XRP Archives - Coinscious

XTZ Performs Best (+73.77%); ADA Offers More Than Its Peers

By | Coinscious Lab, Data Analytics, Market Report | No Comments
30d % Gain

Tezos (XTZ) 

Biggest 30d %
Gain (Sector)

Digital Content

30d % Loss

Pundi X (NPXS) 

Smallest 30d %
Loss (Sector)



Released bi-weekly, this report aims to identify broad trends in the cryptocurrency market. In order to reflect the latest developments in this fast-paced and volatile market, the reports plan to focus on metrics derived from a 30-day rolling window of data, this time from February 28, 2019 to March 28, 2019.

Our universe of analysis includes 50 of some of the most widely used and traded cryptocurrencies, and groups them into sectors that reflect similar utility and valuation models. Through analysis of the recent historical performance of individual cryptocurrencies as well as their sectors, we provide a framework for analysis where investors can identify outperforming cryptocurrencies or sectors by comparing their performance relative to peers.

Constituent Coins/Tokens
Privacycoins XMR, DASH, ZEC, XVG, BCN
Resources SC, GNT
Payments and Settlements XRP, XLM, OMG, NPXS, MKR, PPT
Decentralized Exchanges BTS, ZRX, WAVES
Digital Content TRX, ONT, BAT, STEEM
Data and Information IOTA, VET, LINK, REP
Stablecoin USDT, TUSD, DAI


The performance of major cryptocurrencies over the past month has been good, with 45 out of the 50  cryptocurrencies that we examined up from their values 30 days ago. Bitcoin (BTC), the largest cryptocurrency by market cap, is trading around $4100, still in the sideways trend that began in November last year. However, it is also up 5.00% compared to 30 days ago.

Outside of cryptocurrencies, the S&P 500 has been relatively flat, only up 1.11% from 30 days ago and closing yesterday at $2815.44.

Figure 1 presents the risk versus return trade-off over the past 30 days by plotting mean daily return versus historical daily volatility for various cryptocurrencies.   

Figure 1. Plot of mean daily return against historical daily volatility for individual cryptocurrencies from February 28, 2019 to March 28, 2019. Higher returns at a given level of risk, measured through historical daily volatility, indicates a better investment.

The best performer overall over the past month was Tezos (XTZ), with a total return of 73.77%. Tezos is a self-amending proof-of-work dApp platform that removes the need to hard fork when implementing protocol amendments.

Stakeholders vote for their preferred proposed protocol amendments through a formal and systematic process that has four discrete periods: the Proposal Period, the Exploration or “Testing” Vote Period, the Testing Period, and the Promotion Vote Period. The Tezos community successfully concluded the first round of voting, the Proposal Period, on March 20 and are currently in the Exploration period, casting votes to decide whether the winning proposal will move on to be deployed to the test network.

This news is significant because it is the first time that the self-amending upgrade process has been put into action. According to ​Tezos​, removing the need to hard fork in order to make protocol amendments is an important because “the suggestion or expectation of a fork can divide the community, alter stakeholder incentives, and disrupt the network effects that are formed over time. Because of self-amendment, coordination and execution costs for protocol upgrades are reduced and future innovations can be seamlessly implemented.” ​Tezos’ price went up in the days leading up to the end of the first voting period, so it is possible that growing enthusiasm and positive news about the protocol upgrade was the underlying cause. The success of the first vote also likely caused the subsequent 31% jump on March 20.

The second and third best performing cryptocurrencies were Cardano (ADA) and Basic Attention Token (BAT) with total returns of 55.14% and 39.56% respectively. Cardano is noteworthy in that it offered higher returns than its peers with similar levels of risk, including several other dApp platforms. 

Pundi X (NPXS) was the worst performing cryptocurrency, with total losses of 16.06%.

Figure 2a. Cryptocurrencies with the highest total returns from February 28, 2019 to March 28, 2019.

Figure 2b. Cryptocurrencies with the lowest total returns from February 28, 2019 to March 28, 2019.

Figure 3 shows various performance measures of the nine sectors as well as that of the S&P 500 for comparison and Figure 4 plots the performance over time of each sector. Performance between the sectors was all positive, except for stablecoins with a very small negative return. Total returns ranged from 0.00% (stablecoins) to 23.22% (digital content).

Figure 3. Mean daily returns, historical daily volatility, total returns, maximum drawdown, and ex-post Sharpe ratio for each sector from February 28, 2019 to March 28, 2019. Smaller maximum drawdowns and more positive Sharpe ratios are more desirable. The Sharpe ratio is calculated with the 10 year US Treasury bill rate as the annual risk-free rate.

Figure 4a. Price performance over time of sectors that had positive total returns between February 28, 2019 to March 28, 2019.

Figure 4b. Price performance over time of sectors that had negative total return between February 28, 2019 to March 28, 2019.

Figure 5 shows the correlation between the daily returns of each sector. The S&P 500 had little correlation with most cryptocurrency sectors except for stablecoins, which it had a 0.31 correlation with. Resources, consisting of Siacoin (SC) and Golem (GNT), was the least correlated with the others. Correlations between sectors were more varied and less highly positively correlated than observed in previous months.

Figure 5. Correlation between daily returns of each sector from February 28, 2019 to March 28, 2019. Correlation ranges between -1 and 1. Correlation close to 1 indicates a more positive relationship between the pair of cryptocurrency returns and correlation close to -1 indicates a more negative linear relationship. Correlation close to 0 indicates no linear relationship.

APPENDIX A: Methodology

The daily price data of cryptocurrencies in USD at 4:00 PM EST from February 28, 2019 to March 28, 2019 was used for our calculations.

The prices are the volume weighted average price of the cryptocurrency in USD at 4:00 PM EST each day across all exchanges where Coinscious has data.

To analyze performance by sector, the prices of constituent cryptocurrencies was normalized by dividing by the price on February 28, 2019, then averaged. When calculating the daily returns using this averaged normalized price, it is equivalent to if each sector was represented as an equally weighted portfolio of its constituent cryptocurrencies formed starting February 28, 2019 and the daily returns of the portfolio were calculated. Returns used throughout this report refer to simple returns.

Daily closing price data of the S&P 500 index from Yahoo Finance was also used as a proxy to represent the US equity market. The latest 10 year US Treasury bill rate from YCharts was used for calculations involving a risk-free rate.

In subsequent reports, we may update our universe, sectors, methodology, and analysis to reflect new developments.

APPENDIX B: Terminology

  • Volatility: A measure of the dispersion in the trading price of an instrument over a certain period of time, defined as the standard deviation of an instrument’s returns.
  • Drawdown: A measure of the decline of the trading price of an instrument or investment since the previous peak during a certain period of time. Less negative, less frequent, and shorter drawdowns are more desirable.
  • Maximum drawdown: The maximum peak to trough decline of the trading price of an instrument or investment over a certain period of time. Less negative maximum drawdowns are more desirable.
  • Sharpe ratio: A risk adjusted measure of return that describes the reward per unit of risk. The reward is the average excess returns of an investment against a benchmark or risk-free rate of return, and the risk is the standard deviation of the excess returns. A higher Sharpe ratio is better. Ex-ante Sharpe ratio is calculated with expected returns whereas ex-post Sharpe ratio is calculated with realized historical returns.
  • Correlation: A measure of the linear relationship between two series of random variables, which in the context of finance, can be two series of returns. Correlation ranges between -1 and 1. Correlation close to 1 indicates a more positive relationship between the pair of cryptocurrency returns and correlation close to -1 indicates a more negative linear relationship. Correlation close to 0 indicates no linear relationship.


The information contained herein is for informational purposes only and is not intended as a research report or investment advice. It should not be construed as Coinscious recommending investment in cryptocurrencies or other products or services, or as a solicitation to buy or sell any security or engage in a particular investment strategy. Investment in the crypto market entails substantial risk. Before acting on any information, you should consider whether it is suitable for your particular circumstances and consult all available material, and, if necessary, seek professional advice.

Coinscious and its partners, directors, shareholders and employees may have a position in entities referred to herein or may make purchases and/or sales from time to time, or they may act, or may have acted in the past, as an advisor to certain companies mentioned herein and may receive, or may have received, a remuneration for their services from those companies.

Neither Coinscious or its partners, directors, shareholders or employees shall be liable for any damage, expense or other loss that you may incur out of reliance on any information contained in this report.

Cryptocurrency Triumphs & Troubles

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? U.S. Authorities Charge Leaders of ‘OneCoin’


United States prosecutors announced criminal charges against leaders behind OneCoin, a multibillion-dollar cryptocurrency pyramid scheme. Konstantin Ignatov, Ruja Ignatova, and Mark Scott were charged by the Southern District of New York for wire fraud, securities fraud and money laundering. An estimated $1.2 billion of investor money was laundered to at least 21 countries


⏸  Fidelity May Delay Support for Ethereum


Fidelity Digital Assets (FDAS) advises support for ethereum (ETH) may be delayed due to the latest hard fork upgrades. FDAS quietly launched this quarter and is a new company created by investing giant, Fidelity Investments. FDAS president, Tom Jessop, says they want ether and other cryptocurrencies to be available but the process it’s easy. Bitcoin (BTC) is currently the only digital asset supported on the platform.


?? South Korea Establishes Task Force To Fight Crypto Fraud


South Korean’s Supreme Prosecutors’ Office(SPO) has created a new task force to protect consumers from fraud, crimes, money laundering, and other suspicious activities related to digital currencies. Cryptocurrency related crimes in the country have risen from 53 in 2016 to 4,500 in 2018. 


⚙️ OKEx Lists Tron On C2C Market


Cryptocurrency exchange, OKEx, adds Tron (TRX) to its customer-to-customer (C2C) trading platform. The decentralized C2C platform allows users to trade assets directly with one another. In addition, users can place orders with self-selected exchange rates and pay using cryptocurrency or support fiat currencies.


? eToro Launches Trading Platform & Wallet in the U.S.


eToro, the social investing and trading platform, officially announces the launch of  its fiat-to-crypto platform in the United States. The platform is already available in 140 countries worldwide and will now operate in 30 states and 2 territories in the US. Users will be able to use popular like CopyTrader to follow trading habits of individuals with a proven track record. eToro’s multi-signature wallet provide multi-coin support such as BTC, ETH, LTC, BCH, XRP and XLM


? SIX Teams Up With R3 Corda For New Digital Asset Exchange


The Swiss stock exchange, SIX, selects R3 Corda to provide its ucoming blockchain-based platform. It will use R3’s Corda Enterprise to power the Six Digital Exchange (SDX) platform to allow the issuing and trading digital assets, as well as tokenization of securities and non-bankable assets. 


? Coinbase Clarifies It Never Shared “Personally Identifiable” Customer Data


Coinbase is still in the hot seat since its acquisition of blockchain analytics firm, Neutrino. Tensions raised last Friday when Christine Sandler stated that the exchange’s previous analytics provider was “selling client data to outside sources” in her interview with Cheddar. A spokesperson from Coinbase has now advised Sandler misspoke and clarified the exchange never shared “personally identifiable information with any third-party blockchain analysis vendors.”


? Ripple Launches On Huobi’s OTC Platform


Cryptocurrency exchange, Huobi, now supports Ripple (XRP) on to its over-the-counter (OTC) platform which currently lists assets such as bitcoin, ethereum, tether, EOS, and Huobi Token. Huobi OTC aims to bridge the gap between fiat and digital assets, serving as an escrow for its users.


? Swiss Stock Exchange Launches Ethereum ETP


Swiss’ SIX stock exchange launches its first ethereum-based exchange traded product (ETP) under the ticker AETH. Ethereum ETP, backed by startup Amun AG, is the second cryptocurrency-based ETP to be made available, with bitcoin ETP being the first. SIX plans to list a total of five crypto-based ETP’s which will also include ripple, bitcoin cash and litecoin. These collectively make up the Amun Crypto Basket Index  (HODL5).


? MakerDAO Holds Vote on Fee Hike for DAI


Votes are in! Users and holders of DAI in favour of increasing the DAI stability fee from 1.5% and 3.5%.



MakerDAO is asking token holders of ethereum-backed stablecoin, DAI, to vote on whether to go forth with the proposed increase in stability fees from 1.5% to 3.5%. MakerDAO’s founder, Rune Christensen, states the rationale behind the fee hike is because DAI’s dollar-peg is “almost at a breaking point”. Token holders can cast their votes here.


⚖️ Quadriga Gets 45-Day Extension from Court


The Canadian court has granted troubled crypto exchange, QuadrigaCX, a 45-day extension to allow for the continued search of $140 million worth of missing cryptocurrency. In addition, the court has appointed Peter Wedlake, a retired partner and senior vice president of audit firm Grant Thorton, to be the chief restructuring officer (CRO). The court has also ordered Amazon Web Services to overturn any data on the Quadriga platform originally created by Gerald Cotten, the former CEO.


☑️ Tron (TRX) Partners With Tether (USDT)


Tron Foundation officially announces their newest partnership with Tether. The partnership will bring Tether’s USDT coin onto the TRON network by the end of Q2 2019, and allow the issuance of a TRC20-based USDT. This marks the first major cryptocurrency crossover and will allow TRX and Tether users to transact on the TRON network with access to a price-stable asset.


? Blockchain Watch: Hello Goodbye


Bitcoin whales moved a total of 9820 BTC, worth $38.4 million dollars.


Bitcoin (BTC) and Stellar whales start off the week strong.


Ethereum whales are on the go, moving a total of 174, 450 ETH, worth $22.1 million dollars.


Images courtesy of Shutterstock

⚡️ Cryptocurrencies Go Live!

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?Bitcoin Finally in the Green After 6-Month Losses


Bitcoin (BTC), the world’s largest cryptocurrency by market cap, ends its 6-month losing streak; finally out of the red. It closed at $3,900 at the end of February. Even in this crypto winter, BTC’s bullish momentum show signs of recovery.


? Ethereum Hard Forks Now Activated


Ethereum (ETH), the second largest cryptocurrency by market cap, successfully implements system upgrades Constantinople and St. Petersburg. Originally planned for January, the Constantinople fork was delayed due to concerns about potential security vulnerabilities. Both forks officially took place at block number 7,280,000. The upgrades are intended to increase the network’s efficiency and will reduce miner block rewards from 3 to 2 ETH.


?Facebook & Telegram To Rollout Their Own Digital Currency


Internet messaging giants like Facebook, Telegram and Signal, will be rolling out their own cryptocurrencies in next year. Their intent is to allow users to transfer money directly from their message platforms with the use of blockchain. The projects have been highly secretive and the question still remains whether the tech companies will fully control their cryptocurrency or if it there will be some form of decentralization.


? Kraken Offers $100K Reward For Info On Quadriga’s Missing Funds


Cryptocurrency exchange, Kraken, announced they are offering a $100,000 reward to anyone who can provide the best lead on the discovery of missing coins from recently collapsed Canadian crypto exchange, Quadrigacx. It is estimated that a total of $190 million USD of Quadriga’s client funds remain inaccessible.  Kraken’s official blog highlights what is known to-date and any new tips can be submitted: here.


? Blockchain Watch: Ripple & Stellar On The Move

  • Ripple: 50 separate transactions of 10M XRP each moved from Bittrex to Upbit. The total value of the transactions is equivalent to $159 million USD
  • Stellar22 separate transaction of  10M XLM each moved from Bittrex to an unknown wallet. The total value of the transactions is equivalent to $19 million USD. 


☑️ Ripple Now Listed on Coinbase Pro


Users on crypto exchange, Coinbase Pro, can now buy and sell ripple (XRP) – the third largest cryptocurrency by market cap. Despite regulatory concerns, Coinbase stands its ground and makes XRP available to its users in the US (except New York), Canada, UK, supported EU nations, Singapore and Australia, with more jurisdictions to be added in the near future. Further developments can be found on Coinbase’s official blog post.


? Bitcoin & Ethereum Indices Now Live on Nasdaq


Nasdaq’s integration of Brave New Coin‘s bitcoin liquid index (BLX) and ethereum liquid index (ELX) is now live. The real-time indices are available for clients using Nasdaq’s Global Index Data Service SM (GIDS). This move affirms Nasdaq’s optimism towards cryptocurrency with Adena Friedman, president and CEO, previously expressing it is a “tremendous demonstration of genius and creativity.”


⚖️ US Government Retrieves & Returns Bitcoin to Bitfinex


The United States government has retrieved and returned 27.66270285 BTC (worth $104,000) to cryptocurrency exchange, Bitfinex. A total of 119,756 BTC were stolen during the hack of August 2016; the return represents only 0.023 % of this total.


Accurate Crypto Market Data Ultimately Leads to Winning Model

By | Coinscious Lab, Cryptocurrency | No Comments

The world’s most valuable resource is no longer oil but data. [1] This holds true even for the finance industry. The control that financial companies wield over their data gives them enormous power, and the abundance and quality of data they use changes the very nature of the competition. According to Bloomberg, the financial sector is adopting big data analytics to maintain a competitive advantage in the trading environment” [2]. Quantitative- and high-frequency trading are ubiquitous, indispensable tools in current times, and their full value in cryptocurrency trading are being realized. A key aspect that is still often overlooked in quantitative crypto-trading is the quality of the data being used to design sophisticated prediction models.

In this era of cryptocurrency trading, those with the most data of the highest quality will surely win. In algorithmic trading applications, accuracy is one of the best quality indicators of a data source. It determines the execution prices, the model’s behaviour, and the model’s ability to fit the market efficiently and effectively. In the extreme case, high frequency traders care about order-by-order data to simulate precise market-making algorithms. In order to accurately determine what and how much to trade at a low cost, traders desire the finest scales of accurate data with low latency.

Many algorithmic traders incorporate massive amounts of data into their algorithms to create better pricing models and leverage large volumes of historical data to backtest their trading algorithms. Particularly with recent advances in machine learning, the data-driven approach to modelling is being emphasized more than ever before. Market behaviours are learned from black box models that recognize patterns in big data. This means that the accuracy of the data affects what the model learns and predicts. Thus, the more accurate data you have, the better you can simulate execution quality in algorithms.

Available Sources of High Quality Crypto-Trading Data

There are several companies that provide cryptocurrency market data. Kaiko, CoinAPI, and Coinscious are three well-known crypto data vendors. Most of these companies offer live and historical trading, order books, and OHLCV1  (open, high, low, close, volume) cryptocurrencies. However, what remains unknown, until now, is the quality of data these companies claim to provide. Therefore, the key question is: which data vendor has the highest quality data for you to gain a competitive edge?

The Basics

A simple way to assess data quality is to compare the exchange’s OHLCV data with derived OHLCV data. In the analysis below, the hourly level OHLCV data is computed for December 2018 amongst different data vendors. The error rates were measured over eight well-known exchanges: Binance, Bittrex, Bitfinex, Bitstamp, Bitmex, Huobi Global, Okex, and Coinbase Pro.

Figure 1. OHLC error rates for Bitcoin (BTC), Ethereum (ETH), and Ripple (XRP)2. Given that our budget limits us to purchase just one dataset between Kaiko and CoinAPI, we chose the more expensive one: Kaiko’s data

Figure 2. OHLC error rates for OHLC error rates for four alternative coins (ADA, XLM, TRX, ZRX)

Coinscious data proves to be the most accurate among these data vendors for the top 3 coins (BTC, ETH, and XRP). In average, Coinscious data are 38% better than Kaiko’s data, where the relative errors on OHLC are 39%, 41%, 31%, and 37% respectively (see Figure 1). Similar results have also been shown using four alternative coins (ADA, XLM, TRX, ZRX). Surprisingly, even though Kaiko data is accurate for high and low prices, their open and close prices are quite divergent when compared to Coinscious and CoinAPI.

Error In Trading Volume

In Figure 3 and Figure 4, volume error rates over time reveal the dates when the higher error rates occur. The spike in volume error rates occurs in two scenarios; the first scenario occurs when the volume and volume error rates spikes simultaneously, whereas the second scenario occurs when the volume error rates spike, but volume does not. The former can be attributed to increased latency on exchanges as traffic increases, whereas the latter can be attributed to internal server issues.

Figure 3. Absolute error between exchange volumes versus data vendors’ volumes in December 2018 (the lower, the better). The errors were measured for BTC/USD, ETH/USD, and XRP/USD on the top 7 exchanges3.

Coinscious’ error rates remain relatively low compared to other vendors’ error rates. Overall, it is clear that Coinscious data has the lowest error rates with respect to volume data.

Figure 4. Absolute distance error between exchange volumes versus data vendors’ volumes in December 2018 (the lower, the better). The errors were measured for the following alternative coins: ADA/USD at Bittrex, XLM/USD, TRX/USD, and ZRX/USD at Bitfinex.

The volume quality for alternative coins (i.e., altcoins) was also considered. Eight altcoins were randomly selected from different exchanges, including NEO, TRON, XLM, EOS, LTC, ZRX, and ADA. From the figure above, CoinAPI does not perform well on volumes with respect to these altcoins.

Reason For Data Discrepancies Between Vendors

Now you must be wondering, if the exchange provides public API, why would you need to purchase data? Firstly, public APIs have limited histories of information they provide, and unless a trader has stored historical price data, they would need to gather it from a third-party source. Secondly, even though exchanges provide public APIs, aggregating and preprocessing all possible cryptocurrency pairs for different exchanges is cumbersome, and arguably the most tedious step in developing a trading system. This is especially the case as the data receiving intervals gets coarser as the number of requests for data grows. It is for these reasons that the aforementioned data vendors exist.

More importantly, why do discrepancies in the accuracies exist across different data vendors? There are several possible reasons. It could be due to downtimes of exchange APIs. Or, given the thousands of combinations of cryptocurrency exchanges and trade pairs, there exist API rate limits on all cryptocurrency exchanges, and therefore a large number of data collection clients and complicated infrastructure is required.

While many companies are collecting vast amounts of data across different exchanges and coins, the quality of the data may be hidden underneath the quantity of the data. Especially in this era of a data-driven finance world, success and risk can be heavily dependent on the data quality and the data operations environment. Obtaining the right trading tools and hiring talented traders can certainly help, but even then, tools and people cannot guarantee success if the data is flawed. The cryptocurrency finance market definitely could benefit from having more of data quality analysis in order to understand the granular level of datasets and where they can obtain them.


  1. Open, high, low, close, volume (OHLCV) prices.
  2. Given that our budget limits us to purchase just one dataset between Kaiko and CoinAPI, we chose the more expensive one: Kaiko’s data.
  3. Top 7 exchanges include: Binance, HuobiPro, Bitfinex, Bitmex, OKEx, Bitstamp, and Coinbase.


[1] “The world’s most valuable resource is no longer oil, but data”. The Economist, 6 May 2017, https://www.economist.com/leaders/2017/05/06/the-worlds-most-valuable-resource-is-no-longer-oil-but-data

[2] “3 ways big data is changing financial trading”. Bloomberg, 5 July 2017, https://www.bloomberg.com/professional/blog/3-ways-big-data-changing-financial-trading/