Question

Study the stationarity of stock from Apple Inc. Using a random forest from Scikit-learn to predict stock prices for your chosen stock, and see how far into the future you can go.

Answer 1

Random forest algorithm is based on decision tree algorithm. It considers many decision trees at a time and then learn from the given dataset to build a model. This model is used to predict the values for unknown data.

Say, we have following data available for the stock prices over 10 years:

You may add more data in this.

Then for the above data, the python code will be:

Note: You may look the screenshot of the code to have better understanding of the indentation.

Source Code:

# Random Forest Regression

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
%matplotlib qt
import pandas as pd

# Importing the dataset
dataset = pd.read_csv('Position_Salaries.csv')
X = dataset.iloc[:, 0:1].values
y = dataset.iloc[:, 1].values

# Fitting Random Forest Regression to the dataset
from sklearn.ensemble import RandomForestRegressor
regressor = RandomForestRegressor(n_estimators = 100, random_state = 0)
regressor.fit(X, y)

# Predicting a new result
samp = [[6.5]]
y_pred = regressor.predict(samp)

# Visualising the Random Forest Regression results (higher resolution)
X_grid = np.arange(min(X), max(X), 0.01)
X_grid = X_grid.reshape((len(X_grid), 1))
plt.scatter(X, y, color = 'red')
plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')
plt.title('Truth or Bluff (Random Forest Regression)')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()

Output:

For year 6.5, the following prediction is done:

Plot:

Screenshot of code: