This is one of those topics where fancy buzzwords fly around, but nobody tells you what’s actually happening under the hood. So — here’s the plain version. An AI prompt in finance is basically a pre-written question or instruction fed to an AI model like ChatGPT or Claude to get something specific back: a summary of a company’s earnings call, a trading signal idea, historical pattern detection, etc.
Thank you for reading this post, don't forget to subscribe!
For example, instead of asking “What’s going on with Nvidia’s stock?”, a structured prompt might say:
"Compare NVDA’s last 4 quarterly earnings against analyst expectations. Show beat/miss, revenue trends, and EPS anomalies. Return a table and write a 2-sentence summary."
That’s a prompt. The more structured and consistent your format, the more reliable your results. AI responds to clarity. If you ask in vague terms, you’ll probably get either fluff or hallucinated numbers (looking at you, GPT-4 with imaginary balance sheets 😑).
In my own testing, short prompts like “find undervalued stocks” produce almost worthless output — regurgitated metrics from Yahoo Finance. But when I stack requests like this:
"Scan S&P500 sectors. Surface tickers with P/E < 15, Free Cash Flow growth over last 3 years, but price down YTD. Highlight 3 unusual ones based on pre-pandemic median valuations."
— I get something closer to a real filter. You’ll still want to verify every number… but it gives you a creative lens you just don’t get scrolling through screener tools manually.
To sum up, financial prompts are like hotkeys to actionable insights — if you’re specific.
Structuring prompts for investment research
The way you write your prompt massively impacts what type of answer you get back. This matters a lot in finance — even small changes in wording can turn your prompt from useful to unusable.
I split most market-related prompts into 3 layers:
Dataset scope — what tickers, timeframe, industries are we talking about?
Screening logic — what should we filter for (technicals, fundamentals, sentiment)?
Format output — tables? short bullet takeaways? patterns highlighted?
Here’s a real prompt I used last Thursday before the opening bell (pre-jobs report volatility):
"Using SPY, scan past 12 months of pre-market movements vs 10am economic reports. Show me if there's a pattern of reversal after better-than-expected initial jobless claims. Return your result as a chart with time on X and percent intraday reversal on Y. If no pattern exists, just say so."
And yes — it returned a chart (proxy built from available S&P data), and a decent-sized reversal noted in 5 of the last 8 positive jobs surprises. Surprisingly consistent.
What most beginners miss is the failover instruction. Always add “if no pattern, write that” — otherwise the AI will invent data that looks smart but was never there 😬.
Another tip I picked up from testing with Perplexity: give the prompt an example inside it. Here’s a better format for filtering based on relative valuation:
"Find U.S. banks with Market Cap between $1B-$10B, ROE over 8%, and P/B under 1. Highlight any that look like FITB in March 2020: undervalued even with rising earnings. Return 5 matches."
The key here isn’t just the metrics — it’s the comparative context (“like FITB in March 2020”). It’s a pattern match, not just a screen.
Overall, the success of investment prompts comes down to how precisely you set the AI’s target, not how many words you stuff in.
Best-performing prompt styles for market summaries
Summarizing financial news with AI is a total hit-or-miss unless you structure your requests tightly. Most models will default to generic filler like “The stock moved higher on improved guidance…” unless you block that from happening.
Here are 3 styles I use depending on the situation:
How the news deviates from trend, potential ripple impact
A prompt for the delta summary might look like this:
"Summarize TSLA's Q1 vs Q4 last year. Focus only on margin change, delivery volume shocks, and energy business performance. Give percentage difference and draw 2 key risks moving forward."
The AI summary? Actually usable: it caught the 1% drop in automotive gross margin and the expansion in Megapack revenue line, which most headline writers missed.
Try avoiding long summaries and stick with deltas, comparisons, or deviations. AI is still bad at “what this means” speculation — prompt for facts, not guesses.
Ultimately, bullet-form outputs without speculative language outperform every time.
Using AI prompts for technical analysis signals
Technical analysis is where prompts start behaving weirdly. Why? Because most AI models don’t know your charting platform or which candles you’re staring at. They can’t “see” RSI on your TradingView chart — unless you describe it precisely.
So if you want useful output, you’ll need to tell the AI your conditions as if you’re filing a bug report. Here’s a format I tested successfully:
"I’m tracking $AMD with daily candles. RSI is now 72, price just broke above 50-day MA for the first time in 6 weeks. Volume spiked to 1.3x average. Based on these, what patterns or set-ups historically followed this combination? List event dates if possible."
That kind of prompt triggers actual pattern matching, not just random sentences like “This may be bullish.”
If you’re running multi-ticker setups, here’s one I ran on Tuesday:
"Check past relative strength of SMH vs QQQ when SMH breaks new all-time high and QQQ lags. List prior 3 instances and show what SMH did 5 days after. Ignore false signals where breakout failed within 2 days."
That one gave me actual output with dates and follow-through metrics — a really nice list that let me cross-check against my chart logs.
In the end, AI is not (yet) your chart reader. But it is surprisingly good at:
Backtesting simple setups from known conditions
Summarizing base case probabilities
Returning clean tables if you ask for them
As a final point, always clarify if you want median or average values — AI will make up either one if not told.
Prompt design for company financial reports
Company financials are where LLMs pretend confidence and often hallucinate data — so you need to script these prompts like a stress test. Here’s a setup that works better than most:
"Fetch MSFT recent 10-K. Extract trailing 3-year net income, net margin, and EPS. Show table. Then give 2 major cost drivers explaining any YoY margin drift. Keep it neutral — no speculative commentary."
What’s essential here:
You tell it what document (10-K), so it doesn’t guess from news.
3-year data cuts hallucinations — if it can’t find it, it fails instead of faking.
Neutral tone request removes the usual “analyst-style” redundant language.
Sometimes the AI will still fudge values. So I often end with:
"If any datapoint is uncertain or missing, label as 'N/A' and do not estimate."
Yes, that helps. By about half. It’s not foolproof.
Here’s what a returned table usually looks like (simplified):
Year
Net Income
EPS
Net Margin
2021
$61B
$8.10
36%
2022
$72B
$9.65
38%
2023
$68B
$9.20
34%
Looks good on paper, right? Always verify with SEC filings. I flag anything with sharp movements or matching the prompt too perfectly — that’s usually hallucinated.
Finally, this type of prompt helps you use AI more like a research intern, not a summary monkey.
Integrating AI prompts into trading setups
Using prompts alone is cool, but the real magic is integrating them into an actual system — so the AI becomes part of your prep or execution process, not just a curiosity tool.
Here’s how I wired mine:
Pre-market routine:
- Pull S&P/QQQ overnight news feed dump
- Use a GPT call with: "Summarize 5 macro drivers affecting open. Highlight if futures move is over 1 std dev."
- Output posts to Slack with table: Time / Event / Ticker / Potential Impact
Then during trading hours:
- If stock crosses VWAP+2%, auto-trigger this prompt:
"Track MS weekly option chain. Highlight OI clusters & any unusual volume spikes. Any repeat pattern from past week?"
Using tools like Make or Zapier, you can structure automations to trigger prompts based on price moves, volume thresholds, or schedule. I use browser automations to fire specific prompts in Claude when certain news types hit my RSS feed.
If you’re trading earnings:
- After ER drop:
"Summarize AMZN Q1 beats/misses. Was guidance raised or lowered? Show YoY comparisons only for AWS. Output in 5-word headline and bullet points."
In most cases, this automates the ten minutes you’d otherwise spend refreshing 4 financial sites. It’s not perfect — but it’s fast.
Ultimately, the more tightly your prompts follow your actual process, the more likely you’ll stop checking Bloomberg manually.
