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AlgoLab by theAlgoLab.com is trade execution assistance software. theAlgoLab.com company, software, or it's principals do not provide trading advice or recommendations. If you require personalized professional trading / investing advice, please consult with a licensed broker/CTM. Actual past performance, or simulated past performance does not guarantee future results. Trading futures assumes a high level of risk. theALgoLab.com and it's principals are not registered as investment advisors. Consult with a CPA or financial advisor, or broker to ensure that your strategy utilized is suitable for your investment profile before trading in an actual funded live brokerage account.


Some trading performance results posted at this web site are from back-testing systems during the dates indicated, using specific settings, from a basket of different futures contracts. Some performance results shown here benefit from hind-sight. Some results shown result not from actual funded trading accounts, but from simulated accounts which have certain limitations. Actual results will differ given that simulated results could under, or over compensate the impact of certain market conditions. Actual draw downs could exceed back-testing draw downs when traded on actual trading accounts.  While back-tested results might show profitable returns, once commission, slippage, and fees are considered, actual returns will vary. 

Futures trading has large potential rewards, but also large potential risk. You must be aware of the risks and be willing to accept them in order to invest in the futures markets. Don't trade with money you can't afford to lose. This is neither a solicitation nor an offer to Buy/Sell futures. No representation is being made that any account will or is likely to achieve profits or losses similar to those discussed on this website or on any reports. The past performance of any trading system or methodology is not necessarily indicative of future results. 

August 22, 2019

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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


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:

03/01/2007   7/16/2007

3/17/2008   5/23/2008

03/10/2009   4/21/2010

09/01/2010   05/04/2011

12/19/2011   3/29/2012

06/05/2012   9/28/2012

11/15/2012   3/30/2013

06/06/2013   09/08/2014

10/22/2014   5/22/2015

2/16/2016   4/25/2016

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



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.


<iframe src="https://docs.google.com/spreadsheets/d/1_Ifgq9dEiP7F8Kw-MMmrDLpo7Hf2N-Nwc5rCozfVWfE/pubhtml?gid=0&amp;single=true&amp;widget=true&amp;headers=false"></iframe>




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