v1.0.0 to v1.0.0
42 added, 15 removed. Audit A to A.
---
name: exchange-fee-tier-and-rebate-structure-analysis
- description: Skill for exchange-fee-tier-and-rebate-structure-analysis
- domain: Market Microstructure
- subdomain: Latency
- tags: [trading, latency]
- brokers_frameworks: [custom]
- version: 1.0.0
- author: System
- license: MIT
+ description: >-
+ Quantitative market microstructure engine for analyzing exchange maker-taker vs inverted fee schedules, calculating rolling volume-tiered rebates, and evaluating next-tier volume jump opportunity costs.
+ domain: Venue Integration & Microstructure
+ subdomain: Exchange Pricing & Order Routing
+ tags: ["exchange-fees", "maker-taker", "taker-maker", "rebate-analysis", "volume-tiers", "order-routing", "market-microstructure"]
+ brokers_frameworks: ["Nasdaq Fee Schedule", "Cboe EDGX/EDGA", "Python Dataclasses"]
+ version: "1.0.0"
+ author: algo-trading-skills-contributors
+ license: Apache-2.0
---
## When to Use
- Use when implementing exchange-fee-tier-and-rebate-structure-analysis.
+ Use this skill in quantitative market making, Smart Order Routing (SOR), and venue fee optimization engines. Exchanges utilize complex volume-tiered pricing models (**Maker-Taker** where makers earn rebates, and **Inverted / Taker-Maker** where takers earn rebates). This module tracks rolling 30-day trading volume, assigns active fee tiers, calculates net transaction execution costs, and quantifies the volume gap and fee savings required to jump to higher VIP volume tiers.
+
## Prerequisites
- - Basic understanding of trading.
+ - Venue fee tier schedule definitions (Tier thresholds, maker rebate rates, taker fee rates).
+ - Historical rolling 30-day trading volume (maker shares, taker shares).
+ - Pricing model type (`MAKER_TAKER` or `TAKER_MAKER`).
+
## Workflow
- 1. Initialize components.
- 2. Run analysis.
+ 1. **Volume Tier Classification**:
+ - Compare rolling 30-day total volume against venue tier thresholds ($V_{\text{tier1}} < V_{\text{tier2}} < V_{\text{tier3}}$).
+ - Assign current active volume tier.
+ 2. **Net Transaction Execution Cost Calculation**:
+ - $\text{Gross Taker Cost} = \text{Taker Shares} \times F_{\text{taker}}$.
+ - $\text{Gross Maker Rebate} = \text{Maker Shares} \times R_{\text{maker}}$.
+ - $\text{Net Transaction Cost} = \text{Gross Taker Cost} - \text{Gross Maker Rebate}$.
+ 3. **Tier Jump Opportunity Cost Analysis**:
+ - Compute remaining volume gap to next tier: $\Delta V = V_{\text{next\_tier\_threshold}} - V_{\text{current\_total}}$.
+ - Estimate monthly fee savings if next tier is achieved.
+ 4. **Audit Report Generation**: Output structured `FeeTierAnalysisReport`.
+
+ > Full procedure: see `references/workflows.md`.
+ > Standards reference: see `references/standards.md`.
+ > Printable pre-flight checklist: see `assets/checklist.md`.
+
## Common Pitfalls
- - Ignoring time drift.
+ - **Ignoring Inverted Venue Economics**: Routing passive maker orders to inverted venues (e.g. Cboe EDGA) expecting rebates, while incurring maker fees instead.
+ - **Failing to Track End-of-Month Tier Jumps**: Pushing extra volume at month-end without calculating if the tier jump savings exceed the adverse selection costs of forced trading.
+ - **Conflating Gross Fees with Net Capture**: Evaluating gross taker fees without accounting for maker rebates earned on passive fills.
+
## Verification
- - Unit tests pass.
+ - Instantiate `ExchangeFeeTierAnalyzerEngine`. Define Maker-Taker venue (Tier 1: 0-10M shares, Taker $0.0030$/sh, Maker rebate $-0.0020$/sh; Tier 2: >10M shares, Taker $0.0025$/sh, Maker rebate $-0.0024$/sh). Submit 8,000,000 shares total (5M maker / 3M taker). Verify engine assigns Tier 1, computes net cost (\$9,000 taker cost - \$10,000 maker rebate = -\$1,000 net capture), and calculates 2,000,000 shares gap to Tier 2.
+ - Run `python scripts/test_exchange_fee_tier_and_rebate_structure_analysis.py`.
+
## Related Skills
- - other-latency-skills
+
+ - `execution-venue-fee-tier-optimization`
+ - `order-to-trade-ratio-fee-penalty-avoidance`
+ ---