Pre Trade Discipline Gate

by tradermontyeab8d5cb97b9No license2.9K starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated 3 days ago

Evaluate a local pre-trade checklist before manual order entry, blocking planless, oversized, revenge-risk, market-regime-blocked, or circuit-breaker-blocked entries while journaling the decision for trader-memory-core review.

Includes scriptsBusiness & Finance
AI-generated overview

Offline pre-trade checklist gate that approves, reviews, or blocks planned manual orders and journals the decision.

What it does
Reads a local JSON or YAML checklist of candidate manual orders plus optional market-regime, circuit-breaker, and trader-memory-core artifacts, then applies discipline rules to each actionable candidate. It produces a pre_trade_discipline_decision JSON artifact, a matching markdown report, and an optional JSONL journal row. Decisions are GO, REVIEW_REQUIRED, NO_GO, or NO_ACTIONABLE_ORDERS, and reports can be linked into thesis state without changing review schedules.
When to use it
Use it immediately before placing a manual entry order, especially after a recent loss or when upstream exposure and circuit-breaker decisions exist. It suits traders who want checklist adherence and risk-dollar limits enforced and visible in later reviews.
Requirements
Python 3.9+ and a local JSON or YAML answers file. Optional trader-memory-core thesis state, exposure_decision JSON, and circuit_breaker_decision JSON. It ships executable scripts and runs offline, placing no orders and fetching no market data.

Pre-Trade Discipline Gate

Overview

Evaluate whether a planned manual order should proceed before it is placed at the broker. This skill reads a local checklist plus optional market-regime, circuit-breaker, and trader-memory-core artifacts. It produces a pre_trade_discipline_decision artifact and can link that artifact back to the related thesis without changing the thesis review schedule.

The gate is intentionally offline. It does not place orders, cancel orders, call a broker API, or fetch market data.

When to Use

  • Immediately before placing any manual entry order
  • When a candidate has passed chart validation and position sizing
  • After a recent loss, to avoid revenge trades during the cooldown window
  • When the workflow has an upstream exposure_decision and circuit_breaker_decision
  • When you want checklist adherence to be visible in later trader-memory-core reviews

Prerequisites

  • Python 3.9+
  • A local JSON or YAML answers file with candidate-level checklist answers
  • Optional trader-memory-core thesis state under state/theses/
  • Optional exposure_decision JSON from market-regime-daily / exposure-coach
  • Optional circuit_breaker_decision JSON from drawdown-circuit-breaker

Workflow

Step 1: Prepare the Checklist

Create a JSON or YAML file with candidate answers. Only actionable manual-order intents are gated. Watchlist and ignore intents are journaled as NO_ACTIONABLE_ORDERS.

json
{  "candidates": [    {      "symbol": "AAPL",      "thesis_id": "th_aapl_gm_20260703_0001",      "order_intent": "ENTRY_READY",      "entry_in_written_plan": true,      "stop_predefined": true,      "size_within_plan": true,      "planned_risk_dollars": 500,      "actual_risk_dollars": 500,      "notes": "Entry matches the journaled breakout plan."    }  ]}

Actionable intents are ENTRY_READY, ACTIONABLE, ACTIONABLE_DAY1, and MANUAL_ORDER. Non-actionable intents such as WATCHLIST, DELAYED_EP_WATCH, PEAD_HANDOFF, IGNORE, and REJECTED are recorded but do not create an order permission.

Provide both planned_risk_dollars and actual_risk_dollars for every actionable candidate. Use a finite, non-negative number or numeric string; zero is valid. Treat missing values, booleans, non-numeric strings, NaN, infinities, and negative values as REVIEW_REQUIRED inputs and review them before placing an order.

Step 2: Run the Gate

bash
python3 skills/pre-trade-discipline-gate/scripts/check_pre_trade_discipline.py \  --answers-file state/manual-entry-checklist.json \  --state-dir state/theses \  --market-regime-decision reports/exposure_decision_latest.json \  --circuit-breaker-decision reports/circuit_breaker_decision_latest.json \  --output-dir reports/pre-trade-discipline \  --journal-dir state/journal/pre-trade-discipline

Set --as-of for deterministic testing or backfills:

bash
python3 skills/pre-trade-discipline-gate/scripts/check_pre_trade_discipline.py \  --answers-file state/manual-entry-checklist.json \  --as-of 2026-07-03T12:00:00-04:00

Step 3: Interpret the Decision

DecisionMeaning
GOAll actionable manual-order candidates passed the checklist and upstream gates
REVIEW_REQUIREDInputs are missing, unknown, or journaling failed; do not place orders until reviewed
NO_GOAt least one actionable candidate violated a discipline rule
NO_ACTIONABLE_ORDERSThe file contains no actionable manual orders; nothing should be placed

By default the CLI exits 0 for every valid decision and exits 1 only for input or runtime errors. Use --fail-on-non-go when a shell pipeline should return 2 for any non-GO decision.

Rules

The gate blocks an actionable candidate when:

  • The entry is not confirmed in the written plan
  • The stop is not predefined
  • The size is not confirmed within plan
  • Either risk-dollar field is missing or is not a finite, non-negative number (REVIEW_REQUIRED)
  • actual_risk_dollars exceeds planned_risk_dollars
  • trader-memory-core has a losing exit or partial loss inside the revenge window
  • exposure-coach recommendation is REDUCE_ONLY or CASH_PRIORITY
  • drawdown-circuit-breaker recommendation is COOLDOWN, HALTED, or TRADING_HALTED

Missing or unreadable market-regime or circuit-breaker artifacts produce REVIEW_REQUIRED for actionable orders. If no actionable order exists, the result remains NO_ACTIONABLE_ORDERS.

Outputs

The script writes:

  • pre_trade_discipline_decision_YYYY-MM-DD_HHMMSS.json
  • A matching markdown report unless --json-only is set
  • A JSONL journal row under state/journal/pre-trade-discipline/ when --journal-dir is provided

Each candidate result includes a checklist_answers object with the written-plan, stop, size, risk-dollar, and notes answers used for the decision, so later reviews can audit what was answered at order time. Invalid risk-dollar values are stored as JSON null. Valid JSON decimals that cannot round-trip through a binary float are retained as numeric strings so underflow or precision loss cannot change the gate decision.

If a candidate includes thesis_id and --state-dir is provided, the JSON report is linked into the thesis linked_reports list using trader-memory-core link_report. The skill does not call mark_reviewed and does not change monitoring review dates.

Resources

  • scripts/check_pre_trade_discipline.py - Main CLI and rule engine
  • references/discipline_gate_framework.md - Rule definitions and integration notes
  • skills/trader-memory-core/schemas/thesis.schema.json - Thesis state schema

Key Principles

  1. Manual execution only - The output is a pre-broker checklist gate, not an order router.
  2. Written plan first - No written entry plan, stop, or size confirmation means no manual entry.
  3. Producer-compatible state reading - Revenge-risk detection follows trader-memory-core timestamp and outcome behavior.
  4. Journal without review side effects - The gate links reports to theses without advancing review schedules.

Source and attribution

Source:tradermonty/claude-trading-skillsinskills/pre-trade-discipline-gateat commiteab8d5c

License: No license

Content belongs to its original authors. SourceWeft indexes it from a public repository.

Report or request removal

More from tradermonty/claude-trading-skills

Weekly Performance Digest

tradermonty

Aggregates closed trading theses into a weekly performance report with metrics, pattern breakdowns, and lessons.

Includes scripts
Data & Analytics2.9Kupdated 3 days ago

Trade Performance Coach

tradermonty

Reviews recorded trades for process adherence, risk discipline, execution quality and behavior patterns, producing a coaching report.

Includes scripts
Business & Finance2.9Kupdated 3 days ago

Strategy Pivot Designer

tradermonty

Detects backtest iteration stagnation and generates structurally different strategy pivot proposals for trading strategies.

Includes scripts
Business & Finance2.9Kupdated 3 days ago

Skill Designer

tradermonty

Design new Claude skills from structured idea specifications. Use when the skill auto-generation pipeline needs to produce a Claude CLI prompt that creates a complete skill directory (SKILL.md, references, scripts, tests) following repository conventions.

Includes scripts
Awaiting classification2.9Kupdated 3 days ago

Residual Edge Analyzer

tradermonty

Separate a strategy return series into declared baseline exposure and residual edge with returns-based OLS attribution, HAC inference, rolling stability, alternate-baseline sensitivity, and regime breakdowns. Use when evaluating whether backtest, out-of-sample, or live returns contain independent alpha beyond market, equal-weight, momentum, sector, or user-supplied factor returns; when explaining whether a drawdown came from baseline exposure or strategy-specific behavior; or when a strategy needs an attribution quality gate after backtesting. Do not use for holdings-based Brinson attribution, feature-level Shapley explanations, or analysis from summary metrics without a dated return series.

Includes scripts
Awaiting classification2.9Kupdated 3 days ago

Manifoldbt Backtester

tradermonty

Runs a declarative strategy spec over OHLCV bars with the manifoldbt Rust engine, pairs the fill log into round trips, and emits the eight inputs the backtest-expert skill scores. Use when the user wants to execute a backtest, measure a rule they have described, obtain win rate / average win / average loss / max drawdown from real bars, or feed backtest-expert with measured numbers instead of estimates.

Includes scripts
Awaiting classification2.9Kupdated 3 days ago