mtflow


Namemtflow JSON
Version 0.0.1.dev1 PyPI version JSON
download
home_pagehttps://pfund.ai
SummaryMachine Trading Lifecycle
upload_time2025-02-04 04:16:41
maintainerNone
docs_urlNone
authorStephen Yau
requires_python<4.0,>=3.11
licenseApache-2.0
keywords
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            # mtflow

Machine Trading Lifecycle
- Data management
- Strategy development
- Backtesting
- Model development (if use machine learning)
- Model training (if use machine learning)
- Parameter training / hyperparameter tuning
- MLOps setup (if use machine learning)
- Strategy & Model(s) deployment
- Portfolio monitoring
- Performance analysis


`mtflow` brainstorming:
- data management: Svelte Web UI to visualize data stored in `pfeed`
- StratOps: store strategy scripts and the corresponding backtest results in `pfeed`, can register/retire a strategy, manage its configs, versions, etc.
e.g. register a strategy version that produces the same result as the vectorized one, so that one can safely build on top of that.
- backtesting: save the analytical notebooks/dashboards (`pfund-plot`) used for calculating the backtest results
- MLOps: similar to strategy management, but see if it can be tightly integrated with mlflow, or even build on top of it
- ML training: ...
- deployment: assign dashboards to strategies for monitoring, GUI-based deployment, deployment type (local (single machine), cloud (clusters) etc. ), choose if use kafka in `pfeed` for streaming data
- live trading: monitoring & on-going analysis


`mlflow` reference:
- Experiment Tracking 📝: A set of APIs to log models, params, and results in ML experiments and compare them using an interactive UI.
- Model Packaging 📦: A standard format for packaging a model and its metadata, such as dependency versions, ensuring reliable deployment and strong reproducibility.
- Model Registry 💾: A centralized model store, set of APIs, and UI, to collaboratively manage the full lifecycle of MLflow Models.
- Serving 🚀: Tools for seamless model deployment to batch and real-time scoring on platforms like Docker, Kubernetes, Azure ML, and AWS SageMaker.
- Evaluation 📊: A suite of automated model evaluation tools, seamlessly integrated with experiment tracking to record model performance and visually compare results across multiple models.
- Observability 🔍: Tracing integrations with various GenAI libraries and a Python SDK for manual instrumentation, offering smoother debugging experience and supporting online monitoring.
            

Raw data

            {
    "_id": null,
    "home_page": "https://pfund.ai",
    "name": "mtflow",
    "maintainer": null,
    "docs_url": null,
    "requires_python": "<4.0,>=3.11",
    "maintainer_email": null,
    "keywords": null,
    "author": "Stephen Yau",
    "author_email": "softwareentrepreneer+mtflow@gmail.com",
    "download_url": "https://files.pythonhosted.org/packages/c7/47/4eee0f8290737d14c0f72f5535899ca023a7bea757ed045c4976c8b95bfc/mtflow-0.0.1.dev1.tar.gz",
    "platform": null,
    "description": "# mtflow\n\nMachine Trading Lifecycle\n- Data management\n- Strategy development\n- Backtesting\n- Model development (if use machine learning)\n- Model training (if use machine learning)\n- Parameter training / hyperparameter tuning\n- MLOps setup (if use machine learning)\n- Strategy & Model(s) deployment\n- Portfolio monitoring\n- Performance analysis\n\n\n`mtflow` brainstorming:\n- data management: Svelte Web UI to visualize data stored in `pfeed`\n- StratOps: store strategy scripts and the corresponding backtest results in `pfeed`, can register/retire a strategy, manage its configs, versions, etc.\ne.g. register a strategy version that produces the same result as the vectorized one, so that one can safely build on top of that.\n- backtesting: save the analytical notebooks/dashboards (`pfund-plot`) used for calculating the backtest results\n- MLOps: similar to strategy management, but see if it can be tightly integrated with mlflow, or even build on top of it\n- ML training: ...\n- deployment: assign dashboards to strategies for monitoring, GUI-based deployment, deployment type (local (single machine), cloud (clusters) etc. ), choose if use kafka in `pfeed` for streaming data\n- live trading: monitoring & on-going analysis\n\n\n`mlflow` reference:\n- Experiment Tracking \ud83d\udcdd: A set of APIs to log models, params, and results in ML experiments and compare them using an interactive UI.\n- Model Packaging \ud83d\udce6: A standard format for packaging a model and its metadata, such as dependency versions, ensuring reliable deployment and strong reproducibility.\n- Model Registry \ud83d\udcbe: A centralized model store, set of APIs, and UI, to collaboratively manage the full lifecycle of MLflow Models.\n- Serving \ud83d\ude80: Tools for seamless model deployment to batch and real-time scoring on platforms like Docker, Kubernetes, Azure ML, and AWS SageMaker.\n- Evaluation \ud83d\udcca: A suite of automated model evaluation tools, seamlessly integrated with experiment tracking to record model performance and visually compare results across multiple models.\n- Observability \ud83d\udd0d: Tracing integrations with various GenAI libraries and a Python SDK for manual instrumentation, offering smoother debugging experience and supporting online monitoring.",
    "bugtrack_url": null,
    "license": "Apache-2.0",
    "summary": "Machine Trading Lifecycle",
    "version": "0.0.1.dev1",
    "project_urls": {
        "Documentation": "https://mtflow-docs.pfund.ai",
        "Homepage": "https://pfund.ai",
        "Repository": "https://github.com/PFund-Software-Ltd/mtflow"
    },
    "split_keywords": [],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "c910912413ac381c0845e6539cbac1d4b908c78d1eb259107dd54f7d292642b6",
                "md5": "f3830fc4a4a82e047f46911e48ec39ff",
                "sha256": "5ae74654bc9fa6bb0dff852aa43bdec7fe161b09d8c306039f42beb870b1aa41"
            },
            "downloads": -1,
            "filename": "mtflow-0.0.1.dev1-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "f3830fc4a4a82e047f46911e48ec39ff",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": "<4.0,>=3.11",
            "size": 6528,
            "upload_time": "2025-02-04T04:16:39",
            "upload_time_iso_8601": "2025-02-04T04:16:39.677906Z",
            "url": "https://files.pythonhosted.org/packages/c9/10/912413ac381c0845e6539cbac1d4b908c78d1eb259107dd54f7d292642b6/mtflow-0.0.1.dev1-py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "c7474eee0f8290737d14c0f72f5535899ca023a7bea757ed045c4976c8b95bfc",
                "md5": "86fe1685af03e04deba5c73c36276a11",
                "sha256": "5e727bb2ca9f2103f6b73f0e5193b16bd7faa72294247f7ac1df8798a067598f"
            },
            "downloads": -1,
            "filename": "mtflow-0.0.1.dev1.tar.gz",
            "has_sig": false,
            "md5_digest": "86fe1685af03e04deba5c73c36276a11",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": "<4.0,>=3.11",
            "size": 6129,
            "upload_time": "2025-02-04T04:16:41",
            "upload_time_iso_8601": "2025-02-04T04:16:41.707162Z",
            "url": "https://files.pythonhosted.org/packages/c7/47/4eee0f8290737d14c0f72f5535899ca023a7bea757ed045c4976c8b95bfc/mtflow-0.0.1.dev1.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2025-02-04 04:16:41",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "github_user": "PFund-Software-Ltd",
    "github_project": "mtflow",
    "github_not_found": true,
    "lcname": "mtflow"
}
        
Elapsed time: 0.38913s