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memory-engine.md

Maintain persistent trading memory across sessions to prevent repeated mistakes.

Updated May 19, 2026
Search Tags:#confidence scoring#session context#trading memory#market bias#setup invalidation#anti-revenge#correlation exposure
One-click install
npx skills add https://github.com/sarkcesscrewpay/trading-intelligence-system --skill memory-engine-md
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: memory-engine.md
Source: https://github.com/sarkcesscrewpay/trading-intelligence-system/tree/main/skills/memory_engine_md_trading_intelligence_v_1
Command: npx skills add https://github.com/sarkcesscrewpay/trading-intelligence-system --skill memory-engine-md

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill solves the problem of inconsistent trading behavior by preserving intraday and cross-session context so the model does not “reset” into a new, repeat-mistake mindset each cycle.

Core Features & Use Cases

  • Persistent market memory: Stores previous decisions, active and invalidated setups, daily bias, and session context to maintain continuity over time.
  • Anti-revenge and anti-duplication controls: Prevents chasing failed trades and avoids duplicate alerts by updating existing active setups and enforcing cooling periods after invalidations.
  • Adaptive confidence and regime awareness: Tracks correlation exposure, failed logic patterns, high-confidence behaviors, and market regime shifts to adjust confidence and recommendations appropriately.

Quick Start

Use the memory engine to maintain a live, structured record of bias, setups, invalidations, and confidence across London/NY sessions for continuous, non-repetitive decision-making.

Frequently Asked Questions about memory-engine.md

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I maintain consistent trading decisions across different market sessions?▼

Maintain consistent trading decisions across market sessions by using persistent memory to store previous decisions, daily bias, and active setups so the model avoids resetting into a repeat-mistake mindset each cycle.

What is the best way to prevent revenge trading after a setup invalidation?▼

Prevent revenge trading after setup invalidations by enforcing cooling periods and anti-revenge behavior controls that stop the model from chasing failed trades under shifting market regimes.

How do I avoid duplicate alerts when tracking active trading setups?▼

Avoid duplicate alerts during setup tracking by updating existing active setups within a structured memory format, ensuring the model recognizes and modifies current opportunities rather than creating redundant notifications.

Can I track correlation exposure and market bias across London and New York sessions?▼

Track correlation exposure and market bias across London and New York sessions by maintaining a live, structured record that links session context with adaptive confidence scoring throughout the trading day.

When do I need persistent trading memory for market monitoring workflows?▼

Persistent trading memory is needed for market monitoring workflows when tracking decisions, invalidations, and logic performance across cycles to prevent inconsistent behavior and adapt to market regime shifts.

How does confidence scoring adapt to failed logic patterns during intraday trading?▼

Confidence scoring adapts to failed logic patterns during intraday trading by tracking high-confidence behaviors and correlating them with market regime shifts, adjusting recommendations to prevent repeated mistakes.

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