Deploy Live Trading
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║ ⚠️ LIVE TRADING RISKS REAL CAPITAL ⚠️ ║
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║ • You can lose ALL deployed capital ║
║ • Bugs in strategy code cause significant losses ║
║ • Market conditions change - backtest ≠ live ║
║ • NEVER deploy without thorough backtesting ║
║ • Start with small capital to validate live behavior ║
║ • Monitor deployments actively (daily minimum) ║
║ • Define exit criteria BEFORE deploying ║
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║ THIS IS NOT A SIMULATION ║
║ REAL MONEY WILL BE TRADED ║
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Quick Start
This skill deploys strategies to live trading on Hyperliquid. Use ONLY after thorough backtesting and validation.
Load the tools first:
Use MCPSearch to select: mcp__workbench__deployment_create
Use MCPSearch to select: mcp__workbench__deployment_list
Use MCPSearch to select: mcp__workbench__deployment_stop
BEFORE deploying, complete this checklist:
- Backtested on 6+ months of data
- Sharpe ratio >1.0, max drawdown <20%
- Tested on multiple time periods
- Code reviewed for bugs
- Risk management validated (stop loss, position sizing)
- Credit balance sufficient
- Monitoring plan established
- Exit criteria defined
- Starting with small capital (<10% of intended final size)
If ANY item unchecked, DO NOT DEPLOY
When to use this skill:
- After extensive backtesting shows consistent profitability
- When ready to risk real capital
- When you can monitor the deployment actively
When NOT to use this skill:
- Strategy not thoroughly tested (use
test-trading-strategiesfirst) - Haven't reviewed strategy code
- Don't have monitoring plan
- Can't check deployment daily for first week
- Haven't defined when to stop deployment
Available Tools (6)
deployment_create
Purpose: Deploy strategy to live trading on Hyperliquid
Parameters:
strategy_name(required): Name of strategy to deploysymbol(required): Trading pair (e.g., "BTC-USDT")timeframe(required): Candle interval (1m, 5m, 15m, 30m, 1h, 2h, 4h, 6h, 8h, 12h, 1d)leverage(optional, 1-5): Position multiplier (default: 1)deployment_type(optional): "eoa" (wallet, default) or "vault"vault_name(required for vault): Unique vault namevault_description(optional): Vault description
Returns: Deployment ID, status, wallet address, configuration
Pricing: $0.50 per deployment
Constraints:
- EOA: Max 1 active deployment per wallet
- Vault: Requires 200+ USDC in wallet, unlimited deployments
Use when: All pre-deployment criteria met (see checklist)
deployment_list
Purpose: Monitor active deployments
Parameters: None
Returns: List of all deployments with status, performance, configuration
Pricing: Free
Use when: Checking deployment status, monitoring performance
deployment_start
Purpose: Resume stopped deployment
Parameters:
deployment_id(required): ID of deployment to resume
Returns: Updated deployment status
Pricing: Free
Use when: Restarting previously stopped deployment after validation/fixes
deployment_stop
Purpose: Halt live trading
Parameters:
deployment_id(required): ID of deployment to stop
Returns: Updated deployment status
Pricing: Free
Use when:
- Live performance degrades significantly
- Need to update strategy code
- Market conditions change fundamentally
- ANY red flag triggered (see Red Flags section)
get_credit_balance
Purpose: Check available USDC credits
Parameters: None
Returns: Current credit balance
Pricing: Free
Use when: Before deployment (verify sufficient credits), monitoring spending
get_credit_transactions
Purpose: View credit transaction history
Parameters: None
Returns: List of credit transactions
Pricing: Free
Use when: Auditing spending, tracking costs
Core Concepts
Deployment Types
EOA (Externally Owned Account):
Type: Direct wallet trading
Setup: Immediate (no additional requirements)
Limit: Max 1 active deployment per wallet
Complexity: Lower
Best for: Testing, personal trading, single strategy
Cost: $0.50 to create
Advantages:
✓ Simple setup
✓ Immediate deployment
✓ No minimum balance requirement
Disadvantages:
✗ Only 1 deployment per wallet
✗ No public performance tracking
✗ Personal wallet at risk
Hyperliquid Vault:
Type: Professional vault setup
Setup: Requires 200+ USDC in wallet
Limit: Unlimited deployments
Complexity: Higher
Best for: Multiple strategies, professional trading, public showcasing
Cost: $0.50 per deployment
Advantages:
✓ Unlimited deployments
✓ Public TVL and performance tracking
✓ Professional infrastructure
✓ Separate from personal wallet
Disadvantages:
✗ Requires 200+ USDC setup
✗ More complex configuration
✗ Public performance visibility
Which to choose:
Choose EOA if:
- First deployment (testing live behavior)
- Running single strategy
- Want simple setup
- Don't need multiple simultaneous strategies
Choose Vault if:
- Running multiple strategies
- Want professional setup
- Need public performance tracking
- Trading with significant capital
- Building track record
Leverage Guidelines
Understanding leverage:
Leverage = Position size / Available capital
1x leverage: $1000 capital → $1000 position
2x leverage: $1000 capital → $2000 position
3x leverage: $1000 capital → $3000 position
Key points:
- Leverage multiplies BOTH gains AND losses
- Higher leverage = higher risk
- Liquidation risk increases with leverage
- Start conservative (1-2x)
Recommended leverage by risk profile:
Conservative (1x):
- No amplification
- Lower returns, lower risk
- Recommended for first deployments
- Drawdown ≈ backtest drawdown
Moderate (2-3x):
- 2-3× returns and risk
- Requires careful monitoring
- Only after 1x deployment validated
- Drawdown ≈ 2-3× backtest drawdown
Aggressive (4-5x):
- 4-5× returns and risk
- Very risky, high liquidation chance
- NOT recommended for most users
- Drawdown ≈ 4-5× backtest drawdown
- Can lose entire capital quickly
Leverage and drawdown:
Backtest: 15% max drawdown
1x deployment: 15% expected drawdown
2x deployment: 30% expected drawdown (may hit margin call)
3x deployment: 45% expected drawdown (very likely liquidation)
Rule: Keep leverage low enough that backtest drawdown × leverage < 25%
Risk Management in Live Trading
Position sizing:
Strategy controls position size via code (85-95% margin usage)
Deployment leverage multiplies available margin
Total risk = Strategy position size × Deployment leverage
Example:
- Capital: $1000
- Strategy uses 90% margin
- Deployment leverage: 2x
- Actual position: $1000 × 0.90 × 2 = $1800
Position size is LARGER than capital (risk of liquidation)
Mental stop loss (define BEFORE deploying):
Example thresholds:
- Stop if down 10% from starting capital
- Stop if down 15% from peak
- Stop if drawdown >1.5× backtest max drawdown
Write down your threshold:
"I will stop this deployment if capital drops to $______"
DO NOT move this threshold once deployed (discipline is critical)
Monitoring frequency:
First 24-48 hours: Check every 2-4 hours
First week: Check daily minimum
First month: Check every 2-3 days
After 1 month: Weekly check acceptable (if performing well)
NEVER:
- Deploy and forget
- Ignore for >1 week during first month
- Assume backtest = live performance
Pre-Deployment Checklist
Complete ALL items before deploying:
Strategy Validation
- Backtested on 6+ months (12+ months preferred)
- Sharpe ratio >1.0 (preferably >1.5)
- Max drawdown <20% (acceptable risk level)
- Win rate 45-65% (realistic range)
- Profit factor >1.5 (sufficient edge)
- 50+ trades in test (statistical significance)
- Multi-period validation (consistent across different time ranges)
- Out-of-sample test passed (performed well on unseen data)
Code and Logic Review
- Strategy code reviewed (no obvious bugs)
- No look-ahead bias (not using future data)
- Indicators validated (all indicators available and correct)
- Risk management present (stop loss and position sizing)
- Realistic assumptions (fees, slippage accounted for)
Operational Readiness
- Credit balance sufficient (check with
get_credit_balance) - Deployment type selected (EOA vs Vault)
- Leverage set conservatively (1-2x for first deployment)
- Monitoring plan established (how often will you check?)
- Exit criteria defined (when will you stop?)
- Starting capital decided (how much to deploy?)
- Capital is risk capital (can afford to lose 100%)
IF ANY ITEM UNCHECKED: DO NOT DEPLOY
Deployment Best Practices
Start Small
Initial deployment sizing:
WRONG approach:
- Backtest shows 50% annual return
- Deploy $10,000 immediately
- If strategy fails, lose significant capital
RIGHT approach:
- Deploy $500-1000 initially (5-10% of intended size)
- Monitor for 1-2 weeks
- Validate live behavior matches backtest
- If successful, scale up gradually
- Reduce risk during validation phase
Scaling schedule example:
Week 1-2: $1,000 (test)
Week 3-4: $2,000 (if performing well)
Week 5-6: $4,000 (if still performing well)
Month 2+: Scale to full size gradually
Why start small:
- Live market is different from backtest
- Slippage may be higher
- Execution may differ
- Bugs may only appear in live trading
- Can stop with minimal loss if issues arise
Monitoring Protocol
What to track:
1. P&L vs backtest expectation:
- Is live performance similar to backtest?
- Track daily, weekly, monthly returns
- Compare to backtest metrics
2. Drawdown:
- Current drawdown from peak
- Compare to backtest max drawdown
- If exceeds backtest max × 1.5, be concerned
3. Trade execution:
- Are trades executing as expected?
- Check fill prices (slippage)
- Verify trade frequency matches backtest
4. Win rate and profit factor:
- Track live win rate
- Should be close to backtest win rate
- If diverges >20%, investigate
5. Market regime:
- Has market character changed?
- Trending → ranging or vice versa
- Strategy may stop working if regime shifts
Daily monitoring checklist (first week):
- Check P&L (profit/loss today)
- Check position status (in trade or flat?)
- Check recent trades (executed as expected?)
- Check drawdown (within acceptable range?)
- Note any unusual behavior
Red Flags - Stop Deployment Immediately
STOP deployment if ANY of these occur:
1. Excessive drawdown:
Live drawdown >30% OR >1.5× backtest max drawdown
Example:
- Backtest max drawdown: 15%
- Threshold to stop: 22.5% (1.5× backtest)
- Current live drawdown: 25%
→ STOP IMMEDIATELY
Why: Strategy may be broken or market changed
2. Win rate collapse:
Live win rate <50% of backtest win rate
Example:
- Backtest win rate: 55%
- Threshold to stop: 27.5% (50% of backtest)
- Live win rate after 20 trades: 25%
→ STOP IMMEDIATELY
Why: Strategy logic not working in live market
3. Unexpected trade frequency:
Much higher or lower trade frequency than backtest
Example:
- Backtest: 2-3 trades per day
- Live: 15 trades per day
→ STOP IMMEDIATELY
Why: Strategy may be malfunctioning
4. Consistent losses:
10+ consecutive losing trades (when backtest shows max 5-6)
→ STOP IMMEDIATELY
Why: Strategy edge may have disappeared
5. Technical issues:
- Orders not executing
- Repeated API errors
- Position sizing errors
- Strategy crashes/restarts frequently
→ STOP IMMEDIATELY
Why: Technical problems = unpredictable risk
6. Market regime change:
Market conditions fundamentally different from backtest period
Examples:
- Extreme volatility event (>3× normal)
- Major regulatory news
- Exchange issues
→ STOP, REASSESS, decide if/when to restart
Why: Strategy designed for different conditions
Post-Deployment Analysis
After 1 week of live trading:
1. Compare metrics:
| Metric | Backtest | Live | Variance |
|----------------|----------|-------|----------|
| Sharpe | 1.5 | 1.3 | -13% |
| Drawdown | 12% | 15% | +25% |
| Win rate | 52% | 49% | -6% |
| Profit factor | 1.8 | 1.6 | -11% |
2. Evaluate variance:
- Small variance (<20%) → Expected, continue ✓
- Moderate variance (20-40%) → Monitor closely, may be temporary
- Large variance (>40%) → Significant concern, consider stopping
3. Decision:
- If metrics acceptable: Continue monitoring
- If metrics concerning: Investigate cause
- If red flags present: Stop deployment
After 1 month:
Review:
- Total return vs expectation
- Max drawdown experienced
- Trade execution quality
- Any technical issues
Decide:
- Scale up capital (if performing well)
- Continue same size (if acceptable)
- Scale down or stop (if underperforming)
Common Workflows
Workflow 1: First Deployment (EOA)
Goal: Deploy strategy for first time to validate live behavior
1. Final pre-deployment check:
☑ Backtested 6+ months (Sharpe 1.4, drawdown 14%)
☑ Code reviewed (no bugs found)
☑ Risk management validated
☑ Starting capital: $500 (can afford to lose)
☑ Monitoring plan: Check daily for first week
☑ Exit criteria: Stop if down >20% or drawdown >25%
2. Check credit balance:
get_credit_balance()
→ Balance: 100 USDC ✓ (sufficient for deployment $0.50)
3. Deploy:
deployment_create(
strategy_name="RSIMeanReversion_M",
symbol="BTC-USDT",
timeframe="1h",
leverage=1, # Conservative for first deployment
deployment_type="eoa"
)
→ Deployment ID: abc123
→ Status: Active
→ Cost: $0.50
4. Monitor closely:
Day 1: Check every 4 hours
Day 2-7: Check daily
Track: P&L, drawdown, trade execution
5. After 1 week:
Review performance vs backtest
If good: Continue and consider scaling up
If poor: Stop and analyze what went wrong
Cost: $0.50
Workflow 2: Managing Multiple Strategies (Vault)
Goal: Deploy multiple strategies using Hyperliquid Vault
1. Setup vault (one-time):
- Verify 200+ USDC in wallet
- Decide vault name (unique, descriptive)
2. Deploy first strategy:
deployment_create(
strategy_name="TrendFollower_M",
symbol="BTC-USDT",
timeframe="4h",
leverage=2,
deployment_type="vault",
vault_name="AlgoTrading_Vault_2025",
vault_description="Multi-strategy algorithmic trading vault"
)
→ Vault created successfully
3. Deploy second strategy (same vault):
deployment_create(
strategy_name="MeanReversion_L",
symbol="ETH-USDT",
timeframe="1h",
leverage=1,
deployment_type="vault",
vault_name="AlgoTrading_Vault_2025" # Same vault name
)
4. Monitor all deployments:
deployment_list()
→ Shows both strategies with individual performance
5. Manage independently:
- Can stop one strategy without affecting other
- Each strategy tracks separate P&L
- Vault shows combined performance
Cost: $0.50 per deployment = $1.00 total
Workflow 3: Stopping Underperforming Deployment
Goal: Stop deployment when red flags appear
1. Monitor deployment:
deployment_list()
→ Strategy: MomentumBreakout_H
→ P&L: -18% (started $1000, now $820)
→ Drawdown: 28%
→ Red flag: Drawdown > 1.5× backtest max (15% × 1.5 = 22.5%)
2. Decision: STOP (red flag triggered)
3. Stop deployment:
deployment_stop(deployment_id="abc123")
→ Status: Stopped
→ Final P&L: -$180 (-18%)
4. Analyze what went wrong:
- Review trade history
- Check market conditions during deployment
- Compare to backtest assumptions
- Identify issue (market regime change? bug? bad luck?)
5. Next steps:
- Fix issues if identified (use improve-trading-strategies)
- Re-backtest with improvements
- Deploy again with smaller capital if confident
- Or abandon strategy if fundamentally broken
Cost: Free to stop
Workflow 4: Restarting After Market Change
Goal: Restart deployment after temporary stop
1. Previously stopped deployment due to high volatility event
Stopped during extreme market conditions
2. Market stabilizes:
- Check current market conditions
- Compare to backtest environment
- Decide conditions are favorable again
3. Review strategy:
- Re-backtest on recent data
- Verify strategy still works
- Check no code changes needed
4. Restart deployment:
deployment_start(deployment_id="abc123")
→ Status: Active (resumed)
5. Monitor closely:
- First day: Check multiple times
- Verify execution matches expectations
- Be ready to stop again if issues recur
Cost: Free
Troubleshooting
"Insufficient Credits"
Issue: Cannot create deployment (balance too low)
Solutions:
1. Check balance:
get_credit_balance() → Balance: 0.20 USDC
2. Purchase credits:
- Visit Robonet dashboard
- Add credits to account
- Deployment costs $0.50
3. Retry deployment after purchase
"Max 1 EOA Deployment"
Issue: Trying to create second EOA deployment
Solutions:
1. Stop existing EOA deployment:
deployment_list() → Find existing deployment
deployment_stop(deployment_id="existing_id")
2. Or switch to Hyperliquid Vault:
- Requires 200+ USDC in wallet
- Allows unlimited deployments
- Use deployment_type="vault"
3. Or use different wallet (new EOA)
"Vault Creation Failed"
Issue: Cannot create Hyperliquid Vault
Solutions:
1. Verify 200+ USDC in wallet:
- Check wallet balance on Hyperliquid
- Vault requires minimum balance
2. Check vault name unique:
- Try different vault name
- Vault names must be unique across Hyperliquid
3. Verify wallet permissions:
- Ensure wallet connected properly
- Check Hyperliquid account status
"Live Performance Much Worse Than Backtest"
Issue: Strategy profitable in backtest, losing in live
Common causes & solutions:
1. Slippage higher than expected:
- Market less liquid than backtest assumed
- Solution: Use wider stops, lower frequency trades, or stop deployment
2. Fees not properly accounted:
- Forgot to include fees in backtest
- Solution: Re-backtest with realistic fees (0.05-0.1%)
3. Market regime changed:
- Trending market → ranging market
- Solution: Strategy may not work in current conditions, stop deployment
4. Execution delays:
- Live trades execute slower than backtest assumed
- Solution: Use longer timeframes (1h instead of 5m)
5. Overfitted strategy:
- Strategy memorized past data
- Solution: Simplify strategy, re-backtest, test on out-of-sample data
Decision: If performance -30% worse than backtest, STOP and fix issues
Legal & Compliance
Important disclaimers:
⚠️ Trading crypto perpetuals is HIGH RISK
⚠️ Regulations vary by jurisdiction
⚠️ You are responsible for compliance with local laws
⚠️ This is NOT financial advice
⚠️ Trade at your own risk
⚠️ Only risk capital you can afford to lose 100%
User responsibilities:
- Verify trading is legal in your jurisdiction
- Understand tax implications of trading
- Report trading activity as required by law
- Comply with local financial regulations
- Maintain records of trading activity
Platform disclaimers:
- Robonet provides tools, not financial advice
- Past performance doesn't guarantee future results
- No warranty on strategy performance
- User bears all risk of capital loss
Next Steps
If deployment is performing well:
- Continue monitoring regularly
- Track performance vs backtest expectations
- Consider gradual capital scaling after 1 month
- Document what's working for future strategies
If deployment is underperforming:
- Use
improve-trading-strategiesskill to refine - Re-backtest improvements thoroughly
- Test with small capital again before scaling
After successful deployment:
- Share learnings (what worked, what didn't)
- Consider deploying additional strategies
- Build track record for future deployments
Summary
This skill provides live trading deployment and management:
- 6 tools: deployment_create ($0.50), deployment_list/start/stop (free), account tools (free)
- Risk: HIGH (real capital at risk)
- Purpose: Deploy validated strategies to live trading
Core principle: Thorough preparation → small initial deployment → active monitoring → gradual scaling. Never deploy without extensive backtesting and clear exit criteria.
Critical warnings:
- You can lose ALL deployed capital
- Backtest ≠ live performance (expect differences)
- Start small ($500-1000) to validate live behavior
- Monitor daily for first week, weekly thereafter
- Stop immediately if red flags appear (drawdown >1.5× backtest, win rate collapses, technical issues)
- Define exit criteria BEFORE deploying (don't move goalposts)
Pre-deployment checklist must be 100% complete: Backtest >6 months, Sharpe >1.0, drawdown <20%, code reviewed, monitoring plan, exit criteria, starting small, risk capital only.
Best practice: Treat first deployment as validation phase, not profit phase. Goal is to confirm strategy works live, not to make money immediately. Profits come after validation succeeds.
Remember: This is real money, real risk, real consequences. If uncomfortable with any aspect of deployment, DON'T DEPLOY. It's better to miss opportunity than lose capital.