For the past few days it has been possible to subscribe
to an on-line trade blotter which echoes futures trades done in one of Giller Investments proprietary trading
accounts. Delayed feeds are also available on
Twitter 
and as an RSS Feed at
Feedburner
.
The feeds are the trades, and a VWAP
summary. The trades, which are actual confirmed trades as reported back by our brokerage, are scaled to take account of
the relative sizes of the forecast, the level of volatility, and the account equity — which is currently around
$100,000 for each contract type for this trading account. We are currently trading the
Dow
Jones Industrial Average e-mini contract (ticker symbol YMm, where m is the contract
month) and the NASDAQ-100
Index e-mini contract (ticker symbol NQm).
The strategy is referred to at Giller Investments as the One-Shot strategy. The design goal is to
forecast the intraday price change of the index based on the state of the markets in the morning, shortly after the opening
session. If forecastable, a position is entered over several trades and the position held until the end of the day.
Positions are never held overnight for this strategy. Although some risk management trades might be done,
intraday, the strategy essentially has "one shot" at getting the trade right each day.
As one trade is done, and that trade is certain to be terminated, the trading strategy implemented is a
straightforward barrier trading strategy. However, we will not discuss that specifically here. Here we will
examine the forecasting power of the DJIA system.
This system was originally implemented at the end of September, 2006; and the data presented here was collected
from that implementation to date — i.e. it is entirely out-of-sample.
In the chart above there are four
panels. The two on the left hand side are regressions of the intraday price change onto the forecast. The upper panel
is for all data, and the lower resticted for dates on which trades were done. Regressions are computed both for
simple linear regression and when weighted with the forecast variance. The R² is ≅ 2%, which
corresponds to a correlation coefficient of order 15%. According the the rule of thumb from
Grinold & Kahn's Active Portfolio Management
,
if we could trade every day with negligible costs, this would give a Sharpe Ratio of approximately 2. Note how the linear relationship,
although statistically well established, is difficult to observe by eye due to the low R².
On the right hand side, there is a chart showing each day's forecast at trade time and how that compares to the
trade entry barriers. This illustrates the variability of the scale of the alpha with the local volatility conditions.
Finally, for a contextual reference, we present a chart showing the time series of the index level and the daily point volatility.