How to Develop a Crypto Data Science Marketplace Like Numerai

The blend of AI and cryptocurrency is shaking up industries like finance, healthcare, and cybersecurity in some pretty exciting ways. As we move towards more decentralized models, it’s opening doors for people and businesses to not just secure their data but actually profit from it. Imagine a system where your insights are valuable, and you can control how they’re used, without worrying about privacy or security. 

That’s what’s happening now with the rise of decentralized data marketplaces. It’s about creating a trustworthy, global ecosystem where talent and innovation can thrive, and everyone gets a fair shot at benefiting from the data-driven world we’re building.

Over the last decade, we’ve helped numerous clients build decentralized platforms that empower data scientists and enterprises to collaborate effectively. IdeaUsher has worked with businesses to create marketplaces where data scientists can earn crypto rewards for their predictions while businesses benefit from high-quality, diverse models. This blog serves as our way of passing on valuable information about how you can start developing your own platform that brings transparency, scalability, and trust to crypto data science.

Key Market Takeaways for Crypto Data Science Marketplaces

According to GrandViewResearch, the data marketplace sector is expanding rapidly, with the global market size set to grow from $1.49 billion in 2024 to $5.73 billion by 2030. This growth is driven by the increasing need for data-driven tools across industries, especially in areas like cryptocurrency. Crypto data science marketplaces are a key part of this trend, offering platforms where users can buy, sell, and analyze valuable blockchain-related data, such as trading behaviors and market sentiment.

Source: GrandViewResearch

As interest in crypto data grows, several companies are emerging to meet the demand. Platforms like Kaiko, Amberdata, CryptoQuant, and CoinAPI provide real-time data and advanced analytics, catering to institutional clients, researchers, and developers. These platforms offer essential insights into market movements and blockchain activity, helping to bridge the gap between traditional finance and the decentralized Web3 world.

Partnerships are also playing a major role in the growth of crypto data marketplaces. A notable example is the collaboration between Thomson Reuters and CryptoCompare, which integrates detailed crypto market data into the Eikon platform. This partnership gives traders access to up-to-the-minute information on thousands of tokens, fostering greater transparency and more informed decision-making in the crypto space.

What is a Crypto Data Science Marketplace?

A crypto data science marketplace is a decentralized platform where data scientists contribute machine learning models and are rewarded with cryptocurrency. It integrates blockchain technology to ensure that submissions are transparent, the incentives are fair, and payments are automated without the need for intermediaries.

Key Components 

1. Decentralized Model Submissions – Data scientists around the world can submit predictive models to the marketplace without the need for a central authority.
2. Tokenized Incentives – Contributors are rewarded in cryptocurrency, often based on the quality and performance of their models.
3. Privacy-Preserving Data – Sensitive data is protected using encryption or obfuscation methods to maintain privacy while ensuring the usefulness of the data for training models.
4. Smart Contract Governance – Smart contracts automate various processes, such as payouts, staking mechanisms, and penalties for subpar submissions.

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Comparison with Traditional Model Marketplaces:

Feature	Traditional Marketplaces	Crypto Data Science Marketplaces
Monetization	Prize money, freelance gigs	Crypto rewards, staking yields
Data Privacy	Raw datasets (which can be risky, especially in finance)	Encrypted/obfuscated data
Governance	Centralized (platform-controlled)	Decentralized (powered by smart contracts)
Incentives	One-time competition rewards	Continuous earnings through token rewards
Ownership	The platform owns winning models	Contributors retain IP rights

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Role of Blockchain, Tokens, and Smart Contracts:

• Blockchain ensures that all submissions are immutable, meaning no one can alter or cheat the system. This provides a trustworthy environment for both contributors and consumers of machine learning models.
• Tokens (like NMR, used by Numerai) are used for staking. This means contributors put up a financial stake, showing they are committed to their models’ quality. Poor models or predictions can lose their staked tokens.
• Smart Contracts automatically handle processes like payouts, penalties for poor models, and even the aggregation of multiple models’ predictions. They ensure that the system operates smoothly and fairly without needing intermediaries.

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Overview of Numerai Crypto Data Science Marketplace

Numerai is a blockchain-powered hedge fund that taps into AI predictions from data scientists. They submit machine learning models on encrypted financial data to prevent reverse engineering. The platform uses the NMR token for staking, ensuring participants are committed to their models’ success.

Community Structure and Incentives:

• Weekly Tournaments: Data scientists compete weekly based on the accuracy of their models.
• Leaderboard Rankings: Contributors are ranked based on the quality of their predictions, earning reputation and additional rewards.
• Long-Term Staking: Data scientists can lock up their NMR tokens for months, with longer staked tokens yielding greater rewards as a commitment to long-term contributions.

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Why Are Crypto Data Science Marketplaces Gaining Traction?

Crypto data science marketplaces are gaining traction because they open up access to global talent, letting anyone contribute from anywhere. They also offer trustless, transparent reward systems through smart contracts, which keeps things fair. 

1. Access to Global Data Science Talent

Crypto-native marketplaces eliminate geographical boundaries, allowing anyone with an internet connection to contribute. This opens doors for niche experts, like quants and crypto traders, who may not be easily accessible through traditional hiring methods.

2. Transparent  Incentive Mechanisms

With smart contracts in place, there are no middlemen involved in the distribution of rewards. Staking mechanisms ensure that only serious participants, with a real stake in the outcome, contribute to the marketplace.

3. Immutable Performance Tracking

All submissions and payouts are stored on the blockchain, providing an immutable and transparent record. This prevents issues like manipulation or favoritism that can occur in traditional hedge funds or marketplaces.

4. Programmable Economic Logic with Tokens

Crypto marketplaces offer dynamic rewards that adjust in real-time based on demand. Additionally, mechanisms like token burns (e.g., NMR) reduce the supply, potentially increasing the value for holders, while token holders also have governance power over platform upgrades.

How Does the Numerai Crypto Data Science Marketplace Work?

Numerai operates by using a unique combination of data obfuscation, statistical structure preservation, and decentralized incentives to create a platform where data scientists can build predictive models based on financial data, while maintaining privacy and avoiding reverse engineering of sensitive market information.

Here’s how the key components work:

1. Obfuscated Data with Predictive Power

Numerai needs to provide financial data for data scientists to build predictive models, but without exposing sensitive market patterns that could be reverse-engineered.

Solution: Numerai uses structured obfuscation, this involves transforming the raw data in ways that hide identifiable patterns while retaining essential statistical relationships (such as correlations between assets).

Statistical Structure Preservation

Non-invertible transformations, such as Principal Component Analysis (PCA), Z-Scoring, and Time Slicing, are applied to ensure data remains useful for prediction, while hiding the original signals.

• PCA reduces dimensionality, preserving variance.
• Z-Scoring normalizes features to prevent bias toward large-magnitude values.
• Time Slicing breaks the data into non-contiguous time segments, preventing overfitting to temporal patterns.

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2. Avoiding Overfitting

Out-of-time testing makes sure models are evaluated on future data, so they don’t overfit to the past. Only models that boost the overall ensemble are rewarded, pushing for better contributions. Poor-performing models lose NMR tokens, keeping everyone motivated to bring their A-game.

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3. Meta-Model Aggregation

Uncorrelated Alpha Signals: Numerai aggregates multiple, diverse, and uncorrelated signals (models) to create a meta-model that is more robust and stable than any single model.

Likely Techniques Used:

• Bayesian Model Averaging: Weighs models based on posterior probabilities, adapting to changing market conditions.
• Weighted Voting: Higher stakes give models more influence, with adjustments for the confidence of the contributor.
• Stacking/Ensemble Learning: A meta-learner combines base model outputs to optimize the overall ensemble.

Importance of MMC (Meta Model Contribution) vs. Raw Correlation: Numerai focuses on MMC, which rewards models that improve the overall ensemble, rather than just models that predict well on their own (raw correlation). This ensures diversity and prevents herd behavior.

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4. NMR Staking and the Burn Mechanism

Incentives: Contributors stake NMR tokens when submitting models, ensuring that only confident participants put their models forward.

Burn Mechanism: Poor-performing models result in the burn (loss) of staked NMR tokens. The burn rate is tied to the degree of underperformance, which drives higher-quality submissions.

On-Chain vs. Off-Chain Computations:

• On-Chain: Staking, burns, and payouts are handled transparently using smart contracts.
• Off-Chain: The actual model training and backtesting occur off-chain, due to the high computational cost of these operations.

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5. Advanced Privacy Considerations

Federated Learning: While not currently used, federated learning is considered for real-time model updates. However, it has challenges like latency issues and workflow friction, which prevent it from replacing the current obfuscation method.

Tradeoffs: SMC/Federated Learning vs. Data Obfuscation

Method	Pros	Cons
Data Obfuscation	Fast, scalable	Theoretical re-identification risk
Secure Multi-Party Computation (SMC)	Stronger privacy	High computational overhead
Federated Learning	No raw data sharing	Slow, complex coordination

Would Federated Learning Replace Obfuscation?

Yes, but only for specific use cases,

If Numerai moves to real-time model updates, federated learning could be a good fit for privacy. But it would require significant changes to how contributors work, as they’d need to adapt to a distributed training process. It’s a big shift that could impact the overall user experience.

Right now, the current obfuscation method strikes a solid balance between privacy and usability. It keeps things simple for contributors while still protecting sensitive data. There’s no rush to change it since it’s working well as is.

Benefits of a Crypto Data Science Marketplace for Businesses

Launching a crypto data science marketplace lets businesses tap into a global pool of data scientists, cutting costs on in-house teams. It leverages blockchain for transparent, trustless transactions and aligns incentives through token-based economics.

Technical Advantages

• Trustless Data/Model Exchanges: Blockchain-based smart contracts ensure transparent transactions, eliminating middlemen and ensuring automatic payments based on performance metrics.
• Global Model Sourcing with Enforced Performance: Access to a global talent pool, with staking mechanisms ensuring high-quality models. Poor performers are penalized to maintain platform integrity.
• Reduced Reliance on Internal Data Science Teams
  Crowdsource AI models, continuously refreshing predictive power without the need for expensive in-house teams. Scale capabilities efficiently.
• Auto-Curation of Best Models: AI-driven meta-models automatically aggregate top submissions, and dynamic leaderboards reward the best contributors, driving continuous improvement.

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Business Advantages

• Competitive Edge via Crowd-Sourced Signal Discovery: Leverage diverse global perspectives to uncover hidden market signals and adapt quickly to market changes, outperforming traditional funds.
• Stakeholder Alignment via Token-Based Economics: Token incentives align contributors’ success with business goals, while decentralized governance and long-term reward systems boost engagement.
• Enhanced Market Perception (Web3-Native AI Infrastructure): Position your business as an innovator by integrating AI and blockchain, attracting strategic partnerships, and enhancing your market valuation.
• Lower Infrastructure Costs (via Decentralized Compute Models): Reduce cloud computing expenses as contributors handle compute costs. Scalable, decentralized architecture removes the need for massive internal infrastructure.

How to Develop a Crypto Data Science Marketplace Like Numerai?

We specialize in creating cutting-edge crypto data science marketplaces like Numerai, using blockchain, AI, and DeFi to build secure, scalable platforms. Our goal is to help businesses connect with a global pool of data scientists while ensuring privacy and top-notch performance. Here’s a simple breakdown of how we make that happen, step by step:

1. Define Use Case & Data Domain

First, we work closely with you to define the specific use case and data domain of your marketplace. Whether it’s financial forecasting, climate prediction, or fraud detection, we ensure that the marketplace is tailored to your needs.

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2. Create Obfuscation & Privacy Strategy

Next, we design a robust privacy strategy for the platform. Depending on your requirements, we implement feature transformation, statistical obfuscation, or federated learning. Our focus is to ensure that the platform maintains high-quality, generalizable predictions while safeguarding sensitive data.

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3. Develop Smart Contract Infrastructure

We then build the smart contract infrastructure that powers the marketplace. This includes developing the token staking logic, automating payouts based on model performance, and implementing slashing for underperforming models.

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4. Build Submission & Pipeline

Our team designs an intuitive submission and evaluation pipeline. Contributors can easily upload their models through a user-friendly frontend, while the backend houses an evaluation engine that assesses model performance based on your specific criteria.

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5. Implement Meta Model

To maximize the marketplace’s effectiveness, we implement sophisticated meta-model aggregation logic. This system combines individual model outputs into a unified prediction, utilizing techniques like ensemble learning, dynamic reweighting, and MMC contribution algorithms.

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6. Community & Incentive

Finally, we design a comprehensive community and incentive system. By creating a clear tokenomics structure, we align contributor incentives with business success, and foster ongoing engagement through educational content, challenges, and community interaction channels like Discord.

Common Challenges for a Crypto Data Science Marketplace

After working with numerous clients on building Crypto Data Science Marketplaces, we’ve encountered several challenges that often arise. We’ve learned the best ways to address these issues and ensure smooth, efficient operation. Here’s how we tackle the common problems:

1. Overfitting to Obfuscated Data

Data obfuscation helps protect privacy but can cause models to overfit to noise, missing the true signals in the market. Since the data is transformed, it becomes harder for models to detect meaningful trends, which can lead to overfitting. As a result, models may learn irrelevant patterns or become overly sensitive to small fluctuations.

Solutions:

• Frequent Leaderboard Rotation: We reset rankings regularly to force models to generalize, preventing over-optimization.
• Validation Using Unseen Slices: We test models on future data windows, ensuring that they perform well beyond just the training period. 
• Penalizing High Volatility in Predictions: Models with erratic outputs are downgraded or penalized, encouraging stability and reliability in predictions.

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2. Gaming or Sybil Attacks

Some bad actors might try to game the system by submitting duplicate models or colluding to manipulate rankings. This could result in unfair advantages, with malicious participants skewing the results to maximize their rewards. 

Solutions:

• Stake-Based Gatekeeping: We require a minimum stake in native tokens (like NMR) to participate, making it more expensive for malicious actors to attack.
• Anti-Collusion Checks: We use on-chain analysis to detect suspicious patterns, such as multiple models coming from the same IP address.
• Address Whitelisting or Proof-of-Contribution: For high-stakes cases, we implement KYC or require proof of prior contributions (like a GitHub portfolio) to verify contributors’ legitimacy.

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3. Low-Quality Submissions

Without the right incentives, marketplaces can become flooded with low-effort or random models. This can decrease the overall quality of predictions and make it difficult for high-performing models to stand out. It also increases the chances of “lucky guesses” contributing to high rankings, undermining the credibility of the platform.

Solutions:

• Quality Gating Mechanisms: We reject models that don’t meet a baseline performance threshold, ensuring that only high-quality models are submitted.
• Minimum Score Thresholds: Only reward models that outperform the benchmark, similar to Numerai’s Meta Model Contribution (MMC) filter.
• Gradual Rewards Based on Consistent Performance: Instead of paying for one-time wins, we reward sustained performance, incentivizing long-term improvement.

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4. Scalability & Gas Costs

Running thousands of models on-chain can get very expensive, especially with high Ethereum gas fees. The computational costs can quickly add up, making the platform unsustainable at scale. 

Solutions:

• Off-Chain Computation of Models: We train and evaluate models off-chain, only using the blockchain for final rewards and results, minimizing gas fees.
• zkProof-Based Validation (Future-Ready): We use zero-knowledge proofs to verify model accuracy without fully executing everything on-chain, which helps save on costs.
• Batch Payouts and Model Evaluations: We process rewards on a weekly or monthly basis instead of per transaction, significantly reducing gas fees and improving scalability.

Tools & APIs Needed to Build a Crypto Data Science Marketplace

To build a Crypto Data Science Marketplace, several key tools, APIs, and frameworks are essential to support blockchain interactions, model submissions, evaluation, data privacy, and community engagement. Here’s a breakdown of the core components:

1. Blockchain & Smart Contracts

Core Blockchain Infrastructure

For smart contract deployment, Ethereum, Arbitrum, or Optimism are solid choices, with Arbitrum and Optimism offering lower gas fees. If you need higher transaction throughput, Polygon or Solana can be great alternatives. These options help ensure the marketplace scales efficiently as it grows.

Smart Contract Development

Solidity is the go-to language for writing smart contracts, handling things like staking and rewards. Chainlink Oracles help bring off-chain data on-chain for automatic payouts based on external performance. OpenZeppelin offers secure, pre-tested templates for creating ERC-20 tokens and ERC-721 governance tokens.

Key Smart Contract Functions

• Staking & Slashing: Token holders lock up tokens when submitting models, and they may lose some or all of their tokens for poor performance or malicious activity.
• Automated Payouts: Smart contracts automatically distribute rewards to top-performing models after evaluation.
• Governance Voting: Token holders can vote on platform upgrades or governance decisions, ensuring decentralization and community participation.

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2. Model Submission Infrastructure

Backend APIs for Model Handling

Python with FastAPI or Flask helps you build APIs for model submission and scoring, making it easy to interact with smart contracts. Docker Sandboxing ensures models are securely isolated, protecting against malicious code. Using Celery and Redis helps manage model evaluation tasks efficiently, keeping everything scalable.

Data Pipeline Management

Apache Kafka helps stream model submissions efficiently, acting as a message broker for high throughput. Kubernetes automatically scales compute resources, ensuring smooth performance even during peak times. This combination ensures your system stays fast and responsive as the marketplace grows.

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3. Data Obfuscation & Evaluation Tools

Data Privacy Layer

NumPy, scikit-learn, and pandas are key for preparing and transforming data before it’s evaluated. Custom obfuscation methods like PCA, Fourier transforms, or GANs can help protect sensitive data by masking it. These tools make sure data remains secure while still being useful for model submissions.

Model Validation

• PyCaret, XGBoost, CatBoost: Libraries for benchmarking model performance and evaluating their effectiveness in the marketplace.
• MLflow: A tool to track experiments, monitor model versions, and evaluate metrics, ensuring transparency and reproducibility of model submissions.

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4. Meta Model Aggregation

Ensemble Learning Frameworks

• LightGBM, XGBoost: These fast and efficient gradient boosting algorithms are used for stacking models in the marketplace, improving prediction accuracy.
• StackNet or H2O.ai: Advanced meta-modeling libraries to combine multiple models into a stronger final prediction.

Custom MMC (Meta Model Contribution) Calculators

• Weighted Voting: Assign higher weights to models that are staked with more tokens, influencing the final prediction.
• Bayesian Averaging: Dynamically adjust model weights based on uncertainty, allowing for better handling of model discrepancies.

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5. Community & Dev Tooling

Decentralized Storage

IPFS and Arweave are great for storing model metadata securely and immutably, ensuring transparency. Filecoin is perfect for hosting large datasets, offering a decentralized and cost-effective solution. Together, these tools help keep everything secure and accessible in your marketplace.

Governance & Engagement

• Snapshot.org: A decentralized governance platform that allows token holders to participate in off-chain voting on protocol upgrades and decisions.
• Discord Bots: Automate interactions with the community, such as updating leaderboards, sending notifications, and engaging with users.
• Discourse or Common Room: Platforms for creating community forums to foster discussions, share ideas, and collaborate on improving the marketplace.

Analytics & Monitoring

• Dune Analytics: This platform tracks on-chain data, providing insights into marketplace metrics such as model submissions, rewards, and token burns.
• Grafana + Prometheus: Tools for monitoring the health of your servers, the performance of model evaluations, and tracking system metrics in real time.

Use Case Example: A Crypto-Based ESG Prediction Marketplace

A leading hedge fund came to us with a big challenge: Traditional ESG data is too slow and centralized, plus it doesn’t cover Web3-native companies. They needed real-time sentiment signals from crypto communities to make smarter moves in emerging tokenized assets. So, we stepped in to solve that problem for them.

Key Pain Points:

• No existing datasets tracking ESG sentiment in DAOs, DeFi protocols, or NFT projects.
• Traditional ESG data providers (like Bloomberg) can’t analyze blockchain forums, Discord, or Snapshot votes.
• Social data is noisy, risky, and can be easily manipulated, leading to the potential for overfitting.

The Solution: A Decentralized ESG Signal Marketplace

We built and deployed a crypto data science platform where:

• Data scientists submit ESG prediction models trained on obfuscated Web3 sentiment data.
• Hedge funds access crowd-sourced alpha signals via a Meta Model.
• Contributors earn platform-native tokens for accurate predictions and face penalties for poor performance.

Data Obfuscation Layer

Raw data from Discord messages, governance votes, and Twitter sentiment is hashed and transformed using PCA (Principal Component Analysis). This preserves key statistical relationships while masking identifiable text, ensuring privacy and compliance.

Model Submission & Staking

Data scientists stake platform tokens to submit ESG prediction models. These models are securely evaluated within Dockerized sandboxes to avoid exploits or security issues.

Meta Model Aggregation

We take the top 100 uncorrelated signals and weigh them based on their predictive accuracy, using backtests with past ESG-linked price movements. We also factor in each model’s “Unique ESG Contribution” (UEC), which measures how much it adds diversity to the overall signal set. This ensures we get the most reliable and diverse predictions.

Rewards & Penalties

Accurate models earn token payouts from a shared reward pool, while poorly performing models lose between 20-50% of their staked tokens (which are burned to reduce the token supply). Chainlink Keepers automate the payouts every week.

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Results After 6 Months:

Metric	Result
Accuracy Boost	42% improvement compared to traditional ESG scores in backtests
Data Scientist Contributions	1,200+ data scientists globally contributing
Tokens Burned	$2.1M equivalent burned from low-quality models, creating deflationary pressure
Top Models Usage	Top 3 models used by 7 hedge funds for ESG-weighted crypto portfolios

This platform successfully met the hedge fund’s needs, providing real-time, decentralized ESG signals while addressing key pain points like data quality and security.

Conclusion

Crypto data science marketplaces, like Numerai, are paving the way for the future of open, trustless predictive modeling. By combining token incentives, secure data obfuscation, ensemble learning, and smart contract logic, these platforms foster both innovation and integrity. Businesses can tap into a global pool of AI talent, ensuring scalability, security, and profitability. At Idea Usher, we help enterprises seamlessly integrate these Web3-native data science ecosystems, backed by robust tech stacks, tokenomics, and privacy infrastructure.

Looking to Develop a Crypto Data Science Marketplace Like Numerai?

Unlock the potential of decentralized AI and blockchain by building your own crypto data science marketplace. At Idea Usher, we blend advanced machine learning with Web3 innovation to create platforms where:

• Data scientists compete to submit high-value predictive models
• Contributors earn crypto rewards for accurate insights
• Enterprises tap into crowd-sourced intelligence at scale

Why Choose Us?

• 500,000+ hours of coding expertise – Our ex-MAANG/FAANG engineers deliver robust, scalable architectures.
• Full-stack Web3/AI development – From smart contracts to meta-model aggregation, we handle it all.
• Proven track record – We’ve built everything from DeFi prediction markets to tokenized data ecosystems.

Your Vision + Our Execution = The Next Generation of AI-Powered Finance!

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