Juicy Fruit Stock Options

Utilities for analysing equity call options and covered call positions.

Setup

Install dependencies with:

pip install -r requirements.txt

Directory Layout

app/
├── zen_garden.py           # FastAPI entrypoint with scheduler
├── api/
│   └── routes.py           # REST endpoints to trigger jobs
├── auth/
│   └── users.py            # fastapi-users configuration
├── scheduler/
│   └── jobs.py             # APScheduler jobs
├── services/
│   ├── portfolio_fixer.py
│   └── stock_live_comparison.py
└── utils/
    ├── excel_exporter.py
    └── mongo_client.py

Run the API and scheduler with:

python app/zen_garden.py

Docker

Build and launch the API together with MongoDB using Docker:

./docker-run-stock-app.sh

The service will be available at http://localhost:8000.

Modules

option_analyzer_v5.py

analyze_options(ticker_symbol, min_volume=50, max_expirations=2, min_annual_tv_pct=9.9, max_otm_pct=5.0) -> pandas.DataFrame

from option_analyzer_v5 import analyze_options
df = analyze_options("ORCL")
print(df.head())

option_time_value.py

analyze_options(tickers, min_time_value=0.10) -> pandas.DataFrame

from option_time_value import analyze_options
df = analyze_options(["ORCL", "MSFT"], min_time_value=0.25)

option_optimizer.py

optimize_options(ticker_symbol, min_volume=50, max_expirations=2, min_annual_tv_pct=9.9, max_otm_pct=5.0, min_days=5, max_results=20) -> pandas.DataFrame

from option_optimizer import optimize_options
df = optimize_options("ORCL")

covered_call_analysis.py

analyze_covered_calls(file_path) -> pandas.DataFrame

from covered_call_analysis import analyze_covered_calls
df = analyze_covered_calls("ibkr_positions.csv")

Each module also includes a small main() function so it can be executed directly, e.g. python option_optimizer.py.

Testing

Run the unit tests with:

pytest -q