3 ms·

If such methodology doesn't exist then how are quantitative trading firms in business? Genuinely wondering. Is this because they have so much money to play wit

by dom96 2y ago

If such methodology doesn't exist then how are quantitative trading firms in business? Genuinely wondering. Is this because they have so much money to play with that they can move markets in their favour?

yifanl 2y ago
The one consistent method I know of would be high-frequency trading to front-run orders, which involves maintaining a moving target of state of the art infrastructure (both hardware and software), including a relationship at the markets so you can get an ultra high-speed connection (I'm certain there are rules with this to make it fairer, but I would assume not everyone will be provided a connection simply due to physical limits). But I also assume that's not the type of thing parent comment is asking about - Any rational actor with an opportunity to do this would already be doing this after all.
airstrike 2y ago
How many quantitative trading firms have gone out of business?
Maxatar 2y ago
As a quant myself, we don't try to predict the market, at least not the way that people normally talk about predicting, and we certainly don't move the market in our favor. At least what my firm does, is we look at the current state of the market at any given time point, and test whether the current state of the market satisfies our model of an efficient market. If it does, then there's no action to take, if it doesn't then we determine what kind of violation is present and jump in to close the gap. So a very trivial example would be to take two ETFs, like QQQ and TQQQ. As a simplification a model of an efficient market would have at any moment in the day the change in price of TQQQ = 3x the change in price of QQQ. We then observe the actual state of the market and if the actual change in price of TQQQ matches our model, then there's nothing to do. If it doesn't, then either TQQQ is under priced or it's overpriced or QQQ is underpriced or it's overpriced (or our model is just wrong or some outlier). Depending out what the condition is we buy x dollars worth of TQQQ and sell 3x worth of QQQ or do the opposite. There's no real prediction here, we simply have a model of what an efficient market looks like, we scan the market for violations of that model, and then we perform an action to bring the market back to an efficient state. The model I presented above is incredibly simple and just for illustrative purposes, but in a nutshell, that's our job. We have literally hundreds of models for an efficient market and for every model we have algos that test whether the market satisfies our model, and when the market deviates from our model the algo produces a signal which other algos act.
- plaidfuji 2y ago
  So basically, you’re seeking super low-risk arbitrage opportunities of low-moderate complexity, but like, really high throughput and with really low latency trading?
  - Maxatar 2y ago
    Exactly, over the past 15 years of doing this I can basically recall every single day that my firm had a net loss, with Brexit was the biggest one. Most of the losses were due to technical failures/bugs/networking issues, very few one of them were due to issues with the model. And yes, high throughput and low latency are critical aspects of our trading and they are factored into the model as well, in that for every deviation we observe from our model need to measure how long such a deviation is likely to last and we only trade on those which are likely to last long enough for the trading algo to complete.

by dom96 2y ago

yifanl 2y ago

The one consistent method I know of would be high-frequency trading to front-run orders, which involves maintaining a moving target of state of the art infrastructure (both hardware and software), including a relationship at the markets so you can get an ultra high-speed connection (I'm certain there are rules with this to make it fairer, but I would assume not everyone will be provided a connection simply due to physical limits). But I also assume that's not the type of thing parent comment is asking about - Any rational actor with an opportunity to do this would already be doing this after all.

airstrike 2y ago

How many quantitative trading firms have gone out of business?

Maxatar 2y ago

As a quant myself, we don't try to predict the market, at least not the way that people normally talk about predicting, and we certainly don't move the market in our favor. At least what my firm does, is we look at the current state of the market at any given time point, and test whether the current state of the market satisfies our model of an efficient market. If it does, then there's no action to take, if it doesn't then we determine what kind of violation is present and jump in to close the gap. So a very trivial example would be to take two ETFs, like QQQ and TQQQ. As a simplification a model of an efficient market would have at any moment in the day the change in price of TQQQ = 3x the change in price of QQQ. We then observe the actual state of the market and if the actual change in price of TQQQ matches our model, then there's nothing to do. If it doesn't, then either TQQQ is under priced or it's overpriced or QQQ is underpriced or it's overpriced (or our model is just wrong or some outlier). Depending out what the condition is we buy x dollars worth of TQQQ and sell 3x worth of QQQ or do the opposite. There's no real prediction here, we simply have a model of what an efficient market looks like, we scan the market for violations of that model, and then we perform an action to bring the market back to an efficient state. The model I presented above is incredibly simple and just for illustrative purposes, but in a nutshell, that's our job. We have literally hundreds of models for an efficient market and for every model we have algos that test whether the market satisfies our model, and when the market deviates from our model the algo produces a signal which other algos act.

plaidfuji 2y ago
So basically, you’re seeking super low-risk arbitrage opportunities of low-moderate complexity, but like, really high throughput and with really low latency trading?
- Maxatar 2y ago
  Exactly, over the past 15 years of doing this I can basically recall every single day that my firm had a net loss, with Brexit was the biggest one. Most of the losses were due to technical failures/bugs/networking issues, very few one of them were due to issues with the model. And yes, high throughput and low latency are critical aspects of our trading and they are factored into the model as well, in that for every deviation we observe from our model need to measure how long such a deviation is likely to last and we only trade on those which are likely to last long enough for the trading algo to complete.