Your First Hour
Install QuantIDE, open the stack that ships with it, ask it a real question, and watch a backtest land on the Canvas.
This page gets you from a fresh install to a real backtest with a real number on it. Not a demo, not a sandbox — the same loop you will use every day after this.
Budget an hour. Most of it is one install step that has nothing to do with QuantIDE.
Step 1: Install QuantIDE
Download the build for your platform and open it. QuantIDE is a desktop application — it runs on your machine, keeps your files on your disk, and does not need a browser.
Sign in when it asks. The app opens on the Stacks tab.
Step 2: Install Python
QuantIDE ships no Python of its own. This is deliberate, and it is worth understanding on day one: the app is not a sealed runtime with a fixed list of blessed libraries. It runs whatever is on your machine, which means you are never waiting for us to support the tool you want.
The cost of that choice is this step. The research stack needs Python 3 with a few libraries. Check what you have from any terminal:
python3 --versionIf that prints a version of 3.9 or higher, you are fine. If it errors, install Python 3 from python.org first.
Then install the libraries the research stack expects:
pip install vectorbt quantstatsThe research stack is written to verify the engine before you write anything, and to offer to install what is missing. It asks first, because installing packages changes your machine. If you skipped this step, it will catch it.
Step 3: Open the Quant Research stack
On the Stacks tab you will find one stack already there: Quant Research. It is the only thing that ships with the app, and it exists because an empty shelf teaches nobody what a stack is.
Click it. The workspace opens in two halves — a chat on the right, and the Canvas on the left, currently empty.
Step 4: Ask it something real
Describe a strategy idea in plain English. Not a prompt, not pseudocode — the way you would say it to another person:
Test a 10/50 moving average crossover on AAPL from 2020 to 2024.Watch what happens, because the sequence is the product:
- 01It restates your idea as testable rules — universe, signal, entry, exit, sizing — and names every assumption it had to invent to fill your gaps.
- 02It writes one self-contained Python script into your working folder.
- 03It runs that script and the result appears on the Canvas: an equity curve, a trade list, a metrics panel.
- 04It reads the result back to you like a skeptic, not a salesman.
The script it wrote is a real file in a real folder. Click "Open in Code" in the workspace header and you are looking at it, in an editor, with a terminal. You can run it yourself. You can change a number and run it again. Nothing is hidden behind the chat.
Step 5: Read the answer properly
You now have numbers. The temptation is to look at the return. Resist it — a return figure on its own is the least informative number on the screen.
Look in this order: Sharpe and max drawdown first, then the trade count, then whether the gains came from the whole period or from three good weeks. Fewer than about thirty trades proves nothing at all.
That is a result, not a failure. It usually means a threshold was never crossed, two conditions never overlapped, or an indicator warmup ate your date range. Ask the assistant why — diagnosing that is part of the method, not a detour from it.
What you just learned
You ran the loop the entire product is built around: idea, rules, script, backtest, verdict. Every other page in these docs is either about doing one of those steps better, or about the machinery that made it possible.
That machinery is the Stack, and it is the next thing to understand.
A crossover backtest that looks profitable is not a trading system. It is a hypothesis that survived one weak test. What separates the two is the subject of the Research Method track, and it is the most important thing here.
What Is a Stack?
A stack is a runnable bundle of a skill and the tools it can call. It is the unit QuantIDE is made of — install one, fork it, gut it, publish it.
Understanding Backtesting Metrics
Learn to interpret Sharpe ratio, drawdown, win rate, and other institutional-grade performance metrics.