git404hub

what is revenue-yoy-backtest fr?

mjib007/revenue-yoy-backtest — explained in plain English

Analysis updated 2026-05-18

14Jupyter NotebookAudience · generalComplexity · 1/5LicenseSetup · easy

tl;dr

Educational Jupyter notebooks that backtest buying Taiwanese stocks after strong year-over-year monthly revenue growth.

vibe map

mindmap
  root((revenue-yoy-backtest))
    What it does
      Revenue growth backtest
      Buy and hold simulation
      Three notebook versions
    Tech stack
      Python
      Jupyter and Colab
      FinMind and yfinance
    Use cases
      Test growth thresholds
      Learn backtesting basics
    Audience
      Taiwan stock learners
      Vibe coders

Code map

Detail Auto

An interactive map of this repo's files and how they connect — its source is parsed live in your browser. Click Visualize to build it.

filefunction / class

what do people make with this?

VIBE 1

Backtest a buy-and-hold strategy triggered by monthly revenue growth thresholds.

VIBE 2

Learn how to build a quantitative trading backtest step by step in a notebook.

VIBE 3

Run a strategy test in Google Colab using plain-language commands instead of code.

VIBE 4

Compare win rates across different revenue growth thresholds and holding periods.

what's the stack?

PythonJupyter NotebookGoogle Colabyfinance

how it stacks up fr

mjib007/revenue-yoy-backtestlfrincond/seismic_imaging26onuralpszr/litert-lm-cookbook
Stars141313
LanguageJupyter NotebookJupyter NotebookJupyter Notebook
Setup difficultyeasyhardmoderate
Complexity1/54/53/5
Audiencegeneralresearcherdeveloper

Figures from each repo's GitHub metadata at analysis time.

how do i run it?

Difficulty · easy time til it works · 5min

Runs entirely in Google Colab, no local installation needed unless you want to run it on your own machine.

MIT license: use, copy, modify, and distribute freely, including commercially, as long as you keep the copyright notice.

in plain english

This is a set of Jupyter notebooks for backtesting a stock trading strategy based on year over year monthly revenue growth. The core question it answers is: when a stock's monthly revenue grows by more than a chosen percentage compared to the same month the previous year, what is the historical win rate if you buy and hold the stock for a fixed number of days afterward? The project describes itself as educational material for learning how to build this kind of analysis from scratch. Three notebook versions are provided for different users. A teaching version walks through every step with explanations, meant for first time learners. A single cell version condenses everything into one block that runs after you adjust the parameters. A command input version lets you type plain language instructions, such as a stock ticker, a revenue growth threshold, and a number of days to hold, without touching any code. All three run directly in Google Colab, a free online notebook environment that needs no local installation. Adjustable parameters include the stock ticker, the market (Taiwan Stock Exchange or over the counter), the growth threshold, the number of days to hold after buying, and the backtest start date. Monthly revenue data comes from FinMind, a Taiwanese financial data platform, and stock price data comes from the yfinance library. The project is written in Python. This is intended for learners interested in Taiwanese stock markets who want to understand how to build and test a simple quantitative strategy. The README notes that backtest results are for education only and are not investment advice, and is released under the MIT license.

prompts (copy fr)

prompt 1
Walk me through what year-over-year monthly revenue growth means and why it might predict stock returns.
prompt 2
Help me modify this notebook to backtest multiple stock tickers at once.
prompt 3
Explain how to add a stop-loss rule to this revenue growth backtest.
prompt 4
Show me how to change the chart labels in this notebook to English.

Frequently asked questions

what is revenue-yoy-backtest fr?

Educational Jupyter notebooks that backtest buying Taiwanese stocks after strong year-over-year monthly revenue growth.

What language is revenue-yoy-backtest written in?

Mainly Jupyter Notebook. The stack also includes Python, Jupyter Notebook, Google Colab.

What license does revenue-yoy-backtest use?

MIT license: use, copy, modify, and distribute freely, including commercially, as long as you keep the copyright notice.

How hard is revenue-yoy-backtest to set up?

Setup difficulty is rated easy, with roughly 5min to a first successful run.

Who is revenue-yoy-backtest for?

Mainly general.

peek the repo → explain another one

This repo across BitVibe Labs

double-check against the repo, no cap.