Catalyst is an open-source crypto-asset trading engine written in Python. It is aimed at developers and quantitative traders who want to test trading strategies against historical exchange data and then run the same code in live markets. It is an early adaptation of the Zipline approach, adapted for cryptocurrency exchanges rather than stock markets.
Before you consider it, note the project status: the main repository has not been actively maintained since the end of 2018, and the latest code update dates from September 2021. Version 0.5.21 is the most recent release listed. Several community forks exist and may be more active, so check those if you need ongoing support. Treat Catalyst as a preserved, archived tool rather than a modern, supported platform.
What Catalyst Is and Where It Fits
Catalyst belongs to the family of algorithmic trading frameworks that separate strategy code from the market connection. A strategy is a set of rules that decides when to buy or sell. The framework supplies the data, the order handling and the accounting, so the same rules can be measured on history and then applied to live prices.
The project was built as an adaptation of Zipline, a backtesting library originally developed for equities. Catalyst carried that event-driven design over to cryptocurrency exchanges, which trade around the clock and publish their data in different formats. That history explains both its strengths and its limits: the architecture is familiar to anyone who has used Zipline, but the crypto-specific parts are now several years old.
Who Catalyst Is For
The natural audience is Python developers and quantitative hobbyists who already write code and want an explicit, inspectable trading engine. If you prefer a graphical interface, drag-and-drop strategy building or a managed cloud service, this tool will feel too low-level.
Researchers who want to test ideas on historical crypto data are another group. Because the output is plain Pandas DataFrames, results can be fed into notebooks, plotted and compared across many parameter settings. Students learning how trading systems are structured may also find the source code instructive, as long as they understand that the code is archived.
Typical Use Cases
A common workflow starts with a hypothesis, such as a moving-average crossover on daily Bitcoin candles. You write the rule in Python, run it over several years of historical data, and inspect the equity curve and drawdowns. If the result holds up against the Bitcoin benchmark, you might test it on a different period before going any further.
Another use is comparing strategies side by side. Because every run uses the same data and the same simulation rules, differences in results come from the strategies themselves rather than from inconsistent test conditions. Some users also keep a small live deployment running with minimal capital while they observe how real order execution compares with the simulation.
Key Concepts Behind Backtesting
Backtesting replays historical prices through a strategy and records the trades it would have made. Useful results depend on realistic assumptions. Fees, slippage and the time it takes to fill an order all change the outcome, and a strategy that looks profitable without them can lose money once they are included.
Another common trap is overfitting, where a strategy is tuned until it fits one historical period perfectly but fails on new data. Splitting history into an in-sample period for development and an out-of-sample period for checking is a standard defense. Catalyst gives you the tools to do this, but the discipline has to come from you.
Key features
Simple workflow
Support for major exchanges
Local control of API keys
Historical pricing data
Backtesting and live trading
Performance reports with Pandas
Analysis and machine learning libraries
Bitcoin benchmark
Supported coins
The documentation mentions Bitcoin (BTC) as the primary reference asset, and the engine is built around trading pairs against USDT, with Bitcoin pricing available as a benchmark. The engine connects to several of the largest exchanges by volume. Because the project is archived, confirm the current exchange connectors in the source code before relying on a specific market.
How to install Catalyst on Windows
- 1
Get the official files
Download the program only from its official source, which is the project's GitHub repository at github.com/scrtlabs/catalyst, or from the Releases page there. Avoid mirror sites and re-uploads, since they may carry modified files.
- 2
Verify the download
If the release provides a SHA-256 checksum or a signature, compare the hash of your downloaded file against the published value before running it. A mismatch means the file should be discarded and downloaded again.
- 3
Install Python dependencies
Because Catalyst is a Python project, you will need a compatible Python environment. Create an isolated virtual environment and install the required packages from the project's requirements list, which keeps the engine's dependencies separate from the rest of your system.
- 4
Run a test strategy
Start with the example strategies included with the project. Run one against a small historical window to confirm the installation works and that the output tables are generated correctly.
-
Configure exchange access carefully
Only add exchange API keys when you intend to use live trading. Create keys with trading permissions only, never with withdrawal permissions, and restrict them to the IP address of your machine where the exchange allows it.
System requirements
- Windows with a Python environment that matches the project's supported versions
- Enough disk space to store historical price datasets, which can grow large for minute-level data
- A stable internet connection for downloading data and for any live trading
How to use Catalyst
Catalyst is driven by Python scripts. You define a set of functions that run at the start of a simulation and then on each bar of data, and the engine calls them in order. The workflow below shows the general approach; check the examples in the repository for exact command syntax.
- Set up a clean Python environment and confirm the package imports without errors.
- Load or download historical pricing data for the exchange and market you want to test.
- Write a strategy file that defines the initialization logic and the per-bar trading rules.
- Run a backtest over a chosen date range and save the results.
- Review the performance DataFrame, focusing on returns, drawdowns and trade counts rather than a single headline figure.
- Compare the strategy against the Bitcoin benchmark over the same period.
- Repeat the test on a different time window to check whether the result holds.
- Only if the results are consistent, consider a small live run with strict risk limits.
Pros & cons
- Open source, so the code can be read, audited and modified
- Free to use with no license fee
- Clear separation between strategy logic and exchange connectivity
- Results returned as Pandas DataFrames for easy analysis
- Built-in Bitcoin benchmark for context
- Runs locally, so exchange keys stay on your machine
- Repository is no longer actively maintained
- Last code update dates from 2021, so compatibility with newer libraries and exchange changes is uncertain
- Requires Python programming knowledge; there is no graphical interface
- Community forks may be more active but vary in quality
- Documentation and examples may reflect an older codebase
Is Catalyst safe?
Download Catalyst only from its official GitHub repository or a verified release page, and check the SHA-256 checksum or signature before running it. Do not install builds from unofficial sites, because a modified trading engine can steal exchange keys or move funds. Never enter a seed phrase or wallet recovery phrase into any trading tool or website; a legitimate trading engine has no need for it. Create exchange API keys with trading permissions only and never grant withdrawal rights. Because the project is archived, review the source code yourself before trusting it with real capital, and consider a forked version only after checking its maintenance status and code history. Start with paper trading or very small amounts.
- Official source only. Download from github.com/scrtlabs/catalyst. Ignore ads and lookalike domains.
- Check the SHA-256. Compare the installer hash with the value the developer publishes before running it.
- Never share your seed phrase. No developer, support agent or website — including CryptApps — will ever ask for it.
Alternatives
Catalyst is a code-first research and execution engine. Compared with OctoBot, which offers a web interface and a more guided setup, Catalyst asks you to write strategies directly in Python. Hummingbot focuses on market making and arbitrage, a different trading style from the strategy-testing workflow Catalyst was built for. Gunbot Quant and Quantify also offer analysis-oriented tooling, and StockSharp is a broader multi-market platform in C#. If you want a maintained environment with a lower coding barrier, look at OctoBot or Hummingbot. If you specifically want a Python backtesting engine you can inspect and extend, Catalyst remains a reasonable reference, provided you accept the archived status.
Specifications
- Price
- Free
- Type
- Open source
- License
- —
- Latest version
- 0.5.21
- Last update
- 22 Sept 2021
- Windows support
- Yes (Windows build or installer)
- Source code
- github.com/scrtlabs/catalyst
Frequently asked questions
No. The main repository has not been actively maintained since the end of 2018, and the latest code update is from September 2021. Several community forks exist, and some may be more active, so check their recent commit history before choosing one.
Yes, Catalyst is open-source software and is listed as free. You still pay for exchange fees on any live trades, and you need your own hardware and time to set it up and maintain it.
Yes, Windows is supported through a Windows build or installer. Because it is a Python project, you should set up a dedicated Python environment and confirm that dependencies install correctly before running any strategy.
The documentation mentions Bitcoin (BTC) as the main reference asset, and the engine works with trading pairs against USDT. Support for other assets depends on the exchange data you load, so confirm the available markets in the source code.
Yes. Strategies are written as Python scripts, and results are analyzed with Pandas. Basic Python skills are needed to write and debug a strategy, and some familiarity with data analysis helps you interpret the output.
The engine supports live trading through exchange API keys, but it is archived and may not work correctly with current exchange APIs. Test thoroughly on historical data and in simulation first, and use keys without withdrawal permissions.
Keys are stored and used on your own machine, which is a sound design. The risk comes from the software itself if it has been modified, so download only from the official repository and review the code before use.
Verdict
Catalyst is best suited to Python developers and researchers who want a transparent backtesting engine and can accept an archived codebase. It is not a good first choice for someone looking for a supported, graphical trading product. If you need current exchange compatibility and active support, evaluate OctoBot or Hummingbot first, and treat Catalyst as a reference implementation whose code you can study and adapt.