Open (Investable) Crypto (Asset) Pricing

The fair benchmark for investable crypto portfolios — and an honest map of how much specification choices matter

Data: 30 Dec 2013 to 4 Oct 2026 (666 weeks) · Last updated: 7 Oct 2026 · next update Monday 12 Oct 2026

With the institutionalisation of crypto markets, how to price crypto assets has become a central question. Liu et al. (2022) introduced one of the first factor models for the cross-section of crypto returns. Their model includes a market (CMKT), size (CSMB) and momentum (CMOM) factor. Open Crypto Pricing provides these factors free and open source, constructed to serve as a fair benchmark: one where passive investable portfolios earn zero alpha.

We chose the standard from 249,024 candidate factor sets based on one critical choice: where to compute size and momentum breakpoints. Using all coins creates breakpoints among untradeable microcaps. This misprices the investable cross-section: against the factors of Liu et al. (2022), Bitcoin earns an alpha of about 13% a year. Restricting the breakpoints to coins worth at least $100M fixes both problems: the size premium vanishes, and momentum remains, next to the market, the factor that prices investable coins.

The Standard Download data Methodology

What you get

  • The standard factors: CMKT, CSIZE and CMOM from CoinGecko and CoinMarketCap, updated every Monday after automated validation, with the T-bill rate in the same file.
  • Variants: a 1-day implementation lag, all eight week calendars, all-coin breakpoints, and the exact Liu et al. (2022) construction for comparison.
  • The evidence: all 249,024 candidate factor sets, their non-standard errors (Fieberg et al., 2024) and the full evaluation of fairness and pricing.
  • A coin crosswalk: one id per coin linking CoinMarketCap and CoinGecko, confirmed by prices.

Use the factors in R

The factors can be downloaded as CSV or Parquet files from the Download Data page.

The following example shows how to read the CoinGecko investable factors using R:

# CoinGecko & CoinMarketCap data via the crypto2 package
# install.packages("crypto2")
library(crypto2)

# Or pull the ready-made factor series directly:
url <- "https://opencryptopricing.com/data/factors_cg_investable.csv"
factors <- read.csv(url)   # week_start, week_end, CMKT, CSIZE, CMOM, RF, n_coins

Data sources

Cryptocurrency data is retrieved from both CoinMarketCap and CoinGecko using the crypto2 R package (CRAN) by Sebastian Stoeckl. This package provides the survivorship-bias-free (delisted/inactive coins retained) cryptocurrency cross-section for both data sources (CoinMarketCap and CoinGecko). We treat the choice of data source as one of the specification axes (see Data Sources).

Citation

If you use these data, please cite Stoeckl & Pukrop (2026) and Liu et al. (2022).

@article{liu2022common,
  author  = {Liu, Yukun and Tsyvinski, Aleh and Wu, Xi},
  title   = {Common Risk Factors in Cryptocurrency},
  journal = {The Journal of Finance},
  volume  = {77},
  number  = {2},
  pages   = {1133--1177},
  year    = {2022},
  doi     = {10.1111/jofi.13119}
}


@misc{stoeckl2026opencrypto,
  author      = {Stoeckl, Sebastian and Pukrop, Moritz},
  title       = {Open Crypto Pricing},
  year        = {2026},
  institution = {University of Liechtenstein},
  url         = {https://huggingface.co/datasets/sstoeckl/opencryptoassetpricing}
}

References

Fieberg, C., Günther, S., Poddig, T., & Zaremba, A. (2024). Non-standard errors in the cryptocurrency world. International Review of Financial Analysis, 92, 103106.
Liu, Y., Tsyvinski, A., & Wu, X. (2022). Common risk factors in cryptocurrency. The Journal of Finance, 77(2), 1133–1177. https://doi.org/10.1111/jofi.13119
Stoeckl, S., & Pukrop, M. (2026). Open crypto pricing. University of Liechtenstein. https://huggingface.co/datasets/sstoeckl/opencryptoassetpricing