execution-algo-trading

> **Skill:** Execution Algo Trading | **Domain:** trading | **Category:** execution | **Level:** advanced > **Tags:** `trading`, `execution`, `twap`, `vwap`, `iceberg`, `algo` --- ## 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 --- ## 1. TWAP Executor ```python 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} ``` --- ## 2. VWAP Executor ```python 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) ``` --- ## 3. Implementation Shortfall (Almgren-Chriss) ```python 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 ``` --- ## 4. POV (Percentage of Volume) ```python 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} ``` --- ## 5. Iceberg Order ```python 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) ``` --- ## 6. Slippage & Market Impact Analysis ```python 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} ``` --- ## 7. TCA Report ```python 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, # } ``` --- ## 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 --- ## Related Skills - [Market Making Hft](../market-making-hft.md) - [Market Microstructure](../market-microstructure.md) - [Liquidity Analysis](../liquidity-analysis.md) - [Trading Automation](../trading-autopilot.md)