What Happens If Many Customers Use the Same Trading Signal?

In a recent post we provided benchmarks that show our signals contain alpha. 

One of the most common and very reasonable questions we get from prospective customers is:

“If many firms trade on the same signal, won’t the alpha disappear?”

The concern is simple: if everyone has access to the same information, the first movers capture the edge, and latecomers are left with a diluted or even negative payoff. In quantitative trading this is often framed as alpha decay or signal crowding.

In this post, we’ll walk through how we think about this question in the context of our product: short-term AI signals on highly liquid instruments. We’ll cover:

• When crowding might matter
• How clients’ usage patterns reduce crowding
• The role of custom signals
• Why early adopters have a structural advantage

When Does “Everyone Using the Same Signal” Actually Matter?

Right now, our signals focus on highly liquid symbols and predict the price trend between “now” and the end of the trading day. Three aspects of that design are important:

a) High liquidity

In very liquid markets (e.g., major index futures like the ones we are tackling now), it takes a lot of coordinated volume, in a short window, to measurably move prices or degrade an edge. The order book can absorb substantial flow before the signal itself starts to materially change the behavior it is trying to predict.

b) Zero sum?

The further you move away from the immediate microsecond–millisecond competition, the less the game is purely zero-sum at the individual trade level. Over an intraday horizon, multiple participants can benefit from a similar directional signal without perfectly stepping on each other’s toes.

c) Timing 

Even if many clients use the same directional view, impact depends heavily on when that flow hits the market and how concentrated it is in time:

1. If flow is dispersed across different times of day and different execution styles, the market can absorb it with limited distortion.
2. If flow is highly synchronized, i.e., many participants trading in the same direction in the same short window, then you start to see the kind of impact that can erode the very signal those participants rely on.

In practice, because we operate in highly liquid instruments and most clients have different horizons, risk constraints, and execution logic, that kind of perfect synchronization is rare. And as we’ll see next, how clients actually use the signal further reduces the risk of everyone effectively running the same trade.

Vanilla vs. Blended Strategies: Usage Diversity Helps

We expect two broad types of usage from our customers:

a) “Vanilla” Users: Direct Signal Followers

Some customers will plug our signal directly into their trading and implement simple strategies that follow the signal as is. For example:

1. Go long when the signal is strongly positive
2. Go short when it’s strongly negative
3. Size positions based on confidence scores or risk budgets

These users are the closest to the “everyone trades the same thing” scenario people worry about.

b) "Blended” Users: Signal as a Component, Not the Whole Strategy

We expect the majority of sophisticated customers to treat our signal as one input among several:

1. Using our signal as a filter: Only trade when both their internal models and our signal agree (e.g., trend + microstructure + our LOB-based signal are aligned).
2. Using our signal in ensemble / voting schemes: Combine multiple models and signals with weighted voting to decide when to enter or exit positions

In these blended setups, timing and execution become naturally diversified:

• Different models fire at slightly different times.
• Different horizons (from minutes to hours) spread orders out.
• Different risk and execution settings change how aggressively each firm trades.

All of this reduces synchronization of orders and spreads the impact over time, substantially mitigating the “everyone hits the market at once” problem.

Custom Signals: Deliberate Differentiation

Over time, we expect many of our more advanced customers to ask for custom signals tailored to their specific strategies and workflows. These customizations can include:

a) Different Targets, Not Just Price Trend

Instead of only predicting price direction to end-of-day, a signal might be trained to forecast:

• Short-term volatility
• Liquidity conditions (e.g., depth, spreads, imbalance)
• Microstructure events derived from the limit order book (LOB)

These features can then be plugged into strategies that care more about how you trade (execution) rather than whether you trade.

b) Different Horizons and Event Definitions

We can also customize time horizons and the definition of the prediction window:

• Forecast outcomes 1 minute to several hours ahead, depending on the strategy’s turnover and risk profile.
• Move from strictly time-based horizons (“what happens after Y minutes?”) to event-based horizons, such as:
  
  • After X contracts of volume have traded
  • After the order book has turned over by a given amount
  • After a certain number of quote updates

Event-based targets often align better with execution logic or market impact models than raw clock time.

c) Probability and Liquidity-Oriented Signals

Some customers may want signals focused on trade feasibility, not just direction. For example:

• Probability that a given trade size can be executed within certain price and time constraints
• Probability that sufficient liquidity exists on one side of the book to complete a trade at or near the desired price

These types of signals directly support execution algorithms, routing logic, and dynamic sizing, and are by nature more idiosyncratic across clients. All of these customizations fragment the signal space, making it increasingly unlikely that many institutional users are effectively running the same strategy even if they all start from our core technology stack.

Why Early Adopters Have a Structural Advantage

Finally, there is a time dimension to all of this. Early adopters benefit in at least two ways:

1. First access to the original signals: They get to exploit the signal in its “pure” form before there is any meaningful chance of crowding.
   
   
2. Learning curve and strategy evolution:
   
   • Understand the behavior of the signals across regimes
   • Design and refine strategies that best exploit them
   • Co-develop custom signals and specialized use cases with us

By the time later adopters come in, early users already have:

• Live performance data
• Robust integration with their internal models
• Iterated execution and risk frameworks built around our signals

That accumulated knowledge is itself a durable edge.

In Conclusion

Taken together with the earlier points—highly liquid markets, diverse usage patterns, and the natural fragmentation introduced by custom signals—this is why we’re comfortable with the “what if everyone uses the same signal?” question. Even as adoption grows, there is plenty of room for differentiated implementation, and those who start early have more time to learn, adapt, and build a lasting advantage around the signals they use.

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