Assisted buy

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Problem Statement

This project combines Chronicle Oracle's reliable price feeds with Cartesi's computational capabilities to create a sophisticated price prediction system for digital assets. The integration enables complex computations off-chain while maintaining blockchain security and decentralization. Chronicle Oracle IntegrationReal-Time Price FeedsDirect integration with Chronicle's ETH/USD, BTC/USD, and other major pairs Sub-second price updates for rapid market response Tamper-proof price data verified by Chronicle's validator network Self-kissing mechanism for immediate oracle accessData Validation & AggregationMultiple price sources for enhanced accuracy Outlier detection and filtering Volume-weighted average price (VWAP) calculations Historical price data storage for trend analysisCartesi Machine Learning PipelineOff-Chain ComputationAdvanced ML models running in Cartesi's Linux runtime Support for popular data science libraries (NumPy, Pandas, scikit-learn) Complex technical analysis indicators Neural network-based price predictionPrediction FeaturesMulti-timeframe analysis (short-term, medium-term, long-term) Confidence scoring based on market conditions Volatility forecasting Risk assessment metricsSmart Contract ArchitectureMain ComponentsOracle Interface ContractHandles Chronicle Oracle data ingestion Price feed validation and updates Emergency pause mechanismsPrediction Engine ContractInterfaces with Cartesi Rollups Manages prediction submissions Handles reward distributionUser Interaction ContractStake management Prediction submissions Performance tracking

Solution

This project combines Chronicle Oracle's reliable price feeds with Cartesi's computational capabilities to create a sophisticated price prediction system for digital assets. The integration enables complex computations off-chain while maintaining blockchain security and decentralization. Chronicle Oracle IntegrationReal-Time Price FeedsDirect integration with Chronicle's ETH/USD, BTC/USD, and other major pairs Sub-second price updates for rapid market response Tamper-proof price data verified by Chronicle's validator network Self-kissing mechanism for immediate oracle accessData Validation & AggregationMultiple price sources for enhanced accuracy Outlier detection and filtering Volume-weighted average price (VWAP) calculations Historical price data storage for trend analysisCartesi Machine Learning PipelineOff-Chain ComputationAdvanced ML models running in Cartesi's Linux runtime Support for popular data science libraries (NumPy, Pandas, scikit-learn) Complex technical analysis indicators Neural network-based price predictionPrediction FeaturesMulti-timeframe analysis (short-term, medium-term, long-term) Confidence scoring based on market conditions Volatility forecasting Risk assessment metricsSmart Contract ArchitectureMain ComponentsOracle Interface ContractHandles Chronicle Oracle data ingestion Price feed validation and updates Emergency pause mechanismsPrediction Engine ContractInterfaces with Cartesi Rollups Manages prediction submissions Handles reward distributionUser Interaction ContractStake management Prediction submissions Performance tracking