Buyer Guide

Overview

The ClyptQ Buyer Guide is for strategy investors. This guide covers how to evaluate strategies from the marketplace, deploy them safely, and manage risk effectively.

Why Use ClyptQ?

Transparency and Trust:

All strategy performance is verifiable
Reproducible backtest results
Code-level transparency

Risk Management:

Built-in risk constraints
Real-time monitoring
Automatic stop-loss mechanisms

Accessibility:

Deploy strategies without coding
Diverse strategy selection
Immediate access to infrastructure

Strategy Evaluation

Performance Metrics

Return Metrics:

Total Return: Overall return (%)
Annualized Return: Annualized return (%)
CAGR: Compound Annual Growth Rate (%)

Risk-Adjusted Returns:

Sharpe Ratio: Return vs risk (greater than 1.5 recommended)
Sortino Ratio: Return vs downside risk
Calmar Ratio: Return vs drawdown

Risk Metrics:

Max Drawdown: Maximum loss magnitude (less than 20% recommended)
Volatility: Variability (annualized standard deviation)
VaR (95%): Maximum loss at 95% confidence level

Trading Metrics:

Win Rate: Winning trade ratio
Profit Factor: Total profit / Total loss
Avg Trade Duration: Average holding period

Strategy Profile

Basic Information:

Name: Momentum Crossover Pro
Version: 1.0.0
Author: verified_builder
Description: SMA crossover with liquidity filter

Asset Classes: [crypto]
Market Type: futures
Timeframe: 1m
Leverage: 1x-3x

Performance Summary:

Backtest (2023-01-01 to 2024-12-31):
  Total Return: 45.2%
  Sharpe Ratio: 1.8
  Max Drawdown: 18.3%
  Win Rate: 62%

Paper Trading (30 days):
  Total Return: 8.1%
  Sharpe Ratio: 1.5
  Max Drawdown: 6.2%

Live Trading (60 days, verified):
  Total Return: 15.4%
  Sharpe Ratio: 1.6
  Max Drawdown: 9.1%
  Status: Verified by Platform

Risk Profile:

Max Position: 30% per asset
Max Leverage: 3x
Max Drawdown Limit: 20%
Stop Loss: Enabled
Take Profit: Enabled

Universe: Top 20 liquid crypto
Rebalance Frequency: 1 minute
Order Type: Market (aggressive fill)

Evaluation Checklist

Performance (higher is better):

Total Return greater than 30%/year
Sharpe Ratio greater than 1.5
Win Rate greater than 55%
Profit Factor greater than 1.5

Risk (lower is better):

Max Drawdown less than 20%
Volatility less than 40%
Leverage less than 5x
Position concentration less than 30%

Verification:

1+ year backtest record
Paper trading verification (2+ weeks)
Live trading verification (optional)
Platform verification badge

Transparency:

Strategy logic disclosed
Risk parameters specified
Fee structure transparent
Author reputation verified

Deployment

Step 1: Strategy Selection

Marketplace Filtering:

from clyptq.marketplace import StrategyMarketplace

marketplace = StrategyMarketplace()

# Filter strategies
strategies = marketplace.search(
    asset_class=["crypto"],
    min_sharpe=1.5,
    max_drawdown=0.20,
    min_backtest_duration=365,  # days
    verified_only=True
)

# Sort by Sharpe
strategies = sorted(strategies, key=lambda x: x.sharpe_ratio, reverse=True)

# Top 3
for s in strategies[:3]:
    print(f"{s.name}: Sharpe={s.sharpe_ratio:.2f}, DD={s.max_drawdown:.1%}")

Step 2: Backtesting Verification

Confirm Reproducibility:

from clyptq.apps.trading import TradingDriver

# Download strategy spec
spec = marketplace.download_spec("momentum_crossover_pro")

# Run backtest with same parameters
driver = TradingDriver.from_spec(spec)
driver.load_data(
    items=spec.symbol_source_map.all_symbols(),
    start=datetime(2023, 1, 1),
    end=datetime(2024, 12, 31)
)

results = driver.run()

# Verify performance matches listing
assert abs(results.total_return - listed_return) < 0.01  # Within 1%
assert abs(results.sharpe_ratio - listed_sharpe) < 0.1   # Within 0.1

Step 3: Paper Trading

Real-time Verification (no capital risk):

# Clone spec and set to paper mode
paper_spec = spec.copy()
paper_spec.execution.mode = "paper"

# Set your account size
paper_spec.accounts[0].initial_balance = {"USDT": 10000.0}

# Run
driver = TradingDriver.from_spec(paper_spec)
driver.load_data(
    items=symbols,
    mode="live",
    warmup_ticks=100
)

# Monitor for 1-2 weeks
for result in driver.run():
    print(f"{result.timestamp}: Equity=${result.total_equity:.2f}")

Step 4: Live Deployment (Small)

Small Capital Production Deployment:

# Set to live mode
live_spec = spec.copy()
live_spec.execution.mode = "live"
live_spec.execution.api_key = os.getenv("BINANCE_API_KEY")
live_spec.execution.api_secret = os.getenv("BINANCE_API_SECRET")

# Conservative risk limits (start small)
live_spec.execution.max_position_pct = 0.1   # 10% max per position
live_spec.execution.max_drawdown = 0.10      # Stop at 10% loss

# Use actual account (start with 1-5% of total capital)
live_spec.accounts[0].initial_balance = None  # Use actual balance

driver = TradingDriver.from_spec(live_spec)
driver.load_data(
    items=symbols,
    mode="live",
    warmup_ticks=100
)

# Run with monitoring
for result in driver.run():
    # Log performance
    monitor.log(result)

    # Alert on significant events
    if result.drawdown > 0.05:
        alert.send(f"Drawdown: {result.drawdown:.1%}")

Step 5: Scale Up

Gradual capital increase:

# Week 1-2: 1% of capital
capital_allocation = 0.01

# Week 3-4: 2% of capital (if performance good)
if sharpe > 1.0 and drawdown < 0.10:
    capital_allocation = 0.02

# Month 2: 5% of capital
if sharpe > 1.2 and drawdown < 0.15:
    capital_allocation = 0.05

# Month 3+: Up to 10-20%
if sharpe > 1.5 and drawdown < 0.20:
    capital_allocation = 0.10  # or up to 0.20

Risk Management

Position Limits

Configuration Example:

execution_spec = TradingExecutionSpec(
    # Per-asset limit
    max_position_size={
        "BTC/USDT:USDT": 1.0,  # Max 1 BTC
        "ETH/USDT:USDT": 10.0  # Max 10 ETH
    },

    # Portfolio limit
    max_position_pct=0.2,  # Max 20% per asset

    # Leverage
    max_leverage=3.0,  # Max 3x

    # Drawdown
    max_drawdown=0.15  # Auto-stop at 15% loss
)

Stop Loss & Take Profit

Specify in Intention:

intention = TradingIntention(
    mode="weight",
    values={
        "BTC/USDT:USDT": 0.3
    },

    # Automatic stop-loss/take-profit
    stop_loss={
        "BTC/USDT:USDT": 0.95  # Close at 5% loss
    },
    take_profit={
        "BTC/USDT:USDT": 1.15  # Close at 15% profit
    }
)

Real-Time Monitoring

Track Key Metrics:

# Set up alerts
monitor = PerformanceMonitor()

monitor.add_alert(
    metric="drawdown",
    threshold=0.10,  # 10%
    action=lambda: alert.send("Drawdown exceeds 10%")
)

monitor.add_alert(
    metric="sharpe_ratio",
    threshold=0.5,   # Sharpe below 0.5
    action=lambda: alert.send("Low Sharpe - review strategy")
)

monitor.add_alert(
    metric="unrealized_pnl",
    threshold=-1000,  # -$1000
    action=lambda: alert.send("Large unrealized loss")
)

Emergency Stop

Manual Shutdown:

# Graceful shutdown
driver.stop()
# Maintains current positions, stops new trading

# Emergency close
driver.emergency_close_all()
# Immediately close all positions

Pricing Models

Performance Fee:

Model: Performance-based
Fee: 20% of profits
High Water Mark: Enabled
Min Investment: $1,000

Calculation Example:

Initial capital: $10,000
Profit: $2,000 (20%)
Performance fee: $2,000 × 20% =$ 400
Net profit: $2,000 -$ 400 = $1,600 (16%)

Subscription:

Model: Fixed subscription
Fee: $100/month
Min Investment: $5,000

Hybrid:

Model: Subscription + Performance
Subscription: $50/month
Performance Fee: 10% of profits
Min Investment: $3,000

Best Practices

1. Due Diligence

# DO
- Verify 1+ year backtest
- Run paper trading yourself
- Check author reputation
- Understand risk profile

# DON'T
- Check short backtest only
- Skip paper and go live
- Use unverified builder strategies
- Ignore risk

2. Position Sizing

# DO
- Start small (1-5%)
- Gradual increase
- Diversify across strategies
- Max 20% per strategy

# DON'T
- Deploy full capital at once
- Concentrate in single strategy
- Excessive leverage

3. Monitoring

# DO
- Track real-time metrics
- Daily performance review
- Set up alerts
- Regular re-evaluation

# DON'T
- Neglect monitoring
- Ignore alerts
- Blindly trust

4. Risk Management

# DO
- Conservative risk settings
- Enable stop loss
- Set drawdown limit
- Have emergency stop plan

# DON'T
- No risk limits
- Disable stop loss
- Allow unlimited loss

Common Questions

Q: What if strategy fails? A: Auto-stops at max drawdown limit. Performance fee models only charge on profits. Q: Can I modify strategies? A: Buyers can only adjust risk parameters. Strategy logic can only be modified by Builder. Q: Run multiple strategies simultaneously? A: Yes. Each strategy runs independently, with portfolio-level risk management also supported. Q: Minimum investment? A: Varies by strategy. Typically

1,000-

10,000. Q: Withdrawal restrictions? A: None. Stop strategy and withdraw funds anytime. Q: Performance verification? A: Platform automatically verifies backtest, paper, and live performance. Check for Verified badge.

Support & Resources

Documentation:

Community:

Discord: discord.gg/clyptq
Forum: forum.clyptq.com

Support:

Email: [email protected]
Live Chat: Available on platform

Ecosystem Benefits

For You (Buyer):

Access to verified strategies
Transparent performance metrics
Built-in risk management
Deploy without coding

For Builders:

Verifiable performance
Trust-based monetization
Production infrastructure

For Platform:

Trading Commerce ecosystem
Buyer-Builder trust relationship
Sustainable fee model

Getting Started

Platform Overview

Trading Engine

Overview

Why Use ClyptQ?

Strategy Evaluation

Performance Metrics

Strategy Profile

Evaluation Checklist

Deployment

Step 1: Strategy Selection

Step 2: Backtesting Verification

Step 3: Paper Trading

Step 4: Live Deployment (Small)

Step 5: Scale Up

Risk Management

Position Limits

Stop Loss & Take Profit

Real-Time Monitoring

Emergency Stop

Pricing Models

Best Practices

Common Questions

Support & Resources

Ecosystem Benefits

Getting Started

Platform Overview

Trading Engine

​Overview

​Why Use ClyptQ?

​Strategy Evaluation

​Performance Metrics

​Strategy Profile

​Evaluation Checklist

​Deployment

​Step 1: Strategy Selection

​Step 2: Backtesting Verification

​Step 3: Paper Trading

​Step 4: Live Deployment (Small)

​Step 5: Scale Up

​Risk Management

​Position Limits

​Stop Loss & Take Profit

​Real-Time Monitoring

​Emergency Stop

​Pricing Models

​Best Practices

​Common Questions

​Support & Resources

​Ecosystem Benefits

Overview

Why Use ClyptQ?

Strategy Evaluation

Performance Metrics

Strategy Profile

Evaluation Checklist

Deployment

Step 1: Strategy Selection

Step 2: Backtesting Verification

Step 3: Paper Trading

Step 4: Live Deployment (Small)

Step 5: Scale Up

Risk Management

Position Limits

Stop Loss & Take Profit

Real-Time Monitoring

Emergency Stop

Pricing Models

Best Practices

Common Questions

Support & Resources

Ecosystem Benefits