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Introducing New Market Data to the Web 3.0 Ecosystem

Benefit from dxFeed’s 10+ years of class-leading financial market data presence and its award-winning technologies in the quickly emerging Web 3.0 field. We provide developers and dApps with access to high-quality financial data solutions via blockchain oracle technology.

dxFeed has been offering on-demand, real-time, and historical market prices for a wide asset range, including:

  • cryptocurrencies 
  • equities 
  • indices 
  • derivatives
  • forex

Bring new instruments into your Web 3.0 landscape. dxFeed is actively creating new and unique indices and real-time datasets you can use to broaden the opportunities you build on them. Enjoy new instruments representing asset portfolios, volatility, volumes, sentiment, and many more datasets. Better yet, adapt these datasets to create opportunistic trades, hedges, or other on-chain DeFi and DEX trading products. You might come up with combinations not yet seen or in operation in the industry.

dxFeed focuses on delivering the data platforms and consumers request in ecosystems. Contact us to request any data you’d like to publish via our Nodes. Our expert customer support team is actively engaging with the community to show you the exciting products you can build when you have access to the data you need.

dxFeed’s Presence in Web 3.0

Chainlink is the largest, best-known oracle service for Web 3.0 data. It’s also your first point of entry into dxFeed’s Web 3.0 data publication.

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dxFeed is a founding member of the API3 Alliance. We will be represented as a first-party oracle, providing forex, equities, and commodities data via an API3 beacon.

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SupraOracles has been hotly anticipated as a next-gen oracle service with novel solutions the firm claims to be present in oracle selection and data sourcing.

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Goracle is the first decentralized oracle network on the Algorand blockchain. Goracle currently offers cryptocurrency price pair data and sports data. dxFeed will be providing traditional finance data to the Algorand ecosystem. 

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Charli3 is the platform that provides and verifies data (initially focused on blockchain economic values) for blockchain applications. Charli3 aims to provide powerful, trusted data feeds and building on a peer-reviewed blockchain is crucial.

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Consensus vs. First-Party Oracles

Most Web 3.0 oracles publishing to smart contracts have dividing issues. The biggest philosophical and architectural dividing issue is the nature of the data validation. Blockchain networks have a general philosophy and guiding principle: the desire to be trustless. To achieve this, they operate on a consensus basis.

Blockchains rely on all validating participants to observe and confirm all entries into the shared blockchain ledger. In turn, bad actors can’t disrupt this orderly operation. The validation process results in an element of time delay since a network of blockchain validators needs time to review and reach a consensus.

For smart contracts processing real-world data, the consensus operating model also equally holds true. In the earliest Web 3.0 data designs, for an oracle to publish all data, the data has to go through multi-party review and consensus. This was to ensure the published data is true and accurate. This is still the most popular oracle operating model on the largest networks, like Chainlink.

However, a growing number of Web 3.0 oracle platforms are making available the so-called first-party oracle-based data. In this case, a real-world identifiable content provider is the only data contributor and publisher. The provider’s real-world existence and characteristics are assumed reliable which makes the data true and accurate. In effect, data have a regulator or governmental accreditation, like when a central bank publishes its interest rate updates. Data seekers inherently trust a central bank as a first-party data provider. They’d also consider the central bank as an acceptable source as a Web 3.0 publisher.

First-party oracle systems can, as a result, typically publish data more frequently. These systems might also have more commercial flexibility, offering communication with the original data source and arranging for non-standard commercial models. First-party oracles can still, of course, follow all the automated non-human intervention operating models in all other respects. They’d work the same as a consensus oracle system, based on their design.

Some emerging Web 3.0 oracle networks also allow you to choose between consensus and first-party oracle models on the same network. You can even request hybrid models where groups of identified first parties cooperate in a less burdensome consensus.

dxFeed is enthusiastically pursuing data publication through both models. Its consensus oracle models, like the Chainlink node, along with other contributor nodes contribute data to numerous oracles. dxFeed also announced its intention to launch with API3 which would represent a first-party model.

What Can You Build with Oracle Feeds

You can build a variety of financial applications leveraging oracle financial data. Check the list below from some preliminary use cases. As the market grows, we expect to expand this list to cases we haven’t even envisioned yet.

A future or option product links back to a defined instrument index’s price. It tracks the price and trades based on a predetermined term date or perpetual basis. This is true regardless of the settlement method: product delivery or cash. For cash settlements, a well-structured index gives you pricing and trading access to something you can’t directly gain exposure to, like a market sentiment index.

For decentralized markets, including Automated Market Maker (AMM) and order book-driven markets, a constantly available, trusted index price is the most central requirement for trading. Oracles offer smart-contract accessible price feeds, making this information readily available. Other parties can view and review the feeds’ accuracy. In turn, they’d trust it as a basis for creating orderly, reliable derivatives markets.

Why is this important in DeFi? Benefit from these derivatives’ availability in DeFi in 4 ways:

  1. Create broader accessibility when you compare it with access-controlled centralized exchanges. 
  2. Enable these derivatives to become programmatically-integrated building blocks for more complex instruments or DeFi systems. 
  3. Build oracle-delivered price feeds easily. 
  4. Launch new trading products as quickly as the market demands them.

Prediction markets enable defined event outcome-based payouts. Typically, these would be binary gain or loss payouts, but they can have partial payouts depending on how you define the product.

Prediction markets look much like options when you compare price formulation and trading behavior. However, these instruments can be less standardized when you look closer at their defined characteristics. One example is variable lifespans. You’d define these based on an underlying instrument reaching a particular price, instead of defining an end date for the option and observing if an option is in or out of the money at that time. 

You can also base characteristics to work when any combination of multi-factor events has been satisfied. For example, you can build a Prediction Market that settles when a given NASDAQ 100 index price has been reached AND simultaneously has sunny weather in New York City. This instrument’s pricing and trading are very different from a regular NASDAQ 100  price-driven option.

Exchange-Traded Products are well known in traditional markets. In some cases, they are synthetically backed to create a price return that mimics the ETP index’s net asset value (NAV), instead of holding the underlying basket of instruments directly.

These same concepts are available in crypto. In this case, you can use any oracle-published index to define the unit NAV price of a synthetically-backed spot token in a completely decentralized way. You wouldn’t need an issuer or asset manager to be directly responsible for controlling it. Synthetically-backed products in crypto might hold a value based on stablecoins, like Ethereum or any other crypto instrument defined in its specification. Synthetic tokens can blend features of prediction markets, futures markets, and collateralized loans. You can use any existing index or asset price as the token’s backbone. The token might give a spot instrument the price behavior of an option contract.

Taking this concept a step further, you can use synthetic tokens to create products to use for risk management. In effect, you can create network transaction costs, congestion, volatility, or volumes just by defining the applicable index, and publishing it to a Web 3.0 oracle to track.

Most popular Automated Market Maker (AMM) markets, like Uniswap, are priced on a naive basis. Arbitrageurs keep the available AMM price in line by observing prevailing market prices elsewhere and arbitrage trading the AMM pool to be equal to that external market. This can be very detrimental to asset pool liquidity providers since they’re effectively absorbing this arbitrage as a loss.

The next generation of more intelligent DeFi liquidity pools uses a market price-driven midpoint, i.e., setting an actively-adjusted market midpoint using an oracle. This can greatly increase investor confidence as they contribute assets to liquidity pools. This pricing will also make all AMM markets more efficient.