Theory is cheap.Run the code.
An interactive course on quantitative trading, taught in real Python against real market data. Work through the lessons and run the exercises in your browser: no setup, no videos, no toy datasets.
66 lessons · 12 modules · ~18 hours · $499 once · permanent access
Five lessons. No account needed.
earnings_edge.py
Python · runs in your browser
Sample event history bundled inline, editable before you run it. In the course, the same numbers come live from the Alphanume API.
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. These fragments are from the earnings module's worked example.
1 · Read the argument
Short prose that states a testable hypothesis before any data is touched, so the data can't seduce you later.
“For large-cap names, the earnings move implied by the options market is, on average, larger than the move that gets realized.”
2 · Write and run the code
An editor in the page, wired to live market data from the Alphanume API. You measure the claim yourself.
events["spread"] = (
events["implied_move_pct"]
- events["realized_abs_move_pct"]
)
print(f"Mean: {events['spread'].mean():.2f}pp")3 · Check the result
Output is graded against the expected result, and quizzes attack the reasoning, not the vocabulary.
Mean: 1.34pp
✓ Output matches expected
Leave with a research process, not a watchlist.
The course moves from market mechanism to defensible study, then from a finished study to a repeatable workflow.
- 01Design event studies without look-ahead bias
- 02Research volatility and scheduled catalysts
- 03Build screens around dilution, SPACs, dividends, and attention
- 04Turn a hypothesis into an automated research workflow
The whole syllabus, up front.
12 modules · 66 lessons · ~18 hours
Every lesson listed, every description real. One course, taken in order: foundations and tooling first, then volatility, earnings, index structures, corporate events, and the machinery to run it all.
Systematic Trading with Market Data
66 lessons
Twelve modules from first principles to a running book: volatility, earnings, 0-DTE, dilution, SPACs, dividends, momentum, and the machinery to trade them systematically.
01How Markets Create Repeatable EdgesFree module5 lessons
02The Quant's ToolkitFull course6 lessons
03Research Methods: How to Trust a BacktestFull course5 lessons
04Volatility I: The Volatility Risk PremiumFull course6 lessons
05Volatility II: Earnings and Scheduled CatalystsFull course5 lessons
06The Index Game: SPX, 0-DTE, and RegimesFull course6 lessons
07Event-Driven I: Dilution and the Short SideFull course7 lessons
08Event-Driven II: De-SPACs, Defaults, and DistressFull course5 lessons
09Income and Flows: Dividends and MomentumFull course5 lessons
10Alternative Data and Daily ScreeningFull course5 lessons
11Portfolio, Risk, and TemperamentFull course5 lessons
12Automation and AgentsFull course6 lessons
Alphanume Learn
Own the full course for $499.
One payment. No subscription required. Your course access does not expire, and future revisions and additions are included.
What the purchase includes
- All 66 lessons across 12 modules
- Interactive Python exercises and quizzes
- Saved progress and the final capstone
- The data required to run course exercises
- Future course revisions and additions
Basic Python is helpful, not required. The course is self-paced and takes about 18 hours.
Alphanume Pro is the separate ongoing data product, with the full data platform and general-purpose API access. Active Pro members also receive Learn while subscribed.
Written by Alphanume Research.
Alphanume Research builds the market-data infrastructure behind the course and publishes the research process in the open through Alphanume and The Quant Galore: hypothesis, data, code, result. Some ideas survive testing and some do not, and both outcomes get published.
Nothing in the course asks to be taken on faith. Every claim is a thing you run.
Start with five lessons before you buy.
The free module moves from mechanism-first reasoning to the four-step research loop, then puts that loop into runnable exercises. No account needed.