CM Reference Rates Historical Prices and Methodology Updates

Overview

This post contains the following items:

  1. Launch of Historical Prices for CM Reference Rates
  2. Release of CM Reference Rates Methodology v1.1
  3. Insights and Background on the changes included in the new methodology
  4. Kraken duplicate trades issue
  5. Publication of the CM Reference Rate Methodology online
  6. Roadmap

Historical Prices for CM Reference Rates

  • As promised, Coin Metrics has launched historical reference rates for all 102 assets covered in CM Reference Rates
  • Historical reference rates can be found in the /referencerates endpoint of our API

Release of CM Reference Rates Methodology v1.1

Background

  • Coin Metrics launched the CM Reference Rates on May 15, 2019 using Methodology v1.0
  • The expectation was that Coin Metrics would gain insights into the functioning of the market upon launch and adjust our methodology and quality control processes accordingly
    • Insights were primarily gained from the Quality Control Report, created by Coin Metrics as part of the CM Reference Rates Quality Control Framework
    • The CM Reference Rates Quality Control Framework acts as the governing structure for all machine and human review and expert judgement
    • A sample of the Quality Control Report is included at the end of this post
    • Please reach out to Coin Metrics ([email protected]) for more information on the CM Reference Rates Quality Control Framework
  • Consistent with that expectation, Coin Metrics has made the following modifications associated with the release of the CM Reference Rates Methodology v1.1

Insights and Background on the Changes

Data Contingency Rule Changes

The following changes were made to section 4.5 Data Contingency Rules of the Methodology:

  • Update to the second Data Contingency Rule: instead of using the price of the last observable transaction of the selected market before the start of the first time interval, use the next available time interval’s volume-weighted median price.
    • This update was implemented to give greater weight to transactions that occur closer to the Fixing Time and address low-volume markets in which the last observable transaction before the start of the first time interval can be stale.
  • Update to the third Data Contingency Rule: instead of applying to any 1-minute time interval, only apply to any of the non-first or non-last 1-minute time intervals.
    • The desired outcome was to select the 1-minute time interval closest to the Fixing Time. Thus, the first and last 1-minute time intervals will have dedicated logic (second and fourth Data Contingency Rules) and all other 1-minute time intervals would follow this rule.  
    • As part of the CM Reference Rates Quality Control Framework, Coin Metrics generates a Test Reference Rate for each of the three Fixing Times before the official Fixing Time
      • Thus, there is a Test Reference Rate generated for 23:00 UTC, 22:00 UTC and 21:00 UTC
    • Coin Metrics felt that leveraging one of these rates, subject to the oversight and review requirements, would better represent the Reference Rate given the relative proximity to the Fixing Time as compared to the most recent published reference rate.
  • Update to the fourth Data Contingency Rule: instead of using the last published rate, use the last hourly reference rate in which there were trades during that hour’s Observation Window.
    • As part of the CM Reference Rates Quality Control Framework, Coin Metrics generates a Test Reference Rate for each of the three Fixing Times before the official Fixing Time
      • Thus, there is a Test Reference Rate generated for 23:00 UTC, 22:00 UTC and 21:00 UTC
    • Coin Metrics felt that leveraging one of these rates, subject to the oversight and review requirements, would better represent the Reference Rate given the relative proximity to the Fixing Time as compared to the most recent published reference rate.

Data Source Changes

  1. The Quality Control Report noted instances of low liquidity and identified assets that are in danger of having insufficient trades in the 60-minute calculation window.
  2. Low trades were observed for several reasons:
    1. Trading activity during weekends is lower than on weekdays which affects a small number of infrequently-traded assets
    2. Some stablecoins such as Dai and Gemini Dollar are susceptible to low trades because the CM Reference Rate Methodology selects markets quoted in USD, Bitcoin, or Ether. Special consideration is given to stablecoins where selected markets include markets where the stablecoin itself can be the quote currency and the base currency can be USD, Bitcoin, or Ether to match market convention. The methodology does not select stablecoin:stablecoin markets.
  3. To address  the low liquidity, several changes to the market whitelist were made — expanding the whitelist for some assets and contracting it for others. The net impact of these changes eliminated the issue of low trades in the calculation window for several assets.
    1. In the case of additions, ratings produced by the Market Selection Framework were used to select the next best markets to add.
    2. In the case of removals, expert judgment was applied to remove extremely illiquid markets with prices that differed materially from the other markets.
  4. The changes to the whitelist were
    1. WAX (WAX)
      1. added “upbit-wax-eth-spot”, “bitfinex-wax-eth-spot”, “huobi-wax-btc-spot”, “huobi-wax-eth-spot”
    2. Odyssey (OCN)
      1. added “gate.io-ocn-eth-spot”, “hitbtc-ocn-eth-spot”, “gate.io-ocn-btc-spot”, “huobi-ocn-eth-spot”, “huobi-ocn-btc-spot”
    3. FunFair (FUN)
      1. added “hitbtc-fun-btc-spot”, “hitbtc-fun-eth-spot”
    4. Maker (MKR)
      1. added “hitbtc-mkr-eth-spot”, “ethfinex-mkr-btc-spot”, “ethfinex-mkr-eth-spot”, “ethfinex-mkr-usd-spot”, “bibox-mkr-btc-spot”, “bibox-mkr-eth-spot”
    5. QASH (QASH)
      1. added “cex.io-qash-btc-spot”, “gate.io-qash-eth-spot”, “liquid-qash-eth-spot”, “gate.io-qash-btc-spot”, “huobi-qash-btc-spot”, “huobi-qash-eth-spot”
    6. TenX (PAY)
      1. added “huobi-pay-eth-spot”, “huobi-pay-btc-spot”
    7. Gemini Dollar (GUSD)
      1. added “bibox-btc-gusd-spot”, “bibox-eth-gusd-spot”
    8. Bitcoin Diamond (BCD)
      1. added “hitbtc-bcd-btc-spot”
      2. removed “huobi-bcd-btc-spot”
    9. Bytom (BTM)
      1. added “huobi-btm-btc-spot”
      2. removed “hitbtc-btm-btc-spot”, “hitbtc-btm-eth-spot”

Quality Control Report Sample

The below sample is for illustrative purposes only

Kraken Duplicate Trades

Issue Background

  • Coin Metrics collects trades from Kraken exchange via both WebSocket and HTTP
  • Coin Metrics noticed that Kraken trades received via WebSocket often had a different timestamp from the same trades received from HTTP
  • That, in turn, resulted in some trades received from Kraken being duplicated in the Observation Window

Impact and Resolution

  • Coin Metrics noticed and resolved this issue within 48 hours
  • Coin Metric’s robust Reference Rate methodology meant that the net impact of this issue was minimal
    • Most assets were largely unaffected. For Bitcoin, resolving this issue resulted in a median absolute difference of 0.001 percent and the largest single-day difference is 0.005 percent. For Ethereum, the median absolute difference is 0.001 percent and the largest single-day difference is 0.001 percent.
    • The largest median absolute difference was observed in Project Pai at 1 percent. The largest single-day difference for this asset is 2.5 percent.
  • However, out of an abundance of caution and transparency, Coin Metrics has recalculated and republished all Reference Rates for the impacted time period (May 15th to May 26th)

Publication of the CM Reference Rates Methodology online

  • One of Coin Metrics’ core values is to be open:
    • We believe open data will empower the public to better understand, value, use, and ultimately steward public crypto networks
  • Consistent with that principle, we have added the CM Reference Rate Methodology to our website: https://coinmetrics.io/reference-rates/
  • Check it out and let us know what you think

Roadmap

  • Coin Metrics expects to make consistent updates to the CM Reference Rates product
  • In the short term, those updates may include
    • Inclusion of more assets
    • More publication times (New York, London, Tokyo close)
  • We will keep you updated on our progress

If there is something you would like us to add to our roadmap, let us know at [email protected]

Introducing Coin Metrics’ State of the Network: Issue 1

Introducing CM’s Weekly Newsletter

Dear crypto data enthusiasts,

We’re excited to launch “State of the Network”—CM’s weekly newsletter, bringing you an unbiased, focused view of the crypto market informed by our own network (on-chain) and aggregate market data. You can expect unique insight and recurring weekly data to help you stay informed.

The format and depth of data covered will be improving over the coming weeks so stay tuned! And of course, if you have any feedback or requests, don’t hesitate to let us know at [email protected].

Research Highlight: Ripple

Last week we published a report highlighting discrepancies in Ripple’s escrow reporting. Find the full report in the Coin Metrics blog. A quick summary below:

As part of Coin Metrics standard due diligence process when adding new assets/nodes to our Pro data services, we noticed an abnormality with Ripple’s escrow system reporting of XRP.

Key findings from our research include the following:

  • Two quarterly market reports under-reported the number of XRP released from escrow by a total of 200 million XRP ($84 million at current prices)
  • The “escrow queue” is implemented differently than announced, leading to a faster future release of escrowed funds compared to the announced schedule
  • Other party/parties, potentially associated with Ripple, have released 55 million XRP from an unknown escrow address not connected to the main Ripple escrow account”

Discrepancies such as this only become apparent through auditing and understanding on chain data.

Weekly Deep Dive

A visualization of trades on May 17, 2019

Abstract

On May 17, 2019, the crypto market experienced a sharp decline without any news-related catalyst. Bitcoin’s price decreased from $7,700 to $6,600 in the span of one hour. Similar to the market movement that occurred on April 2, 2019, Coin Metrics believes this movement was engineered to trigger a long squeeze through forced liquidations of long bitcoin futures positions, margin calls on long margin positions, and stop losses on spot markets.

Observations

Market conditions were ripe for a long squeeze:

  1. Bitfinex’s bitcoin long-to-short ratio was at 1.4, indicating more margin long positions than short—the high-end of its recent historical range.
  2. Bitmex’s funding rate on its bitcoin perpetual futures contract on the day prior was 0.07% indicating that Bitmex prices were trading at a premium to spot—again at the high-end of its recent historical range.
  3. Finally, the move happened at 03:00 UTC, typically the point of lowest liquidity during a typical day (see Timing section below)

Focused selling was observed on Bitstamp’s BTC-US Dollar market resulting in a sharp divergence in Bitstamp’s price relative to other major exchanges. Bitstamp was likely targeted because, at the time, it was one of two constituent markets used in the calculation of Bitmex’s bitcoin index. Five days after this movement, Bitmex subsequently added Kraken as a third constituent market, likely in response to this engineered market movement.

Timing

The large price movements on April 2, 2019 and May 17, 2019 both occurred between 03:00 UTC and 05:00 UTC, the time of day where global liquidity is typically at its lowest. This time period coincides with the time when most of the global population is asleep and not at work. The timing of the market movement on May 17, 2019 and the absence of any news-driven catalyst indicate that this market movement was engineered to trigger a squeeze.

Trades Across Exchanges

Some selling was first observed on Bitfinex at approximately 03:00 UTC followed shortly by sustained heavy selling on Bitstamp. The selling on Bitstamp was executed in a way to maximize price impact and a $300 discount was observed on Bitstamp compared to other major exchanges. As the bid side of the order book was wiped completely clean on Bitstamp, a short and volatile period of price discovery followed. Large buying on Binance appears to have stemmed the decline. During this time of market stress, tether-quoted markets temporarily sold for a premium to dollar-quoted markets and none of the bitcoin-tether markets dipped below $7,000. (Tip: zoom in on your browser for greater resolution.)

Here is a closer look at Bitstamp.

Conclusion

In the long-term, crypto prices are driven by market cycles and fundamentals. However, in the short-term, engineered price movements designed to trigger squeezes have been observed from time-to-time. This might be driven by a few factors:

  1. The ability to easily access leverage and the outsized impact of futures markets (particularly Bitmex) incentivize traders to engineer price movements.
  2. The fragmented, 24/7 nature of crypto markets results in an easier ability to have market impact when buying or selling large positions.

This recent market movement is perhaps the clearest indication that these price movements are engineered due to the timing and focused nature of the selling and because the selling was observed on Bitstamp, one of two constituent exchanges for Bitmex’s bitcoin index. Bitmex subsequently added Kraken as a third constituent exchange.

Network Data Insights

Summary Metrics

Top 5

* Ignores Ripple which is not a PoW chain

Network Highlights

  • Someone made an expensive mistake sending litecoin, paying a 200 LTC transaction fee. For comparison, that’s 13x the usual fees paid over a full day.

Source: Coin Metrics, Network Data Pro

  • BCH experienced an intentional reorganization to recover funds accidentally sent to SegWit addresses. This appears to have resulted in a deliberate and coordinated 2-block reorganization. This adds to the debate surrounding intentional reorganizations on Bitcoin that was sparked following a Binance hack which led Binance’s CEO to consider, but ultimately decide against, an intentional reorganization to recover the lost funds.

Illustration of the Bitcoin Cash network splits on 15 May 2019

Source: The Bitcoin Cash Hardfork – Three Interrelated Incidents, BitMex

Market Data Insights

The market has experienced a sharp and broad-based recovery over the past month. Most major assets are up at least 50% and high correlation in returns between assets are observed in the short-term. Bitcoin Cash SV is the biggest mover in the subset of major assets below with a one-month return of 122%.

The short-term moves mask a broader and long-term phenomenon of large dispersion in returns among assets. As an illustration, Binance Coin has returned 164% over the past year while ZCash has lost 66% of its value. Only a handful of assets have a positive return over the past year: Bitcoin, Litecoin, and Binance Coin.

Examining the rolling drawdown paints a similar picture. Certain assets, particularly ZCash, Ripple, Dash, Bitcoin Cash, and Stellar are still far from reaching their all-time highs.

Several interesting narratives can be drawn from the above data:

  • Litecoin has performed well during this market cycle and is one of the few major assets with a positive yearly return. A factor in its strong performance may be market participants pricing in its second block reward halving in August 2019.
  • ZCash is the worst performer in this sample. It also happens to have the highest issuance rates with an annualized inflation in excess of 50% over the past year, likely leading to large selling pressure by miners. By contrast, Bitcoin’s annualized inflation is less than 4%.

Subscribe and Past Issues

If you’d like to get State of the Network in your inbox, please subscribe here. You can see previous issues of State of the Network here.

Check out the Coin Metrics Blog for more in depth research and analysis.

An on-chain analysis of Ripple’s escrow system

‘Coin Metrics recently released version 3.0 of our CM Network Data Pro product. This release includes many new assets, including XRP. When Coin Metrics adds support for any new asset, we do a deep dive into its digital ledger to gain a full comprehension of the ledger and identify any unique behaviors that may affect the creation of our data. For XRP, that meant evaluating its supply schedule and escrow system.

Read more

Ripple, Tezos and stablecoins coming to Pro plus launch of CM Reference Rates

Highlights

  • Launch of CM Network Data Pro v3.0
  • Launch of CM Reference Rates v1.0
  • Announcing our CM Pro Analytics Suite
  • Community upgrades

Email us at [email protected] to contact sales or learn more about any of our new products.

CM Network Data Pro v3.0

Version 3.0 of our institutional network data feed — CM Network Data Pro — is here! Launching May 3rd, v3.0 adds the following assets to a list that already includes BCH, BTC, ETH, LTC, XMR, and ZEC for up to 66 metrics:

  • BNB (pre-mainnet)
  • BSV
  • DAI
  • DASH
  • ETC
  • GRIN
  • MKR (May 6)
  • PAX
  • TUSD
  • USDC
  • XLM (May 6)
  • XRP
  • XTZ (May 6)

Each month, Coin Metrics will launch a new version of Pro that includes more assets, metrics, or features. Version 4.0 (June 3) adds ADA, BNB (mainnet) and USDT and includes new metrics such as mining and exchange net flows. Version 5.0 (July 3) includes real-time(block-by-block) streaming data!

CM Reference Rates v1.0

Later in the month, on May 15th, we’re launching version 1.0 of our CM Reference Rates product. Some feature highlights:

  • CM Reference Rates represent a fixed USD price of one unit of a crypto asset calculated by aggregating trades across approved and vetted trading venues
  • The Rates are produced for 100 of the top crypto assets at midnight UTC, with other daily closes (New York, London, Tokyo) to follow
  • The Rates are used to give an unbiased and auditable USD price for calculating net asset values (NAV) for portfolio valuation and accounting purposes or backtesting trading strategies or other research
  • The Rate methodology adheres to international best practices such as the IOSCO Principles for Financial Benchmarks and includes a quantitative market selection score, a robust time-weighted and volume-weighted median calculation, and automated and human quality control checks
  • The Rates are available through responsive REST API in JSON and CSV format or via CSV file

Analytics and Visualization Dashboard

For clients who prefer to visualize, dashboard, or otherwise create reports of our data, we’re pleased to announce our CM Pro Analytics Suite (sneak peak below). This will go live in June as an option for our network data clients. Some feature highlights:

  • Use powerful time series graphing tools to explore new trends
  • Use pre-built thematic and other dashboards
  • Build your own dashboards or reports
  • Set alerts on the data
  • Integrate our data with your own data sources
  • Mix real-time and end-of-day data in the same dashboard
  • Visualize on your desktop machine or mobile device
  • And more

Community Upgrades

Finally, we’re pleased to announce our Community upgrade project!

Most will happen under the scenes as we transition our legacy Community infrastructure to our new Pro infrastructure. This means institutional-grade reliability and uptime for all our Community data, the release of our new ontology and naming scheme, as well as clear definitions for all metrics. We’ll have more Community upgrades in the months to follow as well so stay tuned.

As far as timing, the transition will happen within the next 3 months. Importantly, we’ll be deprecating our Community API and moving all Community data to our new API. This means that we’ll no longer be supporting the existing API and would urge existing users to transition to the upgraded API.

We’ll have more details as we get closer to the transition so stay tuned!

Email us at [email protected] to contact sales or learn more about any of our new products.