Trading and Execution:
- Trading Cost Estimation
- Implementation shortfall calculation
- Market impact modeling using square root model
- Timing cost estimation
- Optimal Execution Strategy
- Almgren-Chriss optimal execution model
- Time-weighted average price (TWAP) implementation
- Volume-weighted average price (VWAP) considerations
- Dark Pool Integration
- Smart order routing across venues
- Historical fill rate analysis
- Price improvement optimization
- Position Unwinding
- Risk-aware liquidation scheduling
- Market impact minimization
- Multi-day trade planning
- Market Impact Modeling
- Square root impact model
- Volatility-adjusted impact calculation
- Temporary and permanent impact consideration
trading_execution.py:
"""
Trading and Execution Module for Index Solutions
This module provides comprehensive trading and execution capabilities including:
- Trading cost estimation
- Market impact modeling
- Optimal execution strategies
- Dark pool/algorithmic trading integration
- Multi-day trade scheduling
- Position unwinding strategies
"""
import numpy as np
import pandas as pd
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Union, Tuple
from dataclasses import dataclass
from enum import Enum
from risk_analytics import MarketData, RiskAnalytics
class VenueType(Enum):
LIT_EXCHANGE = "lit_exchange"
DARK_POOL = "dark_pool"
ALGO_ENGINE = "algo_engine"
BLOCK_VENUE = "block_venue"
@dataclass
class TradingVenue:
venue_type: VenueType
name: str
fee_structure: Dict[str, float]
historical_fill_rate: float
average_price_improvement: float
typical_latency: float # milliseconds
@dataclass
class TradeInstruction:
symbol: str
side: str # 'BUY' or 'SELL'
quantity: int
price_limit: Optional[float]
urgency: float # 0 to 1, where 1 is most urgent
valid_venues: List[VenueType]
start_time: datetime
end_time: datetime
class TradingCostEstimator:
def __init__(self, market_data: Dict[str, MarketData]):
self.market_data = market_data
def estimate_implementation_shortfall(self, trade: TradeInstruction) -> float:
"""Estimate implementation shortfall for a trade"""
market_data = self.market_data[trade.symbol]
# Basic implementation shortfall model
spread_cost = market_data.bid_ask_spread / 2
market_impact = self._calculate_market_impact(trade, market_data)
timing_cost = self._estimate_timing_cost(trade, market_data)
return spread_cost + market_impact + timing_cost
def _calculate_market_impact(self, trade: TradeInstruction, market_data: MarketData) -> float:
"""Calculate market impact using square root model"""
daily_volume = market_data.avg_daily_volume
participation_rate = trade.quantity / daily_volume
# Square root impact model
impact = market_data.volatility * np.sqrt(participation_rate)
return impact * market_data.price
def _estimate_timing_cost(self, trade: TradeInstruction, market_data: MarketData) -> float:
"""Estimate timing cost based on urgency and volatility"""
duration = (trade.end_time - trade.start_time).total_seconds() / (60 * 60 * 24) # in days
return market_data.volatility * np.sqrt(duration) * trade.urgency
class OptimalExecutionStrategy:
def __init__(self, risk_analytics: RiskAnalytics, cost_estimator: TradingCostEstimator):
self.risk_analytics = risk_analytics
self.cost_estimator = cost_estimator
def generate_trading_schedule(self, trade: TradeInstruction) -> List[Dict]:
"""Generate optimal trading schedule"""
total_duration = (trade.end_time - trade.start_time).total_seconds()
num_intervals = int(total_duration / 300) # 5-minute intervals
# Almgren-Chriss optimal execution model
remaining_time = np.linspace(0, total_duration, num_intervals)
volatility = self.market_data[trade.symbol].volatility
market_impact_factor = self._calculate_market_impact_factor(trade)
# Calculate optimal trajectory
optimal_shares = self._calculate_optimal_trajectory(
trade.quantity, remaining_time, volatility, market_impact_factor
)
return self._create_execution_schedule(trade, optimal_shares)
def _calculate_optimal_trajectory(self, total_shares: int,
remaining_time: np.ndarray,
volatility: float,
market_impact_factor: float) -> np.ndarray:
"""Implement Almgren-Chriss optimal execution model"""
tau = remaining_time[-1] - remaining_time
kappa = np.sqrt(market_impact_factor / volatility)
# Optimal trading trajectory
trajectory = np.sinh(kappa * tau) / np.sinh(kappa * remaining_time[-1])
shares = total_shares * trajectory
return shares
class DarkPoolIntegration:
def __init__(self, venues: List[TradingVenue]):
self.venues = venues
self.venue_stats = {}
def optimize_dark_pool_routing(self, trade: TradeInstruction) -> Dict[str, float]:
"""Optimize order routing across dark pools"""
dark_pools = [v for v in self.venues if v.venue_type == VenueType.DARK_POOL]
# Calculate routing weights based on historical performance
weights = self._calculate_venue_weights(dark_pools, trade)
return {pool.name: weight for pool, weight in zip(dark_pools, weights)}
def _calculate_venue_weights(self, venues: List[TradingVenue],
trade: TradeInstruction) -> np.ndarray:
"""Calculate optimal venue weights based on historical performance"""
fill_rates = np.array([v.historical_fill_rate for v in venues])
price_improvement = np.array([v.average_price_improvement for v in venues])
# Combine metrics with custom weights
score = 0.7 * fill_rates + 0.3 * price_improvement
weights = score / np.sum(score)
return weights
class PositionUnwinding:
def __init__(self, risk_analytics: RiskAnalytics, cost_estimator: TradingCostEstimator):
self.risk_analytics = risk_analytics
self.cost_estimator = cost_estimator
def generate_unwinding_strategy(self, positions: Dict[str, int],
max_days: int,
risk_limit: float) -> Dict[str, List[Dict]]:
"""Generate position unwinding strategy"""
# Calculate position risks and liquidation costs
position_risks = self._calculate_position_risks(positions)
liquidation_costs = self._estimate_liquidation_costs(positions)
# Sort positions by risk-adjusted liquidation cost
risk_cost_ratio = {
symbol: position_risks[symbol] / liquidation_costs[symbol]
for symbol in positions
}
sorted_positions = sorted(
positions.items(),
key=lambda x: risk_cost_ratio[x[0]],
reverse=True
)
return self._create_unwinding_schedule(sorted_positions, max_days, risk_limit)
def _create_unwinding_schedule(self, sorted_positions: List[Tuple],
max_days: int,
risk_limit: float) -> Dict[str, List[Dict]]:
"""Create detailed unwinding schedule"""
schedule = {}
current_date = datetime.now()
for symbol, quantity in sorted_positions:
daily_volume = self.market_data[symbol].avg_daily_volume
max_participation = 0.2 # 20% participation rate limit
# Calculate daily quantities
daily_qty = min(quantity, int(daily_volume * max_participation))
num_days = min(max_days, int(np.ceil(quantity / daily_qty)))
schedule[symbol] = []
remaining_qty = quantity
for day in range(num_days):
trade_qty = min(daily_qty, remaining_qty)
schedule[symbol].append({
'date': current_date + timedelta(days=day),
'quantity': trade_qty,
'expected_impact': self.cost_estimator._calculate_market_impact(
TradeInstruction(
symbol=symbol,
side='SELL',
quantity=trade_qty,
price_limit=None,
urgency=0.5,
valid_venues=[VenueType.LIT_EXCHANGE, VenueType.DARK_POOL],
start_time=datetime.now(),
end_time=datetime.now() + timedelta(days=1)
),
self.market_data[symbol]
)
})
remaining_qty -= trade_qty
return schedule
class TradingExecution:
def __init__(self, risk_analytics: RiskAnalytics):
self.risk_analytics = risk_analytics
self.market_data = {}
self.venues: List[TradingVenue] = []
# Initialize components
self.cost_estimator = TradingCostEstimator(self.market_data)
self.execution_strategy = OptimalExecutionStrategy(risk_analytics, self.cost_estimator)
self.dark_pool_integration = DarkPoolIntegration(self.venues)
self.position_unwinding = PositionUnwinding(risk_analytics, self.cost_estimator)
def add_venue(self, venue: TradingVenue):
"""Add a trading venue"""
self.venues.append(venue)
def update_market_data(self, symbol: str, market_data: MarketData):
"""Update market data for a symbol"""
self.market_data[symbol] = market_data
def execute_trade(self, trade: TradeInstruction) -> Dict:
"""Execute a trade using optimal execution strategy"""
# Generate trading schedule
schedule = self.execution_strategy.generate_trading_schedule(trade)
# If dark pools are valid venues, optimize dark pool routing
if VenueType.DARK_POOL in trade.valid_venues:
dark_pool_allocation = self.dark_pool_integration.optimize_dark_pool_routing(trade)
schedule = self._incorporate_dark_pool_routing(schedule, dark_pool_allocation)
# Estimate trading costs
estimated_costs = self.cost_estimator.estimate_implementation_shortfall(trade)
return {
'schedule': schedule,
'estimated_costs': estimated_costs,
'dark_pool_allocation': dark_pool_allocation if VenueType.DARK_POOL in trade.valid_venues else None
}
def unwind_positions(self, positions: Dict[str, int],
max_days: int = 5,
risk_limit: float = 0.02) -> Dict[str, List[Dict]]:
"""Generate unwinding strategy for positions"""
return self.position_unwinding.generate_unwinding_strategy(
positions, max_days, risk_limit
)
Naixian Zhang 