Customer Stories
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AI-Powered Trading Agents for High-Frequency Financial Markets

FinTech

AI-Powered Trading Agents for High-Frequency Financial Markets

Building resilient trading intelligence in stochastic, ultra-fast environments

2.1

Sharpe Ratio delivers over 2x higher risk-adjusted returns than market benchmarks

1.9

Profit factor

-8.5%

Maximum drawdown

Customer Stories

The entire pipeline operated in a fully automated loop covering training, evaluation, and deployment, all orchestrated through an MLOps stack that supported auto-updates and continuous retraining.

FinTech

Industry

AI/ML Development, Deep Reinforcement Learning (DRL), Trading Agent Architecture Design, Feature Engineering & Data Pipeline Setup, MLOps & Automation

Services

United States

Country

$150,000–$200,000

Budget

Challenge

Managing sub-second trading across volatile markets is nearly impossible with traditional tools. The client needed a scalable, AI-powered trading system.

See what we can do for you

Outcomes We Deliver

Solution

We designed and deployed an end-to-end AI trading platform using Deep Reinforcement Learning (DRL) and built a factory of self-improving trading agents.

Let’s talk about what’s possible

Tech Stack

To designed and deployed an end-to-end AI trading platform, Blackthorn AI applied a production-grade tech stack including:

Python

C++

PyTorch

MLflow

Docker

Python

C++

PyTorch

MLflow

Docker

Roadmap

Project duration

01–03 Weeks

Solution Design & Discovery

Stakeholder interviews, risk profiling, trading objectives, requirements backlog, system architecture blueprint.

04–12 Weeks

Market Data & Feature Engineering

Historical data collection, volatility/price pattern feature extraction, preprocessing, dimensionality reduction.

13–20 Weeks

Exchange Simulator Development

Built custom simulator with order books, liquidity limits, fee modeling, and trend variability injection.

21–32 Weeks

DRL Model Training

Trained and validated DQN, PPO, DDPG, and QFTN models using Ray + PyTorch. Set reward schemes to balance profit/risk.

33–38 Weeks

AI Trader SDK

Converted best models into C++ SDK with built-in controls: stop-loss, retrain, take-profit, emergency halt.

39–46 Weeks

Continuous Learning Loop

Setup automated retraining based on real market feedback. Integrated MLflow for experiment tracking.

47–55 Weeks

Web Platform Development

Built user-friendly web interface for strategy insights, performance dashboards, and manual trade overrides.

56–60 Weeks

Integration & Release

Deployed to live exchanges using Dockerized microservices. Integrated with trading APIs, monitoring, and alerting.

01–03 Weeks

Solution Design & Discovery

Stakeholder interviews, risk profiling, trading objectives, requirements backlog, system architecture blueprint.

04–12 Weeks

Market Data & Feature Engineering

Historical data collection, volatility/price pattern feature extraction, preprocessing, dimensionality reduction.

13–20 Weeks

Exchange Simulator Development

Built custom simulator with order books, liquidity limits, fee modeling, and trend variability injection.

21–32 Weeks

DRL Model Training

Trained and validated DQN, PPO, DDPG, and QFTN models using Ray + PyTorch. Set reward schemes to balance profit/risk.

33–38 Weeks

AI Trader SDK

Converted best models into C++ SDK with built-in controls: stop-loss, retrain, take-profit, emergency halt.

39–46 Weeks

Continuous Learning Loop

Setup automated retraining based on real market feedback. Integrated MLflow for experiment tracking.

47–55 Weeks

Web Platform Development

Built user-friendly web interface for strategy insights, performance dashboards, and manual trade overrides.

56–60 Weeks

Integration & Release

Deployed to live exchanges using Dockerized microservices. Integrated with trading APIs, monitoring, and alerting.

Team Size

1 x AI Solutions Architect

2x Deep Learning Engineers

1x Project Manager

2x MLOps Engineers

2x Data Scientists

8 team members from Blackthorn.ai

1 x AI Solutions Architect

2x Deep Learning Engineers

1x Project Manager

2x MLOps Engineers

2x Data Scientists

Delivering Impact

Beyond the values already highlighted, there’s even more to discover. Our commitment to innovation, client success, and impactful results sets us apart

Book a Meeting

-8.5%

Maximum drawdown

Kept losses under 10% during peak-to-trough declines, ensuring controlled downside risk in volatile markets.

2.1

Sharpe ratio

Demonstrates strong risk-adjusted returns, delivering more than double the excess return compared to volatility benchmarks.

1.9

Profit factor

Generated $1.90 in gross profits for every $1 lost, confirming consistent profitability.

-8.5%

Maximum drawdown

Kept losses under 10% during peak-to-trough declines, ensuring controlled downside risk in volatile markets.

2.1

Sharpe ratio

Demonstrates strong risk-adjusted returns, delivering more than double the excess return compared to volatility benchmarks.

1.9

Profit factor

Generated $1.90 in gross profits for every $1 lost, confirming consistent profitability.

AI-Powered Trading Agents for High-Frequency Financial Markets

The entire pipeline operated in a fully automated loop covering training, evaluation, and deployment, all orchestrated through an MLOps stack that supported auto-updates and continuous retraining.

FinTech

AI/ML Development, Deep Reinforcement Learning (DRL), Trading Agent Architecture Design, Feature Engineering & Data Pipeline Setup, MLOps & Automation

United States

$150,000–$200,000

Managing sub-second trading across volatile markets is nearly impossible with traditional tools. The client needed a scalable, AI-powered trading system.

Solution

To designed and deployed an end-to-end AI trading platform, Blackthorn AI applied a production-grade tech stack including:

Project duration

01–03 Weeks

04–12 Weeks

13–20 Weeks

21–32 Weeks

33–38 Weeks

39–46 Weeks

47–55 Weeks

56–60 Weeks

01–03 Weeks

04–12 Weeks

13–20 Weeks

21–32 Weeks

33–38 Weeks

39–46 Weeks

47–55 Weeks

56–60 Weeks

Team Size

Delivering Impact

-8.5%

2.1

1.9

-8.5%

2.1

1.9

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