- Data pipeline — Market data from Alpaca API or Interactive Brokers TWS. Fundamental data from SEC EDGAR. Store in PostgreSQL or TimescaleDB.
- Analysis framework — Python with pandas, NumPy, and scikit-learn for feature engineering. TA-Lib or custom implementations for technical indicators.
- Strategy engine — Backtesting with vectorbt or Backtrader. Signal generation from combined technical, fundamental, and sentiment inputs.
- Risk management — Position sizing with Kelly Criterion. Max drawdown limits. Correlation-aware portfolio construction.
- Execution layer — Order management via broker APIs. Slippage modeling. Real-time monitoring with Grafana dashboards and Apache Kafka event streams.
Learning Path
Follow this progression from market fundamentals to production trading systems:
Fundamentals
- Understand market microstructure: order books, bid-ask spreads, and execution mechanics
- Learn options pricing theory: Black-Scholes, the Greeks, and implied volatility surfaces
- Study risk management foundations: Kelly Criterion, Value-at-Risk, and max drawdown
- Set up your Python environment with pandas, NumPy, and matplotlib for financial data
Data & Analysis
- Build SEC EDGAR pipelines with edgartools to parse Form 4, 10-K, and 13-F filings
- Implement technical indicators: RSI, Ichimoku Cloud, Stochastic Oscillator, MACD
- Detect insider trading clusters from Form 4 filing patterns using statistical analysis
- Track pre-IPO valuations for SpaceX, OpenAI, and Anthropic via closed-end fund data
Strategy Development
- Backtest strategies with vectorbt or Backtrader using historical market data
- Combine technical, fundamental, and sentiment signals into multi-factor models
- Apply position sizing algorithms: fixed fractional, volatility-adjusted, and Kelly-optimal
- Build narrative detection systems that identify market regime shifts in real time
Production Systems
- Design multi-agent trading architectures with LangGraph for autonomous decision-making
- Connect to broker APIs (Alpaca, Interactive Brokers TWS) for live order execution
- Build real-time monitoring with PostgreSQL, Apache Kafka, and Grafana dashboards
- Implement circuit breakers, kill switches, and automated risk limit enforcement
Why This Guide Exists
Most trading education is written by traders who think in chart patterns and gut feel. This guide is different — it is written by an engineer who treats markets as a distributed systems problem. Every strategy has a mathematical foundation. Every signal has a backtest. Every system has error handling, logging, and monitoring. I built a multi-agent trading system using LangGraph that analyzes SEC filings, computes technical indicators, and executes trades autonomously. These articles document every component of that system.
This guide is for software engineers, data scientists, and quantitative analysts who want to apply engineering discipline to financial markets. You should be comfortable with Python, basic statistics, and API integration. No finance degree required — just the willingness to treat trading as a rigorous engineering problem with working code, real data, and measurable results.
Most trading content is written by traders for traders. This guide is different — it’s written by an engineer, for engineers. We approach markets with the same rigor we apply to distributed systems: mathematical models, backtesting frameworks, risk management algorithms, and data pipelines.
From options pricing theory to SEC filing analysis, each article below tackles a specific aspect of quantitative trading with working code and real data.
📚 Complete Guide Collection (19 Articles)
Insider Trading Detector with Python & Free SEC Data
Detect insider buying clusters from SEC EDGAR Form 4 filings using Python and edgartools. Free, no API key. Complete working code included….
Pre-IPO API: SEC Filings, SPACs & Lockup Data
If you’re building fintech applications, trading bots, or investment research tools, you know the pain: pre-IPO data is fragmented across dozens of SEC filing pages, paywalled databases, and sta…
Track Pre-IPO Valuations: SpaceX, OpenAI & More
Track implied valuations for SpaceX ($2T), OpenAI ($1.3T), and 19 more pre-IPO tech companies using a free API that analyzes publicly traded closed-end fund data….
5 Best Finance APIs for Tracking Pre-IPO Valuations in 2026
Compare the top APIs for tracking private company valuations, including SpaceX, OpenAI, and Anthropic. Free and paid options for fintech developers….
AI Market Signals: What Stock Trends Say This Week
Our AI narrative detection shifted from MIXED to WAR_CRISIS this week. Oil surged 59%, geopolitical risk hit 91.2/100, and the rotation signals are screaming: defense, energy, gold in — tech out….
Engineer’s Guide to RSI, Ichimoku, Stochastic Indicators
Deep dive into RSI, Ichimoku, and Stochastic indicators with Python code. Covers the math, signal generation, and backtesting for quantitative traders….
Risk Management & Position Sizing for Traders
Master risk management and position sizing to protect your trading capital. Covers Kelly criterion, volatility-based sizing, and max drawdown strategies….
Algorithmic Trading: A Practical Guide for Engineers
A practical guide to algorithmic trading for software engineers. Covers backtesting, strategy design, risk management, and live deployment with Python….
Advanced Options Strategies for Engineers: A Practical Guide
Advanced options strategies explained for engineers. Covers spreads, Greeks, volatility trading, hedging techniques, and Python implementation examples….
Stochastic Oscillator in JavaScript for Scalping
Implement the Stochastic Oscillator in JavaScript for crypto and stock scalping. Covers %K, %D calculation, signal generation, and backtesting logic….
Bull Call & Bear Put Spreads: JavaScript Calculator
Build a bull call and bear put spread calculator in JavaScript. Computes max profit, max loss, breakeven points, and visualizes the payoff diagram….
Option Pricing in JS: Forward Implied Volatility
Price options using forward implied volatility in JavaScript. Covers term structure interpolation, vol surface modeling, and Black-Scholes adjustments….
Iron Butterfly Options: Profit Probability in JS
Calculate iron butterfly profit probability with JavaScript. Covers strike selection, max profit/loss, breakeven points, and Monte Carlo simulation….
Iron Condor Profit & Probability with JavaScript
Calculate iron condor profit and probability with JavaScript. Covers strike selection, max gain/loss, breakeven points, and risk-reward visualization….
Linear Regression: A Beginner-Friendly Guide
Learn linear regression from scratch with clear math, Python examples, and real datasets. Perfect for beginners entering machine learning and data science….
Python Finance: Option In-the-Money Probability
Calculate the probability of an option finishing in the money using Python. Covers Black-Scholes, Monte Carlo simulation, and real market data examples….
Monte Carlo Simulations in JS for Finance
Run Monte Carlo simulations in JavaScript for stock price forecasting and portfolio risk analysis. Covers random walks, confidence intervals, and charting….
Ichimoku Cloud in JavaScript: A Trader’s Guide
Build an Ichimoku Cloud indicator in JavaScript for trading. Covers Tenkan, Kijun, Senkou Span calculation, signal interpretation, and chart rendering….
Mastering RSI Calculation in JavaScript for Smarter Trading
Calculate the Relative Strength Index (RSI) in JavaScript from scratch. Covers the math, smoothing methods, signal interpretation, and trading integration….