up

Author: tanchekwei

.github/workflows/build.yml

@@ -34,6 +34,7 @@ jobs:
       - name: Build Release DLL
         shell: cmd
         run: |
+          call "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\VC\Auxiliary\Build\vcvarsall.bat" amd64
           cl /Zc:wchar_t /GS /GL /W3 /O2 /Zc:inline /D NDEBUG /D _WINDOWS /D _USRDLL /D _WINDLL /Oy /Gd /Gy /Oi /GR- /GF /Ot /fp:precise /MT /std:c++17 /LD /EHa /WX- /nologo ^
             /I "." ^
             StrategyOptimizer\StrategyOptimizer.cpp StrategyOptimizer\ResultAnalyzer.cpp StrategyOptimizer\ReplayManager.cpp StrategyOptimizer\OnChartLogging.cpp StrategyOptimizer\Logging.cpp StrategyOptimizer\ConfigManager.cpp StrategyOptimizer\StrategyOptimizerHelpers.cpp StrategyOptimizer\CombinationGenerator.cpp ^
@@ -42,6 +43,7 @@ jobs:
       - name: Build Debug DLL
         shell: cmd
         run: |
+          call "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\VC\Auxiliary\Build\vcvarsall.bat" amd64
           cl /JMC /MP /analyze- /Zc:wchar_t /Z7 /Od /GS /W3 /RTC1 /Zc:inline /D "_WINDOWS" /D "_USRDLL" /D "_WINDLL" /Gd /Gy /GR- /GF /fp:precise /MTd /std:c++17 /LD /EHa /WX- /diagnostics:classic /nologo ^
             /I "." ^
             StrategyOptimizer\StrategyOptimizer.cpp StrategyOptimizer\ResultAnalyzer.cpp StrategyOptimizer\ReplayManager.cpp StrategyOptimizer\OnChartLogging.cpp StrategyOptimizer\Logging.cpp StrategyOptimizer\ConfigManager.cpp StrategyOptimizer\StrategyOptimizerHelpers.cpp StrategyOptimizer\CombinationGenerator.cpp ^

visualizer/README.md

@@ -0,0 +1,34 @@
+# Trading Backtest Visualizer
+
+This Streamlit application provides a comprehensive interface for analyzing trading backtest results exported. It allows you to visualize performance metrics, explore different parameter combinations, and gain deeper insights into your trading strategies.
+
+## Quick Start
+
+Follow these steps to get the application up and running.
+
+### Prerequisites
+
+- Python 3.7+
+- `pip` for package installation
+
+### 1. Install Requirements
+
+Navigate to the `visualizer` directory and install the necessary Python packages using the `requirements.txt` file.
+
+```bash
+pip install -r requirements.txt
+```
+
+### 2. Run the Application
+
+Once the requirements are installed, you can run the Streamlit application.
+
+```bash
+streamlit run app.py
+```d
+
+The application will open in your default web browser.
+
+### 3. Analyze Your Data
+
+When the application starts, you will see a text input field. Enter the path to the folder containing your JSON backtest data files. The application will then load, process, and display the analysis. The repository contains sample data at `src/data` which can be used for demonstration.