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Crypto Currency Market Index

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We are often asked about broad market indicators for different asset classes. Recently Koby finished some research on a Crypto Currency broad market indicator that is predictive of future Bitcoin prices. We call this indicator the Crypto Market Score (CMS).

For the CMS we take once a day snapshots of all coins with a sentiment value. Our universe of covered coins consists of coins that have at one point been in the top 200 market cap of all cryptocurrencies.  Our universe currently stands at approximately 600 coins. We use our patented NLP to read and assign sentiment scores to each Tweet.  Each Tweet is scored from -1 to 1 out to four decimal places.  Because each Tweet is scored independently, we are creating a Tweet count weighted sentiment value.  We aggregate the sentiment from all coins over the last 24 hours by date to aggregate to a daily sentiment value.

We calculate a 14-day rolling sum of the sentiment values and compare it to a 60-day baseline to generate a CMS. Our CMS is a Z-Score with a 14-day summation of Tweet scores for all coins in our universe and compared to a 60-day baseline.

We compared the CMS to forward weekly bitcoin returns. There are a total of 1203 days of data with the CMS ranging from -4.00 to +4.00. In the table below Positive (539) + Negative (664) = our test period 1203 days.

Below are the general return characteristics on the Crypto Market Score. The average 7-day return for BTC was 2.028% for our test period.

As you can see from the return table, days the CMS is positive Bitcoin returns 2.367% over the next 7-days. When the CMS is negative, Bitcoin returns 1.754% over the next 7-days. As the CMS rises the subsequent return of Bitcoin increases; reaching a high of 14.826% for the 8 instances when our CMS is greater than 3. When the CMS is negative, Bitcoin 7-day returns are less than average or in some cases negative. These relationship between the CMS and forward 7-day Bitcoin returns is a positive linear relationship as shown by the monotonic nature of the return characteristics. To learn more about our Market Sentiment Score or Social Market Analytics ContacUS@SocialMarketAnalytics.com.

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SMA releases ‘Crypto Fast’ Sentiment Data Feed

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Social Market Analytics (SMA) the leader in predictive social media data feeds has added the ‘Crypto Fast’ to its suite of API data feeds. SMA’s S-Factor and Activity Cryptocurrency data feeds have been in production since December 2017. “Clients asked for a bespoke feed for a shorter baseline with 1-Hour price projections. Although clients can create their own baselines and metrics with our Activity Feed, clients wanted SMA to do the development work and produce and support the product which has been named ‘Crypto Fast’.”
The SMA Crypto Fast Feed provides faster moving signals than the SMA S-Factor feed. The S-Factor Feed is a 24H lookback with a 20 baseline with decay which supports intraday out to 2-3 trading days. SMA’s Activity Feed is in isolation of what happened in each minute, which can be narrow to HFT or customized to any period including W, M, Q.
Like the calculation of SMA’s S-Score, the ‘Crypto Fast’ is a normalized sentiment score with a shorter 1H lookback period with a 12H baseline to better take into account the high volatility in the cryptocurrency market. For each crypto asset, SMA makes a 1-hour price projections based on its Crypto Fast and price momentum. SMA provides a projected return, as well as a projected range on the return with a 95% confidence interval. The accuracy field reflect how often the subsequent return has fallen within the projected return range historically.

 

Photo credit: SMA has partnered with TheTie to power sentiment www.thetie.io
SMA APIs went into production in 2011 for U.S Equities and have grown to include UK Equities, ETFs, FX, Futures, and Cryptocurrencies. SMA produces over 25 distinct APIs across 6 asset classes www.socialmarketanalytics.com
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Social Market Analytics (SMA) Partners with Coin Metrics to provide Real-Time Sentiment Data Feeds

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Coinmetrics

Coin Metrics and Social Market Analytics (SMA) announced today a partnership to incorporate SMA’s Crypto Currency Data Feed into the Coin Metrics Market Data Platform.

Alternative data such as social media platforms and data feeds have become a vital source of information for traders, particularly in the Crypto Currency Markets. The SMA Crypto Currency Sentiment Feed will offer the Crypto Currency community a tool for including social media sentiment data in their trading and portfolio strategies and expand Coin Metrics market leading Crypto Asset market and network data products.

“As the Crypto Investing market continues to mature, institutional investors are demanding data from trusted partners. These institutions are looking to make data-driven decision by accessing sources of data that they understand from their legacy investing frameworks. We believe that the power of combining sentiment data with granular network and market data is fundamental to building a deeper understanding of crypto assets. Coin Metrics is excited to partner with SMA, who has a long history of providing sentiment data to traditional capital markets participants and share Coin Metrics’ principles and values. The ability to provide an all-in-one Crypto Financial Data solution is a huge convenience for institutions.” Comments Tim Rice Co-Founder and CEO of Coin Metrics.

“Artificial intelligence and Natural Language Processing are moving into our everyday lives at light speed, and perhaps into financial markets even faster than that. We feel strongly at SMA that participants in Crypto Currency markets will benefit from our unique process in this emerging field, both in its approach to filtering social media data and in the analytical methodology used to develop our proprietary metrics. We’re excited to partner with the Coin Metrics team to offer this service through a versatile industry leading platform” said Joe Gits, Co-Founder and CEO of SMA.

About Coin Metrics

Coin Metrics was founded in 2017 as an open-source project to provide the public with actionable and transparent network data. Today, Coin Metrics delivers market and network data, analytics and research to its community and wider industry. https://coinmetrics.io/

About Social Market Analytics, Inc.
Social Market Analytics quantifies social media data for traders, portfolio managers, hedge funds and risk managers using patent pending technology to detect abnormally positive or negative changes in investor sentiment. SMA produces a family of quantitative metrics, called S-Factors™, designed to capture the signature of financial market sentiment. SMA applies these metrics to data captured from social media sources to estimate sentiment for indices, sectors, and individual securities. A time series of these measurements is produced daily and on intraday time scales. For more information, including a User Guide to S-Factors™, please visit www.socialmarketanalytics.com

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Power of Predictive Alpha in a Bear Market

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This year has been tough for most investment strategies.  Firms using traditional sources of data are generating the same underwhelming returns.  Two years ago, Social Market Analytics, Inc.  (SMA)  (Twitter)   launched the SMLCW index in partnership with the CBOE.  This index is re-balanced weekly and comprised of the twenty-five securities selected from the CBOE large cap universe with the highest average S-Score over the prior week.  It’s A long only index of super-cap stocks with unusually positive Twitter conversations.

SMA publishes a family of metrics providing a full representation of the Twitter conversation across equities (US and LSE), commodities, currencies, ETF’s & Cryptos.

S-Score is a normalized representation of the current Twitter conversation of professional investors as identified by Social Market Analytics patented algorithms.  SMA has access to the full Twitter feed through our licensed partnership with Twitter and listens in real-time for any mention of topics and securities of interest.  These Tweets are scanned in real-time for sentiment and influence of the poster and compared to prior conversations over the look back period.  Securities with higher S-Scores subsequently outperform and securities with negative S-Scores under-perform.

SMA S-Scores are predictive over multiple prediction periods.  With seven years of out-of-sample data we can extend our comparison baselines and predict over longer periods.

Year-To-Date the SMLCW index is up over 7.5% while the SP500 is flat.  Subtracting a couple percent for commissions/slippage and the index is still significantly positive. This is not a back-test, this index has been live and on your quote screens for nearly two years.  YTD actual performance chart from the CBOE site is below.

SMLCW - YTD

As mentioned, this is a long only index.  During the recent market drawdown this long index has been performing.  SMA negative S-Score stocks have been moving lower at a significant rate – generating positive alpha.  Below is a chart of the SMLCW index compared to the SP500.  for any questions or to learn more please contact us at:  ContactUs@SocialMarketAnalytics.com.

Thanks,

Joe

 

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Bitcoin Sentiment Based Trading Strategy

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Social Market Analytics (SMA) publishes real time Twitter based sentiment for nearly 300 crypto currencies including Bitcoin.  To view Bitcoin sentiment values and 35 other commodities in real time, go to the CME Active Traders website.   Twitter based sentiment has proven to be strongly predictive for Bitcoin and other commodities.

Today we will review a sentiment-based Z-Score strategy to generate profitable trades for Bitcoin.  This is similar to traditional standard deviation band strategies calculated with price.

When Twitter volume from certified investors is abnormally high use the sentiment of the abnormally large conversation to select entry points.  Strategy overview is below:

CMEBitcoin 1

A visualization of the strategy is below. When the Z-Score of Social Market Analytics Indicative Twitter volume is greater than the threshold and the tone of the conversation is significant enter or modify trades.  Sentiment  > 2 standard deviations and the volume of the conversation is high enter a position.  Positions are modified based on further extensions of the Z-Score.

CMEBitcoin2

Test period is from 1/1/2017 to current.  Overall results below.  For more detailed results on this and other strategies contact ContactUS@SocialMarketAnalytics.com

CMEBitcoin3

SMA has examples of profitable applications of Twitter based sentiment to many coins.

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Social Market Analytics Sentiment Data for Forex Trading

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Social Market Analytics (SMA)  provides real-time sentiment data for equities (North America & LSE), commodities, foreign exchange, Crypto Currencies and ETF’s.

In this blog I am going to explore a trading system using the SMA Twitter based sentiment data to trade a basket of: EURUSD, EURGBP, GBPJPY, GBPUSD ,USDCAD ,USDCHF ,USDJPY.

We will explore two straight forward trading systems:

  • Forex Sentiment RSI: Daily Long/Short Strategy
  • SMA S-Score Based Currency Selection Model

RSI Calculation Methodology 

CurrencyBlog 1

This strategy is a single-factor model solely based on adjusting daily weights according to 3-Day Sentiment RSI on the 7 of the highest daily volume Forex pairs. It is long-short with the assumption that tails act with similar magnitude.

  • Long/Short
    1. RSI >= 50, Long
    2. RSI < 50, Short
  • 50% Long & 50% Short Asset Allocation
    1. Long weights are calculated using only longs
    2. Short weights are calculated using only shorts
  • Daily weight adjusted following:
    1. separately for the long side and the short side

 

currencyBlog2

The strategy significantly improves returns compared to an equal weighted baseline.  Sharpe and Sortino ratios are statistically significant:

  • Sharpe Ratio:
    • 2.77 Jan 03, 2017 to July 19, 2018
    • 3.40 YTD
  • Sortino Ratio:
    • 5.40 Jan 03, 2017 to July 19, 2018
    • 7.46 YTD

The volatility of each leg of the strategy is either kept stable or decreased in comparison with the baseline.

SMA S-Score Based Currency Selection Model

This daily trading strategy is based on the S-Score at 09:10:00 EST and executing a 24-hour hold based on these values at 09:15:00 EST. We find consistency across execution times.  The goal is to assess sentiment and take make a directional trade in agreeance, given that the sentiment falls at least 1 standard deviation from the 20-day mean.

Equal weighted based on standard deviation criteria:

– Long: S-Score > 1

– Short: S-Score < -1

– Baseline: Equal Weighted Portfolio of the 7 Currency pair

Long and short legs are capped at 50% of the daily portfolio, even on the occurrence of an outlier day where all pairs are long, or all pairs are short.

currencyBlog3

 

The strategy drastically improves returns compared an equal weighted baseline.  Up to 40% cumulative over a 19-month period with a consistent annual rate of return.

  • Sharpe Ratio:
    • 2.56 Jan 03, 2017 to July 19, 2018
    • 3.56 YTD
  • Sortino Ratio:
    • 4.93 Jan 03, 2017 to July 19, 2018
    • 7.72 YTD

These are straight forward strategies that illustrate the predictive nature of our dataset.  Twitter and StockTwits based factors.  To learn more about how Social Market Analytics sentiment data can help your trading please contact us at contactus@Socialmarketanalytics.com or Doug Hopkins @ (312) 788-2621.

 

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UIUC Bitcoin Trading System Practicum Presentation

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Every year Social Market Analytics (SMA) is proud to work with the University of Illinois Masters of Science in Financial Engineering Students on a practicum project. In the past we have explored looking at sentiment to predict the VIX, enhancements to traditional indexes and smart beta ETF’s. This year we decided to tackle the most popular topic of the last year – Bitcoin Trading!   We worked with RCM Capital’s Strategy Studio Platform for back testing to develop a Bitcoin trading strategy combining price momentum with sentiment to keep you in the market when Bitcoin is trading up and minimizing draw downs when Bitcoin retreats as it did in early 2018.

Social Market Analytics tracks sentiment on the top 275 market cap currencies, the below Bitcoin strategy performs similarly on other Crypto currencies.

The students did a wonderful job in strategy construction and explanation.  I will undoubtedly leave something important out.  ContactUs@SocialMarketAnalytics.com for details.

At it’s core the strategy buys on a price breakout with a sentiment confirmation.  Exit when price breaks down and is confirmed with sentiment.  Buy when the price crosses above (K) standard deviations over a 21 day moving average of price.  Variable K ranged from .5 to 2. Results shown use a .5 standard deviation multiplier.  Strategy visualization is below.

BitcoinStrategyVisual

Your first trigger is a breakout above K- Standard deviations of the 21 day moving average.

The confirming signal is based on the Social Market analytics S-Score value.  S-Score is a normalized representation of Bitcoin’s Sentiment time series over a look back period and is updated every minute.  It measures the tone of the conversation on Twitter relative to the benchmark time period.  If Bitcoin is breaking out and the sentiment is 2 standard deviations more positive than normal you initiate or add to your position by 50%.  If the conversation is 1 standard deviation more positive than normal  increase the position 25%.  If the standard deviation price break out is not confirmed by sentiment then no position change.

There was no short position initiated with futures.  Exit criteria are opposite entry criteria.  Price break below K – Standard deviations below a moving average. Confirmation with S-Score.

BitcoinResults

Dollar P/L results indicated this portfolio successfully navigates the the bitcoin draw down of early 2018.   2018 in isolation is below.

Bitcoin-2018

Overall performance with Buy & Hold Bitcoin comparison.

BitcoinStats.png

Sharpe ratio and draw down improve dramatically with the momentum and sentiment confirmation.

stats2

Again, please ContactUs@SocialMarketAnalytics.com for more information on our offerings.

Thanks again to the University of Illinois MSFE students and RCM  Capital Markets for contributing to this project.

Regards,

Joe

 

 

 

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