https://github.com/tyrneh/options-implied-probability
OIPD computes the market's expectations about the probable future prices of an asset, based on information contained in options data.
https://github.com/tyrneh/options-implied-probability
finance implied-volatility impliedprobability option-pricing option-trading options options-pricing options-trading probability-distribution risk-neutral risk-neutral-probability rnd volatility-modeling volatility-smile volatility-surface volatility-trading
Last synced: 4 months ago
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OIPD computes the market's expectations about the probable future prices of an asset, based on information contained in options data.
- Host: GitHub
- URL: https://github.com/tyrneh/options-implied-probability
- Owner: tyrneh
- License: apache-2.0
- Created: 2022-10-19T17:59:36.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2026-02-03T20:15:05.000Z (4 months ago)
- Last Synced: 2026-02-04T05:36:34.266Z (4 months ago)
- Topics: finance, implied-volatility, impliedprobability, option-pricing, option-trading, options, options-pricing, options-trading, probability-distribution, risk-neutral, risk-neutral-probability, rnd, volatility-modeling, volatility-smile, volatility-surface, volatility-trading
- Language: Python
- Homepage:
- Size: 26.1 MB
- Stars: 310
- Watchers: 8
- Forks: 47
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://pypi.org/project/oipd/)
[](https://colab.research.google.com/github/tyrneh/options-implied-probability/blob/main/examples/OIPD_colab_demo.ipynb)
[](https://discord.gg/NHxWPGhhSQ)
[](https://pepy.tech/projects/oipd)
# Overview
### OIPD provides 2 capabilities:
**1. It computes the market's expectations about the probable future prices of an asset, based on information contained in options data.**
- While markets don't predict the future with certainty, under the efficient market view, these collective expectations represent the best available estimate of what might happen.
- Traditionally, extracting these “risk-neutral densities” were limited to quants or academics. OIPD makes this capability accessible to everyone.
**2. For options traders, it also offers a simple-to-use but rigorous pipeline to fit an arbitrage-free volatility smile/surface, which can be used to price options.**
- Fitting a vol surface well is a complex and expensive process, with the leading software provider costing $50k USD/month/seat. OIPD open-sources the entire pipeline fairly rigorously, with further improvements in the roadmap.
# Quick start
#### Installation
```bash
pip install oipd
```
### Quickstart tutorial in computing market-implied probability distributions
This quickstart will cover the functionality in **(1) computing market-implied probabilities**. See the [included jupyter notebook ](examples/quickstart_yfinance.ipynb) for a full example on using the automated yfinance connection to download options data and compute market-implied probabilities for Palantir.
For a more technical tutorial including the functionality of **(2) volatility fitting, see the additional jupyter notebooks** in the [examples](examples/) directory, as well as the full documentation [still WIP].
#### A. Usage for computing a probability distribution on a specific future date
```python
import matplotlib.pyplot as plt
from oipd import MarketInputs, ProbCurve, sources
# 1. we download data using the built-in yfinance connection
ticker = "AAPL" # specify the stock ticker
expiries = sources.list_expiry_dates(ticker) # see all expiry dates
single_expiry = expiries[1] # select one of the expiry dates you're interested in
chain, snapshot = sources.fetch_chain(ticker, expiries=single_expiry) # download the options chain data, and a snapshot at the time of download
# 2. fill in the parameters
market = MarketInputs(
valuation_date=snapshot.date, # date on which the options data was downloaded
underlying_price=snapshot.underlying_price, # the price of the underlying stock at the time when the options data was downloaded
risk_free_rate=0.04, # the risk-free rate of return. Use the US Fed or Treasury yields that are closest to the horizon of the expiry date
)
# 3. compute the future probability distribution using the data and parameters
prob = ProbCurve.from_chain(chain, market)
# 4. query the computed result to understand market-implied probabilities and other statistics
prob.plot()
plt.show()
prob_below = prob.prob_below(100) # P(price < 100)
prob_above = prob.prob_above(120) # P(price >= 120)
q50 = prob.quantile(0.50) # median implied price
skew = prob.skew() # skew
```
#### B. Usage for computing probabilities over time
```python
import matplotlib.pyplot as plt
from oipd import MarketInputs, ProbSurface, sources
# 1. download multi-expiry data using the built-in yfinance connection
ticker = "AAPL"
chain_surface, snapshot_surface = sources.fetch_chain(
ticker,
horizon="3m", # auto-fetch all listed expiries inside the horizon
)
# 2. fill in the parameters
surface_market = MarketInputs(
valuation_date=snapshot_surface.date, # date on which the options data was downloaded
underlying_price=snapshot_surface.underlying_price, # price of the underlying stock at download time
risk_free_rate=0.04, # risk-free rate for the horizon
)
# 3. compute the probability surface using the data and parameters
surface = ProbSurface.from_chain(chain_surface, surface_market)
# 4. query and visualize the surface
surface.plot_fan() # Plot a fan chart of price probability over time
plt.show()
# 5. "slice" the surface to get a ProbCurve, and query its statistical properties in the same manner as in example A
surface.expiries # list all the expiry dates that were captured
curve = surface.slice(surface.expiries[0]) # get a slice on the first expiry
curve.prob_below(100) # query probabilities and statistics
curve.kurtosis()
```
OIPD also **supports manual CSV or DataFrame uploads**.
See [more examples](examples/) for demos.
# Community
Pull requests welcome! Reach out on GitHub issues to discuss design choices.
Join the [Discord community](https://discord.gg/NHxWPGhhSQ) to share ideas, discuss strategies, and get support. Message me with your feature requests, and let me know how you use this.