> For the complete documentation index, see [llms.txt](https://candora.gitbook.io/whitepaper/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://candora.gitbook.io/whitepaper/candora-pro/anti-spoof.md). # Anti-Spoof ## Anti-Spoof ### Understanding the reliability of market liquidity Anti-Spoof is [Candora Pro](/whitepaper/candora-pro.md)'s liquidity-reliability analysis system designed to help traders distinguish between stable liquidity and liquidity that may disappear when market conditions become active. Traditional Level 2 order books display all visible liquidity equally. Large buy and sell orders appear identical regardless of whether that liquidity remains available for execution or repeatedly disappears before interacting with the market. As a result, traders often face a fundamental challenge: seeing liquidity does not necessarily mean that liquidity will remain there when it matters. Anti-Spoof addresses this problem by evaluating how displayed liquidity behaves through time rather than treating all visible size as equally meaningful. The objective is not to determine trader intent or identify specific market participants. The objective is to help traders understand which areas of visible liquidity appear behaviorally reliable and which areas appear less stable based on observed market activity. ### Looking beyond displayed size Visible market depth provides an important view of supply and demand, but it represents only a snapshot of current conditions. Large liquidity zones may appear briefly and disappear as price approaches. Orders may repeatedly enter and exit the market without participating in execution. Displayed liquidity may rotate between nearby price levels while creating the appearance of stability. During active market conditions, these behaviors can make the order book difficult to interpret. Anti-Spoof continuously evaluates how liquidity behaves over time in order to provide additional context beyond raw quoted size. Rather than focusing solely on how much liquidity is displayed, the system analyzes how consistently that liquidity behaves as market conditions evolve. ### Behavioral liquidity analysis Anti-Spoof continuously evaluates observable liquidity behavior across the order-book event stream. The system analyzes factors including: * quote persistence * execution participation * cancellation behavior * repeated add-and-remove activity * liquidity stability * localized quote rotation * depth consistency near active price regions Liquidity that consistently remains visible and participates in normal market activity may receive higher reliability weighting. Liquidity that repeatedly appears and disappears before interaction may receive lower reliability weighting. These assessments are not intended to classify liquidity as genuine or deceptive with certainty. Instead, they provide a behavioral interpretation of how displayed liquidity has historically behaved under current market conditions. ### Behaviorally weighted market depth Anti-Spoof transforms raw order-book data into a behaviorally weighted liquidity view. Rather than displaying all liquidity as equally significant, the system emphasizes liquidity that demonstrates greater behavioral consistency while reducing the visual prominence of liquidity that behaves less reliably. This allows traders to identify: * stable versus unstable liquidity zones * persistent versus transient market depth * reinforcing versus withdrawing liquidity * liquidity with stronger execution participation * areas experiencing rapid quote rotation The result is a market view designed to make evolving liquidity conditions easier to interpret during both normal and highly active trading environments. ### Relationship to Liquidity Mirror Anti-Spoof operates alongside [Liquidity Mirror](/whitepaper/candora-pro/liquidity-mirror.md) as part of Candora Pro's market-intelligence framework. While both systems analyze order-book behavior, they focus on different aspects of liquidity. Liquidity Mirror focuses on how liquidity structures evolve across the market, including reinforcement, withdrawal, imbalance formation, and shifting support or resistance zones. Anti-Spoof focuses on the reliability of the liquidity within those structures. Together, the systems help traders understand both how liquidity is evolving and how dependable that liquidity appears to be based on observed market behavior. ### Interpretation without market influence Anti-Spoof operates entirely as an analytical and visualization layer. The system does not place orders, route trades, influence execution, modify market prices, alter matching behavior, or affect participant treatment. The underlying order book remains identical for all market participants. Anti-Spoof changes only how liquidity information is interpreted and presented within Candora Pro. All execution continues to occur through the standard deterministic exchange infrastructure. ### Limitations Anti-Spoof evaluates observable market behavior rather than trader identity, intent, or motivation. Its assessments are based exclusively on how displayed liquidity behaves over time. Reliability may decrease during periods of extreme volatility, fragmented liquidity, excessive quote churn, rapid repricing, delayed market-data propagation, or highly unstable trading conditions where meaningful liquidity persistence becomes difficult to establish. Even behaviorally analyzed liquidity may ultimately prove unreliable. For this reason, Anti-Spoof should be viewed as a decision-support tool designed to improve liquidity interpretation rather than eliminate uncertainty. ### Execution neutrality Anti-Spoof does not suppress, remove, reorder, delay, or interfere with market liquidity. It does not influence execution outcomes, queue priority, participant treatment, matching behavior, or market-state formation. Its role is limited to analyzing observable liquidity behavior and presenting a more interpretable view of order-book conditions. Anti-Spoof helps traders understand the reliability of displayed liquidity. It does not change the liquidity itself.