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mykola-quant/funding-rate-carry-falsification

Analysis updated 2026-05-18

1PythonAudience · researcherComplexity · 3/5LicenseSetup · moderate

TLDR

A research project that pre-registers and then tests a crypto funding-rate trading strategy, finding it loses money after costs.

Mindmap

mindmap
  root((funding carry study))
    What it does
      Pre-registered test
      Funding rate strategy
      Cost analysis
    Tech stack
      Python
      ccxt
      Binance data
    Use cases
      Trading strategy research
      Backtesting methodology
    Audience
      Researchers
      Quant traders

Code map

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filefunction / class

What do people build with it?

USE CASE 1

Study a rigorous example of pre-registered falsification testing applied to a trading strategy.

USE CASE 2

Reuse the funding rate and price data collection scripts for your own crypto research.

USE CASE 3

Learn why a delta-neutral funding carry trade can fail even when raw funding income looks profitable.

What is it built with?

PythonccxtPandas

How does it compare?

mykola-quant/funding-rate-carry-falsification0xustaz/streamgatea-bissell/unleash-lite
Stars111
LanguagePythonPythonPython
Setup difficultymoderatehardhard
Complexity3/54/54/5
Audienceresearcherdeveloperresearcher

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · moderate Time to first run · 30min

Requires running Python scripts against collected Binance data, not a live trading tool.

MIT license: use, modify, and distribute freely, including commercially, as long as you keep the copyright notice.

In plain English

This project is a research study that tests a popular crypto trading idea and shows that it does not actually work. The idea being tested is a strategy called delta-neutral funding rate carry, where a trader holds offsetting long and short positions in a cryptocurrency's spot and futures markets, hoping to collect a profit from the funding rate payments that futures traders pay each other, while staying protected from price swings. Before collecting any data, the author locked in the exact rules of the test in a document called the pre-registration, including the trading signal, the assets to test (Bitcoin, Ethereum, and Solana), the cost assumptions, and the date that separates the data used to build the strategy from the data used to check it honestly. This is done specifically to prevent tweaking the rules after seeing the results, which is a common way research like this becomes misleading. The result is that the strategy loses money in every single combination of asset and variant tested, once realistic trading costs are included. For Bitcoin and Ethereum, the problem is that trading costs are simply too high relative to the funding income available, no version of the timing rule gets close to breaking even. For Solana, the story is different and more useful: the funding income earned was actually higher than the costs, but the strategy still lost money because the long and short legs of the trade did not track each other perfectly, a problem the author calls basis risk. The project also finds that the funding income opportunity shrank over time, consistent with more sophisticated trading firms competing that opportunity away. The repository includes the locked pre-registration document, Python scripts for collecting the funding rate and price data from Binance, running the core analysis, and breaking down exactly why the strategy failed component by component, plus the resulting data files. It is one entry in a public series of similar studies by the same author testing other retail trading signals. The code is released under the MIT license.

Copy-paste prompts

Prompt 1
Explain the pre-registration methodology used in this funding-rate-carry-falsification study.
Prompt 2
Help me understand why the SOL result differs from BTC and ETH in this analysis.
Prompt 3
Walk me through running scripts/collect_funding_history.py to gather my own funding rate data.
Prompt 4
Show me how the component breakdown in diagnose_components.py separates funding PnL from basis drift.

Frequently asked questions

What is funding-rate-carry-falsification?

A research project that pre-registers and then tests a crypto funding-rate trading strategy, finding it loses money after costs.

What language is funding-rate-carry-falsification written in?

Mainly Python. The stack also includes Python, ccxt, Pandas.

What license does funding-rate-carry-falsification use?

MIT license: use, modify, and distribute freely, including commercially, as long as you keep the copyright notice.

How hard is funding-rate-carry-falsification to set up?

Setup difficulty is rated moderate, with roughly 30min to a first successful run.

Who is funding-rate-carry-falsification for?

Mainly researcher.

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