Python and MATLAB notebooks for the strategies behind our research papers
These Python and MATLAB backtesting tutorials show how we implement the strategies behind our published quantitative research papers. Each notebook is designed to help readers reproduce the core logic, experiment with parameters, and understand the research using freely available datasets whenever possible.
We often get asked how we backtest, or how someone outside our team can reproduce the kind of studies we publish. Those questions are fair. A paper without a path to replication is harder to trust, and harder to learn from.
Over the past few years we have published a set of coding tutorials that open up parts of that process. They cover the strategies behind several of our research papers, in both Python and MATLAB, using freely available data where possible: Kenneth French industry portfolios, Alpaca for multi-year free intraday US equity data, and Massive.com (formerly Polygon.io) for free-tier intraday history.
These notebooks are not exact replicas of our internal research stack. We simplify where we can. We translate the core logic so that readers can run it, read it, and experiment with it. The goal is transparency and accessibility, not a one-to-one dump of production code.
Most of the Python notebooks can be run directly in the cloud with Google Colab for free. No local install required. The MATLAB versions need MATLAB.
What These Notebooks Are (and Are Not)
They are educational, research-oriented implementations of the strategies described in our papers.
They are intentionally written to be readable. Across these posts we deliberately avoid more complex coding patterns that might improve computational efficiency but would make the logic harder to follow for someone learning quantitative research.
They are close to what we do in spirit (entries, exits, risk rules, and the broad shape of the data pipeline), but they are not identical to our internal research stack. One of the biggest gaps is data quality.
In Can You Trust Your Intraday Database?, we showed that running the same ORB backtest, with identical code, parameters, and dates, across Massive, Alpaca, IQFeed, Interactive Brokers, and later Databento, produced materially different results. Under H/L stops, final portfolio values ranged from roughly $226k to $726k: more than a threefold gap from data differences alone. The strategy was not the main source of divergence. The data was.
That study documented the structural problems we keep seeing in 1-minute OHLCV feeds:
- phantom highs and lows
- stale bars
- early-close leakage
- tick-to-bar assignment differences
- venue coverage and trade aggregation differences
The practical follow-up is How to Detect and Fix Problems in Intraday Market Data, where we open-sourced concretum_tests, a lightweight Python CLI we use internally to audit 1-minute CSVs. It checks early-close leakage, phantom highs/lows, strong stale bars, and missing bars. Where a fix is defensible, it can clean early-close leakage and optionally reconstruct phantom wicks. It does not invent missing bars, because fabricating fills can create trades that never existed.
That data layer is part of how we work. It is still only one piece of the internal process behind the research. There are other steps we use in practice that are not in these notebooks either. Some of that may appear in future posts if we can simplify it enough to share cleanly.
1. Can Day Trading Really Be Profitable?
Paper: Can Day Trading Really Be Profitable? (Zarattini & Aziz, 2023)
This line of code implements the Opening Range Breakout (ORB) strategy from the paper: define a short opening range, take direction from that range, enter on the next bar, manage risk with stops, and hold to stop or the close.
We published two Python versions so readers can choose based on data access.
Python + Alpaca: 10+ years of free SIP intraday data
ORB Strategy Backtest in Python Using Alpaca (10+ Years of Free Data)
- Runs in Google Colab
- Free Alpaca SIP 1-minute history back to 2016
- Includes both High/Low and ATR stop modes
- Handles adjustments and early-close filtering more cleanly than the earlier free-tier Polygon workflow
This is the version we recommend if you want a long free history without a paid data subscription.
Python + Massive.com (formerly Polygon.io): ~2 years on the free tier
Backtesting the Opening Range Breakout (ORB) Strategy in Python using Polygon.io
- Runs in Google Colab
- Free Massive / Polygon history is limited to roughly the last two years
- Same core ORB logic; useful if you already have a Polygon/Massive key
2. A Century of Profitable Industry Trends
Paper: A Century of Profitable Industry Trends
This study builds a long-only, industry-based trend-following portfolio using Kenneth French‘s freely available daily data for 48 industry portfolios, covering roughly 1926-2024. The open code compares the momentum portfolio against a passive buy-and-hold benchmark. Strategy parameters were not optimized in-sample.
Python: Kenneth French data
Backtest a Profitable Trend-Following Strategy using Python
- Runs in Google Colab
- Downloads French industry and factor files directly
- Includes a full walkthrough notebook and a lighter “easy parameter” notebook for quick experiments
MATLAB: Kenneth French data
Backtest a Profitable Trend-Following Strategy
- Same research design in MATLAB
- Includes a parameter-exploration script so readers can see how lookbacks and related choices affect profitability
- Requires MATLAB (not Colab)
3. Beat the Market: SPY Intraday Momentum
Paper: Beat the Market: An Effective Intraday Momentum Strategy for the S&P500 ETF (SPY)
This family of notebooks backtests the SPY intraday momentum framework from the paper: VWAP, open-relative moves, volatility scaling, and the related execution rules, first in MATLAB, then in Python.
MATLAB + Massive.com / Polygon.io: ~2 years free data
MATLAB: Backtesting “Beat the Market”
- Original MATLAB path used to study the paper strategy
- Free Massive / Polygon history limited to roughly two years
- Requires MATLAB
Python + Massive.com / Polygon.io: ~2 years free data
Backtesting 2 Years of FREE Data Using Python
- Python translation of the MATLAB logic
- Same free-tier data window
- Runs in Google Colab
Python + Alpaca: 7+ years free intraday data, plus overnight gap reversal
Designing a Profitable Intraday Strategy Using Python and Alpaca
- Extends the history to over seven years of free Alpaca intraday data
- Keeps the paper’s intraday momentum edge
- Adds an overnight gap-reversal component (large overnight moves that tend to mean-revert in the first part of the session)
- Runs in Google Colab
This is the version to start with if you want longer free SPY history and the gap enhancement on top of the original paper logic.
Quick Reference
How to Use These
- Read the paper first so the strategy rules are clear.
- Open the matching coding article.
- For Python posts, launch the Google Colab notebook, add any required free API keys (Alpaca or Massive/Polygon), and run all cells.
- For MATLAB posts, paste the scripts into MATLAB and set your API key where needed.
- Change parameters only after you understand the baseline. The published notebooks are meant as transparent starting points, not optimized production systems.
If you are deciding between data vendors for free intraday work: Massive / Polygon is convenient but historically limited on the free tier; Alpaca currently offers a much longer free SIP window for the equity notebooks above; Kenneth French remains the clean open source for the century-long industry study.
We publish research because we care about evidence. We publish code because questions about how the evidence was produced deserve a concrete answer.
These notebooks will not replace a full institutional research stack. They will get you much closer to the logic behind our papers than a PDF alone can. Run them, break them, change the ticker, change the lookback, and see what holds up.
