Cluster Group View - Outlier Chart
Cluster Group View - Outlier Chart
To allow for easy detection of problematic groups of clusters in big data sets with disparate customer or trader cluster volumes, the Outlier chart displays the percentage of high-risk clusters within an group's total clusters.
Outlier Chart display
The Outlier Chart display is organized into the following sections:
- Outlier Chart: Displays the Outlier view of the loaded data as well as filtering and navigation tools.
- Data Selection Panel: Manages the dates available to TT Score and specifies the criteria to use when populating the list.
Interpreting the Outlier Chart
The Outlier Chart displays every cluster group's percentage of clusters with a score above a threshold defined by the user. This allows for easy detection of problematic trading in big data sets with disparate customer or trader cluster volumes.
The color of the dot indicates the severity of the score, with low scores represented as dark green and becoming yellow and finally dark red as risk scores increase.
The position of a dot in the X-axis indicates the number of clusters contained in the dot.
The position of a dot in the Y-axis indicates the percentage of clusters that exceeded the score.
Outlier Chart navigation and filters
Select View: Switch between Individual Clusters, Heat Map, Outlier Chart, and Daily Cluster Groups views.
Group By: Sort the view by trader, account, and instrument.
Outlier threshold: Sets the minimum score to consider as high risk.
Cluster Filters: Filter the view by trader, account, and instrument.
Export: Exports filtered data into a .csv spreadsheet file.
Data Selection Panel
On the left of the screen, the Data Selection Panel shows the daily activity logs that have been selected using the date picker and are available for data visualization. It also includes filters to apply to the
The panel includes the following:
- Score Range: Sets the maximum score of the clusters to include in each view.
- Date Range: Date-based selector to add daily activity data to analyze.
- Models: Sets which type of suspect pattern of trading to analyze in the data.
TT Score uses the following models to analyze data for problematic trading patterns:
- Abusive Messaging: Quote stuffing schemes designed to introduce predictable latency into an exchange's quoting engine or malfunctioning algorithms that might cause market disruptions.
- Cross Trading: A cross trade occurs when a buy order and a sell order for the same instrument are entered for different accounts under the same management, such as a broker or portfolio manager.
- Momentum Ignition: Behaviors that indicate an attempt to create an artificial price movement with aggressive orders followed by an attempt to capitalize on such movement.
- Pinging: The entry of multiple small orders intended to discover hidden book depth followed by a series of order actions designed to force the large order to trade at less desirable prices.
- Spoofing: Patterns of manipulative or disruptive trading activity that involve the placement of a number of orders for which a trader has no intention of executing in an attempt to move the market.
- Wash Account: The same account ID is both the buyer and seller in the same transaction.
- Wash Trader: The same trader ID is both the buyer and seller in the same transaction.
- Products: Sets one or more of the following product types to include in each view:
- Equity-Index Options
- Apply Filters: Applies the selected dates and filters.