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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() | |