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 AnalysisFundamental Data Points:Earnings Per Share (EPS)RevenueNet IncomeEBITDAPrice-to-Earnings (P/E) RatioPrice/Sales RatioGross & Operating MarginsFree Cash Flow YieldInsider TransactionsForward GuidancePEG Ratio (forward estimates)Sell-side blended multiplesInsider-sentiment analytics (in-depth)Options Chain Data Points:Implied Volatility (IV)Delta, Gamma, Theta, Vega, RhoOpen Interest (by strike/expiration)Volume (by strike/expiration)Skew / Term StructureIV Rank/Percentile (after 52-week IV history)Real-time (< 1 min) full chainsWeekly/deep Out-of-the-Money (OTM) strikesDealer gamma/charm exposure mapsProfessional IV surface & minute-level IV PercentilePrice & Volume Historical Data Points:Daily Open, High, Low, Close, Volume (OHLCV)Historical VolatilityMoving Averages (50/100/200-day)Average True Range (ATR)Relative Strength Index (RSI)Moving Average Convergence Divergence (MACD)Bollinger BandsVolume-Weighted Average Price (VWAP)Pivot PointsPrice-momentum metricsIntraday OHLCV (1-minute/5-minute intervals)Tick-level printsReal-time consolidated tapeAlternative Data Points:Social Sentiment (Twitter/X, Reddit)News event detection (headlines)Google Trends search interestCredit-card spending trendsGeolocation foot traffic (Placer.ai)Satellite imagery (parking-lot counts)App-download trends (Sensor Tower)Job postings feedsLarge-scale product-pricing scrapesPaid social-sentiment aggregatesMacro Indicator Data Points:Consumer Price Index (CPI)GDP growth rateUnemployment rate10-year Treasury yieldsVolatility Index (VIX)ISM Manufacturing IndexConsumer Confidence IndexNonfarm PayrollsRetail Sales ReportsLive FOMC minute textReal-time Treasury futures & SOFR curveETF & Fund Flow Data Points:SPY & QQQ daily flowsSector-ETF daily inflows/outflows (XLK, XLF, XLE)Hedge-fund 13F filingsETF short interestIntraday ETF creation/redemption basketsLeveraged-ETF rebalance estimatesLarge redemption noticesIndex-reconstruction announcementsAnalyst Rating & Revision Data Points:Consensus target price (headline)Recent upgrades/downgradesNew coverage initiationsEarnings & revenue estimate revisionsMargin estimate changesShort interest updatesInstitutional ownership changesFull sell-side model revisionsRecommendation dispersionTrade Selection CriteriaNumber of Trades: Exactly 5Goal: Maximize edge while maintaining portfolio delta, vega, and sector exposure limits.Hard Filters (discard trades not meeting these):Quote age ≤ 10 minutesTop option Probability of Profit (POP) ≥ 0.65Top option credit / max loss ratio ≥ 0.33Top option max loss ≤ 0.5% of $100,000 NAV (≤ $500)Selection RulesRank 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 FormatProvide output strictly as a clean, text-wrapped table including only the following columns:TickerStrategyLegsThesis (≤ 30 words, plain language)POPThe 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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