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jblitzar
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github-python-corpus

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# <FILESEP>
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import TimeSeriesSplit, cross_val_score, RandomizedSearchCV
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error, r2_score
from tensorflow.keras.models import Sequential, clone_model as keras_clone_model
from tensorflow.keras.layers import LSTM, Dense, GRU, Dropout
from tensorflow.keras.callbacks import Callback, EarlyStopping
from datetime import datetime, timedelta
from tqdm.auto import tqdm
import yfinance as yf
import ta
from sklearn.ensemble import RandomForestRegressor
from xgboost import XGBRegressor
import warnings
import os
import tensorflow as tf
from tabulate import tabulate
from scipy.stats import randint, uniform
import sklearn.base
import argparse
from sklearn.feature_selection import SelectKBest, f_regression, RFE
from tensorflow.keras.regularizers import l1_l2
from matplotlib.dates import num2date
# Suppress warnings and TensorFlow logging
def suppress_warnings_method():
# Filter out warnings
warnings.filterwarnings("ignore")
# Suppress TensorFlow logging
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
# Suppress TensorFlow verbose logging
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
# Fetch historical stock data from Yahoo Finance
def fetch_stock_data(symbol, start_date, end_date):
"""
Fetch stock data from Yahoo Finance.
"""
data = yf.download(symbol, start=start_date, end=end_date)
return data
# Add technical indicators to the stock data
def add_technical_indicators(data):
"""
Add technical indicators to the dataset.
"""
data["SMA_20"] = ta.trend.sma_indicator(data["Close"], window=20)
data["SMA_50"] = ta.trend.sma_indicator(data["Close"], window=50)
data["RSI"] = ta.momentum.rsi(data["Close"], window=14)
data["MACD"] = ta.trend.macd_diff(data["Close"])
data["BB_upper"], data["BB_middle"], data["BB_lower"] = (
ta.volatility.bollinger_hband_indicator(data["Close"]),
ta.volatility.bollinger_mavg(data["Close"]),
ta.volatility.bollinger_lband_indicator(data["Close"]),
)
# Advanced indicators
data["EMA_20"] = ta.trend.ema_indicator(data["Close"], window=20)
data["ATR"] = ta.volatility.average_true_range(
data["High"], data["Low"], data["Close"]
)
data["ADX"] = ta.trend.adx(data["High"], data["Low"], data["Close"])
data["Stoch_K"] = ta.momentum.stoch(data["High"], data["Low"], data["Close"])
data["Volatility"] = data["Close"].rolling(window=20).std()
data["Price_Change"] = data["Close"].pct_change()
data["Volume_Change"] = data["Volume"].pct_change()
data["High_Low_Range"] = (data["High"] - data["Low"]) / data["Close"]
return data
# Prepare data for model training by scaling and creating sequences
def prepare_data(data, look_back=60):
"""
Prepare data for model training.
"""
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(data)
X, y = [], []
for i in range(look_back, len(scaled_data) - 1): # Note the -1 here
X.append(scaled_data[i - look_back : i])
y.append(scaled_data[i + 1, 0]) # Predicting the next 'Close' price
return np.array(X), np.array(y), scaler
# Create an LSTM model for time series prediction
def create_lstm_model(input_shape):
model = Sequential(
[
LSTM(
units=64,
return_sequences=True,
input_shape=input_shape,