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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.

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.

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

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

Coinscious Market Report

by Coinscious Lab

February 19, 2019

Biggest
30d % Gain

 Pundi X (NPXS)
+47.06%

Largest 30d %
Gain (Sector)

Payments & Settlements
+6.76%

Biggest
30d % Loss

 NEM (XEM)
25.41%

Biggest 30d %
Loss (Sector)

Data & Information
6.70%

Overview

Released bi-weekly, this report aims to identify broad trends in the cryptocurrency market. In order to reflect the latest developments in this fast-paced and volatile market, the reports plan to focus on metrics derived from a 30-day rolling window of data, this time from January 20, 2019 to February 18, 2019.

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

Sector
Constituent Coins/Tokens
Digital Cash BTC, BCH, BSV, LTC, BTG, DOGE, DCR, BCD, DGB
Privacycoins XMR, DASH, ZEC, XVG
DApp Platforms ETH, EOS, ADA, NEO, ETC, XEM, XTZ, QTUM, LSK, AE, ZIL, ICX, BTM, ETP
Resources SC, GNT
Payments and Settlements XRP, XLM, OMG, NPXS, MKR, PPT
Decentralized Exchanges BTS, ZRX, WAVES
Digital Content TRX, ONT, BAT, STEEM
Data and Information IOTA, VET, LINK, REP
Stablecoin USDT, TUSD, DAI

Analysis

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

Outside of cryptocurrencies, the S&P 500 has been performing well, up 3.93% from 30 days ago and closing last Friday at $2,775.60. Figure 1 presents the risk versus return trade-off over the past 30 days by plotting mean daily return versus historical daily volatility for various cryptocurrencies.

Figure 1. Plot of mean daily return against historical daily volatility for individual cryptocurrencies from January 20, 2019 to February 18, 2019. Higher returns at a given level of risk, measured through historical daily volatility, indicates a better investment.

The best performer overall over the past month was Pundi X (NPXS), with a total return of 47.06%. The second and third best cryptocurrency was Litecoin (LTC) and EOS (EOS), with a total return of 42.79% and 23.01% respectively. Pundi X and Litecoin both performed relatively better than the similar cryptocurrencies that make up the rest of their sectors. Pundi X is a token used for payments and settlements, while Litecoin falls into the digital cash category.

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

Figure 2. Mean daily returns, historical daily volatility, total returns, maximum drawdown, and ex-post Sharpe ratio for each sector from January 18, 2019 to February 20, 2019. Less negative maximum drawdowns and more positive Sharpe ratios are more desirable. The Sharpe ratio is calculated with the 10 year US Treasury bill rate as the annual risk-free rate.

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

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

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

Figure 4. Correlation between daily returns of each sector from January 20, 2019 to February 18, 2019. Correlation ranges between -1 and 1. Correlation close to 1 indicates a more positive relationship between the pair of cryptocurrency returns and correlation close to -1 indicates a more negative linear relationship. Correlation close to 0 indicates no linear relationship.

APPENDIX A: Methodology

The daily price data of cryptocurrencies in USD at 4:00 PM EST from January 20, 2019 to February 18, 2019 was used for our calculations.

The prices are the volume weighted average price of the cryptocurrency in USD at 4:00 PM EST each day across all exchanges where Coinscious has data. If there was insufficient good quality data on a cryptocurrency’s value in USD, we would instead use the cryptocurrency’s value in USDT and apply a conversion rate to turn it to USD. If data was still insufficient, then we would find the volume weighted average price of the cryptocurrency in both BTC and ETH, then converted both into USD, and finally took the mean of those values. The conversion rates we use at a given time are the volume weighted average price of USDT, BTC, or ETH to USD at that specific time across all exchanges where Coinscious has data.

To analyze performance by sector, the prices of constituent cryptocurrencies was normalized by dividing by the price on January 20, 2019, then averaged. When calculating the daily returns using this averaged normalized price, it is equivalent to if each sector was represented as an equally weighted portfolio of its constituent cryptocurrencies formed starting January 20, 2019 and the returns of the portfolio were calculated. Returns used throughout this report refer to simple returns.

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

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

APPENDIX B: Terminology

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

Disclaimer

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

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

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

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

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

Coinscious Market Report

by Coinscious Lab

February 4, 2019

Biggest
30d % Gain

  Augur (REP)
+50.39%

Biggest 30d %
Gain (Sector)

Data & Information
+5.31%

Biggest
30d % Loss

 NEM (XEM)
37.86%

Biggest 30d %
Loss (Sector)

Resources
19.58%

Overview

Released bi-weekly, this report aims to identify broad trends in the cryptocurrency market. In order to reflect the latest developments in this fast-paced and volatile market, the reports plan to focus on metrics derived from a 30-day rolling window of data, this time from January 5, 2019 to February 3, 2019.

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

Sector
Constituent Coins/Tokens
Digital Cash BTC, BCH, BSV, LTC, BTG, DOGE, DCR, BCD, DGB
Privacycoins XMR, DASH, ZEC, XVG
DApp Platforms ETH, EOS, ADA, NEO, ETC, XEM, XTZ, QTUM, LSK, AE, ZIL, ICX, BTM, ETP
Resources SC, GNT
Payments and Settlements XRP, XLM, OMG, NPXS, MKR, PPT
Decentralized Exchanges BTS, ZRX, WAVES
Digital Content TRX, ONT, BAT, STEEM
Data and Information IOTA, VET, LINK, REP
Stablecoin USDT, TUSD, DAI

Analysis

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

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

Figure 1. Plot of mean daily return against historical daily volatility for individual cryptocurrencies from January 5, 2019 to February 3, 2019. Higher returns at a given level of risk, measured through historical daily volatility, indicates a better investment.

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

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

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

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

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

Figure 2. Mean daily returns, historical daily volatility, total returns, maximum drawdown, and ex-post Sharpe ratio for each sector from January 5, 2019 to February 3, 2019. Less negative maximum drawdowns and more positive Sharpe ratios are more desirable. The Sharpe ratio is calculated with the 10 year US Treasury bill rate as the annual risk-free rate.

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

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

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

Figure 4. Correlation between daily returns of each sector from January 20, 2019 to February 3, 2019. Correlation ranges between -1 and 1. Correlation close to 1 indicates a more positive relationship between the pair of cryptocurrency returns and correlation close to -1 indicates a more negative linear relationship. Correlation close to 0 indicates no linear relationship.

APPENDIX A: Methodology

The daily price data of cryptocurrencies in USD at 4:00 PM EST from January 5, 2019 to February 3, 2019 was used for our calculations.

The prices are the volume weighted average price of the cryptocurrency in USD at 4:00 PM EST each day across all exchanges where Coinscious has data. If there was insufficient good quality data on a cryptocurrency’s value in USD, we would instead use the cryptocurrency’s value in USDT and apply a conversion rate to turn it to USD. If data was still insufficient, then we would find the volume weighted average price of the cryptocurrency in both BTC and ETH, then converted both into USD, and finally took the mean of those values. The conversion rates we use at a given time are the volume weighted average price of USDT, BTC, or ETH to USD at that specific time across all exchanges where Coinscious has data.

To analyze performance by sector, the prices of constituent cryptocurrencies was normalized by dividing by the price on January 5, 2019, then averaged. When calculating the daily returns using this averaged normalized price, it is equivalent to if each sector was represented as an equally weighted portfolio of its constituent cryptocurrencies formed starting January 5, 2019 and the returns of the portfolio were calculated. Returns used throughout this report refer to simple returns.

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

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

APPENDIX B: Terminology

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

Disclaimer

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

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

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

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

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

Coinscious Market Report

by Coinscious Lab

January 21, 2019

Biggest
30d % Gain

Augur (REP)
+
135.34%

Biggest 30d %
Gain (Sector)

Data & Information
+44.42%

Biggest
30d % Loss

 Bitcoin Cash (BCH)
-33.31%

Biggest 30d %
Loss (Sector)

Digital Cash
-13.81%

Overview

Released bi-weekly, this report aims to identify broad trends in the cryptocurrency market. In order to reflect the latest developments in this fast-paced and volatile market, the reports plan to focus on metrics derived from a 30-day rolling window of data, this time from December 22, 2018 to January 20, 2019.

Our universe of analysis includes 51 of some of the most widely used and traded cryptocurrencies, and groups them into sectors that reflect similar utility and valuation models. Through analysis of the recent historical performance of individual cryptocurrencies as well as their sectors, we provide a framework for analysis where investors can identify outperforming cryptocurrencies or sectors by comparing their performance relative to peers.

Sector
Constituent Coins/Tokens
Digital Cash BTC, BCH, BSV, LTC, BTG, DOGE, DCR, BCD, DGB
Privacycoins XMR, DASH, ZEC, XVG
DApp Platforms ETH, EOS, ADA, NEO, ETC, XEM, XTZ, QTUM, LSK, AE, ZIL, ICX, BTM, ETP
Resources SC, GNT
Payments and Settlements XRP, XLM, OMG, NPXS, MKR, PPT
Decentralized Exchanges BTS, ZRX, WAVES
Digital Content TRX, ONT, BAT, STEEM
Data and Information IOTA, VET, LINK, REP
Stablecoin USDT, TUSD, DAI

Analysis

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

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

Figure 1. Plot of mean daily return against historical daily volatility for individual cryptocurrencies from December 22, 2018 to January 20, 2019. Higher returns at a given level of risk, measured through historical daily volatility, indicates a better investment.

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

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

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

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

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

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

Figure 2. Mean daily returns, historical daily volatility, total returns, maximum drawdown, and ex-post Sharpe ratio for each sector from December 22, 2018 to January 20, 2019. Less negative maximum drawdowns and more positive Sharpe ratios are more desirable. The Sharpe ratio is calculated with the 10 year US Treasury bill rate as the annual risk-free rate

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

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

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

Figure 4. Correlation between daily returns of each sector from December 22, 2018 to January 20, 2019. Correlation ranges between -1 and 1. Correlation close to 1 indicates a more positive relationship between the pair of cryptocurrency returns and correlation close to -1 indicates a more negative linear relationship. Correlation close to 0 indicates no linear relationship.

APPENDIX A: Methodology

The daily price data of cryptocurrencies in USD at 4:00 PM EST from December 22, 2018 to January 20, 2019 was used for our calculations.

The prices are the volume weighted average price of the cryptocurrency in USD at 4:00 PM EST each day across all exchanges where Coinscious has data. If there was insufficient good quality data on a cryptocurrency’s value in USD, we would instead use the cryptocurrency’s value in USDT and apply a conversion rate to turn it to USD. If data was still insufficient, then we would find the volume weighted average price of the cryptocurrency in both BTC and ETH, then converted both into USD, and finally took the mean of those values. The conversion rates we use at a given time are the volume weighted average price of USDT, BTC, or ETH to USD at that specific time across all exchanges where Coinscious has data.

To analyze performance by sector, the prices of constituent cryptocurrencies was normalized by dividing by the price on December 22, 2018, then averaged. When calculating the daily returns using this averaged normalized price, it is equivalent to if each sector was represented as an equally weighted portfolio of its constituent cryptocurrencies formed starting December 22, 2018 and the returns of the portfolio were calculated. Returns used throughout this report refer to simple returns.

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

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

APPENDIX B: Terminology

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

Disclaimer

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

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

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

Market Trend: Bitcoin Definitively Crosses Below $3,000

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

Coinscious Market Report – December 10, 2018


Released bi-weekly, this report aims to identify broad trends in the cryptocurrency market. In order to reflect the latest developments in this fast-paced and volatile market, the reports are planned to focus on metrics derived from a 30-day rolling window of data, this time from November 7, 2018 to December 7, 2018. In this report, we also provide analysis on bitcoin price movements from a technical perspective to see where the market as a whole may be headed in the near future.

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

Introducing Crypto Market Reports by Coinscious Lab

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

We’re excited to share our first Coinscious Market Report by Coinscious Lab. Released bi-weekly, this report aims to identify broad trends in the cryptocurrency market. In order to reflect the latest developments in this fast-paced and volatile market, the reports are planned to focus on metrics derived from a 30-day rolling window of data, this time from October 23, 2018 to November 22, 2018.

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

Read more: Coinscious Market Report: November 23, 2018

Coinscious on Nasdaq NYC

Coinscious on the NASDAQ Big Screen in Times Square

By | Events | No Comments

On August 9, Coinscious appeared on the NASDAQ big screen in New York City’s Times Square. Given our summer tour that has spanned from Canada to US to Europe and all over Asia, it seems appropriate that our logo would make its appearance at the landmark known as the “crossroads of the world.” At Coinscious, we want our brand to represent our extensive work in artificial intelligence, big data, and blockchain as the building blocks for our Coinscious Collective™ platform. These technologies drive cryptocurrency markets and represent a another kind of global crossroad—a level and open playing field rich with opportunity. Our passion for these fields are why we’ve spent time going from country to country this summer, connecting with thought leaders and spreading our message.

Our core team at Coinscious, headquartered in Canada, is composed of experts in blockchain, AI, and big data. Our Coinscious Collective™ platform uses AI to build statistical models. It also uses machine learning to take advantage of financial engineering, quantitative trading, and risk management techniques. In this way, the platform provides investors with a smart, efficient, secure platform for digital asset management and AI-powered investing.

Coinscious has created products and services to help investors find useful and timely market information, manage their assets and investment plans, forecast prices and create quantitative strategies. We created this platform to meet the needs of everyday investors and professional institutions who need to make the most of their time and resources. It’s our hope that, in the future, Coinscious will also aid in the compliance, rapid development, and progress of the digital asset investment market. But for now, we’re really just enjoying seeing our logo light up Times Square.

Coinscious Labs Arbitrage

Coinscious Lab: Arbitrage

By | Coinscious Lab | No Comments

The Challenge of Arbitrage in Crypto Markets

People get excited when they hear about arbitrage opportunities in the crypto market. That excitement is driven by frequently seeing big price differences between exchanges. If you have coding skills, you might think taking advantage of those price differences using arbitrage will be a piece of cake.

The real challenges come as you write, test, and run your code against exchange accounts. Here are just a few of the challenges that come from trying to execute an arbitrage strategy in the crypto market.

Simulation Accuracy

Most developers use simulations to verify their ideas and coding. However, depending on how you execute your simulation, your simulation results might be quite different from your real-world  results. For example, if your simulation is based on the order book (open buy and sell orders), your simulation result might be totally misleading.

Placing Orders

When you see price differences between exchanges, do you check price differences in the order book as well? Do you know if your orders will actually be fulfilled or the timing of those orders?

Capturing Future Opportunities

After you make some arbitrage transactions, you might find one exchange price is always higher than another. Opportunities to take advantage of arbitrage may decrease. How will you continue to profit with limited funds and fewer opportunities?

Technical Challenges

The crypto exchange is not like the stock exchange. Most crypto exchange APIs are not designed for high-frequency trading. Some APIs are poorly designed and unstable. The exchange may go down. The API server could stop responding. Perhaps internal logic or a limit is changed on the exchange side. There are many scenarios where API calls fail. It might be harder than you think to execute a robust arbitrage program without human interaction.

Weighing the Risks

Transaction fees are not cheap in crypto exchanges. If you have transaction success in one exchange, but failed transactions in another, you could lose money very quickly.

Profit Expectations

Of course, everyone would like to know if profit expectations through arbitrage are real and reliable. Many variables come into play when answering this question.

Choosing an Arbitrage Coding Strategy

In addition to two-point arbitrage, you also might hear about triangle or multiple-point arbitrage. Do you understand which strategy is best for you? Does fund size affect your arbitrage strategy? What is the proper fund size you should try? Answering these questions might be harder than you thought.

Bot Parameters

The parameters used by trading bots are also important. There is no “one size fits all” bot that can simply run for you all day every day. Bot parameters have to be set, tested, optimized, and then put into practice in the real world.

Experiment Goal

In this lab experiment, we conducted an arbitrage experiment to determine how to overcome the challenges mentioned above. This lab experiment will also verify what kind of data support is needed to maximize profit and minimize risk.

Profit Snapshots 

Coinscious Labs Arbitrage

This image shows the total amount of transactions completed per day.

 

Coinscious Labs Arbitrage

This image shows a breakdown of all 36 transactions from January 14. The highlighted price in the top right of the image shows that we bought 13.46 LSK at a price of 0.00194800 BTC, on Binance. Correspondingly, this transaction is shown in the Binance transaction history in the snapshot below.

 

Our results in the Binance transaction history

 

Experiment Results

We ended the month with a profit of +0.81616874 BTC (+55.34%), and +80.02477571 LSK (+8.07%).

We see our transactions in the real world, fully automated, with a dynamic configuration of different variables and parameters.

This lab experiment was conducted on several combinations of currencies (such as: BTC/LSK, BTC/USD, BTC/IOTA, BTC/ETH) and on different exchanges.

All experiments were successful. In this Coinscious Lab experiment, we looked at the results of our BTC/LSK arbitrage. For data and information on other experiments, please contact us.

Coinscious Labs Arbitrage Results

Here we see the accumulative results of arbitrage for each day during the month of January.

 

Future Development

Based on different experiments completed by Coinscious Lab, the Coinscious product team will create new product designs that cover the various needs in crypto arbitrage. These designs will not only help traders capture more opportunities, but also will optimize the process through simulation, AI optimization, profit estimation, and so on.

 

Cryptocurrency Arbitrage

What is Arbitrage?

By | Cryptocurrency | No Comments

What Is Arbitrage and How Is It Used in the Coin Market?

The Merriam-Webster dictionary defines arbitrage this way:

The near simultaneous purchase and sale of securities or foreign exchange in different markets in order to profit from price discrepancies.

Let’s break that down further and also understand how arbitrage strategies are used in the cryptocurrency market.

A Simplified Explanation of Arbitrage

Arbitrage is considered a no-risk profit strategy when executed against traditional financial instruments. Why? Because you are buying an asset and selling it simultaneously for a higher price at a profit. It’s considered no risk because there is no hold time or delay between the transactions and the profit is guaranteed.

This type of transaction can be completed with any asset type, but typically the assets are bonds, stocks, currency or other financial instruments.

Here’s a simplified example. A stock is trading for $10 in the New York Stock Exchange (NYSE), but is trading on the Tokyo Stock Exchange (TYO) for $11. You would buy the NYSE stock at the lower price and simultaneously sell the stock in the TYO, making a $1 profit per share.

Because of automated trading systems and high-frequency trades it’s rare for this sort of price discrepancy to occur, making it rare for arbitrage trades like this to happen. Today, when these discrepancies are noticed, they usually only last for a short window of time, measured in seconds or even microseconds. High interest in the “cheap” stock raises its price and subsequently drives down the price of the “expensive” stock. Even before automated trades, arbitrage was viewed as a mechanism for maintaining equilibrium across markets.

What Does Arbitrage Have to Do with Cryptocurrency?

Unlike the stock market, the coin market is ripe for arbitrage. Cryptocurrency don’t support high-frequency trading, which means there are fewer automated trading robots controlling or responding to price fluctuation. There are fewer pressures or controls to ensure pricing equilibrium across exchanges. Also, when new exchanges open, they offer an opportunity to buy in a more established exchange to sell at a profit in the new exchange.

If you plan to pursue arbitrage as a trading strategy, there are three things you must consider:

  • Fees. This can include trading fees, withdrawal or deposit fees, and blockchain fees. You have to calculate if the fees you incur will eat the profit that you anticipate making.
  • Time. Rarely do trades happen simultaneously in the coin market as they can in the automated stock exchanges. You have to weigh the risk that you’ll miss the pricing window if a trade is slowed down for some reason.
  • Risk. Arbitrage in the stock market is considered no risk. Because of the time and fee considerations mentioned above, arbitrage in the coin market is hardly a no-risk venture.

What Do You Need to Profit from Coin Arbitrage?

Reliable information about cryptocurrency is one of the most valuable assets you can possess. In this relatively new market, it’s also one of the hardest assets to find.

If you plan to use arbitrage in the coin market, what information and tools should you look for?

  • Rates: To benefit from arbitrage, you’ll need to know current rates of exchange. Seek out information sites that provide up-to-date coin prices.
  • Opportunities: Try to access a service that can send alerts about potential opportunities. Alerts can help you act on opportunities before other traders do.
  • Simulations: Success in the coin market relies on creating and executing smart trade strategies. Simulation tools can help you validate and improve your trade strategies.
  • Bots: Automation helps traders stay on top of market changes. You can use bots set up to take specific actions based on limits that you set.

You may need nerves of steel to execute coin arbitrage, but having reliable, credible information should remove at least some of the guesswork.