extremely low RRR for automated trading systems

[morehastelessspeed]

I found this paper interesting, although it does seem a bit extreme.

Perhaps somebody with experience of live trading with this kind approach would like to comment!

"The algorithm generated 19.89% profit for the best case in the period of 30 months. This efficiency can be considered a good return, considering a losing trade of 1% of the capital. Even in the case with more consecutive loss trades, like in cases with RRR between 1:15 and 1:40, the algorithm has a positive expectancy in a time."

Păuna, Cristian (2018) Capital and Risk management for automated trading Systems. In: Proceedings of the IE 2018 International Conference.
www.conferenceie.ase.ro

 

[tomorton]

I'm struggling to understand what a strategy would look like which had a risk:reward ratio of 1:50 and generated zero losing trades out of 858 trades taken.

What is "profit ratio" anyway?

 

[morehastelessspeed]

What is "profit ratio" anyway?

Reading the paper by Păuna, I can't see a definition of what exactly he calls "Profit ratio", but he obviously presents it as a measure of profit, as per these graphs also included in the article.

 

[Rufus_Leakey]

I'm struggling to understand what a strategy would look like which had a risk:reward ratio of 1:50 and generated zero losing trades out of 858 trades taken.

The paper has

Using an ATS (TheDaxTrader presented in [8])
...
A. using the historical stock market price data for DAX between 01.06.2015 and 31.12.2017,

The paper's reference link doesn't work, but information about TheDaxTrader can be found at https://thedaxtrader.co.uk/dax-trading-strategy/

My DAX trading strategy is an interesting combination of various technical indicators working together to produce setups and trading opportunities.
...
If the candle turns green whilst the background is green – buy
If the candle turns dark blue whilst the background is red – sell
The dots are the ‘supertrend’ indicator and they are showing us where to put the stop loss for a trade

So that makes it clear, right?

Then the paper has

The back test optimization of the ATS was made using MT4 Strategy Tester [11] with the most precise method based on every tick price available in the historical data.

MT4 does not have the best reputation for backtesting which could explain the results.

On MT4, back-testing on “ Every tick” with a default environment is the highest accuracy possible.

The utilisation of “ Every tick” modelling causes a variety of issues. Prices that are randomly simulated from bar data when the default tick data is downloaded from the broker through MetaTrader’s Strategy Tester. By a process of interpolation, it uses the bar price data together with the tick count to generate the prices for each bar so that they start at the bar open price, touch the bar high and low, ending at the close price.

To summarise, the default testing environment will not cycle through every tick that was delivered at the specific points in history. This will be a lesser issue for higher timeframe EAs, but for scalping EAs, as in the image above, the results will divergence extensively.

Or, maybe "RRR" in the table really stands for reward-to-risk instead of risk-to-reward? Then RRR 1:5 having losses but 1:50 not having losses might be possible.

 

[tomorton]

Thanks guys. I think we can agree this whole subject is unclear and I can't see me putting any more thinking time into this. Thank you anyway for efforts.

 

[morehastelessspeed]

MT4 does not have the best reputation for backtesting which could explain the results.

Good point made about MT4 backtests. The results might just be a quirk, hand-picked for the sake of illustration.

Never mind what is the actual strategy used, I found the article conceptually interesting because it stretches "the general conclusion that a small target is better than a larger one, even [when] the RRR is less than one. Much more small winning trades than losing trades will accumulate an important profit".

Obviously, all other conditions being equal, the greater the stop distance in comparison with the take profit, the less likely the stop will be run before the take profit is reached. However, we know this only serves to make losses fewer and further between, yet these losses will be larger and eventually cancel out many small wins anyway.

How would it make sense to use such an extreme strategy? Would it not lack robustness? With so few losses the statistics might not so be reliable.

Let's assume "RRR 1:50" stands for risking the loss of 50 units for every 1 unit won. Let's also assume the meaning of "Profit ratio" is the returns obtained for the period of study.