--- name: robonet-workbench description: "Use Robonet's MCP server to build, backtest, optimize, and deploy trading strategies. Provides 24 specialized tools for crypto and prediction market trading: (1) Data tools for browsing strategies, symbols, indicators, Allora topics, and backtest results, (2) AI tools for generating strategy ideas and code, optimizing parameters, and enhancing with ML predictions, (3) Backtesting tools for testing strategy performance on historical data, (4) Prediction market tools for Polymarket trading strategies, (5) Deployment tools for live trading on Hyperliquid, (6) Account tools for credit management. Use when: building trading strategies, backtesting strategies, deploying trading bots, working with Hyperliquid or Polymarket, or enhancing strategies with Allora Network ML predictions." ---

Robonet MCP Integration

Overview

Robonet provides an MCP server that enables AI assistants to build, test, and deploy trading strategies. The server offers 24 tools organized into 6 categories: Data Access (8), AI-Powered Strategy Generation (6), Backtesting (2), Prediction Markets (3), Deployment (4), and Account Management (2).

Quick Start

Load the required MCP tools before using them:

Use MCPSearch to select: mcp__workbench__get_all_symbols
Use MCPSearch to select: mcp__workbench__create_strategy
Use MCPSearch to select: mcp__workbench__run_backtest

After loading, call the tools directly to interact with Robonet.

Tool Categories

1. Data Access Tools (Fast, <1s execution)

Browse available resources before building strategies:

• get_all_strategies - List your trading strategies with optional backtest results
• get_strategy_code - View Python source code of a strategy
• get_strategy_versions - Track strategy evolution across versions
• get_all_symbols - List tradeable pairs on Hyperliquid (BTC-USDT, ETH-USDT, etc.)
• get_all_technical_indicators - Browse 170+ indicators (RSI, MACD, Bollinger Bands, etc.)
• get_allora_topics - List Allora Network ML prediction topics
• get_data_availability - Check data ranges before backtesting
• get_latest_backtest_results - View recent backtest performance

Pricing: Most $0.001, some free. Use these liberally to explore.

When to use: Start every workflow by checking available symbols, indicators, or existing strategies before generating new code.

2. AI-Powered Strategy Tools (20-60s execution)

Generate and improve trading strategies:

• generate_ideas - Get AI-generated strategy concepts based on market data
• create_strategy - Generate complete Python strategy from description
• optimize_strategy - Tune parameters for better performance
• enhance_with_allora - Add Allora Network ML predictions to strategy
• refine_strategy - Make targeted code improvements
• create_prediction_market_strategy - Generate Polymarket YES/NO trading logic

Pricing: Real LLM cost + margin ($0.50-$4.50 typical). These are the most expensive tools.

When to use: After understanding available resources, use these to build or improve strategies. Always backtest after generation.

3. Backtesting Tools (20-40s execution)

Test strategy performance on historical data:

• run_backtest - Test crypto trading strategies
• run_prediction_market_backtest - Test Polymarket strategies

Pricing: $0.001 per backtest

Returns: Performance metrics (Sharpe ratio, max drawdown, win rate, total return, profit factor), trade statistics, equity curve data

When to use: After creating or modifying a strategy, always backtest before deploying. Use multiple time periods to validate robustness.

4. Prediction Market Tools

Build Polymarket trading strategies:

• get_all_prediction_events - Browse available prediction markets
• get_prediction_market_data - Analyze YES/NO token price history
• create_prediction_market_strategy - Generate Polymarket strategy code

Pricing: $0.001 for data tools, Real LLM cost + margin for creation

When to use: For prediction market trading strategies on Polymarket (politics, crypto price predictions, economics events)

5. Deployment Tools

Deploy strategies to live trading on Hyperliquid:

• deployment_create - Launch live trading agent (EOA or Hyperliquid Vault)
• deployment_list - Monitor active deployments
• deployment_start - Resume stopped deployment
• deployment_stop - Halt live trading

Pricing: $0.50 to create, free for list/start/stop

Constraints:

• EOA (wallet): Max 1 active deployment per wallet
• Hyperliquid Vault: Requires 200+ USDC in wallet, unlimited deployments

When to use: After thorough backtesting shows positive results. Never deploy without backtesting first.

6. Account Tools

Manage credits and view account info:

• get_credit_balance - Check available USDC credits
• get_credit_transactions - View transaction history

Pricing: Free

When to use: Check balance before expensive operations. Monitor spending via transaction history.

Common Workflows

Workflow 1: Create and Test New Strategy

1. get_all_symbols → See available trading pairs
2. get_all_technical_indicators → Browse indicators
3. create_strategy → Generate Python code from description
4. run_backtest → Test on 6+ months of data
5. If promising: optimize_strategy → Tune parameters
6. If excellent: enhance_with_allora → Add ML signals
7. run_backtest → Validate improvements
8. If ready: deployment_create → Deploy to live trading

Cost: ~$1-5 depending on optimization and enhancement

Workflow 2: Enhance Existing Strategy

1. get_all_strategies (include_latest_backtest=true) → Find strategy
2. get_strategy_code → Review implementation
3. refine_strategy (mode="new") → Make targeted improvements
4. run_backtest → Test changes
5. If better: enhance_with_allora → Add ML predictions
6. run_backtest → Final validation

Cost: ~$0.50-2.00

Workflow 3: Prediction Market Trading

1. get_all_prediction_events → Browse markets
2. get_prediction_market_data → Analyze price history
3. create_prediction_market_strategy → Build YES/NO logic
4. run_prediction_market_backtest → Test performance
5. If profitable: deployment_create → Deploy (when supported)

Cost: ~$0.50-5.00

Workflow 4: Explore Ideas Before Building

1. get_all_symbols → Check available pairs
2. get_allora_topics → See ML prediction coverage
3. generate_ideas (strategy_count=3) → Get AI concepts
4. Pick favorite idea
5. create_strategy → Implement chosen concept
6. run_backtest → Validate

Cost: ~$0.50-4.50 (use generate_ideas to explore cheaply)

Strategy Development Best Practices

Start with Data Exploration

Always check availability before building:

• Use get_data_availability to verify symbol has sufficient history
• Check get_allora_topics if planning ML enhancement
• Review get_all_technical_indicators to know what's available

Always Backtest

Never deploy without backtesting:

• Test on 6+ months of data minimum
• Use multiple time periods (train vs validation)
• Check metrics: Sharpe >1.0, max drawdown <20%, win rate 45-65%
• Compare performance across different market conditions

Cost Management

Tools are priced in tiers:

1. Data tools ($0.001 or free) - Use liberally
2. Backtesting ($0.001) - Use frequently
3. AI generation (LLM cost + margin) - Most expensive
4. Deployment ($0.50) - One-time per deployment

Cost-saving tips:

• Use generate_ideas ($0.05-0.50) before create_strategy ($1-4)
• Check get_latest_backtest_results (free) before running new backtest
• Use refine_strategy ($0.50-1.50) instead of regenerating with create_strategy
• Review get_strategy_code (free) before modifying

Strategy Naming Convention

Follow this pattern: {Name}_{RiskLevel}[_suffix]

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