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**Summary: Discussion between Jeff Liang and Quant Alex Wu on Optimizing Option Order Execution and Slippage Capture** The core topic of their conversation is: **The current option limit order execution is poor (high slippage, low fill rate), essentially due to the lack of professional high-frequency / algorithmic market-making capabilities. They need to upgrade from “cutting meat with a blunt knife” to a sophisticated Delta-hedging + options market-making system.** ### 1. Problem Diagnosis - Current order placement feels like **“cutting meat with a blunt knife”** — poor queue position, low fill probability, and severe slippage. - Jeff provided concrete data: **Average loss of approximately $5.2 per executed option contract** (slightly less than 1 bp), including fees and rebates — still unacceptable. - Even with perpetual futures maker fee rebates helping a bit, the situation “cannot be ignored.” - **Price checking and adjustment frequency is NOT the root cause.** The real drivers are **fill probability** and **queue position**. ### 2. Fundamental Solution Direction (Alex’s View) - A robust **Delta-hedging system** shares significant technical overlap with high-frequency market-making systems for spot, futures, and perpetual contracts. Without this foundation, one is essentially powerless against adverse selection. - Using **maker orders for Delta hedging** is conceptually the same as **Delta-1 market making for inventory risk management** — the analogy made everything “suddenly clear.” - Options market making and Delta-1 market making are **tightly coupled**: - The Delta-1 system handles the Delta exposure of options. - Options themselves can provide protection for Delta-1 positions. ### 3. Technical Difficulty and Implementation Path - This requires entering the realm of **algo trading / HFT**, involving substantial research and engineering resources. - **Language requirement**: Python is **not sufficient**. Must use **C++ and Rust**. - **Target clients**: Institutional clients and high-net-worth individuals engaging in on-exchange block trading. - **Detailed step-by-step roadmap from scratch (Alex’s plan)**: 1. Collect large volumes of **order book data** (snapshots, incremental updates, tick-by-tick trades) for perpetuals + futures + options. 2. Build **fill probability models + queue models**, including: - Limit order arrival intensity - Fill probability - Queue position - Latency modeling 3. First implement and validate on **Delta-1 products**, then extend the backtesting system to support these HFT primitives. 4. Expand from Delta-1 / single option contracts to **all option contracts** (requires major redesign and validation due to performance demands). 5. Develop specialized algorithms for **limit order posting + aggressive crossing** to reduce overall slippage. 6. Finally, conduct small-capital live trading validation. Alex repeatedly emphasized: **“This project is genuine heavy industry.”** ### 4. Consensus - Delta-One research is the foundation for studying option fill probabilities. - Options market making must be deeply integrated with the Delta-hedging system — they cannot be treated separately. - The current phase is **infrastructure building**, requiring patient and significant investment. **Overall Assessment**: Alex provided a highly professional and systematic optimization roadmap, covering data infrastructure, modeling, and execution layers. Jeff focused on the business pain point (real slippage costs). Both fully agree that a fundamental rebuild of the underlying high-frequency system is necessary. This is a classic **quantitative execution optimization** discussion — starting from a clear business problem and pointing directly toward building institutional-grade HFT-level capabilities.

我和Quant Alex @StochAlex07的讨论：Vega*∂Sigma/∂F是否可以被称为Vanna项？ **Compressed Summary: Jeff & Alex Wu Discussion on “whether Vega × ∂Sigma/∂F is a Vanna term?”** ### Core Question (Jeff) Jeff asks why many traders call **Vega × ∂Sigma/∂F** (Smile Delta / Shadow Delta) a “Vanna term” under implied vol surface context. He notes this term represents **spot-induced IV change due to moneyness shift**, but it differs from the classic cross-derivative ∂²P/(∂F ∂Σ). He wants to know if this broader usage is valid. ### Alex’s Clear Verdict **No — it should not be called Vanna.** The “broad interpretation” is **sloppy and mixes two different concepts**: - **Classical BS Vanna**: Second-order Greek — how Vega changes with Spot, or how Delta changes with IV (inside BS PDE). - **Vega × ∂Sigma/∂F (Smile Delta)**: Describes **realized spot–IV dynamics** (how the implied volatility surface itself moves when spot moves). Alex: “They are not the same thing — their explanatory targets are completely different.” ### Why the Confusion Exists - In pure BS, Vanna and Volga were introduced to handle Greeks sensitivity to spot/IV moves. - Spot-induced IV changes are a **separate phenomenon**, addressed by market conventions: - Sticky Strike / Sticky Delta / Sticky Local Vol - Implied Skew (quick proxy for spot-vol linkage) - Calculating full strike-by-strike spot-IV correlations is impractical, so desks focus on **Spot vs ATM IV dynamics**. ### Modern Practical Approaches (Beyond BS Vanna) 1. **Parametric Vol Dynamics** (SEPP / SVI / Vola Dynamics path) Regress dSpot & dATM Vol → derive each strike’s dIV/dF. Focuses on **smile shape**, not stochastic dynamics. Naturally compatible with sticky rules. 2. **Stochastic Volatility Models** (e.g. SABR) Directly links implied skew to **spot-vol covariance**. A Vanna-like term appears in the PDE, but **Bartlett Delta contains no Vanna**. ### Key Takeaways - Smile Delta’s essence is **spot-vol covariance + smile shape**, unrelated to classical BS Vanna. - The popular “Vanna = Smile Delta” shorthand is convenient market talk but **technically inaccurate**. - Real trading desks have long moved beyond pure BS Greeks precisely because BS Vanna alone cannot capture actual vol-surface behavior. **Conclusion**: The terminology is misleading. Vega*∂Sigma/∂F and Vanna explain different objects and should not be conflated.