Wash Trade Type 2 Detection
Description
This test identifies coordinated trading activities involving pairs of users by analyzing trade and order data to uncover pre-arranged transactions and collusion. It filters trades based on duration between order placement and execution, evaluates cumulative trade volumes, and calculates the proportion of total trading activity involving counterparties. The test applies criteria, such as minimum trade value, volume percentages, and average daily volume (ADV), to flag suspicious behaviors.
Use Cases
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Market Integrity Monitoring: Identify coordinated wash trading schemes on exchanges to prevent artificial inflation of trading volumes and prices.
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Regulatory Compliance: Assist financial institutions in adhering to anti-manipulation regulations by detecting group-based collusion and reporting suspicious activities to regulatory bodies.
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Fraud Prevention and Investigation: Support trading platforms and enforcement agencies in uncovering fraudulent trading patterns.
Required Data
Trade Data
| Variable | Description |
|---|---|
timestamp | Timestamp of the trade made (YYYY-MM-DD hh:mm:ss). |
order_id | Unique identifier linking the trade to its associated order. |
user_id | User ID to identify the individual user. |
counterparty_user_id | The user ID of the counterparty in the trade (buyer or seller). |
symbol_pair | The asset symbol; e.g., BTCUSDT, SHIBETH, DOGEUSDT. |
side | Whether the trade is a BUY or SELL. |
price_usd | The price of the symbol in USD. |
price | The execution price of the symbol pair. |
amount | The amount of the symbol being traded. |
Order Data
| Variable | Description |
|---|---|
user_id | Unique identifier for the user who placed the order. |
symbol_pair | The trading pair for the order, e.g., BTCUSDT. |
order_id | Unique identifier for the specific order. |
order_start_time | The timestamp when the order was created. |
order_end_time | The timestamp when the order was last updated. |
Parameters
| Parameter | Description | Type | Default Value | Configurable |
|---|---|---|---|---|
adv_window | Time window (in days) to calculate Average Daily Volume (ADV). | Integer | 7 | Yes |
analysis_window | Time window (in days) to check for wash trades. | Integer | 1 | Yes |
analysis_timedelta | Maximum time (in seconds) between order placement and execution to flag trades. | Integer | 60 | Yes |
analysis_minimum_aggregate_dollar_threshold | Minimum total dollar value required to flag wash trades. | Float | 5000 | Yes |
adv_percentage_threshold | Percentage of ADV that net aggregated trades must exceed to trigger an alert. | Float | 0.1 (10%) | Yes |
analysis_percentage_volume_wash_trading_threshold | Minimum percentage of a user’s cumulative trade volume traded against potential wash trading counterparties. | Float | 0.5 (50%) | Yes |
analysis_maximum_number_counterparties_threshold | Maximum number of counterparties considered for coordinated wash trades. | Integer | 3 | Yes |
analysis_minimum_transaction_count_threshold | Minimum number of trades per user required for analysis. | Integer | 5 | Yes |
create_ticket | Flag to determine if tickets should be created for alerts. | Boolean | True | Yes |
Methodology
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Analyze Historical Trading Activity Examine trading data over a defined period to calculate metrics such as total trading volume and average daily activity. These metrics provide a baseline to identify deviations indicative of abnormal trading patterns.
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Synchronize Trade and Order Data Combine trades with their corresponding order details, focusing on the time difference between order placement and execution. Rapid order-to-trade conversions may signal pre-arranged trades or coordinated behavior.
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Aggregate and Group Trades Consolidate trades by user and counterparty to calculate cumulative trade values and interaction volumes. This helps reveal patterns where pairs of users dominate trading activity, indicating potential collusion.
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Evaluate Cumulative Trade Proportions Assess the share of cumulative trade value for each counterparty relative to the user’s total trading activity. A disproportionate concentration with specific counterparties often highlights coordinated efforts.
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Flag and Summarize Results Identify users with significant deviations based on cumulative trade volumes and interaction patterns.