How do I install the AI Trading Skill skill?

Run npx skillmds@latest add rigneshroot/ai-trading-skill-2 in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is AI Trading Skill safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with AI Trading Skill?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is AI Trading Skill free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published AI Trading Skill?

rigneshroot (@rigneshroot) published this skill. Their other Agent Skills are listed on their SkillMD profile.

AI Trading Skill by rigneshroot — Agent Skill

Name: AI Trading Skill
Author: rigneshroot

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.

rigneshroot 2d2f47d 2.8 KB Updated Sep 17, 2026

File contents

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:

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

User must configure private keys in .env for active trades. The .env.example template shows the 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.

# 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:

python3 -m unittest tests/test_strategies.py

3. Run Historical Backtests

Walk-forward simulation with no lookahead bias:

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:

python3 pnl_calc.py

5. View Signals Breakdown

Inspect per-trade strategy agreement matrix:

python3 show_signals.py

6. Run Execution Orchestrator

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

# 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

---name: ai-trading-skill-2description: 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.---# Explainable Trading Intelligence SkillThis 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 SetupBefore running any script, activate the workspace environment:```bashcd <project-root>source .venv/bin/activate```User must configure private keys in `.env` for active trades. The `.env.example` template shows the required fields.---## Core Operations### 1. Run Market Analysis with Trade RationaleThe 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 profilepython3 analyze.py --symbol ETH --interval 5m --risk-profile conservative# Adjust per-trade risk percentagepython3 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 TestsVerify strategy calculations, rationale engine, and risk layer:```bashpython3 -m unittest tests/test_strategies.py```### 3. Run Historical BacktestsWalk-forward simulation with no lookahead bias:```bashpython3 backtest.pypython3 backtest.py --days 60 --confidence 75 --min-agree 4```### 4. Evaluate Position Sizing ModelsCalculate compounding returns and drawdowns from backtest results:```bashpython3 pnl_calc.py```### 5. View Signals BreakdownInspect per-trade strategy agreement matrix:```bashpython3 show_signals.py```### 6. Run Execution OrchestratorFull execution loop with safety gates, risk circuit breakers, and LLM audit:```bash# One-shot dry tickpython3 main.py# Continuous polling looppython3 main.py --loop```---## ConfigurationEdit `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

Frequently asked questions