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()