Technical Architecture

π Components
1. Solana Data Layer
Source: Pulls real-time wallet activity and trade history from Solana blockchain.
Data Types:
Wallet balances
Trade events (swaps, adds, removes)
Purpose: Serve as the raw material for journaling and pattern analysis.
2. Context Engine
Preprocessing:
Cleans and normalizes incoming Solana data (timestamps, token metadata, trade direction, volumes).
Context Assembly:
Bundles raw trade data into structured units called SCoBs (Structured Context Blocks).
Examples: Trade clusters by day, PnL aggregation, liquidity conditions at time of entry/exit.
Aggregation:
Summarizes historical data across multiple trades or time periods.
Journaling Logic:
Assigns metadata like strategy tags ("scalp", "momentum", "breakout") automatically based on trade patterns.
β Result: Creates clean, minimal, AI-readable context for each user trade session.
3. AI Models
Large Language Models (LLMs):
Fine-tuned prompts using trade context for summarization, pattern recognition, and user Q&A.
Pattern Recognition:
Machine learning layers (optional) for clustering similar successful setups.
Analytics:
Simple statistical models for ROI tracking, win rate estimation, strategy comparisons.
β Result: Personalized, dynamic trading performance insights without manual analysis.
4. Database
Storage:
Journaling entries, pattern labels, PnL histories, model outputs.
Logging:
Historical records of user interactions, queries, and insights generated for replayability.
β Result: Full user history available for retrieval, replay, and reanalysis.
5. Web Application
Frontend:
User dashboard for OptiLog (trade history, summaries, patterns).
Marketplace interface for subscribing to trader journals.
API:
Backend services handling:
Wallet connection
Trade data sync
Query processing to AI models
Auth:
Wallet-based login (no password storage) to ensure security and decentralization.
β Result: Seamless UX between live Solana activity and personalized AI feedback.
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