| Name | mtflow JSON |
| Version |
0.0.1.dev1
JSON |
| download |
| home_page | https://pfund.ai |
| Summary | Machine Trading Lifecycle |
| upload_time | 2025-02-04 04:16:41 |
| maintainer | None |
| docs_url | None |
| author | Stephen Yau |
| requires_python | <4.0,>=3.11 |
| license | Apache-2.0 |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
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# 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.
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"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.",
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