Building an AI Trading Workflow

The Principle: Layers, Not Magic

No single AI tool does everything. The traders getting the most value from AI treat it as a layered system:

• Layer 1 — Data & Signals: Specialised tools (TrendSpider, Trade Ideas, Glassnode) provide real-time market data, chart analysis, and trade signals.
• Layer 2 — Research & Analysis: AI platforms (Claude, ChatGPT, Perplexity) provide deep research, document analysis, and code generation.
• Layer 3 — Decision & Execution: You make the final decision using your defined strategy and execute through your broker.

AI never makes the final call. It accelerates the research that informs your decision.

Workflow 1: Pre-Market Research Pipeline

Time: 30–45 minutes before market open. Designed for swing traders and active investors.

1. Scan for setups (TrendSpider / Trade Ideas) — Run your watchlist through your breakout or trend following scans. Identify 3–5 tickers that triggered overnight signals.
2. Quick news check (Perplexity / ChatGPT with browsing) — For each ticker, ask: "What is the most recent news for [TICKER] in the last 24 hours?" Perplexity is best here because it cites sources. Filter out tickers with pending earnings or material news that could override the technical setup.
3. Deep dive on best candidate (Claude) — For the strongest setup, paste the company's latest 10-Q executive summary or earnings transcript into Claude. Ask: "What are the three biggest risks and three strongest bullish factors for this stock right now?" This adds a fundamental layer to your technical signal.
4. Position sizing (your strategy rules) — Apply your ATR-based position sizing or fixed-fractional rules. No AI needed here — this is mechanical.
5. Execute through your broker — Place the trade with your pre-defined stop loss and target.

Workflow 2: Position Analysis Framework

For traders with open positions. Run weekly or when a position reaches a decision point.

1. Export portfolio data — Download current holdings, entry prices, and P&L from your broker as a CSV.
2. Upload to ChatGPT Code Interpreter — Ask it to generate a correlation matrix, sector breakdown, and drawdown analysis. This gives you a visual portfolio health check in 60 seconds.
3. Risk scenario analysis (Claude) — Paste your holdings into Claude and ask: "If the 10-year Treasury yield rises 75 basis points over the next month, which of these positions are most at risk and why?" Claude's structured reasoning excels at this kind of scenario work.
4. Check technical levels (TrendSpider) — For any position near a key level (stop loss, resistance, support), review the chart with multi-timeframe analysis to decide whether to hold, tighten stops, or take partial profits.

Workflow 3: Weekend Strategy Review

A weekly process for refining your approach. 60–90 minutes on Saturday or Sunday.

1. Journal review — Export your trade log for the week. If you don't journal trades, start — it's the highest-ROI habit in trading.
2. Pattern analysis (Claude) — Paste your trade log into Claude: "Here are my trades from this week. What patterns do you see in my winners vs losers? Am I following my strategy rules consistently?" Claude will identify systematic errors you might not notice yourself.
3. Strategy backtest update (Claude Code or local Python) — Run your strategy's backtest with updated data. If you don't have a backtesting script, ask Claude to write one — see AI Prompts for Trading for templates.
4. Market regime check — Ask Claude or ChatGPT: "Based on these data points [paste VIX level, 200-day MA position, sector rotation data], does the current environment favour trend following or mean reversion?" This informs whether to adjust your strategy allocation for the coming week.

Common Mistakes

• Using AI to confirm a bias you already have. If you're long a stock and ask Claude "why will this stock go up?", you'll get a bullish analysis. Instead ask: "What are the strongest arguments against this position?"
• Skipping verification. AI-generated numbers (historical returns, earnings figures, price levels) must be verified against primary sources. This is non-negotiable.
• Over-optimising. Running 50 backtest variations in Claude Code until you find one that looks profitable is curve-fitting, not research. Test your hypothesis once with reasonable parameters, then forward-test.
• Treating AI output as a trade signal. AI analysis is one input. Your strategy rules, risk management, and market context are the decision framework.