mnemox-ai/tradememory-protocol
Tradememory Protocol
The Tradememory Protocol is a persistent, multi-layered memory and audit framework designed for artificial intelligence trading agents. It provides a structured architecture for agents to maintain episodic, semantic, and procedural knowledge across trading sessions, ensuring that decision-making is informed by long-term recall and historical context.
The framework distinguishes itself through a combination of cryptographic integrity and cognitive modeling. It employs a tamper-evident logging system that uses hashed sequences to verify the history of trading decisions, alongside an outcome-weighted recall mechanism that prioritizes successful and contextually relevant past experiences. By integrating Bayesian semantic induction and iterative strategy evolution loops, the system allows agents to refine their logic based on performance metrics and market conditions.
Beyond its core memory and audit capabilities, the protocol includes comprehensive tools for strategy management, performance benchmarking, and risk oversight. It supports the simulation of trading patterns, the validation of strategy robustness against historical data, and the application of safety rails that monitor performance to trigger automated halts. The framework also tracks computational affective states to dynamically adjust risk appetite based on recent trading outcomes.
The system provides standardized interfaces for connecting agents to local market data and trading terminals, facilitating the ingestion of historical information and the synchronization of execution records.
Features
- Agent Memory Systems - Provides a persistent, multi-layered memory system for AI trading agents with outcome-weighted recall and cryptographic audit trails.
- Multi-Layer Memory Architectures - Organizes agent knowledge into distinct episodic, semantic, procedural, affective, and prospective layers.
- Agent Memory Systems - Provides a persistent, multi-layered memory architecture for AI agents to maintain episodic, semantic, and procedural knowledge across trading sessions.
- Trading Risk Management - Monitors performance metrics like drawdown and streaks to trigger automated halts when predefined risk thresholds are exceeded.
- Pre-Trade Risk Validators - Applies multi-factor risk gates to evaluate prospective trades against active plans and historical performance metrics before execution.
- Cryptographic Audit Trails - Links trade records into a hashed sequence with daily roots to ensure historical data integrity.
- Agent State Persistence - Persists session-specific data, including risk constraints and active positions, to ensure continuity across restarts.
- AI Trading Strategy Automation - Creates and tracks prospective trading strategies with specific entry, exit, and expiry criteria.
- Agent Tool Integrations - Exposes standardized interfaces for connecting AI agents to local market data and trading terminals via tool-calling protocols.
- Trade Rule Inductions - Aggregates episodic trade data into generalized rules using confidence and uncertainty metrics to refine decision-making.
- Trading Session Synchronizers - Polls external terminal interfaces to ingest closed positions and map individual trades to specific market sessions.
- Trading Strategy Robustness Validation - Validates trading strategy robustness against out-of-sample data and diverse market conditions to prevent overfitting.
- Daily Performance Reflectors - Analyzes daily trading metrics to generate structured summaries that inform future agent behavior.
- Trade Reasoning Loggers - Captures comprehensive trade metadata, execution context, and agent reasoning into persistent storage.
- Outcome-Weighted Recallers - Retrieves past trading experiences using scoring that prioritizes successful outcomes and relevant market context.
- Performance-Weighted Recallers - Retrieves past experiences by scoring memories based on performance, recency, and contextual similarity to current market conditions.
- Market Data Aggregators - Retrieves and normalizes historical financial information from multiple broker sources for backtesting and analysis.
- Iterative Strategy Research Loops - Automates strategy evolution by iteratively discovering patterns and validating them against historical data.
- Evolutionary Strategy Optimizers - Generates and validates candidate trading patterns through iterative evolutionary cycles.
- Trade Decision Loggers - Persists agent reasoning and trade outcomes into a secure storage layer to maintain a verifiable audit trail.
- AI Agent Behavior Monitors - Evaluates agent performance over time to identify strategy decay, behavioral drift, and recurring decision-making errors.
- Agent Performance Benchmarks - Executes multi-agent experiments across diverse strategy sets to compare decision-making efficacy.
- Affective State Trackers - Calculates computational analogues of emotional states based on performance metrics to dynamically adjust agent risk appetite.
Star history
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