How I’m Building a System to Choose Better Options Strategies
🧩 From my trading workflow to a tool that ranks option strategies
For almost a year, I’ve been focused on a specific corner of options trading and documenting that process here in 10 Delta Playbook.
Most of my work is built around defined-risk option structures. I write about the full decision-making process behind the trades: what made a setup interesting, how I looked at the risk, why the structure made sense, and what actually happened after the position was opened.
Over time, my workflow became fairly clear:
⚙️ I use TC2000 for technical analysis. It is my main tool for charts, setups, screeners, and reading price action. For someone else, that tool may be TradingView, Thinkorswim, or something completely different. The name of the platform is secondary. What matters is the ability to understand the market context quickly: trend, levels, momentum, compression, extension, and overall structure.
⚙️ Once an idea becomes interesting, I move to OptionStrat. That is where I start working with the actual option structure: strikes, risk profile, reward profile, price scenarios, volatility changes, and time decay.
I wrote more about this workflow in My Stack section.
Although this workflow works well, I kept running into the same gap: the moment between understanding the chart and deciding which options structure actually makes sense, taking into account expiration, volatility, skew, liquidity, expected move, and other market-specific nuances.
With experience, you can look at a chart and understand quite a lot very quickly. You can see when a move is extended, when price is approaching an important zone, when momentum is fading, when the stock is stuck in a range, or when premium might be worth selling.
The harder part comes after that.
Let’s say I like a specific stock. Let’s say I have a general market thesis. Let’s say I am looking at a specific expiration.
The real question becomes:
🤔 Which options strategy should I even look at first? 🤔
That is the problem I want to solve.
Strategy selection is not just a matter of personal preference. The same stock idea can lead to very different option structures depending on the full market context and the way the option chain is priced at that moment.
Sometimes selling premium makes sense. Sometimes a directional structure is cleaner. Sometimes a time spread is more logical. Sometimes the expected move is too expensive or too cheap, and that changes the entire trade logic. Sometimes the best decision is to leave the setup alone.
I tried to improve this process from different angles.
At one point, I launched my own AI tool for reviewing option trade ideas. It runs on a strong OpenAI model, combined with my own prompts and trading context from real setups. Paid subscribers already have access to it and can use it to discuss setups, scenarios, and risks.
It is useful, especially when there is already a concrete idea on the table.
But even that did not solve the main problem :)
I wanted something more structured than a conversation around an idea. I wanted a system that starts with the market itself: ticker, expiration, volatility, option chain, liquidity, skew, expected move, earnings, trend, and extension. Then, based on that context, it should help identify which strategies deserve attention first.
That is how the idea for a new and much-needed tool started 👇👇👇
The working name is Options Decision OS.
The first version has a simple goal:
→ choose a ticker
→ choose an exact expiration
→ and get a ranked list of option strategies that fit the current setup best.
The key unit is:
ticker + expiration
I want to evaluate a specific market situation at a specific point in time. The same stock can produce different answers across different expirations: this Friday, 30–45 days out, and a much later expiration are very different environments, with different time profiles, volatility behavior, liquidity, movement sensitivity, and risk logic.
The first version will be intentionally focused. It will start with a practical universe of tickers that I personally follow and that have enough liquidity for regular options work:
MU, AMD, IWM, QQQ, TLT, SPY, TSLA, NVDA, AAPL, GOOG, SLV, PLTR, MSFT, GLD, AMZN, META, NFLX.
The strategy universe will also be focused. The current working list includes 16 option structures across several practical categories: directional premium selling, debit spreads, iron condors, iron butterflies, broken wing butterflies, calendars, diagonals, straddles, strangles, and back ratio spreads.
The goal is to work with a practical set of option structures and compare them against the current market context in a consistent way.
The main screen I am working toward is called the Strategy Fit Board.
It should show which strategies are stronger, which are weaker, why they received their scores, and what is driving the result. For me, the explanation matters as much as the ranking. I want to know whether the setup is mainly about direction, time, volatility, skew, mean reversion, premium, compression, or expected move.
⚠️ The project is already in active development.
I am building the data core, connecting market data sources, normalizing option chains, working on IV and Greek calculations, checking data freshness, and shaping the deterministic scoring model that will power the Strategy Fit Board.
In the next parts, I want to show how the system is being built from different angles: the product logic, the Strategy Fit Board, the data layer, option-chain analysis, and the path from raw market data to strategy scores.
My goal is to launch the first minimally usable version in early July 💪
I hope some of you will be among the first to try it, not just as a demo, but as a real tool to study, test, and challenge.
Your feedback will matter a lot at this stage.
Always yours,
— Mansur
Disclaimer
All content is for informational purposes only and does not constitute financial advice.Any trades or strategies should be tested in a simulated environment before use.Trading involves risk, and all decisions are the sole responsibility of the reader.