# NOVA for Finance: : Secure DCA Memory with Self-Learning Loops and Confidential Portfolio Analysis Users securely store their Dollar-Cost Averaging (DCA) trading history in a personal, decentralized vault powered by NOVA. By connecting their NEAR wallet, they create a group (e.g., "dca-history"), upload encrypted JSON memos of weekly trades, and dynamically retrieve/update the history. This data is made available to Shade Agents (TEE-based) for confidential portfolio analysis, risk modeling, and self-learning loops (e.g., fine-tuning AI models on historical trades for optimized asset allocation suggestions—without ever exposing plaintext data). ## Use Case Personal Crypto Portfolio Management (e.g., Weekly $100 DCA) In a self-managed DCA strategy, users upload a JSON array of trade memos weekly to their personal NOVA vault (e.g., "dca-history.{user\_account\_id}" for uniqueness). Each entry includes date, operation, provider, portfolio snapshot, and rationale. When performing a weekly update (e.g., on December 31, 2025): * The app retrieves the latest encrypted data from IPFS via NOVA's SDK. * Decrypts it locally using the group key from Shade Agents (TEEs). * Appends a new entry (e.g., {"date": "31-12-2025", "operation": "Swapped 50-worth-usd of NEAR for ETH.", "provider": "", "portfolio": "4.5 NEAR, 1,800 stNEAR, 0.8 SOL, 0.001 BTC, 0.05 ETH", "rationale": "Diversifying into ETH ahead of expected market recovery."}). * Re-uploads the updated JSON as a new encrypted version to IPFS, logging metadata on NEAR for versioning. * For analysis: The app invokes a Shade Agent to process the full history confidentially—e.g., compute risk metrics, simulate scenarios, or fine-tune a lightweight model for future recommendations—outputting verifiable attestations (e.g., "Portfolio risk: Low; Suggested next DCA: 60% NEAR, 40% SOL") without data leaks. This enables automated, privacy-preserving portfolio management, with self-learning loops where agents iteratively improve based on user history, all under user sovereignty. ## How NOVA matches the needs of a personal trading assistant * **User Sovereignty & Privacy**: Personal groups ensure only the user (and temporarily granted agents) access data. Encryption is client-side, with keys in TEEs (Shade Agents) for zero-trust analysis. * **Versioned History with One-Way Updates**: Retrieve and append to JSON without re-encrypting old data; new uploads create immutable versions on IPFS, tracked on NEAR. * **Confidential Analysis via TEEs**: Shade Agents process decrypted data in secure enclaves for portfolio metrics, risk assessment, or AI fine-tuning—e.g., self-learning loops that adapt models to user patterns without exposing history. * **Verifiable & Auditable**: All uploads/logs on NEAR blockchain for tamper-proof auditing; agents provide attestations for transparency. * **Scalability for Weekly Ops**: Low-cost NEAR transactions (\~0.01 NEAR/tx) suit frequent updates; IPFS ensures efficient storage/retrieval. * **AI Agent Integration**: Use NOVA's MCP (Model Context Protocol) to invoke agents for natural language queries like "Analyze my DCA history for Q4 2025" or self-learning (e.g., "Fine-tune allocation model on my trades"). * **Extensible for DeFi Automation**: Integrate with NEAR wallets for seamless wallet connects; extend to trigger DCA ops via intents while logging securely. * **Self-Learning Loops**: Agents can iteratively refine models (e.g., via federated learning proxies) on encrypted history, improving suggestions over time without central servers. This turns NOVA into a backbone for privacy-first personal finance apps, empowering users with confidential AI-driven insights. ## Running the Demos * Set .env file * Rust: `cargo run --bin dca-history`. * JS: `ts-node dca-history.ts`. Expected output: ``` Citizen Account: user.testnet === Step 1: Create or load personal DCA group === Personal group created/loaded: dca-history.user.testnet === Step 2: Retrieve latest DCA history === Latest history retrieved from IPFS: CID = bafybeih... (Decrypted length: 1024 bytes) Current portfolio snapshot: 6.19 NEAR, 1,819 stNEAR, 0.79 SOL, 0.001141 BTC === Step 3: Append new weekly DCA operation === New entry added: {"date": "31-12-2025", "operation": "Swapped 50-worth-usd of NEAR for ETH.", ...} Updated history re-uploaded to IPFS: New CID = bafybeig... === Step 4: Invoke Shade Agent for confidential analysis === Agent attestation: Portfolio risk: Medium; Diversification score: 75%; Suggested next: Increase BTC exposure by 20%. Self-learning loop initiated: Model fine-tuned on 4 weeks of data. === DCA Use Case Demo Complete === Secure, versioned history with privacy-preserving AI analysis. ``` --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://civictech-ou.gitbook.io/nova-docs/use-cases/finance.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.