Delete randomforestML.py

randomforestML.py

@@ -1,86 +0,0 @@
-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'}")