text stringlengths 0 93.6k |
|---|
# <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, |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.