Thanks.
So as I understand everybody here right there is absolutely no marketplace, where you can buy profitable algorithms?!
Then all these EAs, that are being sold on mql5 or collective2 are... scam?!
Can a retail trader succeed in algorithmic trading? (Kevin Davey vs Ernest Chan)
- Thread starter edward22245
- Start date
So as I understand everybody here right there is absolutely no marketplace, where you can buy profitable algorithms?!
Then all these EAs, that are being sold on mql5 or collective2 are... scam?!
Some might be legit, but they're usually long-term with nasty drawdown periods, and single digit annual returns. Something outside the developers comfort level. When you see E-mini S&P daytrading with 100% monthly returns and 2% drawdown, that pretty much screams scam and/or curve fitting.
The reason is bank and large firms algos can only work for them, examples are market making and HFT. Hardware barrier of entry is in 10s of millions.
What you are looking for a strategy can can be deployed on retail level. While those exits, no one in their right mind would disclose it. It is not that difficult to raise funds and trade it, as long as it indeed has positive expectancy.
You will not find it anywhere in public domain.
Great summary of how real world works.
Correct.
However, I once had a grid EA, that was running quite stable for almost two years.
But because of lack of funding, it took its usual road...
caught in drawdown.
But if you run it smoothly with low lot size on a high account, you may earn money for a long time...
And there lies the bait. Yes, in theory a large account, can withstand large draw down if low leverage is used, but most people do not have large accounts, and those that do, will pull the plug if draw down gets to 20 percent or more. I have seen clients closing accounts after 10 percent loss.
If you loose 40 percent, how many you need to make back to get to break even?
Fascinating thread. I like and respect both Kevin and Ernie (and Marcos for that matter), so it's a tricky one. Partly I think it depends on what you mean by 'success', what you mean by 'algo trading', and what you mean by 'ML'. These aren't all well defined terms.
So I think it's possible for pretty much anyone to make money using simple systematic trading strategies. Most of this money comes from being exposed to diversified sources of risk, so no 'secret sauce' or fancy ML techniques are needed. These strategies will mostly be quite slow in nature, so not HFT. They will be based on sources of risk premia that decay very slowly, if at all. They will not be high Sharpe Ratio, but by diversifying over a large number of uncorrelated instruments and a number of different strategies I think an expected SR of 1.0 is feasible. These strategies can be discovered using classical statistical techniques, whether you label these as ML or not is up to you.
Does this count as success? A SR of 1.0 achieved over ten years or more would be top quartile for nearly every hedge fund category. But you will struggle to make a living as a trader with a SR of 1.0, if trading is your only source of income, unless you are very well capitalised (equates to lower risk and return target).
What if you want to make more money; a higher SR? Then you are going to have to move towards (a) the world of HFT and / or (b) the world of weirder, shorter lived alpha-decaying, non linear patterns and/or (c) the world of 'alternative data'. And away from classical linear statistical methods, towards the wacky world of ML. To play in these worlds you are going to need to make serious investment in automated trading technology, but more importantly you are going to have to be able to use ML properly.
The average person using ML in finance does so very badly, and this based on an observation of 'professionals' and doesn't include the hordes of amateurs who've just downloaded a python package and have no idea what they are doing. It's much easier to overfit with fancy ML techniques than with classical ones. Given how much overfitting goes on just using old fashioned grid searches and regressions, it's no surprise that overfitting is absolutely endemic within the neural network, AI, non-linear classifying crowd.
You need a team to do this properly, firstly because of the alpha decay you are going to spend so much time finding new effects you don't have time to do anything else like actually implement them. Secondly, because it's less likely that a single person will have the full range of skills required to test and implement ML based trading strategies. Such people do exist, but they are rare: after all it's rare enough to find people with the full set of skills to test and implement classical trading strategies.
What does this mean for the individual trader? Simply put, don't use ML unless you know exactly what you are doing. And stay away from trading arenas where you need to be able to use ML to discover the edges that exist, plus have access to the technology that will allow you to exploit those edges. There are plenty of areas where you can still compete, but you will have to lower your expectations for SR, and thus increase your bankroll or remain as a part time trader.
GAT
PS you might find my review of Marcos' book interesting
And as a postscript, the strategies in my books still work and I still use them. Because they trade slowly, are based on sources of return that decay extremely slowly, and highly liquid, it's unlikely they will be competed out of existence any time soon. That doesn't apply to faster strategies discovered using ML.
GAT
