I have purchased the book. As a previous poster noted, much of it is derived from previous work put in the public domain by Connors.
However, that doesn't mean it is useless. It never ceases to amaze me how many posters argue opinions without any facts to back up their argument. If you have ever backtested, or much more importantly, actually TRADED strategies, one would realize there IS an edge in trading reversion to the mean systems. If you don't think big institutions use this kind of info, or pay others to find a quantitative edge, then you are a fool. Think of Tom DeMark and how many hedge funds and others have paid for valuable information that is proven to give an edge. And know that most of it is PRICE BASED, not your typical bland 'MACD crossover' crap that IS useless. The RSI that Connors uses is one of the few price derivative indicators that actually has merit and can provide an edge.
To wit, the author of this book has a chapter on one of the RSI systems that can be employed on liquid ETFs. Using the backtest from the inception of the ETF, or back to 1992 (whichever has more history) and testing it through 12/31/2008, Connors states that in this particular system the SPY trade worked 82.1% of the time with an average gain for all trades of +1.23%, and holding the trade on average for 5.2 days. I decided to code it and run it from 1/1/2009 through the present (7/3/2009)...here are the results:
Long trades = 1
Win = 1
Lose = 0
Avg Win = +0.48%
Avg Loss = 0
Short Trades = 6
Win = 5
Lose = 1
Avg Win = +4.09%
Avg Loss = (4.36%)
I know this is a small sample size given the limited amount of data available, but we can see as a whole the 'system' was correct 85.7% of the time with an average profit for ALL trades of +2.37%, giving a profit factor of 4.74.
The database I use apparently has more history than the one Connors used, as I tested the strategy on the SPY and came up with more trades than he. I then went to the particular trades to validate that the parameters were indeed met. Here are the results for the SPY going from 1/1/1989 through 12/31/2008:
Long Trades = 125
Win = 105 (84%)
Loss = 20 (16%)
Avg Win = +1.64%
Avg Loss = (1.69%)
Avg All = +1.12%
Max Consecutive Winners = 13 Trades
Max Consecutive Losers = 1 Trade
Avg Winner Held = 4.14 Days
Avg Loser Held = 11.15 Days
Profit Factor = 5.36
Short Trades = 39
Win = 24 (61.5%)
Lose = 15 (38.5%)
Avg Win = +2.15%
Avg Loss = (2.86%)
Avg All = +0.30%
Max Consecutive Winners = 7 Trades
Max Consecutive Losers = 3 Trades
Avg Winner Held = 5.08 Days
Avg Loser Held = 14.47 Days
Profit Factor = 1.28
Combining the results we come up with 78.7% winning trades for an average gain of 0.93% and a profit factor of 3.05. Not too shabby, and definitely gives one an edge.
Personally, I would incorporate a time-based stop on this system as every trade that went beyond 10 days, with 3 exceptions, was a loser. Oh, and I might note if Monday is another down day in the SPY, AND we manage to hold ABOVE the 200 Day Moving Average, a Long signal will likely fire for this strategy on the close that day.
A few other observations -
This strategy, as well as others in the book, were robust and consistent across the other ETF's that were tested.
The strategies are part of a larger framework that may tell one when to 'step on the gas' or 'ease up' relative to the securities one is trading. For example, if one initiates a trade in a stock shortly after the open when one of these signals is in play, then one might consider adding to their size (managing the risk appropriately, of course)...
Lastly, ideas that come from books like this are a starting point for further research into other ideas. I will elaborate a bit more on that with a strategy I wrote based on ideas such as these presented in Connors' book.
However, that doesn't mean it is useless. It never ceases to amaze me how many posters argue opinions without any facts to back up their argument. If you have ever backtested, or much more importantly, actually TRADED strategies, one would realize there IS an edge in trading reversion to the mean systems. If you don't think big institutions use this kind of info, or pay others to find a quantitative edge, then you are a fool. Think of Tom DeMark and how many hedge funds and others have paid for valuable information that is proven to give an edge. And know that most of it is PRICE BASED, not your typical bland 'MACD crossover' crap that IS useless. The RSI that Connors uses is one of the few price derivative indicators that actually has merit and can provide an edge.
To wit, the author of this book has a chapter on one of the RSI systems that can be employed on liquid ETFs. Using the backtest from the inception of the ETF, or back to 1992 (whichever has more history) and testing it through 12/31/2008, Connors states that in this particular system the SPY trade worked 82.1% of the time with an average gain for all trades of +1.23%, and holding the trade on average for 5.2 days. I decided to code it and run it from 1/1/2009 through the present (7/3/2009)...here are the results:
Long trades = 1
Win = 1
Lose = 0
Avg Win = +0.48%
Avg Loss = 0
Short Trades = 6
Win = 5
Lose = 1
Avg Win = +4.09%
Avg Loss = (4.36%)
I know this is a small sample size given the limited amount of data available, but we can see as a whole the 'system' was correct 85.7% of the time with an average profit for ALL trades of +2.37%, giving a profit factor of 4.74.
The database I use apparently has more history than the one Connors used, as I tested the strategy on the SPY and came up with more trades than he. I then went to the particular trades to validate that the parameters were indeed met. Here are the results for the SPY going from 1/1/1989 through 12/31/2008:
Long Trades = 125
Win = 105 (84%)
Loss = 20 (16%)
Avg Win = +1.64%
Avg Loss = (1.69%)
Avg All = +1.12%
Max Consecutive Winners = 13 Trades
Max Consecutive Losers = 1 Trade
Avg Winner Held = 4.14 Days
Avg Loser Held = 11.15 Days
Profit Factor = 5.36
Short Trades = 39
Win = 24 (61.5%)
Lose = 15 (38.5%)
Avg Win = +2.15%
Avg Loss = (2.86%)
Avg All = +0.30%
Max Consecutive Winners = 7 Trades
Max Consecutive Losers = 3 Trades
Avg Winner Held = 5.08 Days
Avg Loser Held = 14.47 Days
Profit Factor = 1.28
Combining the results we come up with 78.7% winning trades for an average gain of 0.93% and a profit factor of 3.05. Not too shabby, and definitely gives one an edge.
Personally, I would incorporate a time-based stop on this system as every trade that went beyond 10 days, with 3 exceptions, was a loser. Oh, and I might note if Monday is another down day in the SPY, AND we manage to hold ABOVE the 200 Day Moving Average, a Long signal will likely fire for this strategy on the close that day.
A few other observations -
This strategy, as well as others in the book, were robust and consistent across the other ETF's that were tested.
The strategies are part of a larger framework that may tell one when to 'step on the gas' or 'ease up' relative to the securities one is trading. For example, if one initiates a trade in a stock shortly after the open when one of these signals is in play, then one might consider adding to their size (managing the risk appropriately, of course)...
Lastly, ideas that come from books like this are a starting point for further research into other ideas. I will elaborate a bit more on that with a strategy I wrote based on ideas such as these presented in Connors' book.