This increases your risk exponentially. You only have a certain degree of control of losses but not profits.
You guys make fair points about the scaling. However I think that if a model actually works then it is positive expectancy. Therefore scaling
will help it, I will personally do this on demo and see if I can prove myself wrong. That is trade when your model is valid, Stop when invalid and
increase lot size and continue when valid again if it is necessary and reset when profitable. It might be wrong but it might be really
efficient, my scaling is not martingale. I will see for myself.
I don't see why losses and profits have any different degree of control, what is the reason for this?
You guys make fair points about the scaling. However I think that if a model actually works then it is positive expectancy. Therefore
scaling will help it, I will personally do this on demo and see if I can prove myself wrong. That is trade when your model is valid, Stop when
invalid and increase lot size and continue when valid again if it is necessary and reset when profitable. It might be wrong but it might be
really efficient, my scaling is not martingale. I will see for myself.
For a system that works, there is no need to scale. With a fixed % of balance you still scale, but keep the risk the same.
All these loss recover methods never pay out and only hides a system that in essence does not work.
The reason you control loss is that you can decide when to cut the loss. Only Elon Musk and Chuck Norris can control profits because they decide
what price it is. For us normal beings, the market decides what profit it will bring.
Well I am not really trying to have an argument with you guys. Again, some things you said have some logic such as increasing your exposure may
increase your risk, there is no need to scale if a system works, you cant force the market to move in your
direction, you can control your loss by closing your trades.
The rest of what you are both saying I can disagree with. I did not say double on loss, instead I said I don't use martingale. I did not say it was
necessary, instead I said it can improve efficiency of a positive expectancy model.
You say you can control your losses by closing a trade but you think that you cannot also control your profits by closing out a trade? A profit and
a loss are the same phenomenon. infact what you are managing is not profit or loss but exposure to risk.
I think Elon has a lot of trouble controlling losses and even making a profit at all , unless you mean by tweets
I do not disagree with what you are saying, you just don't read what I said properly. You could be right about the scaling but I will see for
myself on demo. This is equivalent to research and a willingness to be wrong and learn
NELODI, I disagree - not in general about the fact that there are differences between real trading and demo, but about the relevance of liquidity
for the average forex retail trader.
Although what your saying is essentially correct, I would argue that the relevance very much depends on the market and on the stop distance:
- if you're scalping for just a few pips, it's of course more important to get filled at a good price than if your stops (SL/TP) are a hundred
- if you're trading forex majors the liquidity usually is so insanely high, that you won't move the price with a relatively ridiculous
single digit lotsize request
Of course liquidity is a real thing, but forex is also the biggest market and not some rare commodity options, so I don't believe that for
anybody with a <100k account, there is a liquidity "problem".
Let's get back to machine learning / neural networks ;-)
500 lots aren't enough to influe eurusd's price :) : a drop
in the ocean.
and i fully agree with starting always with a low deposit and min lots
Chris, i love to question this topic ;)
If a model performs best with normal price and tweaking it with specific features couldnt imporve its acurracy reasonably (/not
overgfitting) how can one explain the bad result i got from the label utilization i used and explained a few sites ago?
I used the same labels but in a different order:
Lebel Method 1 (badly working): Neuron 1= Points in CRV Profitdirection; Neuron 2 Points in CRV Lossdirection; (first as TP wether long
or short, second as SL)
Label Method 2 (pretty well): Neuron1= Points in Long Direction; Neuron 2 Points in ShortDirection;
(TP or SL depending on better CRV)
Prices were exactly the same but in different order. it produced totally different results. the NN needed longer to find out about the pattern and
may got distracted to other "hypothesis". therefore it might be worth considering that specific features ( not only prices could be worth
thinking about). Other ideas/theorys?