XGBoost for stock trend & prices prediction

Using technical indicators as features, I use XGBRegressor from XGBoost library to predict future stock prices.

Let’s start with the imports.

Imports

import os
import numpy as np
import pandas as pd
import xgboost as xgb
import matplotlib.pyplot as plt
from xgboost import plot_importance, plot_tree
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split, GridSearchCV

# Time series decomposition
!pip install stldecompose
from stldecompose import decompose

# Chart drawing
import plotly as py
import plotly.io as pio
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot

# Mute sklearn warnings
from warnings import simplefilter
simplefilter(action='ignore', category=FutureWarning)
simplefilter(action='ignore', category=DeprecationWarning)

# Show charts when running kernel
init_notebook_mode(connected=True)

# Change default background color for all visualizations
layout=go.Layout(paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(250,250,250,0.8)')
fig = go.Figure(layout=layout)
templated_fig = pio.to_templated(fig)
pio.templates['my_template'] = templated_fig.layout.template
pio.templates.default =…