A Bloomberg article today reported that human-run hedge funds trounced quants in 2020 - a turnaround from the experience of recent years. Renaissance Technologies - run by Jim Simons - saw its Institutional Diversified Alpha & Global Equities funds fall by 32% and 31%

The fundamental problem with computer/AI driven trading strats is - and always has been - that the models will never be capable of evaluating novel situations which have not happened before. This was undoing of LTCM, as well as port. insurance which caused 1987 stockmarket crash.
A global pandemic was not in the past datasets, so the bots don't know what to do. AI is only good where you have sizeable and complete datasets that can 'train' the AI to recognized statistical patterns too complex for humans to extract, and use it to make accurate predictions.
This works phenomenally well in situations that regularly recur within the bounds of a confined range of possible outcomes, but it is prone to disastrous error where discontinuities can occur where outcomes can suddenly bear no resemblance to outcomes in past datasets.
How do you teach an AI to determine if there is a cat in photo? You 'train' it by showing it millions of photos labelled by humans that tell the AI whether there was in fact a cat in it or not. The AI pick ups statistical regularities & uses to make inferences on new photos.
It might get so good it works 99.999% of the time. But what happens if a cat trips over a bucket of purple paint and then walks into frame? The AI might never have seen a purple cat before. If colour is a part of its predictive model (likely), it might not recognize it as a cat.
This is why there are fundamental limitations to what AI is capable of. It can never deal w entirely novel situations. When it comes to markets/economics, we have nowhere near enough data. For eg we've had only 1-2 housing crashes in US. You'd need millions for AI to predict em.
Furthermore, the problem with AI driven trading bots is that they actually change the nature of the market itself. The past datasets they rely on become unrepresentative of the current market over time. This is what lead to the portfolio insurance debacle. PI changed the market.
Where a lot of leverage is involved, in my view it is only a matter of time before any quant/AI model blows up. This is also why, incidentally, AI-based fintech lending algos are also very dangerous. Their own lending/credit creation will change the risks they are trying to model
This has a lot in common with how credit ratings on MBSs contributed to the housing bubble & subsequent bust/GFC. The ratings were based on past datasets, which said risk of a crash was negligible. But this assessment led to rapid credit creation which changed the risk function.
AI-based fintech lending will cause another economic blow up at some point. The models will use past data that predicts low defaults to extend too much credit, and default experience will suddenly and discontinuously change when credit stops growing & blow up the models.

More from Trading

Many of you have seen the famous Westrum Organizational Typology model, so prominently featured in State of DevOps Research, Accelerate, DevOps Handbook, etc.

This model was created Dr. Ron Westrum, a widely-cited sociologist who studied the impact of culture on safety


Thanks to Dr. @nicolefv, I was able to interview him for an upcoming episode of the Idealcast! 🤯

It was a very heady experience, and while preparing to interview him, I was startled to discover how much work he's done in healthcare, aviation, spaceflight, but also innovation.

I've read 4+ of his papers, so I thought I was familiar with his work. (Here's one paper:
https://t.co/7X00O67VgS)

I was startled to learn he has also studied in depth what enables innovation. He wrote a wonderful book "Sidewinder: Creative Missile Development at China Lake"


Dr. Westrum writes about China Lake Research Labs: "its design and structure had one purpose: to foster technical creativity. It did; China Lake operated far outside the normal envelope... Sidewinder & others were "impossible" accomplishments,

I love this book because it describes traits of organizations that routinely create and maintain greatness: US space program (Mercury, Gemini, Apollo), US Naval Reactors, Toyota, Team of Teams, Tesla, the tech giants (Amazon, Google, Netflix, Google)
1/ Feels like a good time to tell the story of how I went from broke to a millionaire to broke again in 2017/18 again...

Yesterday was brutal for some people...

Losing life-changing money sucks, losing any money sucks...you can chase the market or you can change your strategy.

2/ The original thread is gone but you can read it here.

https://t.co/cLLNs75rB0

tl;dr
- Traded $32k to $1.2m
- Thought I was a genius
- Made poor investments
- Didn't conserve capital
- Peaked at 150 BTC
- Lost nearly all of it

2 weeks from losing my house + no income. Oops.

3/ I am going to assume you are in it for the money rather than the tech. Yeah, you might Tweet about the amazing blockchaining of cross-border payments and oracles yadda yadda...really, you are in it to make money.

If you are really in it for the tech, go and build something.

4/ Okay, so if you want to make money, trading is super hard, you are trading against:
- Better traders than you
- People who can move markets
- Unknown information

And if you are trading with leverage you might blow up your account with the volatility.

5/ If you are not trading, you are investing. Okay, so what are you investing in?

I made the decision that the crypto with the best opportunity of existing in 10 years is #Bitcoin:
- Solves a genuine problem
- The right tech
- A proven track record

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