icembd/backtesting-swing-trading-wig20-warsaw-exchange-market- — explained in plain English
Analysis updated 2026-05-18
Test whether a moving average buy the dip strategy would have made money on past WIG20 stock data.
Compare SMA, EMA, and HMA signal calculations side by side on the same stock.
Run a batch analysis across a folder of stocks to see which trading rules perform best.
| icembd/backtesting-swing-trading-wig20-warsaw-exchange-market- | 0xallam/my-recipe | 0xhassaan/nn-from-scratch | |
|---|---|---|---|
| Stars | 0 | — | 0 |
| Language | Python | Python | Python |
| Last pushed | — | 2022-11-22 | — |
| Maintenance | — | Dormant | — |
| Setup difficulty | easy | moderate | moderate |
| Complexity | 2/5 | 2/5 | 4/5 |
| Audience | vibe coder | general | developer |
Figures from each repo's GitHub metadata at analysis time.
Needs a CSV dataset of Warsaw Exchange stock data, linked from a companion repository.
This project is a backtesting tool for swing trading strategies on the WIG20, which is a group of major stocks on the Warsaw Stock Exchange in Poland. It comes with a Tkinter graphical interface, so you get a visual window to run tests in rather than just a command line. The author is upfront that this was built for education and entertainment, not for real trading with real money. The core idea is a buy the dip strategy. The engine watches a stock price against a moving signal line. When the price drops below that line by a threshold you set, for example 0.4 percent, it buys. It then waits for the price to climb a set amount above your purchase price, for example 1.2 percent, before selling everything. Every trade includes a 0.3 percent broker fee, and profitable sales lose 19 percent of the gross profit to tax, so the simulation tries to reflect real costs. There is also a volume check that caps trade size at a percentage of typical trading volume, so a simulated trade cannot pretend to move more of the market than is realistic. To figure out that signal line, the tool runs three different moving average calculations side by side over 5 minute data chunks: the Simple Moving Average, the Exponential Moving Average, and the Hull Moving Average. Each has different tradeoffs between smoothness and speed of reaction to price changes, and the README explains the math behind each one. You can point the tool at a single stock or run a batch analysis across a whole folder of stocks. It produces a dashboard showing win rate, number of trades, and net profit in Polish zloty for each of the three algorithms, plus a detailed text file logging every trade in order. A sample dataset of Warsaw Exchange stock data is linked from a companion repository. This is aimed at people curious about testing trading ideas on Polish market data without risking any real money.
A backtesting tool with a graphical interface that simulates buy the dip trading strategies on Warsaw Stock Exchange (WIG20) stocks, for education only.
Mainly Python. The stack also includes Python, Tkinter.
Setup difficulty is rated easy, with roughly 30min to a first successful run.
Mainly vibe coder.
This repo across BitVibe Labs
double-check against the repo, no cap.