NOTICE: The tokens used in the demo app are fixed to a specific set.

We connect to your ETH wallet but we do not pull in balances.

The app is a demo playground. The offering is under construction.

Simulate crypto token risks and rewards

Data 'Legos' that help users know the chances before they transact

Add Chance of XYZ data to your dApp

1  let tokenSimSpec = fetch(portfolioChanceData)
2  let tokenSim = simulate.riskReward(tokenSimSpec)
3  histogram(tokenSim)
Take action to seize opportunity!

Know The Chances

Communicate the chances of liquidation, default, APY and much more. Interactively simulate changes in portfolio HODLings.

Evaluate Options

Simulate the value of NFTs, real options and shock scenarios and more to quickly see the risk/reward in a portfolio.

Regulator Ready

Blockchain proof of analysis and data provenance makes it easier to comply with regulators from around the world.

Uncertainty Measured

Charged Particles

Crypto price distributions

Stress testing

Stock prices

Raw material costs

NFT floors

Parts failure rates

Interest rates

Energy prices

Powered By SIPMath

Uniswaption Case Study

Today finding a token-pair and price range that meets a liquidity provider's (LPs) risk appetite is difficult. LPs have different risk appetites. Liquidity pools have differing yield distributions. Price ranges have different probabilities of hitting a price ceiling or floor. All of these may cause an impaired loss. The app was created in under ten days for the Unicode Hackathon. The goal of the app is to help LPs identify capital efficient liquidity pools and price ranges on UniswapV3.

What is SIPMath?

Based on the pioneering work of Dr. Sam Savage, SIPmath is an open standard for communicating uncertainties as actionable data. This ushers in a new approach to risk management in which risks and opportunities are rolled up like numbers in an accounting system. Chancification is a process of converting the risk and opportunity in eg token price movements into a distribution that can be shared and used to make decisions.

How It Works

The dApp that launches in the NavBar of this site was created by a team of strangers in under four weeks. We came together somewhat randomly during team formation at ETH Global and created a chance of DeFi loan liquidating today dApp (prototype). Team members include DataCritic, i001962, Kit 齋藤飛鳥, noctisatrae, and shyBuilder. We were honored to win three awards for the project....

Probability Data Feeds

We provide probability distributions so you can make your dApp dance with interactive simulation.

Token Correlations

A token's price moves in relationship with other tokens. By preserving the realationships dApps users get a better understanding of porfolio value.


Coming soon! Drop-in Javascript library for creating and interacting with the Chance Data. Load popular data viz libraries and power your dApp.

Chance-data (json): DeFi Tokens

Daily percentage change distributions for a set of DeFi related tokens. Tokens include: ETH,AAVE,BAL,LINK,CRV,DAI,GNO,UNI,YFI. Created on 10-08-2021.

Chance-data (json): Aave aTokens

Daily percentage change distributions for a set of Aave aTokens related tokens. Tokens include aave, aave-aave, bal-aave, bat-aave, crv. Created on 2021-09-29

App Showecase

Uncertainty Has A Shape

The shape of the historical data is compressed into a handful of data points which makes it easy to share and know the data hasn't been changed. Apps, bots, and AI models 'rehydrate' the data when they need to determin the chances of something happening. This can be done at the edge or where and whenever the full shape is needed.

Uncertainties Have Relationships

See how a token's price movement is related to other tokens' price movements. Chance-data captures the shapes and the relationships found in massive amounts of data and compresses it into a few kilobytes.

Sum of independent, identically distributed lognormal distributions

Generalized Metalogs can closely approximate virtually any continuous distribution that may be simulated. It depends on a handful of input parameters. According to Dr. Sam Savage, 'The idea is to run extensive calculations at representative grid points then calculate the a-coefficients for a Metalog at each point. These coefficients are stored in an interpolatable lookup table.'