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)

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3. Implementation Shortfall (Almgren-Chriss)

from xtrading.skills.execution_algo import ISOptimiser

opt = ISOptimiser(
    total_qty=100_000,
    T_hours=4.0,             # execute over 4 hours
    sigma=0.02,              # daily vol
    eta=2.5e-6,              # temporary impact coefficient
    gamma=1e-7,              # permanent impact coefficient
    risk_aversion=1e-6,      # λ: 0 = minimise IS only
)

result = opt.optimal_trajectory(n_intervals=10)
# {
#   "urgency_factor_kappa": 0.000123,
#   "expected_shortfall_bps": 4.2,
#   "schedule": [
#     {"interval": 1, "qty_to_trade": 8234, "remaining_inventory": 91766, "completion_pct": 8.2},
#     ...
#   ]
# }

# Efficient frontier (trade-off: urgency vs cost)
frontier = opt.efficient_frontier()  # DataFrame

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4. POV (Percentage of Volume)

from xtrading.skills.execution_algo import POVExecutor

pov = POVExecutor(
    symbol="GBPUSD",
    total_qty=200_000,
    side="buy",
    target_pov=0.10,          # 10% of market volume
    min_slice_qty=1_000,
    max_slice_qty=20_000,
)

# Called on each new market volume observation
slice_order = pov.on_market_volume(
    interval_volume=50_000,
    mid_price=1.2650,
    spread_bps=1.5,
)

# Summary after execution
summary = pov.execution_summary()
# {"n_fills": 15, "avg_fill_price": 1.2652, "vs_vwap_bps": 1.2, "completion_pct": 85.3}

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5. Iceberg Order

from xtrading.skills.execution_algo import IcebergOrder

ice = IcebergOrder(
    symbol="XAUUSD",
    total_qty=10_000,
    display_qty=500,           # only 500 oz visible at a time
    side="buy",
    limit_price=2000.0,
    randomise_display=True,    # vary display size ±10%
)

# Process fills
status = ice.on_fill(fill_qty=500, fill_price=2000.5)
# {"filled": 500, "total_filled": 500, "remaining": 9500,
#  "visible": 487, "reserve": 9013, "refreshed": True, "complete": False}

print(ice.summary)

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6. Slippage & Market Impact Analysis

from xtrading.skills.execution_algo import SlippageAnalyser, MarketImpactModel

# Post-trade slippage decomposition
decomp = SlippageAnalyser.decompose(
    arrival_price=1.1000,
    avg_fill_price=1.1012,
    vwap_benchmark=1.1008,
    twap_benchmark=1.1005,
    side="buy",
    spread_bps=2.0,
)
# {"implementation_shortfall_bps": 10.9, "market_impact_bps": 8.9,
#  "spread_cost_bps": 2.0, "grade": "B  (Acceptable)"}

# Pre-trade impact estimate
impact = SlippageAnalyser.estimate_market_impact(
    qty=50_000, adv=2_000_000, price=1.1000,
    volatility_daily=0.008, side="buy", model="sqrt"
)
# {"impact_bps": 5.3, "participation_rate": 0.025}

# Full cost model (Almgren-Chriss components)
model = MarketImpactModel(sigma=0.01, adv=1_000_000,
                           bid_ask_spread=0.0002, price=1.1000)
cost = model.total_cost(qty=100_000, execution_time_hours=2.0)
# {"permanent_impact_bps": 2.8, "transient_impact_bps": 1.9,
#  "spread_cost_bps": 0.9, "total_cost_bps": 5.6}

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7. TCA Report

from xtrading.skills.execution_algo import TCAReport
import pandas as pd

fills = pd.DataFrame({
    "timestamp": [...],
    "qty": [1000] * 20,
    "fill_price": [...],
    "mid_price": [...],
})

report = TCAReport(
    symbol="EURUSD", side="buy", fills=fills,
    arrival_price=1.1000, vwap=1.1005, twap=1.1003,
    algorithm="VWAP", benchmark="vwap"
)
d = report.to_dict()
# {
#   "total_cost_bps": 4.2,
#   "quality_score": 91.6,
#   "grade": "A  (Good)",
#   "implementation_shortfall_bps": 2.3,
# }

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Decision Framework

Order Size (% ADV)	Recommended Algorithm	Typical Cost (bps)
< 1%	Market / Limit	1–2
1–5%	TWAP (1–2h)	2–4
5–15%	VWAP (full day)	4–8
15–30%	IS + POV	8–15
> 30%	Iceberg + multi-day	15–30

Usage Conventions

1. qty — shares, lots, or contracts (consistent units throughout)
2. adv — average daily volume in the same units as qty
3. sigma — daily volatility as decimal (0.01 = 1%)
4. spread_bps — round-trip spread cost, not half-spread
5. TCA benchmark — use VWAP for passive strategies, arrival for aggressive

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Related Skills

• Market Making Hft
• Market Microstructure
• Liquidity Analysis
• Trading Automation