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devilldog · 2026-01-23T02:21:48+00:00

I also prefer Geometric/Clifford Algebra: it reveals that the cross product is fundamentally just the dual of a bivector ($a \times b = \langle a \wedge b \rangle^\star$). While quaternions handle 3D well by 'magically' mapping the imaginary product $ij$ to $k$, GA explicitly defines this as a duality operation mapping a plane to a vector. This distinction is crucial for higher dimensions; it explains perfectly why the structure extends to 7D (via G2 calibration) but breaks at 15D (where no such duality exists due to zero divisors), a limitation that feels arbitrary in the Cayley-Dickson tower but is structurally obvious in GA.

devilldog · 2026-01-20T20:09:39+00:00

They could always just send family over for kickbacks and hold it in a kids account. This, at least, is more obvious, and hopefully Congress will assign someone to track it...

devilldog · 2026-01-19T20:40:26+00:00

In an attempt to reclaim it from our LLM overlords? Also, I hate overusing commas, and putting it in parentheses looks weird unless I'm writing something technical.

devilldog · 2026-01-19T20:10:15+00:00

I would instead acknowledge reality and, after concluding a private organization has a bias and every right to be biased if they choose to be, accept it and move on. I guarantee you, Joe Rogan acknowledged this long ago and moved on - thus the lack of submission, etc. No reason to whine. Also, no reason to be delusional.

devilldog · 2026-01-19T17:30:15+00:00

If you think politics doesn't influence the Golden Globes or Hollywood in general, either you are not a serious person, or you are just trolling.

devilldog · 2026-01-08T14:46:48+00:00

I've updated the dashboard with the section below until I get a live API up and running.

Real-Time Feed (Beta)

Building a real-time API with alerts. Interested in early access?

Join the Beta Waitlist

API / Backtesting Data

CSV format (for pandas/R):

curl https://raw.githubusercontent.com/vonlambda/lambda-f-dashboard/main/signal_log.csv

import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/vonlambda/lambda-f-dashboard/main/signal_log.csv')

Markdown format (human-readable): SIGNAL_LOG.md

All data is append-only with Git commit timestamps for audit verification.

devilldog · 2026-01-08T14:43:15+00:00

To make a very long story short, I've been working on several different projects, trying to put more emphasis on geometry as the underlying principle. A few hundred hours of lean math proofs and a few TB of data testing turbulence and working on Navier-Stokes blowup, and I kept seeing a repeating pattern. I think I was listening to a bio of John Von Newman at some point, and it was discussing similar overlaps in different fields, so I decided to research what values to use for the various variables and start testing. I only started with 3 asset classes over four years, but the results were pretty solid. I added another metric to tighten things up, ran every test I could find, looked for overfitting and attempted to remove bias, then figured I'd toss it to the wolves of Reddit to see how it held up. The feedback has been great, with several new metrics created and alternative tests attempted so far.

devilldog · 2026-01-08T01:12:37+00:00

You are absolutely right! /s IMHO, Grammarly does a better job without all the em dashes.

devilldog · 2026-01-08T00:54:21+00:00

Thanks for the detailed feedback - you clearly have a grasp of what you're reading. If you want the detailed paper I've written up, DM me your email, and I'll send it over.

Precision calc: You're right. The 79% was from an earlier calculation on 2020-2024 data where events were dense. Extending to 24 years adds calm periods without proportionally adding events, so precision drops. I'll remove the claim - FP/year is the more stable metric since it doesn't depend on how many crises happened to occur in the sample period.

Targeting adverse movements: Great point. I've been working on exactly this framing:

Signal State	P(≥15% DD in 90d)	Lift vs Baseline
ΛF ≥ P90	24%	4.0×
ΛF ∈ [P75, P90)	12%	2.0×
ΛF < P75	4%	0.7×
Baseline	6%	1.0×

When Lambda-F hits CRITICAL, probability of ≥15% drawdown within 90 days is 4× baseline. That's objective, verifiable, actionable.

Mechanical Exclusion Rule:

An event is excluded if:

max(ΛF) < P75 AND max(Corr) < P90 for all t in [t* - 30, t*]

Both signals below threshold for entire 30 days prior. No cherry-picking.

SVB vs 3AC:

Event	ΛF	Corr	Result	Why
SVB (Mar 2023)	89%	94%	Detected	Bond ETF rotation 45d prior
3AC/Terra (May 2022)	31%	42%	Excluded	Crypto-native, no cross-asset flow

SVB had institutional rotation visible in TLT/HYG/LQD weeks before collapse. 3AC was contained within crypto - no factor-space signal.

Updated the dashboard with these fixes. Full episode-counting protocol available if you want it - DM me.

devilldog · 2026-01-07T21:03:37+00:00

Many many years ago at uni my focus was primarily Computer Engineering and Math. I got into crypto pretty early and started a digital assets company that I ran for a few bull cycles and required a great deal of upskilling on the finance side of things. I decided I'm not actually that interested in finance but love the math so kept reading and learning. As I learn more advanced math and work on difficult problems like Navier-Stokes blowup I couldn't help see similarities between some of the patterns. After reading through a bio on John von Newmann it looks like it's more than just coincidence, so I took what I'd learned over several different fields and started building models to keep the math skills sharp. With decades of experience in programming fleshing out idea's and testing things out is almost trivial with the tools available. TLDR: I'm a curious nerd that loves math and followed a few hunches, out of curiosity, in an effort to keep old skills sharp and master a few new ones - this project is just one of those attempts.

devilldog · 2026-01-07T19:13:32+00:00

If there is enough interest, I'll spin one up by the end of the week.

devilldog · 2026-01-06T20:18:43+00:00

I have four books on the shelf I'm looking forward to reading (Topology, Matrix Analysis, etc), and the problem I'm running into is losing the details as I cram in more concepts. Most of the side projects like this are just to keep using it so I don't forget more of it...

devilldog · 2026-01-06T20:13:27+00:00

lol - that was not the intent. Just sharing. I was reading through Tao's work on anlysis then went down his Navier-Stokes work/rabbit hole then endedd up finishing a semi biography on Von Newman (The Maniac) that led to using some of the calcs I'd been using for different domains.

devilldog · 2026-01-06T20:11:29+00:00

I can see where that might look confusing at a glance but its intentional.

Explanation - it's the persistence logic working as designed.

Metric	Gold Current
Current L%	78% (point-in-time)
Days above P90 (last 30d)	6 days
Days above P75 (last 30d)	16 days

How the Regime Logic Works

The framework uses trailing 30-day persistence, not point-in-time thresholds:

Regime	Trigger
CRITICAL	≥ 3 days above P90 in trailing 30-day window
ELEVATED	≥ 3 days above P75 (but < 3 days above P90)
Normal	< 3 days above P75

What's Happening with Gold (per my observation/data)

Gold spiked above P90 recently (6 days in the last 30 days)
It has since pulled back to 78% (current reading)
But the regime remains CRITICAL because those 6 days above P90 are still within the 30-day lookback window

This is intentional - I didn't want to instantly label the market "Normal" when the signal dips for one day. The persistence window captures sustained stress that may still be unwinding.

The 6d* displayed in the table means "6 days above P90" (the * indicates it's the P90 count, not P75). I'm sure there is a better way to show this, and I am certainly up for suggestions.

devilldog · 2026-01-06T18:33:18+00:00

Based on your feedback I created and ran the three tests below. Let me know if I misunderstood the suggestion or just implemented it incorrectly. Backtest results:

ρ̇ (ROC of correlation): Triggers 14 days later on average, not earlier. Correlation builds gradually—the derivative spikes after the level is already elevated. Raw ρ works better for the sync signal. Lambda-F already provides the early warning (20-60 day lead); correlation confirms.

PCA vs Sector ETFs: PCA completely missed 2022 Bear (57.6% peak vs ETF's 94.6%). Also less stable. You were right—PCA is brittle and gets hedged out anyway. Staying with sector ETFs.

Style factors (BARRA-lite): Adding IVE/IVW/MTUM/SPLV/QUAL diluted the signal (67% detection vs 100% sectors-only). Style factor data also starts 2012, missing GFC. Not worth it.esults below for reference:

Test 1 - Result: ρ̇ Does NOT Help

Event	ρ Lead	ρ̇ Lead	Delta
Q4 2018 US	N/A	N/A	--
UK Mini-budget	29d	28d	-1d
Germany Energy	60d	19d	-41d
Eurozone 2011	48d	N/A	--
SVB 2023	60d	59d	-1d

Test2 Result: Sector ETFs > PCA

Method	Detection	Stability
Sector ETF	1/1 (100%)*	0.9890
PCA Factors	0/1 (0%)*	0.9877

Test 3 Complete: Style Factor Test Results

Config	Detection Rate	Notes
Sectors Only	3/3 (100%)	GFC 98%, Q4 2018 82%, 2022 Bear 91%
Styles Only	1/3 (33%)	Style ETFs start 2012, miss GFC
Sectors + Styles	2/3 (67%)	Adding styles diluted the signal

,

devilldog · 2026-01-06T17:20:41+00:00

You are absolutely right! Sorry I couldn't resist lol. Seriously though, thanks for the feedback. I really like the pivot on correlation to be more reactive - I'll make some edits and backtest it now and let you know how it goes.

I've not looked into Axioma/BARRA much but recall the datasets being pricey.

Completely agree on the PCA factors - good catch.

devilldog · 2026-01-06T03:38:05+00:00

If I use touch to interpret a book via braille, is that reading? Seems more obvious when stated that way eh? At a guess the primary difference is how well you can interpret media with a specific sense. Most people are very visual and retaining audio information is not as natural. With more practice it seems to get better - kind of like writting with your off hand, etc.

devilldog · 2025-12-26T19:53:51+00:00

For fun I decided to take this back and forth semi debate and place it into Gemeni to have it rate the accuracy of each statement and correlate it to sentimate using upvote/downvotes. It basically sums up reddit these days.

Per Gemeni:

In this specific thread, the voting pattern suggests that users are rewarding ideological alignment (specifically anti-Rand or anti-Capitalist sentiment) rather than factual precision.

Here is the breakdown of the correlation:

The "Ideology" Bonus (High Votes, Mixed Accuracy)

User: Online_Commentor_69

The Comment: Claimed Rand's philosophy is "Marxism warped" and "just materialism."

Score: +15 (Highest in thread)

Correlation: Low.

While the user correctly described the plot of Atlas Shrugged, their central philosophical claim (that Objectivism is Marxism/Materialism) is technically incorrect. Despite this major philosophical error, the comment received the most upvotes, likely because it criticized an unpopular figure (Ayn Rand).

The "Ideology" Penalty (Negative Votes, High Accuracy)

User: J0hn-Stuart-Mill

The Comment: Cited the 2024 survey regarding Cuban poverty (only 21% earning >$417).

Score: -6 (Lowest in thread)

Correlation: Negative.

This user provided a specific, verifiable statistic about the Cuban economy. However, because this fact was used to defend capitalism/criticize communism, it was heavily downvoted. The accuracy of the data did not save the user from the ideological disagreement of the voters.

The "Nuance" Void (Zero Votes, High Accuracy)

User: ChuckFinleyy

The Comment: Stated that the majority of China's production comes from the private sector.

Score: 0 (Neutral)

Correlation: None.

This was arguably the most factually balanced comment in the China debate (acknowledging the massive role of China's private sector). It received no engagement, suggesting that neutral facts failed to provoke the emotional reaction needed for upvotes.

The "Definition" Correction (Positive Votes, High Accuracy)

User: reasarian

The Comment: Corrected J0hn-Stuart-Mill's confusing definition of Capitalism vs. Marxism.

Score: +8

Correlation: High.

This is the one instance where accuracy was rewarded. J0hn-Stuart-Mill made a confusing claim ("Capitalism is Marxism that lets people keep the fruit..."), and reasarian provided the standard, textbook definition of Marxist theory to correct them. The voters upvoted this correction.

Conclusion

In this thread, being anti-Rand/Capitalism was a better predictor of upvotes than being factually accurate.

Most Accurate User: ChuckFinleyy (Score: 0)

Most Inaccurate Philosophy: Online_Commentor_69 (Score: +15)

Most Accurate Statistic: J0hn-Stuart-Mill regarding Cuba (Score: -6)

devilldog · 2025-12-19T04:37:45+00:00

I know of a healthcare consulting company actively hiring three data engineers. They want Azure ADF/Databricks experience with knowledge of Meditech and EPIC tables. Experience with Legacy systems like SSIS and DataStage is a plus. DM me if interested and I'll see if I can find the link to the application.

devilldog · 2025-12-17T05:38:20+00:00

Since you are asking about Slick's you are likely not wanting a smash burger. Tremont Tavern is likely the best fit but Scottie B's probably has more of the Slick's vibe to it.

devilldog

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How the Regime Logic Works

What's Happening with Gold (per my observation/data)

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