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Orderly Trader

Automate perpetual futures trading on Orderly Network via Python and x402 VoltPerps APIs.

Updated Feb 18, 2026
Search Tags:#automation#llm#risk management#technical analysis#trading#futures#orderly network
One-click install
npx skills add https://github.com/pavank-hl/agentc-trading --skill orderly-trader
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Orderly Trader
Source: https://github.com/pavank-hl/agentc-trading/tree/main
Command: npx skills add https://github.com/pavank-hl/agentc-trading --skill orderly-trader

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pydantic, pydantic-settings, pyyaml, httpx, websockets, orderly-evm-connector, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates perpetual futures trading on the Orderly Network, enabling an LLM to analyze market data, execute trades, and manage positions autonomously.

Core Features & Use Cases

  • Real-time Market Analysis: Integrates with WebSocket data feeds for 3 symbols (ETH, BTC, SOL) across multiple timeframes.
  • Technical Indicators: Computes a comprehensive suite of indicators (RSI, MACD, Bollinger Bands, VWAP, ATR, etc.) and derivative data (funding rates, open interest).
  • LLM-Driven Trading: Exposes a callable Python API allowing an LLM to control trading cadence, analyze markets, and submit validated trading decisions via the x402 VoltPerps API.
  • Risk Management: Implements a 9-layer risk manager with graduated reserves and validation checks to ensure safe and capital-efficient trading.
  • Use Case: An LLM can use this Skill to continuously monitor market conditions, identify trading opportunities based on technical and derivative signals, and execute trades with defined stop-loss and take-profit levels, all while adhering to risk parameters.

Quick Start

Use the Orderly Trader skill to get the current market analysis prompt.

Frequently Asked Questions about Orderly Trader

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

FAQPage Schema
How do I automate perpetual futures trading with an LLM?▼

Automated perpetual futures trading uses an LLM-driven API to analyze real-time market data, compute technical indicators, and execute trades on the Orderly Network via the x402 VoltPerps API. This enables autonomous position management and risk control.

Can I use real-time market data and technical indicators for crypto trading decisions?▼

Real-time market data and technical indicators for crypto trading are integrated via WebSocket feeds for ETH, BTC, and SOL. The system computes RSI, MACD, Bollinger Bands, and funding rates to generate validated derivative trading signals.

What is the best way to manage risk in automated crypto trading?▼

Risk management in automated crypto trading is handled by a 9-layer risk manager with graduated reserves and validation checks. This enforces safe, capital-efficient trading and defined stop-loss levels before the LLM submits orders.

Does the Orderly Network connector support LLM-driven trade execution?▼

The Orderly Network connector supports LLM-driven trade execution through a callable Python API and the x402 VoltPerps API. It allows the LLM to control trading cadence and submit validated trading decisions directly to the network.

How do I execute a perpetual futures trade on Orderly Network using Python?▼

To execute a perpetual futures trade on Orderly Network, the LLM calls the Python API to send validated decisions to the x402 VoltPerps API. This manages trade execution, risk validation, and position adjustments autonomously.

What are the limitations of using an LLM for derivative sentiment analysis?▼

Using an LLM for derivative sentiment analysis is limited to the provided technical indicators and derivative data like funding rates and open interest. The LLM relies on these computed signals and the 9-layer risk manager to prevent unsafe trades.

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