You will find this AI Smart Trading book as a rapid learning and active trading playbook that shows you to pair ChatGPT with TradingView to turn signals into repeatable rules and clear actions.
To start, we've got short daily rituals that turn premarket scans into a one-page plan for the opening range, and minute-level execution cues that line up perfectly with orders. We've got methods that teach you how to size fixed risk units to keep your dollar loss steady while your position size adapts to volatility so you can predict your exposure. The prompts in the book give you numbers, times, and labels for the source, so you can audit and act on every signal right away. You'll get reliable prompt templates, entries, stops, partial exit schedules, and trailing rules with numeric thresholds. All of this lets you execute without extra arithmetic. The workflow combines watchlist rotation, annotated chart snapshots, and compact trade cards that save each idea to a journal for weekly scoring and continuous improvement. This book covers midday maintenance rituals that force short checks on thesis validity, volume confirmation, and catalyst updates. This means that tightening or standing down happens by rule rather than emotion.
The book also covers a very strong library of stored prompts, along with a simple archive convert prompt, makes prompt engineering a process that you can measure and improve upon each time you use it. You'll be able to make decisions faster, cut out the ambiguity, and get consistent results. This book gives you everything you need to build a prompt-driven desk, from quick ideas to detailed trading triggers.
Key Features
Design workflows that convert raw feeds into repeatable signals with audit trails.
Use prompt templates as features that return numeric fields, timestamps, and outputs.
You can measure the impact by tracking follow-through rates per prompt version and signal type.
Take charge of compact UI outputs, ensuring they map directly to orders for lightning-fast execution.
Arrange outputs and run weekly scoring to ensure prompts evolve into durable trading edges.
Refuse any missing live data and reject fabricated claims.
Align product metrics to outcomes by tracking MAE, MFE, realized PnL, and adherence.
Handle prompt engineering like you would experiment management, using version labels and iterative tests.
