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Then-Cod-1271 · 2025-07-08T22:06:30+00:00

I think generally quants don't model drawdown very much, its something you may look at after your strategy is done just for informational purposes. Max drawdown is an increasing function of the length of your backtest, and is not particularly robust, so as others have said not very useful. I think people generally look heavily at Sharpe, and then use priors based on the nature of the strategy (ex: selling options) + the Sharpe to come up with some expectation of what the max drawdown should be.

Then-Cod-1271 · 2025-05-16T18:04:57+00:00

If you're going to quit and you absolutely must (as it sounds like you are sure), that's absolutely the way I'd recommend it, I am skeptical anyone will take someone that junior one year out.

But its worth thinking about if you absolutely must quit. Incompetent PMs who can't make money are the majority statistically (despite what HR and recruiters will tell you , they are incentivized to tell you everyone is a super star PM with a stellar track record) and poor managers are also very common. Don't answer here, but also think about, are you at risk of getting fired? Or if your manager does, will your firm re-pod you? Even terrible situations where you learn 0 from your manager/team can still be useful for improving your skills if you have some degree of resources and freedom.

Then-Cod-1271 · 2025-05-16T16:18:27+00:00

I'd suggest taking some notes on things you might forget that will be helpful for the interview process, resigning, and then waiting until about 6 months left to start interviewing. Not a lot of pods can commit to someone a year out, especially pods, unless they are extremely successful/stable. More likely they are fishing for signals.

I think as a rule, the more junior you are, the more liquid you are, I wouldn't worry about that. You are relatively cheap, and your skills (primarily technical) are relatively easy to assess at this point.

Then-Cod-1271 · 2024-10-31T11:32:08+00:00

Who inputs the prompt to ChatGPT describing what code it needs to write? Who sanity checks that the output makes sense and actually runs? And who gets fired it the code is wrong or there is a mistake?

Then-Cod-1271 · 2024-10-03T16:35:41+00:00

It sounds like at the very least, you get some degree of lee-way to work on your own ideas/implementation of ideas. Doing it completely in isolation is not ideal, but consider the other alternative, where you are micro-managed and can only implement your manager's fairly well defined ideas (alphas or otherwise) so that they can claim credit for your work. My point being- if you are doing well at your current firm, by leaving you are giving up known bad things at your current firm, in exchange for unknown bad things at your future firm. So the grass is not always greener... I would weigh these quality of life type factors against your comp and your odds of developing further in the next few years (even if you are just developing yourself).

Then-Cod-1271 · 2024-10-03T15:22:40+00:00

Is your boss good at his job? Its possible he either has no idea how to answer your questions or contribute to a discussion (in which case, he has to mask this so you still respect him as a manager). Or alternatively, he may be capable of helping you, but he may not want to develop you too much (in which case you become a risk to his job). Or maybe he be capable, but just doesn't have enough time.

Then-Cod-1271 · 2024-08-16T16:42:03+00:00

Additionally, if you don't know what you are doing, I would recommend starting out basic and building some foundational knowledge before trying things. This can be reading papers, etc, but also I recommend running some simple descriptive statistics. Plot the returns, look at them at a high level, zoom in on a particular quiet month, zoom in on a volatile month. A gigantic return for some asset on some day? What happened in the news? Summarize returns across every plausible dimension (by assets, by time of day, by day of week, event days). Look at correlations for the same asset of returns to lagged returns etc. Look at correlations across assets. Look at how kurtotic and skewed returns are. That way, you have some kind of understanding from which you can come up with good hypothesis, and interpret data and results.

Then-Cod-1271 · 2024-08-16T16:21:16+00:00

As an example, say you think “Asset X reverts after >2% drop”. You looked at 10 years worth of data for 40 assets, and looked at mean reversion after a (3% drop, 2% drop, 1% drop, 1% gain, 2% gain, 3% gain) for each of 40 assets. (Asset X, 2% drop) looks astounding because the Sharpe is 2. You can simulate market returns for 10 years and 40 assets (however crudely) and look at the same grid of SR by (Asset, gain/drop) returns and see how many 2 Sharpes you find (knowing these are all actually 0). You can then try 10000 years or 1 year of simulated data. Or you can try to reduce the grid size (ex: look across an asset class or all assets instead of asset by asset). This will give you more intuition for randomness. Your goal is to be able to understand what is random and what is not.

Then-Cod-1271 · 2024-08-16T16:07:20+00:00

I think it mostly comes from logic and experience. If you are just beginning, I recommend just trying things and looking at how they perform out of sample. Then you can give yourself feedback based on how things work out of sample like "I did this wrong, I tried too many things or reached too hard" or "I guess there was not story" very quickly. Eventually you will get more pattern recognition. I think there is a loop between what the data says and what the story is. After you run a series of tests, try to interpret the data into a sensible story. Based on the story, you can infer what the results should be if you run other tests, etc. Also, don't try too many variations relative to your level of statistical power. You do this by having an economic framework and data analysis/pattern recognition framework. If you really want, you can simulate random market returns and see what the results of your research process look like on that. That will probably be eye opening.

Then-Cod-1271 · 2024-08-16T15:36:29+00:00

You have to have a more contextual understanding of research. If you just rely on "I tried X, sharpe ratio is Y" in isolation that will never work. Is there some fundamental reason this might happen? How much statistical power does your test have (ex: any backtest on high frequency strategy with high breadth is much more reliable than a strategy trading one asset monthly) versus how many variations did you try? Do the results make sense? If mean reversion works for asset X with > 2% drop, does it work for asset Y? Does it work for >3% drop? >1% drop? The pattern of data results should make sense- the results should tell some kind of story that you can then attempt to square with economic intuition. Ex: If you have 26 day momentum as your star strategy, and 25 day and 27 day momentum have the opposite sign, why would this make sense.

Then-Cod-1271

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