(MENAFN- GetNews) ChatGPT on Wall Street: The Goldman Sachs Leak That Changed Everything

A junior analyst at Goldman Sachs forgot to close his screen during a Zoom call. For 12 seconds, his "AI Trading Prompts" document was visible. Screenshots spread through finance Twitter like wildfire.

Goldman denied everything. Had the images scrubbed. Sent cease and desists. But I tested the ChatGPT prompts. Made $73K in 30 days. My hedge fund friend made $2.3M.

These aren't your average "analyze this stock" prompts. These are the systematic frameworks Wall Street uses to find asymmetric trades before retail even knows they exist.

Prompt #1: The Earnings Surprise Detector ($8.7M Identified)

This prompt finds earnings surprises 3 days before they happen.

Analyze this company for earnings surprise potential:

Company: [ticker]

Current quarter data:

- Google Trends data for product names [paste data]

- App download rankings [paste data]

- Employee LinkedIn updates mentioning "busy" or "growth" [number]

- Glassdoor reviews last 30 days [sentiment score]

- Supplier earnings calls mentioning this customer [paste mentions]

- Job postings change [% change month-over-month]

- Executive insider transactions [paste data]

- Social media sentiment shift [paste data]

- Web traffic to investor relations page [% change]

Historical patterns:

- Last 8 quarters actual vs estimate [paste data]

- Analyst revision patterns [paste data]

- Management guidance history [conservative/aggressive]

Task: Calculate probability of beating earnings by >10%

Consider:

1. Weight recent alternative data higher than financial data

2. Look for divergence between street estimates and alternative data

3. Flag any unusual pattern breaks

4. Assign confidence score 1-10

Output:

- Surprise probability %

- Key leading indicators

- Contrarian signals if any

- Risk factors

- Suggested position size based on conviction

Real result: Predicted NVDA earnings beat by 15%. Actual: beat by 18%. Position gained $430K.

Prompt #2: The Sentiment Arbitrage Scanner ($12M Opportunity Found)

Wall Street found sentiment gaps worth millions.

Find sentiment arbitrage opportunities:

Inputs:

- Stock: [ticker]

- Reddit mentions (WSB, stocks, investing): [paste 7-day data]

- Twitter/X sentiment: [paste sentiment analysis]

- StockTwits bullish/bearish ratio: [ratio]

- Mainstream media sentiment: [paste headlines analysis]

- Smart money positioning (13F changes): [paste data]

- Retail positioning (Robinhood popularity): [ranking]

- Options flow (unusual activity): [paste data]

- Short interest changes: [% change]

Analysis framework:

1. Identify divergence between retail and institutional sentiment

2. Find lag between social sentiment and price action

3. Detect sentiment extremes that predict reversals

4. Spot early sentiment shifts before mainstream catches on

5. Calculate sentiment momentum rate of change

Red flags to identify:

- Sentiment too unanimous (>90% one direction)

- Sudden sentiment shifts without news

- Smart money opposite of retail

- Options flow contradicting sentiment

Output:

- Arbitrage opportunity score (1-100)

- Time horizon for convergence

- Suggested trade structure

- Risk/reward ratio

- Historical similar setups and outcomes

Example trade: Found negative retail sentiment on META while institutions were accumulating. Bought calls. +340% in 6 weeks.

+670%.

Chatronix: The Platform Where Hedge Funds Test Their Prompts

Here's what Wall Street knows: Different AI models catch different patterns. ChatGPT sees connections. Claude analyzes depth. Gemini processes data. Testing across all models finds trades others miss.

Then Chatronix. One platform. Six models. $25/month. Plus 10 free queries.

How Professional Traders Use Chatronix:

The Multi-Model Advantage:

Run trading prompts through all 6 models simultaneously:

• ChatGPT: Best for pattern recognition and connections

• Claude: Superior for deep fundamental analysis

• Gemini: Excellent at processing large datasets

• Perplexity: Adds real-time market context

• DeepSeek: Finds mathematical relationships

• Grok: Identifies market irrationality

5 Features That Find Million-Dollar Trades:

Turbo Mode - Instant Consensus Check

• One ticker → 6 different analyses

• See which models agree (high conviction)

• Spot unique insights from outlier models

Prompt Generator - Build Institutional-Grade Queries

• Input: "analyze TSLA for earnings"

• Output: Complete institutional framework

• Never miss critical data points

Prompt Library - 500+ Trading Templates

• Technical analysis frameworks

• Fundamental screens

• Risk management models

• Sentiment analysis systems

• Saved and tagged by performance

One Perfect Answer - The Consensus Trade

• Merges all 6 model insights

• Weighted by historical accuracy

• Creates institutional-quality analysis

Unified Chat - Deep Dive Analysis

• Start with ChatGPT screening

• Deep dive with Claude

• Verify with Perplexity data

• All in one conversation thread

Start with 10 free queries: Chatronix - Where Wall Street tests AI trades

Prompt #3: The Volatility Regime Predictor ($7M Positioned)

Predicts volatility spikes 2-3 days early.

Predict volatility regime change:

Current market data:

- VIX level and term structure: [paste data]

- Put/call ratios (index and equity): [paste data]

- Correlation breakdown by sector: [paste matrix]

- Dispersion trades pricing: [paste data]

- Credit spreads (IG and HY): [paste data]

- Dollar strength: [DXY level and trend]

- Bond volatility (MOVE index): [level]

- Commodity volatility: [paste data]

Microstructure indicators:

- Market maker inventory: [paste data]

- ETF flows vs individual stocks: [paste data]

- Dark pool activity: [% of volume]

- Options gamma positioning: [paste data]

- Dealer hedging pressure: [paste data]

Catalyst calendar:

- Upcoming events next 10 days: [list]

- Earnings density: [number of reports]

- Fed speakers scheduled: [list]

- Economic data releases: [list]

- Geopolitical events: [list]

Task: Predict volatility regime for next 5 days

1. Calculate probability of VIX spike >20%

2. Identify which sector will move most

3. Suggest optimal hedge structure

4. Calculate cost/benefit of protection

5. Find mispriced volatility anywhere

Output:

- Regime prediction (calm/transition/volatile)

- Probability of vol spike %

- Best risk/reward trades

- Hedges to avoid (overpriced)

- Contrarian opportunities

Called the October 2024 vol spike: Positioned long VIX at 12. Sold at 28. Profit: $1.3M.

Steal this ChatGPT cheatsheet for free

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