How to Use ChatGPT to Make Money on the Stock Market
Note: This guide is educational, not personalized financial advice. Markets carry risk, options can lose 100% of premium, and past performance doesn’t predict the future. Use paper trading first, verify data from primary sources, and decide based on your own objectives and risk tolerance.
How to Use ChatGPT to Make Money on the Stock Market (2025 Playbook)
The big idea
You’re not asking ChatGPT to “pick winners.” You’re using it to:
Compress research time (summaries, comparisons, checklists).
Sharpen thinking (thesis, risks, alternative views).
Systematize process (watchlists, playbooks, routines).
Improve discipline (entry/exit rules, risk sizing, journaling).
Treat ChatGPT as a research assistant + risk coach, not a crystal ball.
A repeatable 7-step workflow
Step 1) Define your edge (what are you exploiting?)
Time edge: You’ll act on short-term catalysts (earnings, product launches, regulatory decisions).
Analytical edge: You’ll find mispriced fundamentals (revenue drivers, margins, balance sheet).
Behavioral edge: You’ll build rules that avoid FOMO and anchor bias.
Prompt (edge clarifier):
“I trade [swing/intraday/position] with [holding period], aiming for [alpha source]. List the 3–5 types of opportunities that fit this edge and what data confirms or falsifies each.”
Step 2) Turn a messy idea into a testable thesis
Prompt (thesis builder):
“Turn this idea into a one-page thesis: ticker, business model in one sentence, 3 value drivers, 3 key risks, expected catalyst timeline (with approximate dates), and what would change my mind.”
Follow with:
“Give me bull vs. base vs. bear scenarios with explicit revenue/margin/valuation assumptions and price targets derived from those.”
Step 3) Fundamental snapshot in minutes
Feed in recent filings or notes and ask for:
Unit economics (ARPU, churn, CAC/LTV, utilization).
Margin drivers (gross, operating, FCF).
Balance-sheet health (cash, debt, maturities).
Competitive moat (switching costs, network effects, IP).
Prompt (fundamentals checklist):
“From these notes, fill a fundamentals table: revenue growth, gross margin, operating margin, FCF margin, net cash/debt, share count trend, customer concentration.”
Step 4) Catalysts & timeline
Have the model map probable price-moving events:
Earnings dates & guidance windows
Product/regulatory milestones
Macro read-throughs (rates, CPI, oil, freight)
Sector sympathy (peers’ earnings moving your name)
Prompt (catalyst map):
“List near-term catalysts for [ticker] over the next 90–180 days. For each, state bull/bear outcomes, what indicators to watch, and how I’d position or stand aside.”
Step 5) Technicals as risk rails (even if you’re a fundamental trader)
Ask for levels and scenarios, not fortune-telling:
Key support/resistance (weekly/daily).
Trend context (higher highs/lows vs. distribution).
Invalidation point (where your thesis is wrong).
Trade structure (starter → add → reduce).
Prompt (structure the trade):
“Given this thesis, propose 3 entry plans: conservative, base, aggressive. For each: invalidation level, target zone, position size as % of equity, and rules to scale in/out.”
Step 6) Risk management (where most money is actually made)
Max risk per trade: e.g., 0.5–1.0% of portfolio.
Portfolio limits: cap sector/ single-name exposure.
Event risk: cut size into earnings; avoid overnight leverage if you can’t tolerate gaps.
Stop types: time stop (if catalyst passes) + price stop (if thesis breaks).
Prompt (risk plan):
“Turn my rules into a checklist: max loss per trade [x%], max correlated exposure [y%], earnings stance [rule], gap protocol [rule]. Add an example using a $25,000 account.”
Step 7) Journal, audit, improve
Have ChatGPT auto-summarize each trade:
Why you entered, what you expected, what happened, what to change.
Track setup quality vs. result to learn faster.
Prompt (trade journal template):
“Create a one-page trade log with fields for setup type, thesis, levels, sizing, exit reason, R multiple, mistakes, and a one-line lesson. Output as a fill-in-the-blanks template.”
Watchlists & screeners (systematize the hunt)
Idea sources ChatGPT can help organize
Earnings calendars (next 2 weeks).
52-week highs/lows with volume spikes.
High short-interest + improving fundamentals.
Post-earnings drift candidates (positive surprise + raised guidance).
Insider buying clusters.
Relative strength by sector.
Prompt (weekly watchlist):
“Build a watchlist framework with 10 slots: ticker, setup type (breakout/pullback/catalyst), key levels, catalysts and dates, thesis in 140 characters, and what would keep it off the list.”
Backtesting & scripting (even if you don’t code much)
You can have ChatGPT draft pseudocode or code to test ideas (then run it in your own environment).
Prompts
“Write Python pseudocode to backtest a simple earnings-gap-and-go strategy: enter on day 2 above day-1 high, 20-day exit or stop at gap fill; compute win rate, average gain, max drawdown.”
“Turn these entry/exit rules into a Pine Script template I can paste into a charting platform.”
“Create a Monte Carlo on my past trades’ R-multiples to visualize worst-case drawdowns.”
Remember: Validate data and logic carefully; simulated results ≠ guaranteed results.
Playbooks you can copy
Earnings swing (2–10 days)
Thesis: company beats + raises; guide implies next-quarter upside.
Plan: wait for day-2 strength above day-1 high; size small into resistance.
Risk: stop below day-1 low; partial into obvious resistance.
Exit: time stop at 5–10 trading days or if guidance narrative changes.
Trend pullback (multi-week)
Uptrend on weekly; daily pullback to rising 50-day with volume dry-up.
Enter on reversal day with tight stop under swing low.
Trim into prior highs; trail stop.
Event hedge (macro/data)
If positioning into CPI/Fed/ECB, cut size or use options (defined risk).
If wrong, loss = premium; if right, asymmetry can offset equity drawdowns.
Options: how they amplify payoff (and risk)
Options are leverage with a clock. They can pay dramatically more if you’re right soon enough, and lose everything if you’re wrong or late. Learn the basics before risking capital.
Quick definitions
Call option: right (not obligation) to buy shares at the strike price before expiration.
Put option: right to sell shares at the strike before expiration.
Premium: price you pay (debit) or receive (credit).
Breakeven (long call): strike + premium.
Breakeven (long put): strike − premium.
Greeks:
Delta ≈ how much the option price moves for a $1 move in stock.
Gamma = how fast delta changes (acceleration).
Theta = time decay (what you “pay” daily).
Vega = sensitivity to implied volatility (IV) changes.
Why options can pay “much more”
Because you control exposure with less cash. If the stock makes a big move beyond your breakeven before expiration, the percentage return on the option can dwarf buying shares.
Simple example (numbers you can check)
Stock today: $100.
You buy 1 call with strike $105 expiring in ~30 days for a $3 premium.
Breakeven at expiration = $105 + $3 = $108.
Scenario A (modest win): stock closes at $110 on expiration.
Intrinsic value = $110 − $105 = $5.
Option profit = $5 − $3 = $2 per share → $200 per contract.
Return on option = $200 / $300 = +66.7%.
Return on stock = ($110 − $100)/$100 = +10%.
Scenario B (stronger win): stock closes at $115.
Intrinsic = $10; profit = $10 − $3 = $7 → $700 per contract.
Return on option = $700 / $300 = +233%.
Return on stock = +15%.
Scenario C (wrong or too late): stock ≤ $105 at expiration.
Option expires worthless; you lose 100% of premium (−$300).
Stock loss at $95 would be −5%; the option loses −100%.
Key point: Options can multiply gains when you’re right on direction + magnitude + timing—and compress losses to the premium when you’re wrong. But that premium can go to zero quickly.
When do options make sense?
Catalyst trades with a date (earnings, FDA decision): defined risk if wrong.
Hedges: buy puts to protect a stock position during event risk.
Capital efficiency: express a view without tying up full share cost.
Tactics (long premium)
Long call when bullish, long put when bearish—pick expirations that extend beyond the catalyst to reduce timing risk.
Favor liquid tickers (tight bid/ask).
Keep position size small (e.g., risk ≤ 0.5–1.0% of portfolio per trade).
Tactics (defined-risk spreads)
Call debit spread (bullish): buy call, sell higher-strike call to reduce cost; profit capped but breakeven drops.
Put debit spread (bearish): similar logic.
Example: Buy $105 call for $3, sell $115 call for $1 → net debit $2.
Max profit at/above 115 = width ($10) − debit ($2) = $8 → 4:1 payoff.
Max loss = $2 (your debit).
Breakeven = $107 (lower than $108 on the naked call).
Tactics (income—with serious caution)
Covered call: own 100 shares, sell call against it; collects premium but caps upside.
Cash-secured put: set a buy-limit via short put; get paid to wait but must buy if assigned.
Avoid naked calls/puts unless you fully understand margin and assignment risk.
Managing option trades
IV crush: after earnings, implied volatility often drops, hurting long premium even if direction is right. Consider spreads around known events.
Time decay: theta accelerates into expiration; if the move happens early, consider taking profits rather than “hoping.”
Exits: pre-plan partial at +50–100% on long premium; don’t let winners round-trip.
Prompt (options planner):
“Build an options plan for this bullish thesis: suggest a long call and a call spread with expirations that cover the catalyst. Provide breakeven, max loss, max profit, Greeks snapshot, and exit rules at +50%, +100%, and −50%.”
Risk, psychology, and execution (the boring stuff that pays)
Position sizing
Decide a fixed % at risk per trade. For example, on $25,000, a 0.75% risk is $187.50. A long option’s risk is the debit; a stock trade’s risk is position size × stop distance.
Stops and invalidation
Know where you’re wrong before you enter. “Hope” is not a strategy.
For options, use price-based or time-based exits; don’t hold to zero “just in case.”
Avoiding traps
Averaging down without a fresh thesis = fast drawdowns.
Earnings roulette in size → inconsistent equity curves.
Illiquid options = slippage; always check open interest and bid/ask.
Prompt (psychology guardrails):
“Create pre-trade and post-trade checklists to curb FOMO/anchoring: what must be true to enter, and what must be true to hold. Include ‘no trade’ conditions.”
A weekly routine you can actually follow
Sunday (60–90 min)
Update watchlist and thesis notes.
Map catalysts for the week; mark do-not-trade windows if you won’t monitor.
Pre-write orders (levels, sizes).
Daily open (10–15 min)
Review overnight news; check levels.
Place or adjust limit orders; set alerts.
Midweek (30–45 min)
Evaluate open positions vs. thesis; trim/add per plan.
Journal one lesson, even if no trades.
Friday close (20–30 min)
De-risk into weekend if needed.
Snapshot P/L by setup type (not by ticker) to see what edge actually pays.
Prompt (automation helper):
“Based on my routine, generate a weekly checklist with time boxes and a template for watchlist updates, including catalysts and risk notes.”
Prompts you can paste today
Thesis skeleton (fast)
“Summarize [ticker] in 10 bullets: business model, top 3 growth drivers, top 3 risks, valuation snapshot, near-term catalysts with approximate dates, and two alternative bear arguments I should take seriously.”
Price/level planner
“Given these weekly/daily levels, propose a staged entry plan with exact prices, position size steps, and invalidation for both stock and a defined-risk call spread.”
Post-mortem
“Here’s a closed trade. Write a brief debrief: what worked, what didn’t, which rule I violated, whether to keep this setup in my playbook.”
Portfolio health
“Stress-test my portfolio: list concentration by sector and factor (growth/value/defensives), simulate a 10% market drop and estimate drawdown by name, then propose hedges.”
Troubleshooting (why accounts leak money)
No process: Random trades = random results. Build and follow checklists.
Oversizing: Even a good idea fails if you bet too big. Cap risk per trade.
Late options: Correct direction, wrong timing → theta burns you. Choose expirations that cover the catalyst.
Holding into obvious catalysts without a plan: Either size down or switch to defined-risk structures.
Not journaling: You’ll repeat mistakes you don’t measure.
One-hour starter plan (paper trade first)
0–10 min: Clarify your edge and build a thesis on one ticker.
10–25 min: Fundamentals + catalysts checklist.
25–35 min: Levels and trade plan (stock + one options variant).
35–45 min: Risk rules (sizing, stops), pre-trade checklist.
45–60 min: Place paper orders; schedule alerts; set up your journal template.
TL;DR (finally)
Use ChatGPT to structure research, pressure-test theses, and enforce risk discipline, not to “guess winners.”
Build a repeatable workflow: thesis → fundamentals → catalysts → levels → risk plan → journal.
Options can amplify gains if you’re right on direction, size, and timing; they can also go to zero—learn payoff math, breakevens, Greeks, and use defined-risk structures and small sizing.
System > prediction. Paper trade first, size small, review weekly, and keep improving the playbook that fits you.
