Part 1: Building an AI trading bot w/ Claude + Webull API #AI #Trading #Algo #Claude #Stocks

In this video, I break down the foundation of an autonomous AI trading system I’m building step by step.

The goal is to move from manual decision-making to a fully systemized trading process where data collection, analysis, scoring, and execution are handled programmatically with minimal human intervention.

This is not a finished product. This is part 1 of a build series where I document the architecture, logic, and systems behind the strategy before scaling it into a fully functional automated trading bot.

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What I Built So Far

I started by combining two core components:

1. AI-Based Technical Analysis Layer

I used Claude Code to structure an AI-driven system that can:
• Interpret technical indicators
• Analyze price action contextually
• Evaluate momentum shifts
• Process multi-stock comparisons

This replaces subjective chart reading with a consistent evaluation framework.

2. Market Data Integration Layer

The system is designed around a fixed basket of high-liquidity stocks:
• SPY
• QQQ
• NVDA
• TSLA
• AAPL
• AMD

These assets were chosen because they provide:
• High volume consistency
• Strong correlation with broader market sentiment
• Reliable technical structure for algorithmic analysis

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How the System Works

The workflow is structured into a scoring-based decision engine:

Step 1: Data Collection

The system continuously reads:
• Technical indicators
• Volume and relative volume
• Price momentum
• Market correlation signals

Step 2: Cross-Asset Comparison

Each stock is evaluated not in isolation, but relative to:
• Overall market trend (SPY / QQQ)
• Sector movement behavior
• Strength vs weakness comparisons

Step 3: Trade Scoring Engine

Each setup is assigned a score from:
• 0 to 100

This score represents:
• Confluence of indicators
• Momentum alignment
• Volume confirmation
• Market structure agreement

Only high-confidence setups qualify for execution consideration.

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Risk Management System (Built In)

No trade is executed without predefined protections:
• Dynamic position sizing based on conditions
• Automatic stop-loss calculation
• Portfolio exposure limits
• Risk-adjusted trade filtering

This ensures the system prioritizes capital preservation over frequency.

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Execution Logic

The system does NOT trade constantly.

It only executes when:
• Multiple signals align simultaneously
• Market conditions confirm directionality
• Risk thresholds are satisfied
• Trade score meets a strict threshold

If conditions are not met, the system stays idle.

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What Comes Next in the Series

This is Part 1 of a full build series.

Next steps include:
• Connecting the execution layer through Webull API
• Building real-time data ingestion pipelines
• Automating trade execution based on scoring output
• Refining risk models and position logic
• Backtesting the system against historical market data

Eventually, this will evolve into a fully autonomous trading agent capable of:
• Continuous market scanning
• Real-time decision making
• Automated execution with risk controls

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Goal of This Series

The purpose is not just to build a trading bot.

It’s to demonstrate how modern AI systems can be structured into modular financial automation pipelines that handle:
• Analysis
• Decision-making
• Execution
• Risk control

All as independent but connected systems.

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Видео Part 1: Building an AI trading bot w/ Claude + Webull API #AI #Trading #Algo #Claude #Stocks канала Daniel Thacker