Comparing Twitter Sentiment vs Random Numbers for Entry Signals

This is a cross-post from Trade The Sentiment

One of the concerns that I have of my research into using Twitter Sentiment for entry signals in the market has been that the market has, in general, been in an up-trend over the last year.   With that up-trend, there’s a good change that any entry, made enough times over the course of the year, would have resulted in a profitable trading year.

So…I developed a basic random number generation script in Python that generates a random signal between 0 and 2 to try to mimic the ranges found in the overall market bear/bull sentiment.

With this random signal, I then used the same strategy for an entry signal used in my Twitter Sentiment entries.   The comparisons are below

Trading Rules

• Enter when Bearish Extreme found in Raw Bear/Bull when Sentiment (or Random Signal) > 1.1
• Buy 500 Shares of the SPY ETF. This strategy is Long Only.
• Hold for 6 trading Days.
• Timeframe: Nov 1 2011 to Nov 30 2012
• Commission = $10
• Slippage = $0.05 per share

Using Tradestation, I ran my strategy with both the Twitter Sentiment Signal and the Random Signal without optimizing any of the strategy inputs.  Both systems closed out their last trade before Friday so no trades are held.

The outcome of both systems are provided below.  Below the table, I’ve provided the

Note: The Buy and Hold Rate of Returns are different due to the date of the first trade made by each system. Trade #1 for Random Signals was taken on Nov 9 2011 while Trade #1 for the Twitter Sentiment was taken on Nov 14 2011.

Based on this particular test, I’d say the twitter sentiment entry signals are much more robust and accurate. This signal delivers more profit, more return and much less drawdown than the random signals entries.

Each strategy’s entry / exits are shown in the graphs below.

Twitter Sentiment Signals

Random Signals Entries

This is a cross-post from Trade The Sentiment