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Building AI Trading Systems

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Re: Building AI Trading Systems

#81

Post by DSingularity »

steephills wrote: post #71
smabie wrote: post #27

Earlier quoted context omitted.

The most popular kind of quant trading is using a factor model. The first step is developing some alpha factor, a number that is predictive of how much money you'll make from each stock. So let's say my alpha factor is "companies with good earnings per share will go up." So I first take the EPS for all the stocks in my universe and maybe rank, then zscore. Now I have some positive numbers and some negative numbers. T…

Taking EPS as proxy for alpha is like trying to recognise banana pictures by looking at average yellow content: it is plausible, but roughly 100 years behind the other market participants. Don't do this kind of stuff with your hard earned money...

He was using that as a simple example.

Re: Building AI Trading Systems

#82

Post by Galanwe »

tcgv wrote: post #69
henning wrote: post #9

I tried doing some forecasting with various neural network models after assembling what I thought was a good amount of forex data. The neural net (I tried various architectures) couldn't do any better than chance. After playing around with it and trying to double-check everything, that was as far as I could get. This puts me ahead of most traders, since most of them lose money, then quit. This makes me wonder what ki…

> since some kind of autoregressive time series forecasting system seems pretty unreliable. A few months ago I tried to evaluate autoregressive behavior in stock returns. To my surprise it seemed strong on some periods, but then weak on others [1], and as you said not reliable enough to rely on. My impression is that a lot more information aggregation and processing is required to obtain a sustainable edge worth tran…

You don't have to outperform to have a viable strategy. Harnessing small returns from multiple un correlated sources is a perfectly viable plan.

Re: Building AI Trading Systems

#83

Post by GrumpyNl »

anonu wrote: post #37

Just a reminder: nobody ever wrote about their super successful trading strategy. Its just never happened. If you have the wherewithal to research and build a trading system that works, then you're smart enough to know that the moment you reveal your edge to the world - it disappears. Even if you dont discuss the innards of your strategy, but you talk about your process or the system youre strategy is built on, you'v…

First thing that jumped my mind was moneytron, they were predicting the market, turned out to be a fraud.

Re: Building AI Trading Systems

#84

Post by steephills »

DSingularity wrote: post #81
steephills wrote: post #71

Earlier quoted context omitted.

Taking EPS as proxy for alpha is like trying to recognise banana pictures by looking at average yellow content: it is plausible, but roughly 100 years behind the other market participants. Don't do this kind of stuff with your hard earned money...

He was using that as a simple example.

Yes, I just wanted to make a point: things that have names, that have a paper written about them, that have Wikipedia pages or even Nobel prices attached to them are in the same category. The market has priced them in decades ago.

Thinking you can read Fama papers to take on quant funds, like smabie is claiming at several places in this thread, is like reading Commodore manuals to take on AlphaGo.

Re: Building AI Trading Systems

#85

Post by steephills »

smabie wrote: post #79
AlexCoventry wrote: post #60

Earlier quoted context omitted.

That's the ideal described in quantopian tutorials, but I doubt it often works out that way.

From personal experience, it really does actually work that way. Not all quant firms are running traditional market neutral factor portfolios though.

Really? So you're saying, not all $10tn of quant funds in the US market are managed in the way you just described?

Re: Building AI Trading Systems

#86

Post by steephills »

smabie wrote: post #34
darkteflon wrote: post #32

Earlier quoted context omitted.

Friend, if you are up on Hackernews making comments like this, I can guarantee with near 100% certainty that you are not capable of sustained outperformance in the markets. Whether you realise it now, later or never is no skin off my nose, but sooner would be cheaper. Comments like this are why these "beat-the-market" threads are evergreen on HN. They do a disservice to the community.

The low beta anomaly has persisted for since we have data. If you consider the S&P 500 the market, then yes, it's not difficult to beat it by investing in low beta stocks and leveraging up so your beta is 1. This is a classic and time-tested strategy that will probably always outperform, on a risk-adjusted basis.

Pics or it didn't happen! We should not fall below wallstreetbets standards on HN.

Re: Building AI Trading Systems

#87

Post by user5994461 »

henning wrote: post #9

I tried doing some forecasting with various neural network models after assembling what I thought was a good amount of forex data. The neural net (I tried various architectures) couldn't do any better than chance. After playing around with it and trying to double-check everything, that was as far as I could get. This puts me ahead of most traders, since most of them lose money, then quit. This makes me wonder what ki…

>>> This makes me wonder what kind of trading systems can actually have any kind of edge.

The secret is simply to have an edge.

If you're trading on behalf of clients. You don't care what happens to the market because you don't depend on the high or low to make money.

If you're buying or selling for yourself, same thing. Guess who's buying coal and oil, power plants and refineries and assimilated. They sell what they have and buy what they need.

If you're making money on arbitrage, making sure the New York and the London stock exchange have the same USD to GBP to EUR price and vice versa. You could make money but you better be faster than other corporations and more careful at the same time because you're not the only one doing that. Anytime you buy one side, the other side might have changed because you can balance out.

There are clear factors that drive many markets. When the weather is cold people consume more energy for heating. When it's hot, they go out to make barbecues and buy more sausages. When there is a drought or crop sickness, wiping agriculture exploitation, prices of food and meat go up. That's some examples that are easy to understand.

The stock market is not about speculation. It's about buying real items in the real world and providing services.

Re: Building AI Trading Systems

#88

Post by pinouchon »

For the last year or so I have been working on a ML-based trading system in the domain of crypto with two friends. I made more in 2 months than I used to in a year. This is after thousands of "full positions swings" and millions of trades (short and long). We are now experimenting with different classes of trading strategies to reduce risk.

We would like to find 1 or 2 more people to work on this project, we need people who can tolerate risk and skilled at data engineering: data pipelines, psql, pandas, numpy, data visualisation, setting up servers. Ideally also skilled at machine learning / deep learning and who has tried his hand at trading systems. If interested, my email is in my about info.

Re: Building AI Trading Systems

#89

Post by svara »

smabie wrote: post #34
darkteflon wrote: post #32

Earlier quoted context omitted.

Friend, if you are up on Hackernews making comments like this, I can guarantee with near 100% certainty that you are not capable of sustained outperformance in the markets. Whether you realise it now, later or never is no skin off my nose, but sooner would be cheaper. Comments like this are why these "beat-the-market" threads are evergreen on HN. They do a disservice to the community.

The low beta anomaly has persisted for since we have data. If you consider the S&P 500 the market, then yes, it's not difficult to beat it by investing in low beta stocks and leveraging up so your beta is 1. This is a classic and time-tested strategy that will probably always outperform, on a risk-adjusted basis.

That kind of sounds to good to be true... This [0] seems to suggest that the low-beta anomaly is essentially an artifact of how risk is measured. Toughts?

[0] https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2503174

Re: Building AI Trading Systems

#90

Post by MichaelRazum »

rawoke083600 wrote: post #76
MichaelRazum wrote: post #73

To be honest. I don’t believe a word about the performance using AI. Especially if the article doesn’t present the features and the NN architecture. Its always the question: would a super simple model perform the same way? And very ofter the answer is yes.

Lol this.. I once build a model (btc) that assumes this "we can't know the direction of market so we might as well guess", after spending months reading papers and trying to be "clever" It starts off picking a random market direction (up/down) places bid (sorry I mean makes a trade). Then based on lots of tuning/backtest decided how long to be in position and what is the stoploss.. Think in the end the most "profitab…

Actually I'm not saying that you can't be profitable. Just saying that the fancy NN, won't work like magic in a noisy environment like financial time series. For other time series with periodic and other anomalies you can apply NN and you may have an edge - BUT for that kind of environments also classical methods are working quite good.
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