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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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2015 In Review

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Wow, what a ride 2015 was with the S&P 500 closing slightly down for the year.  As we head into 2016 are you going to continue to look at the same factors as everyone else or maybe try something new?

Below are the returns for stocks with significantly positive and negative pre-market open S-Scores.   Stocks with High pre-market open sentiment scores had a cumulative return of 12.19% versus an SP 500 open to close return of -.38%.  Stocks with a low pre-market open sentiment score had a cumulative open to close performance of -34%.  Stocks with high sentiment scores outperformed and stocks with low sentiment scores under-performed.  With significant Sharpes and Sortinos.  Combining S-Factors with your selection criteria and risk management can add a dynamic new factor to your security selection.

These charts use the S-Factor S-Score.  SMA publishes and family of S-Factors  to clearly identify the tone of the social media conversation.  To learn more go to: https://socialmarketanalytics.com/process.

returns2015

 

Returns2015Tables

Returns2015FullHistory

Returns2015FullHistoryTable

Social Market Analytics has been publishing the performance characteristics of stocks with high and low sentiment over the last four years.  Last year it was difficult to find success with traditional factors.  SMA S-Factors helped our customers generate out-performance.   Please contact Social Market Analytics to explore how sentiment based factors can be included in your models: ContactUs@SocialMarketAnalytics.com.

 

 

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