Backtester Desktop App- Sports Betting Data - ODDS SCRAPING

I have a collection of Excel spreadsheets with historical bet data and I need a Windows-based desktop app, complete with a straightforward GUI, that can back-test the profitability of my sports-betting models. The core task is to ingest those spreadsheets, let me apply custom filters, and instantly return key performance metrics such as yield, ROI, max drawdown ,P/L etc.. The app must handle football, basketball, tennis and other sports data. While testing a strategy I need to be able to narrow results by league, team, specific odds ranges, as well as more nuanced factors like dropping odds or odds movements (%changes),between opening and closing odds for each bookmaker or in average. Whenever I adjust a filter, the results and charts should refresh on the fly so I can see the impact immediately. A clean layout with import-file browsing, filter panels, and an output area for tables and charts is essential. Also ,the user can save the current filter configuration as a Strategy for later use .
The collection of Excel spreadsheets consists of Excel spreadsheets from different sources . Their format remains consistent in the future (new data-same format) .
For these spreadsheets must create a separate script to import it in the app .
For 2 of these files , before were created the scripts , it must make some adjustments-automations to it (see the attached file) .
Also , another source of import , will be an Excel file consisted from odds provided from a Pinnacle-API . So , it must modify these data , from JSON mode in an Excel format , using some-pre-defined filters .
Additionaly , i want to scraping odds-data from https://www.oddssafari.gr and add to these the final score/result for each match .
Finally , using ALREADY EXISTING apps , i would massively scraping odds-data from various leagues/markets from https://www.nowgoal.com and https://www.oddsportal.com . Under the hood I am flexible on the tech stack—whether you prefer C#/WPF, Python with PyQt or Tkinter, or another Windows-friendly framework—so long as the final executable runs smoothly on current Windows versions without extra setup. Deliverables • Compiled Windows executable and full source code • GUI that imports .xlsx/.csv files, applies the filters above, and outputs yield, ROI, max drawdown (plus total profit, hit rate, stake turnover if easily added) • Real-time filtering with responsive tables and optional basic charts • Short user guide covering installation, file import, and feature use Acceptance Criteria The app must read at least 100 k rows in under 10 seconds on a mid-range PC, produce accurate metrics matching a provided sample calculation, and preserve every filter option listed. If you have previous experience with data-driven sports analytics apps or betting back-testers, let me know—looking forward to seeing what you can build.