🔬 Deep research · 2026-10-10

I want a very deep research on how to detect limit order spoofing in…

The request: I want a very deep research on how to detect limit order spoofing in trading. I also want to know how to figure out when the price action enters into a liquidity wall, usually seen on a heatmap of resting liquidity of some kind, how to know if people interacting there are getting absorbed or not for directional bias. I also want to know how to detect ice berg orders and benefit from seeing them. I want to know in what environment volume wise or trading activity wise, when ice berg orders come in or even why people put resting liquidity walls in certain locations of the market or the chart one may be looking at. I want to know every single possible way to use Order flow effectively to have directional bias in signals whether it be through the footprint chart of a fusion of different indicators like the CVD, Volume profile, the one that usually sits on the right side of the charts. I want to know if there are certain regimes and environments like I mentioned before that form certain patterns. I also want to know if it's possible to (as much as I can) synthesize an indicator or clues on what lv 3 data is doing... from having lv 2 data only, also How to approach futures markets by understanding different regimes such as positive or negative gamma or delta and so forth... in regards to already attempting to use order flow and so forth. Also how options market affects price action to my understanding indirectly because hedge funds use the greeks behave a certain way in the market and what can I do to take advantage of Options chain data and lv 2 data for any particular asset.

I’ll build a source-backed guide covering spoofing versus legitimate cancellations, absorption and icebergs, order-flow signals, and options-driven regimes. I’ll distinguish what Level 2 can show from what it can only suggest, with practical examples and failure cases rather than promises of a trading edge.

The research points to a useful distinction: a liquidity wall matters most through how it behaves when trades reach it, rather than its displayed size alone. CVD can show aggressive buying while price stalls because sellers absorb it. Two limits are central to the report: cancellations alone don’t prove spoofing intent, and public options open interest doesn’t reveal who holds the positions or the sign of dealer gamma. I’m turning those into concrete observation checklists, conditional setups, and validation steps. Sources: https://www.cmegroup.com/rulebook/files/cme-group-Rule-575.pdf · https://www.cmegroup.com/articles/faqs/market-by-order-mbo.html

Your deep-research guide is attached: 39 pages with 45 primary references, worked examples, conditional setups, and a testing framework.

It covers:
- Spoofing versus legitimate cancellations, liquidity walls, absorption, and icebergs
- Footprints, CVD, volume profiles, order-flow imbalance, and market regimes
- What Level 2 can suggest, and what it cannot reconstruct from order-level data
- Gamma, delta hedging, vanna/charm, 0DTE, and the limits of public options-chain data
- Confirmation, invalidation, transaction costs, and avoiding look-ahead bias

Short summary: judge what happens when trades actually reach a level, then whether price accepts or rejects it. Treat displayed walls and estimated dealer gamma as conditional context. Neither proves intent or supplies a reliable direction by itself.

Key sources: https://www.cmegroup.com/rulebook/files/cme-group-Rule-575.pdf · https://www.cmegroup.com/articles/faqs/market-by-order-mbo.html

📎 Also made: order-flow-directional-bias-research-guide.pdf (in the Discord thread)