Hermes Registry

trade-performance-coach

v1.0.0Skill

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.

SourceIDtrade-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.

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:

VerdictMeaning
OKNo material process violation found. Outcome appears compatible with the plan.
WARNMinor process or record-quality concern.
REVIEW_REQUIREDMeaningful process, risk, or behavior finding before next similar trade.
RULE_VIOLATIONExplicit user rule appears to have been broken.
COOL_DOWNRepeated violations, drawdown/revenge pattern, or escalation suggests review-only mode.

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.