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Crypto Market Report - Bitcoin SV Stays On Top

Bitcoin SV Stays On Top – Total Return of 232% In Past Month

By | Coinscious Lab, Data Analytics, Market Report | No Comments
Crypto Market Report - Bitcoin SV Stays On Top

Overview Bitcoin SV

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 May 11, 2019 to June 9, 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 was 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 capitalization, is currently trading slightly about $7900 at the time of writing. Bitcoin SV

Outside of cryptocurrencies, the S&P 500 is down 0.28% from 30 days ago and closed last Friday at $2873.34.

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 May 11, 2019 to June 9, 2019. Higher returns at a given level of risk, measured through historical daily volatility, indicates a better investment.

Crypto Market Risk vs. Return - Bitcoin SV Stays On Top

The best performer overall over the past month was Bitcoin SV (BSV), with a total return of 232.00%. Bitcoin SV was created last November after a hard fork to Bitcoin Cash. It aims to restore the original Bitcoin protocol, closely following the concept as described in Satoshi Nakamoto’s white paper.

The second and third best performing cryptocurrencies were Monacoin (MONA) and Chainlink (LINK), with total returns of 160.57% and 67.83% respectively.

Basic Attention Token (BAT) was the worst performing cryptocurrency, with total losses of 7.66%. Basic Attention Token is a digital advertising token used to connect advertisers, content publishers, and content users. It is intended to monetize and reward user attention while also providing advertisers with better ROI.

The second and third worst performing cryptocurrencies were Reputation (REP) and Bytecoin (BCN) with total losses of 5.89%  and 1.28% respectively.

Figure 2a. Mean daily returns, historical daily volatility, total returns, and ex-post Sharpe ratio for each of the five cryptocurrencies with the highest total returns from May 11, 2019 to June 9, 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.

Cryptocurrency Positive Return - Bitcoin SV

Figure 2b. Mean daily returns, historical daily volatility, total returns, and ex-post Sharpe ratio for each of the five cryptocurrencies with the lowest total returns from May 11, 2019 to June 9, 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

Cryptocurrency Negative Return - 2019-06-10

Figure 3 plots daily candlesticks of the prices of BTC and ETH, the two largest cryptocurrencies by market capitalization. We also show BSV the top performer this past month. 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

The 50-day simple moving averages for Bitcoin and Ethereum continue to stay above the 100-day moving averages, a continuation of a long-term bullish signal. In addition, the 50-day simple moving average for Bitcoin SV just crossed above the 100-day moving average in May. This is the start of a long-term bullish signal.

The RSI values of three cryptocurrencies crossed below the 70 overbought threshold from above at various times over the past month; a bearish signal that indicates that upwards momentum has ended. The RSI values are now in between 30 and 70, indicating that they are neither overbought or oversold.

For Bitcoin and Ethereum, the MACD line crossed below MACD signal line around the end of May, and in the case of Bitcoin SV, this just happened on June 8. This is known as a bearish crossover and can also be interpreted as a bearish signal.

Figure 3a. Price of Bitcoin (BTC) in USD at Bitstamp from May 11, 2019 to June 9, 2019.

Price of BTC at Bitstamp - 2019-06-10

Figure 3b. Price of Ether (ETH) in USD at Bitstamp from May 11, 2019 to June 9, 2019.

Price of ETH at Bitstamp - 2019-06-10

Figure 3c. Price of Bitcoin SV (BSV) at Binance in USD from May 11, 2019 to June 9, 2019. BSV

Price of Bitcoin SV at Binance - 2019-06-10

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 May 11, 2019 to June 9, 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.

Cryptocurrency List - Bitcoin SV
Cryptocurrency List - 2019-06-10
Cryptocurrency List - 2019-06-10

APPENDIX B: Methodology

The daily price data of cryptocurrencies in USD at 4:00 PM EST from May 11, 2019 to June 9, 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 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.
  • 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.

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. Bitcoin price.

New 2019 High For BTC, But LINK Is the Bigger Winner

By | Coinscious Lab, Data Analytics, Market Report | No Comments

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 April 27, 2019 to May 26, 2019. LINK

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

Analysis

The performance of major cryptocurrencies over the past month was good, with only 44 out of the 50 cryptocurrencies that we examined up from their values 30 days ago. Bitcoin (BTC), the largest cryptocurrency by market capitalization, is on the rise and is currently trading slightly above $8700 at the time of writing.

Outside of cryptocurrencies, the S&P 500 is down 3.97% from 30 days ago and closed last Friday at $2826.06.

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 April 27, 2019 to May 26, 2019. Higher returns at a given level of risk, measured through historical daily volatility, indicates a better investment.

LINK - Cryptocurrency Risk Return

The best performer overall over the past month was Chainlink (LINK), with a total return of 158.14%. Chainlink aims to be a reliable decentralized data oracle that provides data inputs for smart contracts.

The second and third best performing cryptocurrencies were Bitcoin SV (BSV) and Ether (ETH), with total returns of 73.03% and 59.78% respectively.

Reputation (REP) was the worst performing cryptocurrency, with total losses of 8.69%. Augur is a decentralized oracle and peer-to-peer protocol for prediction markets. Reputation is the cryptocurrency used by reporters during market dispute phases of Augur.

The second and third worst performing cryptocurrencies were Basic Attention Token (BAT) and USD Coin (USDC) with total losses of 7.29% and 4.40% respectively.

Figure 2a. Mean daily returns, historical daily volatility, total returns, and ex-post Sharpe ratio for each of the five cryptocurrencies with the highest total returns from April 27, 2019 to May 26, 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. LINK

Figure 2b. Mean daily returns, historical daily volatility, total returns, and ex-post Sharpe ratio for each of the five cryptocurrencies with the lowest total returns from April 27, 2019 to May 26, 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. We also present the daily candlesticks of the price of Chainlink ( LINK ), the best performer of the past month. 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

The indicators for all three cryptocurrencies share many common features.

First, the 50-day simple moving average continues to stay above the 100-day moving average, a continuation of a long-term bullish signal.

The RSI values of all three cryptocurrencies crossed below the 70 overbought threshold from above; a bearish signal that potentially indicates the end of upwards momentum.

Furthermore, for all three cryptocurrencies, the MACD line is about to cross, or has just crossed below MACD signal line approximately a week ago. This is known as a bearish crossover and can also be interpreted as a bearish signal.

Figure 3a. Price of Bitcoin (BTC) in USD at Bitstamp from April 27, 2019 to May 26, 2019.

Figure 3b. Price of Ether (ETH) in USD at Bitstamp from April 27, 2019 to May 26, 2019.

Figure 3c. Price of ChainLink ( LINK ) in USDT at Binance from April 27, 2019 to May 26, 2019. LINK

Binance LINK USDT

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 April 27, 2019 to May 26, 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 April 27, 2019 to May 26, 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 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.
  • 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.

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. Bitcoin price.

Bitcoin Price Jumps Past $7,600

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

Bitcoin (BTC) 
+39.46%

Biggest
30d % Loss

Zilliqa (ZIL) 
-18.01%

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 April 13, 2019 to May 12, 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 was overall not very good, with only 18 out of the 50 cryptocurrencies that we examined up from their values 30 days ago. Bitcoin (BTC), the largest cryptocurrency by market capitalization, was the best performer over the past month and bitcoin price is $7600 at the time of writing.

Outside of cryptocurrencies, the S&P 500 is down 0.83% from 30 days ago and closed last Friday at $2881.40.

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 April 13, 2019 to May 12, 2019. Higher returns at a given level of risk, measured through historical daily volatility, indicates a better investment. Volatility, on the x-axis, is presented in log scale.

The best performer overall over the past month was Bitcoin (BTC), with a total return of 39.46%.

The second and third best performing cryptocurrencies were Bitcoin Gold (BTG) and Link (LINK), with total returns of 36.42% and 35.55% respectively.

Zilliqa (ZIL) was the worst performing cryptocurrency, with total losses of 18.01%. Zilliqa is a public blockchain platform designed to handle high transaction rates that scale linearly with network size.

The second and third worst performing cryptocurrencies were Siacoin (SC) and Aeternity (AE) with total losses of 17.26% and 14.29% 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 April 13, 2019 to May 12, 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 April 13, 2019 to May 12, 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 Bitcoin price and Ether price, the two largest cryptocurrencies by market capitalization. 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

The indicators for all three cryptocurrencies share many common features.

First, the 50-day simple moving average continues to stay above the 100-day moving average, a continuation of a long-term bullish signal.

Furthermore, for all three cryptocurrencies, the MACD line has crossed below the MACD signal line approximately a week ago. This is known as a bullish crossover and could be interpreted as a bullish signal.

The RSI values of all Ether and XRP are neither oversold or overbought, although both are trending upward and the value for Ether is almost crossing the 70 overbought threshold. Bitcoin’s RSI value is above 70 and indicates that it is overbought. Prices are typically expected to dip after being overbought, but momentum oscillators can also become remain overbought before actually reaching a peak during a strong trend.

Figure 3a. Bitcoin price (BTC) in USD at Bitstamp from April 13, 2019 to May 12, 2019.

Figure 3b. Price of Ether (ETH) in USD at Bitstamp from April 13, 2019 to May 12, 2019.

Figure 3c. Price of XRP (XRP) in USD at Bitstamp from April 13, 2019 to May 12, 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 April 13, 2019 to May 12, 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. Bitcoin price

APPENDIX B: Methodology

The daily price data of cryptocurrencies in USD at 4:00 PM EST from April 13, 2019 to May 12, 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 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. Bitcoin price.

Bitcoin Prices Steadily Increase; Now Holding at $5,400

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

Bitcoin Cash
+55.64%

Biggest
30d % Loss

Maker (MKR)
-23.58%

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 March 30, 2019 to April 28, 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 31 out of the 50 cryptocurrencies that we examined up from their values 30 days ago. Bitcoin (BTC), the largest cryptocurrency by market capitalization, is trading around $5,400. Prices were as high as $5,500 earlier this week. Bitcoin broke above the $4,200 overhead resistance level on April 1 with a sharp jump to $5,000 and has been steadily increasing in value since then.

Outside of cryptocurrencies, the S&P 500 is up 3.72% from 30 days ago and closed last Friday at $2,939.88.

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 30, 2019 to April 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 Bitcoin Cash (BCH), with a total return of 55.64%. Bitcoin Cash is an altcoin created after a hardfork to Bitcoin in August 2017. Bitcoin Cash has larger blocks than Bitcoin, and hence can theoretically process more transactions per second.

The second and third best performing cryptocurrencies were Basic Attention Token (BAT) and Binance Coin (BNB), with total returns of 80.40% and 37.59% respectively.

Maker (MKR) was the worst performing cryptocurrency, with total losses of 23.58%. Maker is a governance token as well as a utility token for the MakerDAO smart contract platform, which backs and stabilizes the value of Dai (DAI), a soft-pegged stablecoin.

The second and third worst performing cryptocurrencies were Waves (Waves) and Steem (STEEM) with total losses of 23.21% and 19.92% 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 30, 2019 to April 28, 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 30, 2019 to April 28, 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, Bitcoin Cash (BCH). 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

The indicators for all three cryptocurrencies share many common features.

First, the 50-day simple moving average continues to stay above the 100-day moving average, a continuation of a long-term bullish signal.

However, for all three cryptocurrencies, the MACD line has crossed below the MACD signal line. This is known as a bearish crossover and could be interpreted as a bearish signal.

Finally, The RSI values of all three cryptocurrencies were in overbought territory above 70 but have since returned to below 70. Prices are typically expected to dip after being overbought, but momentum oscillators can also become oversold multiple times or remain oversold before actually reaching a bottom during a strong uptrend.

Figure 3a. Price of Bitcoin (BTC) in USD at Bitstamp from March 30, 2019 to April 28, 2019.

Figure 3b. Price of Ether (ETH) in USD at Bitstamp from March 30, 2019 to April 28, 2019.

Figure 3c. Price of Bitcoin Cash (BCH) in USD at Bitstamp from March 30, 2019 to April 28, 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 30, 2019 to April 28, 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 30, 2019 to April 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. 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 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.

Investigating Crypto Exchange Price & Volume Patterns

By | Coinscious Lab, Data Analytics, Exchange Report | 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 February 16, 2019 to March 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:

  • OKEx and HitBTC go against the market both in terms of volume correlation and price-volume correlation.
  • Although OKEx has reported the biggest traded volume between February 16, 2019 to March 16, 2019, PCA analysis separates it from the other top exchanges (i.e. Binance and HuobiPro), and shows a low correlation with the market in terms of traded volume.

Performance

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

According to the public exchange data, OKEx has reported the biggest mean daily and hourly volume for the past month.

In the past month, OKEx took first place with a total of $28 billion traded between February 16 to March 16, compared to $25 billion for ZB (2nd) and $20 billion for Binance (3rd) (see Figure 1).

We chose ETH/BTC trading pair to analyze exchange volumes in more depth. ZB traded volume for ETH/BTC was the highest overall, following the trend described in our previous Exchange Report. However, OKEx took first place on March 5 when its volume changed from $78 million to $107 million in a single day. It remained at the top spot until March 12, when its volume dropped back down to $39 million; ZB retook first place. Interestingly, according to BeinCrypto, on March 5 ethereum’s price began to increase rapidly from $130 throughout the earlier part of the day, and reached a daily highly of $143.83 on March 6 [1].

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

Observations

The cryptocurrency market was on an uptrend starting from the morning of Wednesday, April 3, 2019 – the price of BTC went up from $4,000 to $4,800. In fact, because of the BTC pump, we observed that there were huge volume changes for all exchanges except Fcoin; their volume changes remained low.

Figure 3a. Top 10 crypto exchange volume changes on April 3, 2019.

As you can see in Figure 3a, the top 10 exchanges have 1-day volume changes ranging between 73.35% to 315.18%. However, Fcoin is the only exchange with a much lower volume change of 9.25%.

Three days later, on the morning of Saturday, April 6, 2019, the market started to slow down and became tranquil. Again, we can observe this from 1-day volume changes in Figure 3b, where most of the exchanges’ volume changes decreased to around 22-60%. Nevertheless, Fcoin and HitBTC volume changes were up by 18% and 19% respectively.

Figure 3b. Top 10 crypto exchange volume changes on April 6, 2019.  

These strong indications reveal that Fcoin (and possibly HitBTC) is likely faking its volume. The next section delves into a deeper analysis and looks at which specific exchanges violate volume trends in comparison to all exchanges overall.

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 identified a cluster and some outliers (see Figures 3a and 3b). 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-axes indicate similarities 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.

If we zoom in on the market mean, we can identify subgroups of exchanges that are closely related inside the market mean (Figure 3b).

Figure 3a. 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.

Figure 3b. PCA Volume analysis excluding outliers in the first analysis.

To understand the meaning of the two principal components and characterize the outliers, we decided to look at correlation between exchanges. We used volume for the ETH/BTC pair to identify a general trend in the market and determine whether exchanges follow or are against the trend (see Figure 5). Although most exchanges show a similar trend (that is, they follow a similar daily volume trend), OKEx, Bitflyer, BigOne and HitBTC show a daily volume trend that goes against the market. Interestingly, ZB and Zaif follow the mean market trend this time. Also note that compared to the PCA biplot using daily volume data from January 16 to February 16, OKEx and ZB have swapped positions (see Figure 3 from the previous Exchange Report)

Figure 5a. Top 5 negative volume correlations

Figure 5b. Top 5 positive volume correlations

Figure 6. Daily volume correlations between exchanges from February 16, 2019 to March 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.

OKEx, in particular, shows negative correlations with all exchanges except for HitBTC and HuobiPro. This suggests that OKEx, Bitflyer, HitBTC and BigONE are acting against the market.

Again, Upbit and Bittrex show the highest correlation for all exchange pairs, suggesting that they share the same order book [2]. This was also noted in our previous Exchange Report.

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 7. Daily price-volume correlations between exchanges from February 16, 2019 to March 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.

OKEx and HitBTC stand out as exchanges with the highest negative correlation between ETC/BTC price and volume for all exchanges. This shows that not only their volume, but also their ETH prices go against the market. While most exchanges show a positive trend (higher volumes associated with higher prices), OKEx, HitBTC, HuobiPro and BigOne show a negative trend (higher volumes associated with lower prices, and vice versa).

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:

  • Okex and HitBTC go against the market both in terms of volume correlation and price-volume correlation.
  • Although Okex has reported the biggest traded volume in the past month, PCA analysis separates it from the other top exchanges (i.e. Binance and HuobiPro) and shows a low correlation with the market in terms of traded volume.

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).

APPENDIX A: Methodology

The daily volume of cryptocurrencies in USD at 4:00 PM EST from February 16, 2019 to March 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.

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

By | Coinscious Lab, Data Analytics, Market Report | 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, Market Report | 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, Exchange Report | 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, Market Report | 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, Cryptocurrency | No Comments

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

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

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

Available Sources of High Quality Crypto-Trading Data

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

The Basics

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

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

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

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

Error In Trading Volume

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

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

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

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

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

Reason For Data Discrepancies Between Vendors

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

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

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

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/