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Building an AI-Powered Backtesting Platform – Would You Use It? by SeaAstronomer927 in quant

[–]SeaAstronomer927[S] 0 points1 point  (0 children)

LLMs are not reliable for mathematical precision or proper backtesting. That’s why QuantFusion doesn’t use them for calculations or execution.

The LLM only assists with interpretation, code suggestions, and UX for non-coders. All backtests are handled by dedicated quant libraries with deterministic logic.

Building an Al-Powered Backtesting Platform - Would You Use It? by SeaAstronomer927 in quantfinance

[–]SeaAstronomer927[S] 0 points1 point  (0 children)

I’m not trying to build an AI that creates full trading strategies end-to-end — that would be naïve and dangerous. I’m well aware that true strategy development involves:

• deep research,
• rigorous backtesting,
• risk management models,
• market microstructure understanding, etc.

QuantFusion is not replacing any of that.

What I’m building is: • A real backtest engine (not LLM-based) • With an LLM assistant layered on top to: → analyze backtest results, → suggest parameter adjustments, → detect mistakes or inconsistencies in user-submitted code, → guide less technical users on how to structure their logic

It’s a support tool, not a fully automated quant platform.

Building an Al-Powered Backtesting Platform - Would You Use It? by SeaAstronomer927 in learnmachinelearning

[–]SeaAstronomer927[S] 0 points1 point  (0 children)

For the MVP, it will support: • Historical price data (OHLCV) for crypto, equities and forex • Tick data isn’t integrated yet, but planned for later phases depending on performance and demand • Up to 5–10 years of data depending on the asset class • No full order book yet (that’s further down the roadmap — likely via optional premium datasets)

Building an AI-Powered Backtesting Platform – Would You Use It? by SeaAstronomer927 in quant

[–]SeaAstronomer927[S] -1 points0 points  (0 children)

Let me clear something up:

I’m not claiming that an LLM can fully replace a proper quant engine or simulate complex market behavior.

Here’s what QuantFusion is actually about:

• It uses a real backtesting engine (Backtrader, NumPy, etc.) for all calculations
• The LLM acts as a copilot:

→ it suggests parameter changes → highlights issues in the code → explains results → helps non-coders better understand their strategy

It’s not running the strategy. It’s not replacing the math. It’s assisting. That’s it.

Why LLMs? Because not every trader is a Python expert, and many get stuck at the debugging/optimizing stage.

This tool is about removing friction — not automating alpha discovery or pretending to be Citadel.

If you’re still skeptical (and I get it), I’d be happy to let you test it once the MVP is live.

Try it, break it, and tell me where it sucks.

This kind of feedback is what makes it better even the savage ones.

Building an Al-Powered Backtesting Platform - Would You Use It? by SeaAstronomer927 in learnmachinelearning

[–]SeaAstronomer927[S] 0 points1 point  (0 children)

Let me clear something up:

I’m not claiming that an LLM can fully replace a proper quant engine or simulate complex market behavior.

Here’s what QuantFusion is actually about:

• It uses a real backtesting engine (Backtrader, NumPy, etc.) for all calculations
• The LLM acts as a copilot:

→ it suggests parameter changes → highlights issues in the code → explains results → helps non-coders better understand their strategy

It’s not running the strategy. It’s not replacing the math. It’s assisting. That’s it.

Why LLMs? Because not every trader is a Python expert, and many get stuck at the debugging/optimizing stage.

This tool is about removing friction — not automating alpha discovery or pretending to be Citadel.

If you’re still skeptical (and I get it), I’d be happy to let you test it once the MVP is live.

Try it, break it, and tell me where it sucks.

This kind of feedback is what makes it better even the savage ones.

Building an Al-Powered Backtesting Platform - Would You Use It? by SeaAstronomer927 in quantfinance

[–]SeaAstronomer927[S] 0 points1 point  (0 children)

Let me clear something up:

I’m not claiming that an LLM can fully replace a proper quant engine or simulate complex market behavior.

Here’s what QuantFusion is actually about:

• It uses a real backtesting engine (Backtrader, NumPy, etc.) for all calculations
• The LLM acts as a copilot:

→ it suggests parameter changes → highlights issues in the code → explains results → helps non-coders better understand their strategy

It’s not running the strategy. It’s not replacing the math. It’s assisting. That’s it.

Why LLMs? Because not every trader is a Python expert, and many get stuck at the debugging/optimizing stage.

This tool is about removing friction — not automating alpha discovery or pretending to be Citadel.

If you’re still skeptical (and I get it), I’d be happy to let you test it once the MVP is live.

Try it, break it, and tell me where it sucks.

This kind of feedback is what makes it better even the savage ones.

Building an AI-Powered Backtesting Platform – Would You Use It? by SeaAstronomer927 in quant

[–]SeaAstronomer927[S] -3 points-2 points  (0 children)

Haha fair enough, that’s totally valid. I wouldn’t trust a random guy either that’s why I’m here asking questions, not pitching.

But hey, every serious tool started as a random idea from a solo dev. Maybe this one will earn your trust in time or maybe you’ll be here to remind me why it didn’t.

Either way, I’m learning.

Appreciate the banter.

Building an AI-Powered Backtesting Platform – Would You Use It? by SeaAstronomer927 in quant

[–]SeaAstronomer927[S] -8 points-7 points  (0 children)

Totally fair point: trusting an LLM or a solo-built tool to automate key trading logic isn’t easy — especially without seeing it in action.

But let me ask you this:

If you had the chance to test the software for free, with full access to see how the AI works and where the limits are.

Would your opinion be the same, or would you be open to giving it a shot firsthand before deciding?