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 skills add mnemox-ai/tradememory-protocol --skill trading-memorygit 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/skills/mnemox-ai/tradememory-protocol/trading-memory)<a href="https://agentmods.dev/skills/mnemox-ai/tradememory-protocol/trading-memory"><img src="https://agentmods.dev/badge/skills/mnemox-ai/tradememory-protocol/trading-memory/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/skills/mnemox-ai/tradememory-protocol/trading-memory"><img src="https://agentmods.dev/badge/skills/mnemox-ai/tradememory-protocol/trading-memory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00088 | $0.01410 |
| Opus 5 | $0.00044 | $0.00705 |
| Sonnet 5 | $0.00018 | $0.00282 |
| Haiku 4.5 | $0.00009 | $0.00141 |
Grade A, and why
trading-memory 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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Trading Memory
Overview
TradeMemory implements a cognitive memory architecture for trading agents. Every trade is stored with full context (market conditions, strategy, reasoning, confidence) and recalled using Outcome-Weighted Memory (OWM) — a scoring system that surfaces winning trades in similar contexts first.
This is not a trade journal. It's a memory system that learns which past experiences are most relevant to current decisions.
Architecture: 3-Layer Pipeline
L1: Raw Trades → L2: Pattern Discovery → L3: Strategy Adjustments
- L1 (Episodic): Every trade stored as-is with full context. The ground truth.
- L2 (Patterns): Behavioral patterns discovered from L1 data. Disposition effect, session biases, strategy correlations.
- L3 (Adjustments): Concrete strategy adjustments derived from L2 patterns. Parameter changes, rule modifications, strategy retirement.
Outcome-Weighted Memory (OWM) — 5 Memory Types
1. Episodic Memory
Raw trade events. Each record contains: symbol, direction, entry/exit, P&L, strategy, market context, reflection, timestamp.
When to write: After every completed trade. When to read: When recalling past trades for decision-making.
2. Semantic Memory
Strategy knowledge base. Aggregated understanding of what works: "VolBreakout performs best in London session with ATR > $40" is semantic memory.
When to write: Automatically updated when trades are stored via remember_trade.
When to read: When evaluating whether a strategy fits current conditions.
3. Procedural Memory
Behavioral baselines. Tracks execution patterns: average hold times per strategy, lot sizing consistency, stop loss adherence, entry timing precision.
When to write: Automatically computed from trade history. When to read: During behavioral analysis and daily reviews.
4. Affective Memory
Emotional/confidence state. Tracks: current confidence level (0-1), drawdown percentage, win/loss streaks, risk appetite, tilt indicators.
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.
- 10d ago First seen · 132 lines · 88 tokens per session scan A af673baac4cc
trading-memory is a skill published in the GitHub repository mnemox-ai/tradememory-protocol (1,416 stars, last pushed today), licensed MIT. It adds 88 tokens to every session and 1,410 once invoked, about $0.0004 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.
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