Dynamic Fee Adjustment

arb-assist implements sophisticated dynamic fee adjustment mechanisms to optimize transaction success rates while minimizing costs.

Overview

Dynamic fees automatically adjust based on:

• Network congestion levels

• Recent successful transaction fees

• Competition for block space

• Market volatility

• Specific token competition

Priority Fee Sources

Helius API Integration

The most accurate fee estimation comes from Helius:

helius_key = "your-helius-api-key"

Benefits:

• Real-time network statistics

• Percentile-based recommendations

• Historical success rates

• Accurate congestion metrics

Transaction Parsing

Without Helius, arb-assist learns from the blockchain:

helius_key = ""  # Empty = use transaction parsing

How it works:

1. Monitors successful arbitrage transactions

2. Extracts priority fees used

3. Builds statistical model

4. Applies to your transactions

Percentile-Based Fees

Configure fee percentiles for different strategies:

spam_levels = [
  {
    min_cu_percentile = 25,   # 25th percentile (low fees)
    max_cu_percentile = 50,   # 50th percentile (medium fees)
  }
]

Common percentile strategies:

• 0-25: Ultra low cost, may fail often

• 25-50: Budget conscious, moderate success

• 50-75: Balanced approach

• 75-95: High success rate

• 95-99: Maximum priority

Fee Strategies

Random Strategy

fee_strategy = "Random"
min_cu_price = 10_000
max_cu_price = 50_000

Characteristics:

• Unpredictable to competitors

• Good for avoiding detection

• Natural variance in costs

Use when:

• Testing fee ranges

• Avoiding patterns

• General arbitrage

Linear Strategy

fee_strategy = "Linear"
min_cu_price = 10_000
max_cu_price = 100_000
tx_count = 5

Characteristics:

• Gradual fee increase

• Predictable escalation

• Even cost distribution

Example progression (5 transactions):

TX 1: 10,000
TX 2: 32,500
TX 3: 55,000
TX 4: 77,500
TX 5: 100,000

Use when:

• Systematic testing

• Gradual competition

• Budget constraints

Exponential Strategy

fee_strategy = "Exponential"
min_cu_price = 10_000
max_cu_price = 1_000_000

Characteristics:

• Rapid fee escalation

• Aggressive competition

• High final fees

Example progression:

TX 1: 10,000
TX 2: 31,623
TX 3: 100,000
TX 4: 316,227
TX 5: 1,000,000

Use when:

• High-value opportunities

• Extreme competition

• Must-win scenarios

Dynamic Jito Tips

Tip Modes

dynamic_jito_tip_mode = "parsed"     # Learn from chain
dynamic_jito_tip_mode = "tipstream"  # Use API
dynamic_jito_tip_mode = "none"       # Fixed tips

Parsed Mode

Learns optimal tips from successful bundles:

dynamic_jito_tip_mode = "parsed"

Process:

1. Monitors Jito bundle landings

2. Extracts tip amounts

3. Builds tip distribution

4. Applies percentiles

Benefits:

• Self-adjusting

• No external dependencies

• Follows market dynamics

Tipstream Mode

Uses real-time tip floor data:

dynamic_jito_tip_mode = "tipstream"
jito_uuid = "your-uuid"  # Optional tracking

Benefits:

• Most accurate pricing

• Instant updates

• Official Jito data

Tip Percentiles

Configure tip ranges:

jito_levels = [
  {
    min_tip_percentile = 50,  # 50th percentile floor
    max_tip_percentile = 75,  # 75th percentile ceiling
  }
]

Multi-Tier Fee Configuration

Cascading Fee Levels

Different fees for different market conditions:

# Tier 0: Low competition
spam_levels = [
  {
    filter_level = 0,
    min_cu_percentile = 10,
    max_cu_percentile = 25,
    min_cu_price = 1_000,
    max_cu_price = 10_000,
  }
]

# Tier 1: Medium competition
jito_levels = [
  {
    filter_level = 1,
    min_tip_percentile = 50,
    max_tip_percentile = 75,
    min_tip = 5_000,
    max_tip = 50_000,
  }
]

# Tier 2: High competition
fast_levels = [
  {
    filter_level = 2,
    min_tip = 100_000,
    max_tip = 1_000_000,
  }
]

Market-Based Activation

Fees adjust based on profitability:

filter_thresholds = [
  { min_profit = 1_000_000 },   # Low fees OK
  { min_profit = 10_000_000 },  # Medium fees needed
  { min_profit = 100_000_000 }, # High fees justified
]

Fee Optimization Strategies

Conservative Approach

Minimize costs, accept some failures:

# Low percentiles
min_cu_percentile = 10
max_cu_percentile = 30

# Tight ranges
min_cu_price = 5_000
max_cu_price = 20_000

# Random distribution
fee_strategy = "Random"

Balanced Approach

Good success rate, reasonable costs:

# Middle percentiles
min_cu_percentile = 40
max_cu_percentile = 60

# Moderate ranges
min_cu_price = 20_000
max_cu_price = 100_000

# Linear progression
fee_strategy = "Linear"

Aggressive Approach

Maximum success, cost is secondary:

# High percentiles
min_cu_percentile = 75
max_cu_percentile = 95

# Wide ranges
min_cu_price = 50_000
max_cu_price = 1_000_000

# Exponential growth
fee_strategy = "Exponential"

Real-World Examples

Token Launch Scenario

High competition, time-sensitive:

# Use Jito with high tips
dynamic_jito_tip_mode = "tipstream"
jito_levels = [{
  min_tip_percentile = 90,
  max_tip_percentile = 99,
  no_failure_mode = true,
}]

Stable Market Arbitrage

Low competition, volume-based:

# Minimal fees
spam_levels = [{
  min_cu_price = 1_000,
  max_cu_price = 5_000,
  fee_strategy = "Random",
}]

Volatile Market Swings

Rapid changes, mixed competition:

# Multiple strategies
spam_levels = [{
  filter_level = 0,
  bundle_groups = [1, 2],
  min_cu_percentile = 25,
  max_cu_percentile = 50,
}]

jito_levels = [{
  filter_level = 1,
  bundle_groups = [3, 4],
  min_tip_percentile = 50,
  max_tip_percentile = 75,
}]

Monitoring Fee Performance

Key Metrics

Track these metrics:

1. Success Rate by Fee Level

2. Average Cost per Success

3. ROI Impact of Fees

4. Competition Analysis

Log Analysis

Enable logging to track fees:

log_output = true

Analyze patterns:

# Extract fee data
grep "priority_fee" arb-assist.log | awk '{print $NF}' | sort -n | uniq -c

# Success rate by fee
grep -B1 "success\|failed" arb-assist.log | grep "priority_fee"

Fee Efficiency Calculation

Calculate optimal fee ranges:

Efficiency = (Successful Trades * Avg Profit) / Total Fees Paid

ROI = (Total Profit - Total Fees) / Total Fees

Advanced Fee Techniques

Time-Based Adjustments

Adjust fees by time of day:

# Conceptual - not directly configurable
Peak hours (9am-5pm EST): Higher fees
Off-peak (night/weekend): Lower fees
Epoch boundaries: Maximum fees

Token-Specific Fees

Different fees for different tokens:

# Use different bundle groups
mints_to_arb = [2, 2, 3]  # 3 groups

# Group 1-2: Low-value tokens, low fees
spam_levels = [{
  bundle_groups = [1, 2],
  max_cu_price = 50_000,
}]

# Group 3: High-value tokens, high fees
jito_levels = [{
  bundle_groups = [3],
  min_tip = 100_000,
}]

Adaptive Learning

System continuously improves:

1. Initial: Use conservative estimates

2. Learning: Gather success/failure data

3. Optimization: Adjust ranges based on results

4. Maturity: Minimal fees for maximum success

Troubleshooting Fee Issues

Consistent Failures

If transactions consistently fail:

1. Increase minimum percentiles

2. Widen fee ranges

3. Switch to Jito bundles

4. Check RPC latency

Excessive Costs

If fees are too high:

1. Lower maximum percentiles

2. Tighten fee ranges

3. Use parsed mode instead of API

4. Filter for higher profit opportunities

Erratic Success Rates

If success is unpredictable:

1. Switch from Random to Linear strategy

2. Use longer time windows for percentiles

3. Check for network issues

4. Verify competition levels

Best Practices

1. Start Conservative: Begin with low fees and increase gradually

2. Monitor Constantly: Track success rates and adjust

3. Use Multiple Strategies: Different approaches for different scenarios

4. Set Limits: Always define maximum acceptable fees

5. Learn from Data: Let the system adapt to market conditions

6. Regular Reviews: Analyze fee performance weekly

7. Emergency Overrides: Have manual override capabilities

Integration with Strategies

Fees should align with overall strategy:

• High-Frequency: Lower fees, accept some failures

• High-Value: Higher fees, ensure success

• Market Making: Consistent medium fees

• Sniping: Maximum fees for critical moments

Remember: The goal is not the lowest fees, but the best ROI after fees.