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# 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](https://github.com/algorembrant/MTParsee/blob/main/metrics_consideration.pdf) 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 <br>
- run watchdog.py once<br>
- for demonstration, drag-n-drop MT5-trade-report xlsx file into [2] folder
- let the bot do the rest
## MT5 Trade Report
<img width="820" height="200" alt="ReportTester-263254895" src="https://github.com/user-attachments/assets/908ffd2a-2c1e-4a95-91b3-1483e1256cdd" />
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](https://github.com/algorembrant/MTParsee/blob/main/metrics_consideration.pdf).
## All 76 Trading metrics
## Citation
```bibtex
@misc{MTParsee,
author = {Albeos, Rembrant},
title = {{MTParsee}},
year = {2026},
url = {https://github.com/algorembrant/MTParsee},
note = {GitHub repository}
}
```
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