Recreate and Refine Real-Time Bitcoin Index (CFB BRTI) — Millisecond-Level Python Analysis
Budget: $50 – $200 USD
Description:
I’m working on a project to recreate the CFB BRTI (Crypto Facilities Bitcoin Real-Time Index) by aggregating and analyzing live exchange order book data. The goal is to produce a highly accurate, continuously updating estimation of the benchmark and compare it directly to the official BRTI in real time.
A working Python code base already exists, including live data collection and an initial estimation model. I’m now looking for someone to improve the interpolation and estimation logic, especially around millisecond-scale price movements and order book aggregation. If a full rewrite would yield a cleaner or more accurate approach, that’s also an option.
Project Scope:
Improve interpolation and smoothing of the reconstructed index to better match the official CFB BRTI.
Refine order book aggregation depth and weighting across exchanges to more accurately reflect true market behavior.
Optimize timing and data flow for updates every ~50 milliseconds.
Build or refine real-time comparison and visualization of the reconstructed vs. official BRTI.
Analyze discrepancies between the two indices and document findings.
Requirements:
Strong skills in Python for data analysis and processing.
Experience with time-series analysis, data interpolation, or signal smoothing.
Comfort working with real-time or asynchronous data (WebSocket or similar).
Ability to reason about market data and make iterative improvements based on output behavior.
I’m working on a project to recreate the CFB BRTI (Crypto Facilities Bitcoin Real-Time Index) by aggregating and analyzing live exchange order book data. The goal is to produce a highly accurate, continuously updating estimation of the benchmark and compare it directly to the official BRTI in real time.
A working Python code base already exists, including live data collection and an initial estimation model. I’m now looking for someone to improve the interpolation and estimation logic, especially around millisecond-scale price movements and order book aggregation. If a full rewrite would yield a cleaner or more accurate approach, that’s also an option.
Project Scope:
Improve interpolation and smoothing of the reconstructed index to better match the official CFB BRTI.
Refine order book aggregation depth and weighting across exchanges to more accurately reflect true market behavior.
Optimize timing and data flow for updates every ~50 milliseconds.
Build or refine real-time comparison and visualization of the reconstructed vs. official BRTI.
Analyze discrepancies between the two indices and document findings.
Requirements:
Strong skills in Python for data analysis and processing.
Experience with time-series analysis, data interpolation, or signal smoothing.
Comfort working with real-time or asynchronous data (WebSocket or similar).
Ability to reason about market data and make iterative improvements based on output behavior.