check_active_plans
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.
LABSCO SUMMARY
A trading agent has to answer for its own record before it sizes the next trade.
What it takes off you is the discipline: every trade is stored with the reasoning that produced it, recall leans on how trades turned out rather than similarity alone, and a pre-trade check reads sample size, regime experience, streak and drawdown before size is decided. The audit chain sits apart from all of that — records are hashed, chained, and summarised into one daily root, so the decision trail can be verified instead of asserted.
What it is
A memory layer for a trading agent. Trades go in with their context and a written reflection; recall is weighted by outcome, context similarity, recency and confidence; and a separate set of calls discovers, backtests and eliminates candidate patterns. It records decisions rather than making them — it never places a trade or touches an exchange key.
What you get
- A trade stored with entry, exit, PnL, R multiple, max adverse excursion, confidence, regime and a written reflection
- Recall ranked by outcome quality, context similarity, recency and confidence, returned with the score breakdown
- Per-strategy performance aggregates, and behavioural stats: hold times, disposition ratio, lot-size variance and a Kelly comparison
- The agent's current state — confidence, risk appetite, drawdown, win and loss streaks, and a recommended action
- Prospective plans that stay armed until their trigger matches, they expire, or they are cancelled
- OHLCV bars from Binance, LLM-generated candidate patterns, a vectorized backtest reporting Sharpe ratio, win rate, trade count, max drawdown and PnL, and a multi-generation loop that validates survivors out of sample
- Strategy validation over four statistical tests — Deflated Sharpe Ratio, walk-forward, regime and CPCV — from a trade log or a daily returns CSV
- A pre-trade check on whether sample size, memory quality, regime experience, streak and drawdown justify full size, and a Decision Quality Score across five factors
- Trading Decision Records exported for audit, a SHA256 check on one record, a chain walk that reports the first break, and a daily Merkle root
Requirements
uv, to run uvx tradememory-protocol. No exchange credentials: it stores what your agent passes it and nothing more. Trade data stays on the machine — the one outbound call is RFC 3161 timestamping of the daily audit root, a 32-byte hash, which TRADEMEMORY_TSA=off disables. The evolution calls are the exception: they fetch bars from Binance and use Claude to generate candidate patterns, and their run log is held in memory only, so it resets when the server restarts.
Setup effort
One command — uvx tradememory-protocol
USE CASES
01
Logging a trade together with the thinking behind it
Price, PnL and the reflection go in as one record, and the trade can be reopened later with the full context that produced it rather than the numbers alone.
02
Deciding whether you have earned the right to size up
Check whether sample size, regime experience, streak and drawdown support full size, score the decision process itself, and read the agent's current risk state.
03
Finding out which strategies are actually working
Aggregate results per strategy, then read the behaviour underneath them — how long positions are held, whether winners are cut early, how much sizing varies against Kelly.
04
Testing a pattern before it becomes a strategy
Pull the bars, generate candidates from them, backtest each one, then run the loop that validates survivors out of sample and discards the rest.
05
Arming a decision you do not want to make in the moment
Write the plan with its trigger and reasoning now, and check the armed plans against the market context later.
06
Proving the decision trail has not been edited
Export the records, re-hash one of them, walk the chain for the first break, and check the day's Merkle root.
20 tools
Counted once with only startup setup. Other versions, modes or optional credentials may expose a different list.
check_trade_legitimacy
Check if the agent has sufficient data and confidence to trade.
Call this before making any trade decision. Evaluates sample size, memory quality, regime experience, streak state, and drawdown to determine whether the agent has earned the right to trade at full size.
compute_dqs
Compute Decision Quality Score before executing a trade.
Evaluates the quality of the decision *process* (not outcome) across 5 factors: regime match, position sizing vs Kelly, process adherence (OWM similarity), risk state, and historical pattern.
create_trading_plan
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.
evolution_discover_patterns
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.
evolution_evolve_strategy
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 graveyard.
evolution_fetch_market_data
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.
evolution_get_log
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).
evolution_run_backtest
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.
export_audit_trail
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.
get_agent_state
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.
get_behavioral_analysis
Get behavioral analysis from procedural memory.
Returns aggregate trading behavior stats: hold times, disposition ratio, lot sizing variance, and Kelly criterion comparison.
get_daily_root
Get (or rebuild) the daily Merkle root for a UTC date.
The Merkle root summarises every audit_chain entry whose chained_at falls inside the UTC day. Verifying this single 32-byte root proves the integrity of every TDR for that day without re-walking each one.
get_strategy_performance
Get aggregate performance stats per strategy.
Use this to evaluate which strategies are working and which need adjustment.
get_trade_reflection
Get the full context and reflection for a specific trade.
Use this to deep-dive into a particular trade's reasoning and lessons.
recall_memories
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.
remember_trade
Store a trade into OWM multi-layer memory with automatic updates.
Writes to episodic memory and automatically updates semantic (Bayesian), procedural (running averages + hold time + Kelly), and affective (EWMA confidence/streaks). Also writes to trade_records for backward compatibility.
validate_strategy
Validate a trading strategy using statistical tests (DSR + Walk-Forward + Regime + CPCV).
For educational and research purposes only. Not financial advice.
Upload a trade log CSV (QuantConnect format) or daily returns CSV. The tool runs four statistical tests:
- Deflated Sharpe Ratio (DSR) — detects overfitting from multiple testing
- Walk-Forward Validation — checks out-of-sample consistency
- Regime Analysis — performance across bull/bear/crisis markets
- CPCV — cross-validated Sharpe stability across time periods
verify_audit_chain
Verify the integrity of the audit chain.
Walks the chain from from_seq (default: 1, the genesis record) to to_seq (default: latest), checking that every record's prev_hash matches the previous record's data_hash, and that each data_hash equals SHA256(prev_hash || content_hash).
Returns a dict with verified, checked_count, first_break_at, reason. A first_break_at of None with verified=True means the chain is intact across the verified range.
verify_audit_hash
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.
BEFORE YOU INSTALL
What to know first
Tools you get
20 tools when we connected.
Requires
uvx on PATH
Where it runs
Both documented clients run the server on your machine.
CONTEXT COST
Moderate Standing cost
Rated by the size of the 20 tool definitions that ride along on every turn, not by how many there are
INSTALLATION
Connect your client
Server setup
TERMINAL
uvx tradememory-protocolMCP Clients
Claude Desktop
CLAUDE DESKTOP · JSON
{
"mcpServers": {
"tradememory": {
"command": "uvx",
"args": [
"tradememory-protocol"
]
}
}
}Claude Code
CLAUDE CODE · TERMINAL
claude mcp add tradememory -- uvx tradememory-protocol
