A tour of the agent that turns "sounds interesting" into a sized options trade thesis: what it checks, in what order, and the three real cases where the evidence overruled the hunch.
Anatomy of an Options Due Diligence Agent
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The Quantish Research Harness
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An operating system for quantitative trading research: a harness that lets AI agents run strategy development end to end, inside rails that make self-deception structurally difficult. It rejected far more ideas than it accepted, and that is the point.
Refining The 0DTE SPX Breakout Strategy with Evidence-Based Exclusions (2.2 Sharpe)
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Every quant has been there. You’re backtesting a strategy, the overall results look good, but something feels off when you dig into the day-by-day breakdown. You keep digging while your gut says “this feels like data mining,” but the statistics keep pointing to the same uncomfortable truth – some trading days are just bad for business. That’s where I found myself...
Profitably Trading the SPX Opening Range. Code Included.
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This promising strategy comes from Option Alpha’s comprehensive research on trading SPX breakouts with zero-day-to-expiration (0DTE) credit spreads – selling one option while buying a further OTM option for protection, collecting premium with defined risk. If you’re not famliar with Option Alpha, and are serious about trading options, I highly recommend you check them...
Reweighted Price Relative Tracking for Portfolio Optimization
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This is my implementation of the Reweighted Price Relative Tracking strategy documented in the research paper by Zhao-Rong Lai et al. Shout out to the “Paper-to-Profit” substack for calling my attention to it. The code is not meant to be deployed as-is but serves as an educational template for those interested in modern portfolio optimization techniques based on academic research...
KER and FCF Yield Factor Optimization
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This is a factor optimization strategy, written for Quantconnect LEAN (Python). The attached code demonstrates how to combine technical and fundamental factors in an optimized portfolio, focusing on the most heavily-weighted stocks in SPY. Feel free to re-use this, customize it, make it better and re-share. General Architecture The strategy uses a two-factor model combining...
On ML: For ‘Feature’ Consideration
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I’m a big fan of the folks at RobotWealth, the great work they are doing and their efforts to educate others. One of my favorite posts from @Kris goes into great detail about features, the importance of stationarity, and other key considerations when applying ML to trading. I shared this with a friend recently, and he summarized this gem of a post into key takeaways, and I thought to share some...
Trading Earnings Volatility With Options
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This strategy was inspired by u/mori226. The system trades options spreads (diagonals) around earnings volatility, buying when IV is lowest, selling when it is highest. Current work-in-progress is in the embedded PDF below and includes backtest results from my prototyping in OptionStack. Spread Entry Long: One month after earnings, buy cheap LEAP calls.Entry Criteria (all must be met)It’s been at...
Not All NFTs Are Collectibles
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There are NFTs and then there are NFT collectibles. An NFT, or a Non-Fungible Token, is a breakthrough innovation in verifiable ownership. Verifiable ownership is a critical and fundamental component of human society, be it ownership of a property, an idea, an identity, etc. NFT collectibles are an application of NFT technology, and people use it to prove ownership of digital assets that they...
Stock Universe Selection on Multiple Timeframes
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This is my take on scheduled intraday universe selection, written in Python (LEAN). The attached code is not a tradable strategy, it’s a template for others to use in building their own strategies. Feel free to re-use this, customize it, make it better and re-share. General ArchitectureThere are three universe selection phases: Coarse, Fine and Intraday (scheduled). In the Fine and...