Common quantitative trading strategies
This is the second part of our interview with a senior quantitative portfolio manager at a large hedge fund. In the first partwe covered the theoretical phase of creating a quantitative trading strategy. You can read the first part of the interview here. You can read common quantitative trading strategies third part of the interview here. When calibrating on historical data, you can get away with approximations for these. Another aspect of production is that speed is critical.
This entails a lot of matrix manipulation. And that version pipes its output into my trading station, for me common quantitative trading strategies actually start executing on this strategy. And even the successful strategies have a shelf-life of a couple of years before they get arbitraged away, so the above process repeats itself all the time.
I have to common quantitative trading strategies my approach to trading common quantitative trading strategies few years. In my experience all opportunities eventually go away. And indeed one of the biggest challenges in the type of modelling I do is knowing common quantitative trading strategies a live model is obsolete.
All models lose money on some days and weeks. And it is very difficult to recognize when losses are part of a model that is still working and when those losses are signalling the death of the model. Common quantitative trading strategies days especially there are a ton of interesting new data sources out there: Second, the markets themselves. Bankers are always inventing new instruments, and they typically common quantitative trading strategies new inefficiencies. History may not repeat but it certainly rhymes.
For instance, if you want to trade the US interest curve, the Japanese market is a rich source of ideas; Japan went through zero interest rates years before the US. Some of the best trades occur because I apply thinking from regime A within regime B.
Different asset classes have differing degrees of sophistication; you can arbitrage this difference. Fifth, I just keep my eyes and ears open. Different stages in the pipeline require different tools.
The best model in the world will fail if the data inputs are incorrect. No amount of skill can make up for bad data. We received so many excellent questions from readers that we published a third part of this series. Meanwhile, we welcome more of your questions!
If you have any questions or comments, leave them below and our quant will respond to you. Common quantitative trading strategies email address will not be published. Unsurprisingly most of my life is hammering away on new strategies and being stressed about current ones failing. So I guess my question is: Obviously there will be a high standard deviation here, but at least a general estimate would be hugely appreciated.
Do you have any advice for someone who just started as a quant at a systematic hedge fund? How do I become really good at this? What differentiates the ones who succeed from those common quantitative trading strategies do not? By which I mean a combination of procedural rigour, lack of self-deception, and humility in the face of data. Quants tend to get enamored of their models and stick to them at all costs; the intellectual satistfaction of a beautiful model or technology is seductive.
Then one day it all comes crashing down around you. I ask dumb questions, I question everything, I constantly re-examine my own assumptions. This helps me re-invent myself as the market changes. So clearly my answer is not the only way to make money! Hi Thanks for the interesting interview. I know of a few methods, such as using a runs test and z-statistic, wins rate, t-test, chi-squared, etc.
However, all of these require systems common quantitative trading strategies high win rate percentages, and in addition stationarity needs to holding which it usually isnt when the system is going through rough periods.
As we cant determine when it might come back in sync, taking it offline may result in losses through lost opportunity cost of having the system turned off when it gets back in sync. So how do you determine if the model is dead or just having a bad time? Do you know of any useful predictive regime change filters? I dont ask much, do I?! So many fascinating points in this question — it truly deserves a mini essay in response.
Can you comment more on the scale of these automated trading schemes? How much gain per transaction would be considered a good model? What range of timescales are used in your industry? How much money can there be poured into a successful scheme, is this limited by how much money your fund has available or are there typically limits on the trading scheme itself?
If bid-ask is 0. The binding constraint on these trades is usually balance sheet: I need to make sure that the trade pays a decent return on capital locked up. Obviously I use very fat tails and unit correlations in my prognosis.
Incidentally, optimal scale changes over time. I know some of the LTCM folks, and they used to make full points of arbitrage profit on Treasuries, over a span of weeks. A decade later, that same trade would make mere ticks: You have to be aware of and adapt to structural changes in the market, as knowledge diffuses. I personally am comfortable on time scales from a few weeks to a few months.
The two best trades of my career were held for two years each. They blew up, I scaled in aggressively, then rode convergence all the way back to fair value. My partner on the trading desk trades the same instruments and strategies as I do, but holds them for a few hours to a few days at most.
I work for a large-ish fund, and the constraint has almost always been the market itself. Even when the market is as large and liquid as say US Treasuries.
I was wondering how to interpret. Or do you mean that he is calibrating his models such that they take trades in tighter neighbourhoods around an equilibrium value but also have tighter stop outs?
In it, we discuss how production is a whole new ball game, and where to get ideas for new […]. Model deaths seem to last a period of years then come back better than ever sometimes.
Absolutely, and this is a great point. Models do come back from the dead. US T-note futures versus cash is a classic example: So I never say goodbye to a model forever; I have a huge back catalog of ideas whose time may come again.
Thanks for sharing your insights. This is a hard question to answer. See also my reply to Shawn below. I will write up some thoughts on this and publish them separately. What kind of turnaround time do you expect from the engineering colleagues coding up your strategy in Common quantitative trading strategies or Python?
Depends on the strategy. Some strategies are simpler and can be brought live in a matter of days; on the other hand I remember one particular strategy that took several months to instantiate. Artificial intelligence is a hungry beast. It subsists on a continuous input of trillions of data points, incessantly churning, chewing and common quantitative trading strategies out insights.
The data scientist or AI specialist must continuously seek new data sources to feed the beast and fine-tune their creation. To us, this signals growth in the number of AI practitioners deploying functional algorithms in business At least not in the business world. For one thing, they Since man first invented the common quantitative trading strategies, our need to optimize the way we get around has been an almost primeval obsession.
From the advent of the first motorized vehicle to self-driving cars, the auto industry has evolved quickly in its embrace of technology. We are now experiencing what is probably the greatest advancement in the automotive sector since Henry Ford first designed his moving assembly line: From reading your Facebook notifications to measuring common quantitative trading strategies and engine health, What does moving into production entail?
And then, hopefully, I start making money. How long does this entire process take? Are you ever worried that the model-based opportunities you strive to capture will disappear for common quantitative trading strategies Where do you get ideas for new models common quantitative trading strategies trading strategies? But I have a few avenues I resort to pretty consistently. How do you manage the data flow for all these stages and tools?