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.
npx agentmods add commands/mnemox-ai/tradememory-protocol/performancegit 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.svg" alt="Measured on agentmods" 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 | $0.00008 | $0.00503 |
| Opus 5 | $0.00004 | $0.00251 |
| Sonnet 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 5d 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.
- 5d 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 (1,412 stars, last pushed 24d ago), licensed MIT. It adds 8 tokens to every session and 503 once invoked, about $0.0000 per session on Opus 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
portfolio
View TastyTrade portfolio summary (positions and balances).
scan
Universal scan — auto-detects asset class (stock / crypto / index / FX / commodity) and runs the matching protocol. With no args, regenerates scanned/INDEX.md.
scan-flow
Partial scan — refresh ONLY the asset-class flow & positioning section of an existing current.md (Flow & Squeeze for stocks, Crypto/Index/FX/Commodity Flow for non-stocks). Does not re-run charts, 6-pillar, or earnings.
discover
Run a Finviz screener aligned with the framework + current macro regime. Saves a dated candidate list to scanned/SCREENS/.
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.