Counter anti-bot protections

Basic

A trading bot can use multiple strategies to protect itself from other bots:

1. Recognizing Patterns

• The bot can analyze past bot activity and identify recurring patterns, such as placing orders with high frequency or at certain times of the day.

2. Arbitrage Analysis

• The bot can monitor prices on multiple exchanges and identify inconsistencies that may indicate the activity of bots attempting to take advantage of arbitrage opportunities.

3. Imitation of Human Behavior

• The bot can mimic the actions of a real person by using random delays, changing order volumes and placing orders at different times.

4. Use of Antibot Systems

• The bot integrates with anti-bot systems that use advanced algorithms to detect and block bots.

Example

Suppose that a bot detects a recurring pattern in the activity of another bot that places orders to buy a large volume at a price just above the market price, followed by a series of orders to sell a smaller volume at a higher price. The bot can recognize this pattern and take steps to protect against it:

1. Placement of Opposing Orders

• A bot can place sell orders at just below market price to intercept buy orders placed by another bot.

2. Increase in Order Volume

• A bot may increase the volume of its orders to compete with another bot's orders and prevent them from being executed.

3. Changing the Time of Order Placement

• A bot can change the timing of its orders to avoid matching the pattern of another bot.

Using a combination of these strategies, a trading bot can effectively defend itself against other bots and provide a fair and unbiased trading environment.

Basic Code

Disclamer: We provide basic codes to avoid information leaks

Pattern Recognition Code

// import numpy as np import pandas as pd from sklearn.cluster import KMeans

def detect_bot_patterns(orders): """Detects repeating patterns in orders, characteristic for bots.

  Args: orders: Order data in the form of DataFrame.

  Returns: Dictionary with clusters of bot patterns. """" # Convert the DataFrame to a NumPy array X = orders.drop(["timestamp"], axis=1).values

  # Perform KMeans clustering to identify patterns kmeans = KMeans(n_clusters=3) kmeans.fit(X)

  # Get cluster labels labels = kmeans.labels_

  # Analyze the cluster labels and identify potential bot patterns patterns = {} for i in range(kmeans.n_clusters): cluster_orders = orders[labels == i] patterns[f "Cluster {i+1}"] = cluster_orders.describe()

  return patterns


# Example usage: orders = pd.read_csv("orders.csv") patterns = detect_bot_patterns(orders)

# Print the detected patterns for pattern_name, pattern_info in patterns.items(): print(f"{pattern_name}:\n{pattern_info}")

This code uses KMeans clustering to identify recurring patterns in the order data. Each cluster is assigned a label and then descriptive statistics are calculated for the orders in each cluster. This helps to identify patterns, such as placing orders with high frequency at certain times of the day or with a certain volume. By examining the labels and descriptive statistics of clusters, you can identify potential bot patterns. For example, a cluster containing orders with a very high placement frequency and low volume may indicate the activity of a bot attempting to manipulate price.