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.

Index-based backtests often suffer from a hidden form of survivorship bias: using today’s index constituents to simulate historical strategies. In this article, we build a point-in-time index constituency database using Python and Norgate Data, accurately tracking index additions, removals, and re-entries over time. The result is a survivorship bias-free dataset that reflects the true historical composition of major U.S. indices and can be directly integrated into robust quantitative backtests.

Crypto datasets often miss failed or delisted tokens, creating blind spots in research. To address this, we’re sharing the code behind our dataset from Catching Crypto Trends: A Tactical Approach for Bitcoin and Altcoins to support more complete and transparent analysis.

Discover how to backtest the Opening Range Breakout (ORB) strategy using Polygon.io. This guide walks you through executing the backtest, adjusting key parameters, and interpreting results to refine your trading approach.

Analyzing historical stock market data is essential for backtesting trading strategies, identifying tradable edges, and conducting in-depth financial research. However, gathering comprehensive datasets, especially those that include delisted stocks (i.e., stocks that no longer trade due to acquisitions, mergers, buyouts, or bankruptcy) can be challenging. This is where Norgate Data comes in—offering a powerful solution […]

In this article, we present a comprehensive guide to the Python code used to replicate the backtest of the trend-following strategy described in our paper, “A Century of Profitable Industry Trends.” This strategy utilizes Kenneth French’s database to construct a long-only, industry-based trend-following portfolio. The dataset spans daily data from 48 industry portfolios between 1926 and 2024. Our analysis reveals that momentum-based portfolios significantly outperform a passive Buy & Hold approach over the past century. Notably, the strategy’s parameters were not optimized in-sample, indicating potential for enhanced performance with further tuning.

In this article, we discuss using Matlab for a trend-following backtest outlined in “A Century of Profitable Industry Trends”. We use Kenneth French’s data to compare a momentum-based strategy against a Buy & Hold approach. The results show that momentum portfolios can outperform, and we conclude with a Matlab script that lets readers test how different parameters might improve profitability.

Explore our guide on backtesting the S&P500 ETF (SPY) using seven years of free Alpaca data. Learn a proven intraday momentum strategy to potentially beat the market, distilled into clear, actionable insights. Perfect for traders seeking effective, data-driven techniques.

Dive into our latest exploration of Python-based backtesting with two years of free SPY ETF data from Polygon. This post expands on the momentum strategies from ‘Beat the Market’, providing detailed Python code and analysis to assess their profitability and effectiveness.

In the world of finance, MATLAB is a powerful tool used by quantitative researchers to run statistical inferences and backtest systematic trading strategies. In this blog post, we share the MATLAB code that has been used to study the profitability of the strategy presented in our paper “Beat the Market: An Effective Intraday Momentum Strategy […]