Curvefi-Polygon Exchange Trade Data

When it comes to using Curvefi-Polygon, there are several benefits compared to its direct competitors. Curvefi-Polygon is a decentralized exchange (DEX) protocol that operates on the Polygon network, which is a layer 2 scaling solution for Ethereum.

One of the primary benefits of using Curvefi-Polygon is its scalability. The Polygon network is designed to alleviate the scalability issues faced by the Ethereum network, enabling users to conduct transactions faster and at lower costs compared to the Ethereum mainnet. This makes Curvefi-Polygon a more efficient option for traders and liquidity providers.

Another advantage of Curvefi-Polygon is its low transaction fees. By operating on the Polygon network, users can take advantage of its low gas fees, which are significantly lower than those on the Ethereum mainnet. This makes it more cost-effective for users to trade and provide liquidity on the platform.

Furthermore, Curvefi-Polygon offers a wide range of stablecoin assets and liquidity pools. This allows users to easily trade and swap stablecoins with minimal slippage. The platform also implements a unique algorithm that optimizes trading for stablecoins, resulting in lower fees and improved efficiency.

In comparison to its direct competitors like Uniswap, Sushiswap, and Balancer, Curvefi-Polygon stands out with its focus on stablecoin trading, lower transaction fees, and faster transactions. These advantages make it an appealing choice for users looking for a more efficient and cost-effective DEX experience.

Overall, using Curvefi-Polygon provides users with the benefits of scalability, low transaction fees, and a wide selection of stablecoin assets, making it a strong contender in the decentralized exchange landscape.

Curve.fi is a decentralized exchange protocol designed for efficient stablecoin trading. Curve.fi-Polygon is an implementation of Curve.fi on the Polygon blockchain.

The underlying technology behind Curve.fi-Polygon is the Polygon network, which is a layer 2 scaling solution for Ethereum. Polygon uses a set of secure, independent blockchains called sidechains to process transactions off the Ethereum mainnet. These sidechains are connected to the Ethereum mainnet through multiple checkpoints, ensuring the security and integrity of the transactions.

Curve.fi-Polygon functions by utilizing liquidity pools to facilitate stablecoin trading. Users can deposit various stablecoins like USDT, USDC, DAI, and others into these pools. The deposited stablecoins are then utilized to provide liquidity for other users who want to trade between stablecoins or other assets.

Curve.fi-Polygon leverages an automated market maker (AMM) model, which means that trades are executed based on a predefined algorithm without the need for order books. The algorithm keeps the prices of stablecoins stable by maintaining a tight spread and low slippage during trading.

By operating on the Polygon network, Curve.fi-Polygon benefits from faster and cheaper transactions compared to operating directly on the Ethereum mainnet. This makes it more efficient and cost-effective for users to trade stablecoins.

In summary, Curve.fi-Polygon is a decentralized exchange protocol that utilizes liquidity pools and the Polygon network to enable efficient trading of stablecoins. Its underlying technology is based on the sidechain implementation of the Polygon network, providing users with faster and cheaper transactions.

DIA takes a comprehensive approach to collecting trade data from DeFi and NFT exchanges. The process varies depending on the type of exchange being considered.

For centralized exchanges like Coinbase, Kraken, and Binance, DIA scrapers directly collect trades from the exchange databases. This is done using Rest APIs or WebSocket APIs. The frequency of data collection varies, with intervals ranging from 1 to 7 seconds, depending on the exchange.

In the case of decentralized exchanges, DIA retrieves data from various blockchains by subscribing to swap events in liquidity pools. This allows DIA to access trading data directly from the blockchain itself, ensuring data accuracy. Examples of decentralized exchange sources include Uniswap, curve.finance, and PancakeSwap.

When it comes to NFT marketplaces, DIA captures live trading data by retrieving it from the smart contracts of integrated marketplaces. This data is updated with a retrieval period ranging from 20 seconds to 1 minute. By focusing on the actual NFT transactions occurring on these platforms, DIA ensures accuracy and avoids relying on potentially unreliable bids and offer data. Notable NFT integrated exchange sources include Blur, X2Y2, OpenSea, and TofuNFT.

DIA's comprehensive data management strategy, which includes sourcing data from over 80 exchanges and leveraging WebSockets and decentralized node providers, allows them to provide highly accurate and customizable price feeds. By retrieving data as close to the source as possible, DIA ensures precision and reliability in their price feeds for cryptocurrencies, NFTs, and more.

When it comes to building price feed oracles with Curvefi-Polygon trade data, DIA follows a specific process depending on the type of exchange being referred to.

For DeFi exchanges, DIA employs a two-step process. The first step involves data cleaning and outlier detection to ensure that the price estimation process remains resilient against trades that deviate significantly from the current market price. Outliers, such as trades resulting from market manipulation, errors, or flash crashes, are identified and removed to avoid building a feed using data that is far from the median. DIA uses an Interquartile Range (IR) filter to exclude data points and sets that lie outside an acceptable range relative to the interquartile range. Only trades falling into the middle quartiles move forward into further processing.

In the second step, DIA applies price determination methodologies to calculate the final price from the remaining data points. One example is the Volume Weighted Average Price (VWAP), which takes into account the volumes of trades to determine a single price value. Another example is the Moving Average with Interquartile Range Filter (MAIR), where trades collected in a specified time range are ordered by timestamp and weighted against the volume.

For NFT exchanges, the process is different. To determine the floor price of an NFT collection, the on-chain trade data undergoes cleansing filters to exclude market outliers and manipulation techniques. Various pricing methodologies are then applied to determine the final price point. The simplest methodology is the Floor Price, which provides the lowest recorded sale price of an NFT collection within a given time window. However, this method is susceptible to manipulation. To address this, DIA offers advanced methodologies such as the Moving Average of Floor Price, which calculates the moving average of a collection's floor price, customizable to specific use cases.

DIA also implements an interquartile range outlier detection filter for NFT price oracles to filter malicious behavior like wash trading. Additionally, they are open to discussing custom filters and methodologies for specific use cases beyond the implemented techniques described.