robonet-workbenchUse 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.
Install via ClawdBot CLI:
clawdbot install nickemmons/robonet-workbenchRobonet 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).
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_backtestAfter loading, call the tools directly to interact with Robonet.
Browse available resources before building strategies:
get_all_strategies - List your trading strategies with optional backtest resultsget_strategy_code - View Python source code of a strategyget_strategy_versions - Track strategy evolution across versionsget_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 topicsget_data_availability - Check data ranges before backtestingget_latest_backtest_results - View recent backtest performancePricing: 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.
Generate and improve trading strategies:
generate_ideas - Get AI-generated strategy concepts based on market datacreate_strategy - Generate complete Python strategy from descriptionoptimize_strategy - Tune parameters for better performanceenhance_with_allora - Add Allora Network ML predictions to strategyrefine_strategy - Make targeted code improvementscreate_prediction_market_strategy - Generate Polymarket YES/NO trading logicPricing: 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.
Test strategy performance on historical data:
run_backtest - Test crypto trading strategiesrun_prediction_market_backtest - Test Polymarket strategiesPricing: $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.
Build Polymarket trading strategies:
get_all_prediction_events - Browse available prediction marketsget_prediction_market_data - Analyze YES/NO token price historycreate_prediction_market_strategy - Generate Polymarket strategy codePricing: $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)
Deploy strategies to live trading on Hyperliquid:
deployment_create - Launch live trading agent (EOA or Hyperliquid Vault)deployment_list - Monitor active deploymentsdeployment_start - Resume stopped deploymentdeployment_stop - Halt live tradingPricing: $0.50 to create, free for list/start/stop
Constraints:
When to use: After thorough backtesting shows positive results. Never deploy without backtesting first.
Manage credits and view account info:
get_credit_balance - Check available USDC creditsget_credit_transactions - View transaction historyPricing: Free
When to use: Check balance before expensive operations. Monitor spending via transaction history.
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 tradingCost: ~$1-5 depending on optimization and enhancement
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 validationCost: ~$0.50-2.00
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
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 → ValidateCost: ~$0.50-4.50 (use generate_ideas to explore cheaply)
Always check availability before building:
get_data_availability to verify symbol has sufficient historyget_allora_topics if planning ML enhancementget_all_technical_indicators to know what's availableNever deploy without backtesting:
Tools are priced in tiers:
Cost-saving tips:
generate_ideas ($0.05-0.50) before create_strategy ($1-4)get_latest_backtest_results (free) before running new backtestrefine_strategy ($0.50-1.50) instead of regenerating with create_strategyget_strategy_code (free) before modifyingFollow this pattern: {Name}_{RiskLevel}[_suffix]
Examples:
RSIMeanReversion_M - Base strategy, medium riskMomentumBreakout_H_optimized - After optimization, high riskTrendFollower_L_allora - With Allora ML, low riskRisk levels: H (high), M (medium), L (low)
Strategies use the Jesse trading framework with these required methods:
should_long() - Check if conditions met for long entryshould_short() - Check if conditions met for short entrygo_long() - Execute long entry with position sizinggo_short() - Execute short entry with position sizingOptional methods:
on_open_position(order) - Set stop loss, take profit after entryupdate_position() - Trailing stops, position managementshould_cancel_entry() - Cancel unfilled orders170+ technical indicators via jesse.indicators:
Use get_all_technical_indicators to see the full list.
Add ML price predictions to strategies:
Use enhance_with_allora to automatically integrate predictions, or manually add via self.get_predictions() in strategy code.
EOA (Externally Owned Account):
Hyperliquid Vault:
Check balance: get_credit_balance
Purchase credits in Robonet dashboard if needed
Use get_data_availability to check symbol coverage
Try shorter date range or different symbol
BTC-USDT and ETH-USDT have longest history (2020-present)
Entry conditions may be too restrictive
Try longer test period or adjust thresholds
Use get_strategy_code to review logic
Long date ranges (>2 years) or high-frequency timeframes (1m) are slow
Use shorter ranges or lower frequency timeframes
Strategies are linked to API key's wallet
Ensure logged into same account that owns the API key
Refresh "My Strategies" page
For detailed parameter documentation on all 24 tools, see:
The catalog includes:
Create a simple strategy:
Use Robonet MCP to create a momentum strategy for BTC-USDT on 4h timeframe that:
- Enters long when RSI crosses above 30 and price is above 50-day EMA
- Exits with 2% stop loss or 4% take profit
- Uses 95% of available marginBacktest existing strategy:
Backtest my RSIMeanReversion_M strategy on ETH-USDT 1h timeframe from 2024-01-01 to 2024-06-30Optimize parameters:
Optimize the RSI period and stop loss percentage for my MomentumBreakout_H strategy on BTC-USDT 4h from 2024-01-01 to 2024-12-31Add ML predictions:
Enhance my TrendFollower_M strategy with Allora predictions for ETH-USDT 8h timeframe and compare performanceDeploy to live trading:
Deploy my RSIMeanReversion_M_allora strategy to Hyperliquid on BTC-USDT 4h with 2x leverage using EOA deploymentGenerated Mar 1, 2026
A firm uses Robonet to rapidly prototype and backtest new algorithmic trading strategies for crypto markets. Analysts generate strategy ideas with AI, optimize parameters, and deploy to Hyperliquid for live trading, leveraging Allora Network ML predictions for edge.
An individual trader builds and tests automated strategies for Polymarket events like elections or crypto price predictions. They browse prediction events, generate YES/NO trading logic, backtest on historical data, and deploy bots to capitalize on market inefficiencies.
A hedge fund enhances existing trading strategies by integrating Allora Network ML predictions via Robonet. They refine code, run backtests to validate improvements, and deploy optimized strategies to Hyperliquid vaults for scalable live execution.
A solo developer creates and sells custom trading bots using Robonet. They generate strategy code from descriptions, backtest performance, and offer deployment services to clients for passive income on Hyperliquid or Polymarket.
Offer subscription-based access to pre-built, AI-optimized trading strategies deployed on Hyperliquid. Clients pay monthly fees for performance updates and maintenance, generating recurring revenue from strategy licensing.
Provide consulting services to traders and firms for strategy development, using Robonet's tools to backtest, optimize parameters, and enhance with ML predictions. Charge per project or hourly for analysis and code improvements.
Build and sell automated trading bots for Polymarket events, leveraging Robonet to generate and test strategies. Monetize through one-time sales or revenue-sharing models where bots earn a percentage of trading profits.
💬 Integration Tip
Start by loading key MCP tools like get_all_symbols and create_strategy, then follow structured workflows to minimize costs and ensure robust backtesting before live deployment.
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