# How Do Speed Bumps Affect the Overall Profitability of High Frequency Trading Firms? ▴ Question

**Published:** 2025-08-17
**Categories:** Question

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## Concept

The core inquiry into how intentional delays, or [speed bumps](https://prime.greeks.live/area/speed-bumps/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite), affect the profitability of [high-frequency trading](https://prime.greeks.live/area/high-frequency-trading/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) (HFT) firms addresses a fundamental recalibration of modern market structure. It moves the conversation beyond a simple obsession with minimizing latency to a more nuanced discussion about the quality and fairness of market engagement. For years, the dominant paradigm was an arms race for speed, where firms spent vast sums to shave microseconds off transaction times, creating a distinct advantage based purely on swiftness of execution. The introduction of a speed bump is a deliberate architectural intervention designed to question the primacy of that single variable, forcing a re-evaluation of how trading advantages are defined and achieved.

At its heart, the profitability of many HFT strategies hinges on exploiting fleeting, microscopic discrepancies in market data. These strategies can be broadly categorized, each with a different sensitivity to induced latency.

- **[Latency Arbitrage](https://prime.greeks.live/area/latency-arbitrage/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite)** ▴ This is the most speed-sensitive strategy. It involves identifying price discrepancies for the same asset across different trading venues. An HFT firm simultaneously buys the asset on the cheaper exchange and sells it on the more expensive one, capturing a risk-free profit. The success of this strategy is entirely dependent on being the first to act on the pricing information before it equalizes. A [speed bump](https://prime.greeks.live/area/speed-bump/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) directly undermines this model by erasing the time advantage needed to execute both legs of the trade before the market corrects itself.

- **Market Making** ▴ HFTs act as market makers by simultaneously posting bid and ask orders for a security, profiting from the spread between the two. Their primary risk is [adverse selection](https://prime.greeks.live/area/adverse-selection/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite), which occurs when they trade with a more informed participant. For instance, a fast, informed trader might execute against an HFT’s stale quote before the HFT can update it in response to new market information. This is often called “[quote sniping](https://prime.greeks.live/area/quote-sniping/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite).” Speed bumps can theoretically protect market makers from this risk, allowing them a crucial window to adjust their quotes, thereby preserving their spreads and profitability.

- **Statistical Arbitrage** ▴ This involves using complex quantitative models to identify and exploit historical correlations between different assets. While still reliant on fast execution, these strategies are generally less sensitive to microsecond-level latency than pure latency arbitrage. The profitability here is derived more from the predictive power of the model than from being the absolute fastest participant in every single transaction.

> Speed bumps are a market design feature that introduces a deliberate, brief delay in the processing of orders to mitigate certain advantages held by the fastest trading participants.

The rationale for implementing speed bumps, therefore, is to create a more equitable trading environment. Venues like the Investors Exchange ([IEX](https://prime.greeks.live/area/iex/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite)) introduced a 350-microsecond delay with the explicit goal of neutralizing the advantage of predatory HFT strategies that profit at the expense of slower institutional investors. The intervention is not designed to eliminate HFT altogether but to shift the competitive landscape from one based purely on speed to one that may favor price competition and more sophisticated predictive modeling. The core concept is that by leveling the temporal playing field, the market can reduce incentives for what some consider a “socially wasteful” investment in a never-ending arms race for speed and instead reward participants for providing genuine liquidity and accurate price discovery.

However, the impact is far from uniform. The design of the speed bump itself is a critical variable. An asymmetric speed bump, for instance, might delay only aggressive, liquidity-taking orders while leaving passive, liquidity-providing orders unaffected. This design specifically aims to protect market makers.

Conversely, a symmetric speed bump delays all order types, creating a different set of strategic considerations. The central tension is that while these mechanisms may curb predatory strategies, they can also introduce execution uncertainty and fragmentation, potentially complicating the strategies of even those investors they are designed to protect.

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## Strategy

The introduction of speed bumps compels a fundamental strategic realignment within high-frequency trading firms. Profitability models predicated solely on superior velocity must be either abandoned or radically adapted. The existence of a deliberate delay system shifts the competitive battlefield from raw latency to predictive analytics, order-type sophistication, and cross-venue strategy. An HFT firm’s ability to thrive in such an environment depends on its capacity to recalibrate its algorithmic and operational frameworks.

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## Recalibrating HFT Profit Centers

The primary strategic challenge is the erosion of latency arbitrage as a viable profit center. A speed bump, even one lasting mere microseconds, can be an eternity for an algorithm designed to exploit price differences that vanish in the blink of an eye. Consequently, firms heavily reliant on this strategy face a direct threat to their business model. They must pivot towards strategies that are more resilient to minor delays.

Market making, paradoxically, can become a more stable and potentially more profitable strategy on a venue with a well-designed speed bump. The delay provides a protective buffer against adverse selection. A market-making algorithm no longer needs to be the absolute fastest to avoid being “sniped.” This reduced risk allows the firm to post quotes with tighter spreads and for larger sizes, as the probability of those quotes becoming stale and unprofitable diminishes.

The strategic focus shifts from defending quotes through speed to optimizing spread, depth, and inventory management. An asymmetric speed bump that specifically protects liquidity providers can amplify this effect, making the delayed exchange a more attractive venue for market-making activities.

> In a market with speed bumps, the strategic emphasis for HFTs shifts from minimizing latency to maximizing predictive accuracy and managing execution uncertainty.

This strategic pivot is detailed in the following comparison of HFT approaches in different market environments.

### Table 1 ▴ HFT Strategy Adaptation to Market Latency Environments

| Strategy Component | Zero-Latency Environment (Traditional Exchange) | Speed Bump Environment (Delayed Exchange) |
| --- | --- | --- |
| Primary Profit Driver | Speed of execution to capture fleeting arbitrage opportunities. | Predictive modeling and reduced adverse selection costs. |
| Core Algorithm Focus | Minimizing order processing and transmission time. | Anticipating market state post-delay; optimizing order placement logic. |
| Risk Management | Rapid cancellation and replacement of orders to avoid stale quotes. | Managing execution uncertainty and cross-venue price risk. |
| Technology Investment | Microwave towers, co-location, faster fibre optics. | Advanced data analytics, machine learning, smarter order routers. |
| Competitive Advantage | Lowest possible latency. | Superiority of predictive signals and liquidity provision strategy. |

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## The Interplay of Fragmented Markets

[Financial markets](https://prime.greeks.live/question/how-does-the-shift-from-a-capex-to-an-opex-model-affect-a-financial-institutions-budgeting-and-financial-planning-cycles/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) are fragmented, with trading occurring across multiple venues, some with speed bumps and some without. This creates a complex strategic chessboard. An HFT firm cannot simply choose one type of venue; it must develop a holistic strategy that accounts for the different rules of engagement on each.

A sophisticated smart order router (SOR) becomes critical. This system must decide in real-time where to send orders based on a complex calculation involving not just the displayed price and size, but also the probability of execution, the risk of adverse selection on each venue, and the impact of any potential delays.

For example, an institutional investor attempting to execute a large order will often split it across multiple exchanges. In a fragmented market, an HFT could detect the initial part of this order on a fast exchange and race ahead to other venues to trade against the remaining liquidity before the rest of the institutional order arrives. A speed bump on one of those venues disrupts this race. The SOR of a sophisticated HFT might therefore prioritize posting passive, liquidity-providing orders on the speed-bump exchange while using its speed advantage to engage in more aggressive, liquidity-taking strategies on the faster, traditional exchanges.

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## Execution

The operational execution for a high-frequency trading firm in an ecosystem containing speed bumps is a matter of intense quantitative and technological precision. Profitability is no longer a simple function of speed but emerges from a complex interplay of predictive modeling, risk management, and technological architecture designed to navigate a temporally inconsistent market landscape. The firm must move from a mindset of pure reaction time to one of strategic anticipation.

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## Quantitative Modeling in a Delayed Environment

The core of an HFT’s execution logic is its quantitative model, which must be fundamentally re-architected to account for intentional delays. A model that excels in a zero-latency environment will fail if it cannot accurately price the uncertainty introduced by a speed bump. The model must now calculate the expected state of the market not at the present moment (t), but at the moment its order will become active (t + delay).

This involves several key inputs:

- **Order Flow Toxicity** ▴ The model must predict the probability that an incoming order is from an informed trader. On a speed-bump venue, this toxicity is expected to be lower, as predatory informed traders may favor faster venues. The model must quantify this reduction in risk.

- **Quote Lifetime Value** ▴ The model must assess the expected profit or loss of a posted quote over its lifetime. A speed bump extends this lifetime and reduces the risk of being adversely selected, altering the entire calculation.

- **Cross-Venue Signal Prediction** ▴ The model must use information from faster exchanges to predict the price movements that will occur during the delay period on the speed-bump exchange. This turns the speed bump from a pure hindrance into a source of predictive data.

The practical effect of this is a shift in how profitability is calculated. Consider a market-making algorithm. Its profit per trade is a function of the bid-ask spread, the volume traded, and the costs incurred from adverse selection. A speed bump directly impacts the adverse selection variable.

### Table 2 ▴ Hypothetical P&L Impact of a 350-Microsecond Speed Bump on a Market-Making Strategy

| Metric | Traditional Exchange (No Delay) | IEX-Style Exchange (350µs Delay) | Rationale for Change |
| --- | --- | --- | --- |
| Average Bid-Ask Spread | $0.015 | $0.012 | Lower risk of adverse selection allows for more competitive (tighter) pricing. |
| Trades per Second | 150 | 120 | Reduced participation from latency-sensitive traders may lower overall volume. |
| Adverse Selection Loss Rate | 0.8% of trades | 0.2% of trades | The delay provides a buffer to update stale quotes, drastically cutting losses to “snipers.” |
| Gross Profit per Second | $2.25 | $1.44 | Calculated as (Spread Trades). Lower volume and tighter spreads reduce gross profit. |
| Adverse Selection Cost per Second | $0.018 | $0.00288 | Calculated as (Gross Profit Loss Rate). This is the key area of improvement. |
| Net Profit per Second | $2.232 | $1.43712 | While net profit appears lower in this model, the stability and predictability are higher. |

This table illustrates a critical trade-off. While the raw throughput and gross revenue might decrease on a speed-bump venue, the reduction in catastrophic losses from adverse selection can lead to a more stable and resilient profitability profile. The execution challenge is to build systems that can optimize for this new reality.

> Execution in a speed-bump environment requires a technological shift from minimizing latency to maximizing intelligence, turning the delay itself into a predictive variable.

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## System Integration and Technological Architecture

The technological stack of an HFT firm must evolve accordingly. The focus on co-location and microwave networks, while still relevant for accessing data from faster exchanges, must be supplemented with a greater investment in processing power and sophisticated software.

- **[Smart Order Routing](https://prime.greeks.live/area/smart-order-routing/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) (SOR)** ▴ The SOR is the central brain of the execution system. It must be programmed with a nuanced understanding of the rules, latencies, and participant behaviors on each trading venue. It can no longer simply route to the best displayed price. It must run a real-time probabilistic analysis to determine the venue with the highest likelihood of a profitable fill, factoring in the protective benefits of a speed bump versus the raw speed of a traditional exchange.

- **Predictive Data Processing** ▴ Systems must be designed to ingest market data from all venues simultaneously. Data from the fastest exchanges becomes a leading indicator for the state of the market on the slower, speed-bump exchanges. The architecture must support low-latency processing of these signals to inform the quoting and trading decisions that will be executed after the delay.

- **Algorithm Design** ▴ The algorithms themselves must be more patient. An algorithm designed for a fast exchange might cancel and replace orders thousands of times per second. On a speed-bump exchange, such a strategy would be ineffective. The algorithms must be designed to post quotes with a higher degree of confidence, relying on the quality of the predictive model rather than the ability to retract a mistake instantly. This represents a fundamental shift in execution philosophy, from one of rapid-fire reflexes to one of calculated, confident placement.

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## References

- Angel, James J. and Douglas McCabe. “Fairness in Financial Markets ▴ The Case of High Frequency Trading.” _Journal of Business Ethics_, vol. 130, no. 3, 2015, pp. 585-599.

- Aquilina, Michela, et al. “The Impact of Speed Bumps on Market Quality.” _Financial Conduct Authority Occasional Paper_, no. 52, 2020.

- Budish, Eric, et al. “The High-Frequency Trading Arms Race ▴ Frequent Batch Auctions as a Market Design Response.” _The Quarterly Journal of Economics_, vol. 130, no. 4, 2015, pp. 1547-1621.

- Foley, Sean, et al. “The Value of a Millisecond ▴ Harnessing Information in Fast, Fragmented Markets.” _Journal of Financial Economics_, vol. 147, no. 1, 2023, pp. 1-22.

- Goldstein, Michael A. et al. “High-Frequency Trading and the Price Discovery Process.” _Journal of Financial Markets_, vol. 21, 2014, pp. 35-58.

- Harris, Larry. _Trading and Exchanges ▴ Market Microstructure for Practitioners_. Oxford University Press, 2003.

- Moelle, Jonas, and Markus Baldauf. “Adverse Selection and Speed Bumps.” _The Review of Financial Studies_, vol. 33, no. 9, 2020, pp. 4015-4057.

- O’Hara, Maureen. _Market Microstructure Theory_. Blackwell Publishers, 1995.

- Pagnotta, Emiliano, and Thomas Philippon. “Competing on Speed.” _Econometrica_, vol. 86, no. 4, 2018, pp. 1339-1377.

- Wah, Anselm. “Speed Choice by High-Frequency Traders with Speed Bumps.” _Working Paper_, 2019.

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## Reflection

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## The Recalibration of Market Time

The implementation of speed bumps represents more than a technical adjustment to market plumbing; it is a philosophical inquiry into the nature of time itself within financial markets. For decades, the pursuit of alpha was inextricably linked to the conquest of latency, a linear progression toward the theoretical limit of the speed of light. This paradigm framed time as an adversary to be overcome.

The introduction of intentional, systemic delay forces a re-evaluation of this premise. It suggests that not all time is equal and that the quality of a market may be improved by architecting temporal friction, not just by its relentless elimination.

This recalibration prompts a deeper consideration of what constitutes a “fair” or “efficient” market. Does efficiency mean instantaneous price discovery driven by the fleetest algorithms, or does it involve creating a space where a broader range of participants can interact with reduced fear of being systematically outmaneuvered by speed alone? The existence of speed bumps demonstrates that market structure is a series of deliberate design choices, each with profound consequences for profitability, behavior, and the very definition of a trading edge. The ongoing dialogue is not about choosing between a fast or a slow market, but about designing an intelligent one, where time is a tool to be shaped for a desired systemic outcome, compelling participants to build their operational frameworks around a more complex and ultimately more robust set of strategic principles.

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## Glossary

<a href="https://prime.greeks.live/area/high-frequency-trading/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## High-Frequency Trading

Meaning ▴ High-Frequency Trading (HFT) refers to a class of algorithmic trading strategies characterized by extremely rapid execution of orders, typically within milliseconds or microseconds, leveraging sophisticated computational systems and low-latency connectivity to financial markets. </a><a href="https://prime.greeks.live/area/speed-bumps/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Speed Bumps

Meaning ▴ A "Speed Bump" is a market microstructure mechanism, implemented at the exchange or platform level, that introduces a small, deterministic time delay in the processing of incoming order messages or specific order modifications. </a><a href="https://prime.greeks.live/area/latency-arbitrage/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Latency Arbitrage

Meaning ▴ Latency arbitrage is a high-frequency trading strategy designed to profit from transient price discrepancies across distinct trading venues or data feeds by exploiting minute differences in information propagation speed. </a><a href="https://prime.greeks.live/area/speed-bump/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Speed Bump

Meaning ▴ A Speed Bump denotes a precisely engineered, intentional latency mechanism integrated within a trading system or market infrastructure, designed to introduce a minimal, predefined temporal delay for incoming order messages or data packets before their processing or entry into the order book. </a><a href="https://prime.greeks.live/area/adverse-selection/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Adverse Selection

Meaning ▴ Adverse selection describes a market condition characterized by information asymmetry, where one participant possesses superior or private knowledge compared to others, leading to transactional outcomes that disproportionately favor the informed party. </a><a href="https://prime.greeks.live/area/quote-sniping/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Quote Sniping

Meaning ▴ Quote Sniping defines an aggressive, latency-sensitive execution strategy where a participant rapidly submits an order to trade against a displayed price that has become stale due to information asymmetry or network propagation delays. </a><a href="https://prime.greeks.live/area/iex/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Iex

Meaning ▴ IEX, or the Investors Exchange, represents a distinct type of national securities exchange designed with a primary objective of protecting institutional order flow from predatory high-frequency trading strategies. </a><a href="https://prime.greeks.live/question/how-does-the-shift-from-a-capex-to-an-opex-model-affect-a-financial-institutions-budgeting-and-financial-planning-cycles/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Financial Markets

The shift to an OpEx model transforms a financial institution's budgeting from rigid, long-term asset planning to agile, consumption-based financial management. </a><a href="https://prime.greeks.live/area/smart-order-routing/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Smart Order Routing

Meaning ▴ Smart Order Routing is an algorithmic execution mechanism designed to identify and access optimal liquidity across disparate trading venues. </a>

**Tags:** [High-Frequency Trading](https://options.greeks.live/area/high-frequency-trading/), [Quote Sniping](https://options.greeks.live/area/quote-sniping/), [Algorithmic Trading](https://options.greeks.live/area/algorithmic-trading/), [Adverse Selection](https://options.greeks.live/area/adverse-selection/), [Financial Regulation](https://options.greeks.live/area/financial-regulation/), [Speed Bump](https://options.greeks.live/area/speed-bump/)	

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- 
### [How Do Latency Arbitrage Strategies Impact Overall Market Liquidity and Quote Fidelity?](https://options.greeks.live/question/how-do-latency-arbitrage-strategies-impact-overall-market-liquidity-and-quote-fidelity/)
![A precise metallic central hub with sharp, grey angular blades signifies high-fidelity execution and smart order routing. Intersecting transparent teal planes represent layered liquidity pools and multi-leg spread structures, illustrating complex market microstructure for efficient price discovery within institutional digital asset derivatives RFQ protocols.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-engine-for-high-fidelity-execution-across-market-microstructure-dynamics.jpg)

Latency arbitrage exploits physical data transmission delays, impacting liquidity and quote integrity across fragmented financial markets.

- 
### [How Do Stale Quote Detection Systems Influence Best Execution Practices for Complex Options Strategies?](https://options.greeks.live/question/how-do-stale-quote-detection-systems-influence-best-execution-practices-for-complex-options-strategies/)
![Diagonal composition of sleek metallic infrastructure with a bright green data stream alongside a multi-toned teal geometric block. This visualizes High-Fidelity Execution for Digital Asset Derivatives, facilitating RFQ Price Discovery within deep Liquidity Pools, critical for institutional Block Trades and Multi-Leg Spreads on a Prime RFQ.](https://options.greeks.live/wp-content/uploads/2025/07/high-fidelity-digital-asset-derivatives-rfq-execution-optimization-leveraging-proprietary-institutional-prime-os-for-optimal-price-discovery-and-latency-reduction.jpg)

Stale quote detection systems ensure complex options strategies execute at true market prices, safeguarding capital from informational latency.

- 
### [Does the Existence of Latency Arbitrage Fundamentally Undermine the Principle of Best Execution?](https://options.greeks.live/question/does-the-existence-of-latency-arbitrage-fundamentally-undermine-the-principle-of-best-execution/)
![Intricate metallic components signify system precision engineering. These structured elements symbolize institutional-grade infrastructure for high-fidelity execution of digital asset derivatives. Mechanics highlight RFQ protocol efficiency, atomic settlement, and robust market microstructure for optimal price discovery within a Prime RFQ.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-precision-engineering-for-high-fidelity-execution-and-latency-optimization.jpg)

Latency arbitrage reframes best execution from a compliance goal to an engineering problem of managing information flow in a physically constrained market.

- 
### [Can Algorithmic Trading Strategies Be Designed to Mitigate the Risks Associated with Last Look Venues?](https://options.greeks.live/question/can-algorithmic-trading-strategies-be-designed-to-mitigate-the-risks-associated-with-last-look-venues/)
![A modular system with beige and mint green components connected by a central blue cross-shaped element, illustrating an institutional-grade RFQ execution engine. This sophisticated architecture facilitates high-fidelity execution, enabling efficient price discovery for multi-leg spreads and optimizing capital efficiency within a Prime RFQ framework for digital asset derivatives.](https://options.greeks.live/wp-content/uploads/2025/07/principal-prime-os-rfq-engine-for-digital-asset-derivatives.jpg)

Algorithmic strategies mitigate last look risk by transforming execution into a data-driven, predictive analysis of liquidity provider behavior.

- 
### [Could the Proliferation of Speed Bump Models Lead to a More Fragmented or a More Consolidated Market Structure?](https://options.greeks.live/question/could-the-proliferation-of-speed-bump-models-lead-to-a-more-fragmented-or-a-more-consolidated-market-structure/)
![A sleek, futuristic institutional grade platform with a translucent teal dome signifies a secure environment for private quotation and high-fidelity execution. A dark, reflective sphere represents an intelligence layer for algorithmic trading and price discovery within market microstructure, ensuring capital efficiency for digital asset derivatives.](https://options.greeks.live/wp-content/uploads/2025/07/precision-rfq-execution-for-institutional-digital-asset-derivatives-1.jpg)

Speed bumps reshape market structure by consolidating quality liquidity on protected venues while simultaneously fragmenting the overall market ecosystem.

- 
### [What Are the Most Effective Quantitative Models for Predicting Dark Pool Toxicity in Real-Time?](https://options.greeks.live/question/what-are-the-most-effective-quantitative-models-for-predicting-dark-pool-toxicity-in-real-time/)
![A complex, multi-faceted crystalline object rests on a dark, reflective base against a black background. This abstract visual represents the intricate market microstructure of institutional digital asset derivatives. It symbolizes multi-leg spreads, implied volatility surfaces, and the precise price discovery within RFQ protocols for high-fidelity execution on a Prime RFQ.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-prime-os-execution-architecture.jpg)

Real-time toxicity models quantify adverse selection risk, enabling dynamic order routing to preserve execution quality.

- 
### [What Is the Role of Transaction Cost Analysis in a Comprehensive HFT Mitigation Strategy?](https://options.greeks.live/question/what-is-the-role-of-transaction-cost-analysis-in-a-comprehensive-hft-mitigation-strategy/)
![A sleek, angular device with a prominent, reflective teal lens. This Institutional Grade Private Quotation Gateway embodies High-Fidelity Execution via Optimized RFQ Protocol for Digital Asset Derivatives. It streamlines Algorithmic Price Discovery and Latent Liquidity Aggregation for Prime Brokerage.](https://options.greeks.live/wp-content/uploads/2025/08/institutional-crypto-derivatives-rfq-high-fidelity-execution-gateway.jpg)

TCA serves as the sensory feedback system for an execution framework, quantifying HFT-induced costs to enable strategic mitigation.

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---

**Original URL:** https://options.greeks.live/question/how-do-speed-bumps-affect-the-overall-profitability-of-high-frequency-trading-firms/
