AI / Machine Learning8 months12 engineers2025

AI-Powered Trading Platform

How we transformed a legacy trading system into a real-time AI platform that increased trading accuracy by 73%

Client

FinTech Corp

Overview

FinTech Corp is a mid-sized investment firm managing over $2.4B in assets. Their legacy trading infrastructure was built in 2014 — slow, error-prone, and completely blind to modern AI capabilities. They came to Codingace.ai with one goal: survive the next decade of algorithmic trading.

Duration

8 months

Team Size

12 engineers

The Challenge

The existing system processed trades with an average latency of 4.2 seconds, relied on manual rule-based logic, and had zero predictive capability. During high-volatility market events, the platform would crash entirely — costing the firm an estimated $1.2M per incident. The engineering team had accumulated 11 years of technical debt with no clear path forward.

Our Solution

We designed and built a cloud-native, event-driven trading platform from the ground up. At its core is a custom TensorFlow model trained on 15 years of market data, capable of predicting short-term price movements with 78% accuracy. The system processes over 2 million market events per second using Apache Kafka, with a React-based dashboard giving traders real-time visibility into every position.

Before vs. After

The numbers tell the story. Here's exactly what changed after 8 months of work.

Before Codingace.ai

Trade Execution Latency

4.2s

System Uptime

94.1%

Prediction Accuracy

Daily Trade Volume

12K

Manual Interventions/Day

340+

Incident Cost/Year

$4.8M

After Codingace.ai

Trade Execution Latency

38ms

System Uptime

99.97%

Prediction Accuracy

78%

Daily Trade Volume

180K

Manual Interventions/Day

Incident Cost/Year

$0.3M

110x

Faster Execution

15x

More Trades Daily

94%

Cost Reduction

+5.87%

Uptime Improvement

Product Screenshots

A look inside the platform we built.

Real-time Trading Dashboard

Project Timeline

8 months, 5 phases, zero downtime. Here's how we executed the transformation.

Phase 1Weeks 1–4

Discovery & Architecture

Deep-dive audit of the legacy codebase, infrastructure mapping, stakeholder interviews, and design of the new event-driven architecture.

Technical audit reportSystem architecture blueprintRisk assessmentMigration roadmap

Phase 2Weeks 5–12

Core Infrastructure

Built the Kafka event streaming backbone, Kubernetes cluster on AWS, CI/CD pipelines, and the foundational microservices layer.

Event streaming pipelineK8s cluster setupMonitoring & alertingCore microservices

Phase 3Weeks 13–22

AI Model Development

Trained and validated the TensorFlow prediction model on 15 years of historical market data. Implemented real-time inference with sub-50ms response times.

Trained ML modelReal-time inference APIBacktesting frameworkModel monitoring

Phase 4Weeks 23–30

Dashboard & Integration

Built the React trading dashboard with real-time WebSocket feeds, portfolio analytics, and seamless integration with existing broker APIs.

Trading dashboardWebSocket feedsBroker API integrationUser training

Phase 5Weeks 31–34

Migration & Launch

Zero-downtime migration from legacy system, parallel running period, performance validation, and full production cutover.

Zero-downtime migrationPerformance validationProduction launchHandover docs

Tech Stack

The technologies we chose and why — every decision was deliberate.

Frontend

ReactTypeScript

Backend

PythonFastAPI

AI/ML

TensorFlow

Infrastructure

Apache KafkaKubernetes

Cloud

AWS

Database

PostgreSQLRedis

Monitoring

Grafana

DevOps

Terraform

Jennifer's Review

"Codingace.ai didn't just build us a platform — they fundamentally changed how we think about trading technology. The AI model alone has generated an additional $18M in returns in its first six months. The ROI on this project is unlike anything we've seen."

Jennifer Walsh

CTO, FinTech Corp

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