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Coinscious Lab

BTC Surges In Last 2 Weeks To $5,105; Now Holding At $5,000

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

Tezos (XTZ) 
+110.60%

Biggest
30d % Loss

Nano (NANO) 
-53.49%

Overview

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 March 16, 2019 to April 14, 2019.

Our universe of analysis includes 50 of some of the most widely used and traded cryptocurrencies. Please see Appendix A for the complete list.

Analysis

The performance of major cryptocurrencies over the past month has been good, with 41 out of the 50 cryptocurrencies that we examined up from their values 30 days ago. Bitcoin (BTC), the largest cryptocurrency by market capitalization, finally breaks above the $4,200 overhead resistance level on April 1; BTC surges to $5105. Various other cryptocurrencies, including second and third largest cryptocurrencies ether (ETH) and XRP (XRP) also experienced large upwards movements on the same day.

Outside of cryptocurrencies, the S&P 500 is up 3.01% from 30 days ago and closed last Friday at $2907.41.

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 March 16, 2019 to April 14, 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 110.60%. Tezos is a self-amending proof-of-work dApp platform that removes the need to hard fork when implementing protocol amendments.

The second and third best performing cryptocurrencies were Bitcoin Cash (BCH) and IOST (IOST), with total returns of 80.40% and 71.34% respectively.

Nano (NANO) was the worst performing cryptocurrency, with total losses of 53.49%. Nano is a low-latency payment platform designed for peer-to-peer transactions. The second and third worst performing cryptocurrencies were Maker (MKR) and Steem (STEEM) with total losses of 11.49% and 11.42% respectively.

Figure 2a. Mean daily returns, historical daily volatility, total returns, and ex-post Sharpe ratio for each cryptocurrencies with the highest total returns from March 16, 2019 to April 18, 2019. 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 2b. Mean daily returns, historical daily volatility, total returns, and ex-post Sharpe ratio for cryptocurrencies with the lowest total returns from March 16, 2019 to April 18, 2019. 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 3 plots daily candlesticks of the prices of Bitcoin ( BTC ) and Ether (ETH), the two largest cryptocurrencies by market capitalization, as well as the top performer of the past month, Tezos (XTZ). In addition, the following commonly used technical analysis indicators are shown:

  • Simple moving averages (SMA) with periods of 50, 100, and 200 days
  • Relative strength index (RSI) with a period of 14 days
  • Moving average convergence divergence (MACD) with a fast EMA period of 12 days, slow EMA period of 26 days, and a signal period of 9 days

Figure 3a. Price of Bitcoin (BTC) in USD at Bitstamp from March 16, 2019 to April 14, 2019.

Figure 3b. Price of Ether (ETH) in USD at Bitstamp from March 16, 2019 to April 14, 2019.

Figure 3c. Price of Tezos (XTZ) in USD at Bitfinex from March 16, 2019 to April 14, 2019.

APPENDIX A: Cryptocurrencies

Below is a complete list of all cryptocurrencies examined in this market report. In addition, we present the mean daily returns, historical daily volatility, total returns, and ex-post Sharpe ratio for each cryptocurrency from March 16, 2019 to April 18, 2019. 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.

APPENDIX B: Methodology

The daily price data of cryptocurrencies in USD at 4:00 PM EST from March 16, 2019 to April 14, 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. The only exception is Siacoin (SC), where we used the Yahoo Finance price instead due to data quality issues at the time of writing.

Daily closing price data of the S&P 500 index was obtained from from Yahoo Finance. 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 C: 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.

Disclaimer

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.

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

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

Tezos (XTZ) 
+73.77%

Biggest 30d %
Gain (Sector)

Digital Content
+23.22%

Biggest
30d % Loss

Pundi X (NPXS) 
-16.06%

Smallest 30d %
Loss (Sector)

Stablecoins
-0.00%

Overview

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.

Sector
Constituent Coins/Tokens
Digital Cash BTC, BCH, BSV, LTC, BTG, DOGE, DCR, BCD, DGB
Privacycoins XMR, DASH, ZEC, XVG, BCN
DApp Platforms ETH, EOS, ADA, NEO, ETC, XEM, XTZ, QTUM, LSK, AE, ZIL, ICX, BTM, ETP
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

Analysis

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.

Disclaimer

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.

Investigating Disruptive Patterns of Crypto Exchanges

By | Coinscious Lab, Data Analytics | No Comments

Overview

Released monthly, this report aims to analyze and characterize cryptocurrency exchanges according to their volume for the past month, this time from January 16, 2019 to February 16, 2019.

Our universe of analysis uses public exchange data from 18 of some of the most popular exchanges1 (see Figure 1). Through analysis of the recent historical volume and price of individual exchanges we provide a framework for analysis where investors can identify better or fairer exchanges.

Using daily volume data from some of the most widely used cryptocurrency exchanges, we were able to cluster exchanges into three groups based on similarity in volume trends. Some of our findings include the following:

  • Fcoin and HitBTC go against the market both in terms of volume correlation and price-volume correlation.
  • Not only does volume and price seem to go against the market, but returns and volume are positively correlated only for Fcoin and HitBTC, while returns and volume are negatively correlated for the rest of exchanges.
  • Although ZB has reported the biggest traded volume in the past month, ZB volume trend patterns were different from the other top exchanges (Binance, HuobiPro, and OKEx). Generally, it showed a low correlation with the market in terms of traded volume.
  • It is also interesting to note that our analysis (Figure 4b. Top 5 positive correlations) accidentally reveals a shared order book between Upbit and Bittrex.

Performance

Figure 1. Total monthly volume, mean daily volume, max daily volume, mean hourly volume and max hourly volume for each exchange from January 16, 2019 to February 16, 2019 in USD. Monitor exchange performance daily through our Market & Trading Strategy Dashboard: dashboard.coinscious.io

According to the public exchange data, ZB has reported the biggest mean daily and hourly volume for the past month. Binance reportedly lost the crown as the king of cryptocurrency by trade volume on December 11, 2018 to be replaced by OKEx, who held the 1st place at that time, while ZB held 2nd [1].

In the past month, ZB has taken the first spot, with a total of 16 billion dollar traded between January 16th to February 16th, compared to 14 billion USD for OKEx (2nd) and Binance (3rd) (see Figure 1).

We then chose ETH/BTC pair to look at the volume more closely and analyze it in more depth. ZB traded volume for ETH/BTC is low during the whole period, with Binance, HuobiPro and OKEx as top exchanges. However, there was a rapid surge on February 11th, when volume changed from $500,000 USD to $67 million USD in one day, and it has remained high since then (see Figure 2). That same day,  according to Etherscan, daily mining rewards for Ethereum fell to their lowest recorded levels [2].

Figure 2. ETH/BTC pair daily volume for each exchange from January 16, 2019 to February 16, 2019 in USD.

Analysis

In order to categorize exchanges and investigate their trends during the past month, we performed a dimensionality reduction analysis using PCA2 for ETH/BTC pair daily volume. By plotting crypto exchanges according to their first two principal components, we could identify a cluster and some outliers (see Figure 3). The two first principal components explain more than 80% of the variance in our data set and the relative position along the x and y axis indicates similarity between exchanges in terms of traded volume. Thus, exchanges clustered together present similar volume trends, while outliers, namely Okex, HuobiPro, Binance, Fcoin, HitBTC, and ZB, show trends that diverge from the market mean.

Figure 3. PCA Volume analysis for ETH/BTC. The biplot, where the two main principal components are used to represent the exchanges allows us to identify clusters or groups of exchanges that might be correlated according to volume.

To understand the meaning of the two principal components and characterize the outliers, we decided to look at correlation between exchanges using volume for the ETH/BTC pair, to identify a general trend in the market and whether exchanges follow or are against the trend (see Figure 5). Although most exchanges show a similar trends (this is, they follow a similar daily volume trend), Bitflyer, ZB, Zaif, Fcoin, and HitBTC show a daily volume trend that goes against the market.

Figure 4a. Top 5 negative volume correlations

Figure 4b. Top 5 positive volume correlations

Figure 5. Daily volume correlations between exchanges from January 16, 2019 to February 16, 2019 for ETH/BTC. 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.

Fcoin in particular shows negative correlations with all exchanges except for HitBTC. This suggests that Bitflyer, ZB, Zaif, Fcoin, and HitBTC are acting against the market. Moreover, it’s interesting to see that Bitflyer, ZB, Zaif, Fcoin, and HitBTC are all based in Asian countries.

On the other hand, Upbit and Bittrex show the highest correlation for all exchange pairs. This similarity in volume is in line with reports suggesting that they share the same order book, although further analysis of blockchain data will be needed to confirm these claims [3].  

To explore what other factors could contribute to the clustering in different groups after Volume PCA, we calculated the correlation between ETH/BTC price and volume for every exchange pair (see Figure 6).

Figure 6. Daily price-volume correlations between exchanges from January 16, 2019 to February 16, 2019 for ETH/BTC. 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.

Again, Fcoin stands out as the exchange with a highest negative correlation between ETC/BTC price and volume for all exchanges showing that, not only the volume, but ETH price is also against the market3. While most exchanges show a positive trend, with high volumes associated to higher prices, Fcoin shows a negative trend, where higher volumes during the analyzed period are associated with lower prices and vice versa.

This raised suspicions that Fcoin might be manipulating price and volume, especially for the pair that we have analyzed (ETH/BTC). Fcoin has been accused of manipulation in the past. In September, a Chinese investor filed a complaint against the exchange, which presumably induced sudden drops in price through manipulation after investing huge amounts in Fcoin token [4].

Finally, if we look at the correlation between returns (instead of price) and volume for ETH/BTC (Figure 7), HitBTC and Fcoin are again the exchanges going against the general trend. High returns in HitBTC and Fcoin are associated with an increase in volume, while high returns elicit a decrease in volume for the rest of exchanges (negative correlation between returns and volume). Interestingly, HitBTC has also been accused of volume manipulation [5].

Figure 7. Daily returns-volume correlations between exchanges from January 16, 2019 to February 16, 2019 for ETH/BTC. 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.

Summary

Using daily volume data from 18 of some of the most widely used cryptocurrency exchanges and principal component analysis, we identified three clusters of exchanges sharing similar volume trends. We mainly looked at volume correlations and price-volume correlations. Our findings include:

  • Fcoin and HitBTC go against the market both in terms of volume correlation and price-volume correlation.
  • Not only volume and price seem to go against the market, but returns and volume are positively correlated only for Fcoin and HitBTC, while returns and volume are negatively correlated for the rest of exchanges.
  • Although ZB has reported the biggest traded volume in the past month, PCA analysis separates it from the other top exchanges (Binance, HuobiPro, and OKEx) and show a low correlation with the market in terms of traded volume.
  • It is also interesting to note that our analysis (Figure 4b Top 5 positive correlations) accidentally reveals a shared orderbook between Upbit and Bittrex

Here, these findings are simple observations of possibly correlated variables. We share this from the point-of-view of something to look out for. Overall, our exchange analysis has proven useful to study patterns of volume and price activity in the market and identify potential manipulation, that could be confirmed using blockchain data.

Footnotes

1 The scope of this report does not cover futures contracts.

2 PCA is a technique that finds underlying variables that best differentiates your data points. In this article, we visualize and analyze along the two dimensions which the data points varies the most (see Appendix B).

3 Here, the market is refers to the trend of majority of the exchanges.

APPENDIX A: Methodology

The daily volume of cryptocurrencies in USD at 4:00 PM EST from January 16, 2019 to February 16, 2019 was used for our volume ranking. Daily volume and price for the pair ethereum_bitcoin was used for the same time period for the PCA analysis and correlation analysis. Price and volume were normalized such that its distribution had mean value 0 and standard deviation of 1 in order to perform principal component analysis and calculate price-volume correlations. In subsequent reports, we may update our universe, sectors, methodology, and analysis to reflect new developments.

APPENDIX B: Terminology

  • 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.
  • PCA: A statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components, in order to maximize the explained variance.

References

[1] T. (2018, December 11). Binance Losses Top Cryptocurrency Exchange Position to OKEX and ZB.Com. Retrieved from https://coingape.com/

[2] Lavere, M. (2019, February 13). Ethereum (ETH) Mining Reward Hits Lowest Ever.  Retrieved from https://ethereumworldnews.com/

[3] Bitking74. (2017, October 24). UPbit and Bittrex are sharing the same order book. This is a win win for both sides: bring some Korean liquidity to Bittrex, also the Korean users start with nicely filled order books from Bittrex. Go NEO. Neotrader [Online forum]. Retrieved from https://www.reddit.com/r/Neotrader/comments/78fztu/upbit_and_bittrex_are_sharing_the_same_order_book/

[4] J. (2018, September 18). Chinese Investor Loses 700,000 Yuan Due To Fcoin Crypto Manipulation. Retrieved from https://www.coindaily.co/

[5] Sillers, A. (2018, September 12). The evidence of OKex’s fraudulent behavior, which may point to HitBTC as well. Retrieved from https://www.chepicap.com/

Disclaimer

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.

MKR Outperforms in Feb (86% Gains); ONT & BAT Take 2nd & 3rd

By | Coinscious Lab, Data Analytics | No Comments

Coinscious Market Report

by Coinscious Lab

March 4, 2019

Biggest
30d % Gain

Maker (MKR)
+85.56%

Biggest 30d %
Gain (Sector)

Digital Content
+37.38%

Biggest
30d % Loss

Augur (REP)
12.71%

Biggest 30d %
Loss (Sector)

Stablecoins
-0.14%

Overview

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 2, 2019 to March 3, 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.

Sector
Constituent Coins/Tokens
Digital Cash BTC, BCH, BSV, LTC, BTG, DOGE, DCR, BCD, DGB
Privacycoins XMR, DASH, ZEC, XVG
DApp Platforms ETH, EOS, ADA, NEO, ETC, XEM, XTZ, QTUM, LSK, AE, ZIL, ICX, BTM, ETP
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

Analysis

The performance of major cryptocurrencies over the past month has been good, with 44 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 between $3800 and $4000. Despite having surpassed $4000 two weeks ago, giving many hope that this breakout was potentially the start of a new upwards trend, Bitcoin reached and was rejected by the $4250 resistance level. It continues the longstanding sideways trend that began in November last year.

Outside of cryptocurrencies, the S&P 500 has been performing well, up 3.59% from 30 days ago and closing last Friday at $2803.69.

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 2, 2019 to March 3, 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 Maker (MKR), with a total return of 85.56%.  To understand why Maker have outperformed other cryptocurrencies, we need to briefly explain how Maker works first. (For a  full explanation, see their whitepaper.)

MakerDAO is a smart contract platform that backs and stabilizes the value of Dai (DAI), a soft-pegged stablecoin. Unlike most other stablecoins, Dai is collateralized with Ether on the Ethereum blockchain rather than any fiat currency. On this platform, Maker serves multiple purposes:

  • Maker is a governance token that allows holders to vote on system settings
  • Maker is also a utility token that is burned as a fee when settling a Collateralized Debt Position (CDP), a smart contract whose purpose is to create Dai in exchange for collateral, which it then holds in escrow until the borrowed Dai is returned.
  • In the event that CDPs become undercollateralized, likely as a result of market crashes or other adverse events, automatic recapitalization through forced Maker dilution will happen i.e. Maker tokens will be created and sold on the market to raise money to recapitalize the system.

According to an article from MakerDAO’s blog in early February, there are 8200 unique addresses with a non-negligible Dai balance and in January, there were more than 7300 active addresses sending or receiving Dai. In addition, they reported 20% monthly growth in both holders and active addresses.

As the only token that can be used to pay the fee associated with creating Dai and using CDPs, an increase in adoption and demand for Dai means that there will also be additional demand for Maker. In addition, Maker is burned to pay the fee, thereby permanently decreasing the total supply of Maker available (unless more is created during forced Maker dilution for recapitalization). If adoption of Dai continues to grow, as MakerDAO has reported, then there are fundamental reasons to expect the price of Maker to continue to increase as well.

The second and third best performing cryptocurrencies were Ontology (ONT) and Basic Attention Token (BAT) with total returns of 63.94% and 58.31% respectively.

Ontology’s price surge coincides with an article from their blog posted on February 23 that announced the release of the Ontology Development Platform on Google Cloud Platform Marketplace. This makes Ontology one of the first public blockchains to have a development platform on the leading cloud provider marketplaces: Google Cloud, Amazon Web Services, and Microsoft Azure.

Basic Attention Token also benefited from positive news. An article from their blog posted on February 26 announced a partnership between Brave Software and the Tap Network. Brave Software is a privacy browser combined with a blockchain based digital advertising platform that uses Basic Attention Tokens to reward users for their attention. Tap Network is an advertising and data network that connects brands to reward consumers directly using blockchain. This partnership will allow Brave users to redeem Basic Attention Tokens for real-world rewards from over 250 000 brand partners in the TAP Network.

Augur’s reputation token (REP) was the worst performing cryptocurrency, with total losses of 12.72%. This is likely only a pullback from the exuberance that followed the unveiling of the Viel Platform on January 15.

Figure 2a. Cryptocurrencies with the highest total returns from February 2, 2019 to March 3, 2019

Figure 2b. Cryptocurrencies with the lowest total returns form February 2, 2019 to March 3, 2019

Figure 3. Mean daily returns, historical daily volatility, total returns, maximum drawdown, and ex-post Sharpe ratio for each sector from February 2, 2019 to March 3, 2019. Less negative 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 by sectors that had positive returns between February 2, 2019 to March 3, 2019.

Figure 4b. Price performance over time by sectors that had negative returns between February 2, 2019 to March 3, 2019.

Figure 4 shows the correlation between the daily returns of each sector. Stablecoins had low to moderate positive correlation with other sectors. As shown in Figure 2, stablecoins continued to fulfill their intended purpose well by maintaining low volatility, mean daily returns of 0%, and a near zero total return of -0.14% over the observation period. The S&P 500 also had little correlation with any cryptocurrency sectors. As for the other sectors, they had high positive correlation with each other, ranging from 0.66 to 0.94, almost the same compared to 0.66 to 0.95 from two weeks ago. Digital content, the best performing sector, was the least correlated with the others.

Figure 4. Correlation between daily returns of each sector from February 2, 2019 to March 3, 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 2, 2019 to  March 3, 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. If there was insufficient good quality data on a cryptocurrency’s value in USD, we would instead use the cryptocurrency’s value in USDT and apply a conversion rate to turn it to USD. If data was still insufficient, then we would find the volume weighted average price of the cryptocurrency in both BTC and ETH, then converted both into USD, and finally took the mean of those values. The conversion rates we use at a given time are the volume weighted average price of USDT, BTC, or ETH to USD at that specific time 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 2, 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 2, 2019 and the 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.

Disclaimer

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.

Accurate Crypto Market Data Ultimately Leads to Winning Model

By | Coinscious Lab | 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.

Footnotes

  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.

References

[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/

Litecoin & EOS Surge, With Bitcoin Trading Around $4,000

By | Coinscious Lab, Data Analytics | One Comment

Coinscious Market Report

by Coinscious Lab

February 19, 2019

Biggest
30d % Gain

 Pundi X (NPXS)
+47.06%

Largest 30d %
Gain (Sector)

Payments & Settlements
+6.76%

Biggest
30d % Loss

 NEM (XEM)
25.41%

Biggest 30d %
Loss (Sector)

Data & Information
6.70%

Overview

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 January 20, 2019 to February 18, 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.

Sector
Constituent Coins/Tokens
Digital Cash BTC, BCH, BSV, LTC, BTG, DOGE, DCR, BCD, DGB
Privacycoins XMR, DASH, ZEC, XVG
DApp Platforms ETH, EOS, ADA, NEO, ETC, XEM, XTZ, QTUM, LSK, AE, ZIL, ICX, BTM, ETP
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

Analysis

Over the past month, 25 out of the 50 major cryptocurrencies that we examined are up from their values 30 days ago. Bitcoin (BTC), the largest cryptocurrency by market cap, recently broke out of the narrow range from $3,600 and $3,800 that it was confined to. It is currently trading around $3,950 at the time of writing. As is often the case, Bitcoin’s move upwards coincided with good news from other cryptocurrencies as well, with 44 out of the 50 cryptocurrencies having positive returns on February 18th.

Outside of cryptocurrencies, the S&P 500 has been performing well, up 3.93% from 30 days ago and closing last Friday at $2,775.60. 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 January 20, 2019 to February 18, 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 Pundi X (NPXS), with a total return of 47.06%. The second and third best cryptocurrency was Litecoin (LTC) and EOS (EOS), with a total return of 42.79% and 23.01% respectively. Pundi X and Litecoin both performed relatively better than the similar cryptocurrencies that make up the rest of their sectors. Pundi X is a token used for payments and settlements, while Litecoin falls into the digital cash category.

NEM (XEM) was the worst performing cryptocurrency, with total losses of 25.41%. Stellar Lumens (XLM) was the second worst performing cryptocurrency, with total losses of 22.56%. Figure 2 shows various performance measures of the nine sectors as well as that of the S&P 500 for comparison and Figure 3 plots the performance over time of each sector. Total returns ranged from -6.70% (data and information) to 6.76% (payments and settlements). There was a dip in all sectors except for stablecoins around the end of January but all sectors have rebounded, making recoveries with varying degrees of success.

Figure 2. Mean daily returns, historical daily volatility, total returns, maximum drawdown, and ex-post Sharpe ratio for each sector from January 18, 2019 to February 20, 2019. Less negative 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 3a. Price performance over time by sectors that had positive returns between January 20, 2019 to February 18, 2019.

Figure 3b. Price performance over time by sectors that had negative returns between January 20, 2019 to February 18, 2019.

Figure 4 shows the correlation between the daily returns of each sector. As shown in Figure 2, stablecoins continued to fulfill their intended purpose well by maintaining low volatility, mean daily returns near 0%, and a near zero total return of -0.20% over the observation period. Stablecoins had moderate negative correlation with other sectors except for digital content, with which it had near zero correlation. As for the other sectors, they were less positively correlated with each other compared to the numbers we reported two weeks ago. Correlation ranged from 0.54 to 0.92, slightly lower compared to 0.66 to 0.95 previously. The S&P 500 also had near zero correlation with all cryptocurrency sectors.

Figure 4. Correlation between daily returns of each sector from January 20, 2019 to February 18, 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 January 20, 2019 to February 18, 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. If there was insufficient good quality data on a cryptocurrency’s value in USD, we would instead use the cryptocurrency’s value in USDT and apply a conversion rate to turn it to USD. If data was still insufficient, then we would find the volume weighted average price of the cryptocurrency in both BTC and ETH, then converted both into USD, and finally took the mean of those values. The conversion rates we use at a given time are the volume weighted average price of USDT, BTC, or ETH to USD at that specific time 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 January 20, 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 January 20, 2019 and the 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.

Disclaimer

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.

Crypto Bear Market Continues & Remains Anti-Correlated with S&P

By | Coinscious Lab, Data Analytics | No Comments

Coinscious Market Report

by Coinscious Lab

February 4, 2019

Biggest
30d % Gain

  Augur (REP)
+50.39%

Biggest 30d %
Gain (Sector)

Data & Information
+5.31%

Biggest
30d % Loss

 NEM (XEM)
37.86%

Biggest 30d %
Loss (Sector)

Resources
19.58%

Overview

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 January 5, 2019 to February 3, 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.

Sector
Constituent Coins/Tokens
Digital Cash BTC, BCH, BSV, LTC, BTG, DOGE, DCR, BCD, DGB
Privacycoins XMR, DASH, ZEC, XVG
DApp Platforms ETH, EOS, ADA, NEO, ETC, XEM, XTZ, QTUM, LSK, AE, ZIL, ICX, BTM, ETP
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

Analysis

The performance of major cryptocurrencies over the past month has been lacklustre, with only 6 out of the 50 cryptocurrencies that we examined up from their values 30 days ago. Bitcoin (BTC), the largest cryptocurrency by market cap, has been trading between $3800 and $3400 without any major moves and is currently hovering around $3500. Outside of cryptocurrencies, the S&P 500 has been performing well, up 6.90% from 30 days ago and closing last Friday at $2706.53.

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 January 5, 2019 to February 3, 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 Augur’s reputation token (REP), with a total return of 50.39%. This was also the best performing cryptocurrency from our previous market report two weeks ago. The surge in price was attributed to the announcement of the Viel platform on January 15. Viel is a peer-to-peer trading platform for prediction markets and derivatives built on top of Augur, with the goal of making Augur easier to use and more ubiquitous by making transactions faster and cheaper.

NEM (XEM) was the worst performing cryptocurrency, with total losses of 33.31%. Ethereum (ETH) and ICON (ICX) were the second and third worst performers respectively, and all three are part of the dApp platform sector.

Figure 2 shows various performance measures of the nine sectors as well as that of the S&P 500 for comparison and Figure 3 plots the performance over time of each sector. Performance between the sectors was mostly negative with only data and information having positive total returns. Total returns ranged from -19.58% (resources) to 5.31% (data and information).

Data and information has been the best performing sector for three consecutive reports now, corresponding to a time period spanning two months. Reputation, the best performing cryptocurrency, is in the data and information sector, as well as ChainLink (LINK), IOTA (IOTA), and VeChain (VET).

Resources, the worst performing sector, is composed of Siacoin (SC) and Golem (GNT). This is a sector for platforms and tokens that facilitate markets for decentralized resources. In the case of these two tokens, the resources are cloud storage and computing power respectively.

Figure 2. Mean daily returns, historical daily volatility, total returns, maximum drawdown, and ex-post Sharpe ratio for each sector from January 5, 2019 to February 3, 2019. Less negative 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 3a. Price performance over time by sectors that had positive returns between January 5, 2019 to February 3, 2019.

Figure 3b. Price performance over time by sectors that had negative returns between January 5, 2019 to February 3, 2019.

Figure 4 shows the correlation between the daily returns of each sector. Stablecoins had moderate negative correlation with other sectors. As shown in Figure 2, stablecoins continued to fulfill their intended purpose well by maintaining low volatility, mean daily returns near 0%, and a near zero total return of -0.89% over the observation period. The S&P 500 also had little correlation with any cryptocurrency sectors. As for the other sectors, despite their varying performance, they had high positive correlation with each other, ranging from 0.66 to 0.95, but slightly lower compared to 0.77 to 0.97 from two weeks ago.

Figure 4. Correlation between daily returns of each sector from January 20, 2019 to February 3, 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 January 5, 2019 to February 3, 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. If there was insufficient good quality data on a cryptocurrency’s value in USD, we would instead use the cryptocurrency’s value in USDT and apply a conversion rate to turn it to USD. If data was still insufficient, then we would find the volume weighted average price of the cryptocurrency in both BTC and ETH, then converted both into USD, and finally took the mean of those values. The conversion rates we use at a given time are the volume weighted average price of USDT, BTC, or ETH to USD at that specific time 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 January 5, 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 January 5, 2019 and the 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.

Disclaimer

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.

Digital Cash in Crypto Market Plunges 14% While S&P Surges 11%

By | Coinscious Lab, Data Analytics | No Comments

Coinscious Market Report

by Coinscious Lab

January 21, 2019

Biggest
30d % Gain

Augur (REP)
+
135.34%

Biggest 30d %
Gain (Sector)

Data & Information
+44.42%

Biggest
30d % Loss

 Bitcoin Cash (BCH)
-33.31%

Biggest 30d %
Loss (Sector)

Digital Cash
-13.81%

Overview

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 December 22, 2018 to January 20, 2019.

Our universe of analysis includes 51 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.

Sector
Constituent Coins/Tokens
Digital Cash BTC, BCH, BSV, LTC, BTG, DOGE, DCR, BCD, DGB
Privacycoins XMR, DASH, ZEC, XVG
DApp Platforms ETH, EOS, ADA, NEO, ETC, XEM, XTZ, QTUM, LSK, AE, ZIL, ICX, BTM, ETP
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

Analysis

The performance of major cryptocurrencies over the past month has been mixed, with 12 out of the 51 cryptocurrencies that we examined up from their values 30 days ago. Bitcoin (BTC), the largest cryptocurrency by market cap, has been confined between $3500 and $3900 since January 11, and is currently trading at the low end of that range around $3600. Outside of cryptocurrencies, the S&P 500 has been performing well, up 10.51% from 30 days ago and closing last Friday at $2670.71.

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 December 22, 2018 to January 20, 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 Augur’s reputation token (REP), with a total return of 135.34%. The surge in price can be attributed to the announcement of the Viel platform on January 15. Viel is a peer-to-peer trading platform for prediction markets and derivatives built on top of Augur, with the goal of making Augur easier to use and more ubiquitous by making transactions faster and cheaper. Reputation is in the data and information sector, as was the second best overall performer, ChainLink (LINK), which had a total return of 59.72%. Other cryptocurrencies in the data and information sector are IOTA (IOTA) and VeChain (VET).

Bitcoin Cash (BCH) was the worst performing cryptocurrency, with total losses of 33.31%. Bitcoin SV (BSV) was the second weakest, with total losses of  30.32%. Bitcoin Cash and Bitcoin SV both belong to the digital cash sector.

Cobra, the anonymous developer who founded Bitcoin.org, tweeted on January 18, “Bitcoin Cash is dead.” Cobra goes on to claim that Bitcoin Cash needs new leadership, otherwise it’ll be worth $0 in a few years. Cobra also tweeted about Bitcoin SV a little further back on January 7, saying, “Time to sell all my BSV. Worthless shitcoin.” Another high profile member of the cryptocurrency community, Vitalik Buterin, founder of Ethereum, was also critical of Bitcoin SV on Twitter and called it “a pure dumpster fire.”

The tweets don’t appear to coincide with any noticeably large drops in either Bitcoin Cash’s or Bitcoin SV’s prices. Rather, both of them had gradual declines over the past month that fit into a longer term downtrend. However, the negative attention as a result of these tweets is unlikely to present a good opportunity to reverse that downtrend anytime soon.

Several other cryptocurrencies in the digital cash sector, namely Dogecoin (DOGE), Bitcoin (BTC), Bitcoin Gold (BTG), and Bitcoin Diamond (BCD), also had negative returns over the past month.

Figure 2 shows various performance measures of the nine sectors as well as that of the S&P 500 for comparison and Figure 3 plots the performance over time of each sector. Performance between the sectors was mixed, with total returns ranging from -13.81% (digital cash) to 44.42% (data and information). Data and information was also the best performing sector in our previous market report from two weeks ago.

Figure 2. Mean daily returns, historical daily volatility, total returns, maximum drawdown, and ex-post Sharpe ratio for each sector from December 22, 2018 to January 20, 2019. Less negative 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 3a. Price performance over time by sectors that had positive returns between December 22, 2018 to January 20, 2019

Figure 3b. Price performance over time by sectors that had negative returns between December 22, 2018 to January 20, 2019.

Figure 4 shows the correlation between the daily returns of each sector and quantifies some of what we visually observe from Figure 3. Stablecoins had moderate negative correlation with other sectors. As shown in Figure 2, stablecoins continued to fulfill their intended purpose well by maintaining low volatility and mean daily returns near 0%, and a near zero total return of -0.73% over the observation period. As for the other sectors, despite their varying performance, they still had high positive correlation with each other, ranging from 0.77 to 0.97. This is visible is Figure 3 from how sectors often moved up or down together on the same days. The S&P 500 had little correlation with any cryptocurrency sectors.

Figure 4. Correlation between daily returns of each sector from December 22, 2018 to January 20, 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 December 22, 2018 to January 20, 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. If there was insufficient good quality data on a cryptocurrency’s value in USD, we would instead use the cryptocurrency’s value in USDT and apply a conversion rate to turn it to USD. If data was still insufficient, then we would find the volume weighted average price of the cryptocurrency in both BTC and ETH, then converted both into USD, and finally took the mean of those values. The conversion rates we use at a given time are the volume weighted average price of USDT, BTC, or ETH to USD at that specific time 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 December 22, 2018, 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 December 22, 2018 and the 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.

Disclaimer

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.

Waves Dominates Again As Best Overall Crypto Performer In Past Month

By | Coinscious Lab, Data Analytics | No Comments

Coinscious Market Report

by Coinscious Lab

January 7, 2019

Biggest
30d % Gain

  Waves (WAVES)
+99.69%

Biggest 30d %
Gain (Sector)

Data & Information
+51.21%

Biggest
30d % Loss

 Pundi X (NPXS)
-13.07%

Smallest 30d %
Gain (Sector)

Privacycoins
+0.34%

Overview

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 December 8, 2018 to January 6, 2019.

Our universe of analysis includes 51 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.

Sector
Constituent Coins/Tokens
Digital Cash BTC, BCH, BSV, LTC, BTG, DOGE, DCR, BCD, DGB
Privacycoins XMR, DASH, ZEC, BCH, XVG
DApp Platforms ETH, EOS, ADA, NEO, ETC, XEM, XTZ, QTUM, LSK, AE, ZIL, ICX, BTM, ETP
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

Analysis

Cryptocurrencies are holding on to their recovery, with bitcoin (BTC) off the lows and currently trading around $4,100, maintaining approximately the same level for the past two weeks. Other major cryptocurrencies have been performing well, with 45 out of the 51 cryptocurrencies that we examined up from their values 30 days ago. Outside of cryptocurrencies, the S&P 500 continued sliding downwards, down 3.84% from 30 days ago and closing last Friday at $2531.94.

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 December 8, 2018 to January 6, 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 waves (WAVES), with a total return of 99.69%. Waves was also the best performer in our report two weeks ago.

Also worth mentioning is Ethereum (ETH) the second largest currency by market cap at the moment, with total returns of 82.21%. This may be a result of the upcoming Constantinople hard fork, which will happen on block 7080000, around January 16, 2019. Constantinople is a non-contentious fork, meaning that the vast majority of the Ethereum community will be accepting the changes. The five Ethereum Improvement Proposals to be addressed are EIP 1234, EIP 145, EIP 1014, EIP 1052, and EIP 1283. Most notably, EIP 1234 will drop mining rewards per discovered block from three to two ETH, thus decreasing the supply of new ETH.

Pundi X (NPXS) was the weakest performing cryptocurrency, with total losses of 13.07%. Pundi X is a decentralized payment ecosystem that uses NPXS tokens on proprietary physical point-of-sale devices.

Figure 2 shows various performance measures of the nine sectors as well as that of the S&P 500 for comparison and Figure 3 plots the performance over time of each sector. All sectors had positive total returns, with gains ranging from 8% to a little over 51% (excluding stablecoins). Out of all the sectors, data and information performed the best, with a total return of 51.21%. The data and information sector is composed of IOTA (IOTA), VeChain (VET), chainlink (LINK), and augur (REP).

Figure 2. Mean daily returns, historical daily volatility, total returns, maximum drawdown, and ex-post Sharpe ratio for each sector from December 8, 2018 to January 6, 2019. Less negative 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 3a. Price performance over time by sectors that had positive returns between December 8, 2018 to January 6, 2019.

Figure 3b. Price performance over time by sectors that had negative returns between December 8, 2018 to January 6, 2019.

Figure 4 shows the correlation between the daily returns of each sector and quantifies some of what we visually observe from Figure 3. Stablecoins had moderate negative correlation with other sectors. As shown in Figure 2, stablecoins continued to fulfill their intended purpose well by maintaining low volatility and mean daily returns near 0%, and a near zero total return of 0.34% over the observation period. As for the other sectors, they had high positive correlation with each other, ranging from 0.67 to 0.97. The S&P 500 had little correlation with any cryptocurrency sectors.

Figure 4. Correlation between daily returns of each sector from December 8, 2018 to January 6, 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 December 8, 2018 to January 6, 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. If there was insufficient good quality data on a cryptocurrency’s value in USD, we would instead use the cryptocurrency’s value in USDT and apply a conversion rate to turn it to USD. If data was still insufficient, then we would find the volume weighted average price of the cryptocurrency in both BTC and ETH, then converted both into USD, and finally took the mean of those values. The conversion rates we use at a given time are the volume weighted average price of USDT, BTC, or ETH to USD at that specific time 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 December 8, 2018, 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 December 8, 2018 and the 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.

Disclaimer

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.

Crypto Market Recovers: Waves Tops the Chart, Up By 203.50%

By | Coinscious Lab, Data Analytics | No Comments

Coinscious Market Report – December 21, 2018


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 are planned to focus on metrics derived from a 30-day rolling window of data, this time from November 20, 2018 to December 20, 2018. In this report, we also provide analysis on bitcoin price movements from a technical perspective to see where the market as a whole may be headed in the near future.