Dynamic Fee Optimizer¶

34.1 System Overview¶

The Dynamic Fee Optimizer automatically calculates and optimizes trading costs before each order execution. It considers fees, slippage, and latency to dynamically select the lowest-cost execution path across multiple exchanges and accounts—maximizing net returns for market makers and HFT teams.

34.1.1 Core Objectives¶

• Cost Calculation: Automatically calculate expected execution costs (fees, slippage, hidden costs) before each order
• Multi-Venue Comparison: Compare actual cost differences across exchanges and accounts
• Dynamic Routing: Select the lowest total cost execution path
• Real-Time Optimization: Consider fee rates (Maker/Taker), market depth, and latency
• Future Integration: Support liquidity aggregation and split orders across multiple venues

34.2 Architecture Design¶

34.2.1 Microservice Architecture¶

Fee Optimizer Service:

services/fee-optimizer-service/
├── src/
│   ├── main.py
│   ├── model/
│   │   ├── cost_model.py
│   ├── predictor/
│   │   ├── fee_predictor.py
│   ├── scorer/
│   │   ├── venue_scorer.py
│   ├── router/
│   │   ├── fee_optimized_router.py
│   ├── api/
│   │   ├── fee_optimizer_api.py
│   ├── config.py
│   ├── requirements.txt
├── Dockerfile

34.2.2 Core Components¶

• Cost Modeler: Calculates comprehensive trading costs across venues
• Fee Prediction Engine: Real-time cost estimation (fees + slippage + latency)
• Venue Scorer: Scores different execution paths based on cost and speed
• Dynamic Router: Selects lowest-cost execution path
• API Interface: Query optimal trading paths
• Frontend Dashboard: Fee estimation and path selection visualization

34.3 Module Design¶

34.3.1 Cost Modeler (cost_model.py)¶

• Calculates base fee and slippage models

class CostModel:
    def calculate_fee(self, price, quantity, fee_rate):
        return price * quantity * fee_rate

    def estimate_slippage(self, order_quantity, market_depth):
        # Simple linear model - larger orders = more slippage
        if market_depth == 0:
            return 0.005  # 0.5% default slippage
        return min(order_quantity / market_depth, 0.005)

34.3.2 Fee Prediction Engine (fee_predictor.py)¶

• Comprehensive cost prediction

class FeePredictor:
    def __init__(self, cost_model):
        self.cost_model = cost_model

    def predict_total_cost(self, price, quantity, fee_rate, market_depth):
        fee = self.cost_model.calculate_fee(price, quantity, fee_rate)
        slippage_cost = price * quantity * self.cost_model.estimate_slippage(quantity, market_depth)
        return fee + slippage_cost

34.3.3 Venue Scorer (venue_scorer.py)¶

• Scores venues based on total cost, latency, and depth

class VenueScorer:
    def __init__(self, predictor):
        self.predictor = predictor

    def score_venues(self, venues_info, order_details):
        scores = {}
        for venue, info in venues_info.items():
            cost = self.predictor.predict_total_cost(
                price=info["price"],
                quantity=order_details["quantity"],
                fee_rate=info["fee_rate"],
                market_depth=info["depth"]
            )
            latency_penalty = info["latency"] / 1000  # 1ms = 0.001 penalty
            scores[venue] = cost + latency_penalty
        return scores

34.3.4 Dynamic Router (fee_optimized_router.py)¶

• Selects lowest-cost venue for execution

class FeeOptimizedRouter:
    def __init__(self, scorer):
        self.scorer = scorer

    def route(self, venues_info, order_details):
        scores = self.scorer.score_venues(venues_info, order_details)
        return min(scores, key=scores.get)

34.3.5 API Interface (fee_optimizer_api.py)¶

• FastAPI endpoints for venue selection and cost queries

from fastapi import APIRouter
router = APIRouter()

@router.post("/fee_optimizer/choose_venue")
async def choose_best_venue(order_details: dict):
    best_venue = router.route(order_details)
    return {"best_venue": best_venue}

34.3.6 Frontend Dashboard¶

• Real-time multi-exchange quotes, fees, and latency table
• Pre-trade cost estimation preview
• Optimal path highlighting
• Historical cost comparison (before vs after optimization)

34.4 Optimization Flow Example¶

1. Strategy initiates trade request → fee-optimizer-service estimates costs across venues
2. Select optimal cost path → unified execution engine executes order
3. Overall trading costs significantly reduced, net returns improved

34.5 Technology Stack¶

• Python (FastAPI, numpy): Service implementation and calculations
• Redis: Real-time venue data caching
• Docker: Containerization
• React/TypeScript: Frontend dashboard
• Prometheus: Cost optimization metrics

34.6 API Design¶

• POST /fee_optimizer/choose_venue: Select best venue for order
• GET /fee_optimizer/cost_estimate: Get cost estimate for specific order
• GET /fee_optimizer/venue_comparison: Compare costs across venues
• GET /fee_optimizer/optimization_stats: Get optimization performance stats

34.7 Frontend Integration¶

• Real-time venue comparison table
• Pre-trade cost estimation and path selection
• Historical optimization performance visualization

34.8 Implementation Roadmap¶

• Phase 1: Basic cost modeling and venue scoring
• Phase 2: Dynamic routing and API integration
• Phase 3: Advanced optimization, liquidity aggregation, frontend dashboard

34.9 Integration with Existing System¶

• Integrates with unified execution layer for optimal order routing
• Provides cost optimization for all strategies and accounts

34.10 Business Value¶