risk-management — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited risk-management (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
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:
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 |
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.What: Cutting winners short and holding losers too long. Detection: get_behavioral_analysis → disposition_ratio
What: Increasing position size or trade frequency after losses. Detection: Compare lot sizes and trade count in the N trades after a losing streak vs baseline.
What: Taking more trades than the strategy generates signals for. Detection: Compare actual trade count vs strategy signal count.
What: Trading outside designated sessions. Detection: Check trade timestamps against strategy's defined trading windows.
What: Your confidence doesn't match your actual accuracy. Detection: get_behavioral_analysis → confidence calibration curve.
TradeMemory's procedural memory tracks position sizing patterns:
Default: Risk X% of equity per trade (typically 0.25-2%).
Position Size = (Equity × Risk%) / (Entry - StopLoss)Optimal sizing based on historical edge:
Kelly% = WinRate - (LossRate / AvgWin÷AvgLoss)get_behavioral_analysis returns Kelly criterion values per strategy.Procedural memory tracks how consistent your sizing is:
get_agent_state — is confidence reasonable? Any active streaks?remember_trade — include honest reflection/daily-review — is there a systematic problem or just variance?| Mistake | Why It's Bad | Fix |
|---|---|---|
| No pre-session risk check | Walk into the market emotionally unprepared | Always run get_agent_state first |
| Ignoring drawdown thresholds | Small drawdowns become account-threatening drawdowns | Hard stop at 15% drawdown |
| Sizing up after wins | Gives back profits faster when the streak breaks | Keep sizing constant |
| Sizing down after losses | Reduces recovery speed when edge reasserts | Keep sizing constant (unless risk appetite is "low") |
| Skipping daily reviews | Behavioral drift goes undetected for days | Daily reviews are non-negotiable |
| Paper trading with different sizing | Paper P&L doesn't reflect real execution | Same sizing rules for paper and live |
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.