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tos_options_dashboard

★ 95 Open GitHub ↗

Interactive dashboard to filter and analyse stock options contracts (Built using data from ThinkOrSwim's API and Plotly Dash components)

// repository documentation

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# ThinkOrSwim (TOS) Options Dashboard

## Project Description:

An interactive dashboard to filter and analyze stock options contracts (Built using data from ThinkOrSwim's Option Chain API and Plotly Dash components). It is useful for quickly scanning option chains to look for individual profitable options contracts to trade on.

Filter options include:

* Return of Investment (ROI) Range: Percentage of premium received (by selling an contract) over the underlying amount (strike price * 100)
* Delta Range: Represents current probability that the option contract will expire [in-the-money](https://www.investopedia.com/terms/i/inthemoney.asp) (i.e. Option is exercised)
* Option Contract Type: Call/Put/All (both call and put options)
* Days to expiration: No. of days till options contract is set to expire
* Confidence Level: Represents the level of confidence in which the stock price is likely to be within the probability cone. A higher confidence level would result in a wider probability cone. 
* Historical Volatility Period: Changes the width of the probability cone calculation based on past period historical volatility (Past month, past 3 months, past year). For example, historical volatility of 40% would result in a wider probability curve than that based on a historical volatility of 25%.

## Pre-requisites:

1. Set-up a TDAmeritrade Developer account to receive an API key. This key is necessary to authenticate each API call to extract the necessary data (e.g. stock quote date, option chain data) to required to perform financial analysis.

* [TOS Developers Home Page](https://developer.tdameritrade.com/)

* [Reddit: Guide to TOS Developer App set-up](https://www.reddit.com/r/algotrading/comments/914q22/successful_access_to_td_ameritrade_api/)

* If you are using Docker, proceed to step 1a), else go to step 1b).

a) Enter the API key into the Dockerfile.

```dockerfile
     ARG api_key = ENTER_API_KEY_HERE
     ```

b) Set the API key as an environment variable (it should be named TOS_API_KEY).

* [Environment Variables in Windows](https://www.youtube.com/watch?v=IolxqkL7cD8&t=136s)
     * [Environment Variables in Mac/Linux](https://www.youtube.com/watch?v=5iWhQWVXosU)

2. Activate virtual environment in the directory of choice and install the necessary libraries outlined in requirements.txt .

```python
   pip install -r requirements.txt
   ```

3. [Docker Option] To build and run the docker container, run the following lines in the terminal/command prompt. After running docker run, proceed with step 2 of the Usage section.

* [Docker Troubleshooting](https://www.thegeekdiary.com/docker-troubleshooting-conflict-unable-to-delete-image-is-being-used-by-running-container/)

```terminal
   docker build -t tos_options_dashboard .
   docker run -p 8050:8050 tos_options_dashboard
   ```
   
   * Note: You can override the default value of the TOS API Key set in Dockerfile by running the following command during build
   
     ```dockerfile
     docker build --build-arg api_key=<NEW_API_KEY> -t tos_options_dashboard .
     ```

## Usage:

1. Run the python file dashboard.py

```python
   python dashboard.py
   ```

2. The Dashboard would be running on local host (Port: 8050) by default. Open the web browser and enter the corresponding localhost address (http://127.0.0.1:8050/) to view the Dashboard.

3. To start using the Dashboard, activate Ticker mode before entering the stock ticker of interest (e.g. AAPL for Apple Inc. stock).

Alternatively, you can type 'Apple' without activating Ticker mode to search for a particular option underlying.

There are several options property filter options to choose from:

* Return on Investment (ROI) Range: (Default: More than 1%)
   * Delta Range: (Default: Ignore Delta value)
   * Option Contract Type: Call/Put/All (Default: All)
   * Day(s) to Expiration: 0-100 days (Default: 0-14 days)
   * Confidence Level: (Default: 30% Confidence)
   * Historical Volatility Level: (Default: 1 Month)
   * Volaility Estimator: (Default: Log Returns/Standard Deviation)

To proceed with the search function, select on the **Submit** button.

![step3-search](https://raw.githubusercontent.com/bernardcheng/tos_options_dashboard/main/doc_img/step3-search.png)

4. The corresponding historical price charts for the specified stock ticker is generated along with the associated cone of probability (Default: 30% confidence level).

![step4-results](https://raw.githubusercontent.com/bernardcheng/tos_options_dashboard/main/doc_img/step4-results.png)

5. The open interest and volume of the options chain is generated with the rolling estimated volatilities using different estimators. Note: The options chain with expiry date closest to the 'Day(s) to Expiration' field is used before manual selection from the dropdown field (i.e. if option 0-14 days is selected, the graph will show the option chain with number of expiry days closest to 14 first)

* Standard Deviation: Using defintion of standard deviation to derive volatility
   * Parkinson: The Parkinson model uses daily High and Low prices and has no drift term. Its efficiency intuitively comes from the fact that the price range of intraday gives more information regarding the future volatility than two arbitrary points in the series. It uses range the highest value –the lowest value variance instead of a widely used method for estimating variance of Log- transformed stock returns.
   * Garman & Klass: The Garman & Klass volatility estimator which make use of daily Opening, Closing, High and Low prices of the stock. The estimator assumes the underlying process is govern by Brownian motion with zero drift and has no opening jump.
   * Rogers & Satchell: Rogers & Satchell volatility estimator is independent of the drift  and Independent  of opening gaps  weighted average  is the Rogers-Satchell. The estimator makes use of the Open-Close volatility and Close-Open volatility. When the estimator is heavily dominated by opening jumps, its performance degrades to the classical Close-to-Close estimator
   * Yang Zhang: Yang Zhang volatility estimator has the following properties:
      1. Independent of the drift; 
      2. Independent of opening gaps weighted average of Rogers-Satchell, Open-Close and Close-Open volatility; 
      3. When heavily dominated by opening jumps, the performance degrades to classical Close-to-Close volatility estimator.

![step5-results](https://raw.githubusercontent.com/bernardcheng/tos_options_dashboard/main/doc_img/step5-results.png)

6. The dashboard also measures the Call / Put skew of the specified ticker, as well as listing the option contracts that matches the filter requirements in Step 3.

* Put Skew: Defined as the price of 10% OTM puts/10% OTM calls for the next monthly option expiry
   * Call Skew: Defined as the price of 10% OTM calls/10% OTM puts for the next monthly option expiry.

![step6-results](https://raw.githubusercontent.com/bernardcheng/tos_options_dashboard/main/doc_img/step6-results.png)

### Citations
1. Oyediran, Oyelami & Sambo, Eric. (2017). Comparative Analysis of Some Volatility Estimators: An Application to Historical Data from the Nigerian Stock Exchange Market. 4. 13-35.
2. jasonstrimpel (2021) volatility-trading [Source Code]. https://github.com/jasonstrimpel/volatility-trading

tos_options_dashboard

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