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This is a follow up post to the post I made last week. I made some **MAJOR** edits, and this is the final post regarding this project.
Eight months ago I gave ChatGPT $400 and told it to trade for me.
It doubled my money on the first trade. Then it told me it can't see live stock prices.
Classic!
So I did what any rational person would do. I spent eight months building an entire trading platform from scratch, mass-texting Claude in a chat of insanity while slowly losing my mind in the process.
**My first post about this project showed a huge prompt, version 1 —**
CORE STRATEGY BLUEPRINT: QUANT BOT FOR OPTIONS TRADING
Somehow I doubled my money on the first trade, got excited and, so I tore the whole thing down, and tried to make an even better prompt.
**My second post was about the second prompt I made, version 2—**
For this prompt, I was taking screen grabs of live options chains, and feeding them to the prompt, thinking this was the holy grail.
"System Instructions: 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 Opn 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 Opn, 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 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 5 trades satisfy all criteria, clearly indicate: "Fewer than 5 trades meet criteria, do not execute."
I made it in about 18+ trades with the prompt until I realized, taking screen grabs of live options chains, and feeding them to GPT was going to inevitably be a recipe for disaster, and I was likely just getting lucky because the market was on a bull run.
**So, for my third post, I Rebuilt it as a python script, which I built by asking Claude how to build an automated workflow that pulled data and filtered it to pick trades. Version 3 —**
How it works (daily, automated):
Step 0 – Build a Portfolio: Pull S&P 500 → keep $30–$400 stocks with <2% bid/ask. Fetch options (15–45 DTE, 20+ strikes). Keep IV 15–80%. Score liquidity + IV + strikes → top 22. Pull 3 days of Finnhub headlines and summaries
Step 1–7 – Build Credit Spreads: Stream live quotes + options. Drop illiquid strikes (<$0.30 mid or >10% spread). Attach full Greeks. Build bull put / bear call (Δ 15–35%). Use Black-Scholes with IV per strike for PoP. Keep ROI 5–50% and PoP ≥ 60%. Score (ROI×PoP)/100 → pick best 22 → top 9 with sector tags.
Step 8–9 – GPT news filter: 8. For each top trade, GPT reads 3 headlines, flags earnings/FDA/M&A landmines, gives heat 1-10 and Trade/Wait/Skip. 9. Output = clean table + CSV.
Step 10 – AUTOMATE!: 10\_run\_pipeline.py runs everything end-to-end each morning. (\~1000 seconds)
Receipts (quick snapshot) Start: $400 deposited (June 20) Today: \~300% total return Win rate: \~70–80% (varies by week) Style: put-credit / call-credit, 0–33 DTE, avoid earnings & binary events, tight spreads only (I post P&L and trade cards on IG temple\_stuart\_accounting when I remembered.)
The whole pipeline—50 files, soup to nuts—is still here, in its original form: [github.com/stonkyoloer/News\_Spread\_Engine](http://github.com/stonkyoloer/News_Spread_Engine)
**Then I decided, it's time to make a real web app. And now it does something I haven't seen any retail tool do! Version 4 (CURRENT) —**
It scans 500 stocks, runs every single one through a scoring engine, picks the best setups, and hands me a complete trade card with actual suggested positions to take — with a plain English explanation of WHY.
Let me walk you through exactly how it works.
The system pulls from three sources. All free. All real-time.
**(1) Tastytrade** (my brokerage account) gives me 41 data points per stock:
* How expensive options are right now (implied volatility)
* How much the stock actually moves (historical volatility)
* Whether options are cheap or expensive compared to the past year (IV rank)
* The full options chain — every strike, every expiration, live bid/ask prices
* Live Greeks (delta, theta, vega — the math behind options pricing)
**(2) Finnhub** gives me the fundamentals + intelligence:
* financial metrics per stock (revenue, margins, cash flow, debt, everything)
* Analyst ratings (how many say Buy vs Hold vs Sell)
* Insider transactions (are executives buying or selling their own stock?)
* Earnings history (did the company beat or miss expectations?)
* News headlines with dates
**(3) FRED** (the Federal Reserve's database) gives me the big picture:
* VIX (market fear gauge)
* Interest rates
* Unemployment
* Inflation
* GDP
* Consumer confidence
That's the raw material. Now here's what happens to them!
**The scoring engine — how 500 stocks become 8**
Every stock gets scored from 0 to 100 across four categories. Think of it like a report card.
**Vol-Edge (is there a pricing mistake?)**
This answers one question: are options priced higher than they should be?
If a stock moves 11% per year but options are priced like it moves 27%, someone's wrong. That gap is where the edge lives.
The system measures implied vs historical volatility, looks at term structure (are short-term options more expensive than long-term?), and checks the technicals. If options are overpriced, sellers have an edge. If they're underpriced, buyers do.
**Quality (is the company solid?)**
I'm not selling options on a company that might go bankrupt.
This runs a Piotroski F-Score (a 9-point checklist that professors use to spot strong companies), an Altman Z-Score (predicts bankruptcy risk), plus checks on profitability, growth, and efficiency.
A company that's profitable, growing, paying down debt, and generating cash scores high. A company burning cash with declining margins scores low. Simple.
**Regime (what's the economy doing?)**
The market has moods. Sometimes the economy is growing but not too hot (Goldilocks). Sometimes inflation is running wild (Overheating). Sometimes everything's falling apart (Contraction).
The system reads 9 macro indicators from the Fed and classifies the current regime. Then it scores each stock based on how well it fits.
Here's the smart part: if a stock barely moves with the S&P 500 (low correlation), the system dials DOWN the regime score. Because macro doesn't matter much for that stock. A stock with 0.27 S&P correlation gets its regime score cut by 36%. A stock that moves lockstep with the market gets the full score.
**Info-Edge (what's the buzz?)**
This combines five signals:
* Analyst consensus (are the pros bullish?)
* Insider activity (are execs buying their own stock? That's usually a good sign. Selling? Warning sign.)
* Earnings momentum (beating estimates consistently?)
* Options flow (unusual volume in calls vs puts?)
* News sentiment (are headlines getting more positive or negative?)
**The convergence gate — why it's called "convergence"**
Here's the key idea. Any ONE signal can be wrong. Insider buying alone doesn't mean much. High IV rank alone doesn't mean much.
But when multiple independent signals all point the same direction? That's convergence. That's when the probability actually tilts in your favor.
The system requires at least 3 out of 4 categories to score above 50 before it even considers a stock. All 4 above 50 = full position size. 3 of 4 = half size. Less than 3 = no trade, doesn't matter how good one score looks.
**The trade cards — this is the bread and butter!**
For every stock that survives, the system builds an actual trade card.
Not "maybe consider an iron condor." An actual position with real strikes, real prices, real risk.
**Why this trade** (in plain, easy to understand English, not confusing finance-bro jargon):
**Risk warnings:**
**Key stats:**
Everything. One card. No clicking. No digging. Screenshot it and you have the full picture.
All of this information is coming from REAL DATA!
What Claude actually does (and doesn't do)
This is the part people get wrong.
**Claude does NOT:**
* Pick stocks
* Decide what to trade
* Predict the future
* Make any decisions at all
**Claude DOES:**
* Read the plain English signals section of each trade card
* Translate dense numbers into sentences a normal person can understand
The scoring engine is 100% deterministic math. No AI involved. Same inputs = same outputs every time. A CPA could audit every number back to its source.
(I spent a ton of time auditing to make sure the data was complete, and cleaned, and it was not fun!)
Claude's only job is the translation layer. It turns "IV 27.2%, HV 11.2%, IV/HV ratio 2.42" into "Options are priced 2.4x higher than the stock actually moves."
That's it. The robot reads math and explains it in English. I make the decisions.
**The tech stack I used to build this is:**
Next.js + TypeScript — the web app
Tastytrade API — live options data, chains, Greeks
Finnhub API — fundamentals, news, insider data, analyst ratings
FRED API — macro indicators
Claude API — translates scores into plain English (that's ALL it does)
PostgreSQL — stores everything
Vercel — hosting
And by the way it is Opn source — [github.com/Temple-Stuart/temple-stuart-accounting](http://github.com/Temple-Stuart/temple-stuart-accounting) \-- for private use!
**What's next**
Starting tomorrow (Feb 18), I'm running this live. I'm going to fund another account and test it with some real money!
Every week I'll update with:
* What the scanner picked
* What trades I took
* What hit, what didn't
* Running P&L
Every trade documented.
I also have a trade tracker tab built into this repo that uses Plaid to pull the transaction data, and where I map the opening legs to closing legs, and can keep track of every position I take!
In the near future my vision is to build this out in a way where I am able to link the actual position I take to the trade cards the algorithm produces. So I can see the data the algo produced, the position I took, and then my trade log data as well!
For now, the trades get logged in the trade log tab, and the trade suggestions appear in the market intelligence, but I don't think it will be hard to link them up. But that is for another day and another post later down the road.
The whole point of this project is to seek truth. The system either works or it doesn't. The numbers don't lie and they don't care about my feelings.
**This is NOT financial advice.**
I am just a crazy guy who couldn't stop asking AI dumb questions until I accidentally built something that might be useful.
The code is opn source. If something looks broken, tell me!
That's literally how every version of this project got built.
If you made it this far; what would you want to see in the weekly updates? Thinking screenshots of the trade cards, P&L tracking, and maybe a breakdown of the best and worst trades each week.