Smart Portfolios

Screenshots

Problem Statement

🚀 Smart PortfoliosProblem: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.Example:Our MVP lets you invest in a portfolio ofETH + WBTC, rebalanced daily to maintain an optimal ratio. We outperformS&P's CryptoIndex! 🚀Key Features:🎯Defined StrategiesEach product aligns with a specific investment thesis, offering options for different risk profiles and objectives.🛡️Risk ManagementOptimized for financial metrics such as Value at Risk (VaR) or Expected Shortfall (ES), protecting investors' capital.⚙️AutomationOperates with minimal human intervention, reducing costs and errors.🤖Smart RebalancingUses Machine Learning (ML) algorithms based on on-chain data to adjust the portfolio and maximize returns.🔍TransparencyFully auditable on the blockchain, providing trust and security to investors.Value Proposition:🧑‍💻Ease of UseSimplified access to sophisticated investment strategies.💰ProfitabilityOptimized performance adjusted to risk.🔒TransparencyTotal visibility and control over investments.🛡️SecurityCapital protection through risk management and blockchain technology.

Solution

🏦 Decentralized Crypto Investment PlatformA 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.⚙️ Core Components and Functionality:🧠Machine Learning ModelA 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 areinforcement learning modeltrained on thousands of data points.Model parameters can be adjusted and optimized based on backtesting and real-time performance data. Example parameters includefund duration, assets to hold, rebalancing frequency, etc.🐍Python ScriptingPython scripts are used to provide the real-timeoptimal portfolio composition, using live data generated by the ML model.These scripts handle dynamic changes to the portfolio as new data comes in.🔗Core Smart ContractsFetch real-time market data fromweb3 oracles.Interact with open-market smart contracts (e.g.,Uniswap) to execute trades and rebalance portfolios.Calculateportfolio risk metricsand performance indicators.Votingon new fund proposals via smart contracts.Generate logs and reports for users, storing them onTablelandto simplify auditing.🛰️Web3 OraclesUsingChainlinkcustom 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 ensuredata redundancyand reliability.🔄Portfolio RebalancingThe 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 specificmarket events.🗃️Tableland IntegrationAwriteToTablelandfunction handles writing data to your specific Tableland table.In the main POST function, after receiving the prediction from the Flask API,writeToTablelandstores the data inTableland.After successful storage, we generate a hash and update theMLPredictionOraclecontract.Averifyfunction is deployed on-chain to verify the current fund state with the data stored in Tableland.