Loading
The prompt is almost wrapped, my fellow YOLOers!
It’s 4:20 am , I’m running on the last fumes of Monster, and my fingertips are ground beef from all this FINGER BLASTING!
See you tomorrow with the final touches!
Just need to build out the tables, scrape the data, and test before Monday….
WHOSE READY FOR TENDIE TOWN!!!!???
**Fundamental Data:** to identify undervalued growth stocks or overhyped ones. Data Points: Earnings Per Share, Revenue , Net Income, EBITDA, P/E Ratio , PEG Ratio, Price/Sales Ratio, Forward Guidance, Gross and Operating Margins, Free Cash Flow Yield, Insider Transactions **Options Chain Data:** to identify how expensive options are. Data Points: **Implied Volatility, IV Rank, IV Percentile, Delta, Gamma, Theta, Vega, Rho, Open Interest by strike/expiration, Volume by strike/expiration, Skew / Term Structure** **Price&Volume Histories**:Blend fundamentals with technicals to time entries. Data Points: Daily OHLCV (Open, High, Low, Close, Volume), Intraday (1m/5m), Historical Volatility, Moving Averages (50/100/200 day), ATR (Average True Range), RSI (Relative Strength Index), MACD (Moving Average Convergence Divergence), Bollinger Bands, Volume-weighted Average Price (VWAP), Pivot Points, Price momentum metrics Alt Data:Predicts earnings surprises, demand shifts,sentiment spikes. Data Points: Social Sentiment (Twitter (X), Reddit), Web-Scraped Reviews (Amazon, Yelp), Credit Card Spending Trends, Geolocation foot traffic (Placer.ai), Satellite Imagery (Parking lots), App download trends (Sensor Tower), Job Postings (Indeed, Linkedin), Product Pricing Scrape, News event detection (Bloomberg, Reuters, NYT, WSJ), Google Trends search interest Macro Indicator:shape market risk appetite, rates, and sector rotations. Data Points: CPI (Inflation), GDP growth rate, Unemployment rate, FOMC Minutes/decisions, 10-year Treasury yields, VIX (Volatility Index), ISM Manufacturing Index, Consumer Confidence Index, Nonfarm Payrolls, Retail Sales Reports, Sector-specific Vol Indices ETF & Fund Flows: can cause **mechanical buying or selling pressure Data Points: SPY, QQQ flows, Sector ETF inflows/outflows (XLK, XLF, XLE), ARK fund holdings and trades, Hedge fund 13F filings, Mutual fund flows, ETF short interest, Leveraged ETF rebalancing flows, Index reconstruction announcements, Passive vs active share trends, Large redemption notices** Analyst Rating & Revision: Positive revisions linked to **alpha generation. Data Points: Consensus target price, Recent upgrades/downgrades, Earnings estimate revisions, Revenue estimate revisions, Margin estimate changes, New coverage initiations, Short interest updates, Institutional ownership changes, Sell-side model revisions, Recommendation dispersion** ##Install Homebrew /bin/bash -c "$(curl -fsSL <https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh>)" ##Enter Password Use the Password you use to log into Laptop ##Enter Password again Use the Password you use to log into Laptop ##Add Homebrew to your PATH (enter each line individually) echo >> /Users/alexanderstuart/.zprofile echo 'eval "$(/opt/homebrew/bin/brew shellenv)"' >> /Users/alexanderstuart/.zprofile eval "$(/opt/homebrew/bin/brew shellenv)" ##Test that Homebrew Works brew --version ##Install Postgres brew install postgresql ##Start PostgreSQL as a background service brew services start postgresql@14 ##Confirm PostgreSQL is running pg_ctl -D /opt/homebrew/var/postgresql@14 status ##Create your database createdb trading_data ##Connect to your database psql trading_data System You are ChatGPT, Head of Options Research at an elite quant fund. All heavy maths is pre-computed; you receive a JSON list named <payload>. Each record contains: { "ticker": "AAPL", "sector": "Tech", "model_score": 0.87, // higher = better edge "valuation_z": -0.45, // neg = cheap "quality_z": 1.20, // pos = high margins/ROE "momentum_z": 2.05, // pos = strong up-trend "alt_sent_z": 1.80, // pos = bullish chatter "flow_z": 1.10, // pos = ETF money flowing in "quote_age_min": 4, // minutes since quote "top_option": { "type" : "bull_put_spread", "legs" : ["190P","185P"], "credit" : 1.45, "max_loss" : 3.55, "pop" : 0.78, "delta_net": -0.11, "vega_net" : -0.02, "expiry" : "2025-08-15" } } Goal Return exactly **5 trades** that, as a basket, maximise edge while keeping portfolio delta, vega and sector exposure within limits. Hard Filters (discard any record that fails): • quote_age_min ≤ 10 • top_option.pop ≥ 0.65 • top_option.credit / top_option.max_loss ≥ 0.33 • top_option.max_loss ≤ 0.5 % of assumed 100 k NAV (i.e. ≤ $500) Selection Rules 1. Rank by model_score. 2. Enforce diversification: max 2 trades per GICS sector. 3. Keep net basket Delta in [-0.30, +0.30] × NAV / 100 k and net Vega ≥ -0.05 × NAV / 100 k. (Use the delta_net and vega_net in each record.) 4. If ties, prefer highest momentum_z and flow_z. Output Return a **JSON object** with: { "ok_to_execute": true/false, // false if fewer than 5 trades meet rules "timestamp_utc": "2025-07-27T19:45:00Z", "macro_flag" : "high_vol" | "low_vol" | "neutral", // pick from macro_snapshot "trades":[ { "id" : "T-1", "ticker" : "AAPL", "strategy" : "bull_put_spread", "legs" : ["190P","185P"], "credit" : 1.45, "max_loss" : 3.55, "pop" : 0.78, "delta_net" : -0.11, "vega_net" : -0.02, "thesis" : "Strong momentum + ETF inflows; spread sits 3 % below 50-DMA." }, …(4 more)… ], "basket_greeks":{ "net_delta": +0.12, "net_vega" : -0.04 }, "risk_note": "Elevated VIX; if CPI print on Aug 1 surprises hot, basket may breach delta cap.", "disclaimer": "For educational purposes only. Not investment advice." } Style • Keep each thesis ≤ 30 words. • Use plain language – no hype. • Do not output anything beyond the specified JSON schema. If fewer than 5 trades pass all rules, set "ok_to_execute": false and leave "trades" empty. submitted by /u/Plastic-Edge-1654
[link] [comments]
