tradermonty/claude-trading-skills/trade-performance-coach

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 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:

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:

Example Command

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.