Janny

working on credium.io

Trading Bots vs Humans · Everything you need to know

Over the past 10 years we've seen the rise and rise of trading bots and Quantitative Funds and we've seen the fall and fall of traditional Asset Managers and Hedge Funds.
There are a number of reasons for this so we're going to explore them here.
When we look at trading bots vs humans, there are four categories we can split this up into:
  1. Trading bots for retail. These are the bots available through all the common bot platforms that you'll find from a quick Google.
  2. Proprietary trading bots. These are the ones that are developed in house by accomplished Hedge Funds, written by extremely smart finance professionals and statisticians.
  3. Professional (Human) Investors. These are the regular fund managers that you see on the finance channels touting their own positions.
  4. Retail (Human) Investors. This is pretty much everyone else. In the U.K. and Australia they'll live on CFD trading platforms.
What we really want to do is understand the differences between these different groups; humans vs bots on both the retail and institutional side.
Let's start with performance because that's the most important factor.
The average Hedge Fund has underperformed buy and hold by 71.7% over the last 10 years (+5.95% per year for Hedge Funds and +13.12% per year for the S&P 500 net returns over the past 10 years).
However, there is a strong outlier; algorithmically traded Funds (aka Quant Funds).
The performance of funds like Renaissance and Citadel create a force to be reckoned with. They have achieved a very impressive annualised net return over the past 10 years are +37.1% and 22.1% respectively.
However, not all have been successful.
The Quantopian "crowdsourced hedge fund" experiment doesn't seem to be going as planned with overfitting prevalent amongst strategies. Another highly regarded Quant, has suffered tremendous outflows and has had pretty shocking performance of -12.11% in the last 12 months.
See,
You can get a trading bot for just about any asset these days; stocks, forex, cryptocurrencies, you name it.
But it's much harder to find a profitable trading bot.
It is trivial to make a working bot, less so to have a profitable one. - Reddit User
And this is really because they start with the same flawed knowledge. Decades of incorrect assumptions floating around in the public knowledge sphere of Github and Google scholar is only the tip of the iceberg.
Furthermore, profitable trading algorithm developers guard their "edge" like pirates hoard treasure.
The unfortunate reality is, you'd struggle to find a single bot available to the public today that's consistently profitable.
This is the exact reason why we're building Credium. Our only focus is on creating profitable crypto trading bots and for retail investors as well as institutional investors. The founding team have a strong background in finance and are the only bot provider that focusses on profitability as the main goal.
When it comes to retail (human) traders, most lose money over time.
We can actually find out exactly how many of them lose vs win by platform; due to a legal requirement, brokers in the U.K. now have to tell you what percentage of their customers lose money.
If you change your VPN to the U.K. and just Google "CFD Trading" you'll see a page like this:
Literally somewhere between 62.9% and 76.4% of retail traders lose money.
Pretty wild huh?
And it's mainly because of the investor Psychology cycle:
This bring us onto our comparison:

Reasons Why Trading Bots Are better than Humans

  1. Potential for greater performance. The best performing trading algorithms have a performance that's significantly greater than the best performing humans. It's worth noting here though that the majority of bots lose money.
  2. Automation. Bots can automatically execute trades when a particular level has been reached at any time of the day. They react with predictability and logic and when it comes to probability theory and statistics, this approach tends to produce more favourable results over time.
  3. Emotionless. Bots can analyse data without emotion or bias. They understand the implications for a wide range of events and typically wont get wed to their positions.
  4. A.I. A.I. & Machine Learning is beating humans across the board, from Go to Poker. We're right at the beginning of the A.I. age with the advent of self-driving cars and healthcare diagnoses. Credium's crypto trading bot utilises A.I. extensively.
  5. Scale. Bots are able to process significantly more data and in much shorter timeframes than humans. This means they won't miss an important announcement on a portfolio company. It also means they can analyse more companies and thus diversify risk better.
  6. Speed. Speed reduces slippage as reaction time isn't needed and opens up arbitrage opportunities too.
  7. Longevity. Bots can be up and running 24/7. In a product like cryptocurrencies this is especially useful. Humans need to eat, sleep, live. A human typically works 40 hours a week however a bot can work 168 hours a week therefore can capture 4.2x more opportunities.
  8. Consistency. Bots by design are consistent. When they engage in a piece of information or an announcement they'll react in the same way over and over again.
  9. Strategy Diversity. Because bots are able to scale and run at speed, they can also work on different time frames and with different strategies that are uncorrelated which reduces their risk. This is because the P&L volatility is lower which would cause your risk adjusted returns to be better.
  10. In-built backtesting. Every strategy can be backtested multiple times and in multiple environments automatically. This gives you more confidence that the particular strategy works and when you should stick with it or not. This also helps you manage risks better.

Reasons Why Humans are better than Trading Bots

  1. Less susceptible to fake news and pump n dump schemes. Tweet manipulation can sometimes send bots into a frenzy chasing after a particular stock or coin to then be left playing hot potato with each other when they realise the news article wasn't real.
  2. Subjective analysis of data. Sometimes a particular piece of information won't have a first-degree-thinking outcome and will need deeper analysis to understand the implications. Bot's are able to look at words and overall sentiment in a multitude of cases but there can be news events that they don't know how to respond to.
  3. Black swan events. Humans are good at rational thinking. Sometimes black swan events cause bots to lose millions because it's a scenario they haven't encountered before. Human traders are better to respond to severe mispricings and take advantage of them. However, granted, these are rare.
  4. Bug free. Bots are susceptible to bugs and errors. There are times when bots have effectively "fat fingered" a trade and ended up causing a huge price movement in the underlying. Bots can in theory also crash leaving them offline for periods of time.
Well that just about wraps things up for now.
If you like the promise of trading bots and want to get started with trading bots, feel free to give Credium a try.
Disclaimer. Financial markets are risky. Unlike stocks which have intrinsic value and are correlated with the global economy, cryptocurrencies unfortunately aren’t productive assets and they have no intrinsic value. Therefore you should be very conservative if you plan on getting involved with cryptocurrencies; the risks are pretty huge. This post isn’t intended as investment advice so please do your own research before investing in anything.

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Comments

August 15th, 2019

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

August 15th, 2019

such a great question and I’m so glad you asked!

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

If you’re actively trading, human behaviour gets in trouble. Human’s 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’s not replicating human behaviour and just doing what’s most sensible given the data it has.

Good luck with the move too David!

August 16th, 2019

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

August 16th, 2019

As of now, profitable trading bots are like God to me – I’ve heard of them and there’s a great probability that they are everywhere, but I’ve never seen or experienced one myself.

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

August 16th, 2019

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’s pocket.

August 16th, 2019

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’s no good looking at the model after it’s run and then picking the one path that has consistently hit heads and saying ok this is a “profitable model” and therefore we’re 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 “profitable”. If you take any bot that’s 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’t incentivised to produce a profit for you. This is the exact reason we’re building Credium - when we launch we’ll have a page on the site where you can see our daily performance so we’re held accountable to what we produce for clients.

September 21st, 2019

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…

September 21st, 2019

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

September 22nd, 2019

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?

September 22nd, 2019

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

September 24th, 2019

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…

September 24th, 2019

Potentially, let’s look at this together.

Scenario 1: ok so let’s 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’s produce. Let’s 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’s every strategy. The half-life of a strategy is about 1 year, which means if you haven’t 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’s 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’ve 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’ve just hired 1 or 2 may come with their own strategies that they’ve 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’s funny because I’ve heard the “why don’t you keep it yourself” argument so many times. Maybe I’m doing the maths wrong but it never makes sense. Further, if you truly love what you do you’d take the opportunity of scaling and working with a team of ultra-bright people any day of the week.

October 30th, 2019

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…
and, what think about happens when you can’t deliver after getting the $10m?!

October 30th, 2019

pretty simple really, don’t raise $10m if you can’t deliver

November 5th, 2019

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…

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