Browse Robonet Data

Quick Start

This skill provides fast, read-only access to explore Robonet's trading resources before building anything. All tools execute in under 1 second and cost little to nothing.

Load the tools first:

Use MCPSearch to select: mcp__workbench__get_all_symbols
Use MCPSearch to select: mcp__workbench__get_all_technical_indicators
Use MCPSearch to select: mcp__workbench__get_data_availability

Common starting pattern:

1. get_all_symbols → See available trading pairs (BTC-USDT, ETH-USDT, etc.)
2. get_all_technical_indicators → Browse 170+ indicators (RSI, MACD, Bollinger Bands)
3. get_data_availability → Check data ranges before backtesting

When to use this skill:

Start every workflow by exploring available resources
Check data availability before building strategies
Review existing strategies and their performance
Understand what ML predictions are available (Allora topics)
Audit recent backtest results

Available Tools (8)

Strategy Exploration Tools

get_all_strategies - List your trading strategies with optional backtest results

Parameters:
- include_latest_backtest (optional, boolean): Include latest backtest summaries
Returns: List of strategies with names, components, and optionally performance metrics
Pricing: $0.001
Use when: Finding existing strategies to review, enhance, or compare

get_strategy_code - View Python source code of a strategy

Parameters:
- strategy_name (required, string): Name of the strategy
Returns: Complete Python source code
Pricing: Free
Use when: Learning from existing strategies, reviewing before modification, debugging

get_strategy_versions - Track strategy evolution across versions

Parameters:
- base_strategy_name (required, string): Base name without version suffixes
Returns: List of all versions with creation dates and modification history
Pricing: $0.001
Use when: Understanding how a strategy evolved, comparing versions, auditing changes

Market Data Tools

get_all_symbols - List tradeable pairs on Hyperliquid Perpetual

Parameters:
- exchange (optional, string): Filter by exchange name
- active_only (optional, boolean): Only return active symbols (default: true)
Returns: List of symbols with exchange, symbol name, active status, backfill status
Pricing: $0.001
Use when: Choosing which assets to trade, checking what's available before building strategies

get_data_availability - Check data ranges before backtesting

Parameters:
- data_type (optional, string): Type of data (crypto, polymarket, all)
- symbols (optional, array): Specific crypto symbols to check
- exchange (optional, string): Filter crypto by exchange
- asset (optional, string): Filter Polymarket by asset
- include_resolved (optional, boolean): Include resolved Polymarket markets
- only_with_data (optional, boolean): Only show items with available data
Returns: Data availability with date ranges, candle counts, backfill status
Pricing: $0.001
Use when: Before backtesting (verify sufficient data), choosing test date ranges, checking market coverage

Indicator & ML Tools

get_all_technical_indicators - Browse 170+ indicators available in Jesse framework

Parameters:
- category (optional, string): Filter by category (momentum, trend, volatility, volume, overlap, oscillators, cycle, all)
Returns: List of indicators with names, categories, and parameters
Pricing: $0.001
Use when: Exploring indicators for strategy ideas, checking parameter requirements, learning what's available

get_allora_topics - List Allora Network ML prediction topics

Parameters: None
Returns: List of topics with asset names, network IDs, prediction horizons, and prediction types
Pricing: $0.001
Use when: Planning ML enhancement, checking prediction coverage, understanding available horizons (5m, 8h, 24h, 1 week)

Backtest Results Tool

get_latest_backtest_results - View recent backtest performance

Parameters:
- strategy_name (optional, string): Filter by strategy name
- limit (optional, integer, 1-100): Number of results (default: 10)
- include_equity_curve (optional, boolean): Include equity curve timeseries
- equity_curve_max_points (optional, integer, 50-1000): Maximum points for equity curve
Returns: List of backtest records with metrics (Sharpe, drawdown, win rate, total return, profit factor)
Pricing: Free
Use when: Checking if backtest already exists, comparing strategy performance, avoiding redundant backtests

Core Concepts

Symbol Coverage

Crypto Perpetuals (Hyperliquid):

Major pairs: BTC-USDT, ETH-USDT, SOL-USDT, NEAR-USDT
Data history: BTC-USDT and ETH-USDT have longest history (2020-present)
Typical range: Most symbols have 6-24 months of data
Data quality: 1-minute candles available for all symbols

Best practices:

Use get_all_symbols to see complete list
Check get_data_availability for specific symbol history
BTC-USDT and ETH-USDT recommended for initial strategy development (longest history)

Technical Indicators

170+ indicators organized by category:

Momentum (16 indicators): RSI, MACD, Stochastic, ADX, CCI, MFI, ROC, Williams %R, Ultimate Oscillator, etc.
Trend (12 indicators): EMA, SMA, DEMA, TEMA, WMA, Supertrend, Parabolic SAR, VWAP, HMA, etc.
Volatility (8 indicators): Bollinger Bands, ATR, Keltner Channels, Donchian Channels, Standard Deviation, etc.
Volume (10 indicators): OBV, Volume Profile, Chaikin Money Flow, Volume Weighted indicators, etc.
Overlap (8 indicators): Various moving averages and envelopes
Oscillators (6 indicators): Specialized momentum oscillators
Cycle (4 indicators): Market cycle detection indicators

How to use:

1. get_all_technical_indicators(category="momentum") → Browse momentum indicators
2. Pick indicators for your strategy concept
3. Reference indicators in strategy description when building

Note: All indicators are from the Jesse framework (jesse.indicators). Use exact names when creating strategies.

Allora Network ML Predictions

Prediction Coverage:

Assets: BTC, ETH, SOL, NEAR
Horizons: 5 minutes, 8 hours, 24 hours, 1 week
Prediction types:
- Log return (percentage change prediction)
- Absolute price (future price prediction)
Networks:
- Mainnet: 10 production topics
- Testnet: 26 experimental topics

Topic structure:

Asset: BTC
Horizon: 24h
Type: Log return
Network: mainnet

How to use:

1. get_allora_topics() → See all available predictions
2. Match prediction horizon to your strategy timeframe
3. Use enhance_with_allora (from improve-trading-strategies skill) to integrate predictions

Best practices:

Match prediction horizon to strategy timeframe (don't use 5m predictions for daily strategy)
Mainnet topics are production-ready, testnet topics are experimental
Check topic availability before planning ML enhancement

Backtest Result Interpretation

Key Metrics:

Sharpe Ratio (risk-adjusted return):

>2.0: Excellent performance
1.0-2.0: Good performance
0.5-1.0: Acceptable performance
<0.5: Poor performance

Max Drawdown (largest peak-to-trough decline):

<10%: Conservative risk profile
10-20%: Moderate risk profile
20-40%: Aggressive risk profile
>40%: Very risky (reconsider strategy)

Win Rate (percentage of profitable trades):

45-65%: Realistic for most strategies
>70%: Suspicious (possible overfitting or unrealistic fills)
<40%: Needs improvement

Profit Factor (gross profit / gross loss):

>2.0: Excellent
1.5-2.0: Good
1.2-1.5: Acceptable
<1.2: Marginal (risky to deploy)

How to use backtest results:

1. get_latest_backtest_results(strategy_name="MyStrategy") → Check recent tests
2. Review metrics against benchmarks above
3. If metrics good: consider deployment
4. If metrics poor: refine strategy or try different approach

Best Practices

Exploration Workflow

Start every strategy development with data exploration:

1. Explore available assets
   get_all_symbols() → What can I trade?
   get_data_availability(data_type="crypto") → How much history?

2. Understand available tools
   get_all_technical_indicators(category="momentum") → What indicators?
   get_allora_topics() → What ML predictions available?

3. Review existing work
   get_all_strategies(include_latest_backtest=true) → What's already built?
   get_strategy_code(strategy_name="Existing") → Learn from existing code

4. Plan your strategy
   → Use insights from exploration to inform strategy design

Data Availability Checks

Always verify sufficient data before backtesting:

Problem: Backtest fails with "No data available"
Solution:
  1. get_data_availability(symbols=["BTC-USDT"], only_with_data=true)
  2. Check date range returned
  3. Use date range within available data for backtest

Minimum data requirements:

Quick test: 1-3 months (limited validation)
Standard test: 6-12 months (recommended minimum)
Robust test: 12-24 months (ideal for validation)

Cost Optimization

All tools in this skill are cheap (free to $0.001):

Use liberally during exploration
No need to batch queries or optimize calls
Better to over-explore than under-explore

Cost-saving pattern:

1. Browse data (this skill, <$0.01) → Explore thoroughly
2. Generate ideas (design-trading-strategies, $0.05-$1.00) → Cheap exploration
3. Build strategy (build-trading-strategies, $1-$4.50) → Expensive, be sure first

Spending 2-3 minutes exploring data (costs <$0.01) can save dollars in wasted strategy generation.

Learning from Existing Strategies

Use existing strategies as templates:

1. get_all_strategies(include_latest_backtest=true)
   → Find high-performing strategies (Sharpe >1.5)

2. get_strategy_code(strategy_name="HighPerformer")
   → Study the code structure

3. Identify patterns:
   - How are entry conditions structured?
   - What indicators are used?
   - How is position sizing calculated?
   - How is risk management implemented?

4. Apply learnings to new strategy design

Indicator Research

Find the right indicators for your strategy concept:

Strategy Type → Indicator Categories to explore:
- Trend Following → trend, momentum
- Mean Reversion → oscillators, momentum
- Breakout → volatility, volume
- Scalping → momentum, volume
- Swing Trading → trend, overlap

Example exploration:

Building a mean reversion strategy:
1. get_all_technical_indicators(category="oscillators") → See oscillators
2. get_all_technical_indicators(category="momentum") → See momentum indicators
3. Pick RSI (overbought/oversold) + Bollinger Bands (deviation from mean)
4. Use these indicator names when building strategy

Common Workflows

Workflow 1: Pre-Strategy Exploration

Goal: Understand what's available before building anything

1. get_all_symbols()
   → Review available trading pairs
   → Note which symbols interest you

2. get_data_availability(symbols=["BTC-USDT", "ETH-USDT"], only_with_data=true)
   → Check data ranges for chosen symbols
   → Verify sufficient history (6+ months preferred)

3. get_all_technical_indicators(category="all")
   → Browse all 170+ indicators
   → Note which indicators fit your strategy idea

4. get_allora_topics()
   → See ML prediction coverage
   → Check if your asset has predictions available
   → Note prediction horizons

5. Ready to build:
   → If exploring ideas: Use design-trading-strategies skill
   → If ready to code: Use build-trading-strategies skill

Cost: ~$0.005 (essentially free)

Workflow 2: Strategy Audit

Goal: Review existing strategies and their performance

1. get_all_strategies(include_latest_backtest=true)
   → See all strategies with performance data

2. Identify interesting strategies:
   → High Sharpe ratio (>1.5)
   → Acceptable drawdown (<20%)
   → Realistic win rate (45-65%)

3. get_strategy_code(strategy_name="TopPerformer")
   → Review implementation details
   → Understand why it performs well

4. get_strategy_versions(base_strategy_name="TopPerformer")
   → See how strategy evolved
   → Identify what improvements were made

5. Apply learnings:
   → Use as template for new strategies
   → Or enhance further with improve-trading-strategies skill

Cost: Free to $0.003

Workflow 3: Data Coverage Check

Goal: Verify data availability before backtesting

1. Choose your strategy parameters:
   Symbol: BTC-USDT
   Timeframe: 1h
   Test period: 6 months

2. get_data_availability(symbols=["BTC-USDT"], only_with_data=true)
   Returns: "BTC-USDT available from 2020-01-01 to 2025-02-02"

3. Verify coverage:
   ✓ Has 6+ months of data
   ✓ Covers desired test period
   ✓ Ready to backtest

4. If insufficient data:
   → Try shorter test period
   → Or choose different symbol (BTC-USDT and ETH-USDT have longest history)

5. Proceed to testing:
   → Use test-trading-strategies skill to run backtest

Cost: $0.001

Workflow 4: Indicator Research

Goal: Find the right indicators for your strategy concept

Strategy Concept: Mean reversion on cryptocurrency

1. get_all_technical_indicators(category="momentum")
   → Browse momentum indicators (RSI, Stochastic, etc.)

2. get_all_technical_indicators(category="volatility")
   → Browse volatility indicators (Bollinger Bands, ATR, etc.)

3. Select indicators for mean reversion:
   → RSI (identify overbought/oversold)
   → Bollinger Bands (measure deviation from mean)
   → ATR (position sizing based on volatility)

4. Note exact indicator names:
   → "RSI" (not "rsi" or "RelativeStrengthIndex")
   → "BollingerBands" (not "BB" or "bollinger")
   → "ATR" (not "AverageTrueRange")

5. Use exact names in strategy description:
   → When using build-trading-strategies skill
   → Reference indicators precisely: "Use RSI with period 14"

Cost: $0.002

Advanced Usage

Filtering and Optimization

Efficient querying:

# Get only active symbols
get_all_symbols(active_only=true)

# Filter indicators by category
get_all_technical_indicators(category="momentum")

# Check specific symbols only
get_data_availability(symbols=["BTC-USDT", "ETH-USDT"], only_with_data=true)

# Limit backtest results
get_latest_backtest_results(limit=5)

Backtest Result Analysis

Detailed equity curve analysis:

get_latest_backtest_results(
    strategy_name="MyStrategy",
    include_equity_curve=true,
    equity_curve_max_points=500
)

Returns equity curve data for visualizing strategy performance over time.

Use cases:

Identify periods of strong/weak performance
Detect regime changes (strategy works in trending vs ranging markets)
Compare multiple strategies visually

Cross-Asset Research

Compare data availability across assets:

1. get_data_availability(data_type="crypto", only_with_data=true)
   → See all crypto pairs with data

2. Compare:
   - Which symbols have longest history?
   - Which symbols have most recent backfills?
   - Which timeframes are well-covered?

3. Choose optimal symbols for strategy development:
   → BTC-USDT, ETH-USDT: Longest history, most reliable
   → Altcoins: Shorter history, higher risk, potentially higher returns

Troubleshooting

"No Strategies Found"

Issue: get_all_strategies returns empty list

Solutions:

Strategies are linked to your API key's wallet
Ensure you're using the correct API key
If new account, you haven't created strategies yet (use build-trading-strategies skill to create first strategy)

"Symbol Not Found"

Issue: get_data_availability doesn't show expected symbol

Solutions:

Use get_all_symbols() to see complete list of available symbols
Check spelling (BTC-USDT not BTC-USD or BTCUSDT)
Some symbols may not have data backfilled yet (check active_only=false to see inactive symbols)

"No Indicator Matches Description"

Issue: Can't find indicator you're looking for

Solutions:

Use get_all_technical_indicators(category="all") to browse complete list
Search for similar names (RSI vs RelativeStrengthIndex)
Check category filter (momentum indicator won't show if filtering by trend)
Jesse framework uses specific names - use exact names returned by tool

"Backtest Results Missing"

Issue: get_latest_backtest_results doesn't show expected backtest

Solutions:

Check strategy name spelling (case-sensitive)
Backtest may still be running (wait 20-60 seconds)
Backtest may have failed (check for error messages)
Use limit parameter to retrieve more results (default is 10)

Next Steps

After exploring data with this skill:

Generate strategy ideas:

Use design-trading-strategies skill to generate AI-powered strategy concepts
Cost: $0.05-$1.00 per idea generation (cheapest AI tool)
Best when: You want to explore creative concepts before committing to development

Build strategies directly:

Use build-trading-strategies skill to generate complete strategy code
Cost: $1.00-$4.50 per strategy (most expensive AI tool)
Best when: You already know what you want to build

Test existing strategies:

Use test-trading-strategies skill to backtest strategies
Cost: $0.001 per backtest
Best when: You have strategy code and want to validate performance

Improve strategies:

Use improve-trading-strategies skill to refine, optimize, or enhance with ML
Cost: $0.50-$4.00 per operation
Best when: You have an existing strategy that needs improvement

Prediction market trading:

Use trade-prediction-markets skill for Polymarket YES/NO token strategies
Cost: $0.001-$4.50 depending on operation
Best when: You want to trade on real-world events (politics, economics, sports)

Summary

This skill provides fast, cheap, read-only access to Robonet's trading resources:

8 data tools covering strategies, symbols, indicators, ML topics, and backtest results
<1 second execution for all tools
Free to $0.001 cost (essentially free to explore)
Zero risk (read-only operations, no modifications or executions)

Core principle: Explore thoroughly before building. Spending 2-3 minutes browsing data (costs <$0.01) can save dollars in wasted strategy generation and prevent costly mistakes.

Best practice: Start every workflow with this skill, then progress to design → build → improve → test → deploy based on your findings.