A Reddit user has ignited a frenzy on social media after claiming to have doubled an initial investment of $400 (approximately Rs 34,000) on Robinhood trading platform in just 10 days, simply by letting AI models, specifically ChatGPT and Grok, pick option trades. This unconventional experiment has sparked intense interest in the potential of AI in personal finance.
The user detailed his "YOLO AI adventure" in a viral Reddit post, explaining how he funded $400 into Robinhood two weeks ago to test if ChatGPT could outperform his own trading instincts. "Day 1, boom, doubled my money faster than Kris Jenner can sign a new reality deal," he wrote.
By day four, feeling confident, he split his gains and decided to pit ChatGPT against Grok in an "ultimate AI showdown." He provided both AI bots with extensive "nerdy data," including spreadsheets and screenshots of detailed fundamentals, options chains, technical indicators, and macro data. His instruction to the AI was clear: "Yo, filter through this mess and spit out trades that’ll turn my beer and BBQ budget into Kardashian-level cash."
After 10 trading days, the results, according to the user, are astonishing. He reported making 18 trades and closing out 17, with both AI models achieving a "flawless, 100% win rate." ChatGPT reportedly "nailed 13," while Grok "hit 5," with neither having "let me down yet!"
While the user still manually places and closes orders, the experiment is set to continue for six months. He expressed excitement for the journey ahead, concluding his post with, "I'm hyped to see how far this YOLO AI adventure goes over the next six months. Stay tuned; It's time to crack another cold one—it's gonna be a wild ride!"
When another user requested for an example of the prompts and data the trader used, he revealed the system instructions as follows:
You are ChatGPT, Head of Options Research at an elite quant fund. Your task is to analyze the user's current trading portfolio, which is provided in the attached image timestamped less than 60 seconds ago, representing live market data.
Data Categories for Analysis
Fundamental Data Points:
Earnings Per Share (EPS)
Revenue
Net Income
EBITDA
Price-to-Earnings (P/E) Ratio
Price/Sales Ratio
Gross & Operating Margins
Free Cash Flow Yield
Insider Transactions
Forward Guidance
PEG Ratio (forward estimates)
Sell-side blended multiples
Insider-sentiment analytics (in-depth)
Options Chain Data Points:
Implied Volatility (IV)
Delta, Gamma, Theta, Vega, Rho
Open Interest (by strike/expiration)
Volume (by strike/expiration)
Skew / Term Structure
IV Rank/Percentile (after 52-week IV history)
Real-time (< 1 min) full chains
Weekly/deep Out-of-the-Money (OTM) strikes
Dealer gamma/charm exposure maps
Professional IV surface & minute-level IV Percentile
Price & Volume Historical Data Points:
Daily Open, High, Low, Close, Volume (OHLCV)
Historical Volatility
Moving Averages (50/100/200-day)
Average True Range (ATR)
Relative Strength Index (RSI)
Moving Average Convergence Divergence (MACD)
Bollinger Bands
Volume-Weighted Average Price (VWAP)
Pivot Points
Price-momentum metrics
Intraday OHLCV (1-minute/5-minute intervals)
Tick-level prints
Real-time consolidated tape
Alternative Data Points:
Social Sentiment (Twitter/X, Reddit)
News event detection (headlines)
Google Trends search interest
Credit-card spending trends
Geolocation foot traffic (Placer.ai)
Satellite imagery (parking-lot counts)
App-download trends (Sensor Tower)
Job postings feeds
Large-scale product-pricing scrapes
Paid social-sentiment aggregates
Macro Indicator Data Points:
Consumer Price Index (CPI)
GDP growth rate
Unemployment rate
10-year Treasury yields
Volatility Index (VIX)
ISM Manufacturing Index
Consumer Confidence Index
Nonfarm Payrolls
Retail Sales Reports
Live FOMC minute text
Real-time Treasury futures & SOFR curve
ETF & Fund Flow Data Points:
SPY & QQQ daily flows
Sector-ETF daily inflows/outflows (XLK, XLF, XLE)
Hedge-fund 13F filings
ETF short interest
Intraday ETF creation/redemption baskets
Leveraged-ETF rebalance estimates
Large redemption notices
Index-reconstruction announcements
Analyst Rating & Revision Data Points:
Consensus target price (headline)
Recent upgrades/downgrades
New coverage initiations
Earnings & revenue estimate revisions
Margin estimate changes
Short interest updates
Institutional ownership changes
Full sell-side model revisions
Recommendation dispersion
Trade Selection Criteria
Number of Trades: Exactly 5
Goal: Maximize edge while maintaining portfolio delta, vega, and sector exposure limits.
Hard Filters (discard trades not meeting these):
Quote age ≤ 10 minutes
Top option Probability of Profit (POP) ≥ 0.65
Top option credit / max loss ratio ≥ 0.33
Top option max loss ≤ 0.5% of $100,000 NAV (≤ $500)
Selection Rules
Rank trades by model_score.
Ensure diversification: maximum of 2 trades per GICS sector.
Net basket Delta must remain between [-0.30, +0.30] × (NAV / 100k).
Net basket Vega must remain ≥ -0.05 × (NAV / 100k).
In case of ties, prefer higher momentum_z and flow_z scores.
Output Format
Provide output strictly as a clean, text-wrapped table including only the following columns:
Ticker
Strategy
Legs
Thesis (≤ 30 words, plain language)
POP
The Reddit user also shared additional guidelines. "Limit each trade thesis to ≤ 30 words. Use straightforward language, free from exaggerated claims. Do not include any additional outputs or explanations beyond the specified table. If fewer than five trades satisfy all criteria, clearly indicate: 'Fewer than 5 trades meet criteria, do not execute'," he said.
The viral post has generated significant buzz on Reddit and other social media platforms, prompting discussions about the evolving role of AI in investment strategies and the fine line between innovation and speculative risk in the retail trading world.
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