--- name: trade-performance-coach description: >- Review closed trades, partial exits, and monthly trade aggregates for process adherence, risk discipline, execution quality, and evidence-based trading behavior patterns. Use after trader-memory-core and signal-postmortem have produced records, or when the user asks for a post-trade coach, risk-manager style review, rule-adherence review, next-session operating rules, or psychology-aware trading behavior feedback. This skill does not provide buy/sell advice, therapy, or broker execution. --- # Trade Performance Coach ## Overview Trade Performance Coach reviews recorded trade outcomes and journal evidence to help a human trader improve their decision process. It converts closed-trade records, postmortem findings, risk rules, and optional market-regime context into an evidence-based coaching report covering: - process adherence - risk discipline - execution quality - possible trading-behavior patterns - next-session operating rules - coach questions for reflection This skill is intended to fill the support role that a risk manager, desk lead, or trading coach might provide in a professional trading environment. It is strictly a process-review skill: it never recommends entering, exiting, buying, selling, shorting, holding, or sizing a specific security. ## When to Use Use this skill when any of the following are true: - A trade has been closed and the user wants a post-trade coaching review. - A partial close occurred and the user wants to inspect sizing, stop, or exit behavior. - The user has `trader-memory-core` thesis records and `signal-postmortem` findings and wants next-session operating rules. - The user wants a monthly review of recurring process, risk, execution, or behavior patterns. - The user asks for a risk-manager style review of their own recorded trades. - The user asks whether a loss was a process error, execution error, market environment issue, or acceptable variance. - The user wants possible FOMO, revenge-trade, overconfidence, hesitation, stop-moving, or size-creep patterns flagged with evidence. ## When Not to Use Do not use this skill to: - Pick stocks or rank trade candidates. - Approve or reject a live trade as financial advice. - Place orders or draft broker instructions. - Provide therapy, mental-health diagnosis, or personality assessment. - Infer private psychological traits beyond the trade evidence supplied. - Shame the user for losses or rule violations. - Replace `trader-memory-core`; this skill consumes journal/thesis records and produces coaching findings. If the input is incomplete, default to `REVIEW_REQUIRED` or `journal_only` mode and ask for missing records rather than inventing evidence. ## Prerequisites Recommended upstream records: - `trader-memory-core` closed thesis record or journal entry - `signal-postmortem` postmortem findings - original trade plan or trade ticket - actual entry / exit / partial-close actions - user-defined risk plan, if available - optional `market-regime-daily` / `exposure-coach` context No paid API key is required. The deterministic script works from local JSON/YAML-like records. ## Inputs Minimum useful input is one recorded trade or one monthly aggregate. Preferred fields: ```yaml review_type: single_trade | partial_close | monthly_aggregate trade_id: string ticker: string outcome: win | loss | breakeven | mixed planned: thesis: string entry: number stop: number target: number risk_r: number thesis_recorded_before_entry: boolean setup_confirmed: boolean market_regime: allowed | restrictive | cash_priority | unknown actual: entry: number exit: number risk_r: number portfolio_heat_r: number stop_moved: boolean stop_move_planned: boolean entry_before_confirmation: boolean traded_against_regime: boolean risk_plan: max_risk_per_trade_r: number max_portfolio_heat_r: number max_weekly_loss_r: number postmortem: root_cause: thesis_quality | execution | risk_sizing | market_environment | rule_violation | randomness | unknown notes: [string] journal: reflection: string emotions: [string] monthly: trades: [object] consecutive_losses: number rule_violations: number ``` The script tolerates partial records. Missing evidence is marked as `unclear`. ## Workflow ### Step 1 — Collect source records Collect the most recent closed trade record, postmortem, risk plan, and journal notes. ```bash python3 skills/trade-performance-coach/scripts/review_trade_performance.py \ --input reports/trade_memory/closed_thesis_EXMPL.json \ --output-dir reports/trade-performance-coach ``` ### Step 2 — Evaluate process adherence Compare actual actions against the user's documented plan and rules. Check for: - missing pre-entry thesis - setup confirmation skipped - trade taken against market-regime gate - stop moved without a pre-defined rule - exit / partial close inconsistent with plan - incomplete record quality ### Step 3 — Evaluate risk discipline Compare actual risk and heat against the risk plan. Check for: - per-trade risk above max - portfolio heat above max - weekly loss or consecutive-loss escalation - oversized trade after a winner or loser - correlated exposure if provided ### Step 4 — Evaluate execution quality Classify entry, stop, exit, add, trim, and review behavior. Separate clean-process losses from execution mistakes. ### Step 5 — Detect possible behavior patterns Use evidence from journal notes and action flags to tag possible trading behavior patterns. Always tie a tag to evidence and use non-diagnostic language. Supported MVP tags: - `fomo_entry` - `revenge_trade` - `premature_exit` - `overconfidence_after_winner` - `stop_moved` - `size_creep` - `hesitation` - `rule_drift` - `no_pattern_detected` ### Step 6 — Produce next-session operating rules Convert findings into temporary, concrete guardrails. Examples: - require thesis record and screenshot before the next entry - cap risk at 0.5R for the next two trades after a rule violation - switch to review-only mode after repeated revenge-trade evidence - do not chase a missed entry; add to watchlist for the next valid setup ### Step 7 — Human decision gate End every report with a human decision gate. The default action is `journal_only`. Allowed actions: ```text accept_rules / modify_rules / defer / journal_only ``` ## Output The skill produces a JSON report and optionally a Markdown report. Required top-level JSON fields: - `schema_version` - `review_type` - `review_id` - `overall_verdict` - `summary` - `scores` - `process_adherence_findings` - `risk_manager_notes` - `execution_quality_assessment` - `behavioral_pattern_tags` - `next_session_operating_rules` - `coach_questions` - `human_decision_gate` - `disclaimer` Verdicts: | Verdict | Meaning | |---|---| | `OK` | No material process violation found. Outcome appears compatible with the plan. | | `WARN` | Minor process or record-quality concern. | | `REVIEW_REQUIRED` | Meaningful process, risk, or behavior finding before next similar trade. | | `RULE_VIOLATION` | Explicit user rule appears to have been broken. | | `COOL_DOWN` | Repeated violations, drawdown/revenge pattern, or escalation suggests review-only mode. | ## Example Command ```bash python3 skills/trade-performance-coach/scripts/review_trade_performance.py \ --input skills/trade-performance-coach/scripts/tests/fixtures/single_trade_rule_violation_loss.json \ --output-dir reports/trade-performance-coach \ --markdown ``` ## Resources Read these selectively when invoked: - `references/review-framework.md` — five-axis review model, scoring, verdicts - `references/behavior-tags.md` — behavior tag definitions and evidence rules - `references/risk-review-checklist.md` — risk manager checklist and severity rules - `references/output-contract.md` — JSON output contract and schema notes - `references/hermes-integration.md` — suggested Hermes `/post-trade-coach` and monthly coaching integration - `assets/performance_coach_report.schema.json` — machine-readable output schema - `scripts/review_trade_performance.py` — deterministic local reviewer ## Guardrails - This is process-review support, not financial advice. - Do not recommend buying, selling, shorting, holding, or sizing a specific security. - Do not provide therapy or mental-health diagnosis. - Do not infer personality traits. - Do not shame or moralize the user. - Tie every behavior tag to evidence. - Use "possible pattern" language for behavior tags. - Always include a human decision gate. - Default to journal/review mode when data is incomplete.