icembd/warsaw-exchange-stock-market-wig20-csv-gpw-quantsandbox — explained in plain English
Analysis updated 2026-05-18
Backtest a moving-average or RSI trading strategy against real WIG20 price history.
Run Monte Carlo simulations to see a range of possible future price paths for a stock.
Explore how changing volatility or time horizon affects simulated stock behavior.
| icembd/warsaw-exchange-stock-market-wig20-csv-gpw-quantsandbox | 0xhassaan/nn-from-scratch | 3ks/embedoc | |
|---|---|---|---|
| Stars | 0 | 0 | — |
| Language | Python | Python | Python |
| Last pushed | — | — | 2023-06-08 |
| Maintenance | — | — | Dormant |
| Setup difficulty | easy | moderate | hard |
| Complexity | 2/5 | 4/5 | 1/5 |
| Audience | data | developer | developer |
Figures from each repo's GitHub metadata at analysis time.
Just pip install a few packages and run one streamlit command, data is already included.
This project is a self-contained sandbox for studying stocks on the WIG20 index, the main index of the Warsaw Stock Exchange in Poland. Instead of asking you to connect to a live market data feed or sign up for a paid service, it ships with historical daily price data already included as CSV files, covering January through July 2026. That means you can open the project and start experimenting right away, without any data setup. The sandbox is built for testing trading ideas, not for live trading. You can try out simple strategies like moving averages or the RSI indicator against the included historical prices to see how they would have performed. It also includes a Monte Carlo simulation tool, which runs many randomized price scenarios to give a rough sense of the range of outcomes a stock might produce, useful for thinking about risk rather than predicting exact prices. Everything runs through a local dashboard built with Streamlit, a Python tool for building simple web interfaces. From that dashboard you can adjust settings like volatility and time horizon and watch how the simulated stock behavior changes. To use it, you install a handful of Python packages (Streamlit, pandas, numpy, and Plotly) and run one command to launch the app in your browser. The included CSV data covers a specific window of dates for the WIG20 companies. The README notes that the format uses date and time codes, an asset price, and volume for each row. If you need historical data for other Polish stocks or a longer date range, the author offers custom data by request through email, which suggests this project is meant more as a demo and starting point than a fully maintained data service.
A ready-to-run sandbox with built-in historical price data for testing trading strategies on Warsaw Stock Exchange (WIG20) stocks.
Mainly Python. The stack also includes Python, Streamlit, Pandas.
Setup difficulty is rated easy, with roughly 5min to a first successful run.
Mainly data.
This repo across BitVibe Labs
Verify against the repo before relying on details.