TradeMemory is a memory and audit layer for AI trading agents that records trading decisions, outcomes, and context in a tamper-evident history. It is for traders and automated trading systems that need agents to recall past decisions and document their reasoning. Catalogue add-ons provide skills, commands, MCP tools, and related workflow components for using it.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/mnemox-ai/tradememory-protocolWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/mnemox-ai/tradememory-protocol/performance)<a href="https://agentmods.dev/commands/mnemox-ai/tradememory-protocol/performance"><img src="https://agentmods.dev/badge/commands/mnemox-ai/tradememory-protocol/performance/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/mnemox-ai/tradememory-protocol/performance"><img src="https://agentmods.dev/badge/commands/mnemox-ai/tradememory-protocol/performance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00008 | $0.00503 |
| Opus 5.5 | $0.00003 | $0.00201 |
| Sonnet 5.5 | $0.00002 | $0.00101 |
| Haiku 4.5 | $0.00001 | $0.00050 |
Grade A, and why
performance scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Performance Report
Generate aggregate performance statistics per strategy or across all strategies. Shows win rate, profit factor, P&L distribution, best/worst trades, and behavioral metrics.
Workflow
Step 1: Determine Scope
If a strategy name is provided, focus on that strategy. If "all" or nothing is provided, report across all strategies.
Optional filters:
- Date range: Last week, last month, custom range
- Symbol: Filter by trading instrument
- Session: Filter by trading session
Step 2: Fetch Performance Data
Use the get_strategy_performance MCP tool:
get_strategy_performance({
strategy: "VolBreakout", // or null for all
date_from: "2026-01-01",
date_to: "2026-03-16"
})
Step 3: Fetch Behavioral Analysis
Use the get_behavioral_analysis MCP tool for deeper insights:
get_behavioral_analysis({
strategy: "VolBreakout"
})
This returns:
- Disposition ratio: Are you cutting winners short / holding losers long?
- Hold time asymmetry: Winners vs losers average hold time
- Lot sizing variance: Consistency vs Kelly criterion optimal
- Streak analysis: Current and historical win/loss streaks
Step 4: Present Report
Structure the report as:
| Metric | Value |
|---|---|
| Total Trades | N |
| Win Rate | X% |
| Profit Factor | X.XX |
| Total P&L | $X,XXX |
| Avg Win | $XXX |
| Avg Loss | -$XXX |
| Best Trade | $XXX (date, context) |
| Worst Trade | -$XXX (date, context) |
| Max Drawdown | X% |
| Sharpe Ratio | X.XX |
Plus behavioral insights:
- Disposition ratio (target: < 1.0)
- Hold time asymmetry (target: winners held longer)
- Confidence calibration (are high-confidence trades actually better?)
Step 5: Actionable Takeaways
End with 2-3 specific, data-backed recommendations. No vague advice.
Example
User: /performance VolBreakout
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 80 lines · 8 tokens per session scan A cc5c59c063b7
performance is a command published in the GitHub repository mnemox-ai/tradememory-protocol (1,430 stars, last pushed 12d ago), licensed MIT. It adds 8 tokens to every session and 503 once invoked, about $0.0000 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
tax-sanity
Tax awareness sanity-check (NOT tax advice). Walks through entity tax-efficiency, deductions, growth structure, quarterly estimateds, multi-jurisdiction, retirement/long-term questions. Produces a Tax Readiness Checklist for the person's meeting with their accountant.
memory-budget
Define or review memory budgets, ownership, and key memory risks.
alpha
Scan for alpha opportunities and spawn parallel research agents.
positions
Review all open positions with parallel subagents. Decides HOLD or SELL for each position.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.