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import requests
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from textblob import TextBlob
import tweepy
import time
class ExplosiveGrowthBot:
def __init__(self):
self.api_key = "YOUR_BINANCE_API_KEY"
self.base_url = "https://api.binance.com"
self.model = RandomForestClassifier()
self.data = pd.DataFrame()
self.twitter_api = self.setup_twitter_api()
def setup_twitter_api(self):
"""Set up Twitter API for sentiment analysis."""
consumer_key = "YOUR_TWITTER_CONSUMER_KEY"
consumer_secret = "YOUR_TWITTER_CONSUMER_SECRET"
access_token = "YOUR_TWITTER_ACCESS_TOKEN"
access_token_secret = "YOUR_TWITTER_ACCESS_SECRET"
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
return tweepy.API(auth)
def fetch_market_data(self, symbol="BTCUSDT", interval="1h", limit=100):
"""Fetch historical market data from Binance."""
url = f"{self.base_url}/api/v3/klines"
params = {"symbol": symbol, "interval": interval, "limit": limit}
response = requests.get(url, params=params)
if response.status_code == 200:
data = response.json()
df = pd.DataFrame(data, columns=["timestamp", "open", "high", "low", "close", "volume", "_", "_", "_", "_", "_"])
df["close"] = df["close"].astype(float)
df["volume"] = df["volume"].astype(float)
return df
else:
print("Error fetching market data:", response.text)
return None
def analyze_sentiment(self, keyword):
"""Analyze sentiment from Twitter."""
tweets = self.twitter_api.search_tweets(q=keyword, count=100, lang="en")
sentiments = []
for tweet in tweets:
analysis = TextBlob(tweet.text)
sentiments.append(analysis.sentiment.polarity)
return np.mean(sentiments)
def train_model(self, df):
"""Train the AI model to predict explosive growth."""
df["target"] = (df["close"].pct_change() > 0.05).astype(int) # Label: 1 if price increased by >5%
features = df[["close", "volume"]].dropna()
target = df["target"].dropna()
self.model.fit(features[:-1], target)
def predict_growth(self, latest_data):
"""Predict whether the asset will experience explosive growth."""
prediction = self.model.predict([latest_data])
return prediction[0]
def execute_trade(self, symbol, action):
"""Simulate trade execution."""
print(f"Executing {action} trade for {symbol}...")
def run(self):
"""Main loop for the bot."""
symbols_to_watch = ["BTCUSDT", "ETHUSDT", "DOGEUSDT"]
while True:
for symbol in symbols_to_watch:
# Fetch market data
df = self.fetch_market_data(symbol=symbol)
if df is not None:
# Analyze sentiment
sentiment_score = self.analyze_sentiment(symbol.replace("USDT", ""))
print(f"Sentiment score for {symbol}: {sentiment_score}")
# Train model and make predictions
self.train_model(df)
latest_data = df.iloc[-1][["close", "volume"]].values
prediction = self.predict_growth(latest_data)
# Decision-making based on prediction and sentiment
if prediction == 1 and sentiment_score > 0.5: # Strong buy signal
self.execute_trade(symbol, "BUY")
elif prediction == 0 and sentiment_score < -0.5: # Strong sell signal
self.execute_trade(symbol, "SELL")
time.sleep(300) # Wait 5 minutes before checking again
if __name__ == "__main__":
bot = ExplosiveGrowthBot()
bot.run()
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