Use MCPSearch to select: mcp__workbench__create_strategy
Use MCPSearch to select: mcp__workbench__create_prediction_market_strategy

create_strategy(
    strategy_name="RSIMeanReversion_M",
    description="Buy when RSI(14) < 30 and price at lower Bollinger Band (20,2).
                 Sell when RSI > 70 or price at middle Bollinger Band.
                 Stop loss at 2% below entry. Position size 90% of available margin."
)

class MyStrategy(Strategy):
    def should_long(self) -> bool:
        """Check if all conditions are met for long entry"""
        # Return True to signal long entry opportunity
        # Called every candle

    def should_short(self) -> bool:
        """Check if all conditions are met for short entry"""
        # Return True to signal short entry opportunity
        # Called every candle

    def go_long(self):
        """Execute long entry with position sizing"""
        # Calculate position size (qty)
        # Place buy order
        # Set stop loss and take profit in on_open_position()

    def go_short(self):
        """Execute short entry with position sizing"""
        # Calculate position size (qty)
        # Place sell order
        # Set stop loss and take profit in on_open_position()

    def on_open_position(self, order):
        """Set stop loss and take profit after entry"""
        # Called when position opens
        # Set self.stop_loss and self.take_profit

    def update_position(self):
        """Update position (trailing stops, etc.)"""
        # Called every candle while in position
        # Modify stop loss for trailing stops

    def should_cancel_entry(self) -> bool:
        """Cancel unfilled entry orders"""
        # Return True to cancel pending entry order

def go_long(self):
    qty = utils.size_to_qty(self.balance * 0.90, self.price)
    self.buy = qty, self.price

def go_long(self):
    atr = ta.atr(self.candles, period=14)
    # Reduce size in high volatility
    size_multiplier = 0.90 if atr < self.price * 0.02 else 0.70
    qty = utils.size_to_qty(self.balance * size_multiplier, self.price)
    self.buy = qty, self.price

def go_long(self):
    atr = ta.atr(self.candles, period=14)
    stop_distance = atr * 2  # Stop at 2× ATR
    # Risk 2% of balance per trade
    risk_amount = self.balance * 0.02
    qty = risk_amount / stop_distance
    self.buy = qty, self.price

def on_open_position(self, order):
    atr = ta.atr(self.candles, period=14)
    # Stop at 2× ATR below entry (long) or above entry (short)
    self.stop_loss = qty, self.price - (atr * 2)  # Long
    # or
    self.stop_loss = qty, self.price + (atr * 2)  # Short

def on_open_position(self, order):
    atr = ta.atr(self.candles, period=14)
    # Target at 3× ATR (risk/reward = 1.5)
    self.take_profit = qty, self.price + (atr * 3)  # Long

(Use browse-robonet-data skill)
get_all_technical_indicators(category="momentum")

1. browse-robonet-data ($0.001) → Verify resources
2. design-trading-strategies ($0.30) → Explore 3 ideas
3. Pick best idea and refine description
4. create_strategy ($2.50) → Generate once, correctly
Total: $2.80 with high success rate

vs.

1. create_strategy ($2.50) → Vague requirements
2. Doesn't work, try again ($2.50)
3. Still not right ($2.50)
Total: $7.50 with frustration

Entry Conditions:
- Specific indicator with exact parameters
- Exact thresholds
- Multiple conditions with AND/OR logic

Exit Conditions:
- Stop loss method and distance
- Take profit method and target
- Trailing stop if applicable

Position Sizing:
- Percentage of margin to use
- Or risk-based sizing method

Risk Management:
- Maximum loss per trade
- Any position limits

Context:
- Timeframe (5m, 1h, 4h, 1d)
- Market regime (trending, ranging)

"RSI Mean Reversion strategy for BTC-USDT on 1h timeframe.

ENTRY (Long):
- RSI(14) < 30 (oversold)
- Price touches lower Bollinger Band (20-period, 2 std dev)
- Confirm with volume: current volume > 1.2× 20-period average

EXIT (Long):
- Take profit: Price reaches middle Bollinger Band
- Stop loss: 2% below entry price
- Trailing stop: Once profit >3%, trail stop at 1.5% below highest price

POSITION SIZING:
- Use 90% of available margin per trade
- Single position at a time (no pyramiding)

RISK MANAGEMENT:
- Maximum loss: 2% of account per trade
- No new trades if in drawdown >10%"

"Build a profitable BTC strategy using RSI and Bollinger Bands"

"For 1h timeframe..." (helps AI tune indicator parameters appropriately)

1. Explore data (use browse-robonet-data):
   get_all_symbols() → Choose BTC-USDT
   get_all_technical_indicators(category="momentum") → Pick RSI
   get_all_technical_indicators(category="volatility") → Pick Bollinger Bands

2. Optional: Generate ideas (use design-trading-strategies):
   generate_ideas(strategy_count=3) → Get concepts
   Pick best concept as starting point

3. Write detailed description:
   - Entry: RSI < 30 AND price at lower BB
   - Exit: Price at middle BB OR stop loss 2%
   - Sizing: 90% margin
   - Timeframe: 1h

4. Create strategy:
   create_strategy(
       strategy_name="RSIMeanReversion_M",
       description="[detailed description from step 3]"
   )

5. Validate generated code:
   - Check all required methods present
   - Verify indicators match description
   - Confirm risk management included

6. Test immediately (use test-trading-strategies):
   run_backtest(strategy_name="RSIMeanReversion_M", ...)

1. Generate ideas (use design-trading-strategies):
   generate_ideas(strategy_count=3)

   Idea #2: "Bollinger Band Breakout"
   Entry: Price breaks above upper BB with high volume
   Exit: Price returns to middle BB
   Uses: Bollinger Bands, Volume

2. Refine idea into detailed description:
   "Bollinger Band Breakout strategy for ETH-USDT on 4h timeframe.

   ENTRY (Long):
   - Price closes above upper Bollinger Band (20, 2)
   - Current volume > 1.5× 20-period average volume
   - ADX(14) > 25 (confirm trend strength)

   EXIT (Long):
   - Price closes below middle Bollinger Band
   - Or stop loss 3% below entry
   - Or take profit at 9% above entry (3:1 reward:risk)

   POSITION SIZING: 85% of margin
   RISK: Max 3% loss per trade"

3. Create strategy:
   create_strategy(
       strategy_name="BollingerBreakout_H",
       description="[detailed description from step 2]"
   )

4. Test and validate:
   run_backtest(strategy_name="BollingerBreakout_H", ...)

1. Browse prediction markets (use browse-robonet-data):
   get_data_availability(data_type="polymarket")
   → See available markets

2. Analyze market data:
   get_prediction_market_data(condition_id="...")
   → Study YES/NO token price history

3. Write detailed description:
   "Polymarket probability arbitrage strategy for crypto_rolling markets.

   BUY YES TOKEN when:
   - YES token price < 0.40 (implied 40% probability)
   - Market has >$10k volume (sufficient liquidity)
   - Time to resolution > 2 hours (avoid last-minute volatility)

   BUY NO TOKEN when:
   - NO token price < 0.40 (YES price > 0.60)
   - Same liquidity and time criteria

   EXIT:
   - Sell when price reaches 0.55 (15% profit target)
   - Or hold until market resolution
   - Stop loss: Sell if price drops to 0.25 (37.5% loss)

   POSITION SIZING: 5% of capital per market
   MAX POSITIONS: 10 simultaneous markets"

4. Create prediction market strategy:
   create_prediction_market_strategy(
       strategy_name="PolymarketArbitrage_M",
       description="[detailed description from step 3]"
   )

5. Test on historical markets:
   run_prediction_market_backtest(...)

"ETH-USDT swing trading strategy on 1h timeframe with 4h trend filter.

HIGHER TIMEFRAME (4h):
- Only take long trades when 4h EMA(50) is rising
- Only take short trades when 4h EMA(50) is falling

ENTRY TIMEFRAME (1h):
- [standard entry conditions on 1h]
..."

"Entry requires ALL of these conditions (AND logic):
1. RSI(14) < 30
2. Price < Lower Bollinger Band (20, 2)
3. MACD histogram positive (bullish divergence)
4. Volume > 1.3× average

OR entry if these alternative conditions met:
1. Price makes higher low
2. RSI makes higher low (bullish divergence)
3. Volume surge (>2× average)"

"Position sizing based on volatility:
- When ATR(14) < 2% of price: Use 95% margin (low volatility)
- When ATR between 2-4%: Use 85% margin (normal)
- When ATR > 4%: Use 70% margin (high volatility)

This reduces risk during volatile periods."