/trade-performance-coach

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:

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`.

For the numeric fields actually evaluated (`planned.risk_r`, `actual.risk_r`, `risk_plan.max_risk_per_trade_r`, `actual.portfolio_heat_r`, `risk_plan.max_portfolio_heat_r`, and `monthly.consecutive_losses`), supplied non-null values must be finite and nonnegative. Numeric strings and zero are accepted; consecutive losses must be a whole number. Booleans, negative values, NaN, infinity, malformed strings, and conversion overflow are rejected. An explicitly invalid maximum never falls back to planned risk. Missing/null fields retain the partial-record behavior. The CLI validates every source record, including multiple inputs, and returns exit code 2 with a field-specific error before creating or modifying reports when a numeric value is invalid.

This skill remains beta. Numeric validation does not establish production readiness; report-ID path safety and the documented shallow multi-input wrapper still require separate assessment.

Workflow

Step 1 — Collect source records

Collect the most recent closed trade record, postmortem, risk plan, and journal notes.

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 a