ml4t-transaction-costs · diff
git:20260528.303089e to git:20260901.c415df0
10 added, 10 removed. Audit A to A.
---
name: ml4t-transaction-costs
description: "Estimate whether a strategy can survive spread, slippage, and market impact before full simulation. Use when screening strategy feasibility early."
when_to_use: "Use when screening a signal, setting a safety margin, or reasoning about capacity"
dependencies: []
metadata:
book_chapters: "6, 18"
library: ""
---
# Transaction Cost Feasibility
Before you wire costs into a backtest engine, ask a simpler question: does the signal have enough gross edge to pay for trading at all? If not, the strategy should die early.
## The Problem
The full cost of a trade has three layers: explicit costs (commissions, fees), implicit costs (half the bid-ask spread paid on every execution), and market impact (your own order moving the price against you). Most bad strategies fail this economic screen before any engine-specific implementation details matter.
- A strategy with 50 bps gross alpha and 30 bps round-trip costs has a safety margin of only 1.7x -- too thin to survive estimation error.
+ A strategy with 50 bps gross alpha and 30 bps round-trip costs has a safety margin of only 1.7x - too thin to survive estimation error.
## The Pattern
### WRONG
```python
import numpy as np
- # Flat cost assumption -- ignores volume dependence
+ # Flat cost assumption - ignores volume dependence
weights = compute_target_weights(signals)
turnover = np.abs(weights - prev_weights).sum()
- costs = turnover * 0.001 # 10 bps flat -- wrong for large trades
+ costs = turnover * 0.001 # 10 bps flat - wrong for large trades
net_return = gross_return - costs
```
### CORRECT
```python
import numpy as np
weights = compute_target_weights(signals)
turnover = np.abs(weights - prev_weights)
# Volume-dependent square-root impact model
adv = volume_20d_mean # 20-day average daily volume ($)
participation = (turnover * portfolio_aum) / adv
spread_cost = half_spread # ~2-5 bps for liquid equities
impact_cost = 0.1 * np.sqrt(participation) # Almgren-Chriss square-root model
total_cost = spread_cost + impact_cost # per-asset, per-rebalance
net_return = gross_return - (turnover * total_cost).sum()
```
## Cost Stack
| Component | Type | Typical range | Scales with |
|-----------|------|---------------|-------------|
- | Commission | Explicit | 0--5 bps | Trade count |
- | Bid-ask spread | Implicit | 1--50 bps | Asset liquidity |
- | Slippage | Implicit | 1--10 bps | Order urgency |
- | Market impact | Implicit | 5--100+ bps | Trade size / ADV |
+ | Commission | Explicit | 0-5 bps | Trade count |
+ | Bid-ask spread | Implicit | 1-50 bps | Asset liquidity |
+ | Slippage | Implicit | 1-10 bps | Order urgency |
+ | Market impact | Implicit | 5-100+ bps | Trade size / ADV |
| Funding / borrow | Explicit | Variable | Short position size |
## Safety Margin Rule
```python
gross_alpha_bps = 50
round_trip_cost_bps = 20
safety_margin = gross_alpha_bps / round_trip_cost_bps # 2.5x
# Target: safety_margin >= 2.5x
# Below 2.0x: strategy is fragile to cost estimation error
# Below 1.5x: likely unprofitable in practice
```
## Capacity Estimation
```python
import numpy as np
# Maximum AUM before impact erodes alpha
universe_adv = adv_per_asset.sum() # total $ ADV across universe
turnover_rate = 0.20 # 20% monthly turnover
max_participation = 0.05 # trade < 5% of ADV
capacity = max_participation * universe_adv / turnover_rate
print(f"Estimated capacity: ${capacity/1e6:.0f}M")
```
## Guardrails
- - Never backtest without at least spread costs -- it is the irreducible minimum.
+ - Never backtest without at least spread costs - it is the irreducible minimum.
- Flat bps assumptions are only valid for very small portfolios trading liquid names.
- - Higher turnover amplifies cost sensitivity -- under the square-root model, 2x turnover ≈ 2.8x impact cost.
+ - Higher turnover amplifies cost sensitivity - under the square-root model, 2x turnover ≈ 2.8x impact cost.
- Validate cost model against Transaction Cost Analysis (TCA) data when available.
- - Crypto and options have much wider spreads than equities -- use asset-class-specific estimates.
+ - Crypto and options have much wider spreads than equities - use asset-class-specific estimates.
## Hand-Off
If the strategy clears this feasibility screen, encode the actual commission,
slippage, and impact assumptions with `ml4t-cost-model`. This skill is the
economic go/no-go filter; `ml4t-cost-model` is the engine-configuration step.
## Checklist
- [ ] All three cost layers modeled (commission, spread, impact)
- [ ] Market impact scales with trade size relative to ADV (not flat bps)
- [ ] Safety margin >= 2.5x documented (gross alpha / costs)
- [ ] Strategy capacity estimated with participation rate constraint
- [ ] Sensitivity analysis: results reported at 1x, 2x, and 3x base cost assumptions