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/record-trade)<a href="https://agentmods.dev/commands/mnemox-ai/tradememory-protocol/record-trade"><img src="https://agentmods.dev/badge/commands/mnemox-ai/tradememory-protocol/record-trade/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/record-trade"><img src="https://agentmods.dev/badge/commands/mnemox-ai/tradememory-protocol/record-trade.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 | $0.00011 | $0.00469 |
| Opus 5 | $0.00005 | $0.00234 |
| Sonnet 5 | $0.00002 | $0.00094 |
| Haiku 4.5 | $0.00001 | $0.00047 |
Grade A, and why
record-trade 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 9d 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
Record Trade
Store a completed trade into TradeMemory with full context. This writes to all 5 OWM memory layers (episodic, semantic, procedural, affective, prospective) and updates behavioral baselines.
Workflow
Step 1: Gather Trade Details
If trade details are provided in the argument, parse them. Otherwise ask for:
- Symbol: e.g., XAUUSD, BTCUSDT, EURUSD
- Direction: long or short
- Entry price and Exit price
- Lot size (optional, defaults to strategy default)
- P&L in dollars
Step 2: Gather Context
Ask for or infer:
- Strategy: Which strategy triggered this trade (e.g., VolBreakout, IntradayMomentum)
- Market context: Session (London/NY/Asian), volatility regime, trend state
- Reflection: Why did you enter? What was the signal? Would you take it again?
- Confidence: 0.0-1.0, how confident were you at entry?
Step 3: Store via MCP
Use the remember_trade MCP tool to store across all OWM layers:
remember_trade({
symbol, direction, entry_price, exit_price, pnl,
strategy, lot_size, market_context, reflection, confidence
})
This automatically:
- Creates an episodic memory (the raw trade event)
- Updates semantic memory (strategy knowledge base)
- Adjusts procedural memory (hold times, lot sizing patterns)
- Updates affective state (confidence, drawdown, streak tracking)
- Evaluates active prospective plans
Step 4: Confirm and Summarize
Report back:
- Trade stored successfully
- Updated affective state (new confidence level, streak)
- Any active trading plans that were affected
- Similar past trades (top 3 by OWM score) for quick comparison
Example
User: /record-trade XAUUSD long 5180 5210 +$150
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.
- 9d ago First seen · 58 lines · 11 tokens per session scan A ac832ee5b15e
record-trade is a command published in the GitHub repository mnemox-ai/tradememory-protocol (1,416 stars, last pushed 28d ago), licensed MIT. It adds 11 tokens to every session and 469 once invoked, about $0.0001 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
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memory-budget
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alpha
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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.