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ai-trading-skill

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GitHub TrendingGeneralby rigneshroot

Explainable trading intelligence framework. Run multi-layer market analysis, generate structured trade rationales, backtest strategies, evaluate risk profiles, and manage automated execution on Hyperliquid. Use this skill when working with Hyperbot, analyzing markets, backtesting, or generating trade rationale reports.

First seen 5/26/2026

Overview

Explainable Trading Intelligence Skill

This skill equips any agent with the ability to run market analysis, generate explainable trade rationales, backtest strategies, evaluate risk, and manage execution on the Hyperliquid exchange.


Prerequisite Setup

Before running any script, activate the workspace environment:

bash
cd <project-root>
source .venv/bin/activate

User must configure private keys in .env for active trades. See .env.example for required fields.


Core Operations

1. Run Market Analysis with Trade Rationale

The primary output of this framework. Generates a structured trade rationale covering trend, volatility, momentum, key levels, sizing, and invalidity conditions.

bash
# Default analysis (BTC, moderate risk profile)
python3 analyze.py

# Custom asset, interval, and risk profile
python3 analyze.py --symbol ETH --interval 5m --risk-profile conservative

# Adjust per-trade risk percentage
python3 analyze.py --symbol SOL --account-risk-pct 0.5

Available risk profiles: conservative, moderate, aggressive

The output includes four sections:

  • Strategy scoring matrix (all 5 layers)
  • Trade Rationale Engine output (structured breakdown)
  • Risk Assessment (profile-adjusted sizing and warnings)
  • Institutional Context (macro positioning data)

2. Run Automated Tests

Verify strategy calculations, rationale engine, and risk layer:

bash
python3 -m unittest tests/test_strategies.py

3. Run Historical Backtests

Walk-forward simulation with no lookahead bias:

bash
python3 backtest.py
python3 backtest.py --days 60 --confidence 75 --min-agree 4

4. Evaluate Position Sizing Models

Calculate compounding returns and drawdowns from backtest results:

bash
python3 pnl_calc.py

5. View Signals Breakdown

Inspect per-trade strategy agreement matrix:

bash
python3 show_signals.py

6. Run Execution Orchestrator

Full execution loop with safety gates, risk circuit breakers, and LLM audit:

bash
# One-shot dry tick
python3 main.py

# Continuous polling loop
python3 main.py --loop

Configuration

Edit config.yaml to adjust:

  • agree_threshold: Minimum confidence for a strategy to count as agreeing (default: 50)
  • min_agree: Minimum strategies required for consensus (default: 4)
  • symbol: Asset to analyze (default: BTC)
  • interval: Candle timeframe (default: 15m)
  • Individual strategy parameters under the strategies section

Install & Usage

1
Create the skills directory
mkdir -p .claude/skills
2
Download the skill file
mkdir -p .claude/skills && curl -o .claude/skills/ai-trading-skill.md https://raw.githubusercontent.com/rigneshroot/hyperbot-ai-trading-claude-skill/main/SKILL.md
3
Invoke in Claude Code
/ai-trading-skill
View source on GitHub
testing

Security Audits

LicenseUnknownSourceWarnRepositoryPass

Frequently Asked Questions

What is ai-trading-skill?

Explainable trading intelligence framework. Run multi-layer market analysis, generate structured trade rationales, backtest strategies, evaluate risk profiles, and manage automated execution on Hyperliquid. Use this skill when working with Hyperbot, analyzing markets, backtesting, or generating trade rationale reports.

How to install ai-trading-skill?

To install ai-trading-skill: create the skills directory (mkdir -p .claude/skills), then run: mkdir -p .claude/skills && curl -o .claude/skills/ai-trading-skill.md https://raw.githubusercontent.com/rigneshroot/hyperbot-ai-trading-claude-skill/main/SKILL.md. Finally, /ai-trading-skill in Claude Code.

What is ai-trading-skill best for?

ai-trading-skill is a skill categorized under General. It is designed for: testing. Created by rigneshroot.