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Technology Stack

Difficulty expert

Overview

Choosing the right technology stack is critical for building reliable, scalable trading systems.

Language Choices

Python

Best for: Research, prototyping, data analysis, ML 

Pros: Rich ecosystem, easy to learn, great libraries
Cons: Slower execution, GIL limits parallelism
Use: Strategy research, backtesting, data analysis

C++

Best for: Low-latency execution, HFT 

Pros: Maximum performance, fine-grained control
Cons: Complex, longer development time
Use: Execution engines, market data handlers

Rust

Best for: Safe, fast systems 

Pros: Memory safety, performance, modern
Cons: Steep learning curve, smaller ecosystem
Use: Trading infrastructure, risk engines

Java/Kotlin

Best for: Enterprise systems 

Pros: Robust, large ecosystem, good performance
Cons: Verbose, JVM overhead
Use: Banking systems, institutional platforms

Go

Best for: Concurrent services 

Pros: Fast compilation, good concurrency, simple
Cons: Less mature data science ecosystem
Use: Microservices, data pipelines

Data Storage

Use Case Technology Why
Tick data kdb+, InfluxDB Time-series optimized
OHLCV PostgreSQL, TimescaleDB Relational + time-series
Real-time signals Redis In-memory, fast
Trade records PostgreSQL ACID compliance
Logs Elasticsearch Search and analysis
Configuration etcd, Consul Distributed config

Message Queues

Technology Use Case Latency
ZeroMQ Ultra-low latency Microseconds
Kafka High-throughput streaming Milliseconds
RabbitMQ General purpose Milliseconds
Redis Pub/Sub Simple pub/sub Microseconds

Cloud vs. On-Premise

Cloud (AWS, GCP, Azure)

Pros: Scalable, managed services, global reach
Cons: Latency variability, cost at scale, vendor lock-in
Best for: Research, backtesting, non-HFT systems

On-Premise / Co-Location

Pros: Lowest latency, full control, predictable costs
Cons: Capital expenditure, maintenance, limited scale
Best for: HFT, market making, latency-sensitive strategies

Infrastructure Components

┌────────────────────────────────────────────────┐
│                  INFRASTRUCTURE                 │
├─────────┬─────────┬──────────┬────────┬────────┤
│ Network │ Compute │ Storage  │ Monitor│ Deploy │
│         │         │          │        │        │
│ ┌─────┐ │ ┌─────┐ │ ┌──────┐ │┌──────┐│┌──────┐│
│ |VPC  | │ |EC2  | │ |S3    | │|Prom  | │|CI/CD | │
│ |VPC  | │ |EKS  | │ |RDS   | │|Graf  | │|GitLab| │
│ |LB   | │ |Lambda│ │ |Redis | │|ELK   | │|Docker│ │
│ └─────┘ │ └─────┘ │ └──────┘ │└──────┘│└──────┘│
└─────────┴─────────┴──────────┴────────┴────────┘

Research Stack

Language: Python
IDE: Jupyter, VS Code, PyCharm
Libraries: pandas, numpy, scikit-learn, statsmodels
Visualization: matplotlib, plotly, seaborn
Data: yfinance, polygon-api, quandl
Storage: PostgreSQL, S3

Production Trading Stack

Language: Python + C++/Rust for critical paths
Data Pipeline: Kafka + Flink
Storage: kdb+ (tick), PostgreSQL (trades), Redis (signals)
Execution: Custom C++ engine
Monitoring: Prometheus + Grafana + PagerDuty
Deployment: Docker + Kubernetes
CI/CD: GitHub Actions / GitLab CI

Low-Latency Stack

Language: C++ / Rust
Network: Kernel bypass (DPDK, Solarflare)
Exchange: Co-located servers
Clock: Hardware timestamping (PTP)
Monitoring: Custom low-overhead metrics
Deployment: Bare metal

Security

Area Best Practice
API Keys Store in secret manager, never in code
Network VPC, security groups, private subnets
Encryption TLS in transit, encryption at rest
Access IAM, least privilege, MFA
Audit Comprehensive logging, regular reviews

Practical Guidelines

  1. Start Simple — Don't over-engineer
  2. Choose for Team — Use what your team knows
  3. Plan for Scale — But don't prematurely optimize
  4. Automate Everything — CI/CD from day one
  5. Monitor Early — Can't improve what you don't measure
  6. Document — Your future self will thank you
  7. Security First — Breach = game over

Next Steps