Plotly expose dashboard views for human review.
  6. Research outputs are separated from production claims through notes, validation gates, and public/private boundaries.

How this connects to QuantStrat ML

QuantStrat ML is a related research lane, not the same public project. The market-data engine proves ingestion, storage, dashboard review, and source-governance discipline. QuantStrat ML proves backtesting discipline, expected-move research, options/volatility analysis, no-lookahead validation, and model/evaluation artifacts.

Together, they support a public-safe Market & Quant Analytics Lab narrative:

  • Institutional Market Data Engine: market-data engineering and dashboard review.
  • QuantStrat ML Research Framework: model evaluation, backtesting, and volatility research.
  • Market Data Dashboard Docs: governance, dashboard observability, and public/private source boundaries.
  • Production quant runtime: private/internal only.

How this connects to Market Data Dashboard Docs

Market Data Dashboard Docs remains a supporting proof shell for the dashboard and source-governance layer. Institutional Market Data Engine is the clearer primary portfolio title, while the dashboard docs explain related workflow boundaries, validation expectations, and why private runtime state stays private.

Public / private boundary

Public:

  • architecture summaries;
  • sanitized diagrams and code excerpts;
  • public-facing project explanation;
  • synthetic or public-safe examples after review;
  • validation and privacy notes.

Private:

  • API keys, OAuth tokens, .env files, broker credentials, and webhook secrets;
  • SQLite runtime databases and exported account-specific artifacts;
  • private watchlists, local machine paths, trading logs, Discord/n8n/TradingView operational state;
  • production quant runtime code and broker/account-specific workflows.

What this does not claim

  • It is not a live trading system.
  • It is not broker automation.
  • It is not a professional production quant platform.
  • It does not expose account-specific data or private trading records.
  • It does not claim current professional quant trader, quant developer, or quant researcher experience.

Public docs path

Open Market & Quant Analytics Lab Docs Open Institutional Market Data Engine Docs Open Market Data Dashboard Docs Open QuantStrat ML Docs

Professional relevance

This project demonstrates market-data, financial-data, BI, data-analysis, and quant-adjacent analytics relevance through a governed workflow that separates ingestion, dashboard review, model experiments, validation notes, and private runtime state."