Hey, my name is Santosh and I built Beibo: a Python library that uses several AI prediction models (such as Prophet, ARIMA...) to predict stocks returns over a defined period of time.

I have been working on it for a few weeks now. Every detail about how I built it is in the readme, otherwise, feel free to ask me any questions.

Here is the link to the repo: https://github.com/ssantoshp/Beibo

I hope it helps you and if it does, please give it a star!

Disclaimer: I changed the name to Beibo since there might be issues with Oracle Inc's trademark

Here is its answer. How'd it do?

What other factors might we take into account?

import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression

# Load the data
df = pd.read_csv('stock_data.csv')

# Select the features and target
X = df[['feature1', 'feature2', 'feature3']]
y = df['target']

# Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Create and fit the model
model = LinearRegression()
model.fit(X_train, y_train)

# Make predictions on the test set
y_pred = model.predict(X_test)

# Calculate the mean absolute error
mae = np.mean(abs(y_pred - y_test))
print(mae)

This script assumes that you have a CSV file called "stock_data.csv" that contains the historical data for the stock, with columns for the features (e.g. trading volume, market trends) and the target (i.e. the stock price). The script uses a linear regression model to fit the data and make predictions, and it calculates the mean absolute error (MAE) between the predicted and actual stock prices on the test set.

Keep in mind that this is just a simple example, and there are many other factors that can be taken into account when building a stock price prediction model. This script may need to be modified or expanded upon depending on your specific needs and goals.