An integrated desktop toolbox that includes:

• Datasets: Browse and search dataset fields (including Gemini AI semantic search) and import selected fields
• Backtests: View backtest results and import data
• Strategy Generator: Generate strategy code using fields and templates, then export or send to Simulation
• Simulation: Batch simulations interacting with the WorldQuant Brain API, with progress tracking and result export

This app is built with PySide6 for the GUI and supports macOS.

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brain_viewer/
  app.py                    # Integrated entry (four tabs)
  dataset_viewer.py         # Dataset browsing and search (with Gemini)
  backtest_viewer.py        # Backtest data viewer (pairs with data/)
  generator.py              # Strategy templates/parameters and export
  simulation.py             # Simulation widget (integrated into Simulation tab in app.py)
  datasets/                 # Datasets containing *_fields_formatted.csv
  data/                     # Backtest or simulation outputs (CSV/LOG)
  alphas/                   # Strategy files generated by the strategy generator
  templates/                # Strategy templates (.json)

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• Python 3.10+ (3.11 or later recommended)
• OS: macOS (Windows/Linux also possible; install corresponding dependencies yourself)

Required packages (install with pip):

pip install PySide6 pandas matplotlib python-dotenv google-generativeai requests

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1. Create a virtual environment and install dependencies

python3 -m venv .venv
source .venv/bin/activate
pip install -U pip
pip install PySide6 pandas matplotlib python-dotenv google-generativeai requests

2. Configure Gemini API (optional but recommended; used for AI Search)

• Create a .env file at the project root:

cat > .env << 'EOF'
GEMINI_API_KEY=your_api_key
EOF

• dataset_viewer.py automatically reads .env and initializes Gemini:
  • If successful, you can switch the search mode at the top-right to "AI Search"
  • If the key is missing/invalid, it will show "AI Search (API key required)" and disable the feature

3. Launch the integrated application

python app.py

---

• Datasets

  • The left panel lists *_fields_formatted.csv files under the datasets/ directory
  • The right panel shows a table to browse fields, filter, sort, and visualize distributions
  • After selecting fields, click "Import Selected Fields to Generator" to send them to the Strategy Generator
  • Search modes: Normal (keyword) / AI (Gemini semantic search)

• Backtests

  • Reads backtest data under data/ (per your existing data format/process)
  • Can import results to Simulation or Generator (follow the UI prompts)

• Strategy Generator

  • After importing fields from Datasets, combine with templates to build a list of strategies
  • Use "Preview" to review output; or generate files into alphas/
  • "Import to Simulation" sends the selected strategies to the Simulation tab

• Simulation

  • Run batch simulations for strategies imported from the Generator, with progress display and row-by-row highlight
  • Outputs CSV and LOG files under the data/ directory (filenames include timestamps)
  • Requires WorldQuant Brain login credentials (see the next section)

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1. Obtain an API Key:

   • Request a Gemini API Key in Google AI Studio

2. Save the key into .env:

echo "GEMINI_API_KEY=your_api_key" >> .env

3. Ensure packages are installed:

pip install google-generativeai python-dotenv

4. After launching the app, switch to "AI Search" in the top-right of the Datasets tab. Enter a natural-language description of what you are looking for (e.g., "indicators related to earnings momentum"). The system will perform semantic matching to suggest relevant fields.

Common issues:

• If the status bar indicates an invalid API key or the feature is disabled, ensure .env exists, the content is correct, and the terminal process has permission to read it.

---

Location: datasets/

Filename rule: Must match *_fields_formatted.csv, for example: custom_demo_fields_formatted.csv

Required columns (case-sensitive):

• Field (string, field name)
• Description (text description)
• Type (recommended categories such as Vector/Matrix/Scalar)
• Coverage (coverage; recommended as a percentage string like 95%. If numeric, the app will append % automatically)
• Users (integer)
• Alphas (integer)

Minimal viable example (CSV content):

Field,Description,Type,Coverage,Users,Alphas
demo_close,Daily close price,Vector,95%,120,300
demo_volume,Daily volume,Vector,92%,110,280
demo_return_5d,5-day return,Vector,88%,90,250
demo_beta,Market beta estimate,Scalar,80%,60,180
demo_inst_density,Institutional density,Matrix,75%,40,120

Save the above as datasets/custom_demo_fields_formatted.csv, then return to the app and click it in the left list to load. The app will automatically create a corresponding .db (SQLite) in the same directory to accelerate browsing and sorting.

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The Simulation tab interacts with the WorldQuant Brain API. Create a credentials.json in the project root:

{
  "email": "your_email@example.com",
  "password": "your_password"
}

Notes:

• On first use or when required by risk controls, the API may return a URL for biometric verification (persona). Open the link per the UI prompt to complete verification.
• If credentials are expired/invalid, the UI will notify you and stop subsequent simulations.

---

• Integrated app (recommended):

python app.py

• Run individually (for debugging or split work only):
  • Datasets: python dataset_viewer.py
  • Strategy Generator: python generator.py
  • Backtests: python backtest_viewer.py (requires readable data under data/)

simulation.py is integrated as a widget within app.py; running it standalone is not recommended.

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• macOS display issues?
  • This project enforces a light color palette. If the appearance looks abnormal, please update PySide6 and matplotlib.

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• Generated strategy files: alphas/alpha_custom_YYYYmmdd_HHMMSS.py
• Simulation outputs: data/YYYYmmdd_HHMMSS.csv and the corresponding .log