6 Repos
Tools for cleaning and reformatting data for downstream consumption.
Distinguishing note: Focuses on pipeline-oriented schema alignment.
Explore 6 awesome GitHub repositories matching data & databases · Data Pipeline Transformers. Refine with filters or upvote what's useful.
This project is a command-line processor designed for the parsing, filtering, and transformation of structured data streams. It functions as a declarative programming environment that treats data as immutable streams, allowing users to perform complex structural modifications through the composition of small, reusable functions. By utilizing a recursive tree traversal engine, the system enables the navigation, inspection, and modification of deeply nested hierarchical data structures. The engine distinguishes itself through a stream-oriented architecture that processes input records one by on
Cleans and reformats raw data outputs from APIs or databases to match required schemas.
Backtrader is a Python backtesting framework and algorithmic trading platform. It provides a toolkit for developing automated trading rules and simulating investment strategies using historical financial time-series data. The system functions as a quantitative analysis tool, combining a simulation engine for testing trading rules with a financial data visualizer that generates price action charts. It allows for the calculation of technical indicators and the evaluation of portfolio performance through risk-adjusted returns. The platform covers live trading integration via brokerage APIs and
Automatically updates technical indicators using an observer-based processing pipeline.
Backtrader is a Python framework designed for the development, backtesting, and live execution of algorithmic trading strategies. It provides a comprehensive environment for quantitative finance, allowing users to simulate trading logic against historical market data or connect directly to brokerage platforms for automated real-time trading. The project distinguishes itself through a unified event-driven architecture that treats backtesting and live trading with the same API. This consistency is supported by a flexible data-feed abstraction layer that normalizes diverse financial sources, ena
Calculates technical analysis values by chaining mathematical transformations that automatically update as new market data arrives.
Joyagent-jdgenie is an automated data orchestrator designed to centralize the retrieval and processing of information from disparate remote sources. It functions as a framework for building repeatable data pipelines that fetch, clean, and normalize raw input into consistent, structured formats. The system utilizes a schema-driven engine to apply validation rules and structural templates to incoming data, ensuring compatibility across enterprise systems. By employing configuration-based workflow definitions, it allows for the orchestration of modular tasks into automated execution flows, separ
Cleans and reformats incoming data to ensure compatibility for downstream analysis and storage.
react-stockcharts ist eine Finanz-Charting-Bibliothek, die mit React und D3 für die Visualisierung von Marktpreisdaten erstellt wurde. Sie bietet ein System zum Rendern von Aktiencharts und Candlestick-Visualisierungen, um Marktbewegungen darzustellen. Die Bibliothek fungiert als technisches Analysetool, das mathematische Handelsindikatoren auf Preis-Charts berechnet und überlagert. Sie enthält eine interaktive Schnittstelle zur Navigation durch Finanzdaten mittels Zoomen, Panning und dem Hinzufügen manueller geometrischer Annotationen. Das Projekt deckt Finanzdatenvisualisierung, Marktpreisverfolgung und die Erstellung interaktiver Trading-Dashboards ab.
Processes raw price arrays through mathematical pipelines to generate technical analysis overlays.
This is a structured deep learning curriculum for programmers, delivered as a collection of Jupyter notebooks. It teaches the fundamentals of training neural networks for computer vision, natural language processing, tabular data analysis, and collaborative filtering using PyTorch and the fastai library. The course is designed to be hands-on, guiding learners from building a training loop from scratch to fine-tuning pretrained models for a variety of practical tasks. The curriculum distinguishes itself by covering the full lifecycle of a deep learning project, from data preparation and augmen
Provides a declarative API for assembling complete data processing pipelines from scratch.