Skip to content

Strategy 1: Uniform Execution

Overview

The simplest execution strategy: buy a fixed dollar amount each day over the target duration.

Type: Passive / Non-adaptive
Completion: Always 100%
Performance: ~0 bps (by design)

Algorithm

Daily Execution Amount

\[ \text{Daily USD} = \frac{\text{Total USD}}{\text{Target Duration}} \]

For a $1B buyback over 100 days: $10M per day.

Shares Purchased

\[ \text{Shares}_t = \frac{\text{Daily USD}}{P_t} \]

Where \(P_t\) is the stock price on day \(t\).

Implementation

def strategy_1(prices, total_usd, target_duration):
    """Uniform execution: fixed daily amounts."""
    daily_usd = total_usd / target_duration
    usd_per_day = []
    shares_per_day = []

    for t in range(target_duration):
        price = prices[t]
        shares = daily_usd / price
        usd_per_day.append(daily_usd)
        shares_per_day.append(shares)

    total_shares = sum(shares_per_day)
    vwap = total_usd / total_shares
    benchmark = np.mean(prices[:target_duration])
    performance_bps = (benchmark - vwap) / benchmark * 10000

    return performance_bps, target_duration, vwap, benchmark

Characteristics

Pros

  • Simple and predictable
  • Zero market timing risk
  • Easy to explain to stakeholders
  • Guaranteed completion

Cons

  • No price optimization
  • Expected performance = 0 bps
  • Ignores market conditions
  • May buy at unfavorable prices

Use Cases

When to use Strategy 1

  • As a baseline for comparing other strategies
  • When market timing is explicitly forbidden
  • For very liquid stocks with minimal price impact
  • When execution predictability is paramount

Performance Profile

With 10,000 simulations:

Metric Typical Value
Mean Performance ~0 bps
Std Dev ~50 bps
Min -150 to -200 bps
Max +150 to +200 bps

The distribution is symmetric around zero because the strategy has no edge - it simply tracks the market average.

(c) Joerg Osterrieder 2025