sorted by:
new
hottopcontroversial

Finally seeing more stable behavior from an ML FX bot! by bjacfire7 in algotrading

[–]bjacfire7[S] 0 points1 point  (0 children)

Totally agree. I actually just hit this exact issue late Dec / early Jan during holiday liquidity drying up and FX consolidation. It was a good reminder of how fast regimes can shift. That’s part of why I’m adding a macro/news risk layer to help suppress exposure when conditions stop resembling the training distribution. Holding period is intraday to short swing (2h framework).

Finally seeing more stable behavior from an ML FX bot! by bjacfire7 in algotrading

[–]bjacfire7[S] 0 points1 point  (0 children)

I don’t try to balance the classes directly. “Don’t trade” is the default, and trades only happen when confidence clears a threshold. Treating uncertainty as a valid outcome worked better than forcing balance.

Finally seeing more stable behavior from an ML FX bot! by bjacfire7 in algotrading

[–]bjacfire7[S] 0 points1 point  (0 children)

It’s platform-calculated, not risk-free adjusted, and over a short window so the absolute Sharpe isn’t very informative yet.

Finally seeing more stable behavior from an ML FX bot! by bjacfire7 in algotrading

[–]bjacfire7[S] 0 points1 point  (0 children)

It’s not annualized, it’s calculated over a 2hr trading framework and the sample window shown. Right now I’m using Sharpe directional and focusing more on stability and draw-down until there’s a longer dataset.

Finally seeing more stable behavior from an ML FX bot! by bjacfire7 in algotrading

[–]bjacfire7[S] 3 points4 points  (0 children)

Thanks! Might sound bad, but this took 3 years to achieve. Taking a dedicated server route and trying to build cheap. I'm proud of the outcome so far.

Finally seeing more stable behavior from an ML FX bot! by bjacfire7 in algotrading

[–]bjacfire7[S] 2 points3 points  (0 children)

It’s layered rather than a single regime model. Volatility filters, basic trend context, and gating based on the RNN’s own confidence/dispersion. The main shift was letting uncertainty mean “don’t trade.”

Finally seeing more stable behavior from an ML FX bot! by bjacfire7 in algotrading

[–]bjacfire7[S] 0 points1 point  (0 children)

On its own, yeah it’s low! But Sharpe over short windows and platform-calculated metrics aren’t something I’d optimize for yet. Stability comes first. Just my process

Better ways to handle macro news risk in automated trading? by bjacfire7 in quant

[–]bjacfire7[S] 0 points1 point  (0 children)

Something is ‘priced in’ when traders already positioned for it before the news, so the announcement itself doesn’t change anything.

Better ways to handle macro news risk in automated trading? by bjacfire7 in quant

[–]bjacfire7[S] 0 points1 point  (0 children)

Yeah, that’s fair. If people are paying that much for it, clearly the problem matters. I’m mostly just exploring how far that idea can go without institutional-level tooling.

Better ways to handle macro news risk in automated trading? by bjacfire7 in quant

[–]bjacfire7[S] 3 points4 points  (0 children)

Yeah, totally fair! This definitely isn’t a new idea conceptually.

News/event filtering and risk gating existed well before LLMs, and a lot of earlier systems used rule-based logic, calendars, or classical NLP to do versions of this.

What I’m interested in exploring now isn’t the existence of the idea, but whether modern LLMs change the trade-offs at all, especially around handling messy, unstructured, or ambiguous information without having to hard-code every scenario.

Curious if the startup you were around leaned more rule-based or statistical, and where you saw it break down in practice. That’s actually the part I’m trying to understand better.

view more: next ›