{"id":2,"date":"2026-01-14T23:11:25","date_gmt":"2026-01-14T23:11:25","guid":{"rendered":"http:\/\/aiqnotes.com\/?page_id=2"},"modified":"2026-04-18T06:17:50","modified_gmt":"2026-04-18T06:17:50","slug":"manifesto","status":"publish","type":"page","link":"https:\/\/aiqnotes.com\/index.php\/manifesto\/","title":{"rendered":"AIQ notes Manifesto"},"content":{"rendered":"\n

What This Is (and What It Isn’t)<\/h1>\n\n\n\n
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The Short Version<\/h2>\n\n\n\n

AIQ Notes is a research blog about quantitative trading \u2014 including options strategies and volatility analysis. Every note published here follows the same logic: formulate a hypothesis, test it against data, document what works, what doesn’t, and why. AI is the tool that makes this workflow possible at the pace of a single independent researcher. The style is technical but not academic. Think of it as a lab notebook that happens to be public.<\/p>\n\n\n\n

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Why This Exists<\/h2>\n\n\n\n

There’s surprisingly little trading content that focuses on the process \u2014 the kind of structured, data-driven exploration you’d do on your own desk, written up clearly enough that someone else can follow the reasoning and challenge it.<\/p>\n\n\n\n

That’s the gap AIQ Notes tries to fill. The research itself: the questions, the tests, the dead ends, the things that looked promising at 2 AM and fell apart at breakfast.<\/p>\n\n\n\n

If you like the idea of technical research written in plain language, with code and a willingness to be wrong in public \u2014 that’s what we’re trying to do here.<\/p>\n\n\n\n

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What We Actually Do Here<\/h2>\n\n\n\n

The core of this blog is quantitative research applied to two domains:<\/p>\n\n\n\n

Systematic equity trading<\/strong> \u2014 trend persistence, mean reversion, regime detection, risk metrics. The kind of work where you take a market intuition, translate it into a testable rule, and see if the data agrees.<\/p>\n\n\n\n

Options and volatility<\/strong> \u2014 skew dynamics, term structure behavior, delta-hedged returns, vol surface modeling. Options are not just a hedging tool or a leverage device \u2014 they’re a lens for reading what the market is pricing in. Some of the research here lives in that space, alongside broader work on trend-following, regime detection, and portfolio construction.<\/p>\n\n\n\n

Every note follows a structure: there’s a question, there’s a methodology, there’s data, and there’s a conclusion \u2014 which is often “this doesn’t work the way I thought, and here’s what I learned.”<\/p>\n\n\n\n

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The Role of AI in This Project<\/h2>\n\n\n\n

Let’s be direct about this, because it matters.<\/p>\n\n\n\n

AI \u2014 specifically large language models \u2014 is the primary coding and analysis assistant behind AIQ Notes. It generates Python scripts, helps structure statistical tests, drafts visualizations, and significantly accelerates the hypothesis-to-test cycle.<\/p>\n\n\n\n

What AI does not<\/em> do: make trading decisions, validate its own output, or replace judgment. Every AI-generated piece of code is reviewed. Every statistical result is sanity-checked. Every conclusion is mine, not the model’s.<\/p>\n\n\n\n

This project is, among other things, an experiment in whether AI-assisted research can meet the same standard as traditional quant work. The answer so far is yes \u2014 but only if you treat AI as a very fast research assistant, not as an oracle.<\/p>\n\n\n\n

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The Rules We Play By<\/h2>\n\n\n\n

A few principles that apply to every note on this site:<\/p>\n\n\n\n

Assumptions are explicit.<\/strong> If a backtest starts in 2005 instead of 1990, you’ll know why. If a study excludes certain market regimes, it’s stated upfront.<\/p>\n\n\n\n

Data sources are named.<\/strong> No mystery datasets. If something is computed, the logic is shown.<\/p>\n\n\n\n

Failure is documented.<\/strong> A hypothesis that doesn’t survive testing is not a wasted note \u2014 it’s a useful one. The graveyard of dead ideas is part of the research record.<\/p>\n\n\n\n

There’s a wall between research and money.<\/strong> Exploratory work stays exploratory. Only ideas that have been tested across multiple conditions, out-of-sample periods, and stress scenarios get anywhere near real capital. Most don’t make it, and that’s fine.<\/p>\n\n\n\n

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Who This Is For<\/h2>\n\n\n\n

AIQ Notes is written for people who are curious about quantitative approaches to trading and want to see the work, not just the conclusions. You might be a discretionary trader exploring systematic methods, a developer interested in financial data analysis, a student building intuition about how markets behave under the hood, or just someone who likes looking at data and asking “is that actually true?”<\/p>\n\n\n\n

The notes are educational and meant to be accessible. You don’t need a PhD in mathematics to follow along \u2014 but you should be comfortable with the idea that a chart without a methodology behind it is just a picture.<\/p>\n\n\n\n

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What This Blog Is Not<\/h2>\n\n\n\n

AIQ Notes is, first and foremost, a research log. A thinking archive. A place where quantitative ideas go to be tested \u2014 and often to fail gracefully. It is not financial advice and it is not a signal service.<\/p>\n\n\n\n

The objective is simple and long-term: think better, not louder.<\/p>\n\n\n\n

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AIQ Notes<\/strong><\/p>\n\n\n\n

Gaetano Di Prima \u00b7 Independent Trader \u00b7 AI-Assisted Quantitative Research
research@aiqnotes.com<\/p>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

What This Is (and What It Isn’t) The Short Version AIQ Notes is a research blog about quantitative trading \u2014 including options strategies and volatility analysis. Every note published here follows the same logic: formulate a hypothesis, test it against data, document what works, what doesn’t, and why. AI is the tool that makes this […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-2","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/aiqnotes.com\/index.php\/wp-json\/wp\/v2\/pages\/2","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aiqnotes.com\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/aiqnotes.com\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/aiqnotes.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/aiqnotes.com\/index.php\/wp-json\/wp\/v2\/comments?post=2"}],"version-history":[{"count":32,"href":"https:\/\/aiqnotes.com\/index.php\/wp-json\/wp\/v2\/pages\/2\/revisions"}],"predecessor-version":[{"id":475,"href":"https:\/\/aiqnotes.com\/index.php\/wp-json\/wp\/v2\/pages\/2\/revisions\/475"}],"wp:attachment":[{"href":"https:\/\/aiqnotes.com\/index.php\/wp-json\/wp\/v2\/media?parent=2"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}