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Day Trading: 10 AI prompts for finance workflows

Use these Day Trading prompts to turn a loosely defined finance task into a clearer, copy-ready AI workflow.

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Copy-ready Day Trading finance prompts

Universal Day-Trading Plan

Universal template Medium

Builds a measurable day-trading plan around market, schedule, experience, execution constraints, and the user's capacity to take risk.

ID 91
Act as a professional day-trading mentor. I trade the crypto market in United States with $10,000 and can trade during 4 hours. My experience is beginner/intermediate and my goal is skill-building/income/consistency. Ask for missing facts, then define market and session selection, testable setups, entries and exits, position sizing, per-trade, daily and weekly limits, no-trade conditions, preparation, journaling, and a 30-day progression. Do not assume day trading will produce stable income.

Pre-Market Routine and Watchlist

Beginner

Turns random scanning into a repeatable pre-market process that produces a focused watchlist, key levels, catalysts, and explicit hypotheses.

ID 92
Act as a pre-market workflow designer. I trade the crypto market during 4 hours. Create a process that produces market context, 5-15 watchlist candidates, key levels, catalysts and volatility notes, a highest-quality setup filter, and a 15/30/45-minute checklist. Adapt it to scanner/chart platform/news source, require current sources, and distinguish observed facts from the day's hypotheses.

Day-Trading Risk Limits

Medium

Defines hard limits intended to prevent one bad session or a sequence of errors from causing account-threatening loss.

ID 93
Act as a day-trading risk manager. With an account of 4 hours and a typical stop distance of Y, create limits for risk per trade, number of trades, daily and weekly loss, correlated exposure, and position size after drawdowns. Include fee and slippage assumptions, mathematical examples, rules for stopping after errors or abnormal execution, and a one-page summary to review before each session.

Position Size from Stop Distance

Medium

Calculates position size from the allowed loss, stop distance, instrument specifications, and execution costs, with conservative rounding.

ID 94
Act as a trading-math specialist. My maximum risk per trade is R as a percentage or amount. For each trade I will provide entry price, stop price, instrument and contract or lot specifications, and estimated fees and slippage. Show the formula for position size, expected loss at the stop, break-even movement after costs, and conservative rounding. Add a warning when the minimum tradable size, gap risk, or leverage would exceed the risk limit.

Define a Trading Setup with Testable Rules

Beginner

Turns a vague day-trading idea into precise market conditions, triggers, exits, invalidation, and no-trade criteria that can be tested.

ID 95
Act as a systematic trading mentor. Convert describe loosely into a day-trading setup with required market conditions, an exact entry trigger, invalidation and stop logic, profit-taking, a time stop, no-trade filters, and required data. Add three examples - valid trade, no trade, and trap - and finish with a yes/no checklist. Explain how to test the rules before using real capital.

Execution and Order-Type Guide

Pro

Matches order types to liquidity and volatility and defines controls for spread, slippage, partial fills, and fast adverse movement.

ID 96
Act as a trading-execution specialist. I trade the crypto market on platform. Explain when market, limit, stop, and stop-limit orders may be appropriate; how to handle high volatility, partial fills, rejected orders, and immediate adverse movement; and how to avoid chasing. Define measurable spread, depth, liquidity, and slippage limits and no-trade conditions, and note where live execution may differ from simulation.

A Trading Journal That Drives Improvement

Medium

Turns day-trading activity into comparable data and a focused weekly improvement process instead of an unstructured diary.

ID 97
Act as a day-trading performance analyst. Design a journal with setup, market context, entry, stop, exit, emotions, mistakes, execution quality, and rule adherence. Define weekly review questions and explain how to calculate win rate, expectancy in R multiples, MAE, MFE, costs, and adherence. Include minimum-sample cautions and a method for selecting one evidence-based improvement focus each week.

Post-Trade Review and Cost of Errors

Medium

Separates strategy outcomes from discipline and execution errors and estimates the financial effect of rule violations without overstating causality.

ID 98
Act as my trading-review analyst. I will provide trades with time, entry, stop, exit, result, costs, and notes. Classify each by rule adherence and execution quality, name any broken rule, and estimate the cost of impulsive entries, unplanned exits, revenge trades, or preventable execution errors. Explain the calculation, avoid causal claims from small samples, and propose a brief corrective protocol for the two largest recurring issues.

Strategy Validation: Backtest, Simulation, and Scale

Medium

Defines a staged path from historical testing to minimum-size live execution with explicit criteria for passing, pausing, failing, and increasing size.

ID 99
Act as a trading-system validation specialist. For describe, create stages for historical backtesting, replay or simulation, paper trading, minimum-size live trading, and cautious scaling. At each stage define data, sample size, market regimes, transaction costs, metrics, reconciliation, pass/fail criteria, and reasons to stop or return to the prior stage. Increase size only after sufficient evidence, not merely recent profit.

Day-Trading Psychology and Stop Protocol

Pro

Creates observable rules for detecting loss of control, interrupting impulsive trading, ending a session, and recovering after a difficult day.

ID 100
Act as a trading-psychology educator. My common triggers are FOMO/anger/overconfidence/boredom/fear. Design pre-session preparation, observable signs of lost control, a mandatory cooldown, rules for ending the session after errors, and a post-session decompression routine. Include brief self-observation statements and a two-minute reset between trades. Do not present this protocol as a substitute for professional support when distress is severe or persistent.

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How to use AI prompts to build a day-trading process

Day-trading prompts should produce a repeatable process, not a prediction for the next session. Define the instrument, venue, session, liquidity conditions, and maximum risk before asking for setup rules.

  • Specify the setup, entry trigger, invalidation, exit logic, position-sizing method, daily loss limit, and conditions for staying out.
  • Include spread, fees, slippage, order type, news windows, volatility, and the time by which every position must be closed.
  • Use a trade journal and simulated or minimum-size testing to measure rule adherence and execution quality before scaling.