No matter what one’s investment strategy is, financial market volatility is inevitable. It is still possible for experts to project their total returns despite such fluctuations. A number of factors contribute to both stability and volatility of investments, such as their type and other contributing factors. The financial market offers a variety of computational algorithms and models to aid in predicting returns. Quantitative analysts may be able to more accurately forecast investment returns with the help of advancements in data science techniques and methods.
Let’s Start Coding!
import warnings
#warnings.filterwarnings("ignore")
In this code block, we import the Python warnings module, which provides a way to handle warning messages during code execution. In the second line, the “#” symbol indicates that the line is commented out, meaning it is not currently active.
The code would then call the “filterwarnings()” function of the warnings module with the argument “ignore” if the second line were uncommented. With the “ignore” argument, the program ignores all warnings during execution, modifying the behavior of warning messages.
By uncommenting the second line and calling the “filterwarnings()” function with the “ignore” argument, the code imports the warnings module and ignores any warnings that may arise during program execution.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import multiprocessing
from scipy.stats import spearmanr
plt.style.use('bmh')
plt.rcParams['figure.figsize'] = (16, 5)
N_CPU = multiprocessing.cpu_count()