Tradememory Protocol

Store a trade decision with full context into memory. Call this after executing a trade to build your memory bank. Include market_context and reflection for better recall later.

Find past trades with similar market context. Use this before making a trade to learn from past experience. Returns trades with their reflections and outcomes. Uses OWM scoring when episodic memories exist, falls back to keyword matching.

Get aggregate performance stats per strategy. Use this to evaluate which strategies are working and which need adjustment.

Get the full context and reflection for a specific trade. Use this to deep-dive into a particular trade's reasoning and lessons.

Store a trade into OWM multi-layer memory with automatic updates. Writes to episodic memory and automatically updates semantic (Bayesian), procedural (running averages), and affective (EWMA confidence/streaks). Also writes to trade_records for backward compatibility.

Recall memories using OWM outcome-weighted scoring. Queries episodic and semantic memories, scores them by outcome quality, context similarity, recency, confidence, and affective modulation. Returns ranked memories with score breakdown.

Get behavioral analysis from procedural memory. Returns aggregate trading behavior stats: hold times, disposition ratio, lot sizing variance, and Kelly criterion comparison.

Get the current agent affective state (confidence, risk, drawdown). Returns confidence level, risk appetite, drawdown percentage, win/loss streaks, equity tracking, and a recommended action based on current drawdown severity.

No parameters. Call it with no arguments.

Create a prospective trading plan that activates when conditions are met. Stores a rule-based plan in prospective memory. The plan stays active until triggered, expired, or manually cancelled.

Check active trading plans against current market context. Queries all active prospective plans, expires any past their expiry date, and matches remaining plans against the provided context.

Fetch OHLCV market data from Binance for evolution analysis. Downloads historical price bars for backtesting and pattern discovery. Use this before discover_patterns or run_backtest to get data.

Discover trading patterns from market data using LLM analysis. Uses Claude to analyze OHLCV data and generate candidate trading patterns with entry/exit conditions. Each pattern can be backtested afterward.

Backtest a candidate pattern against historical OHLCV data. Takes a pattern dict (from discover_patterns) and runs a vectorized backtest. Returns fitness metrics: Sharpe ratio, win rate, trade count, max drawdown, total PnL.

Run full evolution loop — generate, backtest, select, eliminate. Multi-generation strategy evolution: generates candidate patterns via LLM, backtests on in-sample data, validates survivors on out-of-sample data, eliminates weak hypotheses. Returns graduated strategies and grav...

Get the log of past evolution runs from this session. Returns a list of all evolution runs with their results, including graduated strategies, graveyard, token usage, and backtest counts. Data is in-memory (resets on server restart).

No parameters. Call it with no arguments.

Export Trading Decision Records for audit and compliance review. Provides a complete, tamper-evident record of trading decisions including the memory context (similar trades, beliefs) that informed each decision.

Verify the integrity of a Trading Decision Record. Recomputes the SHA256 data_hash from stored inputs and compares with the hash computed at decision time. A mismatch indicates tampering.