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    "slug": "robonet-workbench",
    "name": "Robonet",
    "source": "tencent",
    "type": "skill",
    "category": "开发工具",
    "sourceUrl": "https://clawhub.ai/nickemmons/robonet-workbench",
    "canonicalUrl": "https://clawhub.ai/nickemmons/robonet-workbench",
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      "shared-references/tool-catalog.md"
    ],
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    "quickSetup": [
      "Download the package from Yavira.",
      "Extract the archive and review SKILL.md first.",
      "Import or place the package into your OpenClaw setup."
    ],
    "agentAssist": {
      "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.",
      "steps": [
        "Download the package from Yavira.",
        "Extract it into a folder your agent can access.",
        "Paste one of the prompts below and point your agent at the extracted folder."
      ],
      "prompts": [
        {
          "label": "New install",
          "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete."
        },
        {
          "label": "Upgrade existing",
          "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run."
        }
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      "checkedAt": "2026-05-07T17:22:31.273Z",
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        "redirectLocation": null,
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      },
      "scope": "source",
      "summary": "Source download looks usable.",
      "detail": "Yavira can redirect you to the upstream package for this source.",
      "primaryActionLabel": "Download for OpenClaw",
      "primaryActionHref": "/downloads/robonet-workbench"
    },
    "validation": {
      "installChecklist": [
        "Use the Yavira download entry.",
        "Review SKILL.md after the package is downloaded.",
        "Confirm the extracted package contains the expected setup assets."
      ],
      "postInstallChecks": [
        "Confirm the extracted package includes the expected docs or setup files.",
        "Validate the skill or prompts are available in your target agent workspace.",
        "Capture any manual follow-up steps the agent could not complete."
      ]
    },
    "downloadPageUrl": "https://openagent3.xyz/downloads/robonet-workbench",
    "agentPageUrl": "https://openagent3.xyz/skills/robonet-workbench/agent",
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    "briefUrl": "https://openagent3.xyz/skills/robonet-workbench/agent.md"
  },
  "agentAssist": {
    "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.",
    "steps": [
      "Download the package from Yavira.",
      "Extract it into a folder your agent can access.",
      "Paste one of the prompts below and point your agent at the extracted folder."
    ],
    "prompts": [
      {
        "label": "New install",
        "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete."
      },
      {
        "label": "Upgrade existing",
        "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run."
      }
    ]
  },
  "documentation": {
    "source": "clawhub",
    "primaryDoc": "SKILL.md",
    "sections": [
      {
        "title": "Overview",
        "body": "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)."
      },
      {
        "title": "Quick Start",
        "body": "Load the required MCP tools before using them:\n\nUse MCPSearch to select: mcp__workbench__get_all_symbols\nUse MCPSearch to select: mcp__workbench__create_strategy\nUse MCPSearch to select: mcp__workbench__run_backtest\n\nAfter loading, call the tools directly to interact with Robonet."
      },
      {
        "title": "1. Data Access Tools (Fast, <1s execution)",
        "body": "Browse available resources before building strategies:\n\nget_all_strategies - List your trading strategies with optional backtest results\nget_strategy_code - View Python source code of a strategy\nget_strategy_versions - Track strategy evolution across versions\nget_all_symbols - List tradeable pairs on Hyperliquid (BTC-USDT, ETH-USDT, etc.)\nget_all_technical_indicators - Browse 170+ indicators (RSI, MACD, Bollinger Bands, etc.)\nget_allora_topics - List Allora Network ML prediction topics\nget_data_availability - Check data ranges before backtesting\nget_latest_backtest_results - View recent backtest performance\n\nPricing: Most $0.001, some free. Use these liberally to explore.\n\nWhen to use: Start every workflow by checking available symbols, indicators, or existing strategies before generating new code."
      },
      {
        "title": "2. AI-Powered Strategy Tools (20-60s execution)",
        "body": "Generate and improve trading strategies:\n\ngenerate_ideas - Get AI-generated strategy concepts based on market data\ncreate_strategy - Generate complete Python strategy from description\noptimize_strategy - Tune parameters for better performance\nenhance_with_allora - Add Allora Network ML predictions to strategy\nrefine_strategy - Make targeted code improvements\ncreate_prediction_market_strategy - Generate Polymarket YES/NO trading logic\n\nPricing: Real LLM cost + margin ($0.50-$4.50 typical). These are the most expensive tools.\n\nWhen to use: After understanding available resources, use these to build or improve strategies. Always backtest after generation."
      },
      {
        "title": "3. Backtesting Tools (20-40s execution)",
        "body": "Test strategy performance on historical data:\n\nrun_backtest - Test crypto trading strategies\nrun_prediction_market_backtest - Test Polymarket strategies\n\nPricing: $0.001 per backtest\n\nReturns: Performance metrics (Sharpe ratio, max drawdown, win rate, total return, profit factor), trade statistics, equity curve data\n\nWhen to use: After creating or modifying a strategy, always backtest before deploying. Use multiple time periods to validate robustness."
      },
      {
        "title": "4. Prediction Market Tools",
        "body": "Build Polymarket trading strategies:\n\nget_all_prediction_events - Browse available prediction markets\nget_prediction_market_data - Analyze YES/NO token price history\ncreate_prediction_market_strategy - Generate Polymarket strategy code\n\nPricing: $0.001 for data tools, Real LLM cost + margin for creation\n\nWhen to use: For prediction market trading strategies on Polymarket (politics, crypto price predictions, economics events)"
      },
      {
        "title": "5. Deployment Tools",
        "body": "Deploy strategies to live trading on Hyperliquid:\n\ndeployment_create - Launch live trading agent (EOA or Hyperliquid Vault)\ndeployment_list - Monitor active deployments\ndeployment_start - Resume stopped deployment\ndeployment_stop - Halt live trading\n\nPricing: $0.50 to create, free for list/start/stop\n\nConstraints:\n\nEOA (wallet): Max 1 active deployment per wallet\nHyperliquid Vault: Requires 200+ USDC in wallet, unlimited deployments\n\nWhen to use: After thorough backtesting shows positive results. Never deploy without backtesting first."
      },
      {
        "title": "6. Account Tools",
        "body": "Manage credits and view account info:\n\nget_credit_balance - Check available USDC credits\nget_credit_transactions - View transaction history\n\nPricing: Free\n\nWhen to use: Check balance before expensive operations. Monitor spending via transaction history."
      },
      {
        "title": "Workflow 1: Create and Test New Strategy",
        "body": "1. get_all_symbols → See available trading pairs\n2. get_all_technical_indicators → Browse indicators\n3. create_strategy → Generate Python code from description\n4. run_backtest → Test on 6+ months of data\n5. If promising: optimize_strategy → Tune parameters\n6. If excellent: enhance_with_allora → Add ML signals\n7. run_backtest → Validate improvements\n8. If ready: deployment_create → Deploy to live trading\n\nCost: ~$1-5 depending on optimization and enhancement"
      },
      {
        "title": "Workflow 2: Enhance Existing Strategy",
        "body": "1. get_all_strategies (include_latest_backtest=true) → Find strategy\n2. get_strategy_code → Review implementation\n3. refine_strategy (mode=\"new\") → Make targeted improvements\n4. run_backtest → Test changes\n5. If better: enhance_with_allora → Add ML predictions\n6. run_backtest → Final validation\n\nCost: ~$0.50-2.00"
      },
      {
        "title": "Workflow 3: Prediction Market Trading",
        "body": "1. get_all_prediction_events → Browse markets\n2. get_prediction_market_data → Analyze price history\n3. create_prediction_market_strategy → Build YES/NO logic\n4. run_prediction_market_backtest → Test performance\n5. If profitable: deployment_create → Deploy (when supported)\n\nCost: ~$0.50-5.00"
      },
      {
        "title": "Workflow 4: Explore Ideas Before Building",
        "body": "1. get_all_symbols → Check available pairs\n2. get_allora_topics → See ML prediction coverage\n3. generate_ideas (strategy_count=3) → Get AI concepts\n4. Pick favorite idea\n5. create_strategy → Implement chosen concept\n6. run_backtest → Validate\n\nCost: ~$0.50-4.50 (use generate_ideas to explore cheaply)"
      },
      {
        "title": "Start with Data Exploration",
        "body": "Always check availability before building:\n\nUse get_data_availability to verify symbol has sufficient history\nCheck get_allora_topics if planning ML enhancement\nReview get_all_technical_indicators to know what's available"
      },
      {
        "title": "Always Backtest",
        "body": "Never deploy without backtesting:\n\nTest on 6+ months of data minimum\nUse multiple time periods (train vs validation)\nCheck metrics: Sharpe >1.0, max drawdown <20%, win rate 45-65%\nCompare performance across different market conditions"
      },
      {
        "title": "Cost Management",
        "body": "Tools are priced in tiers:\n\nData tools ($0.001 or free) - Use liberally\nBacktesting ($0.001) - Use frequently\nAI generation (LLM cost + margin) - Most expensive\nDeployment ($0.50) - One-time per deployment\n\nCost-saving tips:\n\nUse generate_ideas ($0.05-0.50) before create_strategy ($1-4)\nCheck get_latest_backtest_results (free) before running new backtest\nUse refine_strategy ($0.50-1.50) instead of regenerating with create_strategy\nReview get_strategy_code (free) before modifying"
      },
      {
        "title": "Strategy Naming Convention",
        "body": "Follow this pattern: {Name}_{RiskLevel}[_suffix]\n\nExamples:\n\nRSIMeanReversion_M - Base strategy, medium risk\nMomentumBreakout_H_optimized - After optimization, high risk\nTrendFollower_L_allora - With Allora ML, low risk\n\nRisk levels: H (high), M (medium), L (low)"
      },
      {
        "title": "Strategy Framework",
        "body": "Strategies use the Jesse trading framework with these required methods:\n\nshould_long() - Check if conditions met for long entry\nshould_short() - Check if conditions met for short entry\ngo_long() - Execute long entry with position sizing\ngo_short() - Execute short entry with position sizing\n\nOptional methods:\n\non_open_position(order) - Set stop loss, take profit after entry\nupdate_position() - Trailing stops, position management\nshould_cancel_entry() - Cancel unfilled orders"
      },
      {
        "title": "Available Indicators",
        "body": "170+ technical indicators via jesse.indicators:\n\nMomentum: RSI, MACD, Stochastic, ADX, CCI, MFI\nTrend: EMA, SMA, Supertrend, Parabolic SAR, VWAP\nVolatility: Bollinger Bands, ATR, Keltner Channels\nVolume: OBV, Volume Profile, Chaikin Money Flow\nAnd many more...\n\nUse get_all_technical_indicators to see the full list."
      },
      {
        "title": "Allora Network Integration",
        "body": "Add ML price predictions to strategies:\n\nPrediction types: Log return (percentage change) or absolute price\nHorizons: 5m, 8h, 24h, 1 week\nAssets: BTC, ETH, SOL, NEAR\nNetworks: Mainnet (10 topics) and Testnet (26 topics)\n\nUse enhance_with_allora to automatically integrate predictions, or manually add via self.get_predictions() in strategy code."
      },
      {
        "title": "Deployment Options",
        "body": "EOA (Externally Owned Account):\n\nDirect wallet trading\nMax 1 active deployment per wallet\nImmediate deployment\nLower setup complexity\n\nHyperliquid Vault:\n\nRequires 200+ USDC in wallet\nUnlimited deployments\nProfessional vault setup\nPublic TVL and performance tracking"
      },
      {
        "title": "\"Insufficient Credits\" Error",
        "body": "Check balance: get_credit_balance\nPurchase credits in Robonet dashboard if needed"
      },
      {
        "title": "\"No Data Available\" for Backtest",
        "body": "Use get_data_availability to check symbol coverage\nTry shorter date range or different symbol\nBTC-USDT and ETH-USDT have longest history (2020-present)"
      },
      {
        "title": "\"No Trades Generated\" in Backtest",
        "body": "Entry conditions may be too restrictive\nTry longer test period or adjust thresholds\nUse get_strategy_code to review logic"
      },
      {
        "title": "Backtest Takes >2 Minutes",
        "body": "Long date ranges (>2 years) or high-frequency timeframes (1m) are slow\nUse shorter ranges or lower frequency timeframes"
      },
      {
        "title": "Strategy Not Showing in Web Interface",
        "body": "Strategies are linked to API key's wallet\nEnsure logged into same account that owns the API key\nRefresh \"My Strategies\" page"
      },
      {
        "title": "Complete Tool Reference",
        "body": "For detailed parameter documentation on all 24 tools, see:\n\n./shared-references/tool-catalog.md\n\nThe catalog includes:\n\nFull parameter specifications with types and defaults\nReturn value descriptions\nPricing for each tool\nExecution time estimates\nUsage examples"
      },
      {
        "title": "Example Prompts",
        "body": "Create a simple strategy:\n\nUse Robonet MCP to create a momentum strategy for BTC-USDT on 4h timeframe that:\n- Enters long when RSI crosses above 30 and price is above 50-day EMA\n- Exits with 2% stop loss or 4% take profit\n- Uses 95% of available margin\n\nBacktest existing strategy:\n\nBacktest my RSIMeanReversion_M strategy on ETH-USDT 1h timeframe from 2024-01-01 to 2024-06-30\n\nOptimize parameters:\n\nOptimize the RSI period and stop loss percentage for my MomentumBreakout_H strategy on BTC-USDT 4h from 2024-01-01 to 2024-12-31\n\nAdd ML predictions:\n\nEnhance my TrendFollower_M strategy with Allora predictions for ETH-USDT 8h timeframe and compare performance\n\nDeploy to live trading:\n\nDeploy my RSIMeanReversion_M_allora strategy to Hyperliquid on BTC-USDT 4h with 2x leverage using EOA deployment"
      },
      {
        "title": "Security & Access",
        "body": "All tools require valid API key from Robonet\nStrategies are wallet-scoped (only creator can access)\nCredits reserved atomically before execution\nAPI keys never committed to version control\nUse environment variables or secure config for API keys"
      },
      {
        "title": "Resources",
        "body": "Robonet Dashboard: robonet.finance\nAPI Key Management: Dashboard → Settings → API Keys\nCredit Purchase: Dashboard → Settings → Billing\nJesse Framework Docs: jesse.trade\nAllora Network: allora.network\nHyperliquid: hyperliquid.xyz\nSupport: Discord or support@robonet.finance"
      }
    ],
    "body": "Robonet MCP Integration\nOverview\n\nRobonet 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).\n\nQuick Start\n\nLoad the required MCP tools before using them:\n\nUse MCPSearch to select: mcp__workbench__get_all_symbols\nUse MCPSearch to select: mcp__workbench__create_strategy\nUse MCPSearch to select: mcp__workbench__run_backtest\n\n\nAfter loading, call the tools directly to interact with Robonet.\n\nTool Categories\n1. Data Access Tools (Fast, <1s execution)\n\nBrowse available resources before building strategies:\n\nget_all_strategies - List your trading strategies with optional backtest results\nget_strategy_code - View Python source code of a strategy\nget_strategy_versions - Track strategy evolution across versions\nget_all_symbols - List tradeable pairs on Hyperliquid (BTC-USDT, ETH-USDT, etc.)\nget_all_technical_indicators - Browse 170+ indicators (RSI, MACD, Bollinger Bands, etc.)\nget_allora_topics - List Allora Network ML prediction topics\nget_data_availability - Check data ranges before backtesting\nget_latest_backtest_results - View recent backtest performance\n\nPricing: Most $0.001, some free. Use these liberally to explore.\n\nWhen to use: Start every workflow by checking available symbols, indicators, or existing strategies before generating new code.\n\n2. AI-Powered Strategy Tools (20-60s execution)\n\nGenerate and improve trading strategies:\n\ngenerate_ideas - Get AI-generated strategy concepts based on market data\ncreate_strategy - Generate complete Python strategy from description\noptimize_strategy - Tune parameters for better performance\nenhance_with_allora - Add Allora Network ML predictions to strategy\nrefine_strategy - Make targeted code improvements\ncreate_prediction_market_strategy - Generate Polymarket YES/NO trading logic\n\nPricing: Real LLM cost + margin ($0.50-$4.50 typical). These are the most expensive tools.\n\nWhen to use: After understanding available resources, use these to build or improve strategies. Always backtest after generation.\n\n3. Backtesting Tools (20-40s execution)\n\nTest strategy performance on historical data:\n\nrun_backtest - Test crypto trading strategies\nrun_prediction_market_backtest - Test Polymarket strategies\n\nPricing: $0.001 per backtest\n\nReturns: Performance metrics (Sharpe ratio, max drawdown, win rate, total return, profit factor), trade statistics, equity curve data\n\nWhen to use: After creating or modifying a strategy, always backtest before deploying. Use multiple time periods to validate robustness.\n\n4. Prediction Market Tools\n\nBuild Polymarket trading strategies:\n\nget_all_prediction_events - Browse available prediction markets\nget_prediction_market_data - Analyze YES/NO token price history\ncreate_prediction_market_strategy - Generate Polymarket strategy code\n\nPricing: $0.001 for data tools, Real LLM cost + margin for creation\n\nWhen to use: For prediction market trading strategies on Polymarket (politics, crypto price predictions, economics events)\n\n5. Deployment Tools\n\nDeploy strategies to live trading on Hyperliquid:\n\ndeployment_create - Launch live trading agent (EOA or Hyperliquid Vault)\ndeployment_list - Monitor active deployments\ndeployment_start - Resume stopped deployment\ndeployment_stop - Halt live trading\n\nPricing: $0.50 to create, free for list/start/stop\n\nConstraints:\n\nEOA (wallet): Max 1 active deployment per wallet\nHyperliquid Vault: Requires 200+ USDC in wallet, unlimited deployments\n\nWhen to use: After thorough backtesting shows positive results. Never deploy without backtesting first.\n\n6. Account Tools\n\nManage credits and view account info:\n\nget_credit_balance - Check available USDC credits\nget_credit_transactions - View transaction history\n\nPricing: Free\n\nWhen to use: Check balance before expensive operations. Monitor spending via transaction history.\n\nCommon Workflows\nWorkflow 1: Create and Test New Strategy\n1. get_all_symbols → See available trading pairs\n2. get_all_technical_indicators → Browse indicators\n3. create_strategy → Generate Python code from description\n4. run_backtest → Test on 6+ months of data\n5. If promising: optimize_strategy → Tune parameters\n6. If excellent: enhance_with_allora → Add ML signals\n7. run_backtest → Validate improvements\n8. If ready: deployment_create → Deploy to live trading\n\n\nCost: ~$1-5 depending on optimization and enhancement\n\nWorkflow 2: Enhance Existing Strategy\n1. get_all_strategies (include_latest_backtest=true) → Find strategy\n2. get_strategy_code → Review implementation\n3. refine_strategy (mode=\"new\") → Make targeted improvements\n4. run_backtest → Test changes\n5. If better: enhance_with_allora → Add ML predictions\n6. run_backtest → Final validation\n\n\nCost: ~$0.50-2.00\n\nWorkflow 3: Prediction Market Trading\n1. get_all_prediction_events → Browse markets\n2. get_prediction_market_data → Analyze price history\n3. create_prediction_market_strategy → Build YES/NO logic\n4. run_prediction_market_backtest → Test performance\n5. If profitable: deployment_create → Deploy (when supported)\n\n\nCost: ~$0.50-5.00\n\nWorkflow 4: Explore Ideas Before Building\n1. get_all_symbols → Check available pairs\n2. get_allora_topics → See ML prediction coverage\n3. generate_ideas (strategy_count=3) → Get AI concepts\n4. Pick favorite idea\n5. create_strategy → Implement chosen concept\n6. run_backtest → Validate\n\n\nCost: ~$0.50-4.50 (use generate_ideas to explore cheaply)\n\nStrategy Development Best Practices\nStart with Data Exploration\n\nAlways check availability before building:\n\nUse get_data_availability to verify symbol has sufficient history\nCheck get_allora_topics if planning ML enhancement\nReview get_all_technical_indicators to know what's available\nAlways Backtest\n\nNever deploy without backtesting:\n\nTest on 6+ months of data minimum\nUse multiple time periods (train vs validation)\nCheck metrics: Sharpe >1.0, max drawdown <20%, win rate 45-65%\nCompare performance across different market conditions\nCost Management\n\nTools are priced in tiers:\n\nData tools ($0.001 or free) - Use liberally\nBacktesting ($0.001) - Use frequently\nAI generation (LLM cost + margin) - Most expensive\nDeployment ($0.50) - One-time per deployment\n\nCost-saving tips:\n\nUse generate_ideas ($0.05-0.50) before create_strategy ($1-4)\nCheck get_latest_backtest_results (free) before running new backtest\nUse refine_strategy ($0.50-1.50) instead of regenerating with create_strategy\nReview get_strategy_code (free) before modifying\nStrategy Naming Convention\n\nFollow this pattern: {Name}_{RiskLevel}[_suffix]\n\nExamples:\n\nRSIMeanReversion_M - Base strategy, medium risk\nMomentumBreakout_H_optimized - After optimization, high risk\nTrendFollower_L_allora - With Allora ML, low risk\n\nRisk levels: H (high), M (medium), L (low)\n\nTechnical Details\nStrategy Framework\n\nStrategies use the Jesse trading framework with these required methods:\n\nshould_long() - Check if conditions met for long entry\nshould_short() - Check if conditions met for short entry\ngo_long() - Execute long entry with position sizing\ngo_short() - Execute short entry with position sizing\n\nOptional methods:\n\non_open_position(order) - Set stop loss, take profit after entry\nupdate_position() - Trailing stops, position management\nshould_cancel_entry() - Cancel unfilled orders\nAvailable Indicators\n\n170+ technical indicators via jesse.indicators:\n\nMomentum: RSI, MACD, Stochastic, ADX, CCI, MFI\nTrend: EMA, SMA, Supertrend, Parabolic SAR, VWAP\nVolatility: Bollinger Bands, ATR, Keltner Channels\nVolume: OBV, Volume Profile, Chaikin Money Flow\nAnd many more...\n\nUse get_all_technical_indicators to see the full list.\n\nAllora Network Integration\n\nAdd ML price predictions to strategies:\n\nPrediction types: Log return (percentage change) or absolute price\nHorizons: 5m, 8h, 24h, 1 week\nAssets: BTC, ETH, SOL, NEAR\nNetworks: Mainnet (10 topics) and Testnet (26 topics)\n\nUse enhance_with_allora to automatically integrate predictions, or manually add via self.get_predictions() in strategy code.\n\nDeployment Options\n\nEOA (Externally Owned Account):\n\nDirect wallet trading\nMax 1 active deployment per wallet\nImmediate deployment\nLower setup complexity\n\nHyperliquid Vault:\n\nRequires 200+ USDC in wallet\nUnlimited deployments\nProfessional vault setup\nPublic TVL and performance tracking\nTroubleshooting\n\"Insufficient Credits\" Error\n\nCheck balance: get_credit_balance Purchase credits in Robonet dashboard if needed\n\n\"No Data Available\" for Backtest\n\nUse get_data_availability to check symbol coverage Try shorter date range or different symbol BTC-USDT and ETH-USDT have longest history (2020-present)\n\n\"No Trades Generated\" in Backtest\n\nEntry conditions may be too restrictive Try longer test period or adjust thresholds Use get_strategy_code to review logic\n\nBacktest Takes >2 Minutes\n\nLong date ranges (>2 years) or high-frequency timeframes (1m) are slow Use shorter ranges or lower frequency timeframes\n\nStrategy Not Showing in Web Interface\n\nStrategies are linked to API key's wallet Ensure logged into same account that owns the API key Refresh \"My Strategies\" page\n\nComplete Tool Reference\n\nFor detailed parameter documentation on all 24 tools, see:\n\n./shared-references/tool-catalog.md\n\nThe catalog includes:\n\nFull parameter specifications with types and defaults\nReturn value descriptions\nPricing for each tool\nExecution time estimates\nUsage examples\nExample Prompts\n\nCreate a simple strategy:\n\nUse Robonet MCP to create a momentum strategy for BTC-USDT on 4h timeframe that:\n- Enters long when RSI crosses above 30 and price is above 50-day EMA\n- Exits with 2% stop loss or 4% take profit\n- Uses 95% of available margin\n\n\nBacktest existing strategy:\n\nBacktest my RSIMeanReversion_M strategy on ETH-USDT 1h timeframe from 2024-01-01 to 2024-06-30\n\n\nOptimize parameters:\n\nOptimize the RSI period and stop loss percentage for my MomentumBreakout_H strategy on BTC-USDT 4h from 2024-01-01 to 2024-12-31\n\n\nAdd ML predictions:\n\nEnhance my TrendFollower_M strategy with Allora predictions for ETH-USDT 8h timeframe and compare performance\n\n\nDeploy to live trading:\n\nDeploy my RSIMeanReversion_M_allora strategy to Hyperliquid on BTC-USDT 4h with 2x leverage using EOA deployment\n\nSecurity & Access\nAll tools require valid API key from Robonet\nStrategies are wallet-scoped (only creator can access)\nCredits reserved atomically before execution\nAPI keys never committed to version control\nUse environment variables or secure config for API keys\nResources\nRobonet Dashboard: robonet.finance\nAPI Key Management: Dashboard → Settings → API Keys\nCredit Purchase: Dashboard → Settings → Billing\nJesse Framework Docs: jesse.trade\nAllora Network: allora.network\nHyperliquid: hyperliquid.xyz\nSupport: Discord or support@robonet.finance"
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