Golden Encyclopedia | What is Python Network (PYTH)?

Author: Bradley Peak, CoinTelegraph; Compiled by: Deng Tong, Bitchain Vision

1. What is Python?

Python Network is a decentralized oracle (essentially a service that connects blockchain with real-world data) that introduces real-time financial metrics such as stocks, cryptocurrencies and commodity prices into the blockchain.

Originally launched on Solana in 2021, Python is a mission to provide high-quality data for blockchain applications, especially decentralized finance (DeFi), where accurate data feeds are essential for asset pricing and preventing mispriced transactions..

Unlike some oracles that extract data from multiple intermediaries, Python directly obtains data from top financial institutions, including exchanges and trading companies, thus improving the speed and reliability of their data feeds.

2. How to convert real-time data in Python Network

Python Network is updating the way blockchain systems access real-time market data by directly linking to high-quality sources such as trading companies, major financial institutions and exchanges.

Python does not rely on intermediaries, but acquires asset prices directly from first-party providers, including companies from traditional financial and cryptocurrency markets.This enables decentralized applications (DApps) to access accurate, low-latency pricing information, which is critical to tools such as lending platforms, transaction protocols and asset tokenization systems.

What stands out in Python is its commitment to reliability and accuracy.Unlike many oracles, Python uses a unique data aggregation process to acquire multiple data points from the provider and create a single, reliable price information that can resist fluctuations or manipulations.This is crucial in DeFi, as any delay or inaccuracy in price data can lead to problems such as liquidation errors in lending agreements or arbitrage opportunities that harm the market.

Do you know?Python Network publishes data sources on its own blockchain Pythonnet, thus enhancing transparency and security in the blockchain oracle field.

3. How Python Network provides accurate encrypted data

Python Network’s accuracy is driven by powerful verification processes and aggregation models that leverage its vast network of data publishers.

From trading platforms to financial entities, each data provider sends asset prices together with confidence intervals to Python—a metric of price accuracy they provide.Python’s protocol then aggregates these multiple data points to form a single price information for each asset, updated every 400 milliseconds.By using its “pull oracle” design, Python only updates data when requested by the user, rather than continuously pushing it, minimizing blockchain congestion and reducing costs.

To maintain data integrity, Python adopts a weighted aggregation method that can filter out any extreme outliers and pay more attention to more reliable sources.This approach greatly reduces the risk of tampering or data manipulation.The result is a safe and accurate system where asset prices are cross-validated across multiple independent sources, ensuring that DeFi applications can rely on them for accuracy and stability.

Do you know?The first blockchain oracle, Reality Keys, was developed to address the inherent limitations of smart contracts.Blockchains are self-contained and highly secure, but they do not have direct access to external information such as market prices, weather conditions or event results – and this data is crucial for many real-world applications.

4. The main use cases of Python Network

Python Network’s real-time data sources enhance DeFi applications, including DEX, lending platforms, stablecoins, derivatives and earnings optimization.

By providing accurate, decentralized pricing, Python supports responsive trading, efficient clearing, stablecoin value pegging, risk management derivatives and optimized yields, ensuring stability and transparency throughout the DeFi ecosystem.Let’s take a closer look at some of its applications:

• Decentralized Exchange (DEX):Python’s real-time data powers decentralized exchanges, enabling accurate price updates for assets traded across multiple chains.With DEXs such as Drift Protocol on Solana, Python’s low latency data helps maintain efficient price discovery and risk management.For example, Drift leverages Python’s rapid updates to enable features such as perpetual futures and other derivatives, allowing traders to effectively deal with volatile market conditions while keeping trading transparent and secure.

• Lending Platform:For DeFi lending protocols, reliable asset pricing is critical to accurate loan value (LTV) ratios and automatic liquidation.By entering real-time data into the lending platform, Python supports collateral valuation and liquidation events, thereby protecting lenders’ interests and maintaining the stability of the platform.Protocols such as ReactorFusion on ZKsync use Python pricing to effectively process loan value, while Solana uses Python to monitor collateral risk and trigger automatic liquidation, minimizing losses in the volatile market.

• Stablecoin:Stablecoin platforms rely heavily on Python to peg their value to assets such as the US dollar, the euro or other currencies and commodities.Through integration with Python, stablecoins such as Tether’s USDt can maintain their value with frequent, accurate price feedback, which is crucial for stablecoin reserves and protecting users from the risk of decoupling.This stable connection to fiat or cryptocurrency collateralized assets keeps DeFi transactions stable and credible, especially during market volatility.

• Derivatives and Structural Products:In the derivatives market, Python enables the platform to create complex financial instruments such as perpetual swaps, options and structured product vaults.For example, Kwenta and other Synthetix projects use Python sources to provide exposure to digital assets and real-world markets, keeping positions well hedged and reducing the risk of liquidation mismatch.Python’s high-frequency data also supports unique options such as leveraged positions, further advancing DeFi trading options with decentralized price integrity.

• Revenue Optimization and Other DeFi Applications:Earnings Farming and Liquidity Agreements use Python’s price feedback to optimize rewards and manage risks associated with staking or providing liquidity.High-yield investors benefit from real-time data tracking asset performance, helping them maximize returns.In addition, applications in blockchain ecosystems such as Mantle’s Lendle integrate Python to support dynamic earning assets and liquidity pools, thereby facilitating innovation and user engagement in DeFi.

did you know?The biggest project using Python Network is Synthetix on the Optimism blockchain, which relies heavily on Python’s low-latency price source for Synthetix Perpetuals (Perps) v2.The integration allows Synthetix to create 40 new permanent markets, process nearly $15 billion in transaction volume and incur significant staking fees for its users.

5. Timeline: History of Python Network

Over the years, Python Network has been committed to the mission of decentralized financial data, continuously enhancing infrastructure, and supporting the development of DeFi through precise and high-frequency market data sources.

2021: Launched on Solana and offer price for the first time

In April 2021, Python Network was announced and the initial development was supported by Jump Crypto.Python was launched on Solana’s high-performance blockchain in August, providing high-speed, low-latency price feedback to over 30 crypto assets.

By the end of this year, Python has acquired data from about 40 major financial providers, including exchanges and market makers, supporting its goal of providing reliable, real-time data for DeFi applications.

2022: Extend with Python and cross-chain capabilities

Python Network has achieved significant expansion in 2022 by launching Python, a proof-of-authority blockchain forked from Solana.Pythonnet allows faster data aggregation and more frequent updates.

In August of the same year, Python integrated with the Wormhole bridge to reach other blockchains, allowing it to support price feedback on Ethereum, BNB smart chains, etc.This year marks the year for Python’s cross-chain expansion, aiming to provide its high-frequency data to the wider DeFi ecosystem.

2023: Governance Launch and PYTH Token Airdrop

Python launched the PYTH governance token in November 2023.To encourage community participation, Python made an airdrop to distribute PYTH tokens to early users and active DeFi participants, thus granting holders voting rights for protocol changes and development.

This release is an important step in the decentralization of Python governance, allowing communities to participate in decisions about cost structure, network updates and ecosystem development.

2024: Multi-chain and institutional growth

Python continues its multi-chain growth, expanding partnerships and price feedback integration with various DeFi platforms such as Drift Protocol and ReactorFusion.

By mid-2024, Python reported that its total value has exceeded $5 billion and accounts for nearly 10% of the oracle space, highlighting its growing importance as a trusted source of DeFi real-time data in various blockchain networks.effect.

6. Python network comparison link: What is the difference?

The choice between Python and Chainlink depends on project requirements: Python is for high-speed, financially driven data centered around DeFi, while Chainlink is for a wider range of use cases requiring data diversity and strong ecosystem support.

Speaking of oracles, you probably know Chainlink, which is the most widely used decentralized oracle today.Python supports more than 1,600 projects, why do you still need Python?

First, there are significant differences between Python Network and Chainlink on data sources.Python obtains data directly from financial institutions, exchanges and trading companies, ensuring high-quality first-party information from organizations such as Jane Street and Binance.Chainlink usually collects data through independent node operators, which usually get data from aggregators such as CoinMarketCap and BraveNewCoin.

This reliance on repeaters means that Chainlink’s data sources are more diverse, but may not be consistent with Python’s direct source data, especially high-frequency financial data.There are some other key differences; let’s take a closer look at each one.

Cost efficiency and data update model

Python is built around an efficient pull-on model that allows users to request data updates only when needed, thereby significantly reducing transaction costs.Therefore, Python’s updates are almost instant, in just 300-400 milliseconds, tailored for latency-sensitive DeFi applications.

Chainlink, by contrast, often uses push models to update prices regularly based on specific conditions such as price fluctuations or time intervals, which can be expensive and slow.For example, Chainlink updates data every few seconds or minutes based on preset conditions, making it ideal for applications where speed is less important but reliability is critical.

Target audience and use cases

Python focuses on DeFi and financial data applications such as decentralized exchanges, lending and derivatives platforms.Its data sources are optimized for real-time financial transactions, with accurate and high-frequency data being crucial.

However, Chainlink supports a wider range of use cases, including non-financial areas such as insurance, gaming, and supply chains, which require different external data types.

Transparency and governance

Both oracles have governance mechanisms, but Python’s approach is more inclined toward the Web3 spirit.

Python is managed by a decentralized autonomous organization (DAO) that directly integrates community opinions into decisions for agreement changes and updates, ensuring transparency.

Chainlink also integrates community engagement, but concerns about centralization remain due to its multi-signature contract system that gives a few people important control over the data source.

Python’s full on-chain transparency further enhances users’ trust in the authenticity of their data, while Chainlink’s data is still off-chain, requiring users to verify their source separately.

7. The Future of Python

Python Network’s roadmap highlights significant expansion of cross-chain compatibility, supporting Nearand over 50 blockchains including Arbitrum to expand their influence in DeFi.

Python enhances interoperability of DeFi applications by enabling seamless, permissionless data feeds across chains.

Future plans include expanding the coverage of assets outside of cryptocurrencies, integrating commodities, stocks and forex into them, and building them into multi-functional oracles for digital and traditional finance.

Technology improvements are also underway to reduce latency by 20% and increase the number of data providers per feed, thereby enhancing data reliability for high-frequency trading and derivatives platforms.

Finally, Python’s community-driven DAO model will allow stakeholders to guide their strategic direction on issues such as expenses and data integrity.

As the industry matures, these initiatives position Python as the fundamental oracle for secure, real-time data solutions across DeFi and Web3.