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Desktop GUI and CLI toolkit for WorldQuant Brain API workflows, including datasets, operators, alpha generation, simulations, backtests, evolution, and Telegram worker automation.

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## WorldQuant Brain Toolbox

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.

---

### Project Structure Overview

```
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)
```

---

### Prerequisites

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

Required packages (install with pip):

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

---

### Quick Start

1) Create a virtual environment and install dependencies

```bash
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:

```bash
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

```bash
python app.py
```

---

### Modules and Operations

- **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)
  - CLI API refresh uses WQ Brain `OPTIONS /simulations` to discover region-specific universes, then queries `data-sets` / `data-fields` per universe and caches field metadata in `datasets/datasets.sqlite`
  - CLI and GUI dataset caches are separate: the GUI continues to browse `*_fields_formatted.csv`; the CLI reads `datasets/datasets.sqlite` after `datasets refresh`
  - CLI dataset refresh waits for a user-triggered WQ session refresh and retries the same API request when a saved session expires mid-refresh
  - WQ rate limits are handled by respecting `Retry-After` on HTTP 429 and retrying transient 500/502/503/504 responses
  - Simulation workers read `x-ratelimit-limit`, `x-ratelimit-remaining`, and `x-ratelimit-reset` from `POST /simulations`; when the daily remaining count reaches 0, the next submit waits until reset before continuing. Telegram `/status` shows the latest simulation quota.

- **Operators**
  - CLI refresh uses WQ Brain `operators` to cache FASTEXPR operator metadata under `operators/operators.json`
  - When an operator has a `documentation` path, refresh also caches detailed documentation under `operators/docs/<operator>.json`
  - `level: null` is valid metadata and should not be interpreted as unavailable; some access tiers are shown in the website UI rather than this API field

- **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)
- **Background Worker**
  - A persistent worker can monitor Telegram commands and continuously drain pending simulation jobs from `.brain_cli/jobs/`
  - Launching the GUI will also auto-start this worker in the background

---

### Gemini API Setup (AI Search)

1) Obtain an API Key:
   - Request a Gemini API Key in Google AI Studio

2) Save the key into `.env`:

```bash
echo "GEMINI_API_KEY=your_api_key" >> .env
```

3) Ensure packages are installed:

```bash
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.

---

### Add a New Dataset

Location: `datasets/`

GUI CSV 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)

CLI refresh writes to `datasets/datasets.sqlite` and also exposes these metadata columns through `datasets show`, `datasets search`, and `datasets export-fields`:
- `Region` (simulation region, for example `USA`)
- `Delay` (simulation delay, for example `1`)
- `Universe` (simulation universe, for example `TOP3000`)

Use `datasets scopes` to see which region/delay combinations are cached. For live smoke tests, refresh a small slice first:

```bash
python brain_cli.py datasets refresh --dataset-id analyst10 --universes TOP3000,TOP1000
python brain_cli.py datasets scopes
python brain_cli.py datasets show analyst10 --region USA --delay 1
```

Minimal viable example (CSV content):

```csv
Field,Description,Type,Region,Delay,Universe,Coverage,Users,Alphas
demo_close,Daily close price,Vector,USA,1,TOP3000,95%,120,300
demo_volume,Daily volume,Vector,USA,1,TOP3000,92%,110,280
demo_return_5d,5-day return,Vector,USA,1,TOP1000,88%,90,250
demo_beta,Market beta estimate,Scalar,USA,1,TOP500,80%,60,180
demo_inst_density,Institutional density,Matrix,USA,1,TOPSP500,75%,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.

---

### WorldQuant Brain Credentials (for Simulation)

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

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

Notes:
- Use `Check Login` in the Simulation tab before running simulations.
- When biometric verification (persona) is required, the app will try to open the persona page automatically in your browser.
- After finishing the scan in browser, return to the app and click `我已完成驗證` to complete login confirmation.
- Successful GUI or CLI login persists WQ cookies into `session.pkl` / `login_time.pkl`, so later Simulation and Dataset refresh flows can reuse the same login state.
- CLI login stores a pending Persona session in `pending_session.pkl` / `pending_persona.json` while waiting for verification. This avoids creating a new Persona inquiry every time a different CLI command is run.
- `auth login-status` is a passive check: it validates saved cookies with `OPTIONS /simulations` and does not start a new Persona flow. Use `auth login` or Telegram `/refresh` when a new login is actually needed.
- If login is not completed, `Run Simulation` will be blocked and ask you to finish `Check Login` first.
- If credentials are expired/invalid, the UI will notify you and stop subsequent simulations.

---

### CLI Interface (`brain_cli.py`)

A fully-featured headless CLI is available for AI-agent and scripting use. It has no Qt dependency and can be used independently of the desktop app.

#### Quick overview

```
python brain_cli.py <group> <command> [options]

Groups:
  auth       Login status, login, persona completion
  datasets   List, scopes, refresh, show, search, export-fields
  operators  List, refresh, show, search WQ Brain operators
  template   List, show, save, delete, placeholders
  generate   Preview strategies, generate file
  simulate   Enqueue, run, status, stop, results, reconcile, list
  alpha      List, show, history, pnl, correlation, promote, reject registry entries
  backtest   List, show, filter, score, diversity, export
  evolution  Run, from-backtest, auto-run, status, stop, results, list
  telegram   Run Telegram bot polling and send status notifications
  worker     Run the persistent worker loop
```

#### Global flags (usable anywhere in the command line)

| Flag | Description |
|---|---|
| `--json` | Output a machine-readable envelope: `ok`, `status`, `data`, `warnings`, `errors` |
| `--credentials FILE` | Path to `credentials.json` (default: project root) |

#### Examples

```bash
# Check templates
python brain_cli.py template list --json

# Refresh and inspect WQ Brain operators
python brain_cli.py operators refresh --json
python brain_cli.py operators list
python brain_cli.py operators show ts_rank --json
python brain_cli.py operators search group --category Group --json

# Show a specific template
python brain_cli.py template show "[Default] Basic ts_rank"

# Save a new template
python brain_cli.py template save my_alpha --code "rank(close / open)"

# Preview generated strategies from a template + pools
python brain_cli.py generate preview \
  --template-name "[Default] Basic ts_rank" \
  --pool "field=close,open,volume" \
  --pool "window=5,10,20"

# List backtest files
python brain_cli.py backtest list

# Score and show top rows from a backtest CSV
python brain_cli.py backtest score 20250417_231939.csv --top 10 --json

# Diversity-filtered top candidates
python brain_cli.py backtest diversity 20250417_231939.csv --top 20 --min-hamming 0.5

# Run a single simulation (inline code)
python brain_cli.py simulate run \
  --code "rank(ts_mean(close, 20) / close)" \
  --universe TOP3000 --region USA

# Enqueue a batch from a generated .py/.csv/.json strategy file, then run
python brain_cli.py simulate enqueue \
  --params-file alphas/my_strategies.py \
  --decay 4 --truncation 0.08 \
  --json
python brain_cli.py simulate run --job-id <job_id>

# Check job status and get results
python brain_cli.py simulate status <job_id>
python brain_cli.py simulate results <job_id> --json

# Fetch official WQ Brain simulation parameter options
python brain_cli.py simulate options
python brain_cli.py simulate options --region USA
python brain_cli.py simulate options --raw --json

# Reconcile failed items whose WQ simulation URL later completed
python brain_cli.py simulate reconcile <job_id> --json

# Inspect the local alpha registry
python brain_cli.py alpha list --json
python brain_cli.py alpha show <alpha_hash_or_platform_alpha_id> --json
python brain_cli.py alpha history <alpha_hash_or_platform_alpha_id> --json
python brain_cli.py alpha pnl <alpha_hash_or_platform_alpha_id> --format csv --json
python brain_cli.py alpha correlation <base_alpha_id> <target_alpha_id> [more_target_alpha_ids...] --json
python brain_cli.py alpha promote <alpha_hash_or_platform_alpha_id> --reason "good simulation metrics" --json
python brain_cli.py alpha reject <alpha_hash_or_platform_alpha_id> --reason "turnover too high" --json

# Run evolution to generate diverse candidates
python brain_cli.py evolution run \
  --template-name "[Default] Basic ts_rank" \
  --pool "field=close,open,volume,returns" \
  --pool "window=5,10,20,40" \
  --generations 20 --pop-size 50 --top-k 10

# Seed evolution from an existing backtest CSV
python brain_cli.py evolution from-backtest \
  --template-name "[Default] Basic ts_rank" \
  --pool "field=close,open" \
  20250417_231939.csv --top-seed 5

# Closed-loop auto evolution: evolve -> simulate -> feed results back
python brain_cli.py evolution auto-run \
  --template-name "[Default] Basic ts_rank" \
  --pool "field=close,volume" \
  --rounds 3 --generations 10 \
  --universe TOP3000 --region USA --json

# Auth check
python brain_cli.py auth login-status --json
python brain_cli.py auth login
python brain_cli.py auth persona-complete

# Start Telegram bot polling
python brain_cli.py telegram run
python brain_cli.py telegram run --log-level DEBUG

# Send running simulation job progress to Telegram
python brain_cli.py telegram progress
python brain_cli.py telegram progress --job-id <job_id>

# Discover chat IDs from recent bot updates
python brain_cli.py telegram chat-id --json

# Discover and write the latest chat ID into .env
python brain_cli.py telegram chat-id --write-env

# Start the persistent worker
python brain_cli.py worker run
python brain_cli.py worker run --log-level DEBUG

# Check worker status
python brain_cli.py worker status --json
```

#### CLI job state

CLI job state for `simulate` and `evolution` is stored under `.brain_cli/jobs/<job_id>.json`. Use `simulate list` / `evolution list` to view all jobs. Stop a running job from another terminal with `simulate stop <job_id>` or `evolution stop <job_id>`.

Simulation jobs keep `completed_count`, `failed_count`, and `recovered_count` in the job summary. `status=done` means the worker has finished processing the queued items; inspect the summary counts to distinguish full success from completed jobs with failed items. During polling, each simulation item preserves `simulation_url`, `last_poll_status`, `last_progress`, `last_poll_at`, and `alpha_id` when available. Polling retries transient `500`, `502`, `503`, and `504` responses on the same simulation URL using `Retry-After` when present, otherwise capped exponential backoff.

When alpha detail fetch succeeds, the raw `/alphas/<alpha_id>` JSON payload is saved under `data/alpha_details/<alpha_id>.json`; completed job items include `alpha_details_file` when available.

Use `alpha pnl <alpha_hash_or_platform_alpha_id>` to fetch the official daily PnL recordset for one completed alpha from `/alphas/<alpha_id>/recordsets/pnl`. If the identifier is a platform alpha ID, that exact ID is used; if the identifier is a formula hash, the registry uses the formula's `canonical_alpha_id`. The command saves the full payload under `data/alpha_pnl/<alpha_id>.json` by default, or `data/alpha_pnl/<alpha_id>.csv` with `--format csv`; CLI output returns a summary unless `--include-records` is used.

Use `alpha correlation <base_alpha_id> <target_alpha_id> [more_target_alpha_ids...]` to calculate Pearson correlation between the base alpha's daily PnL and one or more target alphas. Targets can also be supplied with `--targets-file` as newline text or a JSON array. The command uses cached `data/alpha_pnl/<alpha_id>.json` files when available, fetches missing PnL from WQ Brain, and supports `--field <pnl_field>`, `--min-overlap <N>`, and `--refresh-pnl`.

When using `simulate enqueue` or `simulate run` with `--params-file` / `--params-json`, command-level simulation settings such as `--decay`, `--delay`, `--neutralization`, `--region`, `--truncation`, and `--universe` are written into each queued params item. This keeps job JSON aligned with the settings that will actually be submitted.

Use `simulate options` to fetch the official `OPTIONS /simulations` schema from WQ Brain using the saved session. The command summarizes allowed settings such as `region`, `universe`, `delay`, `decay`, `neutralization`, `truncation`, `pasteurization`, `nanHandling`, `lookback`, and `testPeriod`; add `--region <REGION>` to show dependent choices for one region, or `--raw --json` for the unmodified API schema.

Simulation jobs send one Telegram message after the job finishes, regardless of how many simulation items it contains. Telegram must be configured with `TELEGRAM_BOT_API_TOKEN` or `TELEGRAM_BOT_TOKEN`, plus `TELEGRAM_CHAT_ID`; if it is not configured, the completion notification is skipped and logged.

If a previous item failed after WQ accepted the simulation, run `simulate reconcile <job_id> --json`. Reconcile checks failed items with `simulation_url`; when WQ now returns `COMPLETE` or `WARNING` with an alpha ID, it fetches `/alphas/<alpha_id>`, appends the result CSV row if missing, updates the alpha registry, moves the item to completed, and increments `recovered_count`.

Alpha registry state is stored in `.brain_cli/alphas.sqlite`. This registry is an index over alpha code, WQ platform alpha IDs, simulation attempts, and lifecycle events; it does not replace job JSON or result CSV files. `simulate enqueue` records candidate alphas, and completed/failed simulations update the registry with metrics, links, errors, and history events.

The registry is formula-family oriented: `alphas.alpha_hash` identifies normalized formula code, while `alpha_platform_ids.alpha_id` tracks every WQ platform alpha generated for that formula. A formula may have many platform alpha IDs because reruns and settings sweeps can create duplicate IDs. `canonical_alpha_id` is the stable representative used for formula-hash operations such as `alpha pnl <alpha_hash>`; `latest_alpha_id` and `latest_result_link` point to the newest observed platform result. `alpha show/history/promote/reject/pnl/correlation` accept either a formula hash or any known platform alpha ID.

CLI authentication reuses the same persisted WQ cookie files as the GUI (`session.pkl` / `login_time.pkl`), matching the open_machine-style login flow.

Important authentication behavior:
- `auth login-status` only checks the current saved session. It does not call `POST /authentication` and therefore does not consume a new Persona inquiry.
- `auth login` starts or resumes a Persona flow. If a pending Persona URL already exists, it reuses that URL/session instead of generating a new one.
- `auth persona-complete` is equivalent to resuming the pending Persona flow from the CLI.
- When login succeeds, saved cookies are written to `session.pkl` and `login_time.pkl`; pending Persona files are cleared.

Telegram `/status` counts jobs directly from the JSON files in `.brain_cli/jobs/`. If an old job remains `pending`, it will be counted as pending even if no process is running. For abandoned simulation jobs with `"pid": null`, mark them `stopped` rather than deleting the file if you want to preserve history.

#### Telegram integration

Telegram support is optional and uses direct Bot API HTTP calls (no extra SDK required). Add these variables to `.env`:

```bash
TELEGRAM_BOT_API_TOKEN=your_bot_token
TELEGRAM_CHAT_ID=your_chat_id
```

After that, start the polling loop:

```bash
python brain_cli.py telegram run
```

Supported Telegram commands:
- `/refresh` / `/refresh_session`: refresh the saved WQ session, including Persona verification handoff with an inline confirmation button
- `/status` / `/stat`: send the current session state plus simulation/evolution job counts
- `/progress` / `/sim_progress [job_id]`: send progress for running simulation jobs, including processed count and active simulation item polling progress
- `/help` / `/start`: show available commands

Recommended Telegram login flow:

1. Start the bot polling loop with `python brain_cli.py telegram run`.
2. Send `/refresh` to the bot in Telegram.
3. Open the Persona URL sent by the bot and complete verification.
4. Press the inline `我已完成驗證` button in Telegram.
5. The same long-running bot process submits the Persona completion request and saves the WQ session.

This is the preferred flow when Persona quota is limited because the Persona URL and pending HTTP session stay in the same process, matching the `open_machine` pattern.

To discover `TELEGRAM_CHAT_ID`, first send a message like `/start` to your bot, then run:

```bash
python brain_cli.py telegram chat-id --json
```

If you want the tool to automatically write the newest discovered chat ID into `.env`, run:

```bash
python brain_cli.py telegram chat-id --write-env
```

If multiple chats have interacted with the bot, the tool will list them and use the most recent one when `--write-env` is set.

When GUI/CLI dataset refresh or simulation flows detect invalid login or expired session state, the app also sends Telegram notifications to the configured chat.

#### Persistent worker behavior

`brain_cli.py worker run` starts a long-lived process that does two things at the same time:

1. Starts Telegram monitoring (if Telegram is configured)
2. Repeatedly scans `.brain_cli/jobs/` for pending simulation jobs and runs them

Foreground `worker run` enables console logging by default. You should see startup lines for the worker state, Telegram monitoring, and simulation job scans, for example:

```text
Starting persistent brain worker…
2026-05-02 10:00:00 INFO [MainThread] Brain worker state written: pid=12345 poll_interval=3s state_file=...
2026-05-02 10:00:00 INFO [MainThread] Telegram monitoring configured: chat_id_configured=yes poll_timeout=60s
2026-05-02 10:00:00 INFO [MainThread] Worker scan: total=0 pending=0 running=0 done=0 failed=0 stopped=0 next_pending=-
2026-05-02 10:01:01 INFO [brain-telegram-worker] Telegram polling active; no updates.
```

Use `--log-level DEBUG` for lower-level Telegram polling details, or `--log-level WARNING` to quiet normal heartbeat logs.

This mirrors the open_machine-style worker pattern: one always-on process watches commands and pending work together.

The desktop GUI auto-starts this worker on launch, so opening `app.py` also brings up the same background processing model.

The Simulation tab now follows the same architecture: clicking **Run Simulation** enqueues a simulation job into `.brain_cli/jobs/`, and the persistent worker executes it. The GUI no longer runs the simulation request loop directly; instead, it polls the job state and updates the table/result flow from worker-owned job progress.

---

### How to Run

- Integrated app (recommended):

```bash
python app.py
```

- Headless CLI (no Qt required):

```bash
python brain_cli.py --help
```

- 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.

---

### FAQ

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

---

### Versions and Outputs

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

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