MT-Parsee

An MT5-trade-report.xlsx file parser constructs statistical graphs for clear visualization of the trading strategy performance. For demonstration, it handles 71,478 cells dataframe to generate 76 rolling trading metrics and graphs with consideration of balance-based and equity-based calculations. Drop the xlsx file once — then let the bot do the rest.

Demonstation

https://github.com/user-attachments/assets/92a9049a-1948-49ed-b96b-75366a9f5b74

Project Structure

├── backend/
│   ├── [1]_main_watchdog.py       # monitors and conduct automation                  
│   ├── [2]_Drop_xlsx_here/        # drog the xlsx file here                   
│   ├── [3]_Process/                                                                                      
│   │   ├── 1_layer.py          # parsing the entire one xlxs                     
│   │   ├── 2_layer.py          # extracts the order&deals tables                
│   │   ├── 3_layer.py          # from 2 csv, merge into one                     
│   │   ├── 4_layer.py          # only all red-rolling_28-metrics (MT5)           
│   │   ├── 5_layer.py          # only all blue-rolling_13-metrics (non-MT5)       
│   │   ├── 6_layer.py          # from 2 csv, all balance-based_41-metrics         
│   │   ├── 7_layer.py          # only all orange (equity-based) rolling_metrics  
│   │   ├── 8_layer.py          # from 1 csv, all equity-based_35metrics          
│   │   └── 9_layer.py          # all balance & equity-based_76metrics             
│   ├── [4]_output_csv_files/   # The "Result": Final processed data ends up here      
│   │   ├── Upload-1_ID/            # This is where the parsed file for first uploaded file
│   │   |   ├── 1_layer_output.csv  # example file
│   │   |   ├── 2_layer_output.csv  # example file
│   │   |   ├── 3_layer_output.csv  # example file 
│   │   |   ├── 4_layer_output.csv  # example file
│   │   |   ├── 5_layer_output.csv  # example file
│   │   |   ├── 6_layer_output.csv  # example file
│   │   |   ├── 7_layer_output.csv  # example file 
│   │   |   ├── 8_layer_output.csv  # example file
│   │   |   ├── 9_layer_output.csv  # example file
│   │   |   └──  visualize_results.py  # 76 plot maker in one tab
│   │   ├── Upload-2_ID/               # This is where the parsed file for second uploaded file
│   │   ├── Upload-3_ID/               # This is where the parsed file for third uploaded file
│   │   └── Upload-4_ID/               # and so on, the pattern goes infinite

Straightforward steps

install python requirements
run watchdog.py once
for demonstration, drag-n-drop MT5-trade-report xlsx file into [2] folder
let the bot do the rest

MT5 Trade Report

The MT5 Trade Report only provides one rolling metric (as shown above), whereas my parser bot provides 76 rolling metrics. All metrics are listed here.

All 76 Trading metrics

Citation

@misc{MTParsee,
  author = {Albeos, Rembrant},
  title = {{MTParsee}},
  year = {2026},
  url = {https://github.com/algorembrant/MTParsee},
  note = {GitHub repository}
}