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AI Quantitative Trading Analytics

AI-powered market analytics for systematic trading.

GAMES N TAPS builds intelligent analytics infrastructure for quantitative trading teams — combining real-time market data processing, AI signal generation, and risk-aware decision support on a unified AWS cloud-native platform.

24/7Continuous analytics
500+Inference RPM baseline
50TBData lake scale
● LIVE SIGNAL ENGINE
Market Momentum+0.84
Risk Exposure32%
AI Confidence91%
Latency Target<120ms
Market Data → AI Analytics → Strategy → Execution

From raw market data to actionable trading intelligence.

Our platform is purpose-built for teams that demand real-time data processing, model-driven analysis, and reliable cloud-native infrastructure.

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Market Data Intelligence

Ingest, normalize, and analyze real-time market signals, historical records, news events, and trading behavior data from multiple sources.

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🧠

AI Signal Generation

Leverage Amazon Bedrock (Claude Sonnet/Opus) for multi-variable inference, feature engineering, scenario analysis, and strategy evaluation at ≥500 RPM.

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🛡️

Risk-Aware Decisioning

Assist strategy teams with position sizing, risk exposure review, and continuous performance feedback loops powered by AI analytics.

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Built for high-frequency analytical workloads.

Scalable analytics, multi-modal inference, and operational reliability for data-intensive trading environments — running 24/7 on AWS.

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Low-Latency Processing

Designed for continuous data pipelines and high-concurrency inference workloads. c7g.12xlarge × 24 compute nodes deliver P95 latency under 120ms.

View architecture →
🧩

Multi-Modal Analysis

Combine structured trading data with image-based signals. Each Bedrock inference request supports text + 2 images (1000×1000) for broader market understanding.

See live demo →
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Feedback Optimization

Close the loop from execution results back to strategy improvement. CloudWatch-driven observability ensures continuous model evaluation.

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Enterprise-grade AWS infrastructure.

Migrated from GCP to AWS us-east-1 — our unified cloud architecture delivers the performance and scalability quantitative trading demands.

🖥️

Compute Layer

Amazon EC2
InstanceCountRole
c7g.12xlarge24×Analytics Core
m7g.2xlarge32×API Services
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Storage Layer

EBS + S3
ServiceConfigScale
EBS gp33000 IOPS24 × 500GB
Amazon S3Standard~50TB
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AI Inference

Amazon Bedrock
ScenarioRPMModel
High Complexity300Claude Sonnet
Standard200Claude Opus
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Observability

CloudWatch
MetricTargetStatus
Availability≥99.9%Active
Snapshots2× dailyActive

Build smarter trading analytics infrastructure.

Contact GAMES N TAPS to discuss AI analytics, quantitative research workflows, and cloud-native trading infrastructure built on AWS.

Platform Overview

The complete AI analytics stack for quantitative trading.

A unified platform that handles data ingestion, AI inference, strategy evaluation, and execution support — all running on production-grade AWS infrastructure with 24/7 operational reliability.

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Multi-Source Ingestion

Ingest real-time market ticks, order book data, news feeds, and exchange-level statistics. Handles structured and unstructured data at scale via S3 data lake (~50TB).

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ETL & Normalization

Batch and streaming ETL pipelines normalize trading records, user behavior logs, and analytical datasets. Full consistency validation (checksum + record comparison).

🗄️

Vector Database

Custom-built vector store enables embedding storage and semantic retrieval for trading signal similarity search and pattern recognition.

❄️

Cold / Hot Data Tiering

S3 Standard for active analytics datasets; S3 Glacier for long-term historical records. Automated lifecycle policies keep costs predictable.

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Data Integrity & Versioning

All migrated data validated with 100% consistency. EBS snapshots run 2× daily. Version management ensures reproducibility for backtesting and model training.

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Privacy & Compliance

User trading data is anonymized and access-controlled via IAM + KMS. Audit trails via CloudTrail. Compliant with North American data privacy requirements.

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Amazon Bedrock Integration

Managed generative AI inference via Claude Sonnet 4.6 / Opus 4.6. No GPU management — scalable on-demand inference with native multi-modal support.

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Multi-Modal Processing

Each inference request supports text + 2 images (1000×1000). Combines structured trading data with visual signal inputs for richer analytical context.

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High-Frequency Inference

Scenario 1: 300 RPM × 24h (complex analytics). Scenario 2: 200 RPM × 24h (standard). Combined ≥500 RPM sustained without degradation.

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Signal Generation

AI-assisted feature engineering, scenario analysis, and strategy evaluation. Long-context reasoning for multi-variable market conditions.

Bedrock Inference Specifications

ScenarioModelRPMInput TokensOutput TokensImages/ReqDuration
High Complexity AnalyticsClaude Sonnet 4.63002006002 × 1000×100024h/day
Standard AnalyticsClaude Opus 4.62002004002 × 1000×100024h/day
📊

Position & Exposure Analysis

AI-assisted position sizing recommendations and risk exposure reviews. Continuous feedback from execution results back into strategy evaluation.

⚠️

Real-Time Alerting

CloudWatch alarms trigger on performance degradation, resource anomalies, and service failures. P95 latency monitoring ensures SLA compliance.

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Strategy Feedback Loops

Trade outcome data feeds back into model evaluation pipelines. Continuous improvement cycle reduces drift and improves signal accuracy over time.

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Multi-Variable Scenario Modeling

Claude's long-context reasoning enables complex multi-variable scenario analysis, stress testing, and correlation modeling across asset classes.

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Centralized Monitoring

Amazon CloudWatch tracks CPU utilization, model inference latency, task queue length, and storage I/O. Data retention: 3–6 months.

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Unified Logging

CloudWatch Logs centralizes application logs, AI inference logs, and LLM call records. All logs archived to S3 for audit and compliance.

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Auto Scaling

EC2 Auto Scaling Groups expand capacity under high-concurrency conditions. Bedrock scales inference demand without manual provisioning.

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Security & Audit

IAM roles + KMS encryption + VPC Security Groups + NACL + CloudTrail. Fine-grained access control with full audit trail for all data access.

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Cost Management

Pay-as-you-go Bedrock inference with token usage monitoring. Future Savings Plans / Reserved Instances roadmap for ~$300K/month optimization.

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Multi-AZ Availability

All critical components deployed across multiple Availability Zones in us-east-1. Target SLA: ≥99.9% monthly uptime. VPN/PrivateLink for internal connectivity.

RegionServiceConfigurationPurpose
us-east-1EC2 c7g.12xlarge24 instances, On-Demand, Linux, Multi-AZAnalytics Core
us-east-1EC2 m7g.2xlarge32 instances, On-Demand, Linux, Multi-AZAPI Layer
us-east-1EBS gp324 × 500GB, 3000 IOPS, 150MB/s, 2× daily snapshotBlock Storage
us-east-1Amazon S350TB Standard + Glacier lifecycleData Lake
us-east-1Bedrock (Sonnet)300 RPM, 200 in / 600 out tokens, multi-modalAI Inference
us-east-1Bedrock (Opus)200 RPM, 200 in / 400 out tokens, multi-modalAI Inference
us-east-1CloudWatchMetrics, Logs, Alarms, 3–6 month retentionObservability
us-east-1VPCMulti-AZ, public + private subnets, VPN/PrivateLinkNetwork
us-east-1IAM + KMS + CloudTrailRole-based access, encryption, audit logsSecurity

Ready to see the platform in action?

Explore our live analytics dashboard or reach out to discuss how the platform can support your quantitative trading workflows.

🏗️ System Architecture

Cloud-native AI analytics architecture on AWS.

A modular, multi-layer architecture that separates access, compute, AI inference, data storage, and governance — purpose-built for quantitative trading workloads in us-east-1.

Layer 1

🌐 Access Layer

Users, administrators, and exchange interfaces connect via secure API endpoints. VPC + Security Groups enforce network isolation.

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Layer 2

🖥️ Compute Layer

24 × c7g.12xlarge (analytics) + 32 × m7g.2xlarge (API services). Graviton-optimized, Multi-AZ, Auto Scaling enabled.

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Layer 3

🧠 AI Inference

Amazon Bedrock: Claude Sonnet (300 RPM) + Opus (200 RPM). Multi-modal text + image processing. On-demand, no GPU management.

Layer 4

💾 Data Layer

EBS gp3 (24 × 500GB) for active workloads. S3 Standard + Glacier (~50TB) for the data lake. Vector DB for semantic retrieval.

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Layer 5

📊 Operations

CloudWatch metrics + logs + alarms. IAM + KMS encryption. CloudTrail auditing. 2× daily EBS snapshots with recovery testing.

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Layer 6

🔁 Continuous Improvement

Trade outcomes feed back into model tuning and strategy optimization. Performance metrics drive cost and scaling decisions.

  • 1

    Application & API Services

    m7g.2xlarge × 32 handles request routing, data processing orchestration, and API endpoint management. Graviton ARM architecture delivers optimal price-performance for service workloads.

  • 2

    Core Analytics & AI Compute

    c7g.12xlarge × 24 powers the analytics core: feature engineering, signal processing, and high-concurrency data analysis. 48 vCPUs per instance, purpose-built for compute-intensive workloads.

  • 3

    AI Agent Orchestration (Bedrock)

    Managed Claude inference with ≥500 RPM combined throughput. Supports multi-modal inputs (text + images), long-context reasoning, and adaptive strategy analysis without infrastructure overhead.

  • 4

    Data Pipeline & ETL

    Batch + streaming ingestion pipelines process trading records, market data, and analytical outputs. Full integrity validation with checksum-based comparison ensures 100% data consistency post-migration.

  • 5

    Storage Architecture

    Three-tier storage: EBS gp3 (active block storage, 3000 IOPS), S3 Standard (hot analytical data), S3 Glacier (historical archive). NoSQL vector DB for embedding retrieval.

  • 6

    Observability & Security

    CloudWatch unified monitoring. IAM fine-grained access control. KMS at-rest encryption. VPC network isolation. CloudTrail audit trail. Multi-AZ for ≥99.9% availability SLA.

🔐

Identity & Access (IAM)

Unified identity management via AWS IAM. Role-based access control with least-privilege policies. Separate roles for analytics, API, and Bedrock services.

🌐

Network Isolation (VPC)

Multi-AZ VPC with separate public/private subnets. Security Groups + NACLs enforce traffic rules. VPN/PrivateLink for internal system connectivity.

🔑

Encryption at Rest (KMS)

All data encrypted using AWS KMS. EBS volumes, S3 objects, and database records are encrypted. Key rotation managed automatically.

📋

Audit & Compliance (CloudTrail)

CloudTrail records all API calls and access events. Logs archived to S3 for compliance auditing. Satisfies North American individual data privacy requirements.

🕵️

Data Masking & Access Control

User trading data and exchange credentials are anonymized before storage. Access to raw data requires explicit IAM role authorization and is fully audited.

🤖

AI Model Security

Bedrock access is controlled via IAM policies. All model invocation logs (LLM calls, inference results) are captured in CloudWatch Logs for behavior auditing.

GCP → AWS Migration Map

Migrated from Google Cloud Platform to AWS us-east-1 with zero data loss. 7-week execution: May–July 2026.

Source (GCP)Migration MethodTarget (AWS)Feasibility
Compute Engine (VM)Image export + re-deployment (Terraform)EC2 c7g / m7gHigh
Persistent DiskSnapshot export + block transferEBS gp3 (500GB)High
Cloud Storagegsutil / parallel transfer / AWS CLIAmazon S3 (~50TB)High
Application Services (VM)Re-deployment + config managementEC2 application servicesHigh
Vertex AI / External APIsAPI adapter + Prompt optimizationAmazon Bedrock (Claude)High
ETL PipelinesWorkflow reconfigurationEC2-based ETL + BedrockHigh
PLANNING
May 15–18
DESIGN
May 20–25
DEPLOY
May 22–30
TESTING
Jun 1–10
HANDOVER
Jun 12–20
GO-LIVE
Jul 2026

Explore the platform in detail.

See all platform capabilities, infrastructure specs, and AI inference configurations.

Live Analytics Dashboard

Real-time AI signal engine — simulated live.

This demonstration simulates the GAMES N TAPS analytics pipeline: market data ingestion → AI inference via Bedrock → signal generation → risk assessment. All data is simulated for demonstration.

AI Signal Engine — us-east-1
LIVE
MARKET ANALYTICS SIGNAL (normalized)
Market Momentum
+0.84
Risk Exposure
32%
AI Confidence
91%
P95 Latency
87ms
AI-Generated Signal Log

🤖 Bedrock Inference

0
requests/min (combined)
Sonnet: 300 RPM Opus: 200 RPM

📊 Signal Strength

Equity Momentum72%
Volatility Index45%
Sentiment Score83%
Cross-Asset Corr.61%

🏗️ Infrastructure Status

EC2 c7g (×24) CPU 62%
EC2 m7g (×32) CPU 41%
EBS gp3 I/O 2841 IOPS
S3 Data Lake 50.2 TB
Availability 99.97%

⚙️ Demo Controls

🏢 About GAMES N TAPS

AI-driven quantitative trading analytics, built for scale.

GAMES N TAPS is a quantitative trading technology company focused on the North American market, specializing in AI-driven data analytics and strategy support services. Headquartered in Dublin, Ireland — operating infrastructure in AWS us-east-1.

🎯

Our Mission

Build high-performance, AI-driven quantitative trading analytics infrastructure that enables systematic trading teams to make better decisions with real-time, model-powered insights.

🌍

Our Market

Focused on North American trading markets, covering continuous 24/7 trading sessions. Platform designed for scale: from startup quant teams to institutional analytics operations.

☁️

Our Infrastructure

Built on AWS us-east-1, migrated from GCP. Unified compute (EC2 Graviton), storage (S3 + EBS), and AI inference (Bedrock) — ~$300K/month production infrastructure.

The team behind the platform.

ZY

Zhang Yuan

Solution Architect
AWS SAP
FJ

Fang Jiating

Senior Engineer
AWS SAP
WL

Wang Lei

Project Manager
AWS DAS

Project milestones.

GCP → AWS migration timeline — May to July 2026.

May 15–18

Planning Phase

Requirements analysis, GCP architecture assessment, AWS target design, resource planning and cost estimation.

May 20–25

Architecture Design

AWS architecture documentation, VPC design, IAM policy definition, Bedrock integration planning, cost model validation (~$300K/mo).

May 22–30

Deployment & Migration

EC2/EBS/S3 deployment. 50TB data migration with checksum validation. Application re-deployment and Graviton ARM compatibility testing.

Jun 1–10

Testing & Validation

Load testing at ≥500 RPM, multi-modal inference validation, stability testing, 99.9% availability verification, performance benchmarking.

Jun 12–20

Handover & Documentation

System delivery documentation, operational runbooks, team training, and project acceptance verification.

Jul 2026

🚀 Production Go-Live

Full platform operational on AWS us-east-1. 7×24 continuous analytics running. Savings Plans optimization roadmap activated.

📧

Technical Inquiries

For platform and infrastructure questions:
it@gamesntaps.vip

💼

Business & Partnerships

For business collaboration:
office@gamesntaps.vip

Work with GAMES N TAPS.

Interested in our platform, partnership opportunities, or joining the team? Get in touch with us directly.

📬 Contact

Get in touch with GAMES N TAPS.

Whether you want to discuss AI analytics infrastructure, quantitative research workflows, or partnership opportunities — we'd love to hear from you.

📧

Technical Contact

Infrastructure, platform, and technical inquiries.
it@gamesntaps.vip

💼

Business Contact

Partnership, business development, and general inquiries.
office@gamesntaps.vip

🌐

Website

gamesntaps.vip
AI Quantitative Trading Analytics Platform

📍

Registered Address

Apartment 146, Meridian Court
Royal Canal Park, Dublin, Ireland

⏱️

Response Time

We typically respond within 24 hours for technical inquiries and 48 hours for business proposals.

Send us a message

✅

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Thank you for reaching out. Our team will review your inquiry and respond within 24–48 hours.