Optimize Trade Execution With Predictive Analytics

Blockhouse's predictive analytics and algorithms are designed to uncover hidden costs, optimize execution, and enhance P&L - for modern trading desks.

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Move beyond static benchmarks and rules based algorithms. Leverage dynamic, real-time trading strategies that connect data to measurable execution outcomes across global financial markets.

A Modern Solution for Trade Execution

An integrated suite of algorithms, analytics, SDKs, and research to cut hidden trading costs and enhance returns.

Algorithms

Analytics

SDK Infrastructure

Research

Algorithms

Enhance Your Executions Across Global Financial Markets

Customized Execution Algorithms for All Asset Classes and Strategies

We design, test, deploy, and monitor algorithms tailored for a wide range of strategies, including multi-asset and multi-legged trades. Our solutions support rates, equities, futures, and crypto markets, with more asset classes on the horizon.

Dynamic Order Routing with Machine Learning
Cutting Edge Research and Transparent Results
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Analytics

Analyze your executions and uncover hidden costs.

Actionable reports and intelligence

Our reports go beyond standard metrics such as markouts, delivering actionable intelligence on comprehensive market impacts, algorithmic benchmarks, and counterparty relationships.

Pre-Trade and Post-Trade Dashboards
Compliance with Best Execution

SDK

Analyze your Trade Data in 20 Lines of Code

Integrate your trade execution systems with our high performance SDK - designed for speed, precision, flexibility, and security. Use Python and Rest API's to integrate advanced analytics and algorithms, across asset classes and strategies, with minimal code.

Dive into our SDK

Asset Classes

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from blockhouse import Transfer

# Initialize the client
transfer_client = Transfer("your-api-key")

# Define the bucket name and file name
bucket_name = 'blockhouse-sdk'  # Use this specific bucket name
file_name = 'your-file'

# Transfer the file
file_upload_response = transfer_client.send_file(file_name, bucket_name)
print("File Upload Response:", file_upload_response)

# Then use the Equities client to process uploaded data
from blockhouse import Equities
equities_client = Equities("your-api-key")
result = equities_client.analyze_uploaded_file(file_name)
Output

Process

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Data Ingestion + Pre-Processing

Effective trade execution starts with clean data. Blockhouse standardizes formats, resolves discrepancies, and normalizes inputs to ensure accuracy, optimizing every data point for analytics and execution.

Analytical Alignment

Trading desks need precise insights aligned with execution goals. Blockhouse integrates pre-trade, post-trade, and compliance data into a unified framework, enhancing decision-making, returns, and adaptability.

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Model Development

Blockhouse uses adaptive AI to minimize slippage, reduce adverse selection, and optimize order flow for better execution in complex markets.

Testing & Deployment

Blockhouse validates execution with backtesting and A/B testing, ensuring low-latency deployment and consistent results in volatile markets.

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Proven Impact

People

A $50M AUM hedge fund focused on spot equities, operating out of the Midwest.

Challenge

The fund relied on outsourced TCA, which only measured mark-outs quarterly, and lacked real-time price impact analysis. This limited visibility of execution quality, causing hidden costs, and eroded returns.

Solution

Blockhouse ingested the fund’s blotter, applied real-time TCA, and analyzed $3M in notional volume over one month. We identified instruments with over 8% slippage and uncovered $30K in potential savings by optimizing execution.

Impact

With data-driven insights, the fund adjusted execution strategies, switched venues, and reduced market impact. As a result, they outperformed TWAP/VWAP benchmarks and improved execution efficiency, increasing net returns in the following month.

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FAQs

Frequently Asked Questions

Who is the Blockhouse product suite designed for?
How does Blockhouse impact your bottom line?
How does Blockhouse integrate with existing trading infrastructure?
What is your typical implementation timeline? Do you offer Proof-of-Concept (PoC) implementations?
What asset classes does Blockhouse support?
What pricing models do you offer?
How does Blockhouse handle sensitive trading data and ensure security?
How does Blockhouse compare to in-house TCA tools or third-party execution algos?
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