Edited by Pranav Udeshi
Mar 6, 2025
The Cost of Building Execution Algos In-House vs. Partnering with Experts
The Hidden Cost of Building Execution Algorithms In-House
Execution algorithms are critical to institutional trading, but developing them in-house is a resource-intensive challenge. Firms that underestimate the complexity often suffer from poor execution quality, increased market impact, and inefficiencies that silently erode returns.
An execution algorithm isn’t just code—it’s an optimization engine that must continuously adapt to market microstructure, order flow dynamics, and an adversarial trading environment. HFTs and market makers have spent decades refining execution into a systematic, data-driven process, leveraging predictive models and reinforcement learning to minimize costs. Competing at that level requires more than a few quants—it demands a fully integrated research and engineering effort.
The real question isn’t whether you can build an execution stack, but whether your trading desk has the bandwidth, data infrastructure, and cross-functional expertise to develop, test, and optimize execution at a competitive level. If getting it wrong costs measurable basis points, how much are you already losing
The Execution Algo Assembly Line: A Continuous Arms Race
Building an execution algorithm isn’t a one-time project—it’s an ongoing, resource-intensive process. Every component—data pipeline, signal generation, and low-latency execution—requires constant investment to stay competitive. Firms that try to build in-house face a logistical and technical grind, where maintaining an edge demands continuous iteration across multiple specialized domains.
Data Curation & Normalization
Execution quality starts with data. Firms must acquire, clean, and normalize high-resolution tick data, historical trade records, and market microstructure feeds across fragmented venues. Fixed income lacks centralized pricing, while equities, crypto, and FX require ultra-low-latency ingestion. Maintaining real-time usability at scale is a continuous engineering burden.
Feature Engineering & Market Microstructure Analysis
Extracting predictive execution signals requires more than basic regression. Market impact, cross-impact, adverse selection, and liquidity shifts must be modeled under adversarial conditions. Machine learning can improve predictive power, but introduces new challenges—feature drift, non-stationarity, and overfitting to transient market conditions. A robust feature pipeline requires continuous research, validation, and iteration to remain effective.
Strategy Development & Execution Logic
Signals alone don’t create alpha—execution logic does. Strategies must adapt dynamically to venue liquidity, volatility, and order flow imbalances. Balancing execution performance (slippage, impact) with risk (fill probability, adverse selection) requires constant parameter tuning and reinforcement learning. Static strategies can be gamified and degrade fast.
Backtesting & Performance Benchmarking
Accurately assessing strategy performance in real markets requires more than traditional backtesting on historical trade and market data. Market impact is dynamic, and static backtests fail to capture how trades influence future prices. At Blockhouse, our advanced simulation engine models market impact, regime shifts, and other adversarial conditions to deliver execution backtests that closely reflect real-world trading. Additionally, our backtesting framework integrates advanced Transaction Cost Analysis (TCA) features, providing deeper insights that inform and optimize future executions. Effective strategy evaluation demands rigorous falsification testing rather than confirmation bias, ensuring robustness across diverse market environments.
Deployment & Infrastructure Scaling
Institutional execution operates at a production scale, requiring ultra-low-latency infrastructure, colocation, and direct market access. Every microsecond of delay impacts execution quality. Real-time risk controls, kill switches, and redundant infrastructure are mandatory to prevent catastrophic execution errors. Maintaining execution efficiency demands continuous investment in network optimization, hardware acceleration, and dynamic parameter tuning at the infrastructure level.
Execution research is an ongoing race against market evolution. Signals decay, strategies degrade, and without continuous iteration, execution quality deteriorates. Staying competitive requires constant refinements to signals, execution algorithms, and market structure insights—demanding a specialized research function. Without this, firms risk running outdated execution logic against counterparties who are always optimizing.
The True Cost of Building In-House
Building an execution algorithm isn’t just about writing code—it requires ongoing investment in research, infrastructure, and continuous optimization. Competing at the highest level means adapting to market dynamics in real-time, but doing so in-house comes at a steep cost. Below is a breakdown of what it takes to build and maintain an execution research team.
The Human Capital Investment
Hiring top execution talent is expensive. The best quants, market microstructure analysts, and engineers already command $500K+ compensation packages at leading firms. Competing for this talent requires not just competitive salaries but also retention incentives, research budgets, and long-term investments in talent development.
A fully staffed execution team requires 11–16 specialists, spanning data curation, feature engineering, execution research, backtesting, and infrastructure.
Total Headcount: 11–16 specialists
Total Personnel Cost: $5M–$10M+ annually, excluding retention bonuses and research costs.
But talent alone isn’t enough. Execution requires infrastructure, compute power, and scalable deployment.
Infrastructure & Compute Costs
Even with the right team, execution at scale demands market data, compute clusters, and direct exchange connectivity—without them, even the best execution logic fails.
Total infrastructure cost: $3M–$10M annually, before ongoing upgrades.
Without scale-ready infrastructure, firms suffer from higher market impact, increased transaction costs, and execution inefficiencies—negating any competitive edge.
The Opportunity Cost of Delayed Optimization
Even with a team and infrastructure in place, execution models take 12–18 months to become competitive. During this time:
• Execution costs remain high, leading to excess slippage and market impact.
• Desks lose efficiency, increasing transaction costs and eroding alpha.
• Constant refinements are required, pulling quants and engineers away from alpha-generating strategies.
To quantify the impact, assume a firm trades $1B annually:
If a firm takes 12–18 months to develop in-house execution, they incur $300K–$450K in excess costs per billion traded. For firms executing $10B+ annually, these inefficiencies translate to $3M–$5M in lost savings before even reaching competitive performance.
Firms face $10M–$20M+ in annual costs and 12–18 months of inefficiencies when building execution in-house. The question isn’t whether firms can build execution, but whether they should.
Why Execution Providers Outperform In-House Builds
Blockhouse delivers institutional-grade execution from day one—without the hiring, infrastructure, or research burden.
Building execution in-house takes 12–18 months before reaching competitive performance. Instead, firms can integrate plug-and-play SDKs and start optimizing execution immediately. Blockhouse’s pre-trained execution models, built on trillions in trading flow, dynamically adjust for market impact, adverse selection, and cross-asset interactions. Firms looking to enhance an analytical edge can integrate Blockhouse’s quantitative research models, leveraging real-time execution analytics, market impact forecasting, and machine learning-driven optimization that refines execution dynamically.
For firms seeking an informational edge, Blockhouse acquires and trains models on exclusive proprietary datasets , giving traders deeper insights into market microstructure, order book liquidity, and venue-specific execution costs. These insights refine routing logic, execution strategies, and trade placement, unlocking hidden liquidity and improving execution quality.
Access is a competitive advantage. Blockhouse partners with institutional-grade smart order routers, unlocking dark pools, internal crossing networks, and alternative liquidity sources typically unavailable to most firms. This ensures higher fill rates, lower market impact, and optimized execution across fragmented liquidity venues.
Infrastructure is the foundation of execution. Blockhouse invests in high-performance computing, colocation, and ultra-low-latency networking, providing firms with execution speed and efficiency typically reserved for top-tier trading desks. Instead of committing millions to custom infrastructure, firms can leverage Blockhouse’s execution stack from day one.
Top firms already optimize for alpha—Blockhouse ensures they execute with the same level of precision. Speed, intelligence, access, and infrastructure—fully integrated, continuously optimized, and available immediately.
The Future of Execution is Hybrid
Winning firms aren’t choosing between in-house and outsourced execution—they’re blending both. Relying solely on internal models means falling behind, while firms leveraging execution providers like Blockhouse stay ahead with continuously optimized, real-time adaptive execution strategies.
Static execution is obsolete. Modern execution requires predictive models, proprietary datasets, institutional-grade access, and ultra-low-latency infrastructure—elements that are expensive and complex to build in-house. Firms that integrate Blockhouse don’t replace their edge—they enhance it, reducing slippage, improving market impact, and optimizing execution across fragmented venues.
If you’re still relying solely on internally built execution algos, you’re already behind. The firms winning the execution game aren’t just the ones with the best research—they’re the ones with the best execution partnerships.
Get Started with Blockhouse Today
Maximize your execution edge without the overhead. Integrate Blockhouse in minutes and start optimizing execution immediately. Reach out to see how Blockhouse can fit into your trading operation.
The Hidden Cost of Building Execution Algorithms In-House
Execution algorithms are critical to institutional trading, but developing them in-house is a resource-intensive challenge. Firms that underestimate the complexity often suffer from poor execution quality, increased market impact, and inefficiencies that silently erode returns.
An execution algorithm isn’t just code—it’s an optimization engine that must continuously adapt to market microstructure, order flow dynamics, and an adversarial trading environment. HFTs and market makers have spent decades refining execution into a systematic, data-driven process, leveraging predictive models and reinforcement learning to minimize costs. Competing at that level requires more than a few quants—it demands a fully integrated research and engineering effort.
The real question isn’t whether you can build an execution stack, but whether your trading desk has the bandwidth, data infrastructure, and cross-functional expertise to develop, test, and optimize execution at a competitive level. If getting it wrong costs measurable basis points, how much are you already losing
The Execution Algo Assembly Line: A Continuous Arms Race
Building an execution algorithm isn’t a one-time project—it’s an ongoing, resource-intensive process. Every component—data pipeline, signal generation, and low-latency execution—requires constant investment to stay competitive. Firms that try to build in-house face a logistical and technical grind, where maintaining an edge demands continuous iteration across multiple specialized domains.
Data Curation & Normalization
Execution quality starts with data. Firms must acquire, clean, and normalize high-resolution tick data, historical trade records, and market microstructure feeds across fragmented venues. Fixed income lacks centralized pricing, while equities, crypto, and FX require ultra-low-latency ingestion. Maintaining real-time usability at scale is a continuous engineering burden.
Feature Engineering & Market Microstructure Analysis
Extracting predictive execution signals requires more than basic regression. Market impact, cross-impact, adverse selection, and liquidity shifts must be modeled under adversarial conditions. Machine learning can improve predictive power, but introduces new challenges—feature drift, non-stationarity, and overfitting to transient market conditions. A robust feature pipeline requires continuous research, validation, and iteration to remain effective.
Strategy Development & Execution Logic
Signals alone don’t create alpha—execution logic does. Strategies must adapt dynamically to venue liquidity, volatility, and order flow imbalances. Balancing execution performance (slippage, impact) with risk (fill probability, adverse selection) requires constant parameter tuning and reinforcement learning. Static strategies can be gamified and degrade fast.
Backtesting & Performance Benchmarking
Accurately assessing strategy performance in real markets requires more than traditional backtesting on historical trade and market data. Market impact is dynamic, and static backtests fail to capture how trades influence future prices. At Blockhouse, our advanced simulation engine models market impact, regime shifts, and other adversarial conditions to deliver execution backtests that closely reflect real-world trading. Additionally, our backtesting framework integrates advanced Transaction Cost Analysis (TCA) features, providing deeper insights that inform and optimize future executions. Effective strategy evaluation demands rigorous falsification testing rather than confirmation bias, ensuring robustness across diverse market environments.
Deployment & Infrastructure Scaling
Institutional execution operates at a production scale, requiring ultra-low-latency infrastructure, colocation, and direct market access. Every microsecond of delay impacts execution quality. Real-time risk controls, kill switches, and redundant infrastructure are mandatory to prevent catastrophic execution errors. Maintaining execution efficiency demands continuous investment in network optimization, hardware acceleration, and dynamic parameter tuning at the infrastructure level.
Execution research is an ongoing race against market evolution. Signals decay, strategies degrade, and without continuous iteration, execution quality deteriorates. Staying competitive requires constant refinements to signals, execution algorithms, and market structure insights—demanding a specialized research function. Without this, firms risk running outdated execution logic against counterparties who are always optimizing.
The True Cost of Building In-House
Building an execution algorithm isn’t just about writing code—it requires ongoing investment in research, infrastructure, and continuous optimization. Competing at the highest level means adapting to market dynamics in real-time, but doing so in-house comes at a steep cost. Below is a breakdown of what it takes to build and maintain an execution research team.
The Human Capital Investment
Hiring top execution talent is expensive. The best quants, market microstructure analysts, and engineers already command $500K+ compensation packages at leading firms. Competing for this talent requires not just competitive salaries but also retention incentives, research budgets, and long-term investments in talent development.
A fully staffed execution team requires 11–16 specialists, spanning data curation, feature engineering, execution research, backtesting, and infrastructure.
Total Headcount: 11–16 specialists
Total Personnel Cost: $5M–$10M+ annually, excluding retention bonuses and research costs.
But talent alone isn’t enough. Execution requires infrastructure, compute power, and scalable deployment.
Infrastructure & Compute Costs
Even with the right team, execution at scale demands market data, compute clusters, and direct exchange connectivity—without them, even the best execution logic fails.
Total infrastructure cost: $3M–$10M annually, before ongoing upgrades.
Without scale-ready infrastructure, firms suffer from higher market impact, increased transaction costs, and execution inefficiencies—negating any competitive edge.
The Opportunity Cost of Delayed Optimization
Even with a team and infrastructure in place, execution models take 12–18 months to become competitive. During this time:
• Execution costs remain high, leading to excess slippage and market impact.
• Desks lose efficiency, increasing transaction costs and eroding alpha.
• Constant refinements are required, pulling quants and engineers away from alpha-generating strategies.
To quantify the impact, assume a firm trades $1B annually:
If a firm takes 12–18 months to develop in-house execution, they incur $300K–$450K in excess costs per billion traded. For firms executing $10B+ annually, these inefficiencies translate to $3M–$5M in lost savings before even reaching competitive performance.
Firms face $10M–$20M+ in annual costs and 12–18 months of inefficiencies when building execution in-house. The question isn’t whether firms can build execution, but whether they should.
Why Execution Providers Outperform In-House Builds
Blockhouse delivers institutional-grade execution from day one—without the hiring, infrastructure, or research burden.
Building execution in-house takes 12–18 months before reaching competitive performance. Instead, firms can integrate plug-and-play SDKs and start optimizing execution immediately. Blockhouse’s pre-trained execution models, built on trillions in trading flow, dynamically adjust for market impact, adverse selection, and cross-asset interactions. Firms looking to enhance an analytical edge can integrate Blockhouse’s quantitative research models, leveraging real-time execution analytics, market impact forecasting, and machine learning-driven optimization that refines execution dynamically.
For firms seeking an informational edge, Blockhouse acquires and trains models on exclusive proprietary datasets , giving traders deeper insights into market microstructure, order book liquidity, and venue-specific execution costs. These insights refine routing logic, execution strategies, and trade placement, unlocking hidden liquidity and improving execution quality.
Access is a competitive advantage. Blockhouse partners with institutional-grade smart order routers, unlocking dark pools, internal crossing networks, and alternative liquidity sources typically unavailable to most firms. This ensures higher fill rates, lower market impact, and optimized execution across fragmented liquidity venues.
Infrastructure is the foundation of execution. Blockhouse invests in high-performance computing, colocation, and ultra-low-latency networking, providing firms with execution speed and efficiency typically reserved for top-tier trading desks. Instead of committing millions to custom infrastructure, firms can leverage Blockhouse’s execution stack from day one.
Top firms already optimize for alpha—Blockhouse ensures they execute with the same level of precision. Speed, intelligence, access, and infrastructure—fully integrated, continuously optimized, and available immediately.
The Future of Execution is Hybrid
Winning firms aren’t choosing between in-house and outsourced execution—they’re blending both. Relying solely on internal models means falling behind, while firms leveraging execution providers like Blockhouse stay ahead with continuously optimized, real-time adaptive execution strategies.
Static execution is obsolete. Modern execution requires predictive models, proprietary datasets, institutional-grade access, and ultra-low-latency infrastructure—elements that are expensive and complex to build in-house. Firms that integrate Blockhouse don’t replace their edge—they enhance it, reducing slippage, improving market impact, and optimizing execution across fragmented venues.
If you’re still relying solely on internally built execution algos, you’re already behind. The firms winning the execution game aren’t just the ones with the best research—they’re the ones with the best execution partnerships.
Get Started with Blockhouse Today
Maximize your execution edge without the overhead. Integrate Blockhouse in minutes and start optimizing execution immediately. Reach out to see how Blockhouse can fit into your trading operation.
The Hidden Cost of Building Execution Algorithms In-House
Execution algorithms are critical to institutional trading, but developing them in-house is a resource-intensive challenge. Firms that underestimate the complexity often suffer from poor execution quality, increased market impact, and inefficiencies that silently erode returns.
An execution algorithm isn’t just code—it’s an optimization engine that must continuously adapt to market microstructure, order flow dynamics, and an adversarial trading environment. HFTs and market makers have spent decades refining execution into a systematic, data-driven process, leveraging predictive models and reinforcement learning to minimize costs. Competing at that level requires more than a few quants—it demands a fully integrated research and engineering effort.
The real question isn’t whether you can build an execution stack, but whether your trading desk has the bandwidth, data infrastructure, and cross-functional expertise to develop, test, and optimize execution at a competitive level. If getting it wrong costs measurable basis points, how much are you already losing
The Execution Algo Assembly Line: A Continuous Arms Race
Building an execution algorithm isn’t a one-time project—it’s an ongoing, resource-intensive process. Every component—data pipeline, signal generation, and low-latency execution—requires constant investment to stay competitive. Firms that try to build in-house face a logistical and technical grind, where maintaining an edge demands continuous iteration across multiple specialized domains.
Data Curation & Normalization
Execution quality starts with data. Firms must acquire, clean, and normalize high-resolution tick data, historical trade records, and market microstructure feeds across fragmented venues. Fixed income lacks centralized pricing, while equities, crypto, and FX require ultra-low-latency ingestion. Maintaining real-time usability at scale is a continuous engineering burden.
Feature Engineering & Market Microstructure Analysis
Extracting predictive execution signals requires more than basic regression. Market impact, cross-impact, adverse selection, and liquidity shifts must be modeled under adversarial conditions. Machine learning can improve predictive power, but introduces new challenges—feature drift, non-stationarity, and overfitting to transient market conditions. A robust feature pipeline requires continuous research, validation, and iteration to remain effective.
Strategy Development & Execution Logic
Signals alone don’t create alpha—execution logic does. Strategies must adapt dynamically to venue liquidity, volatility, and order flow imbalances. Balancing execution performance (slippage, impact) with risk (fill probability, adverse selection) requires constant parameter tuning and reinforcement learning. Static strategies can be gamified and degrade fast.
Backtesting & Performance Benchmarking
Accurately assessing strategy performance in real markets requires more than traditional backtesting on historical trade and market data. Market impact is dynamic, and static backtests fail to capture how trades influence future prices. At Blockhouse, our advanced simulation engine models market impact, regime shifts, and other adversarial conditions to deliver execution backtests that closely reflect real-world trading. Additionally, our backtesting framework integrates advanced Transaction Cost Analysis (TCA) features, providing deeper insights that inform and optimize future executions. Effective strategy evaluation demands rigorous falsification testing rather than confirmation bias, ensuring robustness across diverse market environments.
Deployment & Infrastructure Scaling
Institutional execution operates at a production scale, requiring ultra-low-latency infrastructure, colocation, and direct market access. Every microsecond of delay impacts execution quality. Real-time risk controls, kill switches, and redundant infrastructure are mandatory to prevent catastrophic execution errors. Maintaining execution efficiency demands continuous investment in network optimization, hardware acceleration, and dynamic parameter tuning at the infrastructure level.
Execution research is an ongoing race against market evolution. Signals decay, strategies degrade, and without continuous iteration, execution quality deteriorates. Staying competitive requires constant refinements to signals, execution algorithms, and market structure insights—demanding a specialized research function. Without this, firms risk running outdated execution logic against counterparties who are always optimizing.
The True Cost of Building In-House
Building an execution algorithm isn’t just about writing code—it requires ongoing investment in research, infrastructure, and continuous optimization. Competing at the highest level means adapting to market dynamics in real-time, but doing so in-house comes at a steep cost. Below is a breakdown of what it takes to build and maintain an execution research team.
The Human Capital Investment
Hiring top execution talent is expensive. The best quants, market microstructure analysts, and engineers already command $500K+ compensation packages at leading firms. Competing for this talent requires not just competitive salaries but also retention incentives, research budgets, and long-term investments in talent development.
A fully staffed execution team requires 11–16 specialists, spanning data curation, feature engineering, execution research, backtesting, and infrastructure.
Total Headcount: 11–16 specialists
Total Personnel Cost: $5M–$10M+ annually, excluding retention bonuses and research costs.
But talent alone isn’t enough. Execution requires infrastructure, compute power, and scalable deployment.
Infrastructure & Compute Costs
Even with the right team, execution at scale demands market data, compute clusters, and direct exchange connectivity—without them, even the best execution logic fails.
Total infrastructure cost: $3M–$10M annually, before ongoing upgrades.
Without scale-ready infrastructure, firms suffer from higher market impact, increased transaction costs, and execution inefficiencies—negating any competitive edge.
The Opportunity Cost of Delayed Optimization
Even with a team and infrastructure in place, execution models take 12–18 months to become competitive. During this time:
• Execution costs remain high, leading to excess slippage and market impact.
• Desks lose efficiency, increasing transaction costs and eroding alpha.
• Constant refinements are required, pulling quants and engineers away from alpha-generating strategies.
To quantify the impact, assume a firm trades $1B annually:
If a firm takes 12–18 months to develop in-house execution, they incur $300K–$450K in excess costs per billion traded. For firms executing $10B+ annually, these inefficiencies translate to $3M–$5M in lost savings before even reaching competitive performance.
Firms face $10M–$20M+ in annual costs and 12–18 months of inefficiencies when building execution in-house. The question isn’t whether firms can build execution, but whether they should.
Why Execution Providers Outperform In-House Builds
Blockhouse delivers institutional-grade execution from day one—without the hiring, infrastructure, or research burden.
Building execution in-house takes 12–18 months before reaching competitive performance. Instead, firms can integrate plug-and-play SDKs and start optimizing execution immediately. Blockhouse’s pre-trained execution models, built on trillions in trading flow, dynamically adjust for market impact, adverse selection, and cross-asset interactions. Firms looking to enhance an analytical edge can integrate Blockhouse’s quantitative research models, leveraging real-time execution analytics, market impact forecasting, and machine learning-driven optimization that refines execution dynamically.
For firms seeking an informational edge, Blockhouse acquires and trains models on exclusive proprietary datasets , giving traders deeper insights into market microstructure, order book liquidity, and venue-specific execution costs. These insights refine routing logic, execution strategies, and trade placement, unlocking hidden liquidity and improving execution quality.
Access is a competitive advantage. Blockhouse partners with institutional-grade smart order routers, unlocking dark pools, internal crossing networks, and alternative liquidity sources typically unavailable to most firms. This ensures higher fill rates, lower market impact, and optimized execution across fragmented liquidity venues.
Infrastructure is the foundation of execution. Blockhouse invests in high-performance computing, colocation, and ultra-low-latency networking, providing firms with execution speed and efficiency typically reserved for top-tier trading desks. Instead of committing millions to custom infrastructure, firms can leverage Blockhouse’s execution stack from day one.
Top firms already optimize for alpha—Blockhouse ensures they execute with the same level of precision. Speed, intelligence, access, and infrastructure—fully integrated, continuously optimized, and available immediately.
The Future of Execution is Hybrid
Winning firms aren’t choosing between in-house and outsourced execution—they’re blending both. Relying solely on internal models means falling behind, while firms leveraging execution providers like Blockhouse stay ahead with continuously optimized, real-time adaptive execution strategies.
Static execution is obsolete. Modern execution requires predictive models, proprietary datasets, institutional-grade access, and ultra-low-latency infrastructure—elements that are expensive and complex to build in-house. Firms that integrate Blockhouse don’t replace their edge—they enhance it, reducing slippage, improving market impact, and optimizing execution across fragmented venues.
If you’re still relying solely on internally built execution algos, you’re already behind. The firms winning the execution game aren’t just the ones with the best research—they’re the ones with the best execution partnerships.
Get Started with Blockhouse Today
Maximize your execution edge without the overhead. Integrate Blockhouse in minutes and start optimizing execution immediately. Reach out to see how Blockhouse can fit into your trading operation.