Social Media Monitoring for sniping

Basic

The bot can monitor social media sentiment to identify tokens with strong demand and growth potential.

A trading bot can monitor social media for token sniping by performing the following actions:

1. Data collection

• The bot collects data from social networks such as Twitter, Telegram and Discord using APIs or third-party services.

2. Data Analysis

• The bot analyzes the collected data to identify keywords, hashtags, and topics related to upcoming token listings or other events that could cause prices to spike.

3. Filtering Results

• The bot filters the analysis results to focus on the most promising sniping opportunities. For example, it can filter out tweets with a high number of retweets or likes.

4. Definition of Listing Time

• The bot uses the collected data to determine the approximate listing time of a token. This can be done by analyzing exchange announcements, project announcements, or social media posts from industry insiders.

5. Placement of Warrants

• When the listing time is determined, the bot places limit orders to buy the token at or just above the listing price.

Example

Suppose a bot monitors Twitter looking for mentions of a new token listing called "XYZ". The bot can collect tweets containing the hashtag "#XYZListing" and analyze the number of retweets and likes for each tweet.

Based on this analysis, the bot can determine that there is significant interest in the XYZ listing and decide to snap the token. The bot can also use the Twitter API to determine the approximate time of the listing, which is 4:00 p.m. Eastern Time.

When 4:00 pm ET occurs, the bot automatically places a limit order to buy XYZ at a listing price of 0.1 ETH. The order is quickly executed and the bot successfully snaps the token.

By integrating social media monitoring into its strategy, a trading bot can increase its chances of token sniping at listing and profiting from price volatility.

Basic Code

Disclamer: We provide basic codes to avoid information leaks

Social Media Monitoring Code

// import tweepy from tweepy import Stream from tweepy import OAuthHandler from tweepy.streaming import StreamListener

class MyStreamListener(StreamListener):

    def on_data(self, data): # Process received data from Twitter # ....

    def on_error(self, status): # Handle errors # ....

# Set your Twitter API keys consumer_key = "YOUR_CONSUMER_KEY" consumer_secret = "YOUR_CONSUMER_SECRET" access_token = "YOUR_ACCESS_TOKEN" access_token_secret = "YOUR_ACCESS_TOKEN_SECRET"

# Set up Twitter authorization auth = OAuthHandler(consumer_key, consumer_secret) auth.set_access_token(access_token, access_token_secret)

# Create a stream listener instance listener = MyStreamListener()

# Create an instance of stream stream = Stream(auth, listener)

# Filter tweets by keywords keywords = ["#XYZListing", "#NewToken"] stream.filter(track=keywords)