Design Trading Strategies

Design Trading Strategies

Quick Start

This skill generates AI-powered trading strategy concepts before you commit to expensive development. It's the cheapest AI tool in Robonet ($0.05-$1.00 vs $1-$4.50 for full strategy creation).

Load the tool first:

Use MCPSearch to select: mcp__workbench__generate_ideas

Basic usage:

generate_ideas(strategy_count=3)

Returns 3 creative strategy concepts with descriptions of market conditions, entry/exit logic, and rationale.

When to use this skill:

• Exploring new markets or timeframes
• Stuck for strategy ideas
• Want to validate approach before expensive development
• Need inspiration from current market data
• Brainstorming session before building

When to skip this skill:

• You already know exactly what you want to build → Go directly to build-trading-strategies
• Iterating on existing strategy → Use improve-trading-strategies

The generate_ideas Tool

Purpose: Creates innovative strategy concepts based on current Hyperliquid market data

Parameters:

• strategy_count (optional, integer, 1-10): Number of ideas to generate (default: 1)

Returns: List of strategy concepts including:

• Strategy name: Descriptive name for the concept
• Market conditions: What market regime it's designed for (trending, ranging, volatile, etc.)
• Entry logic: When to enter positions
• Exit logic: When to exit positions
• Risk management: Stop loss and position sizing approach
• Indicators: Which technical indicators to use
• Rationale: Why this strategy might work

Pricing: Real LLM cost + margin (max $1.00)

• Typical cost: $0.05-$0.50 depending on number of ideas
• Significantly cheaper than create_strategy ($1-$4.50)

Execution Time: ~20-40 seconds

Example output:

Strategy 1: "Bollinger Band Mean Reversion"
Market conditions: Ranging market with clear support/resistance
Entry logic: Enter long when price touches lower band and RSI <30
Exit logic: Exit at middle band or when RSI >70
Risk management: 2% position size, stop loss at 1.5× ATR below entry
Indicators: Bollinger Bands (20, 2), RSI (14), ATR (14)
Rationale: Markets tend to revert to the mean in ranging conditions.
           Bollinger Bands identify extremes, RSI confirms oversold/overbought.

Core Concepts

Strategy Archetypes

Understanding common patterns helps evaluate AI-generated ideas:

1. Trend Following

• Market: Trending markets (strong directional movement)
• Logic: Enter in direction of trend, ride momentum
• Indicators: Moving averages (EMA, SMA), ADX, MACD
• Risk: False breakouts, whipsaws in ranging markets
• Example: "Buy when price crosses above 50 EMA and ADX >25"

2. Mean Reversion

• Market: Ranging markets (price oscillates around mean)
• Logic: Buy oversold, sell overbought, profit from reversion
• Indicators: RSI, Bollinger Bands, Stochastic
• Risk: Trending markets (price may not revert)
• Example: "Buy when RSI <30 and at lower Bollinger Band"

3. Breakout

• Market: Consolidation followed by expansion
• Logic: Enter when price breaks support/resistance with volume
• Indicators: Bollinger Bands, ATR, Volume, Donchian Channels
• Risk: False breakouts, low win rate (requires large profit factor)
• Example: "Buy on breakout above 20-day high with volume >1.5× average"

4. Momentum

• Market: Strong directional moves
• Logic: Enter when momentum accelerates, exit when it fades
• Indicators: MACD, Stochastic, Rate of Change (ROC), Williams %R
• Risk: Late entries, momentum reversals
• Example: "Buy when MACD crosses above signal line and price makes new high"

5. Arbitrage/Market Making

• Market: Any (exploits inefficiencies or spreads)
• Logic: Profit from price discrepancies or spread capture
• Indicators: Spread analysis, correlation, volume
• Risk: Low margins, requires high frequency, execution risk
• Example: "Capture spread between related assets or maker/taker fees"

Realistic Performance Expectations

Set realistic expectations to evaluate generated ideas:

Sharpe Ratio (risk-adjusted return):

• >2.0: Exceptional (rare for algorithmic strategies)
• 1.0-2.0: Good (achievable with solid strategy)
• 0.5-1.0: Acceptable (worth testing)
• <0.5: Poor (likely not profitable after costs)

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

• <10%: Conservative (lower returns, safer)
• 10-20%: Moderate (balanced risk/reward)
• 20-40%: Aggressive (higher returns, higher risk)
• >40%: Very risky (difficult to recover from)

Win Rate (percentage of profitable trades):

• 45-65%: Realistic for most strategies
• >70%: Suspicious (likely overfitting or unrealistic assumptions)
• <40%: Needs improvement or higher profit factor

Typical characteristics of good strategies:

• Clear entry and exit rules (not ambiguous)
• Realistic indicator parameters (not over-optimized)
• Appropriate for market regime (trend-following in trending markets)
• Risk management included (stop loss, position sizing)
• Based on sound trading principles (not curve-fitted)

Market Regime Awareness

Different strategies work in different market conditions:

Trending Markets (strong directional movement):

• Best: Trend following, momentum strategies
• Worst: Mean reversion (buys dips that keep dipping)
• Current state: Check BTC/ETH price action (making new highs/lows?)

Ranging Markets (sideways price action):

• Best: Mean reversion, oscillator-based strategies
• Worst: Trend following (whipsawed by false signals)
• Current state: Price oscillating between clear support/resistance?

Volatile Markets (large price swings):

• Best: Breakout strategies, volatility-based position sizing
• Worst: Tight stop losses (get stopped out frequently)
• Current state: ATR elevated? Wide intraday ranges?

Low Volatility Markets (compressed ranges):

• Best: Range trading, spread capture
• Worst: Momentum strategies (insufficient movement)
• Current state: ATR compressed? Narrow ranges?

How generate_ideas uses market data:

• Analyzes current Hyperliquid market data
• Considers recent volatility, trends, and patterns
• Generates ideas appropriate for current conditions
• You should still validate ideas against your own market view

Best Practices

Cost Optimization

Why use generate_ideas before create_strategy:

Without generate_ideas:
1. create_strategy("vague idea") → $2.50
2. Code doesn't match expectations → Wasted
3. create_strategy("try again") → $2.50
4. Still not quite right → Wasted
Total cost: $5.00, no strategy yet

With generate_ideas:
1. generate_ideas(strategy_count=3) → $0.30
2. Review 3 concepts, pick best
3. create_strategy("refined concept from idea #2") → $2.50
4. Code matches expectations → Success
Total cost: $2.80, working strategy

Savings: $2.20 (44% reduction) + better outcome

Effective Idea Generation

Request multiple ideas to compare approaches:

generate_ideas(strategy_count=3-5)

Why 3-5 ideas:

• See multiple approaches to same problem
• Compare trade-offs (risk vs return, complexity vs simplicity)
• Identify common themes (e.g., all ideas use RSI → probably important)
• Cost is still minimal ($0.15-$0.50 total)

Avoid:

• strategy_count=1: Limited perspective, may miss better approaches
• strategy_count=10: Diminishing returns, most ideas will be variations

Critical Evaluation of AI Ideas

Not all AI-generated ideas are good. Evaluate critically:

Red flags (be skeptical of these):

• Unrealistic parameters: "Buy when RSI exactly equals 27.6" (over-optimized)
• Too many indicators: Uses 8+ indicators (overfitting risk)
• Vague logic: "Enter when conditions are favorable" (not actionable)
• No risk management: Doesn't mention stop loss or position sizing
• Ignores market regime: Mean reversion strategy for strongly trending market
• Contradictory logic: "Buy when RSI <30 AND RSI >70" (impossible)

Green flags (good ideas have these):

• Clear entry conditions: Specific, measurable criteria
• Clear exit conditions: Defined profit target and stop loss
• Appropriate indicators: 2-4 indicators that complement each other
• Market regime awareness: Strategy matches current conditions
• Risk management: Position sizing and stop loss defined
• Sound rationale: Based on trading principles, not curve-fitting

Example evaluation:

Idea: "Buy when price is above 200 EMA, RSI crosses above 50, and MACD crosses bullish"
✓ Clear entry conditions (3 specific criteria)
✓ Indicators complement each other (trend + momentum + momentum confirmation)
✓ Appropriate for trending markets
? Missing exit logic (need to ask for refinement)
? No position sizing mentioned (need to specify)

Verdict: Good foundation, needs exit logic and risk management details

Iterative Refinement

Use ideas as starting points, not final solutions:

1. generate_ideas(strategy_count=3) → Get initial concepts

2. Evaluate ideas:
   - Idea #1: Too complex (8 indicators)
   - Idea #2: Good concept, but missing exit logic
   - Idea #3: Too risky (no stop loss)

3. Pick best idea (#2) and refine:
   - Use browse-robonet-data to verify indicators available
   - Add missing details (exit logic, risk management)
   - Create refined concept description

4. Proceed to build-trading-strategies:
   - Use refined concept as input
   - Get working code on first try

Exploring Different Markets

Generate ideas for specific symbols or timeframes:

# First, check available symbols
(Use browse-robonet-data skill)
get_all_symbols()
get_data_availability(symbols=["BTC-USDT", "ETH-USDT"],

# Generate ideas (generate_ideas pulls current market data automatically)
generate_ideas(strategy_count=3)

# AI will analyze current market conditions and propose strategies

Note: generate_ideas automatically analyzes current Hyperliquid market data. You don't need to specify symbol or timeframe—it considers the current market environment.

Common Workflows

Workflow 1: Exploring New Market

Goal: Generate ideas for trading a new asset

1. Browse available assets (use browse-robonet-data skill):
   get_all_symbols() → See what's available
   get_data_availability(symbols=["SOL-USDT"]) → Check history

2. Generate ideas:
   generate_ideas(strategy_count=3) → Get 3 concepts

3. Evaluate ideas:
   - Which idea fits current market regime?
   - Which idea has clearest logic?
   - Which idea has best risk/reward?

4. Select best idea:
   - Pick idea with clearest logic and appropriate risk

5. Build strategy (use build-trading-strategies skill):
   - Use selected idea as description input
   - Get working code in one attempt

Cost: ~$0.20-$0.50

Workflow 2: Brainstorming Session

Goal: Explore multiple approaches before committing to development

1. Generate initial batch:
   generate_ideas(strategy_count=5) → Get 5 diverse concepts

2. Group ideas by type:
   - Trend following: Ideas #1, #3
   - Mean reversion: Ideas #2, #4
   - Breakout: Idea #5

3. Compare approaches:
   - Trend following: Higher win rate but slower trades
   - Mean reversion: More trades but lower win rate
   - Breakout: Highest risk/reward but lowest frequency

4. Choose based on goals:
   - Want frequent trades? → Mean reversion
   - Want high win rate? → Trend following
   - Want big winners? → Breakout

5. Proceed to development:
   - Build chosen strategy type
   - Test thoroughly before deployment

Cost: ~$0.30-$0.70

Workflow 3: Validating Your Own Idea

Goal: See if your strategy concept is viable before building

1. You have an idea:
   "I want to trade BTC mean reversion using RSI"

2. Generate AI ideas:
   generate_ideas(strategy_count=3) → See what AI suggests

3. Compare:
   - Do any AI ideas match your concept?
   - What do AI ideas do differently?
   - Are there obvious flaws in your idea that AI avoids?

4. Refine your idea:
   - Incorporate AI insights
   - Add missing details (exit logic, risk management)
   - Validate indicator selection

5. Build refined strategy:
   - Use your idea + AI insights as input
   - Proceed to build-trading-strategies skill

Cost: ~$0.15-$0.40

Workflow 4: Market Regime Analysis

Goal: Understand what strategies work in current market

1. Generate ideas (AI analyzes current market automatically):
   generate_ideas(strategy_count=5)

2. Identify patterns in generated ideas:
   - All ideas trend-following? → Market is trending
   - All ideas mean-reversion? → Market is ranging
   - Mix of approaches? → Mixed/transitional market

3. Use insights to guide strategy selection:
   - Build strategy aligned with current regime
   - Be aware of regime change risk
   - Consider multi-regime strategies

4. Proceed to development:
   - Build strategy appropriate for current market

Cost: ~$0.30-$0.70

Advanced Usage

Combining with Browse Data

Optimal workflow for thorough exploration:

1. Browse data first (use browse-robonet-data skill):
   get_all_symbols() → Available assets
   get_all_technical_indicators(category="momentum") → Indicators
   get_allora_topics() → ML predictions available

2. Generate ideas:
   generate_ideas(strategy_count=4) → AI concepts

3. Cross-reference:
   - Do generated ideas use available indicators?
   - Can I enhance with Allora ML (check topics)?
   - Do I have sufficient data history?

4. Select idea that aligns with:
   - Available data
   - Available indicators
   - ML prediction coverage

5. Build with confidence:
   - All resources verified
   - No surprises during development

Extracting Patterns Across Ideas

Learn from multiple idea generations:

Generate ideas multiple times and track patterns:
- Session 1: generate_ideas(strategy_count=3) → Uses EMA, RSI, MACD
- Session 2: generate_ideas(strategy_count=3) → Uses EMA, ATR, Bollinger
- Session 3: generate_ideas(strategy_count=3) → Uses EMA, Volume, ADX

Pattern identified: EMA appears in 9/9 ideas → Fundamental indicator
→ Include EMA in your custom strategy design

Troubleshooting

"Generated Ideas Are Too Generic"

Issue: Ideas lack specific details or actionable logic

Solutions:

• This is expected—ideas are concepts, not complete strategies
• Use ideas as starting points, not final solutions
• Refine the best idea and use it as input to build-trading-strategies
• Ideas are meant to inspire, not to be implemented directly

"Ideas Don't Match My Market View"

Issue: AI generates trend-following ideas but you think market is ranging

Solutions:

• AI analyzes current data, but markets change
• Use ideas for inspiration, not gospel
• Modify concept to match your view
• Generate new ideas and select one that aligns better
• Remember: You have final say, AI provides suggestions

"All Ideas Look Similar"

Issue: 5 ideas generated but they're all variations of same approach

Solutions:

• This can happen if market regime is very clear (strong trend → all trend-following)
• Try generating another batch (different ideas each time)
• If pattern persists, market may strongly favor one approach
• Consider building the repeated strategy type (market is telegraphing opportunity)

"Ideas Use Indicators I Don't Understand"

Issue: AI suggests indicators you're unfamiliar with

Solutions:

• Use browse-robonet-data skill: get_all_technical_indicators() to see all indicators
• Research indicator (Google "ADX indicator trading" for examples)
• Ask AI to explain indicator in strategy description when building
• Choose ideas with familiar indicators if uncomfortable

Next Steps

After generating and evaluating ideas:

Build the strategy:

• Use build-trading-strategies skill to transform concept into code
• Cost: $1.00-$4.50 per strategy
• Provide refined idea description as input
• Get complete Python strategy code

Test the strategy:

• Use test-trading-strategies skill to backtest generated ideas
• Cost: $0.001 per backtest
• Validate performance before committing to expensive development

Improve the strategy:

• If you already have a strategy and want to refine it
• Use improve-trading-strategies skill
• Cost: $0.50-$4.00 per operation

Deploy to production:

• After thorough testing shows strong performance
• Use deploy-live-trading skill (HIGH RISK)
• Cost: $0.50 deployment fee
• NEVER deploy without extensive backtesting

Summary

This skill provides cheap AI-powered strategy ideation before expensive development:

• 1 tool: generate_ideas (1-10 concepts per call)
• Cost: $0.05-$1.00 (significantly cheaper than building $1-$4.50)
• Execution: 20-40 seconds
• Purpose: Explore concepts, validate ideas, get inspiration

Core principle: Spend $0.30 exploring 3 ideas before spending $3.00 building. This saves money and improves outcomes.

Best practice: Browse data first (browse-robonet-data), generate ideas (this skill), then build best idea (build-trading-strategies). This workflow minimizes waste and maximizes success rate.

Remember: AI-generated ideas are starting points, not final solutions. Evaluate critically, refine, and validate before building.