The next frontier is here: Oracles with human insight

The chasm between blockchains and traditional finance has been bridged at lightning speed thanks to blockchain Oracles, but ghosts in the system persist. Data, the very source of modern Oracles’ power, can also be its undoing.

An overreliance on quantitative data, which is often skewed, can be crippling and stifling to decentralised finance (DeFi) applications. What if the solution to this problem is an Oracle that can give both qualitative and quantitative insights? 

Nitin Gaur, founder and director of IBM Digital Asset Lab, recently made a detailed case for Oracles in an article published on Cointelegraph, titled “The rise of Oracles: Institutional investors need trusted crypto market data.” 

Efficient systems

In “The Rise of Oracles” Gaur mentions Nobel laureate Eugene Fama’s efficient market hypothesis (EMH), which can be distilled into the known aphorism, “prices fully reflect all available information.” Explained in short, EMH says that stock price movements are impossible to predict in the short-term, and that new information affects prices almost immediately. Arguably, the stock market is thus ultimately, and perfectly, efficient. 

While Fama has his champions and detractors, EMH serves as a fitting segue to address the need for correct and reliable market data in cryptocurrency and DeFi. Fama’s theory relies wholly on accurate and reliable information. If the information is off, the price is wrong. 

Crypto exchanges have traditionally been the key source of data for oracles, and there have been instances of wash trading and closed pools, distorting data which then ripples through the entire DeFi system.Fama would say that these systems were inefficient. 

When two worlds collide

Financial institutions rely on traditional market data (price) and trade-related data, such as asset class volatility, volume and trade-specific data, and value-added data. These metrics play a fundamental role in risk model framework, quantitative trading, price and valuation, portfolio construction and management, and, yes, cryptocurrency finance, as Gaur notes. 

Newer data models, more suited to digital assets, include volume-weighted average price, time-weighted average price, total value locked, and crypto volatility index, among many others. It’s a complicated system of measurement metrics and market data, Gaur notes, “due to a collision of value systems.” 

We must discern between what he calls the global macro and the crypto macro. Global macro trends, such as inflation and money supply, impact global demand and supply curves, while crypto macro trends have an effect on the play between sectors such as Web 3.0, layer one, layer two, DeFi, and non-fungible tokens. When the two macros meet, things tend to get complicated. 

Modeling a hypothesis on empirical data to arrive at an investment theory can, in Gaur’s words, be tricky. “This gives rise to oracles that aim to resolve the issues of trusted data coming into the blockchain transaction system or a mediation layer between the crypto and traditional finance layers,” he says. But he leaves us hanging at the mention of the “tricky” issue with empirical data. 

Beyond hard data

He makes a clear case for valid, trusted and reliable market data, which has come to be an imperative requirement for greater institutional and mainstream adoption, but what about qualitative data? 

Oracles today are brilliant at calculating observational hard data. For example, you ask an oracle what the temperature is today, and it will give you a scientific breakdown that would make Fama’s head spin, but what if you asked whether today is a nice day? What if users could speculate on an unlimited number of markets by relying on an Oracle as a Service (OaaS) powered by user governance consensus? 

Using an OaaS, different entities can request data from our governance – it’s not restricted to resolving markets. The OptionRoom forecast protocol is unique in that it introduces the human element required to give insights beyond what computers can comprehend. 

The nature of our forecast protocol and OaaS, means that it relies heavily on platform participation, which is driven by various incentives, including utility token rewards for protocol participants, governance token farming, and pool winnings distribution to pool creators. 

Using our fully decentralised forecast protocol, users can create a new market using a $ROOM utility token, enabling token holders to create their unique market access without the need for approval by a centralised authority. Platform participants that stake the $COURT governance token can vote on the resolution of oracle requests.

Quantitative data remains vital, which is why our OaaS can also plug into external platforms to provide DeFi protocols with a fully decentralised, governance-based data source and verification solution.

Quantitative data paints a vivid outline, given the data is correct, but sometimes we need qualitative reasoning to add colour. The OptionRoom Oracle believes in the hard numbers, but in reaching an efficient solution, we need the full picture, colour and all.

(Disclaimer: The author is the CEO at OptionRoom)