Autotrading AlgoLab stock indexes can be more profitable on a risk adjusted basis than buying stocks
AlgoLab stock index can be more profitable on a risk adjusted basis than buying stocks
Trading Systems Performance Expectations
Algorithmic trading systems have been developed by optimizing system rules, and parameters on historical data. This does not guarantee that past performance will be as profitable, or will exhibit similar characteristics to real-time trading results. Given enough variables, it is possible to curve fit a system to historical data, and any relationship between those rules and future, unseen data may be random. AlgoLab has taken steps to reduce the number of variables and system rules to reduce the degrees of freedom which will reduce the chance of curve fitting. Current market regimes can and do change, and rules that previously predicted future prices may no longer work.
From 2007 to 2016 there were 9 periods where the stock market went up (bull market), and 9 periods in between when the equities markets were flat or down.
On average, during BULL market periods, the market GAINED 1.5 times what it lost during BEAR/FLAT periods. So, taking an outright long position in the equities markets by buying stocks or mutual funds would result in, on average, 1.5 times higher return during bull market periods than bear market / flat market periods.
Rather than taking an outright long position by buying stocks, use an AlgoLab trading system because on average, an AlgoLab long only system will earn 3 times more profit in BULL periods than what it will loose in BEAR/FLAT periods (rather than 1.5 to 1 for taking an outright long position).
(NOTE: For this tutorial, I used a development-only version of AlgoLab with features that are not available in the current release of Performance Viewer. You could replicate this test using the Performance Viewer with the SuperSystem multisystem. Results will be similar)
Below is a backtest of the PivotBreakout system with settings:
containment = 20
Trend = 1000
BarRes = 60 minutes
Stop = 4
Capital = $50,000
Risk = .25
Bias = LONG ONLY
Symbols = ES,YM,NQ (S&P 500 e-mini, DJIA index, NASDAQ index)
I used the Specific Dates Filter in AlgoLab and created a list of 9 BULL MARKET date ranges in a spreadsheet, then copied and pasted the values into the spcfici dates filter in AlgoLab. This back test performance metrics cover the 9 BULL MARKET periods only.
Here are the BULL MARKET dates ranges:
The PivotBreak strategy generated an annual average of 14.67% return over the 9 bull market periods spanning from 2007 to 2016 with an average annual drawdown of 7.93%.
Then, I ran a backtest using the same system, same settings, LONG only again, but this time, for the 9 BEAR market periods. Since my spreadsheet list was a list of dates for bull markets, all I had to do to 'flip' the dates to bear market was select the button "disallow trades between", and this resulted in taking trades only during the bear market date ranges.
The PivotBreak strategy generated an annual average of -6.12% return over the 9 bear market periods spanning from 2007 to 2016 with an average annual drawdown of 10.24%.
Total profit BULL MARKETS = $65,548
Total profit BEAR MARKETS = $-24,627
ALGOLAB BULL MARKET / BEAR MARKET profit ratio = 2.66
BUY AND HOLD STOCKS BULL MARKET / BEAR MARKET profit ratio = 1.5
Below is the historical backtest performance report including all dates (both bull and bear market periods - note the "Specific Dates Filter" is turned off.
I ran this same test using the SuperSystem MultiSystem and the ratio was 4 times more profit during bull market periods than bear market periods.
Details of my study using various other AlgoLab trading systems is below.