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master_trading_v2

Collapse deterministic trading pipeline steps into a single bash script invocation.

2|4|Updated Apr 27, 2026
Search Tags:#token optimization#Alpaca#paper trading#strategy execution#autonomous trading#trading orchestration
One-click install
npx skills add https://github.com/khoks/ClaudeTrading --skill master-trading-v2
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: master_trading_v2
Source: https://github.com/khoks/ClaudeTrading/tree/main/.claude/skills/master_trading_v2
Command: npx skills add https://github.com/khoks/ClaudeTrading --skill master-trading-v2

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires jq, and includes scripts (resource) components.

What problem does it solve?

The v1 master_trading skill walks every deterministic pipeline step (market check, safe trading filter, strategy execution, state persistence) via separate LLM tool calls, wasting ~45k input tokens per tick with no LLM judgment required. This skill eliminates that overhead by moving all deterministic orchestration to a single bash script, delivering identical trading behavior at a fraction of the context cost.

Core Features & Use Cases

  • Script-orchestrated tick pipeline: Collapses all deterministic trading steps into one bash invocation, removing redundant LLM round-trips for non-judgmental tasks.
  • LLM observability layer: Shifts the LLM's role to surfacing only anomalies (strategy errors, concentration risks, equity drops, repeat-fire events) that require human intelligence to spot.
  • Wire-compatible with v1: Uses the exact same trading semantics, H1B cooldown rules, paper-only Alpaca integration, and persistence layout as master_trading v1, so switching between versions is seamless.
  • Use Case: For autonomous paper trading systems that run frequent scheduled ticks during market hours, this skill reduces per-tick compute cost and context bloat while maintaining full trading safety and observability.

Quick Start

Run the master_trading_v2 skill during a scheduled trading tick or when manually invoking /master_trading_v2 for a one-shot paper trading cycle, then review the surfaced anomaly summary for any issues requiring operator attention.

Frequently Asked Questions about master_trading_v2

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

FAQPage Schema
How do I reduce LLM token consumption during autonomous paper trading ticks?▼

To reduce token consumption during paper trading ticks, collapse all deterministic pipeline steps into a single bash script invocation. This eliminates redundant LLM tool calls for non-judgmental tasks like market checks and state persistence.

Why does my scheduled trading orchestration use so many input tokens per tick?▼

Scheduled trading orchestration wastes tokens when an LLM walks every deterministic step via separate tool calls. Moving the entire pipeline into one bash script prevents the context bloat caused by these unnecessary LLM round-trips.

How do I run a paper trading cycle with Alpaca using a single script?▼

Running a paper trading cycle with Alpaca requires invoking a single bash script that handles market checks, safe trading filters, strategy execution, and state persistence automatically without separate LLM tool calls.

Do I need jq to execute the master_trading_v2 tick pipeline?▼

Yes, you need jq installed to execute the tick pipeline. The bash script depends on jq to process JSON data from the Alpaca paper trading integration and manage the state persistence layer.

Can I replace my v1 trading skill without changing H1B cooldown rules or persistence?▼

Yes, you can replace the v1 skill seamlessly because this version is wire-compatible. It preserves the exact trading semantics, H1B cooldown rules, and persistence layout of master_trading v1.

What is the best way to monitor trading anomalies without wasting LLM context?▼

The best way to monitor anomalies without wasting context is shifting the LLM's role to an observability layer. The bash script surfaces only strategy errors, concentration risks, and equity drops that require human intelligence.

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