Create randomforestML.py

randomforestML.py

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+import os
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+from binance.client import Client
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import classification_report
+import ta
+
+# Connect to Binance (Fill your own API keys if live)
+# client = Client(api_key, api_secret)
+client = Client()
+
+# File to store the historical data
+DATA_FILE = "btc_data.csv"
+symbol = "BTCUSDT"
+interval = Client.KLINE_INTERVAL_4HOUR
+
+# Load existing data or download fresh
+if os.path.exists(DATA_FILE):
+    print("Loading existing data...")
+    df = pd.read_csv(DATA_FILE, index_col=0, parse_dates=True)
+    last_timestamp = df.index[-1]
+    # Binance gives data in 15min intervals, so move forward
+    start_time = last_timestamp + timedelta(minutes=15)
+    start_str = start_time.strftime("%d %B %Y %H:%M:%S")
+    
+    print(f"Downloading new data from {start_str}...")
+    new_klines = client.get_historical_klines(symbol, interval, start_str)
+    if new_klines:
+        new_df = pd.DataFrame(new_klines, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume',
+                                                    'close_time', 'quote_av', 'trades', 'tb_base_av', 'tb_quote_av', 'ignore'])
+        new_df = new_df[['timestamp', 'open', 'high', 'low', 'close', 'volume']]
+        new_df[['open', 'high', 'low', 'close', 'volume']] = new_df[['open', 'high', 'low', 'close', 'volume']].astype(float)
+        new_df['timestamp'] = pd.to_datetime(new_df['timestamp'], unit='ms')
+        new_df = new_df.set_index('timestamp')
+        
+        # Append and remove any duplicates (just in case)
+        df = pd.concat([df, new_df])
+        df = df[~df.index.duplicated(keep='first')]
+        df.to_csv(DATA_FILE)
+else:
+    print("Downloading all data from scratch...")
+    klinesT = client.get_historical_klines(symbol, interval, "01 December 2021")
+    df = pd.DataFrame(klinesT, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume',
+                                        'close_time', 'quote_av', 'trades', 'tb_base_av', 'tb_quote_av', 'ignore'])
+    df = df[['timestamp', 'open', 'high', 'low', 'close', 'volume']]
+    df[['open', 'high', 'low', 'close', 'volume']] = df[['open', 'high', 'low', 'close', 'volume']].astype(float)
+    df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
+    df = df.set_index('timestamp')
+    df.to_csv(DATA_FILE)
+
+# Feature Engineering: Add technical indicators
+df['rsi'] = ta.momentum.RSIIndicator(df['close'], window=14).rsi()
+df['sma_fast'] = df['close'].rolling(window=5).mean()
+df['sma_slow'] = df['close'].rolling(window=20).mean()
+df['macd'] = ta.trend.MACD(df['close']).macd()
+df['ema'] = df['close'].ewm(span=10, adjust=False).mean()
+
+# Create target: 1 if next close > current close, else 0
+df['target'] = np.where(df['close'].shift(-1) > df['close'], 1, 0)
+
+# Drop rows with NaN values
+df = df.dropna()
+
+# Features and Target
+features = ['rsi', 'sma_fast', 'sma_slow', 'macd', 'ema']
+X = df[features]
+y = df['target']
+
+# Train/Test split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)
+
+# Train Random Forest
+model = RandomForestClassifier(n_estimators=100, random_state=42)
+model.fit(X_train, y_train)
+
+# Evaluate
+y_pred = model.predict(X_test)
+print(classification_report(y_test, y_pred))
+
+# Predict next movement
+latest_features = X.iloc[-1].values.reshape(1, -1)
+predicted_direction = model.predict(latest_features)
+print(f"Predicted next movement: {'UP' if predicted_direction[0] == 1 else 'DOWN'}")