I built a live crypto quant system from scratch with no coding background, using AI as my execution partner. Here's the full 2-month path.

Around January 20th I made a decision: build an algorithmic trading system using AI. No coding background. No ML experience. No idea what I was getting into.

Two months later: 5 live strategies running 24/7 across BTC, ETH, SOL, XRP, and DOGE. Here's the complete path, including everything that didn't work.

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Why I started

5 years of emotional trading. Greed, fear, blown accounts. I finally accepted something: human nature is not fixable through discipline. Greed and fear are structural, not habitual. The only way out is to remove the human from the decision entirely.

Quantitative trading does exactly that. Rules and algorithms replace emotion. The question was whether someone without a technical background could actually build this.

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The tool that made it possible: OpenClaw

OpenClaw is an AI agent framework — it connects a language model (I use Claude) to real tools: file system, terminal, SSH, APIs. It doesn't just answer questions. It executes.

The key difference from standard AI assistants: it has persistent memory. Rules established today apply tomorrow. Mistakes logged now don't get repeated. It builds genuine understanding of how you work over time.

I handle direction and judgment. OpenClaw handles implementation.

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The complete path

Month 1: Infrastructure

• Set up Google Cloud server ($8-30/month — far cheaper than buying a dedicated machine)
• Connected via SSH (first on laptop, later switched to Termius on mobile — significantly more convenient)
• Data pipeline: Bybit for candlesticks, CoinGlass for liquidations/long-short/OI/CVD
• Pulling historical data failed repeatedly — timeouts, auth errors, format issues. Eventually let OpenClaw handle it. It tried several approaches and got it working.

Feature engineering Started with 200+ features. Most were noise. Filtered down to 60-70, each with clear economic logic behind it.

Label design Triple-barrier labeling produced a 0.65 long/short ratio — heavily biased short because the stop was tighter than the take-profit. Switched to ATR-based binary classification. Ratio normalized.

Model selection Transformer: 3 weeks, validation accuracy stuck at 54%. LightGBM: 1 day, 67.5%. Kept LightGBM. Interpretability matters more than theoretical ceiling when you're debugging a live system.

WFO parameter optimization 11-dimensional parameter space. CMA-ES optimizer, 500 trials per window. Modified Calmar as optimization target (annualized return / max drawdown, 3% floor). Fibonacci time-decay weighted averaging for final parameter selection.

Month 2: Live deployment

5 symbols went live. First 3 days:

• NaN features running silently for 2 days (timestamp bug, no errors logged)
• 28 entry signals, zero executions over 5.5 hours (exchange API spec changed, old code rejected silently)
• Exit logic in wrong order (cancelled stop-loss before closing position — position ran unprotected on failure)
• Floating point precision rejected a position close
• Fix introduced a new bug (syntax error, no check before upload)

Each one was found and fixed. Each one added a monitoring layer.

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What actually makes this work

The AI handles execution. I handle judgment.

Every decision about strategy logic, risk parameters, and what to build next — that's mine. OpenClaw implements, debugs, monitors, and alerts. The collaboration quality improves over time because the system accumulates context.

One person with this setup can cover roughly what used to require a small team.

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Current status

Day 3 live. Starting equity $902. Real P&L posted daily. No filters.

Happy to answer questions on any specific part — data pipeline, WFO setup, the AI collaboration model, or live execution architecture.

X: @dayou_tech