Command
Data
Command
/daily-review
Run a daily reflection on recent trades and behavioral patterns
Install
$ npx -y skills add mnemox-ai/tradememory-protocol --agent claude-codeHow it fires
How this command gets triggered: by you, by Claude, or both.
- Fires itselfClaude auto-loads it when your prompt matches the work.
- You can call itInvoke it directly when you want it.
- Slash command
/daily-review
Context preview
What this command does when you run it.
Run a daily reflection on recent trades and behavioral patterns
Command definition
daily-review.mddescription: Run a daily reflection on recent trades and behavioral patterns
argument-hint: "[date or 'today']"
Daily Review
Trigger an AI-powered reflection on recent trading activity. Analyzes trades, identifies behavioral patterns, updates affective state, and produces actionable insights.
Workflow
Step 1: Determine Review Period
If a date is provided, review that day's trades. Default: review all trades since last reflection.
Step 2: Gather Data
Load the `trading-memory` skill context, then:
1. Use `get_strategy_performance` to pull recent trade stats 2. Use `get_behavioral_analysis` to check behavioral drift 3. Use `get_agent_state` to read current affective state 4. Use `recall_memories` with recent context to surface relevant patterns
Step 3: Analyze Patterns
The reflection should cover:
**Trade Execution Quality**
- Did entries match the strategy rules? Or were they impulsive?
- Were stop losses honored? Any manual overrides?
- Position sizing: consistent with risk rules or erratic?
**Behavioral Drift Detection**
- Disposition effect: cutting winners, holding losers?
- Revenge trading: increased size after losses?
- Overtrading: more trades than the strategy signals justify?
- Session discipline: trading outside designated sessions?
**Strategy Performance**
- Which strategies fired today?
- Win/loss breakdown per strategy
- Any strategy consistently underperforming?
Step 4: Update Affective State
Based on the review, the affective state should be recalibrated:
- Confidence: up after good execution, down after poor discipline
- Risk appetite: reduce after drawdown, normalize after recovery
- Streak awareness: flag tilt risk after consecutive losses
Step 5: Produce Report
Structure:
## Daily Review — [Date]
### Summary
- Trades today: N (W wins, L losses)
- P&L: $XXX
- Best: [trade details]
- Worst: [trade details]
### Behavioral Check
- Disposition ratio: X.X (target < 1.0)
- Hold time balance: [OK / Winners cut short / Losers held too long]
- Position sizing: [Consistent / Erratic]
### Insights
1. [Specific, data-backed observation]
2. [Specific, data-backed observation]
### Tomorrow's Focus
- [One concrete action item based on today's data]
Important Notes
- Requires trades in the database. If no recent trades, report "no activity" instead of generating fluff.
- LLM reflection requires `ANTHROPIC_API_KEY`. Without it, uses rule-based analysis (still useful, less nuanced).
- Be honest. If performance is bad, say so. No sugar-coating.
Example
User: /daily-review today
Read more
description: Run a daily reflection on recent trades and behavioral patterns argument-hint: "[date or 'today']"
Daily Review
Trigger an AI-powered reflection on recent trading activity. Analyzes trades, identifies behavioral patterns, updates affective state, and produces actionable insights.
Workflow
Step 1: Determine Review Period
If a date is provided, review that day's trades. Default: review all trades since last reflection.
Step 2: Gather Data
Load the `trading-memory` skill context, then:
1. Use `get_strategy_performance` to pull recent trade stats 2. Use `get_behavioral_analysis` to check behavioral drift 3. Use `get_agent_state` to read current affective state 4. Use `recall_memories` with recent context to surface relevant patterns
Step 3: Analyze Patterns
The reflection should cover:
**Trade Execution Quality**
- Did entries match the strategy rules? Or were they impulsive?
- Were stop losses honored? Any manual overrides?
- Position sizing: consistent with risk rules or erratic?
**Behavioral Drift Detection**
- Disposition effect: cutting winners, holding losers?
- Revenge trading: increased size after losses?
- Overtrading: more trades than the strategy signals justify?
- Session discipline: trading outside designated sessions?
**Strategy Performance**
- Which strategies fired today?
- Win/loss breakdown per strategy
- Any strategy consistently underperforming?
Step 4: Update Affective State
Based on the review, the affective state should be recalibrated:
- Confidence: up after good execution, down after poor discipline
- Risk appetite: reduce after drawdown, normalize after recovery
- Streak awareness: flag tilt risk after consecutive losses
Step 5: Produce Report
Structure:
## Daily Review — [Date] ### Summary - Trades today: N (W wins, L losses) - P&L: $XXX - Best: [trade details] - Worst: [trade details] ### Behavioral Check - Disposition ratio: X.X (target < 1.0) - Hold time balance: [OK / Winners cut short / Losers held too long] - Position sizing: [Consistent / Erratic] ### Insights 1. [Specific, data-backed observation] 2. [Specific, data-backed observation] ### Tomorrow's Focus - [One concrete action item based on today's data]
Important Notes
- Requires trades in the database. If no recent trades, report "no activity" instead of generating fluff.
- LLM reflection requires `ANTHROPIC_API_KEY`. Without it, uses rule-based analysis (still useful, less nuanced).
- Be honest. If performance is bad, say so. No sugar-coating.
Example
User: /daily-review today
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