FinLab Getting Started: Build Your First Backtest in 3 Minutes

The fastest path is to choose an execution environment, download closing price data, write the first stock-selection condition, then run backtest.sim(). If you want AI-assisted strategy writing, start directly from studio.finlab.tw.

Use case	Recommended entry	Next step
No installation, test a strategy immediately	studio.finlab.tw	Describe the strategy in natural language
First time using FinLab	Google Colab	Run the examples on this page
Existing Python environment	Local pip install finlab	Download data and backtest
Complex dependency environment	Docker	Open JupyterLab

Related guides: Data Download, Backtesting, and FAQ.

Installation

We recommend starting with the AI workflow used on the FinLab homepage. Install the Python package only when you want to write Python or notebooks yourself. Supports Windows, macOS, Linux, and Colab; Docker also available.

AI Tool (Recommended) Studio, No Install Google Colab Local Python Docker

Install Codex or Claude Desktop first, then give this prompt to AI:

Run npx skills add https://github.com/koreal6803/finlab-ai --skill finlab, then backtest: "a multi-factor stock selection strategy"

Once installed, you can ask AI in natural language to query Taiwan / US data, write strategies, run backtests, and interpret performance.

Requirement

Requires Node.js 18+. After installing Codex or Claude Desktop, re-run the command above.

Visit studio.finlab.tw to write strategies through AI conversation — no installation required.

# Run in a new Colab notebook
!pip install finlab

pip install finlab

Compatibility note

If you encounter dependency issues, try the Docker version instead.

docker pull finlab/jupyter-finlab
docker run -p 8888:8888 finlab/jupyter-finlab
# Open JupyterLab using the URL shown in the terminal

Download Data

Use the following code to download data. You can browse available datasets.

Taiwan MarketUS Market

from finlab import data

close = data.get('price:收盤價')
close.tail()

from finlab import data

data.set_market('us')
close = data.get('price:adj_close')
close.tail()

Write a Strategy

Use simple Pandas syntax to write strategy logic. For example, a 300-day high breakout strategy:

Taiwan MarketUS Market

from finlab import data

close = data.get('price:收盤價')

# Buy stocks making 300-day highs
position = close >= close.rolling(300).max()
position

from finlab import data

data.set_market('us')
close = data.get('price:adj_close')
close = close[close.index.dayofweek < 5]  # remove weekends

# Buy stocks making 300-day highs
position = close >= close.rolling(300).max()
position

Backtest

from finlab import backtest

report = backtest.sim(position, resample='M')
report.display()