{"repo":"flowdrivenml/market-data-stream-processor","free":true,"listed":false,"github":"https://github.com/flowdrivenml/market-data-stream-processor","clone":"git clone https://github.com/flowdrivenml/market-data-stream-processor.git","description":"Cross-exchange crypto market data and feature-engineering toolkit for order books, trades, open interest, funding, liquidations, and options. Python research prototype with lessons on fixed-time bars and ML.","language":"Python","stars":15,"topics":["datastreaming","financial-data","futures","heatmaps","liquidation","liquidation-map","marketdata","options","orderbook-snapshots","quantitative-finance"],"license":null,"category":"trading","readme_excerpt":"Crypto Market Data Aggregation Lab [!IMPORTANT] This repository is both a reusable market feature-engineering toolkit and an engineering postmortem . The code still has practical value. Its exchange adapters, flow processors, price-level aggregation, cross-exchange synthesis, and generated feature dictionaries can be imported into another Python project as a local plugin. The part that failed was the original assumption that fixed one-minute bars should be the main representation for machine-learning training. Fixed-time bars contain radically different amounts of information. A quiet minute and an extremely active minute both become one training row. This makes samples statistically inconsistent and encourages models to overfit to volatility regimes, activity regimes, and arbitrary clock boundaries. The code is therefore useful for: - feature prototyping; - dashboards and market summaries; - exploratory research; - cross-exchange normalization; - local feature generation; - integration into another data pipeline. It should not be treated as a production market-data collector or a lossless ML dataset generator without additional infrastructure. --- Quick Navigation - What This Project Is - Why the Code Is Still Useful - Using It as a Local Plugin - Architecture - Technology Used - Feature Engineering - Generated Feature Groups - How the Processing Works - Using Individual Flow Modules - Using the Combined Processor - What Worked - The Main Problem With Fixed-Time Bars - Probl","default_branch":null,"files":null,"tree":[],"storefront":"/r/flowdrivenml","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/flowdrivenml/market-data-stream-processor/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}