If you were redirected from: Is Facebook's Prophet the Time-Series Messiah or Just a Very Naughty Boy?, here's the article.

Otherwise hi

• Virtual Peter Cotton can help redirect.
• My home page page contains papers, working papers, articles
• There's a custom GPT for the microprediction book here.
• Medium blog
• Linked-In content.
• Robust portfolio theory reading list.
• My thanks for reaching out page predicts if my niche expertise can help you.

I'm a career quant, applied mathematician, open-source developer, entrepreneur and dad.

• Portfolio and ensemble construction (e.g. paper and blog where I unified the two sides of portfolio theory).
• OTC microstructure (my day job so not public .. but see this or that or the other).
• Thurstone models (contests, LLM samplers etc)
• Derivative-free optimization
• Time-series
• Sports analytics
• Collective Intelligence, Decentralized AI
• LLM uses for alpha, code gen, prediction and instrumentation (e.g. pi).

I wrote a book containing some views on data science. I'm a believer in engineering pipelines that anyone else can improve without asking permission, and in the eventual inversion of control between humans and machine in the "microprediction domain" (frequently repeated quantitative tasks). This relates to the efficiency of markets, noting the important caveat made clear in the Indispensable Markets Hypothesis paper that markets can be indispensible yet not perfectly efficient.

Said book is a meditation on the power of mini-markets and algorithmic statistical agents. It predates and extends phrases like "DeAI" and "Info Finance" (Buterin). Read the awards and reviews or chat with the Microprediction GPT.

I enjoy reminding the kids that an old guy can still beat them sometimes.

• I used the options market to effortlessly beat 97% of participants in the year-long M6 contest - see the post or article.

• I'm winning a popular web3 prediction contest (the Synth bittensor subnet) for 3 out of 4 currencies.

• But I'm only 4th in my own game at www.monteprediction.com and invite you to beat me there.

A long-running game where you hurl a million 11-dimensional Monte Carlo samples at my server.

1. Open this colab notebook or script (yes there's an R version),
2. Change the email, at minimum,
3. Run it. Every weekend.
4. Check your scores at www.monteprediction.com

The notebook also describes the scoring mechanism. Ask questions in the slack (see bottom of leaderboard for slack invite).

Now, here's some attempt to introduce you to my open source work ...

The humpDay package is intended to help you choose a derivative-free optimizer for your use case.

My work on unifying Hierarchical Risk Parity with minimum variance portfolio optimization sits in the precise package. See slides from a recent talk.

The timemachines package enumerates online methods and makes some effort to evaluate univariate methods against the corpus of time-series drawn from the microprediction platform. It is an attempt to reduce everything to relatively pure functions:

$$ f : (y_t, state; k) \mapsto ( [\hat{y}(t+1),\hat{y}(t+2),\dots,\hat{y}(t+k) ], [\sigma(t+1),\dots,\sigma(t+k)], posterior\ state)) $$

where $\sigma(t+l)$ estimates the standard error of the prediction $\hat{y}(t+l)$.

These packages aspire to advance online autonomous prediction in a small way, but also help me notice if anyone else does.

The winning package includes my recently published fast algorithm for inferring relative ability from win probabilities, at any scale. As explained in the paper the uses extend well beyond the pricing of quinellas!

My firstdown repo contains analysis aspiring to ruin great game of football. See Wilmott paper and for heaven's sake, don't stretch out for the first down. That's obviously nuts.

• manifoldbot - A bot that uses LLMs to trade on manifold prediction markets.
• randomcov - A set of quirky correlation and covariance matrix generators (I'd love your ideas).
• embarrassingly - A speculative approach to robust optimization that sends impure objective functions to optimizers.
• pandemic - Ornstein-Uhlenbeck epidemic simulation (related paper)
• momentum - My most personally re-used mini package ... for incremental mean, var, skew, kurtosis.
• muid - Memorable Unique Identifiers ... try to figure out how that can't be an oxymoron.
• timeseries-notebooks - Lots of examples of using open source timeseries packages.
• correlationbounds - Mini package for conf bounds
• building_an_open_ai_network - Book related.
• recalibrate - Utils related to Platt scaling etc.

The real-time system previously maintained by yours truly has entered a trisoloran dehydrated state but will hopefully be revived at a future date, after one of my three hundred ChatGPT generated scientific grant proposals is successful. Here's how some of the open-source stuff used to propagate down into the "algo fight club".

1. The "/skaters" provide canonical, single-line of code access to functionality drawn from packages like river, pydlm, tbats, pmdarima, statsmodels.tsa, neuralprophet, Facebook Prophet, Uber's orbit, Facebook's greykite and more.

2. The StreamSkater makes it easy to use any "skater".

3. Choices are sometimes advised by Elo ratings, but anyone can do what they want.

4. It's not too hard to use my HumpDay package for offline meta-param tweaking, et cetera.

5. It's not too hard to use my precise package for online ensembling.

A few repos that drove this:

• The muid identifier package is explained in this video.
• microconventions captures things common to client and server, and may answer many of your more specific questions about prediction horizons, et cetera.
• rediz contains server side code. For the brave.
• There are other rats and mice like getjson, runthis and momentum.