week_start date32 | q int32 | ret float64 | n int32 | char string | eval string | lag int32 | source string | calendar string |
|---|---|---|---|---|---|---|---|---|
2015-04-04 | 0 | -0.288642 | 11 | age | cap1m | 0 | cg | fri |
2015-04-11 | 0 | -0.035007 | 12 | age | cap1m | 0 | cg | fri |
2015-04-18 | 0 | -0.160307 | 12 | age | cap1m | 0 | cg | fri |
2015-04-25 | 0 | -0.091705 | 12 | age | cap1m | 0 | cg | fri |
2015-05-02 | 0 | -0.118622 | 12 | age | cap1m | 0 | cg | fri |
2015-05-09 | 0 | 0.177178 | 11 | age | cap1m | 0 | cg | fri |
2015-05-30 | 0 | -0.041516 | 13 | age | cap1m | 0 | cg | fri |
2015-06-06 | 0 | -0.014629 | 14 | age | cap1m | 0 | cg | fri |
2015-06-13 | 0 | -0.056369 | 14 | age | cap1m | 0 | cg | fri |
2015-08-01 | 0 | 0.029896 | 15 | age | cap1m | 0 | cg | fri |
2015-08-08 | 0 | -0.016693 | 16 | age | cap1m | 0 | cg | fri |
2015-08-15 | 0 | -0.046562 | 14 | age | cap1m | 0 | cg | fri |
2015-09-05 | 0 | -0.194738 | 13 | age | cap1m | 0 | cg | fri |
2015-09-12 | 0 | -0.10899 | 13 | age | cap1m | 0 | cg | fri |
2015-09-26 | 0 | -0.111917 | 13 | age | cap1m | 0 | cg | fri |
2015-10-03 | 0 | -0.088724 | 13 | age | cap1m | 0 | cg | fri |
2015-10-10 | 0 | -0.184718 | 13 | age | cap1m | 0 | cg | fri |
2015-10-17 | 0 | 0.01254 | 13 | age | cap1m | 0 | cg | fri |
2015-10-24 | 0 | 0.411562 | 14 | age | cap1m | 0 | cg | fri |
2015-10-31 | 0 | -0.192456 | 14 | age | cap1m | 0 | cg | fri |
2015-11-07 | 0 | 0.078107 | 14 | age | cap1m | 0 | cg | fri |
2015-11-14 | 0 | 0.107764 | 14 | age | cap1m | 0 | cg | fri |
2015-12-12 | 0 | -0.099029 | 14 | age | cap1m | 0 | cg | fri |
2016-01-02 | 0 | 0.059624 | 15 | age | cap1m | 0 | cg | fri |
2016-01-16 | 0 | 0.349819 | 13 | age | cap1m | 0 | cg | fri |
2016-01-23 | 0 | 0.408731 | 14 | age | cap1m | 0 | cg | fri |
2016-01-30 | 0 | 0.038265 | 17 | age | cap1m | 0 | cg | fri |
2016-02-06 | 0 | 0.983661 | 16 | age | cap1m | 0 | cg | fri |
2016-02-13 | 0 | -0.307815 | 17 | age | cap1m | 0 | cg | fri |
2016-02-20 | 0 | 0.458212 | 20 | age | cap1m | 0 | cg | fri |
2016-02-27 | 0 | 0.75365 | 22 | age | cap1m | 0 | cg | fri |
2016-03-05 | 0 | 0.198154 | 20 | age | cap1m | 0 | cg | fri |
2016-03-12 | 0 | -0.203313 | 22 | age | cap1m | 0 | cg | fri |
2016-03-19 | 0 | 0.026555 | 21 | age | cap1m | 0 | cg | fri |
2016-03-26 | 0 | 0.046387 | 23 | age | cap1m | 0 | cg | fri |
2016-04-02 | 0 | -0.117545 | 25 | age | cap1m | 0 | cg | fri |
2016-04-09 | 0 | -0.174664 | 25 | age | cap1m | 0 | cg | fri |
2016-04-16 | 0 | -0.063058 | 23 | age | cap1m | 0 | cg | fri |
2016-04-23 | 0 | -0.153053 | 23 | age | cap1m | 0 | cg | fri |
2016-04-30 | 0 | 0.023051 | 24 | age | cap1m | 0 | cg | fri |
2016-05-07 | 0 | 0.091644 | 24 | age | cap1m | 0 | cg | fri |
2016-05-14 | 0 | 0.386606 | 23 | age | cap1m | 0 | cg | fri |
2016-05-21 | 0 | -0.33922 | 22 | age | cap1m | 0 | cg | fri |
2016-05-28 | 0 | 0.090238 | 22 | age | cap1m | 0 | cg | fri |
2016-06-04 | 0 | -0.004863 | 23 | age | cap1m | 0 | cg | fri |
2016-06-11 | 0 | -0.594548 | 23 | age | cap1m | 0 | cg | fri |
2016-06-18 | 0 | 0.39512 | 24 | age | cap1m | 0 | cg | fri |
2016-06-25 | 0 | -0.115328 | 23 | age | cap1m | 0 | cg | fri |
2016-07-02 | 0 | 0.208363 | 25 | age | cap1m | 0 | cg | fri |
2016-07-09 | 0 | 0.641411 | 27 | age | cap1m | 0 | cg | fri |
2016-07-16 | 0 | -0.009099 | 26 | age | cap1m | 0 | cg | fri |
2016-07-23 | 0 | -0.21111 | 28 | age | cap1m | 0 | cg | fri |
2016-07-30 | 0 | 0.168642 | 28 | age | cap1m | 0 | cg | fri |
2016-08-06 | 0 | -0.197073 | 26 | age | cap1m | 0 | cg | fri |
2016-08-13 | 0 | -0.041537 | 29 | age | cap1m | 0 | cg | fri |
2016-08-20 | 0 | -0.229913 | 28 | age | cap1m | 0 | cg | fri |
2016-08-27 | 0 | -0.075535 | 26 | age | cap1m | 0 | cg | fri |
2016-09-03 | 0 | -0.053173 | 28 | age | cap1m | 0 | cg | fri |
2016-09-10 | 0 | -0.088861 | 31 | age | cap1m | 0 | cg | fri |
2016-09-17 | 0 | 0.053913 | 32 | age | cap1m | 0 | cg | fri |
2016-09-24 | 0 | -0.102217 | 33 | age | cap1m | 0 | cg | fri |
2016-10-01 | 0 | -0.045704 | 31 | age | cap1m | 0 | cg | fri |
2016-10-08 | 0 | -0.213018 | 31 | age | cap1m | 0 | cg | fri |
2016-10-15 | 0 | -0.030754 | 31 | age | cap1m | 0 | cg | fri |
2016-10-22 | 0 | -0.250018 | 32 | age | cap1m | 0 | cg | fri |
2016-10-29 | 0 | 0.134098 | 35 | age | cap1m | 0 | cg | fri |
2016-11-05 | 0 | -0.051639 | 32 | age | cap1m | 0 | cg | fri |
2016-11-12 | 0 | -0.130064 | 27 | age | cap1m | 0 | cg | fri |
2016-11-19 | 0 | -0.077436 | 29 | age | cap1m | 0 | cg | fri |
2016-11-26 | 0 | -0.080984 | 29 | age | cap1m | 0 | cg | fri |
2016-12-03 | 0 | -0.013511 | 26 | age | cap1m | 0 | cg | fri |
2016-12-10 | 0 | 0.010245 | 27 | age | cap1m | 0 | cg | fri |
2016-12-17 | 0 | 0.058443 | 29 | age | cap1m | 0 | cg | fri |
2016-12-24 | 0 | -0.005773 | 30 | age | cap1m | 0 | cg | fri |
2016-12-31 | 0 | 0.052533 | 31 | age | cap1m | 0 | cg | fri |
2017-01-07 | 0 | 0.061852 | 34 | age | cap1m | 0 | cg | fri |
2017-01-14 | 0 | 0.000266 | 30 | age | cap1m | 0 | cg | fri |
2017-01-21 | 0 | -0.022517 | 32 | age | cap1m | 0 | cg | fri |
2017-01-28 | 0 | -0.062314 | 33 | age | cap1m | 0 | cg | fri |
2017-02-04 | 0 | -0.114449 | 38 | age | cap1m | 0 | cg | fri |
2017-02-11 | 0 | -0.047229 | 39 | age | cap1m | 0 | cg | fri |
2017-02-18 | 0 | -0.17493 | 38 | age | cap1m | 0 | cg | fri |
2017-02-25 | 0 | -0.079113 | 34 | age | cap1m | 0 | cg | fri |
2017-03-04 | 0 | 0.106213 | 37 | age | cap1m | 0 | cg | fri |
2017-03-11 | 0 | 0.264888 | 36 | age | cap1m | 0 | cg | fri |
2017-03-18 | 0 | 0.221805 | 43 | age | cap1m | 0 | cg | fri |
2017-03-25 | 0 | 0.443024 | 44 | age | cap1m | 0 | cg | fri |
2017-04-08 | 0 | 0.141467 | 52 | age | cap1m | 0 | cg | fri |
2017-04-15 | 0 | 0.026493 | 57 | age | cap1m | 0 | cg | fri |
2017-04-22 | 0 | 0.228791 | 64 | age | cap1m | 0 | cg | fri |
2017-04-29 | 0 | 0.017857 | 65 | age | cap1m | 0 | cg | fri |
2017-05-06 | 0 | -0.273194 | 65 | age | cap1m | 0 | cg | fri |
2017-05-13 | 0 | 0.007278 | 70 | age | cap1m | 0 | cg | fri |
2017-05-20 | 0 | 0.215517 | 71 | age | cap1m | 0 | cg | fri |
2017-05-27 | 0 | 0.232722 | 79 | age | cap1m | 0 | cg | fri |
2017-06-03 | 0 | 0.196413 | 78 | age | cap1m | 0 | cg | fri |
2017-06-10 | 0 | 0.021365 | 80 | age | cap1m | 0 | cg | fri |
2017-06-17 | 0 | 0.060895 | 90 | age | cap1m | 0 | cg | fri |
2017-06-24 | 0 | -0.044308 | 94 | age | cap1m | 0 | cg | fri |
2017-07-01 | 0 | -0.063107 | 95 | age | cap1m | 0 | cg | fri |
Open Crypto Pricing
Weekly cryptocurrency risk factors — market (CMKT), size (CSIZE), momentum (CMOM) — from CoinGecko and CoinMarketCap, chosen as the FAIR benchmark for investable crypto portfolios, plus the full specification multiverse and evaluation behind that choice and a crosswalk of both providers' coin identifiers. Website and methodology: https://opencryptopricing.com
Standard series: 2013-12-30 to 2026-10-04 (weekly), updated every Monday.
The standard factor model
ISO weeks (Monday to Sunday, close Sunday 24:00 UTC); coins with previous-week average market cap >= $1M; stablecoins, wrapped/bridged/staked tokens and tokenised traditional assets excluded; value weights (previous-week average cap); terciles; breakpoints from coins >= $100M (all coins assigned); at least 5 coins per leg; no winsorisation.
- CMKT: value-weighted market return in excess of the 3-month T-bill (RF, 365-day accrual)
- CSIZE: small minus big (terciles of previous-week average cap)
- CMOM: winners minus losers (terciles of the return over the previous two weeks)
It gives passive investable portfolios (BTC, ETH, top-N, index replicas) the smallest alphas and prices investable characteristic portfolios best among 249,024 candidate designs; see the methodology page for the evaluation and why it differs from Liu, Tsyvinski & Wu (2022).
Files
factors/factors_{cg,cmc}_investable.{csv,parquet}: the standard (CoinGecko is canonical). Columns: week_start, week_end, CMKT, CSIZE, CMOM, RF, n_coins.factors/factors_{cg,cmc}_investable_lag1.*: with a 1-day implementation lag.factors/factors_{cg,cmc}_all.*: same design with all-coin breakpoints (comparison only).factors/daily/factors_{cg,cmc}_investable_daily.*andfactors/monthly/..._monthly.*: daily and monthly returns of exactly the standard's weekly portfolios (buy-and-hold within the week; daily returns compound to the weekly series; months are complete calendar months).factors/calendars/factors_{src}_{cal}[_lag1].*: the standard on 8 week calendars (sharp= Liu et al.: Jan 1-7 = week 1;mon..sun= 7-day weeks closing on that weekday).factors/liu/factors_{src}_liu_{all,investable}.*: Liu et al. (2022) exact construction.crosswalk/coin_crosswalk.{csv,parquet}: one row per coin: our iduid(never reused),cmc_id,cg_id(CoinGecko slug),cg_numeric_id,name,symbol;match_confidence= high / medium / low for matched pairs (each confirmed against both providers' prices), cg_only / cmc_only for coins listed by one provider only (pairs still under review are not published as matches);match_basis= evidence used (contract, links, name+symbol, slug, DefiLlama);defillama_check= agrees / disagrees / suggests_pair_we_lack; flagsis_stablecoin,is_derived_token,is_tokenized_tradfi. In the CSV files missing values are empty fields.- Research vintage 2026-10-07:
multiverse/: specification of all 249,024 worlds (Fieberg, Günther, Poddig & Zaremba 2024 decision tree x breakpoint universe x source x calendar, plus Liu-exact worlds), their weekly factors (factors/calendar=*/source=*/, CMKT NOT in excess of RF here), Sharpe ratios and non-standard errors.evaluation/: fairness (passive alphas), pricing (37 characteristic long-shorts), out-of- sample Sharpe per world x model; finalists, bootstrap and spanning tests.test_assets/: 18 passive investable indices and investable characteristic portfolios.
Cleaning (all series)
Positive price, market cap and volume; coin-days worth more than Bitcoin removed; daily reversal filter (>= 300% and net < 50% over two days); daily returns above 1,000% removed; weekly returns only from complete daily data (Fieberg et al. 2024, Sec. 2.3).
Legacy
Files under data/ are from the May 2026 pipeline and are no longer updated.
Citation
Stoeckl, S. and Pukrop, M. (2026). Open Crypto Pricing. University of Liechtenstein. https://opencryptopricing.com. Please also cite Liu, Tsyvinski and Wu (2022), JF 77(2), and Fieberg, Günther, Poddig and Zaremba (2024), IRFA 92.
Licence: CC BY 4.0 (our factor series, multiverse, evaluation and crosswalk). Underlying market data are from CoinGecko and CoinMarketCap and subject to their terms.
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