--- title: "automl-gs vs machine-learning-for-trading" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/minimaxir-automl-gs-vs-stefan-jansen-machine-learning-for-trading" tools: ["minimaxir-automl-gs", "stefan-jansen-machine-learning-for-trading"] --- # automl-gs vs machine-learning-for-trading *GraphCanon updated Sep 30, 2026* ## Verdict Pick automl-gs if automl-gs: Python tool for automated machine-learning model creation from CSV data; pick machine-learning-for-trading if machine-learning-for-trading is a comprehensive repository for implementing machine learning techniques in trading, covering data sourcing, backtesting, and live execution strategies, all documented in Jupyter Notebooks. [automl-gs](https://github.com/minimaxir/automl-gs) reports 1.9k GitHub stars, 178 forks, and 28 open issues, last pushed Oct 22, 2019. [machine-learning-for-trading](https://ml4trading.io) has 21k stars, 5.6k forks, and 3 open issues, last pushed Sep 17, 2026. Figures are from public GitHub metadata via [automl-gs's repository](https://github.com/minimaxir/automl-gs) and [machine-learning-for-trading's repository](https://github.com/stefan-jansen/machine-learning-for-trading). | | [automl-gs](/tools/minimaxir-automl-gs.md) | [machine-learning-for-trading](/tools/stefan-jansen-machine-learning-for-trading.md) | | --- | --- | --- | | Tagline | Automatically generate machine-learning models and code with input CSV and target field | Code for Machine Learning for Trading, 3rd edition, from data sourcing to live execution. | | Stars | 1,867 | 20,925 | | Forks | 178 | 5,620 | | Open issues | 28 | 3 | | Language | Python | Jupyter Notebook | | Adopt for | automl-gs: Python tool for automated machine-learning model creation from CSV data | machine-learning-for-trading is a comprehensive repository for implementing machine learning techniques in trading, covering data sourcing, backtesting, and live execution strategies, all documented in Jupyter Notebooks. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | Data & Retrieval, Model Training | AI Agents, Data & Retrieval, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [automl-gs](/tools/minimaxir-automl-gs.md) | [machine-learning-for-trading](/tools/stefan-jansen-machine-learning-for-trading.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 2534d | 0d | | Open issues (now) | 28 | 3 | | Stars delta | -2 (30d) | +445 (30d) | | Open issues delta | 0 (30d) | -2 (30d) | | Full report | [trust report](/tools/minimaxir-automl-gs/trust.md) | [trust report](/tools/stefan-jansen-machine-learning-for-trading/trust.md) | ## Decision facts: automl-gs - **Adopt for:** automl-gs: Python tool for automated machine-learning model creation from CSV data ## Decision facts: machine-learning-for-trading - **Requirements:** Min 8 GB RAM; Requires Docker; Requires a C/C++ compiler and Python headers for local setup on macOS, Linux, or WSL2.; Docker setup is recommended for macOS with Intel and Windows (via WSL2). - **Adopt for:** machine-learning-for-trading is a comprehensive repository for implementing machine learning techniques in trading, covering data sourcing, backtesting, and live execution strategies, all documented in Jupyter Notebooks. ## Choose when ### Choose automl-gs if… - automl-gs is primarily Python; machine-learning-for-trading is Jupyter Notebook. - Tags unique to automl-gs: automl, keras, machine-learning, python. - Need to rapidly prototype models with limited ML expertise ### Choose machine-learning-for-trading if… - machine-learning-for-trading is primarily Jupyter Notebook; automl-gs is Python. - Requirements: Min 8 GB RAM; Requires Docker; Requires a C/C++ compiler and Python headers for local setup on macOS, Linux, or WSL2.; Docker setup is recommended for macOS with Intel and Windows (via WSL2).. - Tags unique to machine-learning-for-trading: algorithmic-trading, artificial-intelligence, backtesting, data-science. - Also covers AI Agents. - machine-learning-for-trading ships Docker support for self-hosted deployment. - Use when you need a complete workflow from data sourcing to live trading execution, as the repository covers all stages of the trading process. ## When NOT to use automl-gs - Complex feature engineering or non-standard data inputs required - Sensitive about licensing of the generated code ## When NOT to use machine-learning-for-trading - Avoid if you are working on Windows without WSL2, as the repository does not support installation into Windows Python. - Not recommended for users who do not have access to a C/C++ compiler and Python headers, as the local setup requires these for compiling dependencies. - Not ideal for those who prefer a lightweight setup, as the local environment requires about 16 GB of disk space. ## Common questions ### What is the difference between automl-gs and machine-learning-for-trading? automl-gs: Automatically generate machine-learning models and code with input CSV and target field. machine-learning-for-trading: Code for Machine Learning for Trading, 3rd edition, from data sourcing to live execution.. See the comparison table for live GitHub stats and shared categories. ### When should I choose automl-gs over machine-learning-for-trading? Choose automl-gs over machine-learning-for-trading when automl-gs is primarily Python; machine-learning-for-trading is Jupyter Notebook; Tags unique to automl-gs: automl, keras, machine-learning, python; Need to rapidly prototype models with limited ML expertise. ### When should I choose machine-learning-for-trading over automl-gs? Choose machine-learning-for-trading over automl-gs when machine-learning-for-trading is primarily Jupyter Notebook; automl-gs is Python; Requirements: Min 8 GB RAM; Requires Docker; Requires a C/C++ compiler and Python headers for local setup on macOS, Linux, or WSL2.; Docker setup is recommended for macOS with Intel and Windows (via WSL2).; Tags unique to machine-learning-for-trading: algorithmic-trading, artificial-intelligence, backtesting, data-science; Also covers AI Agents; machine-learning-for-trading ships Docker support for self-hosted deployment; Use when you need a complete workflow from data sourcing to live trading execution, as the repository covers all stages of the trading process. ### When should I avoid automl-gs? Complex feature engineering or non-standard data inputs required Sensitive about licensing of the generated code ### When should I avoid machine-learning-for-trading? Avoid if you are working on Windows without WSL2, as the repository does not support installation into Windows Python. Not recommended for users who do not have access to a C/C++ compiler and Python headers, as the local setup requires these for compiling dependencies. Not ideal for those who prefer a lightweight setup, as the local environment requires about 16 GB of disk space. ### Is automl-gs or machine-learning-for-trading more popular on GitHub? machine-learning-for-trading has more GitHub stars (20,925 vs 1,867). Stars measure visibility, not whether either tool fits your constraints. ### Are automl-gs and machine-learning-for-trading open source? Yes - both are open-source projects on GitHub (automl-gs: MIT, machine-learning-for-trading: MIT). ### Where can I find alternatives to automl-gs or machine-learning-for-trading? GraphCanon lists graph-backed alternatives at [automl-gs alternatives](/tools/minimaxir-automl-gs/alternatives) and [machine-learning-for-trading alternatives](/tools/stefan-jansen-machine-learning-for-trading/alternatives) ([automl-gs markdown twin](/tools/minimaxir-automl-gs/alternatives.md), [machine-learning-for-trading markdown twin](/tools/stefan-jansen-machine-learning-for-trading/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/minimaxir-automl-gs-vs-stefan-jansen-machine-learning-for-trading.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, automl-gs or machine-learning-for-trading? automl-gs: Dormant. machine-learning-for-trading: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for automl-gs and machine-learning-for-trading? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [automl-gs trust report](/tools/minimaxir-automl-gs/trust); [machine-learning-for-trading trust report](/tools/stefan-jansen-machine-learning-for-trading/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=minimaxir-automl-gs`](/api/graphcanon/graph?tool=minimaxir-automl-gs) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_