Theory is cheap.
Run the code.
Alphanume Learn is an interactive curriculum on systematic trading, taught in real Python against live market data. Lessons are read and run in the browser: no setup, no videos, no toy datasets. The same API that serves production strategies serves every lesson.
The introduction is free. No account needed.
earnings_edge.py
Python · sample history
# One real question: do options overprice earnings moves? # Sample history: 12 straight earnings quarters, in percent. implied = [4.6, 4.1, 4.9, 4.4, 3.8, 4.2, 5.1, 4.7, 4.3, 3.9, 4.8, 4.5] realized = [2.1, 6.8, 1.4, 3.2, 4.1, 2.7, 4.0, 1.8, 2.5, 4.6, 1.1, 2.9] edge = [i - r for i, r in zip(implied, realized)] print(f"avg edge : {sum(edge) / len(edge):+.2f}pp") print(f"implied > realized : {sum(e > 0 for e in edge)} of {len(edge)} quarters")
avg edge : +1.34pp implied > realized : 9 of 12 quarters
✓ Output matches expected
Every lesson is a working session.
No lectures to sit through. A lesson states a claim, hands you the data, and grades what your code prints.
- Read the argument
- Each lesson opens with a testable claim about how a market behaves, stated in plain prose before any data is touched. The claim comes first so the data cannot seduce you later.
- Write and run the code
- An editor in the page, wired to live market data from the Alphanume API. You measure the claim yourself, in real Python, with nothing to install.
- Check the result
- Output is graded against the expected result, and quizzes attack the reasoning rather than the vocabulary. You leave each lesson knowing whether you actually got it.
One research loop, run across every niche worth trading.
- 01
Why edges exist
Who is forced to act, why the pressure repeats, and what that leaves on the table.
- 02
The research loop
Hypothesis, data, measurement, attack. The loop every lesson repeats until it is a reflex.
- 03
How to trust a backtest
Lookahead, survivorship, costs, sample size. The ways research lies, and how to catch it lying.
- 04
Volatility and earnings
The volatility risk premium, implied against realized, and the scheduled catalysts that reprice it.
- 05
Index structures
SPX, 0DTE, and regime behavior at the index level, where the flows are largest.
- 06
Event-driven trades
Dilution, de-SPACs, distress, dividends, momentum, attention data. Mechanism first, then the trade.
- 07
Portfolio, risk, automation
Running the whole thing as a book, then teaching machines to run it without you watching.
Every theme lands in a live exercise against the Alphanume API within a lesson or two. The full syllabus is public: every lesson listed, every description real.
Taught against the datasets production strategies trade.
The curriculum is not a sidecar with sample CSVs. Exercises query the same point-in-time datasets documented on this site: dilution events, implied moves, index regimes, attention data. What you learn to measure in a lesson is a thing you can go trade with the same key.
Taught from a public track record.
Alphanume Learn is written by the quant behind Alphanume Research and The Quant Galore, where the research is published in the open: hypothesis, data, code, result. Some ideas survive testing and some do not, and both outcomes get published. Nothing in the curriculum asks to be taken on faith. Every claim is a thing you run.
The introduction is free. Pro opens the full curriculum.
- Free$0
- The introductory Alphanume Learn modules, free forever. The first lesson needs no account at all.
- Pro$99/month, or $75/month billed $900 yearly
- The full curriculum, plus full access to the Alphanume data platform: complete point-in-time history, real-time data, and production API access. One membership covers both.
The first lesson takes about ten minutes.
Read the argument, run the code, decide with evidence. That is the whole method, and it starts working on you in lesson one.