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



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.



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 or Doug Hopkins @ (312) 788-2621.


Today I will explore decile groupings based on S-Scores, and  plot cumulative subsequent returns. We typically focus on an S-Score > 2 for subsequent positive movements in stock prices, and an S-Score < -2 for negative movements in stock price.

Our metrics identify when a conversation becomes significantly more positive or negative than normal.  Most stocks have normal conversations on any given day.  On these days there are other factors driving the security. “Normal” conversation securities will typically follow the market, as you see in the SMA data set.  High sentiment out-performs and low sentiment under-performs,  Open to Close, and Close to Close, across Twitter and StockTwits.

The only filter we add is that the prior day’s closing price must be above $5, to avoid penny stocks.  Total return time series are used for returns, and time series are equal weighted.

The first chart illustrates subsequent Open to Close returns based on S-Score deciles at 9:10 a.m. Eastern time. As you can see, the deciles are in order with top decile securities out-performing and bottom decile securities under-performing.  SPY is represented by the black line and the universe is blue.


Pre-Market Close deciles are below.  S-Scores are taken at 3:40 p.m. Eastern and Close to Close returns are calculated.  Again, high S-Score securities out-perform and low S-Score securities under-perform, with the universe in the middle.


StockTwits is the largest chat community for active traders.  Its users are professional traders discussing long and short positions. The below chart looks at S-Score decile returns based on StockTwits conversations.

Data is consistent across deciles.  A unique characteristic of the StockTwits feed is that there are significant short conversations.  The lowest two deciles have negative returns.  This is a function of the StockTwits community being able to short securities by direct short selling or taking net short options positions.

StockTwits PreOpen

Pre-market close deciles are below.

StockTwits CLose-close

To learn more about Social Market Analytics and the products we offer please visit our website, or contact us here.



Last year was a good year for SMA data.  High sentiment securities outperformed and low sentiment securities underperformed with good Sharpe’s and Sortino’s.  The below tables contain returns and Sharpe/Sortino ratios for the full history of Social Market Analytics S-Factor data.   Correlations to standard factors continue to be near zero. I’m sure our data can help in your investment process, contact us to learn more.
Five-year return summary:


 Sharpe / Sortino


By Kim Gits,  CFO of Social Market Analytics

Are you prepared for the shift in attitudes and expectations of the next generation of investors? What is the next step for social media use in the capital markets?  How far will you go in implementing a social strategy to retain/attract investors?  Once in house, how will you communicate with them?

Much has been said about this new generation of investors – the Millennials.  Their generation is larger than even the Boomer generation.  They will be recipients of the largest wealth transfer in history.  They grew up with cellphones and instant access to information via the internet.  They are social and mobile.

But what does this mean to firms who wish to court this generation of investors?

In the last few years, we have seen Twitter and other social media sites like StockTwits and Seeking Alpha come of age as reliable data sources.  At Social Market Analytics, we are incredibly excited about these questions and the changes in investing that are on the horizon.

I’d break down the market response to this paradigm shift in waves.  The first wave many years ago was to develop a consistent firm-wide policy for employee use of social media.  Much legal angst went into creating these edicts.  Many of the Boomer and even GenX population saw it as a fad that would pass and certainly never saw themselves as Twitter users (Deja-vu for me when thinking about the adoption of the internet in the late 80s).  This first wave marked the beginning of a new means of communication.  For us at SMA it represented a new source of data as “smart money” finally had the clearance to Tweet their thoughts.

I’d describe the second wave as the re-posting of individual Tweets – something we began seeing as early as 2012.  A few firms began streaming Twitter posts as they related to stocks and the markets.  But let me ask you – with over 500,000 tweets, about stocks, a day (and growing) is it really possible to read all of those Tweets and still get any work done?  Do you really care that your brother-in-law’s third cousin thinks Apple is going up (unless of course he’s Warren Buffet)?

The third wave was adoption of social media data by hedge funds and quant traders.  Always on the lookout for new ways to generate alpha, this group has been adding various forms of social media data to their trading strategies.  SMA and its partners have been at the forefront of research in alpha generation strategies using S-FactorsTM, our social media metrics.  Growth at this level continues with more advanced strategies using multiple asset classes.

The final wave as I see it is the native integration of social media and its rich knowledge sharing capabilities into the investing platform.  Not only will investors be alerted to what they want to know in real-time, they will also have the ability to communicate socially with their brokers and execute transactions on a mobile platform based on either alerts or social communications from their broker.

As you address these coming changes, we are prepared to help.  SMA finds the real-time “smart-money” conversations in social media.  Knowledge sharing is known to be important to this new generation of investors.  SMA’s differentiation is that our proprietary filtering eliminates the spammers, scammers and naïve-user conversations.  Our metrics are based on the social media posts of “smart-money”.  Also, SMA’s unique normalization process helps users find hidden stock conversations that might otherwise be overwhelmed by the likes of Apple, Google and Facebook.  Our data and metrics are engineered to perform at the highest levels and we offer fine-grained customization to meet the needs of your specific customer base.  User-defined thresholds of our metrics let investors listen to only the conversations that are meaningful to them.  To learn more, please contact us at