Your Bio Is Worth Gold!
Larry Connors' Trading Lesson of The Day | May 15, 2025
In my new AI for Professional Traders Field Manual, I show how simply by adding your bio to your first prompt, AI will substantially improve the outcome.
How much will it improve it…by a vast amount.
Here’s how…
I’m going to share with you a basic prompt traders will use in AI.
The prompt is “What is Mean Reversion”.
Ask a simple question to AI, and get a simple (and often poor and incomplete) answer.
Add your bio to this simple question, and your bio can often turn the response into gold.
Here’s The Prompting Rule
In your initial prompt, tell it everything pertinent about yourself.
The more you tell the AI about itself, the better it’s able to customize answers to your intelligence.
If you don’t, it has no idea who it’s talking to.
If you do, especially if you have strong knowledge in any part of trading, its answers will be significantly better.
Watch what happens when you prompt the AI incorrectly, vs prompting it professionally.
Here’s an example tied into mean reversion trading.
Here’s AI’s response to the question “What is Mean Reversion?”
This was done with no context.
The AI has no idea who the user is, and it automatically defaults to “Robinhood trader” mode in its answer.
In fact, it doesn’t even know the person who asked the question even trades!
This is an amateur mistake and the output from the AI is not only pedestrian – it’s cartoonish including emojis and comparing mean reversion to TikTok videos!
Prompt:
What is mean reversion?
ChatGPT:
Mean Reversion—Quick & Friendly Guide
What it means
• When a number (price, score, temperature—anything!) shoots way above its usual level…
• 📉 …or dives way below it…
• ⏳ …the odds increase that it will drift back toward its long-term average, just like a stretched rubber band snapping closer to center.
Why it happens
• 🤯 Big moves often come from short-lived forces—emotion, hype, panic, supply shocks.
• 🧠 As cooler heads prevail or new info arrives, those temporary pushes fade.
• 🎯 The “pull” toward the average shows up because the extreme levels simply aren’t sustainable forever.
Everyday examples
• 🪙 Coin flips: Ten heads in a row? Keep flipping and the overall heads ratio gravitates toward 50%.
• 🏀 Sports streaks: A player on a hot shooting run usually cools off toward season averages.
• 🍔 Restaurant wait times: After a viral TikTok, lines get crazy—then settle once the buzz dies down.
• 💰 Stock prices: A sudden spike or crash often retraces part of the move when the initial frenzy wears off.
Key takeaway
• Extremes are exciting, but they’re often temporary. Mean reversion reminds us that nature, markets, and even viral trends tend to balance out over time.
Is this response helpful to us as professionals?
No, of course not. It teaches by analogy (the lowest and simplest form of teaching) and it’s comparing mean reversion trading to things like viral TikTok videos.
These are the types of answers the overwhelming number of traders who use AI are receiving.
Professional Note – Once you see emojis in the answer, it’s basically over. The AI is now executing at a low level (IQ under 100) and you have virtually no chance of ever getting professional-level knowledge and information from it.
My suggestion when this happens – start the conversation over (and prompt it correctly).
Now Let’s Do This Professionally and Correctly
Here’s what happens when I tell the AI who is asking the question.
Its response to the question “What is mean reversion” is as different as night and day.
Based on the following answer, I can now build upon my decades of published knowledge in mean reversion trading – simply by telling it who I am (and you should do the same – let it know who you are).
Larry Connors:
I’m Larry Connors, 45-year trading veteran, author of over a dozen books, including those on quantitative trading strategies – especially with equities and ETFs.
I pioneered the use of mean reversion trading in equities beginning in the mid-1990s, along with taking the then-dormant 14-period RSI created by Welles Wilder in the 1970s and creating the 2-period RSI in 2003, which still maintains statistical proof more than two decades later – even after being in the public domain during that period of time.
I’ve also written some of the original mean reversion research on the VIX in 1995, a few years after Professor Whaley created the index.
I’m founder of Connors Research, a firm dedicated to bringing out cutting edge strategies, especially in the equity and ETF markets, along with founding TradingMarkets.com in 1999.
Along the way, my books have sold hundreds of thousands of copies, they’ve been translated in multiple languages including Japanese, Korean, German. Spanish, Italian, Russian and Portuguese.
I’m also the winner of the Charles H. Dow Award from the Market Technicians Association. I’ve spoken at dozens of industry conferences around the world, have appeared on financial markets shows many times, and have been quoted by many of the largest financial media outlets, including the Wall Street Journal, New York Times, Bloomberg, and many others.
Recently, I was the first person on Wall Street to teach professional traders how to professionally prompt AI in order to improve their trading skills and knowledge, and was also the first to teach professional traders how to build high-performing strategies with AI. This course ran over 4 weeks, for a total of 30 hours, with 1,100 pages of high-level course material presented.
I’ve now expanded into “Cognitive Analysis,” the fourth and likely most important form of analysis for professional traders.
First, there was Fundamental Analysis, then Technical Analysis, and then Quantitative Analysis.
The final frontier is “Cognitive Analysis,” empowering professionals to combine their own knowledge with AI’s knowledge in order to gain insights into the markets that none have ever had up until now.
My question for you is… What is Mean Reversion?
ChatGPT:
Mean reversion is the statistical tendency for a variable that has strayed materially from its long-term equilibrium level to migrate back toward that level over time.
In financial markets, the “variable” is usually price, spread, or volatility; the “equilibrium” is an empirically observed distributional center – often the arithmetic mean, sometimes a more sophisticated stationary value such as the mean of a residual after removing trend.
Whenever prices overshoot that center because of liquidity dislocations, sentiment cascades, or forced flows, the forward-looking expected return becomes skewed in the opposite direction: the further a security drifts from its statistical anchor, the higher the probability‐weighted pull back toward it.
In practice, mean reversion presumes three conditions.
First, the underlying series must be at least weakly stationary once extraneous regime effects are stripped out; otherwise the “mean” itself is a moving target and reversion has no definable magnet.
Second, there must be a persistent mechanism – behavioural reflexes, inventory constraints, risk limits—that supplies restoring order flow faster than new information can justify the deviation.
Third, the time horizon of that mechanism must be shorter than the horizon over which fresh valuation-relevant information accumulates.
When those conditions hold, you can model the path back to equilibrium with an Ornstein-Uhlenbeck (OU) framework, estimate its half-life, and translate that decay constant into trade entry, exit, and position-sizing rules.
Your own two-period RSI is a canonical operationalization: it turns the OU intuition into a bounded oscillator that flags statistically stretched closes and prescribes a fade whose expected value has remained positive across two decades of public scrutiny.
Mean reversion sits conceptually opposite trend following. Trend assumes serial correlation in returns; mean reversion assumes negative serial correlation once shocks are normalized by liquidity and volatility.
Traders harness AI to quantify the state-dependent pull of reversion more precisely—ingesting higher-order microstructure signals, real-time positioning data, and exogenous regime tags—to decide when the “rubber band” is taut enough to justify fixed-risk, asymmetrically skewed entries.
That fusion of domain insight and machine-amplified context is what elevates mean-reversion edges from incremental to exponential, especially in liquid equity and ETF universes where overreaction and forced rebalancing are endemic.
Same Question Asked; Which Answer Will Make You A Better Trader?
Based upon the two very different responses from the exact same question, who is more likely going to succeed, especially in building high-performing mean-reversion strategies?
The anonymous trader who got the TikTok answer?
Or the one who told the AI in his initial prompt his professional background?
The first answer is useless to serious traders.
Yet this is how most traders, and the masses prompt today.
Prompt with no bio, no objective, no role definition – the AI answers “What is mean reversion?” with cartoon metaphors and a rubber-band emoji.
Prompt with a professional bio – the same AI engine pivots all the way up to Ornstein-Uhlenbeck decay math, half-life estimation, and position-sizing logic used by institutional quants.
My Strong Suggestion – add your bio in your first prompt when discussing anything related to trading.
You’ll see a significant improvement in the AI’s output because your bio (especially if you have a trading background) really is worth gold.
This is excerpted from Chapter 4 of my AI for Professionals Traders Field Manual. To learn more on how AI can significantly improve your trading performance and knowledge, click here.
For Every Issue of Larry Connors’ Trading Lesson Of The Day, Click Here.
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