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"Most trading strategies are not tested rigorously enough" After having spent many-many years in the financial sector, I don't even know whether I should laugh

"Most trading strategies are not tested rigorously enough" After having spent many-many years in the financial sector, I don't even know whether I should laugh or cry. :) The industry is not based on science, well, 99% of it isn't. Traders can be considered being the master of the universe just because pure luck. Well-researched, tested strategies are thrown out because they're not profitable enough to the senior management. If a model seems to bring in high profits then even the very makers of the model do not want to let it into production, the management will just use it because nobody cares of tail risk. And so on. Great industry. :)

collyw 12y ago
The other thing that strikes me as bizarre is the way the everything seems so coupled. Its like the exact opposite of good software engineering - we build the systems to be so interlinked that everything affects everything else. Like when the oil price came down, it was threatening to put the UK (a net importer) into recession.
IndianAstronaut 12y ago
My experience across industries is that people don't know basic statistics or the value of statistics. I even came across a manager of a data science team at a major company who did not know anything about statistical testing.
- superuser2 12y ago
  ... what Data science is a buzzword substitution for statistics. How you could possibly have someone in a "data science" role without a statistics education is baffling.
  - stdbrouw 12y ago
    It's really not. Statisticians usually receive only minimal training in statistical programming, data cleaning, data gathering and warehousing and so on – yet those are all essential to data science. On top of that, when dealing with bigger data sets, many common statistical tests become irrelevant, as their main purpose is to make it possible to reason about small sample sizes. Of course, I don't mean to imply that a data scientist shouldn't have at least a basic statistical grounding: they do. But there's roles in data science for people with varying skills in programming, ops, statistics, ML, visualization and so on.
patcheudor 12y ago
A number of years ago a friend and myself worked out what could best be described as a medium frequency trading strategy based on historical trading data. This was when high frequency trading was still in its infancy. The algorithm would find certain stocks with a high frequency of highs and lows. In simulation, it worked perfectly. We were making loads of "virtual money." This got us excited enough to unleash the algorithm on the world. Guess what the problem was? All the testing in the world can't account for the market dynamics your algorithm itself creates. That was our biggest downfall. Time and time again it worked in simulation but in the real world, its trading actions based on historical data was in fact impacting the market in unexpected ways that we were ultimately unable to account for.
washedup 12y ago
This is exactly why I am on my way out. I have worked as a quant for traders for the past 5 years, and I am running out of patience. It's tough because Chicago is such a heavy financial hub. I have decided to move to a new city and try to exit trading all together.

collyw 12y ago

The other thing that strikes me as bizarre is the way the everything seems so coupled. Its like the exact opposite of good software engineering - we build the systems to be so interlinked that everything affects everything else. Like when the oil price came down, it was threatening to put the UK (a net importer) into recession.

IndianAstronaut 12y ago

My experience across industries is that people don't know basic statistics or the value of statistics. I even came across a manager of a data science team at a major company who did not know anything about statistical testing.

superuser2 12y ago
... what Data science is a buzzword substitution for statistics. How you could possibly have someone in a "data science" role without a statistics education is baffling.
- stdbrouw 12y ago
  It's really not. Statisticians usually receive only minimal training in statistical programming, data cleaning, data gathering and warehousing and so on – yet those are all essential to data science. On top of that, when dealing with bigger data sets, many common statistical tests become irrelevant, as their main purpose is to make it possible to reason about small sample sizes. Of course, I don't mean to imply that a data scientist shouldn't have at least a basic statistical grounding: they do. But there's roles in data science for people with varying skills in programming, ops, statistics, ML, visualization and so on.

patcheudor 12y ago

A number of years ago a friend and myself worked out what could best be described as a medium frequency trading strategy based on historical trading data. This was when high frequency trading was still in its infancy. The algorithm would find certain stocks with a high frequency of highs and lows. In simulation, it worked perfectly. We were making loads of "virtual money." This got us excited enough to unleash the algorithm on the world. Guess what the problem was? All the testing in the world can't account for the market dynamics your algorithm itself creates. That was our biggest downfall. Time and time again it worked in simulation but in the real world, its trading actions based on historical data was in fact impacting the market in unexpected ways that we were ultimately unable to account for.

washedup 12y ago

This is exactly why I am on my way out. I have worked as a quant for traders for the past 5 years, and I am running out of patience. It's tough because Chicago is such a heavy financial hub. I have decided to move to a new city and try to exit trading all together.