Options-Project

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Options-Project

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Research on options using machine learning algorithms trained on historical data.

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# Options Project

## Description

**Goal:** Use Greeks and sentiment features derived from end-of-day (EOD) option snapshots to predict performance of
different option strategies (e.g. vertical spreads).

[Alpha Vantage](https://www.alphavantage.co), [Questrade API](https://www.questrade.com/api),
and [FRED](https://fred.stlouisfed.org/categories/115) are used to retrieve historical stock prices, current stock
prices, and treasury yields (respectively).

For more details, I have written an [article](https://www.linkedin.com/pulse/parameterizing-option-delta-curves-jack-tan)
explaining the methodology use in section 4.1 below.

## Overview

- **[Part 1: Closing price, splits and
  dividends](https://github.com/jacktan1/Options-Project/blob/master/src/P1_adj_close_and_dividends.py)**
    - Retrieve historical closing prices
    - Adjust for splits
    - Estimate dividend time series

- **[Part 2: Treasury Yields](https://github.com/jacktan1/Options-Project/blob/master/src/P2_treasury_yields.py)**
    - Retrieve market yields on constant maturity securities
    - Convert linearly interpolated interest rates to continuous rates

- **[Part 3: Preprocess Options](https://github.com/jacktan1/Options-Project/blob/master/src/P3_preprocess_options.py)**
    - Filter options data for specified ticker
    - Remove errors caused by stock splits
    - Adjust options by split factors
    - Attach dividend and closing prices on data/exp date(s)

- **[Part 4: Engineer Features](https://github.com/jacktan1/Options-Project/blob/master/src/P4_model_features.py)**
    - **[4.1 - Greeks](https://github.com/jacktan1/Options-Project/tree/master/src/greeks)**
        - Calculate Greeks from clean option spread
        - **[Delta](https://github.com/jacktan1/Options-Project/blob/master/src/greeks/delta.py)**
            - For each of call & put, we parameterize: "skew", in-the-money (ITM) spread, out-of-the-money (OTM) spread
            - Interpolation at 1, 2, 3, 6 and 12 months constant maturity
        - **[VIX](https://github.com/jacktan1/Options-Project/blob/master/src/greeks/vix.py)**
            - Modified stock-specific VIX calculation based
              on [CBOE VIX](https://cdn.cboe.com/resources/vix/vixwhite.pdf#page=4)
            - Parameterization
                - Call & put VIXs
                - Interpolation at 1, 2, 3, 6 and 12 months constant maturity
        - **[Gamma](https://github.com/jacktan1/Options-Project/blob/master/src/greeks/gamma.py)**
            - No parameterization yet (future)
        - **Theta** (future)
    - **[4.2 - Custom Input Features](https://github.com/jacktan1/Options-Project/blob/master/src/custom_features/custom_inputs.py)**
        - Use daily change in open
          interest ([methodology - EDA](https://github.com/jacktan1/Options-Project/blob/master/EDA/EDA_1.ipynb)) and
          volume to parameterize options via linear regression
            - Years until expiry vs. adjusted moneyness ratio (7 variations on sample weights)
            - Call, put slopes and intercepts (2 parameters per variation)

- **[Part 5: Fit & Predict Models](https://github.com/jacktan1/Options-Project/blob/master/src/models)**
    - **Fit** - For each model type:
        - Fit separate sub-models that predict EOD price `[1, 5, 10, 15, 20, 40, 65, 90, 130, 260, 390, 520]` days out
        - Generate 2-dimensional probability density kernels (1 per sub-model) using multi-variate kernel density
          estimates
    - **Predict** - For each expiry date in test options data date:
        - Use the two nearest sub-model kernels and their discrete predictions to linearly interpolate a 1-dimensional
          density plot for each [data date, expiration date]
    - **Model Types**:
        - **[Baseline model](https://github.com/jacktan1/Options-Project/blob/master/src/models/baseline_model.py)**
            - Simply uses previous day's EOD price as predictor for target
        - Baseline model with target standardization and autoregression
        - **XGBoost model**
            - De-trend target variable to be stationary (ARIMA, Augmented Dickey-Fuller)

- **[Part 6: Trading Strategies](https://github.com/jacktan1/Options-Project/blob/master/src/option_strats)**
  - Select the most suitable option(s) per [data date, expiration date] based on prediction PDF
  - **Strategies:**
    - **[Bull call spread](https://github.com/jacktan1/Options-Project/blob/master/src/option_strats/bull_call_spread.py)**

### Demonstration of parts 5 & 6

- **[Baseline model](https://github.com/jacktan1/Options-Project/blob/master/src/P5-0_baseline_model.ipynb)**
- **[XGBoost model - in progress](https://github.com/jacktan1/Options-Project/blob/master/src/P5-2_xgboost_model.ipynb)**

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