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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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Low capital vs. high capital account performance continued. Trade by trade analysis

January 14, 2019

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Avoiding drawdowns (part 1/2)

March 18, 2018

Is is possible to reduce drawdowns in your account by manually adjusting your AlgoLab settings?

 

AlgoLab was designed to be fully automatic, but it does offer the some manual controls like using the pause button, adjusting the risk value (leverage), and customizing the symbol portfolio.

 

Can we improve the results of your AlgoLab by overriding the automatic operation, by using your discretion to apply some of these custom settings? In this blog post I'll attempt to test a theory that I have about using a recent string of winning closed trades as an indicator to improve the risk/reward ratio.

 

RISK REWARD RATIO (GAIN/PAIN RATIO)

 

When I measure trading results, I do not like to use profits as the only measure of how well a system performs. What is more important, is to measure how much profit a system generates for every unit of RISK taken. A system that earns 100% profit with a 50% drawdown (loss) at some point prior to earning the 100% profit is NOT AS GOOD as a system that earns a 50% profit with a 10% drawdown because you can simply increase the leverage by multiplying the number of contracts by 5 to increase the 10% drawdown to match the 50% drawdown in the former case - resulting in a 250% return!

 

A better way to measure the performance of a system is risk/reward, or PG - Pain/Gain metric, which is the maximum drawdown (loss) divided by the maximum profit that the system generated. The LOWER the number, the more profit per unit of risk used.

 

THE TEST

 

If there is any correlation between previous winning trades and future probability of a winning trade, then I should be able to measure it by increasing the number of contracts traded for a trade relative to the % of previous winning trades prior to that trade.

 

THE PRELIMINARY RESULTS

 

There is a SLIGHT decease in the overall GP ratio if the number of contracts traded increases with the increase in % winning trades over the previous 20 closed trades. In order words, For each trade, I consider the previous 20 closed trades and calculate the number of profitable trades in that set of 20, then divide that by 20 giving me a percentage of winning trades. For this preliminary test, rather than attempting to relate actual number of contracts to the percentage value, I simply multiplied the trade profit by the % of winning trades of the last 20 trades. This would be impossible to do in reality, as you would have to trade fractions of a contract in order to accomplish it, but for our purposes, this will show if this is might be a valid indicator.

 

The GP ratio decreased from 2.1 to 1.9 when increasing the number of contracts traded when the percentage of winning trades out of last 20 trades is higher.

 

A short (previous 20 trades) streak of winning trades seems to lead to more winning trades.

 

 

There is also a slightly larger decrease in the overall GP ratio if the number of contracts traded increases with the increase in % of LOSING TRADES over the previous 75 TRADES. This is totally opposite to my previous test, and suggests that when there has been a very long period of losses, then it may be profitable to increase your risk (increase the number of contracts traded). 

 

The GP ratio decreased from 2.1 to 1.65 when increasing the number of contracts traded when the percentage of LOSING trades out of the last 75 trades is higher.

 

A long (previous 75 trades) streak of losing trades seems to lead to more winning trades.

 

THE FINAL RESULTS

 

To know for sure if these decreases in GP from the preliminary tests are good enough to trade in the "real world", I converted the % winning and losing trades value to an actual number of contracts to trade.

 

Unfortunately, when converting to whole contracts and including the brokerage fees and standard slippage costs, the result is that neither the winning % indicator or the losing % indicator made much of a difference to the overall results. This is probably not worth pursuing any further.

 

It does seem like some AlgoLab users are indeed avoiding the drawdowns, but I have been unable to convert their discretionary actions to rules that when tested, improve the results. I WILL KEEP LOOKING THOUGH!

 

 

 

 

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