Trading Bots vs Humans 路 Everything you need to know

Trading Bots vs Humans 路 Everything you need to know

Over the past 10 years we鈥檝e seen the rise and rise of trading bots and Quantitative Funds and we鈥檝e seen the fall and fall of traditional Asset Managers and Hedge Funds.

In building your crypto trading bot, what did you find was the most difficult human behavior to encapsulate with an algorithm?

such a great question and I鈥檓 so glad you asked!

I鈥檇 probably say we did everything we could to not replicate human behaviour :slight_smile:

If you鈥檙e actively trading, human behaviour gets in trouble. Human鈥檚 have pack mentality which means you chase after a stock as it goes higher and end up getting into terrible trades. This is also one of the reasons day traders typically never make money. The algorithm wins because it鈥檚 not replicating human behaviour and just doing what鈥檚 most sensible given the data it has.

Good luck with the move too David!

Excellent insights. Thanks for the read! As a company that is preparing to launch an algo fund, it鈥檚 always nice to have some new perspectives.

As of now, profitable trading bots are like God to me 鈥 I鈥檝e heard of them and there鈥檚 a great probability that they are everywhere, but I鈥檝e never seen or experienced one myself.

But, as you said, I too think they鈥檒l become more common in the near future due to the rapid advancement in A.I. and ML.

Most trading bots that show up on Google search are so not reliable. Anyone who goes with one must have an iron heart to be putting their money straight into another person鈥檚 pocket.

if you run a monte carlo simulation on a coin toss (assume heads=profit, tails=loss) you end up with a bunch of paths producing a profit and a bunch not producing a profit. Now, it鈥檚 no good looking at the model after it鈥檚 run and then picking the one path that has consistently hit heads and saying ok this is a 鈥減rofitable model鈥 and therefore we鈥檙e awesome. This is basically what everyone touting profitable bots does, they either overfit or take a favourable subsample of data to show that theirs is 鈥減rofitable鈥. If you take any bot that鈥檚 in the public domain today, I guarantee that on a long enough timeline the survival rate goes to zero. The reason for this is simply that the bot developers aren鈥檛 incentivised to produce a profit for you. This is the exact reason we鈥檙e building Credium - when we launch we鈥檒l have a page on the site where you can see our daily performance so we鈥檙e held accountable to what we produce for clients.

as the article points out the most successful trading bots are proprietary;
there is no reason for a developer/creator/owner to open a successful trading bot/program to the public鈥

yes totally agree Paul - if it was successful opening it up will mean it鈥檒l stop working (alpha decay). The only way to avoid this is to raise capital and scale it yourself

Janny, we are agreeing on opening of the trading will mean diminished returns or stop of working.
How does this fit with your involvement with Credium - which is looking for customers?

good question - so we鈥檙e not opening up any of the trades or trade history but we鈥檙e opening up a lot of aggregated metrics. It would be pretty difficult to back out the underlying algorithms because we鈥檙e running 4 right now and they鈥檙e uncorrelated. It鈥檚 more of a fund that鈥檚 open to retail as well. We don鈥檛 need lock up periods because out portfolio turnover is fairly high so it鈥檚 easy for us to rebalance whenever we have deposit/redemptions. Appreciate you asking!

in my personal opinion if a program/bot generates revenue it is better for the creator/developer to keep the gains for themselves instead of running a business, pleasing investors and making gains for customers鈥

Potentially, let鈥檚 look at this together.

Scenario 1: ok so let鈥檚 say you spent a year developing an algorithm, and that algorithm makes 50% annual returns - this is pretty decent for one guy to achieve compared to what world-leading HF鈥檚 produce. Let鈥檚 also say you were lucky enough to be in a decent paying field before so you have $100k yourself you can run. After year 1 this is what you have: $50k returns (which you have to pay tax on). Probably tough to live on that given you may be accustomed to a higher cost of living given the previous field you were in. You also need to take into account that Alpha decay hit鈥檚 every strategy. The half-life of a strategy is about 1 year, which means if you haven鈥檛 come up with a new one after your first year you now have a strategy which only makes 25% annual returns.

Scenario 2: You take the strategy you built and acquire investors. Let鈥檚 say you manage to scramble together $10m on a 2 and 20 structure and you hit the same 50% in year 1. You now have $1.2m returns that you can use to go out and hire more quants and you鈥檝e built an impressive track record (you just made $3.8m for your investors) on which you can probably go out and raise $50m in year 2. With the quants you鈥檝e just hired 1 or 2 may come with their own strategies that they鈥檝e already built. So you now have a $50m fund, 5 quants, 2 new strategies and all you had to do was talk to a few investors every now and then.

It鈥檚 funny because I鈥檝e heard the 鈥渨hy don鈥檛 you keep it yourself鈥 argument so many times. Maybe I鈥檓 doing the maths wrong but it never makes sense. Further, if you truly love what you do you鈥檇 take the opportunity of scaling and working with a team of ultra-bright people any day of the week.

yes, maybe the math is wrong - it is one thing to get 50% return on $100k and other thing to get 50% return $1.2m or $10m鈥
and, what think about happens when you can鈥檛 deliver after getting the $10m?!

pretty simple really, don鈥檛 raise $10m if you can鈥檛 deliver

I work in the area of automated crypto-trading just as you are; I did send you an invitation on linked-in and if you like to consider another system in addition to what you have we can continue the interaction鈥