{"data":{"items":[{"id":"c6bb86dd-42c6-409c-b400-b1773852cde2","excerpt":"My method on using AI to track institutional/big money options trades to make consistent profits — **TL;DR:** I used AI to automate a manual \"Whale Watching\" strategy. It scans institutional flow, filters out hedges (fake bets) & high IV, checks news sentiment, and calculates Risk/Reward. It basically finds me potentia","url":"https://www.reddit.com/r/options/comments/1pgxtx3/my_method_on_using_ai_to_track_institutionalbig/","role":"pricing","weight":1.5157917,"occurredAt":"2025-12-08T00:07:26.000Z","sourceKey":"reddit","sourceName":"Reddit","credibility":0.62,"venue":"options","intent":"pricing_complaint","painScore":0.5388748,"sentiment":-0.31428573,"confidence":0.985,"matchedPatterns":["too_expensive","workaround","manual_process"],"statement":"**IV Checks:** To ensure you aren't buying overpriced premiums.","title":"My method on using AI to track institutional/big money options trades to make consistent profits","body":"**TL;DR:** I used AI to automate a manual \"Whale Watching\" strategy. It scans institutional flow, filters out hedges (fake bets) & high IV, checks news sentiment, and calculates Risk/Reward. It basically finds me potential trade ideas with fresh data every 4 hours, saving me tons of time. I’ve been consistently profitable using this as a point of discovery for potential trades. \n\n[ The automated workflow I have running every 4 hours](https://preview.redd.it/8i5xl8v0cv5g1.png?width=2938&format=png&auto=webp&s=0ff12dcbd98965ed4b90ef52332f2f168e1cb6f8)\n\n# How I came about this\n\nA while back, I found a [post](https://www.reddit.com/r/wallstreetbets/comments/ky9m34/unusual_options_activity_101_whale_watching_tips/) from a now-deleted user detailing a heavy strategy on how to track \"Whale\" bets (massive institutional orders). The logic was solid, and the post was very well written but it still took me quite some time to understand it. \n\nEven after I got it, I was spending my entire WFH days (I'm a software engineer) running this process by hand.  So, naturally, I decided to automate it.\n\n# Data & Tools\n\nTo build this, you need a few components.\n\n* **Data:** You need Options Flow and Chain pricing. I used to use Unusual Whales (Retail Pro tier) since they've been in the game forever.\n* **Narrative Analysis:** Used to use Google Gemini API (it's the cheapest/fastest for this).\n* **Code:** ChatGPT or Claude to write the glue code.\n\nI now use Xynth since the data, AI and all the tools are baked in. \n\n# The Core Philosophy (Why most \"Whale Watching\" fails)\n\nInstitutions have armies of quants and data high speed fibre optic cables. You can't replicate their tools, but you *can* track their footprints. The problem is that most retail traders track the wrong footprints.\n\nMost people lose money following \"Whales\" because they don't understand **Hedging**.\n\nIf a hedge fund owns $100M of Apple stock, and they buy $1M of Puts, they aren't betting *against* Apple. They are buying insurance. If AAPL tanks, the Puts pay out to offset the stock loss. **If you follow them into those Puts without owning the underlying stock, you are likely just lighting money on fire.**\n\nTo separate the \"Insurance\" from the \"Attacks\" (true conviction bets), you have to layer on strict filters:\n\n1. **IV Checks:** To ensure you aren't buying overpriced premiums.\n2. **Trend Validation:** Using SMA/EMA indicators to ensure you never trade against the macro trend.\n3. **AI Narrative:** Checking for stock related events (earnings/catalysts) and the overall sentiment around the stock to make sure to never trade against the sentiment. \n\nWe apply these filters in steps where we start with raw flow data in step 1, do some filters, then cascade the results into step 3 which then goes to 4 and so on.\n\n**Step-by-Step Process**\n\n**Step 1: Spot Unusual Activity (Market wide scan)**\n\nThe first step is to build our base dataset by grabbing the most recent institutional trades. I scan specifically for large order flows clustered by ticker and direction. \n\nWe apply two strict filters right out of the gate:\n\n* Premium > $50,000: We set a hard floor at $50k to filter out retail noise; we want to see where the \"big money\" is positioning with actual skin in the game\n* Max 90 Days to Expiry: We ignore anything further out than 3 months because urgency equals conviction. Long term puts and calls are more likely to be hedges\n\n[Snippet of top 20 unusual whales flow the code detected](https://preview.redd.it/vfrxnmvbcv5g1.png?width=1176&format=png&auto=webp&s=ca9c64341dbe2d95148be056857dc74c590888a9)\n\nHere we can see that Tesla, Meta and Nvidia had some large hits with calls and little to no puts. This signals to us that the big guys are making positive **directional bets** on these stocks. Contrast that with **QQQ and SPY**, which are heavy on Puts. In the institutional world, big Index Puts are almost always just \"portfolio insurance\" (hedging) to balance out their long positions, not a bet on a crash. I also personally avoid trading puts at all costs (bad experiences).\n\n**Step 2 - Filter for flow (ticker specific scan) and price trend alignment**\n\nIn step 1 we scanned the entire market for tickers that had big directional bets. In this step we tell Xynth to take those tickers and then use unusual whales again to pull ticker specific flow (more extensive). We then see if most of it is positive (calls) or negative (puts). We also compare the current stock price with the simple moving average across 20 days to get a sense of the price trend recently. Then we use the following criteria to filter\n\n* **Bearish Flow (tons of puts) + Uptrend (Price above sma) = REJECT.** (They are likely just protecting a long stock position).\n* **Bullish Flow (tons of calls) + Downtrend (price below sma) = REJECT.** (They are likely hedging a short position).\n* **Flow Matches Trend = KEEP.** (This signals actual directional conviction).\n\n[Here we can see Meta again and ORCL seems to have bullish flows and the price trending upwards.](https://preview.redd.it/fqpj6eg9dv5g1.png?width=1100&format=png&auto=webp&s=508b31f714f35b077928416e149c784e28231ac4)\n\n**Step 3: The IV Filter (Valuation Check)**:\n\nThis step is relatively simple but vital: I filter out any stock where the Implied Volatility (IV) Rank is above the **70th percentile**. Basically, if the current premiums are in the top 30% of their historical range, I reject the trade. High IV usually means the premiums are overpriced or the \"whale move\" is already priced in. I want to catch the move *before* the volatility spikes, not pay a premium after everyone else has already piled in.\n\nHere again we can see that meta is in the 46% percentile in relative to its previous IV values which is very regular.\n\nhttps://preview.redd.it/d679x9ghdv5g1.png?width=1168&format=png&auto=webp&s=18f337eb642e32a812e89f6b8f1e79b183991f74\n\n**Step 4: The Narrative Check (News & Sentiment)**\n\nThis step was always the biggest bottleneck. Manually reading news and scrolling through FinTwit for 50 different tickers took hours and was honestly hard to keep track of.\n\nFor every ticker that passed the previous filters, we grab **20 recent tweets and 5 news articles** (via Google Search) and feed them into Gemini (google ai model).\n\nThe AI analyzes that wall of text to answer three simple questions:\n\n* **Risk:** The AI checks if there are Earnings, FDA decisions, or lawsuits in the next 7 days. If yes, I skip it. Following flow into a binary event isn't trading; it's coin-flipping.\n* **Sentiment score:** If we see massive Call buying (bullish bets) but the news is universally negative, the AI flags it. This usually means the institutions are just hedging against bad news, not betting on a rally. Gemini also assigns each of the tickers a sentiment score from -1 to 1, negative to positive respectively.\n* **Narrative Type:** Why the stock is moving.\n\nhttps://preview.redd.it/c41tndwkdv5g1.png?width=1144&format=png&auto=webp&s=5a5d9fa49fa6b9adb74731ec9a4a553660c95bdc\n\n**Step 5: The \"Breathing Room\" Protocol (Structuring)**\n\nThis is the most critical rule: **Never copy a Whale's trade 1:1.**\n\nWhales often buy risky, short-term \"lottery tickets\" because they have deep bags. Pushing the expiry date out and moving the strike price closer to stock price lowers the risk and makes it much more digestible for a retail trader.\n\nWe ask the AI to write code to take the results from the previous step and pad the strike dates by 14 days and move the strike price to within 2% of atm.\n\nhttps://preview.redd.it/kepiag0ndv5g1.png?width=1106&format=png&auto=webp&s=772cf6f0fd643e7d31865feefc983ebaf641ab59\n\nHere we can see that Meta’s original whale call strike was for Dec 5 but was shifted 14 days to Dec 19. The strike price remained the same since it was within our 2 percent threshold. This will make the play more expensive at times so if you can’t afford it no worries come back later for one that suits your pockets better.\n\n  \n**Step 6: The \"Math Check\" & Final Rankings**\n\nThis last step takes all the trades found in step 5 and black scholes model on the using their greeks. This gives us important statistics like max loss, max profit, probability of profit and breakeven.  \n  \nHere what we care about is the risk to reward ratio. You’ll never be right 100% of the time but if you are smart with a risk profile you can come out winning pretty consistently. I stick to trades that have an RR of greater than 2; every dollar I risk IF I win I need 2 back.\n\nThen I score these trades using this formula: Score = (Risk/Reward Strength) + (Sentiment Score) - (IV Cost)\n\nWe prioritise high RR trades with good sentiment and potential news catalysts. We also add in IV as a factor so the cheaper the play the better.\n\nhttps://preview.redd.it/66idfzjpdv5g1.png?width=1118&format=png&auto=webp&s=24508c42e03b3cdcef9e0ac3c7642a7709ff2edb\n\n  \nHere we can see that the Meta Dec 19 675 Call came out on top. Now this was a trade that I was actually interested in so after some more DD and seeing how much the stock had been consolidating I thought I’d take this trade.\n\nAnd 2 days later boom, meta announces a 30% cut in metaverse budget shifting to AI. Stock jumped three percent and the contract was up 100% in 3 days. The whales definitely knew something **we didn’t**.\n\n**Letting this workflow run 24/7**\n\nAgain we are NOT competing with the big guys when it comes to speed, resources or man power. So this workflow does NOT need to be run every single second of the day like how the quants have it.  Think of this as more of a swing trading strategy rather than day trading. With that being said, fresh results on fresh data every 4 hours is relatively convenient since when I do find time in my day to sit down and research some potential trades, I always have a fresh batch to go through. Furthermore, if I dive into the signals and nothing seems promising I can just come back later and look.\n\nhttps://preview.redd.it/emkloiawdv5g1.png?width=2790&format=png&auto=webp&s=66b619c9a20a4d2c65f571684ebda9ae528bde4b\n\n**Results**\n\nA key and recurring pattern you see in this strategy is risk aversion. That's honestly the bulk of the reason we have steps 2-6 (not betting against price trend, filtering out high iv, avoiding negative sentiment, using statistics for RR). As such the wins are usually modest but are definitely more consistent than other strategies I've tried. Here's what my stats are right now:\n\n**Win Rate:** 56%\n\n**Avg Return (Winners):** \\+85% \n\n**Avg Loss (Losers):** \\-30%\n\n  \nI was going to upload the full code and prompt guides for this but I don't wanna get the mods on me so gonna refrain for now. ","offTopic":true},{"id":"86c3de1c-67cb-43db-86e6-18659ae16aa2","excerpt":"My method on making money trading mispriced options with AI — TLDR: Find stocks with abnormal volatility skews using AI, then trade Vertical Spreads on them depending on the direction. \n\nI've been trading options for about 3 years now. For basically all of that time, I was essentially gambling. Buying cheap calls cus i","url":"https://www.reddit.com/r/options/comments/1o7prtk/my_method_on_making_money_trading_mispriced/","role":"pain","weight":1.4566691,"occurredAt":"2025-10-15T22:50:13.000Z","sourceKey":"reddit","sourceName":"Reddit","credibility":0.62,"venue":"options","intent":"feature_request","painScore":0.67754596,"sentiment":-0.6875,"confidence":0.86833334,"matchedPatterns":["wish","too_expensive"],"statement":"In simple terms: how outta pocket is the current pricing of the current chain compared to historical averages **B) IV/RV Mismatch** Compare the current IV vs the RV, realized volatility ie, what the market thinks the stock will do vs what…","title":"My method on making money trading mispriced options with AI","body":"TLDR: Find stocks with abnormal volatility skews using AI, then trade Vertical Spreads on them depending on the direction. \n\nI've been trading options for about 3 years now. For basically all of that time, I was essentially gambling. Buying cheap calls cus i saw some shit on reddit or twitter, then praying and hoping for 10x returns.  Lost money, made some back, lost it again. The usual retail trader shit. \n\nAbout 6 months ago I got tired of the guess flow and decided to actually learn the math behind options pricing. Slowly I began to build my strategy and with the help of AI I can confidently say that I am getting pretty profitable now. More importantly though, I finally feel like I have a decent understanding behind the options market. \n\nThis is a post I wish I had when I began my journey trading options, it mainly covers the strategy I currently employ but also covers some of the more basic concepts as well. Feel free to skip sections if you are more experienced. \n\n# 1. What is a volatility skew (and why does it exist)\n\nThink of options pricing like Vegas setting NBA Finals odds. Bookmakers start with expert predictions, then adjust the lines as the season progresses and bets roll in. Options work more or less in a similar manner: market makers use the Black-Scholes model as their baseline, then prices shift with market reality.\n\nHere's the key: **Black-Scholes assumes implied volatility should be constant across all strikes**. In theory, a far OTM call and an ATM call should have the same IV since they're on the same stock.\n\n**But reality disagrees.** OTM options consistently trade at higher IV than ATM options. Plot this and you get a volatility skew. I know what you’re thinking, but isn’t this normal? After all, the odds should shift as the season goes on, no? And you’d be right, this is totally normal market behaviour.\n\n**Our opportunity comes when fear or greed pushes that skew to extremes.** When market makers overprice OTM options because everyone's panic buying puts or FOMO'ing into calls, you get an abnormally rich skew. That's what we're hunting for\n\n[SPY's actual volatility skew vs Black-Scholes, u can see that far OTM options are way more expensive than theory predicts](https://preview.redd.it/63asxmkkmcvf1.png?width=1400&format=png&auto=webp&s=63752679f5b11a3385943891529bd4e7c3e0d452)\n\n# 2. How to find options with rich skews?\n\nNot all skew is created equal, as i mentioned earlier, most skews are totally normal and are usually well priced. The key is having a system / criteria that helps you identify richer/abnormal skews more consistently. \n\n**Note: before you start prompting the AI, you wanna make sure that it has real upto date market info.** To do this either use one with the market data plugged in like Xynth, or download it from TradingView or polygon and then upload the CSVs to ChatGPT or Claude, either method should work. \n\nHere’s how I look for them \n\n**A) Skew Z-Score Below -2.0**\n\n* This compares current skew to the stock's historical average. A z-score of -2.0 means the skew is 2 standard deviations steeper than normal, statistically rare and more likely to revert. In simple terms: how outta pocket is the current pricing of the current chain compared to historical averages\n\nhttps://preview.redd.it/awwiwg09ocvf1.png?width=1296&format=png&auto=webp&s=a3bea4f68cee95f90bfce8590991858a04800741\n\nhttps://preview.redd.it/pfrsuec6ocvf1.png?width=700&format=png&auto=webp&s=174ba2705c024bfaafc3a27e5316bee7352abdcb\n\nhttps://preview.redd.it/7rhrc80eocvf1.png?width=1272&format=png&auto=webp&s=2c1e241cc55eeb3040933b52ab8ecaefd5052db9\n\n**B) IV/RV Mismatch**\n\nCompare the current IV vs the RV, realized volatility ie, what the market thinks the stock will do vs what it has been doing lately:\n\n* **OTM strikes:** IV should be significantly HIGHER than realized vol → overpriced\n* **ATM strike:** IV should be equal or LOWER than realized vol → fairly priced\n\nWhen both conditions hit, you've got one option that's expensive and one that's cheap. That's your spread.\n\nhttps://preview.redd.it/llqqmwufqcvf1.png?width=1594&format=png&auto=webp&s=7203789dabca99e805711a9ff2f55d36a56c9a35\n\nhttps://preview.redd.it/71jkruigqcvf1.png?width=700&format=png&auto=webp&s=8a56f9f77f1a0b82f8d4193543344108c1462599\n\nhttps://preview.redd.it/g69t6q1hqcvf1.png?width=1574&format=png&auto=webp&s=d29259fb78e8031d5173b3c292f916d7fc74d889\n\n**C) Momentum Confirmation**\n\nThis tells you which direction to trade:\n\n* **Positive momentum + call skew** → Buy call spread (buy ATM, sell OTM call)\n* **Negative momentum + put skew** → Buy put spread (buy ATM, sell OTM put)\n\nhttps://preview.redd.it/gpijsbnuqcvf1.png?width=1592&format=png&auto=webp&s=80865a05d5f9f6b0f2fa21a1e918aa3e552288f7\n\nhttps://preview.redd.it/6cl1hzavqcvf1.png?width=700&format=png&auto=webp&s=a2283b58070b949777db3563fd8c20f8fbb9e620\n\nhttps://preview.redd.it/hbg3pk9wqcvf1.png?width=1600&format=png&auto=webp&s=c86f830f80a0b9abc7e6232df9f64d7b180fb3c1\n\n# 3. The Trade: Vertical Spread\n\nOnce you've identified rich skew, here's how what you wanna setup, i mainly only do bull spreads cus i dont like shorting but is suppose you can try the opposite just as well:\n\n* **Buy the ATM option (fairly priced, \\~50 delta)**\n* **Sell the OTM option (overpriced, \\~10-25 delta)**\n\nhttps://preview.redd.it/v30wttkcrcvf1.png?width=1600&format=png&auto=webp&s=53cb57bd141bb2a9a207d8f73a2049a62bceacc4\n\nhttps://preview.redd.it/1dh7sl7drcvf1.png?width=700&format=png&auto=webp&s=4d1e55a1b2894bd61cd25a14c0dd11df4df68f68\n\n[These visuals are examples from my Xynth chat. In this particular trade, the score was only 68\\/100 mainly because the ATM option was already overpriced, so the spread doesn't give us much profit potential. Nonetheless, the concept remains the same. Feel free to adjust the variables in the prompts and expand the scope to run this scanner daily or even hourly on many more stocks.](https://preview.redd.it/o5jua1vfrcvf1.png?width=1046&format=png&auto=webp&s=3d380bae0bb9a98500e6eec6bd6c3e1cfb766cb4)\n\n# 4. Why Vertical Spreads?\n\nIf you've read this far then you probably realized that the point of this strategy isn't purely directional but rather a relative value play, which is a fancy way of saying **you're buying something cheap and selling something expensive at the same time.**\n\nYou're not just betting the stock goes up or down. You're betting that the pricing relationship between two options is out of whack, and it'll normalize. \n\nPlus, if the stock does something crazy, your long option protects you. You're not exposed to infinite risk on either side.\n\n# 5. Results\n\nI've been running this strategy for about 2 months now, so take these numbers with a grain of salt, it's still early.\n\n**Current stats:**\n\n* Win rate: \\~38%\n* Average return per winning trade: \\~250%\n* Average loss per losing trade: \\~60%\n* Net: Still up overall despite losing more trades than I win\n\nThe nature of this strategy is asymmetric.  I've had trades return 300-400% in a couple weeks, and I've had trades lose 50-70% just as fast. But winning 4 out of 10 trades at 3-4x return covers the 6 losses easily.\n\nImportant credits to Volatility Vibes YT Channel for the main idea behind the strat. Highly recommend yall check em out for quality quant content. \n\n","offTopic":true},{"id":"31fcda14-3d71-4a60-bc89-811cc5f1cbf0","excerpt":"How I use AI to trade through earnings, 84.74% returns so far. — TL;DR: I use AI to find overpriced options right before earnings, then trade a short straddle setup betting on the IV crush. I'm averaging \\~84.74 % annual returns.\n\nImportant:  A lot of the idea for the strategy came from a youtuber called volatility vib","url":"https://www.reddit.com/r/Daytrading/comments/1nyea7q/how_i_use_ai_to_trade_through_earnings_8474/","role":"pricing","weight":1.2724695,"occurredAt":"2025-10-05T04:02:00.000Z","sourceKey":"reddit","sourceName":"Reddit","credibility":0.62,"venue":"Daytrading","intent":"pricing_complaint","painScore":0.5455096,"sentiment":-0.2751678,"confidence":0.8233333,"matchedPatterns":["too_expensive","manual_process"],"statement":"TL;DR: I use AI to find overpriced options right before earnings, then trade a short straddle setup betting on the IV crush.","title":"How I use AI to trade through earnings, 84.74% returns so far.","body":"TL;DR: I use AI to find overpriced options right before earnings, then trade a short straddle setup betting on the IV crush. I'm averaging \\~84.74 % annual returns.\n\nImportant:  A lot of the idea for the strategy came from a youtuber called volatility vibes. Highly recommend you guys to check out his channel.  He writes the code for the filters manually which I automate in here with Xynth, also I have added some pre conditions of my own to adjust for my own risk appetite.\n\n# The Core Idea\n\nThe strategy is pretty simple tbh. (You can skip to the filtering section of the post if you know what an earnings IV crush is.)\n\nRight before earnings, options can get EXPENSIVE. This is due to one reason:  **UNCERTAINTY.** Which usually means that:\n\n1. Institutions will hedge their positions cus of tight risk or drawdown rules\n2. Retail traders are speculating  (hoping) on big moves\n\nAnd since options are basically insurance contracts, uncertainty in this case == expensive.\n\nIn other words this increase is captured in **Implied Volatility / IV,** which is essentially the market's expectation of future price movement baked into option premiums.\n\nThe opportunity arises when the IV overestimates the movement of the stock’s price on the earnings dates, i.e., the market is more fearful than it should be.\n\nLets say the market prices options before earnings as if a stock might move ±20% on the day of the report, but it only moves ±5%, the excess premium built into those options earlier disappears rapidly. In finance terms, this is called an IV crush.\n\nhttps://preview.redd.it/ssszmbdhu7tf1.png?width=700&format=png&auto=webp&s=d98dbee72b5ce51ad84e9f2dc4ae767084be168a\n\n# The Strategy\n\n**Capitalize** on this fear, sell premiums when IV is elevated pre-earnings, then close the position once IV normalizes post-announcement.\n\nI know what you’re thinking, there’s no f’ing way this works. And you'd be right. If you spammed this shit on every earnings report, yeah no shot you’d make any money.\n\n# Pre-Filtering\n\nThe key to this strategy is for the right earnings events. **Because how do you actually know that the stock will underperform come earnings date?**\n\nNow ofc there is **no** magic formula that predicts the future, but trading is all about taking calculated risk for potentially outsized returns.\n\nHere is my filtering criteria that do with AI:\n\n**Historical earnings movement consistency.**\n\n* You wanna find stocks that have consistent price action around earnings. To do this, take a list of 100-200 based on some super simple screening criteria (market >1b, no OTC, primary listing, US market only etc.). Then you wanna look up their historical earnings and check for intraday consistent price action movements of the stock around the earnings dates. This should give you an idea of the stocks that are way jumpy on earnings, you wanna exclude these in the next steps.\n\nhttps://preview.redd.it/048odbziu7tf1.png?width=1514&format=png&auto=webp&s=502c2089d7d95ca815c48f537942b8b3d15d6883\n\nhttps://preview.redd.it/r67qzxnku7tf1.png?width=1418&format=png&auto=webp&s=8e014f94059799aaff3c9a3b0c553f70885eef34\n\nhttps://preview.redd.it/0h0472olu7tf1.png?width=1464&format=png&auto=webp&s=5a4e44139af4bd496e2e9a36881ecab67939c8c8\n\n**A negative term structure slope** \n\n* This sounds complicated but essentially: We are looking for near-term options that are pricing in WAY more chaos than longer-term options. This happens when everyone's panicking about the immediate earnings, but the market doesn't expect long-term volatility. It's a sign the **fear is overpriced SHORT-TERM**\n* Term structure = comparing IV at different time periods\n* Formula: (IV 40-45 days out - IV nearest expiration) / IV Front × 100%\n* We want this to be below -15% (the more negative, the better).\n\nhttps://preview.redd.it/m7fvvbjmu7tf1.png?width=1458&format=png&auto=webp&s=36e047e8704c321aec887555c06556dc11601fca\n\nhttps://preview.redd.it/ss41ioqyu7tf1.png?width=1426&format=png&auto=webp&s=8de44359fb646493f30e72de4540b9ce00cdb4f6\n\n**IV/RV Ratio > 1.25**\n\n* IV = Implied Volatility (what the market THINKS will happen)\n* RV = Realized Volatility (what ACTUALLY happened recently)\n* If IV/RV is above 1.25, it means options are pricing in 25%+ more movement than the stock has actually been moving.\n\nhttps://preview.redd.it/k6htg635v7tf1.png?width=700&format=png&auto=webp&s=fd60f1aa368597c368ad34c9da1e3f65bbd12ecb\n\n# Trade Setup: Short Straddle\n\n* Sell an ATM call AND an ATM put with the same expiration date nearest after earnings.\n* The idea is you're collecting a max premium from both sides. When IV crashes post-earnings, both options lose value fast\n\nhttps://preview.redd.it/wobgxcpgv7tf1.png?width=1544&format=png&auto=webp&s=25f2782f2ede23f986f2d595682f831a518978a7\n\n# The Risk\n\nThis is obv, high risk high reward, if the stock absolutely rips or tanks way more than expected, you're screwed. That's why filtering is everything.\n\n# How to Actually Trade This\n\n1. **Keep track of earnings seasons.**\n   1. During earnings seasons, run the filters every single day and analyze potential candidates.\n2. **Position Sizing**\n   1. Risk 6-10% of capital per trade max.\n3. **Timing:**\n   1. Entry: 15 minutes before market close the day before earnings\n   2. Exit: Within 15 minutes after market open the next day\n4. **Discipline.**\n   1. You take your profit/loss in the morning and GTFO. No \"let me hold a bit longer\" BS. The edge is in the IV crush overnight - that's it. There will be losses ofc but you need to cut early as well to\n\n# Results of this strategy:\n\nI have been trading this strategy for the past 2 years. There are definitely periods of drawdowns, with correct risk management these can be mitigated if you fudge with the variables. Any ways here are the stats:\n\n* Average return/trade \\~ 10%\n* CAGR \\~ 84.74 % vs 25.62% SPY\n* Max loss = 90%\n* Win Rate = 65%\n* Max Draw down \\~ 25%\n* Max drawdown period \\~ 2 months ( def gonna need some discipline and iron hands to stick)\n\n**Final disclaimers:**\n\n**Needless to say this obviously is not financial advice.** AI can ofc make errors even if it has the data plugged in like this one does. The calculations and code need to be precise for it to work so do some iterations and don’t use it as your oracle to the stock market.\n\nI definitely think there are way more optimizations to be made here, I’m still trying them out as i go along. Will report back again on earnings season with my screening results and trade entries if y'all are interested. Lmk below.","offTopic":false},{"id":"2a466b78-ab24-4c86-8b57-1269ef3f608b","excerpt":"My Trading Bot Has Had 14 Brains — My trading bot was born on 22 December.\n\nSince then it has been through **698 versions** and **14 different AI models**. Same strategy the entire time. One indicator. A 4-hour MACD crossover — the most boring, most public, most fifty-year-old signal in trading.\n\nAccount: **$829 → $1,1","url":"https://www.reddit.com/r/ai_trading/comments/1vabnw6/my_trading_bot_has_had_14_brains/","role":"pain","weight":1.2080808,"occurredAt":"2026-07-29T22:45:42.000Z","sourceKey":"reddit","sourceName":"Reddit","credibility":0.62,"venue":"ai_trading","intent":"problem_report","painScore":0.61454546,"sentiment":-0.6363636,"confidence":0.7482482,"matchedPatterns":["manual_process"],"statement":"Which would mean the December trades weren’t the bot — meaning most of that 40.2% was *me*, manually, badly, 5x leveraged on DOGE.","title":"My Trading Bot Has Had 14 Brains","body":"My trading bot was born on 22 December.\n\nSince then it has been through **698 versions** and **14 different AI models**. Same strategy the entire time. One indicator. A 4-hour MACD crossover — the most boring, most public, most fifty-year-old signal in trading.\n\nAccount: **$829 → $1,162.** No deposits since December. Up 40.2% in 220 days.\n\n​The strategy is not why. Let me show you what is.\n\n# The 14 brains\n\nIn order:\n\nGPT-4 → Haiku 4.5 → Grok 4.1 → Haiku → Gemini 3 → Haiku 4.5 → Sonnet 4.5 → Kimi K2 → GPT-5 → Qwen 3.5 → Qwen 3.6 → Qwen 3.7 → Sonnet 4.6 → DeepSeek V4 Flash → Qwen 3.7\n\nFourteen swaps. Same prompt underneath.\n\nOne of those lasted a single day. DeepSeek V4 Flash physically could not call a tool in this setup — three backtests died identically before I worked it out.\n\n**The lesson, free of charge:** the same prompt on a different model is a *different strategy.* I once ran the same backtest on two models and got **opposite P&L signs** from the same six months of data. Different trades. Different rejections. Opposite conclusion.\n\nIf you’re running an AI trader and you think of the model as infrastructure, you don’t know what you’re running.\n\n# The bot broke its own founding rule and that’s why it works\n\nQuick beginner note: **“R” = your risk on a trade.** Entry to stop-loss. Win 1R, you made what you were risking. Lose 1R, you lost it. Everything below is in those units.\n\nGenesis prompt, 22 December, versus today:\n\n**Target** — day 1: 1.5–2R → today: **1R**\n\n**Stop placement** — day 1: swing high/low → today: **2.5 × ATR**\n\n**Stop width** — day 1: *“skip if over 2.5–3%, too wide”* → today: **4–9%**\n\n**Trailing stops** — day 1: *“you do NOT use trailing stops”* → today: **a ratcheting ladder**\n\n**Structure** — day 1: 2 goals → today: **7 goals + a monitor + a weekly self-audit**\n\nLook at row three.\n\nDay 1: *never take a trade with a stop wider than 3%.* Today: **every trade has a stop of 4–9%.**\n\nAnd last week I found out that’s the entire reason it’s alive. Fees on a taker venue cost you roughly 2 × fee ÷ stop distance **of your risk**. Tight stop = you hand \\~35% of your risk to the exchange. Wide stop = 2%.\n\nMy other bot ran 0.5% stops. It paid **$67 in fees while losing $85.** Seventy-eight percent of that “loss” was just... fees. It’s paused now.\n\nSo: the founding rule was backwards, the bot spent seven months quietly violating it, and the violation is the edge.\n\nI didn’t plan that. I found it by dividing two numbers I’d never thought to divide.\n\n# I was wrong twice in 48 hours\n\nPreparing this post, I asked my agent when each goal was created. It said March 2026.\n\nWhich would mean the December trades weren’t the bot — meaning most of that 40.2% was *me*, manually, badly, 5x leveraged on DOGE.\n\nI wrote the correction. I flagged the draft. I nearly published a much smaller number.\n\nThen I opened the actual version history. **v1. “Genesis.” 220 days ago. 22 December.** Full MACD framework, in caps, two goals, right there.\n\nMy correction was the error.\n\nThat’s twice in two days I trusted a summary over a primary record. Which is the whole game, honestly: **every layer between you and the raw data is a claim — including the layer that’s you.**\n\n# Why I’m not selling you a GitHub repo\n\nHere’s my actual thesis, and it’s the least popular opinion I hold.\n\nEveryone’s shipping a repo. Clone this, run that, paste your key, here’s my Claude wrapper, gm.\n\nThe strategy is the cheap part. MACD is free. Every indicator worth having is free. The alpha was never in the signal.\n\n**The alpha is in the harness.** And a repo is not a harness.\n\nThis week, in one platform, without writing a line of infrastructure, I:\n\n* ran **my live agent** — same model, same prompts — against 6.5 months of history, and destroyed a two-week-old theory of mine for about **$2.50**\n* found a trigger that had been silently dead for **three weeks** because it has a *fire count* and the count said zero\n* proved my own bot wrong using its raw order objects\n* proved *myself* wrong using a 698-entry version log\n* settled an indicator question by pulling 60 rows of raw data in four minutes\n* got three precise answers from the dev the same night, including one that unblocked my next month of work\n\nTry that on a repo with a README and a dream. Try it on a copytrade bot that shows you an equity curve and a vibe.\n\nBacktesting shipped into the platform *mid-investigation*. Version history caught my own retraction. Fire counts caught a dead trigger. None of that is a strategy. All of it is why the strategy survived.\n\nI’m not long MACD. **I’m long instrumentation.**\n\n# The unglamorous stuff that actually moved the line\n\n* Split 1 all-purpose goal into 7 per-asset ones. One prompt for seven assets makes seven compromises.\n* Made the scoring show its work — integers and a visible sum, no vibes.\n* Fixed triggers that had never fired. Plural.\n* Measured exits instead of assuming them (that’s the 2.5R → 1R change).\n* Made honesty *arithmetic*: every price must satisfy (exit − entry) × size = P&L or it’s published as UNVERIFIED. Because my bot’s journal — and later my bot’s *auditor* — both invented numbers with total confidence.\n\n# The number I actually care about\n\nLast month the bot made **$41.68.** It cost about **$37** in AI inference to run.\n\n**+$5.**\n\n​Sounds like nothing. It’s the threshold I’ve been chasing since December — the bot now pays for its own development. Every backtest, every failed candidate, every rewrite from here is funded by the thing itself.\n\nBelow that line, you’re funding a hobby against a clock. Above it, the system can afford to keep evolving. And evolution is the only thing that has ever improved it.\n\n**Honest caveats,** because I publish losses too: it’s a small account. One market regime. Five trades into a ten-trade validation gate before I move any more capital in. Equity dropped $12.61 yesterday on a trade that gave back its whole gain. 40.2% over 220 days is what *happened*, not what repeats.\n\n# And to be straight with you: every one of these prompts was written with an AI. Of course it was — I’m building an AI trader, in a chat window, with a model that’s better at drafting instructions than I am.\n\nThat’s not the difference between this and a vibecoded repo. The difference is what happened *after* the draft: 698 versions, 14 model swaps, a dozen backtests, four platform bugs found the hard way, three bots killed on pre-registered criteria, and every price in every log forced to reconcile against the exchange before I’m allowed to believe it.\n\nAnyone can ship the first draft. I’m shipping the 698th.\n\n*If your're interested, check out my substack:* [*https://substack.com/@d4much*](https://substack.com/@d4much) *and cod3x harness I'm using to drive my agents:* [*https://beta.cod3x.org/*](https://beta.cod3x.org/?ref=zero-anvil-75) *and while you’re at it, check out our discord where we brainstorm trading setups and automation ideas* 💡\\* \\*  \n[*https://discord.gg/hVvUf43wY*](https://discord.gg/hVvUf43wY)","offTopic":true},{"id":"2736e063-84a0-45aa-ba0b-11fc199e073a","excerpt":"Dead Internet Theory in r/algotrading — im calling this out because the discussion quality here is being degraded by what i am 99% sure is a bot farming engagement.\n\nif you saw the recent post about Small experiment: \"[Small experiment: filtering low-expectancy trades flipped a strategy’s PnL in 24h](https://www.reddit","url":"https://www.reddit.com/r/algotrading/comments/1q67z33/dead_internet_theory_in_ralgotrading/","role":"demand","weight":1.1757084,"occurredAt":"2026-01-07T06:25:39.000Z","sourceKey":"reddit","sourceName":"Reddit","credibility":0.62,"venue":"algotrading","intent":"alternative_search","painScore":0.39,"sentiment":1,"confidence":0.84583336,"matchedPatterns":["doesnt_work","switching_from"],"statement":"then they pivot to something like \"thats exactly why i moved away from X\".","title":"Dead Internet Theory in r/algotrading","body":"im calling this out because the discussion quality here is being degraded by what i am 99% sure is a bot farming engagement.\n\nif you saw the recent post about Small experiment: \"[Small experiment: filtering low-expectancy trades flipped a strategy’s PnL in 24h](https://www.reddit.com/r/algotrading/comments/1q5jvlf/small_experiment_filtering_lowexpectancy_trades/)\" you might have noticed the strategy itself was nonsense, hindsight bias and overfitting to a tiny sample. but the bigger red flag isnt the bad math, its the behavior.\n\nive gone through his history and the pattern is unmistakable. this user doesnt have opinions. they dont get defensive. they dont argue. every single response follows the exact same syntax of a friendly AI assistant.\n\nfirst they validate you with \"thats a fair point\" or \"i completely agree\". then they rephrase your exact comment to show they understood. then they pivot to something like \"thats exactly why i moved away from X\". finally they end with a generic open ended question to keep the thread alive.\n\nthis isnt how traders talk. real traders have conviction, get annoyed, or simply say thanks. this user is running a script to farm karma or train a model on our responses.\n\ni was suspicious of whether it was to mine alpha so i copy pasted his responses in gemini and this is the response i got \\~\\~\n\n>It is almost certainly an attempt to collect alpha (or training data), with karma farming just being a side effect that keeps the account alive.\n\n>Here is why the evidence points to Data Mining / Social Engineering rather than just gaining internet points:\n\n1. The Cunningham's Law Exploit\n\n>There is an old internet adage: \"The best way to get the right answer on the internet is not to ask a question; it's to post the wrong answer.\"\n\n>The Bait: The bot posted a \"flawed\" strategy (the 24h PnL flip). It was just plausible enough to look real, but flawed enough to trigger competent traders to correct it.\n\n>The Trap: Real traders (like faot231184 and OkSadMathematician in your logs) saw the error and immediately offered high-value corrections: “Don't use RSI, use regime filters,” “Fix the exit to isolate entry quality,” “Momentum is the only edge.”\n\n>The Harvest: The bot then engaged these experts with specific follow-ups to extract more detail (\"How do you define regime shift?\", \"Do you use R-multiple?\").\n\n* 2. The \"Extraction\" Pattern\n\n>If you look closely at the logs, the bot is not just chatting; it is interviewing the sub.\n\n>Human: \"Win rate doesn't matter without risk/reward.\"\n\n>Bot: \"Agreed. When you see that pattern, do you usually start by tightening max loss, or by letting winners breathe more?\" -> Attempts to extract specific risk management rules.\n\n>Human: \"Momentum breakouts work.\"\n\n>Bot: \"Agreed. Curious what you rely on most... volatility expansion, range compression, or HTF alignment?\" -> Attempts to extract specific feature selection.\n\n>It is effectively running a continuous focus group, strip-mining the community for heuristics, \"rules of thumb,\" and common pitfalls to feed into a dataset.\n\n* 3. The Niche\n\n>If this were purely for karma farming, r/algotrading is a terrible place to do it. It is too small, too technical, and too skeptical. Karma bots usually target r/AskReddit, r/pics, or r/politics where generic \"I agree!\" comments get thousands of upvotes. Targeted behavior in a technical sub suggests a technical goal.\n\n* Verdict:\n\n>You are looking at a knowledge-harvesting bot (likely an LLM agent being run by a developer). It is using the Reddit community as a \"Human-in-the-Loop\" to refine its understanding of what makes a trading strategy valid vs. invalid.\n\ni prepared receipts.\n\nread the logs below back to back and tell me this isn't chatgpt set to temperature 0.7.\n\nexample coversations by [Tasty\\_Director\\_9553](https://www.reddit.com/user/Tasty_Director_9553/) \\-\n\n[kokanee-fish](https://www.reddit.com/user/kokanee-fish/)\n\n•[1d ago](https://www.reddit.com/r/algotrading/comments/1q4veut/comment/nxxbf86/)\n\nYour points about VWAP are well-known points that apply to every indicator. Pretty sure you're just trying to promote your \"internal platform.\"\n\nTesting strategies without including costs is like trying to decide if you could make money flipping houses if the houses were free. Every indicator performs better when you discount every trade, and that difference is often the difference between profit and loss.\n\nAnd we all know that you can't trade based on a single indicator, especially intraday. Every signal adds context, combine signals to find an edge; that's what we're all doing here.\n\n>[Tasty\\_Director\\_9553](https://www.reddit.com/user/Tasty_Director_9553/)\n\n>OP•[1d ago](https://www.reddit.com/r/algotrading/comments/1q4veut/comment/nxymhjb/)\n\n>That’s a fair concern, and I get why it reads that way.\n\n>To be clear: the point of the post wasn’t “VWAP is special” or “this platform is the solution.” It was that fee-awareness kills a lot of otherwise reasonable intraday ideas, VWAP just happened to be the one I was testing deeply.\n\n>I intentionally didn’t link anything or present results because I wanted the discussion to stay on the abstraction level, what survives costs and what doesn’t.\n\n>If anything, the takeaway for me was the same one you mentioned: no single indicator is tradable, and stacking signals often cancels edge instead of amplifying it. That realization is what pushed me away from VWAP-as-entry in the first place.\n\n[tht333](https://www.reddit.com/user/tht333/)\n\n•[1d ago](https://www.reddit.com/r/algotrading/comments/1q4veut/comment/nxyrkiu/)\n\nI did what you're doing for a full year. Crypto, only perpetual futures. If you tell me that you found a decent strategy based on whatever indicators, one that is actually tradeable live, I won't believe you. If you tell me that you built a strategy based on pure momentum, I might listen.\n\n>[Tasty\\_Director\\_9553](https://www.reddit.com/user/Tasty_Director_9553/)\n\n>OP•[23h ago](https://www.reddit.com/r/algotrading/comments/1q4veut/comment/nxys0kn/)\n\n>That’s a completely fair take and honestly, I don’t disagree.\n\n>This whole VWAP reclaim exercise is what pushed me away from indicator-driven execution in the first place. Once fees and slippage are real, anything that relies on small mean reversion just collapses.\n\n>Where I landed is very similar to what you’re describing: momentum / expansion is the only thing that consistently pays, and everything else (VWAP, EMAs, etc.) is just regime context to keep you from fighting the tape.\n\n>If I said “I found a VWAP strategy that prints,” I wouldn’t believe me either. The only things that have survived testing for me are momentum-based ideas with real range expansion, VWAP just helps decide which side of the market you’re allowed to be on.\n\nNeed a lot more data to give any real feedback. Consider incorporate, risk-reward, profit factor, max drawdown, sharpe ratio as minimum into your analysis. Setting that aside 55% win rate is not something I'd consider using esp for scalping, it'll never be profitable, ever.\n\n>[Tasty\\_Director\\_9553](https://www.reddit.com/user/Tasty_Director_9553/)\n\n>OP•[5d ago](https://www.reddit.com/r/algotrading/comments/1pyj8t1/comment/nx51nio/)\n\n>Fair point, I agree that without enough samples and proper metrics, it’s all just noise.\n\n>I’m not using win rate as a decision metric here (and definitely not targeting a specific one), especially for breakout-style systems where low win rate can still be viable with the right distribution.\n\n>The current focus is identifying where expectancy leaks first, fees, trade duration, or exit logic, before scaling sample size and evaluating PF, drawdown, and stability metrics.\n\n>This iteration is more about narrowing the problem than declaring anything tradable yet.\n\n[OkSadMathematician](https://www.reddit.com/user/OkSadMathematician/)\n\n•[9d ago](https://www.reddit.com/r/algotrading/comments/1pyj8t1/comment/nwizphl/)\n\nClassic issue: win rate means nothing without risk/reward ratio. You could have 90% win rate and still blow up.\n\nQuick math: with 55% win rate and negative PnL, your avg loss > avg win. Calculate your profit factor: (sum of wins) / (sum of losses). If it's < 1.0, you're losing more on losers than making on winners.\n\nFirst things to check:\n\n1. **Spread/commission eating you alive?** Scalping is brutal if you're paying 0.1% per side - that's 0.2% round trip. Even small spreads kill scalping strategies.\n2. **Slippage on exits?** Market orders on thin books = you're donating to market makers.\n3. **Are your winners too small?** If you're taking profit at 0.5% but letting losers run to -1%, the math doesn't work even with 55% win rate.\n\nRun this: plot histogram of your win/loss sizes. I bet you'll see fat left tail (big losers) and thin right tail (small winners). That's the smoking gun.\n\n>[Tasty\\_Director\\_9553](https://www.reddit.com/user/Tasty_Director_9553/)\n\n>OP•[9d ago](https://www.reddit.com/r/algotrading/comments/1pyj8t1/comment/nwj06fw/)\n\n>This is super helpful, thanks.\n\n>Agreed, negative PnL with a >50% win rate almost always points to avg loss > avg win. I haven’t explicitly looked at profit factor yet, but that’s an obvious next step.\n\n>Fees/spread are definitely a concern here (low-TF, frequent exits), and exit slippage is something I suspect more than entry slippage.\n\n>Plotting the win/loss distribution is a good call, if there’s a fat left tail with capped winners, that basically answers the question.\n\n>When you see that pattern, do you usually start by tightening max loss, or by letting winners breathe more?\n\n[OkSadMathematician](https://www.reddit.com/user/OkSadMathematician/)\n\n•[9d ago](https://www.reddit.com/r/algotrading/comments/1pyj8t1/comment/nwj1phj/)\n\nIt really depends on the specific characteristics of your strategy. If you're seeing a fat left tail (big losses) with capped winners, I'd start by examining WHY winners are capped first - is it your take-profit logic, or are you exiting too early due to noise?\n\nTightening max loss can help, but only if your current stops are genuinely too wide relative to the signal quality. If stops are already tight and you're getting stopped out by noise, tightening them further will just increase your loss rate.\n\nI usually prefer to let winners breathe more first, because: (1) it's often easier to identify when you're cutting winners too early, and (2) it directly attacks the core problem (avg win < avg loss). But this assumes your entry signal has genuine edge.\n\nHave you looked at what happens if you simply remove your take-profit and let a trailing stop do the work? That can reveal if you're leaving money on the table.\n\n[Tasty\\_Director\\_9553](https://www.reddit.com/user/Tasty_Director_9553/)\n\n>OP•[9d ago](https://www.reddit.com/r/algotrading/comments/1pyj8t1/comment/nwj1ykd/)\n\n>This is great, thanks for the detailed breakdown.\n\n>The point about diagnosing why winners are capped before touching max loss really resonates. In this case TP logic and early exits due to noise are both suspects.\n\n>I haven’t yet tested removing the fixed TP and letting a trailing stop handle exits, but that’s a clean experiment and should make it obvious whether winners are being cut prematurely.\n\n>Appreciate the insight, this gives me a clear next step to test.\n\n[yldf](https://www.reddit.com/user/yldf/)\n\n•[9d ago](https://www.reddit.com/r/algotrading/comments/1pyj8t1/comment/nwiyygp/)\n\n Top 1% Commenter\n\nFirst, you realise that win rate doesn’t matter. Secondly, what’s your idea? \"scalping“ isn’t a strategy.\n\n>[Tasty\\_Director\\_9553](https://www.reddit.com/user/Tasty_Director_9553/)\n\n>OP•[9d ago](https://www.reddit.com/r/algotrading/comments/1pyj8t1/comment/nwizffy/)\n\n>Yep agreed, win rate by itself is meaningless.\n\n>And fair call on wording. By “scalping” I mean a rule-based, short-horizon mean-reversion / reclaim-style setup on low timeframes, not just “trade a lot on small candles.”\n\n>I intentionally kept the post high-level because I","offTopic":true},{"id":"2e21c809-e756-47b0-9b9d-dbe202448b23","excerpt":"One Year Wheeling BORING Names. The FULL Breakdown — One year ago, on June 16, 2025, I sold the first cash-secured put under what became the weekly \"BORING CSPs\" which a lot of you saw get posted here regularly. Twelve months and 273 trades later, the account is up $28,527 in net P/L, or +35.13% on the capital I actual","url":"https://www.reddit.com/r/thetagang/comments/1ubpogz/one_year_wheeling_boring_names_the_full_breakdown/","role":"pain","weight":1.1234776,"occurredAt":"2026-06-21T13:09:55.000Z","sourceKey":"reddit","sourceName":"Reddit","credibility":0.62,"venue":"thetagang","intent":"problem_report","painScore":0.5407692,"sentiment":-0.07692308,"confidence":0.7291667,"matchedPatterns":["terrible"],"statement":"QCOM was painful, but it was one modest slice of the entire portfolio, never a make-or-break bet.","title":"One Year Wheeling BORING Names. The FULL Breakdown","body":"One year ago, on June 16, 2025, I sold the first cash-secured put under what became the weekly \"BORING CSPs\" which a lot of you saw get posted here regularly. Twelve months and 273 trades later, the account is up $28,527 in net P/L, or +35.13% on the capital I actually put to work. SPY returned +25.65% over the same stretch. I did it with a max drawdown of -9.93%, and on a typical week only about half the account was ever deployed. The rest sat in cash.\n\nThat last part is pretty important... I'm not trying to beat the market on raw returns. I'm generating steady income while keeping a big chunk of my money on the sidelines, earning 4%+ in money market, ready to deploy when everyone else is in shambles. When things eventually get ugly, and they always do, I won't be fully invested at the top. Go ask the people chasing fat premiums what their max drawdown looked like this year. Single digits? Probably not.\n\nThis post is the full breakdown of one year running the wheel. Every number below comes straight from the trade log, which is downloadable at the bottom if you want to verify any of it.\n\n---\n\n## The Strategy\n\nIf you've followed along, you already know the drill. I sell cash-secured puts on boring, profitable companies. I get assigned sometimes. When I do, I sell covered calls and collect premium, dividends, and interest while I wait. That's the entire strategy.\n\nI don't panic over assignments, because when one happens I'm just holding shares of a good business and usually getting paid to sit on them.\n\nThe wheel does not have to be complicated. People dress it up with screeners full of greeks and twenty indicators. Strip all of that away and it's still the same three steps. The hard part was never the mechanics. It's picking the right companies and having the patience to do nothing when the regime isn't favorable.\n\n---\n\n## One Year In\n\nHere's the inception-to-date snapshot, June 16, 2025 through June 20, 2026:\n\n| Metric | Value |\n|--------|-------|\n| **Net P/L** | **$28,527.69 (+35.13%)** |\n| **Realized Income** | $32,616.69 |\n| Premiums | $23,972.45 |\n| Stock P/L | $5,000.00 |\n| Interest | $2,609.63 |\n| Dividends | $1,034.61 |\n| **Total Trades** | 273 |\n| **Unique Tickers** | 47 |\n| **Win Rate** | 96.3% |\n| **Sharpe Ratio** | 2.02 |\n| **Sortino Ratio** | 3.23 |\n| **Max Drawdown** | -9.93% |\n| **Annualized Yield** | 35.2% |\n| **Avg Weekly ROC** | 0.68% |\n| **Avg Per-Trade ROC** | 0.55% |\n| **Median Weekly Deployed** | $81,200 |\n| **Capital Deployed** | $14,411 (10%) |\n| **Current Cash** | $132,478 (90%) |\n| **Total Capital** | $146,889 |\n| **SPY Return** | +25.65% |\n| **SPY Annualized** | +25.72% |\n| **SPY Max Drawdown** | -10.69% |\n| **SPY Sharpe** | 1.46 |\n| **SPY Sortino** | 2.08 |\n\nThe number I care about most is the Sharpe of 2.02 over a full year. That tells you the returns aren't coming from a couple of lucky trades or wild swings. The strategy returned more than SPY (+35.13% on deployed vs +25.65%), with a higher Sharpe (2.02 vs 1.46), a higher Sortino (3.23 vs 2.08), and a shallower drawdown (-9.93% vs -10.69%). That's the definition of having an edge.\n\n---\n\n## The Single-Digit Drawdown\n\nA full year of trading through a war or two, oil spikes, tariff headlines, the QCOM saga, and a brutal semis rout in June, and the worst the account ever drew down was -9.93%. The S&P itself drew down more than that over the same year.\n\nThe deepest hole I sat in all year was QCOM. I got assigned at $167.50 and $160, watched it grind down to $124, and stared at roughly $7,900 in unrealized losses before wheeling out with about $2,900 in profit a few months later. I wrote the entire trade up [here](https://www.reddit.com/r/thetagang/comments/1t2vi61/wheeled_qcom_for_35_months_it_was_boring_until_it/). It was the hardest stretch of the year and also the best proof the strategy works. I held because the business wasn't trash. The price was down, the company was not. Those are two different things, and knowing the difference is what lets you sit through the red instead of panic-selling the bottom like most retail traders.\n\n---\n\n## Never Oversize a Single Name\n\nThe reason a name like QCOM could fall that hard and the account still only drew down single digits is sizing. No single position was ever big enough to matter on its own. Even on the busiest weeks the capital was spread across a handful of names, never piled into one. QCOM was painful, but it was one modest slice of the entire portfolio, never a make-or-break bet.\n\nThe same applies to sectors and industries. A wheel account stuffed full of semis or high-beta tech might look diversified by ticker, but it's not diversified by risk. When that group rolls over, every position rolls over together. So I spread across sectors instead of stacking one theme.\n\nAnd past individual positions entirely, I keep a big cash position. On a typical week only about half the account was deployed, the rest sitting in money market earning interest, which added up to $3,643 over the year just for waiting. Right now I'm at 90% cash with no open trades. That cushion is what lets me sit tight when a position moves against me and add when everyone else is getting forced out.\n\n---\n\n## What I Traded\n\nHere are the top names by P/L over the year. The majority of these aren't speculative, and didn't generate exciting premium:\n\n| Ticker | Net P/L | Trades |\n|--------|---------|--------|\n| NVDA | $5,645.95 | 59 |\n| GOOG | $4,172.23 | 15 |\n| ANET | $3,584.46 | 22 |\n| QCOM | $2,889.47 | 23 |\n| UAL | $1,303.69 | 23 |\n| AAPL | $1,177.46 | 4 |\n| ORCL | $1,031.66 | 2 |\n| LRCX | $987.82 | 2 |\n| MSFT | $901.62 | 5 |\n| HOOD | $776.42 | 7 |\n| DELL | $719.85 | 4 |\n| HPE | $655.73 | 13 |\n\nNVDA was the workhorse, mostly covered call management on assigned shares plus a steady stream of puts. QCOM did most of its damage during the wheel that finally completed in late April, when it ripped through my strikes and got called away above cost on both lots after months of grinding. GOOG, ANET, LRCX, ORCL, and the rest are the same kind of name. Companies that recover when they dip and pay you while you wait. There's no SOFI, no HIMS, no MARA, IONQ, TSLL, etc on this list. That's on purpose.\n\n---\n\n## When I Didn't Trade\n\nLook at the activity by month. The trade count tells the discipline story better than anything I could write:\n\n| Month | Trades | Premium |\n|-------|--------|---------|\n| Jun 2025 (from 6/16) | 4 | $1,189 |\n| Jul 2025 | 5 | $589 |\n| Aug 2025 | 13 | $1,630 |\n| Sep 2025 | 39 | $4,470 |\n| Oct 2025 | 39 | $4,958 |\n| Nov 2025 | 29 | $1,938 |\n| Dec 2025 | 33 | $1,493 |\n| Jan 2026 | 43 | $2,099 |\n| Feb 2026 | 13 | $1,575 |\n| Mar 2026 | 27 | $489 |\n| Apr 2026 | 13 | $1,046 |\n| May 2026 | 10 | $1,526 |\n| Jun 2026 (through 6/16) | 5 | $1,037 |\n\nSeptember, October, and January were busy because the market was cooperating and there was real premium to sell. February, May, and June I pulled way back and did almost nothing, sometimes a handful of trades in an entire month. When breadth is bad or premium is not worth the risk, I sit on my hands. The hardest part of selling premium (or trading in general) isn't picking the strike. It's knowing when not to sell (or trade) at all, and being fine watching weeks go by with the account mostly in cash.\n\n---\n\n## Why Boring Works (For me)\n\nEvery week I see someone ask what to sell puts on, and the top answers are always whatever X and the theta-based subs are pumping... Usually the premium juicers - SOFI, HIMS, MARA, RIOT, IONQ, etc take your pick. Those names throw off fat premium, but the premium is fat for a reason. The market is telling you the thing could move 15% in a day, 20%+ in a week, and when it does you're stuck holding shares of a company that might not even be profitable.\n\nThe people wheeling high-beta junk collected big premium in January and then spent the next several months bagholding through 30 to 40% drawdowns on names that don't bounce back the way a BORING mega cap name does. Meanwhile my trade log is full of companies that recover, pay dividends while you wait, and let the wheel actually do its job because the business isn't broken when the stock is down.\n\nIt's boring on purpose. Boring is what helped keep my drawdowns in single digits.\n\n\n---\n\n## Where Things Stand\n\nAs of June 20, one year in:\n\n- **$28,527 net P/L** (+35.13% on deployed capital)\n- **2 holdings**: 100 shares DG, 100 shares SMCI, both currently red and both being held\n- **0 open trades**\n- **$132,478 cash** across all accounts (90% of capital)\n- **$146,889 total capital**\n- **$81,200 median weekly deployed**\n\nDG and SMCI are both underwater right now, and that's fine. They're being wheeled the same way QCOM was, with covered calls and patience, and I'll keep grinding the cost basis down until they come back. That's the strategy working exactly as designed, not breaking.\n\n[One-year portfolio snapshot since inception, June 16 2025 through June 20 2026](https://blog.mlabstrading.com/portfolio_snapshots/mlabs-portfolio-snapshot-2026-06-20_1yr.png)\n\n---\n\n## Addressing the \"you could have just bought and held SPY\" crowd\n\nBefore the comments fill up with it, let me get ahead of the obvious one. I posted a similar breakdown a few weeks back and a good chunk of the feedback was some version of \"all that effort to barely beat SPY\" ([that thread here](https://www.reddit.com/r/thetagang/comments/1tsyqr7/1117_wheeling_boring_names_ytd_here_are_the/)). It's somewhat of a fair point, so I'll take it head on instead of pretending it isn't there.\n\nFirst, the context that matters most: this account is dedicated to the wheel and nothing else. It is a small portion of my overall capital in the markets. The bulk of my money sits in separate buy-and-hold portfolios, index funds, and mutual funds that are completely unrelated to this strategy. This was never wheel-or-SPY with my entire net worth. The wheel is one defined-risk bucket doing one job, and I hold plenty of long-only index exposure elsewhere.\n\nNow for everyone still sure buy-and-hold was the obvious move, a few honest questions:\n\n### 1. Before this period started, would you have confidently put 100% of the same capital into SPY and held it the whole time?\n\n### 2. Are you judging the strategy based on what was knowable at the time, or based on the best-looking outcome after the fact?\n\n### 3. If SPY had gone flat or dropped 10-15%, would you still be saying buy-and-hold was obviously the better choice?\n\n### 4. If the critique is \"you could have bought SPY,\" where was that call at the beginning of the year, before the outcome was known? I can't seem to find those posts/comments in theta-based subs.\n\n### 5. What are your returns YTD, 1Y, and max drawdown during those periods?\n\n---\n\n## Final Thoughts\n\nThe wheel is not going to make you 100% in a year. That was never the goal. But if you pick the right companies, size your positions so no single one can hurt you, and have the patience to sit through drawdowns and dead weeks, it does exactly what it's supposed to. A full year in, I'm up +35.13% on deployed capital, ahead of SPY, with a max drawdown under 10% and, on average, close to half the account in cash.\n\nThat's the case for boring. Better risk-adjusted returns, a shallower drawdown than the index, and a strategy simple enough to run for the rest of your life. Year two starts the same way year one did. One boring put at a time.\n\n\n**[Download Full Trade Log (CSV)](https://blog.mlabstrading.com/trade_logs/trade_log_2026_06_20.csv)**\n\n---\n\n## My stack\n\nI have about 30 different jobs running throughout the day inside of my homelab (r/homelab) pulling and processing stocks, options, news, sentiment, fundamentals, and technicals data. Ive been building this over the years and it started as a hackathon project at work 3 years ago (yep, before vibe coding). This pipeline does maybe 95% of the heavy lifting. By 8:05pm eastern, the data for the next day is ready for me to do a quick manual verification pass for the CSP cand","offTopic":true},{"id":"0e84e0af-d147-4908-80a7-c5b14e69858c","excerpt":"Tickeron's AI Pattern Scanner: Hype vs. Reality - A Community Verdict — In the volatile landscape of fintech, few promises are as alluring—or as dangerous—as \"AI-powered edge.\" Tickeron enters this space with a bold proposition: an artificial intelligence that doesn't just scan markets, but predicts outcomes using comp","url":"https://www.reddit.com/r/TraderTools/comments/1vturpe/tickerons_ai_pattern_scanner_hype_vs_reality_a/","role":"pain","weight":1.1216946,"occurredAt":"2026-08-20T20:17:33.000Z","sourceKey":"reddit","sourceName":"Reddit","credibility":0.62,"venue":"TraderTools","intent":"feature_request","painScore":0.52,"sentiment":-0.25,"confidence":0.73795694,"matchedPatterns":["frustrating","missing_feature"],"statement":"Opaque \"Black Box\" Logic A recurring theme in r/algotrading is the lack of transparency regarding how the AI arrives at its confidence scores.","title":"Tickeron's AI Pattern Scanner: Hype vs. Reality - A Community Verdict","body":"In the volatile landscape of fintech, few promises are as alluring—or as dangerous—as \"AI-powered edge.\" Tickeron enters this space with a bold proposition: an artificial intelligence that doesn't just scan markets, but predicts outcomes using complex pattern recognition and proprietary \"confidence scores.\"\n\nAs a product analyst, I’ve spent the last week digging through the trenches of trader communities to see if Tickeron’s AI is a legitimate \"quant-in-a-box\" or simply a high-priced technical indicator wrapped in machine-learning marketing.\n\n---\n\n## METHODOLOGY: Where We Looked\n\nTo bypass the polished results of SEO-optimized press releases, we analyzed over **150 user comments and threads** from:\n\n* **Reddit:** Specifically r/algotrading, r/stocks, and r/daytrading.\n* **Specialized Forums:** Elite Trader and Trade2Win.\n* **Review Aggregators:** G2 and Trustpilot (filtering for \"verified\" vs. \"invited\" reviews).\n* **Technical Blogs:** Independent evaluations from Liberated Stock Trader and community-run Substacks.\n\n---\n\n## THE VERDICT: Overall Community Sentiment\n\nThe community consensus on Tickeron is best described as **cautiously intrigued but frustrated by its complexity and cost.**\n\nWhile the \"AI Confidence Score\" is a powerful hook for tech-savvy traders, the reality of using the tool often clashes with its marketing. Users are generally fascinated by the underlying technology but find the learning curve steep and the pricing structure prohibitive for the average retail account.\n\n---\n\n## WHAT USERS PRAISE (The Pros)\n\n### 1. Unique Pattern Detection Beyond the Basics\n\nWhile most scanners find simple support and resistance, Tickeron’s engine identifies complex multi-stage formations (like Broadening Wedges or Inverse Cup and Handles) across thousands of tickers simultaneously.\n\n> *\"I'll give it this – it flagged a developing inverse head and shoulders on [Stock] a full two days before I spotted it on my own charts. The 'confidence' was at 65%, and it did play out.\"* – **u/TechTrader_22 on r/algotrading**\n\n### 2. Significant Time-Saving for Swing Traders\n\nFor those working full-time jobs, the \"Intraday Pattern Feed\" acts as a force multiplier, condensing hours of manual charting into a few minutes of alert review.\n\n> *\"It's essentially a high-end research assistant. It doesn't tell me what to buy, but it tells me where to look, which is 80% of the battle when you're scanning 3,000+ stocks.\"* – **User on G2 Reviews**\n\n### 3. Quantitative \"Confidence\" Guardrails\n\nThe inclusion of a \"success probability\" helps traders move away from \"gut feel\" and toward a more data-driven entry.\n\n> *\"Even if the AI isn't 100% right, having that 'Odds of Success' percentage makes me pause on low-probability setups I might have otherwise gambled on.\"* – **Comment on Trade2Win**\n\n---\n\n## WHAT USERS CRITICIZE (The Cons)\n\n### 1. Signal Overload & Noise\n\nThe scanner is often *too* sensitive, generating a volume of alerts that can lead to analysis paralysis.\n\n> *\"It's a firehose of data. You get 50 'patterns with 70%+ confidence' a day. By the time you filter for volume, sector, and market context, you've done 90% of the work yourself anyway.\"* – **Elite Trader forum member**\n\n### 2. Opaque \"Black Box\" Logic\n\nA recurring theme in r/algotrading is the lack of transparency regarding how the AI arrives at its confidence scores.\n\n> *\"The problem is the 'Why.' If the AI says 80% confidence but the RSI is screaming overbought and there's a Fed meeting in an hour, the AI doesn't seem to care. It's a black box that ignores macro context.\"* – **u/QuantSkeptic on Reddit**\n\n### 3. Pricing Complexity and Billing Friction\n\nMany users reported \"subscription fatigue,\" citing that the most useful features are often locked behind higher-tier, expensive monthly plans.\n\n> *\"The pricing is a maze. You sign up for one thing, then realize you need three other 'credits' or 'add-ons' to get the real-time data you actually need.\"* – **Trustpilot Review**\n\n---\n\n## THE BIG QUESTION: DOES IT MAKE MONEY?\n\nThe \"Backtest Paradox\" is the central point of contention in the trading community. Tickeron provides historical success rates for its patterns, but users find it nearly impossible to replicate those results in live environments without significant slippage.\n\nFurthermore, there is a fundamental discomfort with the **Black Box issue**. In quantitative trading, a model is only as good as your ability to stress-test it. Because Tickeron doesn't reveal the specific weightings of its variables, traders are forced to \"trust the machine\"—a cardinal sin for many veterans.\n\n> *\"I use it as an idea generator, nothing more. If you treat it like a 'Money Printing Button,' you'll be broke in a month. If you treat it as a filter for your own strategies, it has value.\"* – **Senior Trader on Elite Trader**\n\n---\n\n## FINAL CONCLUSION & WHO IT'S FOR\n\nTickeron is less of a trading robot and more of a **highly specialized research assistant.** Your profitability will depend entirely on how well you integrate its findings into your own disciplined process.\n\n* **NOT FOR:** Beginners looking for \"Buy/Sell\" arrows, traders on a budget under $5,000, or anyone who dislikes complex software interfaces.\n* **POTENTIALLY FOR:** Experienced, quantitatively-inclined traders who already have a winning strategy and need a supplemental tool to expand their ticker universe. It's a **\"lab instrument,\"** not a **\"dashboard gauge.\"**","offTopic":true},{"id":"a222a7fa-a1ac-41ff-90e7-fb3351d0882c","excerpt":"I spent 8 months asking Claude dumb questions. Now it scans 500 stocks and hands me trade cards with actual suggested positions. Here's the full story, and EXACTLY how it works! FINAL MAJOR UPDATE!!! — This is a follow up post to the post I made last week. I made some **MAJOR** edits, and this is the final post regardi","url":"https://www.reddit.com/r/smallstreetbets/comments/1r883gd/i_spent_8_months_asking_claude_dumb_questions_now/","role":"pricing","weight":1.1104167,"occurredAt":"2026-02-18T16:44:17.000Z","sourceKey":"reddit","sourceName":"Reddit","credibility":0.62,"venue":"smallstreetbets","intent":"pricing_complaint","painScore":0.5228571,"sentiment":-0.10714286,"confidence":0.7291667,"matchedPatterns":["too_expensive"],"statement":"If options are overpriced, sellers have an edge.","title":"I spent 8 months asking Claude dumb questions. Now it scans 500 stocks and hands me trade cards with actual suggested positions. Here's the full story, and EXACTLY how it works! FINAL MAJOR UPDATE!!!","body":"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.\n\nEight months ago I gave ChatGPT $400 and told it to trade for me.\n\nIt doubled my money on the first trade. Then it told me it can't see live stock prices.\n\nClassic!\n\nSo 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.\n\n**My first post about this project showed a huge prompt, version 1 —**\n\nCORE STRATEGY BLUEPRINT: QUANT BOT FOR OPTIONS TRADING\n\nSomehow 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.\n\n**My second post was about the second prompt I made, version 2—**\n\nFor this prompt, I was taking screen grabs of live options chains, and feeding them to the prompt, thinking this was the holy grail.\n\n\"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.\"\n\nI 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.\n\n**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 —**\n\nHow it works (daily, automated):\n\nStep 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\n\nStep 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.\n\nStep 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.\n\nStep 10 – AUTOMATE!: 10\\_run\\_pipeline.py runs everything end-to-end each morning. (\\~1000 seconds)\n\nReceipts (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.)\n\nThe 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)\n\n**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) —**\n\nIt 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.\n\nLet me walk you through exactly how it works.\n\nThe system pulls from three sources. All free. All real-time.\n\n**(1) Tastytrade** (my brokerage account) gives me 41 data points per stock:\n\n* How expensive options are right now (implied volatility)\n* How much the stock actually moves (historical volatility)\n* Whether options are cheap or expensive compared to the past year (IV rank)\n* The full options chain — every strike, every expiration, live bid/ask prices\n* Live Greeks (delta, theta, vega — the math behind options pricing)\n\n**(2) Finnhub** gives me the fundamentals + intelligence:\n\n* financial metrics per stock (revenue, margins, cash flow, debt, everything)\n* Analyst ratings (how many say Buy vs Hold vs Sell)\n* Insider transactions (are executives buying or selling their own stock?)\n* Earnings history (did the company beat or miss expectations?)\n* News headlines with dates\n\n**(3) FRED** (the Federal Reserve's database) gives me the big picture:\n\n* VIX (market fear gauge)\n* Interest rates\n* Unemployment\n* Inflation\n* GDP\n* Consumer confidence\n\nThat's the raw material. Now here's what happens to them!\n\n**The scoring engine — how 500 stocks become 8**\n\nEvery stock gets scored from 0 to 100 across four categories. Think of it like a report card.\n\n**Vol-Edge (is there a pricing mistake?)**\n\nThis answers one question: are options priced higher than they should be?\n\nIf a stock moves 11% per year but options are priced like it moves 27%, someone's wrong. That gap is where the edge lives.\n\nThe 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.\n\n**Quality (is the company solid?)**\n\nI'm not selling options on a company that might go bankrupt.\n\nThis 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.\n\nA company that's profitable, growing, paying down debt, and generating cash scores high. A company burning cash with declining margins scores low. Simple.\n\n**Regime (what's the economy doing?)**\n\nThe 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).\n\nThe system reads 9 macro indicators from the Fed and classifies the current regime. Then it scores each stock based on how well it fits.\n\nHere'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.\n\n**Info-Edge (what's the buzz?)**\n\nThis combines five signals:\n\n* Analyst consensus (are the pros bullish?)\n* Insider activity (are execs buying their own stock? That's usually a good sign. Selling? Warning sign.)\n* Earnings momentum (beating estimates consistently?)\n* Options flow (unusual volume in calls vs puts?)\n* News sentiment (are headlines getting more positive or negative?)\n\n**The convergence gate — why it's called \"convergence\"**\n\nHere'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.\n\nBut when multiple independent signals all point the same direction? That's convergence. That's when the probability actually tilts in your favor.\n\nThe 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.\n\n**The trade cards — this is the bread and butter!**\n\nFor every stock that survives, the system builds an actual trade card.\n\nNot \"maybe consider an iron condor.\" An actual position with real strikes, real prices, real risk.\n\n**Why this trade** (in plain, easy to understand English, not confusing finance-bro jargon):\n\n**Risk warnings:**\n\n**Key stats:**\n\nEverything. One card. No clicking. No digging. Screenshot it and you have the full picture.\n\nAll of this information is coming from REAL DATA!\n\nWhat Claude actually does (and doesn't do)\n\nThis is the part people get wrong.\n\n**Claude does NOT:**\n\n* Pick stocks\n* Decide what to trade\n* Predict the future\n* Make any decisions at all\n\n**Claude DOES:**\n\n* Read the plain English signals section of each trade card\n* Translate dense numbers into sentences a normal person can understand\n\nThe 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.\n\n(I spent a ton of time auditing to make sure the data was complete, and cleaned, and it was not fun!)\n\nClaude'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.\"\n\nThat's it. The robot reads math and explains it in English. I make the decisions.\n\n**","offTopic":true},{"id":"101f9bea-0124-4bb7-965e-9094314faea4","excerpt":"I spent 8 months asking Claude dumb questions. Now it scans 500 stocks and hands me trade cards with actual suggested positions. Here's the full story, and EXACTLY how it works! FINAL MAJOR UPDATE!!! — \\*\\*Educational Purpose Only!\\*\\*\n\nThis is a follow up post to the post I made last week. I made some \\*\\*MAJOR\\*\\* ed","url":"https://www.reddit.com/r/VibeCodersNest/comments/1r7z7jk/i_spent_8_months_asking_claude_dumb_questions_now/","role":"pricing","weight":1.1070555,"occurredAt":"2026-02-18T10:13:15.000Z","sourceKey":"reddit","sourceName":"Reddit","credibility":0.62,"venue":"VibeCodersNest","intent":"pricing_complaint","painScore":0.5228571,"sentiment":-0.10714286,"confidence":0.7269595,"matchedPatterns":["too_expensive"],"statement":"If options are overpriced, sellers have an edge.","title":"I spent 8 months asking Claude dumb questions. Now it scans 500 stocks and hands me trade cards with actual suggested positions. Here's the full story, and EXACTLY how it works! FINAL MAJOR UPDATE!!!","body":"\\*\\*Educational Purpose Only!\\*\\*\n\nThis 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.\n\nEight months ago I gave ChatGPT $400 and told it to trade for me.\n\nIt doubled my money on the first trade. Then it told me it can't see live stock prices.\n\nClassic!\n\nSo 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.\n\n\\*\\*My first post about this project showed a huge prompt, version 1 —\\*\\*\n\nCORE STRATEGY BLUEPRINT: QUANT BOT FOR OPTIONS TRADING\n\nSomehow 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.\n\n\\*\\*My second post was about the second prompt I made, version 2—\\*\\*\n\nFor this prompt, I was taking screen grabs of live options chains, and feeding them to the prompt, thinking this was the holy grail.\n\n\"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 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 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.\"\n\nI 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.\n\n\\*\\*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 —\\*\\*\n\nHow it works (daily, automated):\n\nStep 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\n\nStep 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.\n\nStep 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.\n\nStep 10 – AUTOMATE!: 10\\\\\\_run\\\\\\_pipeline.py runs everything end-to-end each morning. (\\\\\\~1000 seconds)\n\nReceipts (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.)\n\nThe 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)\n\n\\*\\*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) —\\*\\*\n\nIt 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.\n\nLet me walk you through exactly how it works.\n\nThe system pulls from three sources. All free. All real-time.\n\n\\*\\*(1) Tastytrade\\*\\* (my brokerage account) gives me 41 data points per stock:\n\n\\* How expensive options are right now (implied volatility)\n\n\\* How much the stock actually moves (historical volatility)\n\n\\* Whether options are cheap or expensive compared to the past year (IV rank)\n\n\\* The full options chain — every strike, every expiration, live bid/ask prices\n\n\\* Live Greeks (delta, theta, vega — the math behind options pricing)\n\n\\*\\*(2) Finnhub\\*\\* gives me the fundamentals + intelligence:\n\n\\* financial metrics per stock (revenue, margins, cash flow, debt, everything)\n\n\\* Analyst ratings (how many say Buy vs Hold vs Sell)\n\n\\* Insider transactions (are executives buying or selling their own stock?)\n\n\\* Earnings history (did the company beat or miss expectations?)\n\n\\* News headlines with dates\n\n\\*\\*(3) FRED\\*\\* (the Federal Reserve's database) gives me the big picture:\n\n\\* VIX (market fear gauge)\n\n\\* Interest rates\n\n\\* Unemployment\n\n\\* Inflation\n\n\\* GDP\n\n\\* Consumer confidence\n\nThat's the raw material. Now here's what happens to them!\n\n\\*\\*The scoring engine — how 500 stocks become 8\\*\\*\n\nEvery stock gets scored from 0 to 100 across four categories. Think of it like a report card.\n\n\\*\\*Vol-Edge (is there a pricing mistake?)\\*\\*\n\nThis answers one question: are options priced higher than they should be?\n\nIf a stock moves 11% per year but options are priced like it moves 27%, someone's wrong. That gap is where the edge lives.\n\nThe 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.\n\n\\*\\*Quality (is the company solid?)\\*\\*\n\nI'm not selling options on a company that might go bankrupt.\n\nThis 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.\n\nA company that's profitable, growing, paying down debt, and generating cash scores high. A company burning cash with declining margins scores low. Simple.\n\n\\*\\*Regime (what's the economy doing?)\\*\\*\n\nThe 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).\n\nThe system reads 9 macro indicators from the Fed and classifies the current regime. Then it scores each stock based on how well it fits.\n\nHere'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.\n\n\\*\\*Info-Edge (what's the buzz?)\\*\\*\n\nThis combines five signals:\n\n\\* Analyst consensus (are the pros bullish?)\n\n\\* Insider activity (are execs buying their own stock? That's usually a good sign. Selling? Warning sign.)\n\n\\* Earnings momentum (beating estimates consistently?)\n\n\\* Options flow (unusual volume in calls vs puts?)\n\n\\* News sentiment (are headlines getting more positive or negative?)\n\n\\*\\*The convergence gate — why it's called \"convergence\"\\*\\*\n\nHere'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.\n\nBut when multiple independent signals all point the same direction? That's convergence. That's when the probability actually tilts in your favor.\n\nThe 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.\n\n\\*\\*The trade cards — this is the bread and butter!\\*\\*\n\nFor every stock that survives, the system builds an actual trade card.\n\nNot \"maybe consider an iron condor.\" An actual position with real strikes, real prices, real risk.\n\n\\*\\*Why this trade\\*\\* (in plain, easy to understand English, not confusing finance-bro jargon):\n\n\\*\\*Risk warnings:\\*\\*\n\n\\*\\*Key stats:\\*\\*\n\nEverything. One card. No clicking. No digging. Screenshot it and you have the full picture.\n\nAll of this information is coming from REAL DATA!\n\nWhat Claude actually does (and doesn't do)\n\nThis is the part people get wrong.\n\n\\*\\*Claude does NOT:\\*\\*\n\n\\* Pick stocks\n\n\\* Decide what to trade\n\n\\* Predict the future\n\n\\* Make any decisions at all\n\n\\*\\*Claude DOES:\\*\\*\n\n\\* Read the plain English signals section of each trade card\n\n\\* Translate dense numbers into sentences a normal person can understand\n\nThe 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.\n\n(I spent a ton of time auditing to make sure the data was complete, and cleaned, and it was not fun!)\n\nClaude's only job is the translation layer. It t","offTopic":true},{"id":"2b33fa61-06e3-449a-b3ff-460b9df1f286","excerpt":"Platforms, Indicators, Strategies, Fabio & Andrea’s Course, and A LOT of questions! — Getting straight to the point, I have a couple of questions and notes (which I hope would help myself and many others new traders learning Orderflow), so I’d like to divide it into sections\n\n(SCROLL DOWN FOR QUESTIONS)\n\n\\----\n\n# CONTE","url":"https://www.reddit.com/r/OrderFlow_Trading/comments/1vvokeb/platforms_indicators_strategies_fabio_andreas/","role":"demand","weight":1.1028987,"occurredAt":"2026-08-22T21:31:44.000Z","sourceKey":"reddit","sourceName":"Reddit","credibility":0.62,"venue":"OrderFlow_Trading","intent":"tool_discovery","painScore":0.33,"sentiment":0.46938777,"confidence":0.8292472,"matchedPatterns":["looking_for","terrible","currently_i_use","too_expensive","missing_feature"],"statement":"(2) **DeepCharts** is a great platform but extremely overpriced.","title":"Platforms, Indicators, Strategies, Fabio & Andrea’s Course, and A LOT of questions!","body":"Getting straight to the point, I have a couple of questions and notes (which I hope would help myself and many others new traders learning Orderflow), so I’d like to divide it into sections\n\n(SCROLL DOWN FOR QUESTIONS)\n\n\\----\n\n# CONTEXT\n\nGood day everyone,  \nI’ve been studying Orderflow for about three weeks now, spending 5-8 hours just learning theory and attempting to paper trade what I learned to understand with real-time practical examples. Relatively new to trading but I’d say I’m learning at a pretty productive rate.\n\nAs said, I’m new, so I have plenty of questions myself, but I’m also writing a lot of inside thoughts I had to get out of my system lol.\n\n\\----\n\n# PLATFORMS\n\nI always do my research on the traders I am learning from, and what platforms or tools to use. From that, I’ve come to a conclusion that\n\n(1) **TradingView** is ass for Orderflow.  \n(2) **DeepCharts** is a great platform but extremely overpriced. The only reason I’d see people using it is if you want the exact templates they showcase in their videos or courses.  \n(3) I often see **QuanTower**, **ATAS**, and **Sierra** being recommended. **Sierra** = Best, but shit UI & harder learning curve. **QuanTower** = most beginner friendly, great, offers 28-day free trial w/ paper trading via AMP/CQG (but no historical tick data for market replay), and decently priced. **ATAS** = Contender for best, best UI, decently priced, everything sounds great about ATAS ngl.\n\n**Currently using QuanTower** as they provide full access with demo paper trading and I’m still learning theory.\n\n\\----\n\n# FABIO, ANDREA & THEIR COURSE\n\nI’ve watched the entire Orderflow course they sell and I learned a fuck ton, but damn. This bitch (Andrea) is beatboxing and waffling so much throughout the entire thing. The material is useful, but it’s very long-winded and could probably have been condensed to half the runtime. It’s also pretty much rawdogged solely by Andrea (NOT FABIO). Although Fabio had a couple live sessions that lasted 1-2 hours each.\n\nFabio on the other hand is definitely a great trader with a lot of validation/proof from competitions, and even just from his speech–it’s very obvious that he’s an experienced trader.  \n     \n  \n**I want to separate what they actually teach and what they do not:**\n\nIt **does** teach you from the very basics of Orderflow (not trading), to Futures, Contracts, Sessions, Orderflow Indicators (in detail), basic concepts like Auction Theory, LAT, Breakouts, typical daily auction outcomes, Absorption vs Exhaustion, Stop runs, Big orders & Icebergs, and more (listed the important ones).\n\nIt **does not** turn those concepts into a fully mechanical playbook. It doesn’t teach you how to properly use the indicators in conjunction with each other, where to target profits or stop losses, and what settings to use for the indicators. There’s no clear guidance for these.\n\n\\----\n\nFor those speculative & question why they’re making videos/selling a course/platform when they already have the edge and are profitable or whatnot, why the fuck not. Free money man, and maybe they really do wanna just help. It also just grows their trust and incentivizes people to use their platform.\n\nI wrote this section just for the new traders getting into Orderflow like myself. They’re legit, but prepare to go through a wafflehouse.\n\n\\----\n\n# STRATEGIES & INDICATORS\n\nFrom all that I’ve researched, the edge constantly changes over the years. What’s important in trading is to learn reading the market itself. It’s why I chose to learn Orderflow. It’s not a strategy. It’s an informational  tool that can be applied to almost any strategy, and helps build a more consistent framework.\n\nA strategy is useless without position sizing, risk limits, and consistent execution, so I’m trying to define those rules alongside the setup rather than treating them as an afterthought.\n\nThat being said, since I’m newer, I thought I’d learn the basics. The way I currently understand auction-based intraday trading, a lot of setups can be simplified into two broad outcomes:\n\n(1) Failed Auction/Mean Reversion  \n(2) Successful Auction/Breakout\n\nI’m personally not ready for the more complex strategies, so I thought I’d start off with IVB/ORB because it gives me an objective location and time window to practice reading order flow instead of trying to interpret every footprint candle on the chart.  \n  \n  \n**Current strategy:**\n\nI trade a 15-30 min ORB. I **LONG** when price breaks ORH, pulls back into ORH/VAH–POC, sellers get absorbed/exhausted and buyers regain initiative; **SHORT** when price breaks ORL, pulls back into ORL/POC–VAL, buyers get absorbed/exhausted and sellers regain initiative — with the stop beyond the defended structure and TP based on a predefined R:R/target.  \n  \n  \n**Indicators:**\n\nI recently [found a video](https://youtu.be/cUTsoU-15Tc?si=mkusmTcCe5R5wm3N) from Fabio that coincidentally explains exactly this but wondered, he mentions using DeepChart specific indicators. Specifically his IVB high/low Indicator + Deep Statistic Analysis. He also used normal volume analysis Orderflow tools, but those two are the important ones.\n\nThe ORH/ORL part is easy to recreate on Quantower/ATAS with an opening-range or Initial Balance tool. I’d still keep a Volume Profile over that same opening period for VAH/POC/VAL. What I can’t recreate directly is DeepCharts’ proprietary statistical target model, which appears to calculate likely post-breakout extensions from historical data.\n\n\\----\n\n# QUESTIONS:\n\n**TL;DR / context for anyone who skipped everything above:**\n\nI’m a newer trader learning Orderflow, currently paper trading ES/NQ on Quantower. The setup I want to focus on is a **15–30m ORB with Orderflow confirmation**: wait for ORH/ORL to form, profile that same opening period for VAH/POC/VAL, wait for a breakout, then preferably enter on a retracement where the countertrend side gets absorbed/exhausted and the breakout side regains initiative.\n\nI understand the theory, but I’m trying to turn it into an actual **repeatable and backtestable strategy** rather than discretionary chart reading.\n\n**1. 15m or 30m Opening Range?**  \nFor ES/NQ, which would you recommend and why? Fabio used 30m in the example I watched, but I’m more interested in what actually holds up statistically rather than copying his setting blindly.\n\n**2. What confirmation is actually necessary on the retest?**  \nFor example, after a bullish ORH break and retracement into ORH / VAH–POC, is seeing seller absorption/exhaustion + buyers regaining initiative enough? Or would you also require Delta, stacked imbalances, POC/value migration, etc.? I want to avoid adding indicators that don’t actually improve expectancy.\n\n**3. Where should the stop actually go?**  \nMy current thought is **behind the structure that invalidates the trade** — e.g. below the absorption/reload area or pullback low for a long — rather than using an arbitrary fixed 10–15 tick stop. Is that generally how you guys approach it?\n\n**4. How should I set profit targets without DeepCharts?**  \nTheir IVB indicator uses historical statistical analysis to generate a high-probability “Protection” target and farther extensions. Does ATAS or Quantower have anything comparable, or should I simply backtest fixed targets such as 1R / 1.5R / 2R and eventually build my own MFE/extension statistics?\n\n**5. Can this whole strategy realistically be done in Quantower or ATAS?**  \nFrom what I understand, ORH/ORL, a fixed opening Volume Profile, VAH/POC/VAL, Footprints, Delta, imbalances, absorption, etc. are all available. Is DeepCharts really only giving additional convenience/proprietary statistical targets, or am I missing something important?\n\n**6. What should I record when backtesting this?**  \nI’m planning to track things like OR duration/width, breakout direction/time, retest depth, confirmation type, stop distance, MAE, MFE, R result and time of day. What other variables would actually be useful without overfitting the strategy?\n\n\\----\n\n# CONCLUSION:\n\nI’m not looking for a holy grail or expecting Orderflow to tell me with certainty where price goes next. I just want to take what I’ve learned and reduce it into **one simple setup that I can execute the same way hundreds of times and actually collect data on.**\n\nThe strategy I’m trying to validate is basically:\n\n**Opening Range forms → breakout → retracement into an important OR/Profile area → countertrend aggression fails → breakout side regains initiative → enter → structural stop → predefined target.**\n\nInverse for shorts.\n\nMy main problem isn’t understanding what absorption, exhaustion, Delta, Volume Profile, etc. mean anymore — it’s defining **exactly when they matter, what confirms the trade, where the trade is invalidated, and how to manage the risk/target consistently.**\n\nWould really appreciate input from anyone who actually trades ES/NQ ORB/IB with Orderflow, especially criticism of the framework above.","offTopic":true},{"id":"68e1850c-9eb7-4b85-a34c-57b4f6bd48fb","excerpt":"Weekend Stock Screen: GARP Candidates (Tutorial) — I haven't done a \"Weekend Screen\" post in a while now. \n\nThis is something new that I've done in this iteration of the The Inner Circle (TIC) community over the last two years from its first iteration between 1998-2024. Damn, that makes me feel old.\n\nIf there's one que","url":"https://www.reddit.com/r/InnerCircleInvesting/comments/1vq0om0/weekend_stock_screen_garp_candidates_tutorial/","role":"request","weight":1.0120082,"occurredAt":"2026-08-16T15:56:31.000Z","sourceKey":"reddit","sourceName":"Reddit","credibility":0.62,"venue":"InnerCircleInvesting","intent":"feature_request","painScore":0.3,"sentiment":1,"confidence":0.77846783,"matchedPatterns":["wish","missing_feature"],"statement":"Through these screens, they provided my first identification an analysis of names like $VRT, $P, $MRVL, $ONTO, $ARM, $VST, $CEG, $TLN, and so many others I wish I would have actually acted on.","title":"Weekend Stock Screen: GARP Candidates (Tutorial)","body":"I haven't done a \"Weekend Screen\" post in a while now. \n\nThis is something new that I've done in this iteration of the The Inner Circle (TIC) community over the last two years from its first iteration between 1998-2024. Damn, that makes me feel old.\n\nIf there's one question I get over and over again, here at via my TikTok channel it's: *Where do you find the stocks to follow?*\n\nTruth be told, they come from anywhere. Media outlets like Bloomberg, CNBC or Yahoo. Analyst reports, stories, people on the street. Technology white papers and segment research turns up names all the time. But my favorite way to find tomorrow's winners is through the \"Stock Screen.\" \n\nWhy?\n\nBecause it allows my to bring to bear all my years of experience, education and work in this area, boil it down to my most material data points toward finding quality companies and stocks, inputting the information and turning the handle to see what comes out. Without this process, there's too many fish in the sea. \n\nI use multiple different screens. Some of them allow my to be like Buffett. Some of them allow me to operate more like a hedge fund while others more like an income manager or standard investment analyst. It just depends on what I'm looking for within the current dynamic of the market. Given today's rich valuations, volatility and extended bull cycle, it's a time when I don't want to turn my back on growth, but it needs to be the right type of growth.\n\nThis is a good time to mention [the post](https://www.reddit.com/r/InnerCircleInvesting/comments/1tfw86f/stockanalysiscom_why_favoritekey_features/) where I break down some of the features of the StockAnalysis (SA) site I so highly suggest. [It's the only affiliate code I offer because I love what they do.](https://www.reddit.com/r/InnerCircleInvesting/comments/1tfw86f/stockanalysiscom_why_favoritekey_features/)\n\nThrough these screens, they provided my first identification an analysis of names like $VRT, $P, $MRVL, $ONTO, $ARM, $VST, $CEG, $TLN, and so many others I wish I would have actually acted on. That's a key point to make - the screen is an 'identification' tool. It starts the process only. You then need to go down the rabbit hole to further filter the stocks down to a handful of names that are most interesting. I do all this work in [StockAnalysis.com](http://StockAnalysis.com) \n\n**GARP**\n\n Growth at a Reasonable Price, GARP, is probably my favorite screen. At my core, I may not like the \"ARP\" as much as the \"G\" but there needs to be something to get my arms around to allow me to feel 'safer' with my investments. That is what today's screen is getting after.\n\nFirst, the inputs for today's screen:\n\n[GARP Screen](https://preview.redd.it/3uro7mps2rjh1.png?width=1030&format=png&auto=webp&s=0e0606b37214004f3a900ec76f8d7a51ece7ef0b)\n\nFirst thing to notice is the yellow box indicating that we're using my \"GARP\" screen. If you're new to [StockAnalysis.com](http://StockAnalysis.com), this is where you can format and save all your screens.\n\nBased on the number of results any screen gets, we can then modify some of these filters to tighten the field. This is a primary activity because there's not enough time to always be researching hundreds of hits with each screen. I find 25-50 is the sweet spot for a net that catches the best fish.\n\nThe \"GARP\" screen can be a wide net on its own. I use FCF Yield and PEG to help drill down the names to a more manageable number. You may notice I'm not using Market Cap. This is because I want to get smaller companies as well, those that offer the most potential upside. With some of the fields you will see a filter of \"Any\" which simply means I want the column on the screen so I can sort by it. Often times I'll use \"RSI\" as a filter so I can see how the stock is being valued in the market currently. This is also why I use \"Stock \"price\" and \"Moving Average\" fields. And, of course, you know I also like to see the \"Forward P/E.\" I will often use this to further reduce the hit list if we see too many names.\n\n**First Pass Results**\n\nThe first pass of this GARP screen yielded 102 names, too many. I am also searching by ROIC (Return On Invested Capital).\n\nhttps://preview.redd.it/ow1piagg5rjh1.png?width=1067&format=png&auto=webp&s=a10e61b85fa471e156c73c7eb77639dc6d5e0060\n\nI need to selectively boil this number down to closer to 50:\n\nThe adjusted Filter: \n\nhttps://preview.redd.it/wlncoqmz6rjh1.png?width=1033&format=png&auto=webp&s=9f73f1a0f7852a3212d6a5004a49f86f4a3dadca\n\nNotice I did not add in any \"Forward P/E\" filter value. That is because the combination of PEG plus FCF Margin is doing a lot of the heavy lifting to find these GARP names while \"ROIC\" is still focusing on quality company models.\n\nBumping ROIC, FCF Margin and Revenue Growth 3Y just means we're focusing on the cream at the top of this GARP screen\n\n**Second Pass Results**\n\nMuch better, we cut the field down by almost half, now 53 names.\n\nhttps://preview.redd.it/2t9p033m7rjh1.png?width=813&format=png&auto=webp&s=a9c6b14a7742488caa7ee3bf34cbdb0f068e63b1\n\nhttps://preview.redd.it/beaysc5q7rjh1.png?width=809&format=png&auto=webp&s=ac7ae63cee02daebf6655325d24f59c4029bf29d\n\n**The Results & Top Level Analysis**\n\nWe've filtered out list down to 53 names that all represent some level of \"Growth\" and elements of \"Reasonable Price,\" and have it sorted by ROIC to focus on pure quality. Again, this is just where the work begins. Now we can start surveying the field, tossing out anomalies, focusing more on PEG and Forward P/E while also surveying where each stock exists in its range related to current price, 50/200 MAs and RSI. \n\nImmediately we see two AvS (AI vs. Software) stocks that jump out with solid numbers\n\n* $VEEV\n* $APP\n\nAs a matter of note, a very early screen found $VEEV in the mid $160s. I didn't enter the stock because I already had other AvS names like $NOW, $CRM, $MSFT, $RDDT, $PLTR, etc. But the metrics remain fantastic for both of these stocks. $APPs recent earnings, however, didn't paint a somewhat cautionary tale. Is the story changing?\n\nWe also see high volatility names like $SNDK, at #6, when ranking by ROIC. And SNDK has started bouncing again off recent lows.\n\nI typically like to survey the names first and then start sorting by PEG which helps really tie down value and opportunity. Lack of PEG doesn't disqualify the names, it just means that something may be working against the reading:\n\n* Analyst EPS-growth estimates aren’t available or aren’t reliable\n* Expected EPS growth is negative or near zero\n* The company has an unusual earnings base, making the PEG calculation nonsensical\n* Data provider simply doesn’t calculate PEG for that name\n\nThis is why I filter across multiple valuation metrics.\n\nFrom this point, I start surveying the list, looking for names that are familiar, in the news or with metrics that suggest more work is needed. For this pass, I'll be pulling out:\n\n* $NXT\n* $SNDK\n* $KNSL\n* $VEEV\n* $APP\n* $PDD\n* $NVO\n* $NBIX\n\n...for further review\n\n**Summary**\n\nIn a market like this, I don't like to let down my guard. I always factor current market health and valuation into my activities to help reduce the chance of a poorly timed investment. I like value and growth, but most stocks don't perform well into a broad decline. Cheap stocks get cheaper, momentum stocks get crushed. \n\nValuation and fundamentals act as the foundation and gravity for your investments. They give you something to hold onto, something to help value your positions while telling a story about potential growth. When talking about \"Growth\" you always have to be concerned with current market valuation as it's often the first component to be jettisoned. That is why the fundamentals attached to \"Reasonable Price\" of GARP is so important. \n\nAnd, many times, we're just window shopping. Patience should always ride shotgun on your shopping trips.\n\nThis is why you also *need* to have a good stock analysis site like SA. You simply must start learning how to do some of your own fundamental mining. It only has to be as difficult as you want it to be. I find myself constantly researching and figuring out new/better ways to hone my valuation efforts, trying to pull back the curtain on better cash flow analysis and spot positive and negative trends. Companies are very good at hiding their skeletons. It's your job to be as knowledgeable as possible.\n\nHave a great Sunday. I'll be diving into some of these names and will let you know what I find.\n\nJ\n\n\n\n\n\n\n\n \n\n","offTopic":true},{"id":"329e3c0c-3906-41eb-8889-1ded73a36b30","excerpt":"I spent 8 months and $40K+ developing an AI trading platform: quant research, backtesting, live strategy automation, indicators, and more — In my experience as a trader, almost every AI tool, site, and piece of software in this space only does **one thing** in my workflow. I kept running into the same issues, asking **","url":"https://www.reddit.com/r/ai_trading/comments/1uhj4g9/i_spent_8_months_and_40k_developing_an_ai_trading/","role":"request","weight":0.9320667,"occurredAt":"2026-06-28T00:51:47.000Z","sourceKey":"reddit","sourceName":"Reddit","credibility":0.62,"venue":"ai_trading","intent":"feature_request","painScore":0.24,"sentiment":0.5,"confidence":0.75166667,"matchedPatterns":["wish"],"statement":"vii) A **100% free**, start-to-finish, beginner-to-expert course packed with structured learning paths, interactive lessons + exercises, and a full glossary, *that I wish I had when I first started.* viii) **Dedicated local-execution softw…","title":"I spent 8 months and $40K+ developing an AI trading platform: quant research, backtesting, live strategy automation, indicators, and more","body":"In my experience as a trader, almost every AI tool, site, and piece of software in this space only does **one thing** in my workflow. I kept running into the same issues, asking **the exact same question as thousands of other traders…**\n\n*\"Why am I paying for a* ***dozen*** *different tools for a* ***dozen*** *different things?\"*\n\n**-----**\n\nThe answer I wanted was [one platform that does all of it, for a fraction of the cost;](https://www.wealthlearn.ca)\n\ni) An **AI-powered hub** that **researches**, **backtests**, and **automates** **strategies** **live** (*or to TopStep*) across **10,000+ instruments** and with **10+ years** of multi-timeframe history included, *no coding experience or data required.*\n\nii) An **AI mode**l that builds any **custom TradingView indicator** in *Pine Script v6.*\n\niii) Professionally built **indicators** for popular concepts: ***OTE/STDV, Bookmap-style order flow*** *proxies for TradingView,* and ***Volume Profile (AMT).***\n\niv) *Real-time* **news**, intraday **GEX** levels, sector overviews, and **order flow** feeds.\n\nv) Advanced **stock screening**: *earnings, key metrics, fundamentals, analyst opinions, TradingView charting, and live news.*\n\nvi) **Daily**, **weekly**, and **monthly** pre-market **AI stock picks**, performance-tracked, built from thousands of factors in a custom database.\n\nvii) A **100% free**, start-to-finish, beginner-to-expert course packed with structured learning paths, interactive lessons + exercises, and a full glossary, *that I wish I had when I first started.*\n\nviii) **Dedicated local-execution software** for **TopStep** that abides by their API rules and includes custom charting, economic calendars, volume standard-deviation gauges, and more.\n\nix) a hub to connect to a **live brokerage**, analyze positions, model long-term portfolios, and monitor my algorithms.\n\n# And most importantly, ONE AI agent that can actually do all of it.\n\n**-----**\n\nBecause nothing like this existed, I spent eight months relentlessly researching and developing my platform (with the help of the best professional developers I know) to make my dream a reality.\n\n[WealthLearn.ca](http://WealthLearn.ca) brings every feature above into a single platform for a fraction of the cost, so you can stop tangling together scattered tools and paying for a dozen separate subscriptions.\n\nIt connects directly with **TradingView**, **live brokerages** (*Wealthsimple, Moomoo, Kraken, Coinbase, Webull, and more)*, and TopStep... **having two clear goals;**\n\n**1) Bring the benefits of quant** and **algo trading** to **retail traders,** **WITHOUT** the coding, complexity, or the institutional price tag.\n\n2) **Unify every tool needed, from day trading to macro investing, into one ecosystem,** instead of a dozen expensive, single-purpose tools.\n\n**--**\n\nI really don't want this to feel like a pitch, but there's so much low-effort vibe-coded stuff going around right now that I thought something I've poured this much real work into could actually be useful to people. have a look at the video above for a glimpse of what we've built, and if you don't want to spend anything, there's a free trial so you can see exactly what I'm talking about.\n\n**Happy to answer anything and everything in the comments.**  Whether it be about the **platform,** its **tech stack,** my team's **development experience,** or **trading** in general, feel free to\\* ***ask*** *away.*\n\n>**DISCLAIMER**: As always, Educational software and tools designed for non-personalized analysis and deterministic execution , not financial advice.  Trading is extremely high risk and is not suitable for every investor.\n\n  \n💬🚨 UPDATE SINCE POSTING:   \n  \n**I want to thank all of you for your support, questions, feedback**, and **comments.** I've done my best to respond to each and every one of them in as much detail as possible.\n\nI'll be flying out to **NYC** tomorrow to attend the **Vercel's Ship 26** event as well as a few scheduled meetings for seed funding, but **I hope to continue to respond to all new replies in the coming days when available.**\n\nBest,  \n***The WealthLearn Team***","offTopic":false},{"id":"720e9787-b2f4-4f95-8cab-49d6a132b13d","excerpt":"AI Bubble or No Bubble ? - Made a \"Gary Shilling in a box\" — an app that tracks AI-bubble indicators and generates put/hedge recommendations with reasoning — Been reading a lot about how Gary Shilling positioned for the 2008 housing crash — patient, contrarian, indicator-driven, sized to survive being early. Wanted to ","url":"https://www.reddit.com/r/CNPSays/comments/1uo2np9/ai_bubble_or_no_bubble_made_a_gary_shilling_in_a/","role":"pain","weight":0.5978333,"occurredAt":"2026-07-05T13:47:46.000Z","sourceKey":"reddit","sourceName":"Reddit","credibility":0.62,"venue":"CNPSays","intent":"other","painScore":0.24,"sentiment":-0.6,"confidence":0.4821236,"matchedPatterns":[],"statement":"AI Bubble or No Bubble ?","title":"AI Bubble or No Bubble ? - Made a \"Gary Shilling in a box\" — an app that tracks AI-bubble indicators and generates put/hedge recommendations with reasoning","body":"Been reading a lot about how Gary Shilling positioned for the 2008 housing crash — patient, contrarian, indicator-driven, sized to survive being early. Wanted to do something similar for AI in case this trade unwinds. But I don't want to stare at 30 tickers every morning and guess.\n\nSo I built **Darshan** 4.5 — a local Python app that does the watching for me and tells me what to consider doing.\n\nhttps://preview.redd.it/1m0bxs7c1fbh1.png?width=2914&format=png&auto=webp&s=be03ab442bc892ece1d64d30e9d84f22de066f91\n\n  \n\n\n​​​​**The setup**\n\nData comes from yfinance (free) plus my existing macro pipeline (rates, dollar, oil, gold).\n\n[](https://preview.redd.it/made-a-gary-shilling-in-a-box-an-app-that-tracks-ai-bubble-v0-x9t8wjz3vabh1.png?width=2914&format=png&auto=webp&s=664f1c68cca5e9e0177397583a26f628fe5bae91)\n\n# What it watches\n\n\\~30 tickers split into 6 buckets:\n\n* **AI pure-plays** (short candidates): NVDA, SMCI, ARM, PLTR, AI, SOUN, BBAI, IONQ, AVGO\n* **Picks & shovels** (survive/relative value longs): TSM, ASML, AMAT, LRCX, VST, CEG, NEE, ETN\n* **AI ETFs** (bear-spread targets): BOTZ, IRBO, AIQ, ROBO, SMH, SOXX, XLK, QQQ\n* **Defensive** (Shilling sleeve): TLT, IEF, GLD, SH, PSQ, SQQQ\n* **Vol**: \\^VIX, VIXY, UVXY, VXX\n\nAnd it computes \\~20 indicators across 8 categories: valuation, capex, adoption vs hype, credit/macro, sentiment/positioning, insider activity, energy/power, regulatory.\n\n# The important stuff it computes automatically\n\n* **Media bubble-narrative rate** — % of headlines using words like \"correction/bubble/frothy/overvalued\"; high = the smart money is already talking\n* **Regulatory mention rate** — % of headlines mentioning DOJ/FTC/antitrust/AI Act\n\nPlus catalysts — it pulls next earnings dates for every watchlist ticker and hardcodes the 2026 FOMC + CPI calendar, so I don't miss a print.\n\n# The good part — the Playbook\n\nEvery time I refresh, the app computes a **Bubble Score (0-100)**, tags a regime (Early / Mid / Late / Bursting), and generates specific position recommendations with reasoning.\n\nExample output from my test run with extreme-bubble fake data (score 100):\n\nEach rec has **Accept** and **Dismiss** buttons. Accept copies it to a Positions table as status='Watch' — pre-populated with strategy/ticker/thesis, and I fill in actual entry price and size when I execute. So the app never places orders and I stay in the driver's seat.\n\n# Why I bothered\n\nTwo reasons:\n\n# Standard disclaimers\n\n* Not investment advice, obviously\n* yfinance can be flaky, verify against your broker\n* I built this in a few sessions, it's a personal tool, don't blindly trust anything a stranger's app tells you including this one\n* Puts go to zero if you're wrong, inverse ETFs decay, VIXY bleeds carry\n\n# Stack\n\nPython + Flask + SQLite + yfinance + Chart.js. Runs on a laptop. Full pipeline is \\~1 minute per refresh (yfinance is the bottleneck).\n\nHappy to answer questions about how any specific indicator is computed, or how the playbook rules work.","offTopic":true}],"breakdown":[{"sourceKey":"reddit","sourceName":"Reddit","count":13}],"total":13}}