
Trade Performance Coach
FreeEnhance your trading decisions through evidence-based reviews.
Free · Opens the source repo
What Trade Performance Coach does
The Trade Performance Coach skill is designed to assist traders in improving their trading decisions by reviewing closed trades and journal entries. By analyzing recorded outcomes, this skill provides insights into process adherence, risk discipline, execution quality, and potential behavior patterns. It serves as a support tool akin to a risk manager or trading coach, focusing on evidence-based evaluations rather than providing direct trading advice. The skill processes data from closed trades, postmortem analyses, and user-defined risk plans to generate comprehensive reports that highlight areas for improvement and suggest operating rules for future trades.
When utilizing the Trade Performance Coach, traders can expect a structured review process. The skill evaluates actual trading actions against pre-defined plans and rules, identifying discrepancies such as missed thesis records or trades taken against market conditions. It also assesses risk management practices, ensuring that trades align with the user's risk tolerance and portfolio strategy. The output includes actionable insights and questions for reflection, empowering traders to refine their strategies and decision-making processes.
This skill is particularly useful for traders who maintain detailed records of their trades and seek to understand the underlying reasons for their successes and failures. By focusing on evidence from past trades, the Trade Performance Coach helps users identify behavioral patterns such as overconfidence or revenge trading, which can adversely affect performance. The skill is not intended for stock selection or live trade execution but rather for retrospective analysis and improvement of trading practices, making it a valuable addition for serious traders looking to enhance their skills through systematic review.
When to use it
Use this skill after closing a trade or at the end of a trading month to review performance and refine strategies.
When not to use it
Do not use this skill for real-time trading decisions or to provide financial advice; its focus is solely on reviewing past trades.
What you can build with it
Post-Trade Review
After closing a trade, use the skill to analyze the decision-making process and adherence to your trading plan.
Monthly Performance Review
At the end of each month, review aggregated trade data to identify patterns and areas for improvement.
Behavior Pattern Detection
Utilize the skill to flag potential behavioral issues such as FOMO or overconfidence based on your trading history.
How to install Trade Performance Coach
View source1. Install with the skills CLI
npx skills add tradermonty/claude-trading-skills/trade-performance-coach --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by tradermontyTrade 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-corethesis records andsignal-postmortemfindings 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-coreclosed thesis record or journal entrysignal-postmortempostmortem findings- original trade plan or trade ticket
- actual entry / exit / partial-close actions
- user-defined risk plan, if available
- optional
market-regime-daily/exposure-coachcontext
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_entryrevenge_tradepremature_exitoverconfidence_after_winnerstop_movedsize_creephesitationrule_driftno_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_versionreview_typereview_idoverall_verdictsummaryscoresprocess_adherence_findingsrisk_manager_notesexecution_quality_assessmentbehavioral_pattern_tagsnext_session_operating_rulescoach_questionshuman_decision_gatedisclaimer
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
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, verdictsreferences/behavior-tags.md— behavior tag definitions and evidence rulesreferences/risk-review-checklist.md— risk manager checklist and severity rulesreferences/output-contract.md— JSON output contract and schema notesreferences/hermes-integration.md— suggested Hermes/post-trade-coachand monthly coaching integrationassets/performance_coach_report.schema.json— machine-readable output schemascripts/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.
Frequently asked questions about Trade Performance Coach
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