You already trade
You know what a stop, a spread and a drawdown are, and you have a setup you trust. The course builds on that instead of starting from what a candle is.
Academy
Quant.
A structured course for traders who want to automate their trading: from written rules to Python, honest backtests, validation and a system that runs.
From rules you follow to rules a computer runs
Eleven modules take one idea from a discretionary setup to a tested, validated and monitored system. Each lesson ends with a Lab exercise and a short self-check.
Systematic trading is not a shortcut to an edge. It is a way of writing your edge down precisely enough that a computer can test it, and of finding out — before real money does — whether it was ever there.
The first two modules are available now: the mindset and workflow of a quant trader, and enough Python to hold price data, compute a signal and plot an equity curve. The remaining modules are outlined below and will open in order.
0%
- 11
- 9
- 2 h 15 min
Traders first, programmers second
You do not code yet
Every programming idea is explained the first time it appears, through trading examples. School algebra is the only maths you need to start.
You want evidence, not promises
The goal is a system you can test, question and audit. Nothing here promises returns, and every example runs on synthetic data.
Eleven modules, one workflow
Idea, rules, test, validate, run. Modules open in order; outlined modules list the lessons planned for them.
From Discretion to Rules
What changes when trading decisions become written rules that a computer can test and run — and what stays exactly the same.
Python for Traders
Enough Python and pandas to hold price data, compute returns and indicators, generate a first signal and plot an equity curve — taught through trading examples.
Market Data and Its Traps
Where price data comes from, what it quietly hides, and how to clean it before it misleads a backtest.
5 planned lessons
Statistics That Matter
The small set of statistics a systematic trader uses every week — distributions, volatility, correlation and sample size — and the ways each one misleads.
5 planned lessons
Turning a Setup into Rules
Translating a discretionary setup into unambiguous entry, exit and sizing rules that code can execute and a backtest can measure.
5 planned lessons
Backtesting Honestly
Building a simple backtester, then making it honest — realistic fills and costs, no look-ahead, and metrics that describe risk as well as return.
5 planned lessons
Validation: Walk-Forward, Monte Carlo, Overfitting
Testing whether a backtest result is likely to survive new data — out-of-sample periods, walk-forward analysis, Monte Carlo resampling and overfitting checks.
5 planned lessons
Risk and Portfolio
Managing risk across a whole system — position sizing, drawdown limits, correlation between strategies, and combining several strategies into one book.
5 planned lessons
Execution and Broker APIs
How orders reach the market from code — order types, broker APIs, paper trading, and the engineering a system needs to run reliably.
5 planned lessons
Going Live
Moving from paper trading to small live size — deployment, monitoring, comparing live results with the backtest, and deciding in advance when to stop.
4 planned lessons
Capstone
One complete project from idea to paper trading, using every stage of the workflow and documented so that another person could audit it.
4 planned lessons