Risk Management¶
ArBot implements a comprehensive risk management framework designed to protect your capital while maximizing arbitrage opportunities. The system operates at multiple levels - from individual trade risk to portfolio-wide exposure management.
Risk Management Philosophy¶
Multi-Layer Protection¶
Defense in Depth:
1. Position Level: Individual trade risk controls
2. Portfolio Level: Overall exposure management
3. System Level: Technical and operational safeguards
4. Market Level: External condition monitoring
Risk-First Approach: - Capital preservation over profit maximization - Controlled exposure to market volatility - Systematic risk measurement and monitoring - Automated risk response mechanisms
Position-Level Risk Controls¶
Stop Loss Management¶
Automatic Stop Losses:
{
"risk_management": {
"stop_loss_percent": 2.0,
"enable_trailing_stops": true,
"stop_loss_method": "percentage"
}
}
Stop Loss Mechanisms: - Percentage-based: Fixed percentage from entry price - Volatility-based: Based on market volatility (ATR) - Time-based: Maximum holding period limits - Trailing stops: Dynamic adjustment with favorable moves
Implementation:
def check_stop_loss(position, current_price):
entry_price = position.entry_price
stop_loss_threshold = config.stop_loss_percent / 100
if position.side == "long":
stop_price = entry_price * (1 - stop_loss_threshold)
if current_price <= stop_price:
return True # Trigger stop loss
elif position.side == "short":
stop_price = entry_price * (1 + stop_loss_threshold)
if current_price >= stop_price:
return True # Trigger stop loss
return False
Position Sizing¶
Fixed Position Sizing:
Dynamic Position Sizing:
def calculate_position_size(signal, account_balance):
# Kelly Criterion implementation
win_rate = get_historical_win_rate()
avg_win = get_average_win()
avg_loss = get_average_loss()
kelly_fraction = (win_rate * avg_win - (1 - win_rate) * avg_loss) / avg_win
# Apply safety factor
safe_kelly = kelly_fraction * 0.25 # Use 25% of Kelly
# Calculate position size
position_size = account_balance * safe_kelly
# Apply maximum limits
return min(position_size, config.max_position_size)
Position Sizing Methods: - Fixed Amount: Consistent USD amount per trade - Percentage of Balance: Fixed percentage of total capital - Kelly Criterion: Mathematically optimal sizing - Risk Parity: Equal risk contribution per position
Slippage Protection¶
Slippage Estimation:
def estimate_slippage(symbol, trade_size, market_impact_model):
# Get order book depth
orderbook = get_orderbook(symbol)
# Calculate market impact
available_liquidity = sum(orderbook.bids[:10]) # Top 10 levels
size_ratio = trade_size / available_liquidity
# Estimate slippage based on size and volatility
base_slippage = config.slippage_tolerance
impact_slippage = size_ratio * market_impact_model.coefficient
return base_slippage + impact_slippage
Slippage Controls: - Pre-trade estimation: Calculate expected slippage - Dynamic adjustment: Adjust for market conditions - Size limits: Limit trades based on liquidity - Execution monitoring: Track actual vs expected slippage
Portfolio-Level Risk Management¶
Drawdown Protection¶
Maximum Drawdown Limits:
{
"risk_management": {
"max_drawdown_percent": 5.0,
"drawdown_calculation": "peak_to_trough",
"recovery_threshold": 50.0
}
}
Drawdown Monitoring:
class DrawdownMonitor:
def __init__(self, max_drawdown_percent):
self.max_drawdown = max_drawdown_percent / 100
self.peak_balance = 0
self.current_drawdown = 0
def update(self, current_balance):
# Update peak balance
if current_balance > self.peak_balance:
self.peak_balance = current_balance
# Calculate current drawdown
self.current_drawdown = (self.peak_balance - current_balance) / self.peak_balance
# Check if limit exceeded
if self.current_drawdown > self.max_drawdown:
return "STOP_TRADING" # Emergency stop
return "CONTINUE"
Drawdown Response Actions: 1. Warning Level (50% of limit): Reduce position sizes 2. Critical Level (80% of limit): Stop new positions 3. Maximum Level (100% of limit): Close all positions
Concentration Risk¶
Exposure Limits:
{
"risk_management": {
"max_concurrent_trades": 3,
"max_symbol_exposure": 0.2,
"max_exchange_exposure": 0.4,
"correlation_threshold": 0.7
}
}
Concentration Monitoring:
def check_concentration_risk(new_position, current_portfolio):
# Check symbol concentration
symbol_exposure = calculate_symbol_exposure(new_position.symbol, current_portfolio)
if symbol_exposure > config.max_symbol_exposure:
return False
# Check exchange concentration
exchange_exposure = calculate_exchange_exposure(new_position.exchange, current_portfolio)
if exchange_exposure > config.max_exchange_exposure:
return False
# Check correlation risk
if check_correlation_risk(new_position, current_portfolio):
return False
return True
Balance Protection¶
Minimum Balance Threshold:
{
"risk_management": {
"balance_threshold_percent": 10.0,
"reserve_balance": 1000.0,
"emergency_liquidation": true
}
}
Balance Monitoring: - Threshold alerts: Warn when approaching minimum - Trading suspension: Stop new trades below threshold - Emergency procedures: Automated position closure
System-Level Risk Controls¶
Technical Risk Management¶
Connection Monitoring:
class ConnectionHealthMonitor:
def __init__(self):
self.exchange_status = {}
self.last_heartbeat = {}
def monitor_exchanges(self):
for exchange in self.exchanges:
# Check WebSocket connection
if not exchange.is_connected():
self.handle_disconnection(exchange)
# Check API response times
if exchange.latency > MAX_LATENCY:
self.handle_high_latency(exchange)
# Check data freshness
if time.time() - exchange.last_update > MAX_DATA_AGE:
self.handle_stale_data(exchange)
Risk Mitigation: - Redundant connections: Multiple data feeds - Circuit breakers: Automatic trading suspension - Failover mechanisms: Backup systems activation - Data validation: Quality checks on all inputs
Operational Risk¶
Error Handling:
def handle_trading_error(error, context):
error_severity = classify_error(error)
if error_severity == "CRITICAL":
# Stop all trading immediately
emergency_stop()
send_alert("Critical error - trading stopped")
elif error_severity == "HIGH":
# Suspend affected exchange/symbol
suspend_trading(context.exchange, context.symbol)
elif error_severity == "MEDIUM":
# Reduce position sizes
reduce_position_sizes(factor=0.5)
# Log all errors for analysis
log_error(error, context, error_severity)
Error Classification: - Critical: System failures, API key issues - High: Exchange disconnections, order failures - Medium: Network delays, data inconsistencies - Low: Minor warnings, performance issues
Regulatory Risk¶
Compliance Monitoring:
def check_regulatory_compliance(trade):
# Check position limits
if trade.size > REGULATORY_POSITION_LIMIT:
return False
# Check geographic restrictions
if trade.exchange in RESTRICTED_EXCHANGES:
return False
# Check token restrictions
if trade.symbol in RESTRICTED_SYMBOLS:
return False
return True
Risk Metrics and Monitoring¶
Real-Time Risk Metrics¶
Portfolio Risk Dashboard:
{
"current_drawdown": 2.3,
"max_drawdown": 5.0,
"var_95": -0.8, # 95% Value at Risk
"expected_shortfall": -1.2,
"sharpe_ratio": 2.1,
"sortino_ratio": 3.2,
"beta": 0.3,
"correlation_btc": 0.15
}
Position-Level Metrics:
{
"unrealized_pnl": 45.67,
"unrealized_pnl_percent": 0.89,
"time_in_position": 3600, # seconds
"stop_loss_distance": 1.2,
"profit_target_distance": 2.8
}
Risk Reporting¶
Daily Risk Report: - Portfolio performance summary - Risk metric trends - Violation alerts and responses - Market condition analysis
Weekly Risk Review: - Strategy performance analysis - Risk parameter optimization - Stress test results - Regulatory compliance check
Advanced Risk Features¶
Stress Testing¶
Scenario Analysis:
def stress_test_portfolio(portfolio, scenarios):
results = {}
for scenario_name, scenario in scenarios.items():
# Apply market shock
shocked_prices = apply_shock(current_prices, scenario)
# Calculate portfolio impact
portfolio_pnl = calculate_portfolio_pnl(portfolio, shocked_prices)
results[scenario_name] = {
"portfolio_pnl": portfolio_pnl,
"worst_position": find_worst_position(portfolio, shocked_prices),
"recovery_time": estimate_recovery_time(portfolio_pnl)
}
return results
Stress Scenarios: - Market Crash: -20% across all assets - Crypto Winter: -50% sustained decline - Exchange Hack: Single exchange shutdown - Regulatory Ban: Specific region/token restrictions
Dynamic Risk Adjustment¶
Market Regime Detection:
def detect_market_regime(price_history, volatility_history):
current_volatility = calculate_current_volatility()
volatility_percentile = get_volatility_percentile(current_volatility)
if volatility_percentile > 90:
return "HIGH_VOLATILITY"
elif volatility_percentile < 10:
return "LOW_VOLATILITY"
else:
return "NORMAL"
def adjust_risk_parameters(market_regime):
if market_regime == "HIGH_VOLATILITY":
# Reduce position sizes and increase stops
config.trade_amount_usd *= 0.5
config.stop_loss_percent *= 0.8
elif market_regime == "LOW_VOLATILITY":
# Increase position sizes slightly
config.trade_amount_usd *= 1.2
config.min_profit_threshold *= 0.8
Risk-Adjusted Performance¶
Performance Metrics:
def calculate_risk_adjusted_returns(returns, risk_free_rate=0.02):
excess_returns = returns - risk_free_rate
metrics = {
"sharpe_ratio": excess_returns.mean() / returns.std(),
"sortino_ratio": excess_returns.mean() / returns[returns < 0].std(),
"calmar_ratio": returns.mean() / max_drawdown,
"omega_ratio": calculate_omega_ratio(returns),
"tail_ratio": calculate_tail_ratio(returns)
}
return metrics
Risk Configuration Examples¶
Conservative Profile¶
Ultra-Safe Settings:
{
"risk_management": {
"max_drawdown_percent": 2.0,
"stop_loss_percent": 1.0,
"max_concurrent_trades": 1,
"balance_threshold_percent": 20.0,
"position_sizing_method": "fixed"
},
"arbitrage": {
"min_profit_threshold": 0.015,
"max_position_size": 500.0,
"use_trend_filter": true
}
}
Moderate Profile¶
Balanced Risk-Reward:
{
"risk_management": {
"max_drawdown_percent": 5.0,
"stop_loss_percent": 2.0,
"max_concurrent_trades": 3,
"balance_threshold_percent": 10.0,
"position_sizing_method": "percentage"
},
"arbitrage": {
"min_profit_threshold": 0.005,
"max_position_size": 1000.0
}
}
Aggressive Profile¶
Higher Risk Tolerance:
{
"risk_management": {
"max_drawdown_percent": 10.0,
"stop_loss_percent": 3.0,
"max_concurrent_trades": 5,
"balance_threshold_percent": 5.0,
"position_sizing_method": "kelly"
},
"arbitrage": {
"min_profit_threshold": 0.003,
"max_position_size": 2000.0
}
}
Best Practices¶
Risk Management Principles¶
- Never Risk More Than You Can Afford to Lose
- Diversify Across Exchanges and Symbols
- Monitor Risk Metrics Continuously
- Adjust Parameters Based on Market Conditions
- Have Emergency Procedures Ready
Implementation Guidelines¶
Daily Procedures: - Review overnight positions - Check risk metric alerts - Validate system connectivity - Monitor market conditions
Weekly Reviews: - Analyze performance vs risk - Optimize risk parameters - Stress test portfolio - Review correlation patterns
Monthly Analysis: - Comprehensive risk assessment - Strategy performance review - Risk parameter backtesting - Regulatory compliance audit
Risk Warning
Cryptocurrency trading involves substantial risk of loss. Past performance does not guarantee future results. Never trade with funds you cannot afford to lose.
Risk Monitoring
Set up automated alerts for all risk metrics. Real-time monitoring is essential for effective risk management in fast-moving crypto markets.
Risk vs Reward
Higher returns typically come with higher risks. Find the right balance for your risk tolerance and investment objectives through careful backtesting and gradual position size increases.
Created: July 12, 2025