Create App.py

App.py

@@ -0,0 +1,97 @@
+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()
+