Good news for ethereum:0xce24439f2d9c6a2289f741120fe202248b666666 !🔔 Starting June 30, 08:00 UTC, @binance will officially open trading and enable Spot Algo Trading Bots for the following pairs: 🔹 RE/U 🔹 XPL/U Start Trading Now!👉

@aooyoo3o I’m neutral at this point, this means I’ll just hold my BTC spot, not gonna buy more. Regarding to the macro market timing I suggest you guys follow @Pentosh1 @AlgodTrading @GCRClassic and track them/enable notification.

Describe it. Generate it. Deploy it. 🚀 What if you could use Claude Code directly to build your Hummingbot strategies? Imagine prompting: "Build a V2 controller that adjusts spreads based on volatility" ...and instantly getting clean, compliant Python. Zero boilerplate. 🐍✨ We want to hear from the Claude Code & algo trading community: If this workflow existed, what’s the VERY FIRST prompt you’d type? Sound off below! 👇

**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.