Category: Finance & Trading

Finance & Trading is where orthogonal.info explores the intersection of software engineering and quantitative finance. This category covers algorithmic trading systems, market data analysis, SEC filing automation, and the Python-based tooling that makes it all possible. If you have ever wanted to build your own trading signals, backtest a strategy with real data, or automate the retrieval of financial filings, the guides here walk you through the engineering — not just the theory.

With 20 posts and counting, this is a growing collection of practical, code-first content for engineers who want to apply their skills to financial markets.

Key Topics Covered

Algorithmic trading systems — Designing, building, and deploying multi-agent trading systems using Python, LangGraph, and event-driven architectures with proper risk management layers.
Market data and APIs — Integrating with Yahoo Finance, Alpha Vantage, Polygon.io, FRED, and broker APIs to build reliable, real-time and historical data pipelines.
SEC EDGAR and financial filings — Automating 10-K, 10-Q, and 13-F retrieval and analysis using the SEC EDGAR full-text search API, CIK/ticker mapping, and structured data extraction.
Backtesting and strategy evaluation — Building backtesting frameworks with pandas, NumPy, and Backtrader, including walk-forward analysis, Monte Carlo simulation, and avoiding common pitfalls like look-ahead bias.
Options and derivatives analysis — Greeks calculation, volatility surface modeling, and options strategy evaluation using QuantLib and custom Python tooling.
Portfolio construction and risk — Mean-variance optimization, factor models, value-at-risk (VaR), and position sizing strategies for systematic portfolios.
Data engineering for finance — Storing tick data in PostgreSQL and TimescaleDB, building ETL pipelines, and managing the unique challenges of financial time-series data.

Who This Content Is For
This category is tailored for software engineers exploring quantitative finance, data scientists building trading models, self-directed investors who want to automate their research, and fintech developers building market-facing applications. You do not need a finance degree — the content assumes strong programming skills and teaches the domain concepts as they arise. A working knowledge of Python and basic statistics is helpful.

What You Will Learn
By working through the Finance & Trading articles, you will learn how to build end-to-end trading pipelines — from ingesting raw market data and SEC filings, through signal generation and backtesting, to execution and monitoring. You will understand how to structure a multi-agent analysis system, avoid the most common quantitative pitfalls, and leverage open-source Python libraries to do work that once required expensive proprietary platforms. Each post includes working code, real data sources, and honest discussion of limitations.

Dive into the posts below to start building your own quantitative edge.