├── .github ├── FUNDING.yml └── workflows │ └── python-publish.yml ├── .gitignore ├── LICENSE ├── README.md ├── optopsy ├── __init__.py ├── checks.py ├── core.py ├── datafeeds.py ├── definitions.py ├── rules.py └── strategies.py ├── requirements.txt ├── samples ├── data │ └── sample_spx_data.csv ├── spx_singles_example.py ├── spx_straddles_example.py └── spx_strangles_example.py ├── setup.py └── tests ├── __init__.py ├── conftest.py ├── test_checks.py ├── test_data └── data.csv ├── test_datafeeds.py ├── test_rules.py └── test_strategies.py /.github/FUNDING.yml: -------------------------------------------------------------------------------- 1 | # These are supported funding model platforms 2 | 3 | github: michaelchu 4 | patreon: # Replace with a single Patreon username 5 | open_collective: # Replace with a single Open Collective username 6 | ko_fi: # Replace with a single Ko-fi username 7 | tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel 8 | community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry 9 | liberapay: # Replace with a single Liberapay username 10 | issuehunt: # Replace with a single IssueHunt username 11 | otechie: # Replace with a single Otechie username 12 | custom: # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2'] 13 | -------------------------------------------------------------------------------- /.github/workflows/python-publish.yml: -------------------------------------------------------------------------------- 1 | # This workflows will upload a Python Package using Twine when a release is created 2 | # For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries 3 | 4 | name: Upload Python Package 5 | 6 | on: 7 | release: 8 | types: [created] 9 | 10 | jobs: 11 | deploy: 12 | 13 | runs-on: ubuntu-latest 14 | 15 | steps: 16 | - uses: actions/checkout@v2 17 | - name: Set up Python 18 | uses: actions/setup-python@v2 19 | with: 20 | python-version: '3.x' 21 | - name: Install dependencies 22 | run: | 23 | python -m pip install --upgrade pip 24 | pip install setuptools wheel twine 25 | - name: Build and publish 26 | env: 27 | TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }} 28 | TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }} 29 | run: | 30 | python setup.py sdist bdist_wheel 31 | twine upload dist/* 32 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | ### Python template 2 | 3 | # Byte-compiled / optimized / DLL files 4 | __pycache__/ 5 | *.py[cod] 6 | *$py.class 7 | 8 | # Virtual environments 9 | optopsy-env 10 | 11 | # C extensions 12 | *.so 13 | 14 | # Distribution / packaging 15 | .Python 16 | build/ 17 | develop-eggs/ 18 | dist/ 19 | downloads/ 20 | eggs/ 21 | .eggs/ 22 | lib/ 23 | lib64/ 24 | parts/ 25 | sdist/ 26 | var/ 27 | wheels/ 28 | *.egg-info/ 29 | .installed.cfg 30 | *.egg 31 | MANIFEST 32 | 33 | .DS_Store 34 | .idea 35 | .vscode 36 | 37 | /tests/.pytest_cache/ 38 | 39 | *.swp 40 | *.swo 41 | 42 | /venv/ -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | [![Downloads](https://pepy.tech/badge/optopsy)](https://pepy.tech/project/optopsy) 2 | [![Black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black) 3 | 4 | # Optopsy 5 | 6 | Optopsy is a nimble backtesting and statistics library for option strategies, it is designed to answer questions like 7 | "How do straddles perform on the SPX?" or "Which strikes and/or expiration dates should I choose to make the most potential profit?" 8 | 9 | Use cases for Optopsy: 10 | * Generate option strategies from raw option chain datasets for your own analysis 11 | * Discover performance statistics on **percentage change** for various options strategies on a given stock 12 | 13 | ## Supported Option Strategies 14 | * Calls/Puts 15 | * Straddles/Strangles 16 | * Vertical Call/Put Spreads 17 | 18 | ## Documentation 19 | Please see the [wiki](https://github.com/michaelchu/optopsy/wiki) for API reference. 20 | 21 | ## Usage 22 | 23 | ### Use Your Data 24 | * Use data from any source, just provide a Pandas dataframe with the required columns when calling optopsy functions. 25 | 26 | ### Dependencies 27 | You will need Python 3.6 or newer and Pandas 0.23.1 or newer and Numpy 1.14.3 or newer. 28 | 29 | ### Installation 30 | ``` 31 | pip install optopsy==2.0.1 32 | ``` 33 | 34 | ### Example 35 | 36 | Let's see how long calls perform on the SPX on a small demo dataset on the SPX: 37 | 38 | **Note:** As of July 2024, the link below is broken, however DeltaNeutral still provides free data [here](https://historicaloptiondata.com/free-data/). 39 | You should still be able to proceed by mapping the columns according to the current format of the sample data as shown below. 40 | 41 | ~~Download the following data sample from DeltaNeutral: http://www.deltaneutral.com/files/Sample_SPX_20151001_to_20151030.csv~~ 42 | 43 | This dataset is for the month of October in 2015, lets load it into Optopsy. First create a small helper function 44 | that returns a file path to our file. We will store it under a folder named 'data', in the same directory as the working python file. 45 | ``` 46 | def filepath(): 47 | curr_file = os.path.abspath(os.path.dirname(__file__)) 48 | return os.path.join(curr_file, "./data/Sample_SPX_20151001_to_20151030.csv") 49 | ``` 50 | 51 | Next lets use this function to pass in the file path string into Optopsy's `csv_data()` function, we will map the column 52 | indices using the defined function parameters. We are omitting the `start_date` and `end_date` parameters in this call because 53 | we want to include the entire dataset. The numeric values represent the column number as found in the sample file, the 54 | numbers are 0-indexed: 55 | ``` 56 | import optopsy as op 57 | 58 | spx_data = op.csv_data( 59 | filepath(), 60 | underlying_symbol=0, 61 | underlying_price=1, 62 | option_type=5, 63 | expiration=6, 64 | quote_date=7, 65 | strike=8, 66 | bid=10, 67 | ask=11, 68 | ) 69 | ``` 70 | The `csv_data()` function is a convenience function. Under the hood it uses Panda's `read_csv()` function to do the import. 71 | There are other parameters that can help with loading the csv data, consult the code/future documentation to see how to use them. 72 | 73 | Optopsy is a small simple library that offloads the heavy work of backtesting option strategies, the API is designed to be simple 74 | and easy to implement into your regular Panda's data analysis workflow. As such, we just need to call the `long_calls()` function 75 | to have Optopsy generate all combinations of a simple long call strategy for the specified time period and return a DataFrame. Here we 76 | also use Panda's `round()` function afterwards to return statistics within two decimal places. 77 | 78 | ``` 79 | long_calls_spx_pct_chgs = op.long_calls(spx_data).round(2) 80 | ``` 81 | 82 | The function will returned a Pandas DataFrame containing the statistics on the **percentange changes** of running long calls in all *valid* combinations on the SPX: 83 | 84 | | | dte_range | otm_pct_range | count | mean | std | min | 25% | 50% | 75% | max | 85 | |----|-------------|-----------------|---------|--------|-------|-------|-------|-------|-------|-------| 86 | | 0 | (0, 7] | (-0.5, -0.45] | 155 | 0.03 | 0.02 | -0.02 | 0.01 | 0.02 | 0.04 | 0.11 | 87 | | 1 | (0, 7] | (-0.45, -0.4] | 201 | 0.04 | 0.03 | -0.02 | 0.01 | 0.03 | 0.06 | 0.12 | 88 | | 2 | (0, 7] | (-0.4, -0.35] | 247 | 0.04 | 0.03 | -0.02 | 0.02 | 0.04 | 0.07 | 0.13 | 89 | | 3 | (0, 7] | (-0.35, -0.3] | 296 | 0.05 | 0.04 | -0.02 | 0.02 | 0.04 | 0.08 | 0.15 | 90 | | 4 | (0, 7] | (-0.3, -0.25] | 329 | 0.05 | 0.05 | -0.03 | 0.02 | 0.05 | 0.09 | 0.17 | 91 | | 5 | (0, 7] | (-0.25, -0.2] | 352 | 0.06 | 0.05 | -0.03 | 0.02 | 0.05 | 0.1 | 0.2 | 92 | | 6 | (0, 7] | (-0.2, -0.15] | 383 | 0.08 | 0.07 | -0.04 | 0.03 | 0.07 | 0.13 | 0.26 | 93 | | 7 | (0, 7] | (-0.15, -0.1] | 417 | 0.11 | 0.09 | -0.06 | 0.04 | 0.09 | 0.17 | 0.37 | 94 | | 8 | (0, 7] | (-0.1, -0.05] | 461 | 0.18 | 0.16 | -0.12 | 0.07 | 0.15 | 0.28 | 0.69 | 95 | | 9 | (0, 7] | (-0.05, -0.0] | 505 | 0.64 | 1.03 | -1 | 0.14 | 0.37 | 0.87 | 7.62 | 96 | | 10 | (0, 7] | (-0.0, 0.05] | 269 | 2.34 | 8.65 | -1 | -1 | -0.89 | 1.16 | 68 | 97 | | 11 | (0, 7] | (0.05, 0.1] | 2 | -1 | 0 | -1 | -1 | -1 | -1 | -1 | 98 | | 12 | (7, 14] | (-0.5, -0.45] | 70 | 0.06 | 0.03 | 0.02 | 0.03 | 0.07 | 0.08 | 0.12 | 99 | | 13 | (7, 14] | (-0.45, -0.4] | 165 | 0.09 | 0.04 | 0.02 | 0.06 | 0.08 | 0.1 | 0.17 | 100 | | 14 | (7, 14] | (-0.4, -0.35] | 197 | 0.09 | 0.04 | 0.02 | 0.07 | 0.09 | 0.12 | 0.19 | 101 | | 15 | (7, 14] | (-0.35, -0.3] | 235 | 0.11 | 0.04 | 0.02 | 0.09 | 0.1 | 0.13 | 0.21 | 102 | | 16 | (7, 14] | (-0.3, -0.25] | 265 | 0.13 | 0.05 | 0.03 | 0.1 | 0.12 | 0.15 | 0.25 | 103 | | 17 | (7, 14] | (-0.25, -0.2] | 280 | 0.15 | 0.06 | 0.03 | 0.11 | 0.14 | 0.18 | 0.3 | 104 | | 18 | (7, 14] | (-0.2, -0.15] | 307 | 0.18 | 0.08 | 0.04 | 0.14 | 0.18 | 0.23 | 0.38 | 105 | | 19 | (7, 14] | (-0.15, -0.1] | 332 | 0.25 | 0.11 | 0.05 | 0.18 | 0.24 | 0.31 | 0.54 | 106 | | 20 | (7, 14] | (-0.1, -0.05] | 370 | 0.4 | 0.18 | 0.07 | 0.29 | 0.39 | 0.52 | 0.97 | 107 | | 21 | (7, 14] | (-0.05, -0.0] | 404 | 1.02 | 0.68 | -0.46 | 0.58 | 0.86 | 1.32 | 4.4 | 108 | | 22 | (7, 14] | (-0.0, 0.05] | 388 | 1.52 | 4.45 | -1 | -0.99 | -0.73 | 2.65 | 32 | 109 | | 23 | (7, 14] | (0.05, 0.1] | 36 | -0.93 | 0.06 | -1 | -1 | -0.94 | -0.87 | -0.83 | 110 | | 24 | (14, 21] | (-0.5, -0.45] | 6 | 0.1 | 0.01 | 0.09 | 0.09 | 0.1 | 0.1 | 0.1 | 111 | | 25 | (14, 21] | (-0.45, -0.4] | 66 | 0.14 | 0.04 | 0.09 | 0.11 | 0.14 | 0.17 | 0.23 | 112 | | 26 | (14, 21] | (-0.4, -0.35] | 91 | 0.16 | 0.04 | 0.1 | 0.12 | 0.16 | 0.2 | 0.25 | 113 | | 27 | (14, 21] | (-0.35, -0.3] | 135 | 0.18 | 0.05 | 0.11 | 0.13 | 0.17 | 0.21 | 0.28 | 114 | | 28 | (14, 21] | (-0.3, -0.25] | 149 | 0.2 | 0.05 | 0.12 | 0.15 | 0.2 | 0.25 | 0.33 | 115 | | 29 | (14, 21] | (-0.25, -0.2] | 160 | 0.24 | 0.06 | 0.14 | 0.18 | 0.23 | 0.29 | 0.4 | 116 | | 30 | (14, 21] | (-0.2, -0.15] | 174 | 0.3 | 0.08 | 0.17 | 0.23 | 0.29 | 0.35 | 0.51 | 117 | | 31 | (14, 21] | (-0.15, -0.1] | 187 | 0.4 | 0.11 | 0.22 | 0.3 | 0.38 | 0.48 | 0.7 | 118 | | 32 | (14, 21] | (-0.1, -0.05] | 211 | 0.63 | 0.19 | 0.32 | 0.47 | 0.6 | 0.75 | 1.16 | 119 | | 33 | (14, 21] | (-0.05, -0.0] | 229 | 1.39 | 0.53 | 0.58 | 1 | 1.3 | 1.73 | 3.1 | 120 | | 34 | (14, 21] | (-0.0, 0.05] | 252 | 2.58 | 2.92 | -1 | -1 | 2.72 | 4.56 | 10.1 | 121 | | 35 | (14, 21] | (0.05, 0.1] | 93 | -0.82 | 0.92 | -1 | -1 | -1 | -1 | 6.39 | 122 | | 36 | (21, 28] | (-0.5, -0.45] | 1 | 0.11 | nan | 0.11 | 0.11 | 0.11 | 0.11 | 0.11 | 123 | | 37 | (21, 28] | (-0.45, -0.4] | 21 | 0.15 | 0.03 | 0.11 | 0.12 | 0.15 | 0.17 | 0.23 | 124 | | 38 | (21, 28] | (-0.4, -0.35] | 39 | 0.2 | 0.06 | 0.12 | 0.16 | 0.18 | 0.24 | 0.32 | 125 | | 39 | (21, 28] | (-0.35, -0.3] | 61 | 0.21 | 0.06 | 0.13 | 0.17 | 0.2 | 0.26 | 0.35 | 126 | | 40 | (21, 28] | (-0.3, -0.25] | 75 | 0.25 | 0.08 | 0.14 | 0.2 | 0.24 | 0.31 | 0.41 | 127 | | 41 | (21, 28] | (-0.25, -0.2] | 79 | 0.3 | 0.09 | 0.17 | 0.23 | 0.27 | 0.37 | 0.49 | 128 | | 42 | (21, 28] | (-0.2, -0.15] | 87 | 0.37 | 0.11 | 0.2 | 0.29 | 0.34 | 0.45 | 0.62 | 129 | | 43 | (21, 28] | (-0.15, -0.1] | 93 | 0.48 | 0.15 | 0.26 | 0.37 | 0.46 | 0.58 | 0.85 | 130 | | 44 | (21, 28] | (-0.1, -0.05] | 105 | 0.74 | 0.24 | 0.36 | 0.56 | 0.71 | 0.89 | 1.39 | 131 | | 45 | (21, 28] | (-0.05, -0.0] | 114 | 1.45 | 0.54 | 0.62 | 1.05 | 1.34 | 1.73 | 3.28 | 132 | | 46 | (21, 28] | (-0.0, 0.05] | 125 | 2.97 | 3.38 | -1 | 1.29 | 2.58 | 4.21 | 17.15 | 133 | | 47 | (21, 28] | (0.05, 0.1] | 85 | 0.82 | 5.3 | -1 | -1 | -1 | -1 | 19.5 | 134 | | 48 | (28, 35] | (-0.4, -0.35] | 5 | 0.31 | 0.01 | 0.3 | 0.3 | 0.31 | 0.32 | 0.32 | 135 | | 49 | (28, 35] | (-0.35, -0.3] | 7 | 0.34 | 0.01 | 0.32 | 0.33 | 0.35 | 0.35 | 0.36 | 136 | | 50 | (28, 35] | (-0.3, -0.25] | 12 | 0.39 | 0.02 | 0.36 | 0.37 | 0.39 | 0.4 | 0.42 | 137 | | 51 | (28, 35] | (-0.25, -0.2] | 13 | 0.46 | 0.02 | 0.42 | 0.44 | 0.45 | 0.47 | 0.49 | 138 | | 52 | (28, 35] | (-0.2, -0.15] | 14 | 0.55 | 0.04 | 0.5 | 0.53 | 0.55 | 0.58 | 0.62 | 139 | | 53 | (28, 35] | (-0.15, -0.1] | 15 | 0.73 | 0.07 | 0.63 | 0.67 | 0.72 | 0.77 | 0.84 | 140 | | 54 | (28, 35] | (-0.1, -0.05] | 17 | 1.06 | 0.14 | 0.86 | 0.94 | 1.05 | 1.17 | 1.32 | 141 | | 55 | (28, 35] | (-0.05, -0.0] | 19 | 1.95 | 0.44 | 1.36 | 1.58 | 1.87 | 2.26 | 2.79 | 142 | | 56 | (28, 35] | (-0.0, 0.05] | 20 | 5.72 | 2.23 | 2.94 | 3.85 | 5.23 | 7.33 | 9.97 | 143 | | 57 | (28, 35] | (0.05, 0.1] | 21 | 3.53 | 5.47 | -1 | -1 | -1 | 10.38 | 11.32 | 144 | 145 | There are more customization options for Optopsy's strategy functions, consult the codebase/future documentation to see how it can be used to adjust the results, such as increasing/decreasing 146 | the intervals and other data to be returned. 147 | -------------------------------------------------------------------------------- /optopsy/__init__.py: -------------------------------------------------------------------------------- 1 | from .strategies import * 2 | from .datafeeds import * 3 | -------------------------------------------------------------------------------- /optopsy/checks.py: -------------------------------------------------------------------------------- 1 | expected_types = { 2 | "underlying_symbol": ("object",), 3 | "underlying_price": ("int64", "float64"), 4 | "option_type": ("object",), 5 | "expiration": ("datetime64[ns]",), 6 | "quote_date": ("datetime64[ns]",), 7 | "strike": ("int64", "float64"), 8 | "bid": ("int64", "float64"), 9 | "ask": ("int64", "float64"), 10 | } 11 | 12 | 13 | def _run_checks(params, data): 14 | for k, v in params.items(): 15 | if k in param_checks: 16 | param_checks[k](k, v) 17 | _check_data_types(data) 18 | 19 | 20 | def _check_positive_integer(key, value): 21 | if value <= 0 or not isinstance(value, int): 22 | raise ValueError(f"Invalid setting for {key}, must be positive integer") 23 | 24 | 25 | def _check_positive_integer_inclusive(key, value): 26 | if value < 0 or not isinstance(value, int): 27 | raise ValueError(f"Invalid setting for {key}, must be positive integer, or 0") 28 | 29 | 30 | def _check_positive_float(key, value): 31 | if value <= 0 or not isinstance(value, float): 32 | raise ValueError(f"Invalid setting for {key}, must be positive float type") 33 | 34 | 35 | def _check_side(key, value): 36 | if value != "long" and value != "short": 37 | raise ValueError(f"Invalid setting for '{key}', must be only 'long' or short'") 38 | 39 | 40 | def _check_bool_type(key, value): 41 | if not isinstance(value, bool): 42 | raise ValueError(f"Invalid setting for {key}, must be boolean type") 43 | 44 | 45 | def _check_list_type(key, value): 46 | if not isinstance(value, list): 47 | raise ValueError(f"Invalid setting for {key}, must be a list type") 48 | 49 | 50 | def _check_data_types(data): 51 | df_type_dict = data.dtypes.astype(str).to_dict() 52 | for k, et in expected_types.items(): 53 | if k not in df_type_dict: 54 | raise ValueError("Expected column: {k} not found in DataFrame") 55 | if all(df_type_dict[k] != t for t in et): 56 | raise ValueError( 57 | f"{df_type_dict[k]} of {k} does not match expected types: {expected_types[k]}" 58 | ) 59 | 60 | 61 | param_checks = { 62 | "dte_interval": _check_positive_integer, 63 | "max_entry_dte": _check_positive_integer, 64 | "exit_dte": _check_positive_integer_inclusive, 65 | "otm_pct_interval": _check_positive_float, 66 | "max_otm_pct": _check_positive_float, 67 | "min_bid_ask": _check_positive_float, 68 | "side": _check_side, 69 | "drop_nan": _check_bool_type, 70 | "raw": _check_bool_type, 71 | } 72 | -------------------------------------------------------------------------------- /optopsy/core.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | from functools import reduce 4 | from .definitions import * 5 | from .checks import _run_checks 6 | 7 | pd.set_option("expand_frame_repr", False) 8 | pd.set_option("display.max_rows", None, "display.max_columns", None) 9 | 10 | 11 | def _assign_dte(data): 12 | return data.assign(dte=lambda r: (r["expiration"] - r["quote_date"]).dt.days) 13 | 14 | 15 | def _trim(data, col, lower, upper): 16 | return data.loc[(data[col] >= lower) & (data[col] <= upper)] 17 | 18 | 19 | def _ltrim(data, col, lower): 20 | return data.loc[data[col] >= lower] 21 | 22 | 23 | def _rtrim(data, col, upper): 24 | return data.loc[data[col] <= upper] 25 | 26 | 27 | def _get(data, col, val): 28 | return data.loc[data[col] == val] 29 | 30 | 31 | def _remove_min_bid_ask(data, min_bid_ask): 32 | return data.loc[(data["bid"] > min_bid_ask) & (data["ask"] > min_bid_ask)] 33 | 34 | 35 | def _remove_invalid_evaluated_options(data): 36 | return data.loc[ 37 | (data["dte_exit"] <= data["dte_entry"]) 38 | & (data["dte_entry"] != data["dte_exit"]) 39 | ] 40 | 41 | 42 | def _cut_options_by_dte(data, dte_interval, max_entry_dte): 43 | dte_intervals = list(range(0, max_entry_dte, dte_interval)) 44 | data["dte_range"] = pd.cut(data["dte_entry"], dte_intervals) 45 | return data 46 | 47 | 48 | def _cut_options_by_otm(data, otm_pct_interval, max_otm_pct_interval): 49 | # consider using np.linspace in future 50 | otm_pct_intervals = [ 51 | round(i, 2) 52 | for i in list( 53 | np.arange( 54 | max_otm_pct_interval * -1, 55 | max_otm_pct_interval, 56 | otm_pct_interval, 57 | ) 58 | ) 59 | ] 60 | data["otm_pct_range"] = pd.cut(data["otm_pct_entry"], otm_pct_intervals) 61 | return data 62 | 63 | 64 | def _group_by_intervals(data, cols, drop_na): 65 | # this is a bottleneck, try to optimize 66 | grouped_dataset = data.groupby(cols)["pct_change"].describe() 67 | 68 | # if any non-count columns return NaN remove the row 69 | if drop_na: 70 | subset = [col for col in grouped_dataset.columns if "_count" not in col] 71 | grouped_dataset = grouped_dataset.dropna(subset=subset, how="all") 72 | 73 | return grouped_dataset 74 | 75 | 76 | def _evaluate_options(data, **kwargs): 77 | 78 | # trim option chains with strikes too far out from current price 79 | data = data.pipe(_calculate_otm_pct).pipe( 80 | _trim, 81 | "otm_pct", 82 | lower=kwargs["max_otm_pct"] * -1, 83 | upper=kwargs["max_otm_pct"], 84 | ) 85 | 86 | # remove option chains that are worthless, it's unrealistic to enter 87 | # trades with worthless options 88 | entries = _remove_min_bid_ask(data, kwargs["min_bid_ask"]) 89 | 90 | # to reduce unnecessary computation, filter for options with the desired exit DTE 91 | exits = _get(data, "dte", kwargs["exit_dte"]) 92 | 93 | return ( 94 | entries.merge( 95 | right=exits, 96 | on=["underlying_symbol", "option_type", "expiration", "strike"], 97 | suffixes=("_entry", "_exit"), 98 | ) 99 | # by default we use the midpoint spread price to calculate entry and exit costs 100 | .assign(entry=lambda r: (r["bid_entry"] + r["ask_entry"]) / 2) 101 | .assign(exit=lambda r: (r["bid_exit"] + r["ask_exit"]) / 2) 102 | .pipe(_remove_invalid_evaluated_options) 103 | )[evaluated_cols] 104 | 105 | 106 | def _evaluate_all_options(data, **kwargs): 107 | return ( 108 | data.pipe(_assign_dte) 109 | .pipe(_trim, "dte", kwargs["exit_dte"], kwargs["max_entry_dte"]) 110 | .pipe(_evaluate_options, **kwargs) 111 | .pipe(_cut_options_by_dte, kwargs["dte_interval"], kwargs["max_entry_dte"]) 112 | .pipe( 113 | _cut_options_by_otm, 114 | kwargs["otm_pct_interval"], 115 | kwargs["max_otm_pct"], 116 | ) 117 | ) 118 | 119 | 120 | def _calls(data): 121 | return data[data.option_type.str.lower().str.startswith("c")] 122 | 123 | 124 | def _puts(data): 125 | return data[data.option_type.str.lower().str.startswith("p")] 126 | 127 | 128 | def _calculate_otm_pct(data): 129 | return data.assign( 130 | otm_pct=lambda r: round((r["strike"] - r["underlying_price"]) / r["strike"], 2) 131 | ) 132 | 133 | 134 | def _apply_ratios(data, leg_def): 135 | for idx in range(1, len(leg_def) + 1): 136 | entry_col = f"entry_leg{idx}" 137 | exit_col = f"exit_leg{idx}" 138 | entry_kwargs = {entry_col: lambda r: r[entry_col] * leg_def[idx - 1][0].value} 139 | exit_kwargs = {exit_col: lambda r: r[exit_col] * leg_def[idx - 1][0].value} 140 | data = data.assign(**entry_kwargs).assign(**exit_kwargs) 141 | 142 | return data 143 | 144 | 145 | def _assign_profit(data, leg_def, suffixes): 146 | data = _apply_ratios(data, leg_def) 147 | 148 | # determine all entry and exit columns 149 | entry_cols = ["entry" + s for s in suffixes] 150 | exit_cols = ["exit" + s for s in suffixes] 151 | 152 | # calculate the total entry costs and exit proceeds 153 | data["total_entry_cost"] = data.loc[:, entry_cols].sum(axis=1) 154 | data["total_exit_proceeds"] = data.loc[:, exit_cols].sum(axis=1) 155 | 156 | data["pct_change"] = ( 157 | data["total_exit_proceeds"] - data["total_entry_cost"] 158 | ) / data["total_entry_cost"].abs() 159 | 160 | return data 161 | 162 | 163 | def _strategy_engine(data, leg_def, join_on=None, rules=None): 164 | if len(leg_def) == 1: 165 | data["pct_change"] = (data["exit"] - data["entry"]) / data["entry"].abs() 166 | return leg_def[0][1](data) 167 | 168 | def _rule_func(d, r, ld): 169 | return d if r is None else r(d, ld) 170 | 171 | partials = [leg[1](data) for leg in leg_def] 172 | suffixes = [f"_leg{idx}" for idx in range(1, len(leg_def) + 1)] 173 | 174 | # noinspection PyTypeChecker 175 | return ( 176 | reduce( 177 | lambda left, right: pd.merge( 178 | left, right, on=join_on, how="inner", suffixes=suffixes 179 | ), 180 | partials, 181 | ) 182 | .pipe(_rule_func, rules, leg_def) 183 | .pipe(_assign_profit, leg_def, suffixes) 184 | ) 185 | 186 | 187 | def _process_strategy(data, **context): 188 | _run_checks(context["params"], data) 189 | return ( 190 | _evaluate_all_options( 191 | data, 192 | dte_interval=context["params"]["dte_interval"], 193 | max_entry_dte=context["params"]["max_entry_dte"], 194 | exit_dte=context["params"]["exit_dte"], 195 | otm_pct_interval=context["params"]["otm_pct_interval"], 196 | max_otm_pct=context["params"]["max_otm_pct"], 197 | min_bid_ask=context["params"]["min_bid_ask"], 198 | ) 199 | .pipe( 200 | _strategy_engine, 201 | context["leg_def"], 202 | context.get("join_on"), 203 | context.get("rules"), 204 | ) 205 | .pipe( 206 | _format_output, 207 | context["params"], 208 | context["internal_cols"], 209 | context["external_cols"], 210 | ) 211 | ) 212 | 213 | 214 | def _format_output(data, params, internal_cols, external_cols): 215 | if params["raw"]: 216 | return data[internal_cols].reset_index(drop=True) 217 | 218 | return data.pipe( 219 | _group_by_intervals, external_cols, params["drop_nan"] 220 | ).reset_index() 221 | -------------------------------------------------------------------------------- /optopsy/datafeeds.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | from .core import _trim, _ltrim, _rtrim 3 | from .checks import _check_data_types 4 | 5 | default_kwargs = { 6 | "start_date": None, 7 | "end_date": None, 8 | "underlying_symbol": 0, 9 | "underlying_price": 1, 10 | "option_type": 2, 11 | "expiration": 3, 12 | "quote_date": 4, 13 | "strike": 5, 14 | "bid": 6, 15 | "ask": 7, 16 | } 17 | 18 | 19 | def _trim_dates(data, start_date, end_date): 20 | if start_date is not None and end_date is not None: 21 | return _trim(data, "expiration", start_date, end_date) 22 | elif start_date is None and end_date is not None: 23 | return _rtrim(data, "expiration", end_date) 24 | elif start_date is not None and end_date is None: 25 | return _ltrim(data, "expiration", start_date) 26 | else: 27 | return data 28 | 29 | 30 | def _trim_cols(data, column_mapping): 31 | cols = [c for c, _ in column_mapping if c is not None] 32 | return data.iloc[:, cols] 33 | 34 | 35 | def _standardize_cols(data, column_mapping): 36 | col_names = list(data.columns) 37 | cols = {col_names[idx]: label for idx, label in column_mapping if idx is not None} 38 | return data.rename(columns=cols) 39 | 40 | 41 | def _infer_date_cols(data): 42 | data["expiration"] = pd.to_datetime(data.expiration, infer_datetime_format=True) 43 | data["quote_date"] = pd.to_datetime(data.quote_date, infer_datetime_format=True) 44 | return data 45 | 46 | 47 | # noinspection PyIncorrectDocstring 48 | def csv_data(file_path, **kwargs): 49 | """ 50 | Uses pandas DataFrame.read_csv function to import data from CSV files. 51 | It will automatically generate standardized headers for this library to use. 52 | 53 | Args: 54 | file_path: str, path to csv file 55 | start_date: datetime, start date of data set to consider, date is inclusive 56 | end_date: datetime, end date of data set to consider, date is inclusive 57 | underlying_symbol: int, index of column containing underlying symbol of option chain 58 | underlying_price: int, index of column containing underlying stock price 59 | quote_date: int, index of column containing quote date of option chain 60 | expiration: int, index of column containing expiration of option chain 61 | strike: int, index of column containing strike price of option chain 62 | option_type: int, index of column containing option type of option chain 63 | bid: int, index of column containing bid price of option chain 64 | ask: int, index of column containing ask price of option chain 65 | 66 | Returns: 67 | DataFrame: A dataframe of option chains with standardized columns 68 | 69 | """ 70 | params = {**default_kwargs, **kwargs} 71 | 72 | column_mapping = [ 73 | (params["underlying_symbol"], "underlying_symbol"), 74 | (params["underlying_price"], "underlying_price"), 75 | (params["option_type"], "option_type"), 76 | (params["expiration"], "expiration"), 77 | (params["quote_date"], "quote_date"), 78 | (params["strike"], "strike"), 79 | (params["bid"], "bid"), 80 | (params["ask"], "ask"), 81 | ] 82 | 83 | return ( 84 | pd.read_csv(file_path) 85 | .pipe(_standardize_cols, column_mapping) 86 | .pipe(_trim_cols, column_mapping) 87 | .pipe(_infer_date_cols) 88 | .pipe(_trim_dates, params["start_date"], params["end_date"]) 89 | ) 90 | -------------------------------------------------------------------------------- /optopsy/definitions.py: -------------------------------------------------------------------------------- 1 | # columns of options after evaluation 2 | evaluated_cols = [ 3 | "underlying_symbol", 4 | "option_type", 5 | "expiration", 6 | "dte_entry", 7 | "strike", 8 | "otm_pct_entry", 9 | "underlying_price_entry", 10 | "underlying_price_exit", 11 | "entry", 12 | "exit", 13 | ] 14 | 15 | # columns of dataframe after generating strategy 16 | single_strike_internal_cols = [ 17 | "underlying_symbol", 18 | "underlying_price_entry", 19 | "option_type", 20 | "expiration", 21 | "dte_entry", 22 | "strike", 23 | "entry", 24 | "exit", 25 | "pct_change", 26 | ] 27 | 28 | 29 | straddle_internal_cols = [ 30 | "underlying_symbol", 31 | "underlying_price_entry", 32 | "expiration", 33 | "dte_entry", 34 | "option_type_leg1", 35 | "option_type_leg2", 36 | "strike", 37 | "total_entry_cost", 38 | "total_exit_proceeds", 39 | "pct_change", 40 | ] 41 | 42 | 43 | double_strike_internal_cols = [ 44 | "underlying_symbol", 45 | "underlying_price_entry_leg1", 46 | "expiration", 47 | "dte_entry", 48 | "option_type_leg1", 49 | "strike_leg1", 50 | "option_type_leg2", 51 | "strike_leg2", 52 | "total_entry_cost", 53 | "total_exit_proceeds", 54 | "pct_change", 55 | ] 56 | 57 | triple_strike_internal_cols = [ 58 | "underlying_symbol", 59 | "underlying_price_entry", 60 | "expiration", 61 | "dte_entry", 62 | "option_type_leg1", 63 | "strike_leg1", 64 | "option_type_leg2", 65 | "strike_leg2", 66 | "option_type_leg3", 67 | "strike_leg3", 68 | "entry", 69 | "exit", 70 | "long_profit", 71 | "short_profit", 72 | "long_pct_change", 73 | "short_pct_change", 74 | ] 75 | 76 | quadruple_strike_internal_cols = [ 77 | "underlying_symbol", 78 | "underlying_price_entry", 79 | "expiration", 80 | "dte_entry", 81 | "dte_range", 82 | "option_type_leg1", 83 | "strike_leg1", 84 | "option_type_leg2", 85 | "strike_leg2", 86 | "option_type_leg3", 87 | "strike_leg3", 88 | "option_type_leg4", 89 | "strike_leg4", 90 | "entry", 91 | "exit", 92 | "long_profit", 93 | "short_profit", 94 | "long_pct_change", 95 | "short_pct_change", 96 | ] 97 | 98 | # base columns of dataframe after aggregation(minus the calculated columns) 99 | single_strike_external_cols = ["dte_range", "otm_pct_range"] 100 | double_strike_external_cols = ["dte_range", "otm_pct_range_leg1", "otm_pct_range_leg2"] 101 | triple_strike_external_cols = [ 102 | "dte_range", 103 | "otm_pct_range_leg1", 104 | "otm_pct_range_leg2", 105 | "otm_pct_range_leg3", 106 | ] 107 | quadruple_strike_external_cols = [ 108 | "dte_range", 109 | "otm_pct_range_leg1", 110 | "otm_pct_range_leg2", 111 | "otm_pct_range_leg3", 112 | "otm_pct_range_leg4", 113 | ] 114 | -------------------------------------------------------------------------------- /optopsy/rules.py: -------------------------------------------------------------------------------- 1 | def _rule_non_overlapping_strike(data, leg_def): 2 | leg_count = len(leg_def) 3 | if leg_count == 1: 4 | return data 5 | 6 | query = " & ".join( 7 | [f"strike_leg{leg + 1} > strike_leg{leg}" for leg in range(1, leg_count)] 8 | ) 9 | 10 | return data.query(query) 11 | -------------------------------------------------------------------------------- /optopsy/strategies.py: -------------------------------------------------------------------------------- 1 | from .core import _calls, _puts, _process_strategy 2 | from .definitions import ( 3 | single_strike_external_cols, 4 | single_strike_internal_cols, 5 | double_strike_external_cols, 6 | double_strike_internal_cols, 7 | straddle_internal_cols, 8 | ) 9 | from .rules import _rule_non_overlapping_strike 10 | from enum import Enum 11 | 12 | default_kwargs = { 13 | "dte_interval": 7, 14 | "max_entry_dte": 90, 15 | "exit_dte": 0, 16 | "otm_pct_interval": 0.05, 17 | "max_otm_pct": 0.5, 18 | "min_bid_ask": 0.05, 19 | "drop_nan": True, 20 | "raw": False, 21 | } 22 | 23 | 24 | class Side(Enum): 25 | long = 1 26 | short = -1 27 | 28 | 29 | def _singles(data, leg_def, **kwargs): 30 | params = {**default_kwargs, **kwargs} 31 | return _process_strategy( 32 | data, 33 | internal_cols=single_strike_internal_cols, 34 | external_cols=single_strike_external_cols, 35 | leg_def=leg_def, 36 | params=params, 37 | ) 38 | 39 | 40 | def _straddles(data, leg_def, **kwargs): 41 | params = {**default_kwargs, **kwargs} 42 | 43 | return _process_strategy( 44 | data, 45 | internal_cols=straddle_internal_cols, 46 | external_cols=single_strike_external_cols, 47 | leg_def=leg_def, 48 | join_on=[ 49 | "underlying_symbol", 50 | "expiration", 51 | "strike", 52 | "dte_entry", 53 | "dte_range", 54 | "otm_pct_range", 55 | "underlying_price_entry", 56 | ], 57 | params=params, 58 | ) 59 | 60 | 61 | def _strangles(data, leg_def, **kwargs): 62 | params = {**default_kwargs, **kwargs} 63 | return _process_strategy( 64 | data, 65 | internal_cols=double_strike_internal_cols, 66 | external_cols=double_strike_external_cols, 67 | leg_def=leg_def, 68 | rules=_rule_non_overlapping_strike, 69 | join_on=["underlying_symbol", "expiration", "dte_entry", "dte_range"], 70 | params=params, 71 | ) 72 | 73 | 74 | def _call_spread(data, leg_def, **kwargs): 75 | params = {**default_kwargs, **kwargs} 76 | return _process_strategy( 77 | data, 78 | internal_cols=double_strike_internal_cols, 79 | external_cols=double_strike_external_cols, 80 | leg_def=leg_def, 81 | rules=_rule_non_overlapping_strike, 82 | join_on=["underlying_symbol", "expiration", "dte_entry", "dte_range"], 83 | params=params, 84 | ) 85 | 86 | 87 | def _put_spread(data, leg_def, **kwargs): 88 | params = {**default_kwargs, **kwargs} 89 | return _process_strategy( 90 | data, 91 | internal_cols=double_strike_internal_cols, 92 | external_cols=double_strike_external_cols, 93 | leg_def=leg_def, 94 | rules=_rule_non_overlapping_strike, 95 | join_on=["underlying_symbol", "expiration", "dte_entry", "dte_range"], 96 | params=params, 97 | ) 98 | 99 | 100 | def long_calls(data, **kwargs): 101 | return _singles(data, [(Side.long, _calls)], **kwargs) 102 | 103 | 104 | def long_puts(data, **kwargs): 105 | return _singles(data, [(Side.long, _puts)], **kwargs) 106 | 107 | 108 | def short_calls(data, **kwargs): 109 | return _singles(data, [(Side.short, _calls)], **kwargs) 110 | 111 | 112 | def short_puts(data, **kwargs): 113 | return _singles(data, [(Side.short, _puts)], **kwargs) 114 | 115 | 116 | def long_straddles(data, **kwargs): 117 | return _straddles(data, [(Side.long, _puts), (Side.long, _calls)], **kwargs) 118 | 119 | 120 | def short_straddles(data, **kwargs): 121 | return _straddles(data, [(Side.short, _puts), (Side.short, _calls)], **kwargs) 122 | 123 | 124 | def long_strangles(data, **kwargs): 125 | return _strangles(data, [(Side.long, _puts), (Side.long, _calls)], **kwargs) 126 | 127 | 128 | def short_strangles(data, **kwargs): 129 | return _strangles(data, [(Side.short, _puts), (Side.short, _calls)], **kwargs) 130 | 131 | 132 | def long_call_spread(data, **kwargs): 133 | return _call_spread(data, [(Side.long, _calls), (Side.short, _calls)], **kwargs) 134 | 135 | 136 | def short_call_spread(data, **kwargs): 137 | return _call_spread(data, [(Side.short, _calls), (Side.long, _calls)], **kwargs) 138 | 139 | 140 | def long_put_spread(data, **kwargs): 141 | return _put_spread(data, [(Side.short, _puts), (Side.long, _puts)], **kwargs) 142 | 143 | 144 | def short_put_spread(data, **kwargs): 145 | return _put_spread(data, [(Side.long, _puts), (Side.short, _puts)], **kwargs) 146 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | # It is recommended to install Miniconda3 for Python 3.6.1 and Pandas 2 | pandas>=0.23.1 3 | pytest>=3.10.0 4 | numpy>=1.14.3 5 | -------------------------------------------------------------------------------- /samples/data/sample_spx_data.csv: -------------------------------------------------------------------------------- 1 | underlying,underlying_last, exchange,optionroot,optionext,type,expiration,quotedate,strike,last,bid,ask,volume,openinterest,impliedvol,delta,gamma,theta,vega,optionalias 2 | SPX,1921.42,*,SPX151016C00400000,,call,10/16/2015,10/01/2015,400,0,1518.7,1525.2,0,0,0.2589,1,0,-1.2854,0,SPX151016C00400000 3 | SPX,1921.42,*,SPX151016C00500000,,call,10/16/2015,10/01/2015,500,0,1418.8,1425.2,0,0,0.2589,1,0,-1.6068,0,SPX151016C00500000 4 | SPX,1921.42,*,SPX151016C00600000,,call,10/16/2015,10/01/2015,600,0,1318.8,1325.2,0,0,0.2589,1,0,-1.9282,0,SPX151016C00600000 5 | SPX,1921.42,*,SPX151016C00700000,,call,10/16/2015,10/01/2015,700,0,1218.8,1225.3,0,0,0.2589,1,0,-2.2495,0,SPX151016C00700000 6 | SPX,1921.42,*,SPX151016C00750000,,call,10/16/2015,10/01/2015,750,0,1168.8,1175.3,0,0,0.2589,1,0,-2.4102,0,SPX151016C00750000 7 | -------------------------------------------------------------------------------- /samples/spx_singles_example.py: -------------------------------------------------------------------------------- 1 | import os 2 | import optopsy as op 3 | import tabulate as tb 4 | 5 | 6 | def filepath(): 7 | curr_file = os.path.abspath(os.path.dirname(__file__)) 8 | 9 | # for demo purposes only, download your copy of data from sites such as: 10 | # CBOE Datashop: https://datashop.cboe.com/ 11 | # HistoricalOptionData: https://www.historicaloptiondata.com/ 12 | # DeltaNeutral: http://www.deltaneutral.com/ 13 | 14 | # following file was downloaded from: http://www.deltaneutral.com/files/Sample_SPX_20151001_to_20151030.csv 15 | return os.path.join(curr_file, "./data/Sample_SPX_20151001_to_20151030.csv") 16 | 17 | 18 | def run_strategy(): 19 | 20 | # indices for the column params are 0-indexed 21 | spx_data = op.csv_data( 22 | filepath(), 23 | underlying_symbol=0, 24 | underlying_price=1, 25 | option_type=5, 26 | expiration=6, 27 | quote_date=7, 28 | strike=8, 29 | bid=10, 30 | ask=11, 31 | ) 32 | 33 | # Backtest all single calls(long) on the SPX 34 | 35 | # All public optopsy functions return a regular Pandas DataFrame so you can use 36 | # regular pandas functions as you see fit to analyse the dataset 37 | long_single_calls = op.long_calls(spx_data).round(2) 38 | 39 | print("Statistics for SPX long calls from 2015-10-01 to 2015-10-30 \n") 40 | print( 41 | tb.tabulate( 42 | long_single_calls, 43 | headers=long_single_calls.columns, 44 | tablefmt="github", 45 | numalign="right", 46 | ) 47 | ) 48 | 49 | 50 | if __name__ == "__main__": 51 | import timeit 52 | 53 | start = timeit.default_timer() 54 | 55 | # All the program statements 56 | run_strategy() 57 | 58 | stop = timeit.default_timer() 59 | execution_time = round(stop - start, 0) 60 | 61 | print("Program Executed in " + str(execution_time)) # It returns time in seconds 62 | -------------------------------------------------------------------------------- /samples/spx_straddles_example.py: -------------------------------------------------------------------------------- 1 | import os 2 | import optopsy as op 3 | import tabulate as tb 4 | 5 | 6 | def filepath(): 7 | curr_file = os.path.abspath(os.path.dirname(__file__)) 8 | 9 | # for demo purposes only, download your copy of data from sites such as: 10 | # CBOE Datashop: https://datashop.cboe.com/ 11 | # HistoricalOptionData: https://www.historicaloptiondata.com/ 12 | # DeltaNeutral: http://www.deltaneutral.com/ 13 | 14 | # following file was downloaded from: http://www.deltaneutral.com/files/Sample_SPX_20151001_to_20151030.csv 15 | return os.path.join(curr_file, "./data/Sample_SPX_20151001_to_20151030.csv") 16 | 17 | 18 | def run_strategy(): 19 | 20 | # indices for the column params are 0-indexed 21 | spx_data = op.csv_data( 22 | filepath(), 23 | underlying_symbol=0, 24 | underlying_price=1, 25 | option_type=5, 26 | expiration=6, 27 | quote_date=7, 28 | strike=8, 29 | bid=10, 30 | ask=11, 31 | ) 32 | 33 | # Backtest all straddes(long) on the SPX 34 | 35 | # All public optopsy functions return a regular Pandas DataFrame so you can use 36 | # regular pandas functions as you see fit to analyse the dataset 37 | straddles = op.long_straddles(spx_data).round(2) 38 | 39 | print("Statistics for SPX straddles from 2015-10-01 to 2015-10-30 \n") 40 | print( 41 | tb.tabulate( 42 | straddles, 43 | headers=straddles.columns, 44 | tablefmt="github", 45 | numalign="right", 46 | ) 47 | ) 48 | 49 | 50 | if __name__ == "__main__": 51 | import timeit 52 | 53 | start = timeit.default_timer() 54 | 55 | # All the program statements 56 | run_strategy() 57 | 58 | stop = timeit.default_timer() 59 | execution_time = round(stop - start, 0) 60 | 61 | print("Program Executed in " + str(execution_time)) # It returns time in seconds 62 | -------------------------------------------------------------------------------- /samples/spx_strangles_example.py: -------------------------------------------------------------------------------- 1 | import os 2 | import optopsy as op 3 | import tabulate as tb 4 | 5 | 6 | def filepath(): 7 | curr_file = os.path.abspath(os.path.dirname(__file__)) 8 | 9 | # for demo purposes only, download your copy of data from sites such as: 10 | # CBOE Datashop: https://datashop.cboe.com/ 11 | # HistoricalOptionData: https://www.historicaloptiondata.com/ 12 | # DeltaNeutral: http://www.deltaneutral.com/ 13 | 14 | # following file was downloaded from: http://www.deltaneutral.com/files/Sample_SPX_20151001_to_20151030.csv 15 | return os.path.join(curr_file, "./data/Sample_SPX_20151001_to_20151030.csv") 16 | 17 | 18 | def run_strategy(): 19 | 20 | # indices for the column params are 0-indexed 21 | spx_data = op.csv_data( 22 | filepath(), 23 | underlying_symbol=0, 24 | underlying_price=1, 25 | option_type=5, 26 | expiration=6, 27 | quote_date=7, 28 | strike=8, 29 | bid=10, 30 | ask=11, 31 | ) 32 | 33 | # Backtest all strangles(long) on the SPX 34 | 35 | # All public optopsy functions return a regular Pandas DataFrame so you can use 36 | # regular pandas functions as you see fit to analyse the dataset 37 | strangles = op.long_strangles(spx_data).round(2) 38 | 39 | print("Statistics for SPX strangles from 2015-10-01 to 2015-10-30 \n") 40 | print( 41 | tb.tabulate( 42 | strangles, 43 | headers=strangles.columns, 44 | tablefmt="github", 45 | numalign="right", 46 | ) 47 | ) 48 | 49 | 50 | if __name__ == "__main__": 51 | import timeit 52 | 53 | start = timeit.default_timer() 54 | 55 | # All the program statements 56 | run_strategy() 57 | 58 | stop = timeit.default_timer() 59 | execution_time = round(stop - start, 0) 60 | 61 | print("Program Executed in " + str(execution_time)) # It returns time in seconds 62 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup 2 | 3 | setup( 4 | name="optopsy", 5 | description="A nimble backtesting and statistics library for options strategies", 6 | long_description=open("README.md").read(), 7 | long_description_content_type="text/markdown", 8 | version="2.0.1", 9 | url="https://github.com/michaelchu/optopsy", 10 | author="Michael Chu", 11 | author_email="mchchu88@gmail.com", 12 | license="GPL-3.0-or-later", 13 | classifiers=[ 14 | "Operating System :: OS Independent", 15 | "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)", 16 | "Programming Language :: Python :: 3.6", 17 | ], 18 | packages=["optopsy"], 19 | install_requires=["pandas", "numpy"], 20 | ) 21 | -------------------------------------------------------------------------------- /tests/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/michaelchu/optopsy/b1cf036d7e6f420c335384fbd8db02b6ad0e281c/tests/__init__.py -------------------------------------------------------------------------------- /tests/conftest.py: -------------------------------------------------------------------------------- 1 | import pytest 2 | import pandas as pd 3 | import datetime as datetime 4 | 5 | 6 | @pytest.fixture(scope="module") 7 | def data(): 8 | exp_date = datetime.datetime(2018, 1, 31) 9 | quote_dates = [datetime.datetime(2018, 1, 1), datetime.datetime(2018, 1, 31)] 10 | cols = [ 11 | "underlying_symbol", 12 | "underlying_price", 13 | "option_type", 14 | "expiration", 15 | "quote_date", 16 | "strike", 17 | "bid", 18 | "ask", 19 | ] 20 | d = [ 21 | ["SPX", 213.93, "call", exp_date, quote_dates[0], 212.5, 7.35, 7.45], 22 | ["SPX", 213.93, "call", exp_date, quote_dates[0], 215.0, 6.00, 6.05], 23 | ["SPX", 213.93, "put", exp_date, quote_dates[0], 212.5, 5.70, 5.80], 24 | ["SPX", 213.93, "put", exp_date, quote_dates[0], 215.0, 7.10, 7.20], 25 | ["SPX", 220, "call", exp_date, quote_dates[1], 212.5, 7.45, 7.55], 26 | ["SPX", 220, "call", exp_date, quote_dates[1], 215.0, 4.96, 5.05], 27 | ["SPX", 220, "put", exp_date, quote_dates[1], 212.5, 0.0, 0.0], 28 | ["SPX", 220, "put", exp_date, quote_dates[1], 215.0, 0.0, 0.0], 29 | ] 30 | return pd.DataFrame(data=d, columns=cols) 31 | -------------------------------------------------------------------------------- /tests/test_checks.py: -------------------------------------------------------------------------------- 1 | import pytest 2 | import optopsy.checks as op 3 | 4 | 5 | def test_check_positive_integer(): 6 | with pytest.raises(ValueError): 7 | op._check_positive_integer("some key", -1) 8 | op._check_positive_integer("some key", 0) 9 | op._check_positive_integer("some key", 1.0) 10 | 11 | assert op._check_positive_integer("some key", 1) is None 12 | 13 | 14 | def test_check_positive_integer_inclusive(): 15 | with pytest.raises(ValueError): 16 | op._check_positive_integer_inclusive("some key", -1) 17 | op._check_positive_integer_inclusive("some key", 1.0) 18 | 19 | assert op._check_positive_integer_inclusive("some key", 1) is None 20 | assert op._check_positive_integer_inclusive("some key", 0) is None 21 | 22 | 23 | def test_check_positive_float(): 24 | with pytest.raises(ValueError): 25 | op._check_positive_float("some key", -1) 26 | op._check_positive_float("some key", 0) 27 | op._check_positive_float("some key", 1) 28 | 29 | assert op._check_positive_float("some key", 1.0) is None 30 | 31 | 32 | def test_check_side(): 33 | with pytest.raises(ValueError): 34 | op._check_side("some key", "invalid") 35 | 36 | assert op._check_side("some key", "short") is None 37 | assert op._check_side("some key", "long") is None 38 | 39 | 40 | def test_check_bool_type(): 41 | with pytest.raises(ValueError): 42 | op._check_bool_type("some key", "invalid") 43 | 44 | assert op._check_bool_type("some key", True) is None 45 | assert op._check_bool_type("some key", False) is None 46 | 47 | 48 | def test_check_list_type(): 49 | with pytest.raises(ValueError): 50 | op._check_list_type("some key", "invalid") 51 | 52 | assert op._check_list_type("some key", []) is None 53 | 54 | 55 | def test_check_data_types(): 56 | import pandas as pd 57 | 58 | invalid_cols = {"some_col": ["some val"]} 59 | invalid_types = {"underlying_symbol": [123]} 60 | 61 | with pytest.raises(ValueError, match="Expected column"): 62 | op._check_data_types(pd.DataFrame(invalid_cols)) 63 | 64 | with pytest.raises( 65 | ValueError, match="underlying_symbol does not match expected types" 66 | ): 67 | op._check_data_types(pd.DataFrame(invalid_types)) 68 | -------------------------------------------------------------------------------- /tests/test_data/data.csv: -------------------------------------------------------------------------------- 1 | col1,col2,col3,col4,col5,col6,col7,col8,col9 2 | SPX,359.69,call,1/20/1990,1/2/1990,225,135.5,135.5,0 3 | SPX,359.69,call,1/20/2000,1/2/2000,320,40.9,40.9,0 4 | SPX,359.69,call,1/20/2010,1/2/2010,325,35.9,35.9,0 5 | SPX,359.69,call,1/20/2020,1/2/2020,330,30.9,30.9,0 -------------------------------------------------------------------------------- /tests/test_datafeeds.py: -------------------------------------------------------------------------------- 1 | import os 2 | from datetime import datetime 3 | import optopsy as op 4 | 5 | 6 | def filepath(): 7 | curr_file = os.path.abspath(os.path.dirname(__file__)) 8 | return os.path.join(curr_file, "./test_data/data.csv") 9 | 10 | 11 | def test_import_csv_file(): 12 | data = op.datafeeds.csv_data( 13 | filepath(), 14 | underlying_symbol=0, 15 | underlying_price=1, 16 | option_type=2, 17 | expiration=3, 18 | quote_date=4, 19 | strike=5, 20 | bid=6, 21 | ask=7, 22 | ) 23 | 24 | expected_columns = [ 25 | "underlying_symbol", 26 | "underlying_price", 27 | "option_type", 28 | "expiration", 29 | "quote_date", 30 | "strike", 31 | "bid", 32 | "ask", 33 | ] 34 | assert list(data.columns) == expected_columns 35 | assert not data.empty 36 | 37 | 38 | def test_import_csv_with_date_range(): 39 | data = op.datafeeds.csv_data( 40 | filepath(), 41 | start_date=datetime(1990, 1, 1), 42 | end_date=datetime(1990, 12, 31), 43 | underlying_symbol=0, 44 | underlying_price=1, 45 | option_type=2, 46 | expiration=3, 47 | quote_date=4, 48 | strike=5, 49 | bid=6, 50 | ask=7, 51 | ) 52 | assert len(data) == 1 53 | assert data.iloc[0]["expiration"] == datetime(1990, 1, 20) 54 | 55 | 56 | def test_import_csv_with_start_date(): 57 | data = op.datafeeds.csv_data( 58 | filepath(), 59 | start_date=datetime(2000, 1, 1), 60 | underlying_symbol=0, 61 | underlying_price=1, 62 | option_type=2, 63 | expiration=3, 64 | quote_date=4, 65 | strike=5, 66 | bid=6, 67 | ask=7, 68 | ) 69 | assert len(data) == 3 70 | assert data.iloc[0]["expiration"] == datetime(2000, 1, 20) 71 | assert data.iloc[1]["expiration"] == datetime(2010, 1, 20) 72 | assert data.iloc[2]["expiration"] == datetime(2020, 1, 20) 73 | 74 | 75 | def test_import_csv_with_end_date(): 76 | data = op.datafeeds.csv_data( 77 | filepath(), 78 | end_date=datetime(2010, 1, 1), 79 | underlying_symbol=0, 80 | underlying_price=1, 81 | option_type=2, 82 | expiration=3, 83 | quote_date=4, 84 | strike=5, 85 | bid=6, 86 | ask=7, 87 | ) 88 | assert len(data) == 2 89 | assert data.iloc[0]["expiration"] == datetime(1990, 1, 20) 90 | assert data.iloc[1]["expiration"] == datetime(2000, 1, 20) 91 | 92 | 93 | def test_import_csv_with_no_date_range(): 94 | data = op.datafeeds.csv_data( 95 | filepath(), 96 | underlying_symbol=0, 97 | underlying_price=1, 98 | option_type=2, 99 | expiration=3, 100 | quote_date=4, 101 | strike=5, 102 | bid=6, 103 | ask=7, 104 | ) 105 | assert len(data) == 4 106 | assert data.iloc[0]["expiration"] == datetime(1990, 1, 20) 107 | assert data.iloc[1]["expiration"] == datetime(2000, 1, 20) 108 | assert data.iloc[2]["expiration"] == datetime(2010, 1, 20) 109 | assert data.iloc[3]["expiration"] == datetime(2020, 1, 20) 110 | -------------------------------------------------------------------------------- /tests/test_rules.py: -------------------------------------------------------------------------------- 1 | from optopsy.strategies import Side 2 | from optopsy.core import _calls 3 | from optopsy.rules import _rule_non_overlapping_strike 4 | 5 | 6 | def test_no_overlapping_strikes(data): 7 | leg_def = [(Side.long, _calls)] 8 | result = _rule_non_overlapping_strike(_calls(data), leg_def) 9 | assert len(result) == 4 10 | assert "call" in list(result["option_type"].values) 11 | -------------------------------------------------------------------------------- /tests/test_strategies.py: -------------------------------------------------------------------------------- 1 | from optopsy.strategies import * 2 | from optopsy.definitions import * 3 | 4 | 5 | describe_cols = [ 6 | "count", 7 | "mean", 8 | "std", 9 | "min", 10 | "25%", 11 | "50%", 12 | "75%", 13 | "max", 14 | ] 15 | 16 | 17 | def test_single_long_calls_raw(data): 18 | results = long_calls(data, raw=True) 19 | assert len(results) == 2 20 | assert list(results.columns) == single_strike_internal_cols 21 | assert "call" in list(results["option_type"].values) 22 | assert round(results.iloc[0]["pct_change"], 2) == 0.01 23 | assert round(results.iloc[1]["pct_change"], 2) == -0.17 24 | 25 | 26 | def test_single_long_puts_raw(data): 27 | results = long_puts(data, raw=True) 28 | assert len(results) == 2 29 | assert list(results.columns) == single_strike_internal_cols 30 | assert "put" in list(results["option_type"].values) 31 | assert round(results.iloc[0]["pct_change"], 2) == -1 32 | assert round(results.iloc[1]["pct_change"], 2) == -1 33 | 34 | 35 | def test_single_short_calls_raw(data): 36 | results = short_calls(data, raw=True) 37 | assert len(results) == 2 38 | assert list(results.columns) == single_strike_internal_cols 39 | assert "call" in list(results["option_type"].values) 40 | assert round(results.iloc[0]["pct_change"], 2) == 0.01 41 | assert round(results.iloc[1]["pct_change"], 2) == -0.17 42 | 43 | 44 | def test_single_short_puts_raw(data): 45 | results = short_puts(data, raw=True) 46 | assert len(results) == 2 47 | assert list(results.columns) == single_strike_internal_cols 48 | assert "put" in list(results["option_type"].values) 49 | assert round(results.iloc[0]["pct_change"], 2) == -1 50 | assert round(results.iloc[1]["pct_change"], 2) == -1 51 | 52 | 53 | def test_singles_long_calls(data): 54 | results = long_calls(data) 55 | assert len(results) == 1 56 | assert results.iloc[0]["count"] == 2.0 57 | assert round(results.iloc[0]["mean"], 2) == -0.08 58 | assert list(results.columns) == single_strike_external_cols + describe_cols 59 | 60 | 61 | def test_singles_long_puts(data): 62 | results = long_puts(data) 63 | assert len(results) == 1 64 | assert results.iloc[0]["count"] == 2.0 65 | assert round(results.iloc[0]["mean"], 2) == -1.0 66 | assert list(results.columns) == single_strike_external_cols + describe_cols 67 | 68 | 69 | def test_singles_short_calls(data): 70 | results = short_calls(data) 71 | assert len(results) == 1 72 | assert results.iloc[0]["count"] == 2.0 73 | assert round(results.iloc[0]["mean"], 2) == -0.08 74 | assert list(results.columns) == single_strike_external_cols + describe_cols 75 | 76 | 77 | def test_singles_short_puts(data): 78 | results = short_puts(data) 79 | assert len(results) == 1 80 | assert results.iloc[0]["count"] == 2.0 81 | assert round(results.iloc[0]["mean"], 2) == -1.0 82 | assert list(results.columns) == single_strike_external_cols + describe_cols 83 | 84 | 85 | def test_straddles_long_raw(data): 86 | results = long_straddles(data, raw=True) 87 | assert list(results.columns) == straddle_internal_cols 88 | assert results.iloc[0]["option_type_leg1"] == "put" 89 | assert results.iloc[0]["option_type_leg2"] == "call" 90 | assert round(results.iloc[0]["pct_change"], 2) == -0.43 91 | assert round(results.iloc[1]["pct_change"], 2) == -0.62 92 | 93 | 94 | def test_straddles_short_raw(data): 95 | results = short_straddles(data, raw=True) 96 | assert list(results.columns) == straddle_internal_cols 97 | assert results.iloc[0]["option_type_leg1"] == "put" 98 | assert results.iloc[0]["option_type_leg2"] == "call" 99 | assert round(results.iloc[0]["pct_change"], 2) == 0.43 100 | assert round(results.iloc[1]["pct_change"], 2) == 0.62 101 | 102 | 103 | def test_long_straddles(data): 104 | results = long_straddles(data) 105 | assert len(results) == 1 106 | assert results.iloc[0]["count"] == 2.0 107 | assert round(results.iloc[0]["mean"], 2) == -0.52 108 | assert list(results.columns) == single_strike_external_cols + describe_cols 109 | 110 | 111 | def test_short_straddles(data): 112 | results = short_straddles(data) 113 | assert len(results) == 1 114 | assert results.iloc[0]["count"] == 2.0 115 | assert round(results.iloc[0]["mean"], 2) == 0.52 116 | assert list(results.columns) == single_strike_external_cols + describe_cols 117 | 118 | 119 | def test_strangles_long_raw(data): 120 | results = long_strangles(data, raw=True) 121 | assert len(results) == 1 122 | assert list(results.columns) == double_strike_internal_cols 123 | assert results.iloc[0]["option_type_leg1"] == "put" 124 | assert results.iloc[0]["option_type_leg2"] == "call" 125 | assert round(results.iloc[0]["pct_change"], 2) == -0.57 126 | 127 | 128 | def test_strangles_short_raw(data): 129 | results = short_strangles(data, raw=True) 130 | assert len(results) == 1 131 | assert list(results.columns) == double_strike_internal_cols 132 | assert results.iloc[0]["option_type_leg1"] == "put" 133 | assert results.iloc[0]["option_type_leg2"] == "call" 134 | assert round(results.iloc[0]["pct_change"], 2) == 0.57 135 | 136 | 137 | def test_long_strangles(data): 138 | results = long_strangles(data) 139 | assert len(results) == 1 140 | assert results.iloc[0]["count"] == 1.0 141 | assert round(results.iloc[0]["mean"], 2) == -0.57 142 | assert list(results.columns) == double_strike_external_cols + describe_cols 143 | 144 | 145 | def test_short_strangles(data): 146 | results = short_strangles(data) 147 | assert len(results) == 1 148 | assert results.iloc[0]["count"] == 1.0 149 | assert round(results.iloc[0]["mean"], 2) == 0.57 150 | assert list(results.columns) == double_strike_external_cols + describe_cols 151 | 152 | 153 | def test_long_call_spread_raw(data): 154 | results = long_call_spread(data, raw=True) 155 | assert len(results) == 1 156 | assert list(results.columns) == double_strike_internal_cols 157 | assert results.iloc[0]["option_type_leg1"] == "call" 158 | assert results.iloc[0]["option_type_leg2"] == "call" 159 | assert round(results.iloc[0]["pct_change"], 2) == 0.81 160 | 161 | 162 | def test_long_put_spread_raw(data): 163 | results = long_put_spread(data, raw=True) 164 | assert len(results) == 1 165 | assert list(results.columns) == double_strike_internal_cols 166 | assert results.iloc[0]["option_type_leg1"] == "put" 167 | assert results.iloc[0]["option_type_leg2"] == "put" 168 | assert round(results.iloc[0]["pct_change"], 2) == -1 169 | 170 | 171 | def test_short_call_spread_raw(data): 172 | results = short_call_spread(data, raw=True) 173 | assert len(results) == 1 174 | assert list(results.columns) == double_strike_internal_cols 175 | assert results.iloc[0]["option_type_leg1"] == "call" 176 | assert results.iloc[0]["option_type_leg2"] == "call" 177 | assert round(results.iloc[0]["pct_change"], 2) == -0.81 178 | 179 | 180 | def test_short_put_spread_raw(data): 181 | results = short_put_spread(data, raw=True) 182 | assert len(results) == 1 183 | assert list(results.columns) == double_strike_internal_cols 184 | assert results.iloc[0]["option_type_leg1"] == "put" 185 | assert results.iloc[0]["option_type_leg2"] == "put" 186 | assert round(results.iloc[0]["pct_change"], 2) == 1 187 | --------------------------------------------------------------------------------