microprediction (Peter's Repos - see also Home Page)

If you were redirected from a site that used to host my blog or: Is Facebook's Prophet the Time-Series Messiah or Just a Very Naughty Boy? in particular, here's the article.

Otherwise hi

My home page page contains papers, working papers, articles
Medium blog
Linked-In content.
My thanks for reaching out page has open positions and also predicts if my niche expertise can help you in some other manner.

I'm a career quant, applied mathematician, open-source developer, entrepreneur and father of three girls.

Interests

Portfolio and ensemble construction (e.g. paper and blog where I unified the two sides of portfolio theory - more reading if you wish, and here's a broader papers list on the topic.
OTC microstructure (past work here and there and there but mostly private).
Thurstone models (contests, ratings etc)
Derivative-free optimization (see humpday)
Time-series (see timemachines)
Sports analytics
Collective Intelligence
Artificial judgemental prediction

Microprediction (custom GPT for book)

A few years ago I wrote a book predicting that data science would splinter into little agents.

I've long been 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). I'm starting to think this applies to judgemental prediction (less frequently or never repeated) also. I have more faith in small markets than most, noting the important caveat made clear in the Indispensable Markets Hypothesis paper that markets can be indispensible yet not perfectly efficient.

So yeah, the book was a meditation on the power of mini-markets and algorithmic statistical agents - a thesis that went from unlikely to almost self-evident as LLMs arrived. It predates phrases like "DeAI" and "Info Finance" (Buterin) not to mention the general explosion of interest in prediction markets ... but despite this shift in the zietgeist the ideas have a long way to go as far as seeping into general software engineering consciousness in concerned (judging by this market anyway). I guess you can read the awards and reviews or chat with the Microprediction GPT.

Beating the kids

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.

Monteprediction.com is ...

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

Open this colab notebook or script (yes there's an R version),
Change the email, at minimum,
Run it. Every weekend.
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 ...

Derivative-free optimizer comparisons

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

Schur Complementary Portfolios

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

Incremental time-series and benchmarking

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 $\hat{y}(t+l)$.

Benchmarking overview

| Topic | Package | Elo ratings | Methods | Data sources | |------------------------|-------------------|-------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------| | Univariate time-series | timemachines | Timeseries Elo ratings | Most popular packages (list) | microprediction streams | | Global derivative-free optimization | humpday | Optimizer Elo ratings | Most popular packages (list) | A mix of classic and new objectives | | Covariance, precision, correlation | precise | See notebooks | cov and portfolio lists |Stocks, electricity etc |

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

Winning

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 p

Microprediction

Install / Use

README