Category: Coding

Explore the Python and MATLAB notebooks behind our published quantitative research papers. Learn how we implement, backtest, and analyze trading strategies using free datasets and reproducible code, with Google Colab support for most Python tutorials.

Download a historical economic calendar covering decades of U.S. macroeconomic release dates, including FOMC, CPI, Non-Farm Payrolls, GDP, Retail Sales, and more. Export clean CSV files for quantitative research, event studies, and trading strategy backtests.

A complete Python procedure for constructing an S&P 1500 GICS database from Norgate Data. From Individual Stocks to Entire Industries Most quantitative equity research starts with individual securities. We download historical stock prices, construct a survivorship-bias-free universe, and test signals across hundreds or thousands of companies. However, many portfolio managers are more interested in systematic […]

Learn how to build a survivorship-bias-free equity database, historical index constituents, and a complete futures database using Norgate Data and Python.

Learn how to transform a quantitative volatility strategy into an automated trading system using Python, Interactive Brokers, and Market-on-Close execution.

Reliable intraday market data is one of the most overlooked requirements in quantitative research. In this follow-up article, we introduce concretum_tests, a Python tool designed to detect and handle some of the most common structural problems found in 1-minute OHLCV datasets, including phantom highs/lows, early-close leakage, stale bars, and missing bars.

We also explain which issues can be corrected cleanly, which require probabilistic reconstruction, and which should never be “fixed” at all.

A complete Python pipeline for downloading, organizing, and stitching futures contracts using Norgate Data, including contract specs, individual contracts, continuous futures, and front month volume decomposition for research ready backtests.

A complete, reproducible ORB (Opening Range Breakout) strategy backtest in Python using over 10 years of free Alpaca SIP intraday data. This research demonstrates how to build and evaluate a professional intraday trading system with ATR and High/Low stop modes, proper adjustment handling, early close filtering, and full performance analytics, all runnable directly in Google Colab with no local setup required.

A step-by-step guide to downloading and building complete Kraken intraday data, including quarterly updates, merge scripts, and validation against Binance.

This tutorial shows how to download daily and intraday data from Massive.com and build a total-return equity dataset that treats splits, dividends, and market hours correctly. We combine split-adjusted closes, raw traded prices, and dividend events to compute daily total return, and aggregate intraday bars to cover the full session up to 15:45. The result is a coherent database that preserves real traded levels while enabling accurate total-return calculations.