🚀 Smart Portfolios

Problem: Traditional investors find the cryptocurrency market highly volatile and complex to manage without prior experience in on-chain tools.

Solution: A decentralized platform offering tokenized investment products in crypto assets, managed automatically and transparently through a DAO.

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Example:

Our MVP lets you invest in a portfolio of ETH + WBTC, rebalanced daily to maintain an optimal ratio. We outperform S&P's CryptoIndex! 🚀

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Key Features:

🎯 Defined Strategies

Each product aligns with a specific investment thesis, offering options for different risk profiles and objectives.

🛡️ Risk Management

Optimized for financial metrics such as Value at Risk (VaR) or Expected Shortfall (ES), protecting investors' capital.

⚙️ Automation

Operates with minimal human intervention, reducing costs and errors.

🤖 Smart Rebalancing

Uses Machine Learning (ML) algorithms based on on-chain data to adjust the portfolio and maximize returns.

🔍 Transparency

Fully auditable on the blockchain, providing trust and security to investors.

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Value Proposition:

🧑‍💻 Ease of Use

Simplified access to sophisticated investment strategies.

💰 Profitability

Optimized performance adjusted to risk.

🔒 Transparency

Total visibility and control over investments.

🛡️ Security

Capital protection through risk management and blockchain technology.

🏦 Decentralized Crypto Investment Platform

A decentralized platform that automates the management of crypto investment portfolios. It leverages a combination of machine learning, Python scripting, and web3 oracles to achieve this. Users can propose parameters for new funds, and then ML + data take over the management.

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⚙️ Core Components and Functionality:

🧠 Machine Learning Model

A core component of the platform, this model analyzes on-chain data and market trends to generate investment signals.

• The model type (e.g., regression, classification, reinforcement learning) can be tailored to each investment strategy.
• For our MVP, we use a reinforcement learning model trained on thousands of data points.
• Model parameters can be adjusted and optimized based on backtesting and real-time performance data. Example parameters include fund duration, assets to hold, rebalancing frequency, etc.

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🐍 Python Scripting

• Python scripts are used to provide the real-time optimal portfolio composition, using live data generated by the ML model.
• These scripts handle dynamic changes to the portfolio as new data comes in.

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🔗 Core Smart Contracts

• Fetch real-time market data from web3 oracles.
• Interact with open-market smart contracts (e.g., Uniswap) to execute trades and rebalance portfolios.
• Calculate portfolio risk metrics and performance indicators.
• Voting on new fund proposals via smart contracts.
• Generate logs and reports for users, storing them on Tableland to simplify auditing.

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🛰️ Web3 Oracles

• Using Chainlink custom oracles, we ensure that the ML model receives accurate, untampered data.
• Oracles act as a bridge between the blockchain and external data sources, providing real-time market data critical for generating investment signals.
• The platform integrates with multiple oracles to ensure data redundancy and reliability.

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🔄 Portfolio Rebalancing

• The platform automatically rebalances portfolios based on the signals generated by the ML model.
• Ensures that the portfolio remains aligned with the chosen investment strategy and risk tolerance.
• Rebalancing can be triggered at regular intervals or in response to specific market events.

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🗃️ Tableland Integration

• A writeToTableland function handles writing data to your specific Tableland table.
• In the main POST function, after receiving the prediction from the Flask API, writeToTableland stores the data in Tableland.
• After successful storage, we generate a hash and update the MLPredictionOracle contract.
• A verify function is deployed on-chain to verify the current fund state with the data stored in Tableland.