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u/DegenWhale_

▲ 28 r/ASX_Bets

Would anyone read/watch nerdy ASX mining factor model content?

Any interest in nerdy ASX mining quant content?

Thinking about posting some stuff around mining-focused factor models and unstructured data extraction.

Not normal PE / EV EBITDA screening. More like:

  • extracting drilling results from ASX announcements
  • ranking hits through intercepts, grades, depth, width
  • parsing production reports into structured data
  • turning messy PDFs/decks/announcements into quant signals
  • building mining-specific factor models and backtests

Basically: using LLMs + Python/Rust to pull the messy mining data that actually matters, structure it properly, and test whether it has signal.

Not selling anything — no course, no paid group, no signals. If I ever monetise it, it’d probably just be normal blog/YouTube ads.

Too niche, or would people actually read/watch this?

****Edit***

Should have added If interested - Would you prefer quant heavy aka deep maths/code? or the concepts explained whilst keeping things relevant for traders (less code more simple application / no phds required)

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u/DegenWhale_ — 4 days ago
▲ 1 r/quantfinance

Need thoughts: restarted my quant blog after 15 years — what content would people actually watch?

Restarted my blog after 15 years and thinking about doing some YouTube too.

Trying to work out what factor model / quant content people actually want.

Not selling a stack or pitching some magic model. More interested in the messy parts of retail quant research, and novel trading ideas.

Quick social proof: screenshot is from a broad-market model on an S&P 500 / Nasdaq 100 universe. Obvious caveats: short live history, bull market. But this kind of LLM/unstructured-data workflow wasn’t really practical two years ago.

IBKR SNAP

I’ll start with factor models using daily data, then later get into the more niche stuff.

I grew up trading from Australia, so I’ve got a strong commodity bias. Normal screening is pretty weak in these sectors: accounting data mostly messy/useless, and stale by the time it lands.

There are some juicy signals in things like production updates, drilling results, hedging, costs, etc. I’m doing a few more things here with unstructured data extraction and commodity-specific signals that I haven’t really seen people talk about much.

So I’m trying to work out which content angle people would actually watch:

1. Codex / AI-assisted “how to build” series
More “bash through the whole research workflow with modern tools” than quant theory/math.

Stuff like:

  • pulling market/company data and building a usable database
  • building daily-data factor models first
  • making factor models dynamic instead of overfit static screens
  • auditing/correcting AI slop
  • adding unstructured data from filings, PDFs, decks, and updates
  • working out what retail should actually target instead of copying fund-style quant

Basically: for people who understand trading but aren’t strong coders yet. How far can you get using Codex/LLMs as leverage without letting the AI fill your pipeline with bullshit?

2. Deep code / engineering breakdowns
More traditional quant/code breakdown than guided build-along.

Stuff like:

  • actual parsing/extraction methods for messy financial and commodity data
  • database/schema design for factor research
  • performance tradeoffs: CPU, GPU, Python, Rust
  • dynamic factor internals: weighting, decay, redundancy, regimes
  • data quality audits and failure modes
  • retail edges vs institutional constraints

Basically: for nerds only — pure math/code/pipeline guts.

Would you rather watch:

  1. Codex-assisted build-throughs for traders who aren’t strong coders yet
  2. Deep code / quant breakdowns for nerds only
  3. Commodity/mining deep-dives where I get more niche and show why normal factor metrics often suck there

Or something else entirely? Keen to hear what people would actually watch/read, so hit me with ideas.

u/DegenWhale_ — 4 days ago
▲ 1 r/tinnitus

Hi everyone

Anyone with tinnitus want to help test an Android app?

I’m building a tinnitus-focused acrn type sound app for Android.

Basic idea:

  1. match your tinnitus pitch
  2. build a fully customizable sound session around it
  3. try different tones, masking noise, blends
  4. save what feels useful/comfortable

Instead of just choosing white noise or a fixed tone sequence, the app lets you push the sound pretty deep.

You can build around your own matched tinnitus pitch, layer tone patterns with masking noise, blend textures, change speed/randomness/range, use pulsing and notching, try faster moving tones, melody-style patterns, experimental sounds and other weird tinnitus-focused stuff — then save the setups that actually feel useful.

Main things I need tested or would love feedback on:

  • Does sound playback work properly on your phone?
  • Are any sounds too harsh, broken, or weird?
  • Do the tone/masking/blend options make sense?
  • Is the onboarding confusing?
  • Is the menu easy to understand?
  • Is there anything you’d change before release?

It’s Android only for now and will be shared through Google Play testing.

The test build is free — no subscription, no ads, no weird signup stuff. I’ll just ask a few quick questions about your tinnitus, matched frequency, phone/audio setup, and what worked or felt confusing.

If you’re open to testing an early build, comment or dm me and try I’ll follow up asap.

also disclosure: I'm the developer and yes I have tinnitus myself after too many loud music/DJ nights when I was young and silly :P

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u/DegenWhale_ — 16 days ago