We have been trading NASDAQ-100 futures under a our One-Shot strategy, and publishing that trade data to Twittersince April, this year. The following chart shows the performance of this trading, to date. Put simply, the 1st. of September was very bad. We got into the wrong position and rode it down. Some money was made back by trading during the month, but the month as a whole was a poor one for this strategy. Fortunately, today (the 1st. of October) was very good. Good enough to cancel out its antecent date, but in the wrong month to make the performance numbers look nicer.

To summarize our analysis of the first quarter of publicized index futures trading on Twitter, I am including a table of monthly returns for an simple aggregate portfolio of both strategies.

In addition, you can now view the daily profit reports for both strategies directly on this website. The DJIA Index Futures and NASDAQ-100 Index Futures systems are presented separately, and the data is updated every day.

As we noted in the previous post, on (almost) the first quarter's trading for DJIA Index Futures , we have also been trading NASDAQ-100 futures under a variant of the same strategy, and also publishing that trade data to Twitter, for the same period.

One can see that the system is unprofitable, and remains within the drawdown it entered roughly in the middle of April. A table of monthly returns for this model account, is given below.

On 9th. April, we started a Web 2.0 experiment by publishing an RSS Feed and publishing trade data to twitter, for an intraday index futures trading strategy operated by our firm. This strategy has been running in this context for approximately three months now, and the daily performance for the trading of DJIA futures is exhibited in the charts below. (We will exhibit the NASDAQ-100 strategy in a following post — that variant has lost money to date.)

One can see that the system is profitable, but remains within the drawdown it entered roughly in the middle of May. A table of monthly returns for this model account, is given below.

On 9th. April, we started a Web 2.0 experiment by publishing an RSS Feed and publishing trade data to twitter.

In the charts above and below, we exhibit the daily trading performance of our Web 2.0 systems since the inception of the experiment (the first published date for the RSS feed). The reason I am not including back history for these systems is so that the analysis is totally out-of-sample — there is no selection bias issue which might exist were I to publish charts that exhibit prior positive performance. The structure of the performance charts is described in our earlier post on the strategy.

For the month of May, 2009, we see a divergence between the two strategies. Both exhibit the mean reversion I suggested might occur in the prior update. The DJIA contracts were essentially flat on the month, whereas the NASDAQ-100 contracts lost money — in fact, they most more than they'd made in April. I am posting this data on an up day in which we have a big long position, so the data for the first of June may be different at the end of the day (to be a pessimist, let's say I'm marking the top for the day).

On 9th. April, we started a Web 2.0 experiment by publishing an RSS Feed and publishing trade data to twitter.

In the chart below we show the combined daily performance (return on account equity) of the equity index trading strategies referenced in our experiment. This is just short of a month's data, and the systems have made money, so the Sharpe Ratio and annualized returns metrics look incredible. These numbers are better than I expect, and I expect the Law of Large Numbers to bring them both back to more reasonable values, as time passes and we do more trading. Nevertheless, it's nicer than starting with a drawdown.

The chart has three panes. The upper one is a histogram of daily returns with a fitted Generalized Error Distribution. The middle on is a time series of daily returns and of the cumulative daily returns. The lower one is a histogram of drawdowns with a fitted Gamma Distribution.