execution-algo-trading

Skill: Execution Algo Trading | Domain: trading | Category: execution | Level: advanced Tags: trading, execution, twap, vwap, iceberg, algo

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Execution Algorithm Trading Skill

Overview

Implements institutional-grade execution algorithms used by buy-side desks to minimise market impact and transaction costs when executing large orders.

Python Module

xtrading/skills/execution_algo.py

Stack

• numpy — Numerical computations, binomial tree, random generation
• pandas — VWAP calculation, fill data management
• scipy — Statistical computations

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1. TWAP Executor

from datetime import datetime, timedelta
from xtrading.skills.execution_algo import TWAPExecutor

now = datetime.now()
twap = TWAPExecutor(
    symbol="EURUSD",
    total_qty=100_000,
    side="buy",
    start_time=now,
    end_time=now + timedelta(hours=4),
    n_slices=20,
    randomise_size=True,    # add ±15% size variation
    randomise_time=True,    # add ±10% timing jitter
)

schedule = twap.build_schedule()
# schedule.n_slices = 20
# schedule.estimated_cost_bps ≈ 3.0
# schedule.slices[i].target_time, .quantity, .order_type

# Simulate against historical prices
import pandas as pd
prices = pd.Series(...)  # mid prices with DatetimeIndex
result = twap.simulate_execution(prices, spread_bps=2.0)
# {"avg_fill_price": 1.1005, "twap_benchmark": 1.1003,
#  "vs_benchmark_bps": 1.8, "total_cost_bps": 3.8}

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2. VWAP Executor

from xtrading.skills.execution_algo import VWAPExecutor
import numpy as np

# Custom intraday volume profile
profile = np.array([0.10, 0.08, 0.06, 0.05, 0.04, 0.04,
                    0.04, 0.04, 0.05, 0.06, 0.07, 0.08,
                    0.09, 0.10, 0.10, 0.10])

vwap = VWAPExecutor(
    symbol="XAUUSD",
    total_qty=50_000,
    side="sell",
    start_time=now,
    end_time=now + timedelta(hours=8),
    volume_profile=profile,
    participation_cap=0.15,   # max 15% of any interval
)

schedule = vwap.build_schedule(avg_interval_volume=10_000)
# schedule.participation_rate ≈ 0.031 (3.1% of daily volume)

# Calculate realised VWAP
import pandas as pd
vwap_price = vwap.calculate_volume_weighted_price(prices, volumes)