Forecasting Stock Using Deep Reinforcement Learning
In recent years, Deep Reinforcement Learning (DRL) has emerged as a promising approach for forecasting stock prices and optimizing trading strategies.
DRL combines the power of deep learning, which excels at learning complex representations from raw data, with reinforcement learning, a framework that enables agents to learn optimal actions in a given environment through trial and error. By leveraging DRL, researchers and investors can develop models capable of understanding intricate market dynamics, analyzing historical data, and making informed decisions on buying, selling, or holding stocks.
Let’s Start Coding!
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory
from subprocess import check_output
#print(check_output(["ls", "../input"]).decode("utf8"))
# Any results you write to the current directory are saved as output.
import time
import copy
import numpy as np
import chainer
import chainer.functions as F
import chainer.links as L
from plotly import tools
from plotly.graph_objs import *
from plotly.offline import init_notebook_mode, iplot, iplot_mpl
from tqdm import tqdm_notebook as tqdm
init_notebook_mode()