QuantLabsNet.com Public Quant Analytics Group

Introduction

After 30+ years of coding experience, I decided to challenge myself: build an entire trading application using 100% C++ with no compromises.

The result? A humbling failure.

But failure is the best teacher, and I want to share what happened, what I learned, and what the community taught me during a recent live stream discussion.

Part 1: The Starting Point

When I began this project, my thinking was:

• "C++ is faster than Python"

• "Complete control over everything"

• "No dependencies, no baggage"

• "Professional-grade system"

This approach seemed logical. C++ is the language of high-frequency trading. Bloomberg uses it. Most speed-critical trading systems use it.

What could go wrong?

Part 2: Where It Went Wrong

Issue 1: Complexity Overload

Pure C++ means you're responsible for:

• Memory management (no garbage collection)

• Thread synchronization (mutexes, condition variables, atomic operations)

• Data structures (building everything from scratch)

• Network protocols (implementing WebSockets, HTTP, etc.)

• Error handling (no friendly exceptions like Python)

Every component required reinventing the wheel. This created endless complexity that slowed development to a crawl.

Issue 2: Development Speed Trade-offs

Yes, C++ is faster. But speed has two dimensions:

1. Execution speed

2. Development speed

I was gaining execution speed but losing massive amounts of development speed. Features that would take hours in Python took days in C++.

Issue 3: Multi-Threading Complexity

Several viewers during the live stream asked about distributing workloads across cores in C++. This is where things get complicated.

Key points discussed:

• Intel's Core Ultra 2 has stopped multithreading altogether

• Bloomberg programming videos show they disable hyperthreading

• Thread synchronization adds layers of complexity

• Race conditions, deadlocks, and debugging challenges multiply

The hardware itself is evolving to address multi-threading issues, but software solutions remain challenging.

Part 3: Data Management Challenges

A viewer shared a real-world problem:

• They trade energy/power markets

• They have 30-50 TB of weather data

• This data cannot be stored locally

This highlighted a critical issue: pure C++ solutions often lack the ecosystem for handling massive datasets efficiently.

Cloud-based databases become inevitable at this scale, but integrating them with a pure C++ architecture adds another layer of complexity.

Part 4: AI Integration Insights

The community had fantastic insights about using AI tools (Claude, Deepseek, etc.) in trading system development:

Recommended Practices:

1. Build on existing frameworks rather than starting from scratch

2. Ask AI to create changelogs for version tracking

3. Save working copies of files until new versions compile successfully

4. Gather data in CSV files for post-trade analysis

5. Use AI as a tool to understand code, not blindly copy it

The WebSocket Challenge:

One viewer asked about building a WebSocket connection between Rithmic and NinjaTrader using AI. My advice? Use AI to help, but ensure you understand every piece of code you're implementing.

Part 5: Community Suggestions

Suggestion 1: Market Regime Indicator (Markov Model)

A brilliant suggestion came in: build a market regime indicator using a Markov model to identify different market states:

• Trending markets

• Mean-reverting markets

• High volatility periods

• Low volatility periods

This would allow the system to adjust strategies based on current market conditions rather than using a one-size-fits-all approach.

Suggestion 2: Hybrid Architecture

The community consensus: use C++ for performance-critical components while leveraging Python (or other languages) for rapid development.

Part 6: Key Lessons Learned

1. 100% C++ was overengineering for my actual needs

2. Community knowledge is invaluable — questions and insights were better than anything I could have figured out alone

3. Start small — don't try to build Bloomberg from scratch on day one

4. Token costs matter — if using Claude or similar for trading bots running 5 days a week, budget accordingly

5. Fail fast, learn faster — the failure taught me more than any success ever could

Part 7: What's Next?

I'm considering updating my HFT book based on these lessons. The market has changed significantly, and the tooling has evolved.

Pure C++ might still have a place, but the hybrid approach seems more practical for most developers.

Questions for the Forum

1. Has anyone else attempted a "pure" language approach for trading systems? What were your results?

2. How do you handle multi-core/multi-threading challenges in your own systems?

3. For those using AI tools in trading system development — what's your workflow?

4. What's your take on the Python vs. C++ trade-off for different types of trading strategies?

5. Would a market regime indicator using Markov models be useful for your trading approach?

Related Resources:

• 📺 Full YouTube Replay: https://www.youtube.com/watch?v=z3k5ZQDBpmI

• 📚 HFT Book (considering updates)

Profile Info:

30 years of coding experience. Started in 1991. Still learning. Still building. Still failing forward.

Looking forward to your thoughts and experiences!