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 risk-managementgit 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/risk-management)<a href="https://agentmods.dev/skills/mnemox-ai/tradememory-protocol/risk-management"><img src="https://agentmods.dev/badge/skills/mnemox-ai/tradememory-protocol/risk-management/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/risk-management"><img src="https://agentmods.dev/badge/skills/mnemox-ai/tradememory-protocol/risk-management.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 | $0.00089 | $0.01509 |
| Opus 5 | $0.00044 | $0.00754 |
| Sonnet 5 | $0.00018 | $0.00302 |
| Haiku 4.5 | $0.00009 | $0.00151 |
Grade A, and why
risk-management 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 12d 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 — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Risk Management
Overview
Risk management in TradeMemory is behavioral, not just mathematical. Traditional risk management calculates position sizes and stop losses. TradeMemory adds a behavioral layer: it monitors your execution patterns, detects emotional drift, and flags when you're deviating from your own rules.
The system tracks two kinds of risk:
- Position risk — How much capital is at stake on each trade
- Behavioral risk — Are you making decisions rationally or emotionally
Affective State Model
TradeMemory maintains a real-time emotional state model for the trading agent:
| Dimension | Range | What It Tracks |
|---|---|---|
| Confidence | 0.0 - 1.0 | Self-assessed confidence, calibrated against outcomes |
| Drawdown | 0% - 100% | Current peak-to-trough equity drawdown |
| Win Streak | 0 - N | Consecutive winning trades |
| Loss Streak | 0 - N | Consecutive losing trades |
| Risk Appetite | low / normal / high | Derived from confidence + drawdown + streaks |
How Affective State Updates
- After a win: Confidence += f(P&L magnitude), win streak ++, loss streak reset
- After a loss: Confidence -= f(P&L magnitude), loss streak ++, win streak reset
- Drawdown crossing thresholds: Risk appetite auto-reduces at 5%, 10%, 15% drawdown
- Daily review: Confidence recalibrated against actual hit rate
Using Affective State
Check get_agent_state before every trading session:
get_agent_state() → {
confidence: 0.42,
drawdown: 8.3%,
win_streak: 0,
loss_streak: 3,
risk_appetite: "low"
}
Action rules:
risk_appetite == "low"→ Reduce position size by 50% or skip marginal setupsloss_streak >= 3→ Stop trading for the session. Review, don't revenge trade.confidence < 0.3→ Paper trade only until confidence recoversdrawdown > 15%→ Hard stop. No new positions until daily review.
Behavioral Risk Indicators
1. Disposition Effect
What: Cutting winners short and holding losers too long.
Detection: get_behavioral_analysis → disposition_ratio
- Ratio < 1.0 = Good (holding winners longer than losers)
- Ratio > 1.5 = Problem (losers held 50% longer than winners)
- Ratio > 2.0 = Critical (classic retail trader failure mode)
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.
- 12d ago First seen · 145 lines · 89 tokens per session scan A a73ad4852853
risk-management is a skill published in the GitHub repository mnemox-ai/tradememory-protocol (1,417 stars, last pushed 3d ago), licensed MIT. It adds 89 tokens to every session and 1,509 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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