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Predictive Real-Time Alerting on Commodities

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Social Market Analytics (SMA) tracks real-time sentiment on equities, commodities, currencies, ETF’s and crypto currencies.  SMA has the most powerful and customizable Alerting API combining Twitter sentiment and pricing metrics.  Users receive custom real-time sentiment alerts on instruments in their watch list.  For example, on December 11, 2018, SMA’s alerting system sent an alert on Corn at 12:12 pm CT when corn was @ $385.25. Below is the email and mobile alert.

Cornalert

Mobile

Subsequent to the alert, corn moved lower starting at 12:17pm CT. The price continued to move lower the remainder of the day and closed at $383.25. (See chart below)

Corn Alert

The above alert was based on SMA’s rolling 24-hour sentiment. SMA also calculates a Long-term sentiment with longer price projection periods.  Corn’s long-term S-Factor flipped from positive to negative on November 14th. 12/10 was the first day the long-term S-Factor for corn reached a significantly negative level of -1.5 standard deviations more negative than the longer-term baseline conversation. For more information please contactUs@SocialMarketAnalytics.com

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Social Market Analytics (SMA) Trading Strategy on Natural Gas Futures

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Social Market Analytics, Inc. (SMA) has been the leading provider of predictive quantitative signal in the alternative data space for 7 years. Over the years, we have developed patented algorithms that use machine learning and natural language processing to provide content that generates alpha. Our NLP is unique because of our proprietary processes that tag sentiment weights based on the language used in finance per asset class. The processing of tweets for futures and commodities is different from what we use for equities.

There have been a lot of conversations around Natural Gas futures in social media lately. Through our partner CME Group , people have been monitoring social sentiment from SMA in real time on their active trader site.

Recently, we did as study on using SMA signals to create a Long Short trading strategy using Natural Gas futures. In the example here, we are using front month futures contract and trading daily using a combination of S-Score (a measure of unusual sentiment) and SV-Score (a measure of unusual twitter volume activity).

The strategy buys contracts when the sentiment (S-Score) is positive. We scale up the long position when the sentiment is positive, and the volume of tweets is also significantly high (SV-Score). Conversely, when the sentiment is negative, we sell contracts and go short. We sell more contracts when sentiment is negative with significantly high number of tweets. For this study, we use a maximum of 100 contracts when going long and 100 contracts when taking short positions.

The sentiment strategy performs significantly better than the Natural gas prices, returning 87.42% YTD. We also avoid the volatility in the price and get a Sharpe of 3.53 on the strategy.

NG

A PnL curve of investment of $300,000 in 100,000 contracts on Jan 1, 2018 is shown in the chart below.

The strategy is profitable throughout the  period, and the maximum drawdown is only 7.19%, which is significantly better than the 29.72% drawdown in the natural gas prices YTD.

NG2

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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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