Improving TWAP Execution with Direction-to-Close Signals

Executive Summary

Quantum Signals produces live direction-to-close signals for highly liquid equities and futures markets. These signals capture persistent intraday market dynamics and can be used as a timing overlay for execution algorithms.

This note presents a simple, transparent benchmark evaluating whether such signals improve execution of a fixed parent order relative to a neutral TWAP schedule, applied to E-mini S&P 500 (ES) and E-mini Nasdaq 100 (NQ) futures. We simulate the execution of a daily parent order using two strategies:

• A standard TWAP schedule.
• A signal-aware execution schedule that symmetrically tilts the trading trajectory earlier or later depending on the signal direction, using an aggressiveness level of λ=1.

The simulation assumes zero transaction costs. Since any fixed per-trade cost would apply uniformly to both strategies, it would not affect the relative improvement reported.

Across the tested sample, the signal-aware schedule reduces implementation shortfall relative to TWAP across all periods tested. Across 2024 and 2025, the average daily improvement is 2.55 basis points, ranging from 0.09 to 9.80 basis points across years, quarters, symbols and directions.

The benchmark isolates the value of predictive timing while keeping the total traded quantity identical between strategies. The results suggest that intraday direction-to-close signals can provide measurable execution value when used as an overlay of standard execution schedules. While the benchmark is conducted on ES and NQ futures, the same approach applies directly to correlated instruments including MES, MNQ, SPY, and QQQ.

1. Signal Overview

Quantum Signals provides live direction-to-close signals for highly liquid futures markets, generated by proprietary neural networks based on Temporal Fusion Transformer (TFT) architectures.

The signals are updated each minute. At each update, the model predicts the direction of price movement between the current 5-minute window and the close of the session, defined as:

Average midprice over [15:55, 16:00] − Average midprice over [t, t+5min]

The signal takes three values: UP, STABLE, or DOWN, indicating whether the model expects price to rise, remain broadly flat, or fall between the current moment and the session close.

2. Benchmark Design

To evaluate the execution value of direction-to-close signals, we construct a simple benchmark comparing a standard TWAP schedule with a signal-aware execution schedule, isolating the effect of predictive timing while keeping all other elements identical.

The benchmark is conducted on E-mini S&P 500 (ES) and E-mini Nasdaq 100 (NQ) futures over the full years 2024 and 2025. All results are out-of-sample. Buy and sell orders are benchmarked separately for each instrument, yielding four instrument-direction combinations.

Each simulation assumes a parent order of Q = 10,000,000 USD notional, executed over regular trading hours from 9:45 to 16:00 ET — a horizon of 374 one-minute intervals. Execution begins at 9:45 rather than the session open to avoid opening volatility and to allow the model sufficient intraday history before generating predictions. The simulation assumes zero transaction costs. Since any fixed per-trade cost would apply uniformly to both strategies, it would not affect the relative improvement reported.

3. Execution Strategies

3.1 Baseline TWAP

The baseline strategy executes the parent order using a standard Time-Weighted Average Price (TWAP) schedule. The parent order notional Q is converted into a number of units using the session open price P₀:

N = Q / P₀

These units are then divided evenly across the T = 374 one-minute intervals, yielding a constant slice size:

q₀ = N / T

All calculations are performed using fractional units throughout the simulation.

3.2 Signal-Aware Execution Schedule

The signal-aware strategy dynamically adjusts the slice size at each interval based on the signal direction. At each minute t, the algorithm first computes the baseline quantity required to complete the order on time given remaining inventory:

b_t = remaining units / remaining intervals

The executed quantity is then adjusted according to the signal:

q_t = b_t (1 + λ s_t)

where  s_t ∈ {−1, 0, +1} represents the signal direction relative to the order side, and λ is an aggressiveness parameter. In this benchmark, λ = 1. When the signal is favorable, execution is accelerated relative to the neutral trajectory; when unfavorable, it is slowed.

To prevent excessive inventory accumulation, a guardrail is applied: if b_t exceeds twice the initial TWAP slice size q₀=N/T, the signal is ignored and execution reverts to the neutral pace q_t=b_t. This ensures the strategy never defers too aggressively and can always complete the full parent order by the session close.

A second guardrail prevents execution from tapering to sub-TWAP pace late in the session: if b_t  falls below q₀, the executed quantity is floored at q₀ for the remainder of the session, ensuring a smooth completion without anomalous slowdown at the close.

3.3 Example

Setup: 

• Buy Order 
• Q = 1,000 units
• T = 10 intervals
• q₀ = N/T = 100 units
• λ = 1

guardrail triggers if b_t> 2 × q₀ = 200 units

Table 1: Signal aware execution example with signal values

What to note: Five consecutive DOWN signals (t3–t7) cause execution to pause entirely, letting inventory accumulate. By t8 the catch-up pace b_t = 622/3 ≈ 207 units — exceeding the guardrail threshold of 2 × q₀ = 200. The guardrail overrides the signal at t8 and t9, forcing execution at the neutral catch-up pace regardless of signal direction. This ensures the full order completes by t10 without aggressive end-of-session trading. Total executed: 1,000 units across all 10 intervals.

4. Performance Metric

Performance is measured using implementation shortfall relative to the session open price. For a sell order on day d, shortfall is defined as:

IS_d = (Average Execution Price − Open Price) / Open Price × 10,000

expressed in basis points. For a buy order, the sign is reversed:

IS_d = (Open Price − Average Execution Price) / Open Price × 10,000

Under this convention, a more negative IS indicates worse execution — the strategy bought above the open (for buys) or sold below the open (for sells) — while a more positive IS indicates better execution. In both cases, the signal-aware strategy improves execution by shifting the trajectory toward more favorable prices: accelerating when price is expected to move against the order, and slowing when it is expected to move in a favorable direction.

The daily improvement from using the signal-aware schedule is:

Δ_d = IS_d(Signal Strategy) − IS_d(TWAP)

A positive Δ_d means the signal-aware schedule achieved better execution than TWAP on that day — regardless of the sign of IS itself. For example, a signal strategy with IS = −0.20 bps outperforms a TWAP with IS = −1.93 bps, yielding Δ_d = +1.73 bps. Equally, a signal strategy with IS = +4.17 bps outperforms a TWAP with IS = +1.93 bps on a sell order, yielding Δ_d = +2.24 bps. In both cases the improvement metric is positive and directly comparable across instruments and order directions.

5. Annual Reports Summary

Tabe 2: Implementation shortfall improvement for 2025

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Table 3: Implementation shortfall improvement for 2024

6. Interpretation

The signal-aware schedule outperforms TWAP across all 32 quarter-symbol-direction combinations within two full years with an average daily improvement of 2.55 basis points across 2024 and 2025. The consistency of this result — positive across all quarters, both instruments, and both order directions — suggests the improvement reflects a systematic execution edge rather than a period-specific effect.

The magnitude of improvement varies with market conditions, ranging from 0.09 to 9.80 basis points across the sample. This variation is expected for a timing-based strategy: in periods of stronger intraday directional persistence, the signal provides more opportunities to meaningfully shift the execution trajectory, while in quieter periods the improvement is more modest. Crucially, the strategy never materially underperforms TWAP — the guardrail mechanism ensures that execution pace never falls so far behind the neutral schedule that the strategy is forced into aggressive catch-up trading.

NQ consistently shows larger improvements than ES, averaging approximately 3.1 bps versus 2.0 bps annually. This is consistent with NQ exhibiting stronger intraday directional dynamics, which the model is better positioned to exploit for timing purposes.

The benchmark does not attempt to replicate a full broker execution stack. It isolates the value of predictive timing relative to a neutral TWAP baseline, keeping total traded quantity and execution horizon identical between strategies. Given the slice sizes relative to the liquidity of ES and NQ, market impact beyond the zero-cost assumption is considered negligible. The results suggest that direction-to-close signals can serve as an effective timing overlay on top of standard execution schedules, with applicability extending to correlated instruments such as MES, MNQ, SPY, and QQQ.

7. Quarterly breakdown

Table 4: Quarterly breakdown for 2024

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Table 5: Quarterly breakdown for 2025

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