ml4t-transaction-costs git:20260528.303089eA

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---
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.

## The Pattern

### WRONG

```python
import numpy as np

# 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
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 |
| 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.
- 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.
- 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.

## 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