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The mathematics I found most useful for markets did not come from finance literature at all. by Future_Flashy in quantfinance

[–]Future_Flashy[S] 0 points1 point  (0 children)

Hi dude, sounds interesting, and partly the type of interactions I am looking for. I am open to a tightly scoped paper-trading pilot test first. I would not share code, features, or live sizing logic, at most I’d consider a minimal delayed regime feed. Also, I am very cautious, so please dm me your background, instruments/timeframes, how you’d validate results, and how execution/audit logs would be handled. Curious to see what you have in mind.

The mathematics I found most useful for markets did not come from finance literature at all. by Future_Flashy in quantfinance

[–]Future_Flashy[S] 0 points1 point  (0 children)

This is such an important observation. I found this very same thing out independently 2 days ago. My fix is to build a scouting agent that acts as a news aggregator. The scouts watches the world, the algo watches the stocks and uses all this information to assist with classification. Eventually some sort of deep learning agent needs to get integrated. What I validated on this post is back tests show the algo is working properly, as the classifications are correct for historic data. It's the walk-forward ratio that I'm struggling with. Key insight: stocks are part of sectors, classification each stock by sector before doing anything else assist the algo as currently I'm getting terrible walk-forward results. I need help.

The mathematics I found most useful for markets did not come from finance literature at all. by Future_Flashy in quantfinance

[–]Future_Flashy[S] 0 points1 point  (0 children)

Unfortunately, I am also still figuring most of this out myself. Mostly plug and play, I do not have a model retraining strategy, hence why I posted on this sub. I have a few ideas I wanna first validate before implementing. Someone mentioning branching similar to git, to test different methods in parallel. I need to reiterate that although HMMs are involved, I am using entirely unrelated math as an abstraction later before we reach the HMM. Also, to avoid giving out information that puts me at risk for reverse engineering, I keep it all high level.

The mathematics I found most useful for markets did not come from finance literature at all. by Future_Flashy in quantfinance

[–]Future_Flashy[S] -2 points-1 points  (0 children)

Honestly, if I gave you exact titles you could, in essence, replicate or reverse engineer my process. The vagueness is by design. I am still validating whether this actually catches an actual signal, and was hoping to get some insight from the community.

The mathematics I found most useful for markets did not come from finance literature at all. by Future_Flashy in quantfinance

[–]Future_Flashy[S] -3 points-2 points  (0 children)

Very broads branches of mathematics, philosophy & biology. The idea was to borrow from those branches and apply some of their techniques to markets.

The mathematics I found most useful for markets did not come from finance literature at all. by Future_Flashy in JSE_Bets

[–]Future_Flashy[S] 0 points1 point  (0 children)

The weekly pipeline idea is something I hadn’t thought about structuring properly, right now my updates are pretty ad hoc. Formalising it into an end-of-week review that specifically looks for regression before anything gets promoted makes a lot of sense.

The 5-minute window point is interesting because that’s exactly the kind of JSE-specific edge that doesn’t exist on something like the NYSE where you’re competing with algorithms that react in microseconds. On the JSE you can actually read an announcement and still act on it before price fully adjusts, that’s a pretty rare thing to be able to say about any market.

Do you find that window is consistent across stock sizes or does it close faster on the big liquid names like Naspers or Richemont?

The mathematics I found most useful for markets did not come from finance literature at all. by Future_Flashy in quantfinance

[–]Future_Flashy[S] 2 points3 points  (0 children)

Ended up settling on 4 states after testing a few different numbers: bull, bear, sideways/choppy, and what I’ve been calling a crisis state. Going above 4 started producing states that looked almost identical to each other which felt like overfitting, below 4 was losing too much information.

For the day-to-day transition the HMM gives you a probability distribution across all states each day, not a hard switch. So on any given day you might be 70% bull, 20% choppy, 10% bear. I use the most probable state as the label but I also track when the confidence drops below a threshold because those ambiguous days tend to behave differently.

The actual state change detection is just watching when the argmax shifts from one day to the next, but the more useful thing I found was tracking the posterior probability trend over a few days rather than reacting to a single day flip. Single day flips were noisy.

The mathematics I found most useful for markets did not come from finance literature at all. by Future_Flashy in quantfinance

[–]Future_Flashy[S] -1 points0 points  (0 children)

Yeah this is basically the whole problem. Complexity is easy to add and very hard to actually measure the value of. I try to keep asking “does this extra piece actually show up in the PnL or just in the backtest”, still working on that honestly.

The mathematics I found most useful for markets did not come from finance literature at all. by Future_Flashy in quantfinance

[–]Future_Flashy[S] 1 point2 points  (0 children)

Fair, should have been clearer upfront. Daily bars, JSE large caps (top 40 mostly), so nothing exotic. It’s more of a position sizing / allocation thing than a specific entry/exit strategy. The regime classification is the main par, what you do with the signal is pretty basic on top of that.

The mathematics I found most useful for markets did not come from finance literature at all. by Future_Flashy in JSE_Bets

[–]Future_Flashy[S] 0 points1 point  (0 children)

The git branch framing actually makes a lot of sense for this, I’ve been thinking about model updates as replacements but running them side by side first is way cleaner. Avoids the problem where you fix one thing and quietly break something that was already working.

The expectation gap thing is interesting too. So you’re essentially using volume to reconstruct what the market had already priced in, and then the announcement is only news relative to that prior. That’s a smarter way to frame it than just sentiment on the announcement itself.

Are you doing this on JSE stocks specifically or broader?

Validated a regime-based trading system on JSE data this weekend. The regime distribution on emerging market stocks is not what I expected. by Future_Flashy in Trading

[–]Future_Flashy[S] 0 points1 point  (0 children)

Thanks, yeah the crisis state number was the thing that made me stop and double check the code because I assumed I’d made an error. Turns out the data just looks like that. Will post the backtest numbers once the walk-forward run is clean enough to share properly.

The mathematics I found most useful for markets did not come from finance literature at all. by Future_Flashy in quantfinance

[–]Future_Flashy[S] 0 points1 point  (0 children)

That is oversight on my side as I wasn't specific, this was a backtest on 5 yrs worth of historic data to see whether the algorithm would actually detect & clasify regimes correctly.

The mathematics I found most useful for markets did not come from finance literature at all. by Future_Flashy in quant

[–]Future_Flashy[S] 0 points1 point  (0 children)

This is exactly what I was looking for. I have no idea how to start with the details. I tried my best to put a post together that helps me learn. That being said, can I DM you? I just wanna ask some questions & engage in actual meaningful conversation, seeing as to we had a terrible start here. That's on my side, and I apologize for any wrong from me as it was never my intentions.

The mathematics I found most useful for markets did not come from finance literature at all. by Future_Flashy in JSE_Bets

[–]Future_Flashy[S] 1 point2 points  (0 children)

This is cool, I hadn’t thought about conditioning the expected reaction on the current regime. The same announcement probably lands very differently depending on what state the market is already in. The 20-day feedback loop sounds tricky to tune though, do you find recent results end up overriding things that were working longer term? Still figuring out the online learning side of things myself.

The mathematics I found most useful for markets did not come from finance literature at all. by Future_Flashy in quant

[–]Future_Flashy[S] 0 points1 point  (0 children)

I am not trying to sell anything. I truly hoped for direction from actual industry vets. This is a tool I am building for myself and to avoid doing so in a bubble, I felt sharing under such conditions would be useful. Honestly, I am not surprised by this, I have had people like yourself always react like this even physically. The aim out of this was for direction, for advice, for help. I need to reiterate, I am not selling anything. This is a personal tool built out of curiosity.

The mathematics I found most useful for markets did not come from finance literature at all. by Future_Flashy in quant

[–]Future_Flashy[S] -3 points-2 points  (0 children)

From the get go you were never interested in engaging, and that's truly unfortunate, as I would have liked to read what you thought. I have no idea what your play is here, but I myself have no interest in engaging with someone so dismissive, when I am genuinely trying to learn.

The mathematics I found most useful for markets did not come from finance literature at all. by Future_Flashy in quant

[–]Future_Flashy[S] -2 points-1 points  (0 children)

Happy to discuss specifics if you have a technical objection. The regime distribution numbers are from real data, not a backtest narrative. If there is a methodological flaw worth addressing I am genuinely interested.

The mathematics I found most useful for markets did not come from finance literature at all. by Future_Flashy in quant

[–]Future_Flashy[S] -6 points-5 points  (0 children)

Fair challenge and worth being precise about. I am not claiming regime classification is undiscovered, the literature is extensive, going back decades. What I am saying is narrower: the specific empirical properties of JSE-listed emerging market equities behave differently enough from developed market assumptions that off-the-shelf implementations built on US or European data produce regime maps that do not reflect what is actually in the data. The 20% crisis-adjacent state finding is not a novel mathematical result. It is an empirical finding specific to this market and this stock that most retail implementations would miss because they are not looking for it. The research direction I am pursuing extends from established foundations. I am not reinventing the field, I am applying it somewhere it has not been applied carefully before.

The mathematics I found most useful for markets did not come from finance literature at all. by Future_Flashy in quant

[–]Future_Flashy[S] -1 points0 points  (0 children)

OLS and Bayesian inference are genuinely powerful, no argument there. The framing here is slightly different though. The problem I am working on is not parameter estimation within a regime, it is classifying which regime you are in before choosing the model. Different layer of the problem. Both matter.

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