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Have you ever wondered how adults make decisions about what to buy and sell in the stock market? Though it may appear like a bewildering professional world, have no fear ! Today, we’ll look at CatBoost, a useful tool that can be used to forecast stock price fluctuations. Think of it as a weather prediction for money!
Building a Stock Price Prediction Model with CatBoost: A Hands-On TutorialMachine learning can simplify the difficult challenge of predicting share prices. CatBoost is one example of a machine learning tool. We’ll look into CatBoost’s role in stock price prediction in this blog article. Even a novice can follow along as we define all the important terms and topics. We will also go through the methods required to produce these predictions and examine a few examples.
It’s difficult to predict stock values these days; it’s like trying to estimate what color gumball will come out of the machine next. But CatBoost is like a superintelligent cat that can sift through a ton of data and make well-informed assumptions. It looks at prior stock prices news from the company, and even the day of the week ! CatBoost can identify trends and make predictions about future price changes by analyzing all of this data.
Assume that you are a baseball card collector. The cost of some players’ cards increases due to their greater fame. Similar to this, successful businesses typically have greater stock prices. Like a superfan, CatBoost keeps track of every player’s stats and use that information to predict which cards will increase in value over time!
Key Terminologies
- Algorithm: An algorithm is a series of guidelines or actions that address an issue. One example of such algorithm is CatBoost.
- Data: Information collected for analysis. In stock prediction, this includes past stock prices, trading volume, etc.
- Model: A model is an illustration of a system that has the ability to forecast results from input data.
- Training Data: Information that teaches the model how to forecast.
- Testing Data: Precise information meant to gauge how well the model predicts outcomes.
- Prediction: The result that the model projects from the data it receives.
How to Use CatBoost to Predict Share PricesSteps to Predict Share Prices Using CatBoostInitially, we need to gather historical stock prices and other relevant information. This data is divided into two sets: training data and testing data.
Step 2: Prepare the DataNext, we tidy up the data (deleting any errors or null values) and put it in a format that CatBoost can understand.
Step 3: Train the ModelNext, we train the CatBoost model to identify patterns and generate predictions using the training data. By supplying the model with data and letting it learn, this is accomplished.
Step 4: Test the ModelAfter training, we test the model using the testing data to see how well it predicts stock prices. This helps us understand the model’s accuracy.
Step 5: Make PredictionsFinally, we use the trained model and new input data to anticipate future stock values.
As an illustration, forecast the price of XYZ Corporation.Let’s look at a simple share price prediction example for XYZ Corporation.
Data GatheringLet’s say we have information on the stock prices of XYZ Corporation going back five years. The daily closing prices, trade volume, and other pertinent data are included in this data.
Data PreparationWe clean this data and prepare it in a table format like this:
Date
| Closing Price
| Volume
| Other Info
|
|---|
|
2020-01-01
|
100
|
5000
|
…
|
|---|
|
2020-01-02
|
102
|
5200
|
…
|
|---|
|
…
|
…
|
…
|
…
|
|---|
|
2024-01-01
|
150
|
4800
|
…
|
|---|
Training the ModelOur CatBoost model is trained using these data. The closing price relationship to volume, and other variables among other patterns in the data are taught to the model.
Testing the ModelNext, we run a test on a new set of data to determine how well the model predicts the closing prices.
Making PredictionsWe utilize the model to forecast XYZ Corporation future prices after testing. Assume for the moment that the model projects the stock price to reach, $152 by tomorrow.
Step-by-Step Guide to Predicting Stock Prices Using CatBoostStep 1: Importing Libraries and Loading the DatasetExplanation:
- Libraries are sets of prewritten code that are useful for doing repetitive operations. We require libraries for machine learning, data manipulation, and visualization for this work.
- We can train, and test our model using historical stock price data from Yahoo Finance.
In order to retrieve stock data from Yahoo Finance into our system, we must first install the catboost and yfinance libraries.
Python
pip install catboost yfinance
# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from catboost import CatBoostRegressor
from sklearn.model_selection import train_test_split
import yfinance as yf
import ipywidgets as widgets
from IPython.display import display
import seaborn as sns
Now, let’s load the historical stock price data for Apple Inc. (AAPL) from Yahoo Finance. We will fetch data from January 1, 2015, to December 31, 2020.
Python
# Load the dataset from Yahoo Finance
ticker = 'AAPL'
data = yf.download(ticker, start="2015-01-01", end="2020-12-31")
# Display the first few rows of the dataset
data.head()
Output:
[*********************100%%**********************] 1 of 1 completed
Open High Low Close Adj Close Volume
Date
2015-01-02 27.847500 27.860001 26.837500 27.332500 24.402174 212818400
2015-01-05 27.072500 27.162500 26.352501 26.562500 23.714720 257142000
2015-01-06 26.635000 26.857500 26.157499 26.565001 23.716957 263188400
2015-01-07 26.799999 27.049999 26.674999 26.937500 24.049520 160423600
2015-01-08 27.307501 28.037500 27.174999 27.972500 24.973562 237458000 Step 2: Data PreparationExplanation:
- Data preparation involves cleaning the data and selecting relevant features that will be used to train the machine learning model.
- Feature engineering is the process of creating new features or modifying existing ones to improve the performance of the model.
First, let’s reset the index of our DataFrame and extract the date components (year, month, day) as additional features.
Python
# Data preparation: Adding 'Date' column and resetting the index
data['Date'] = data.index
data.reset_index(drop=True, inplace=True)
# Feature engineering: Adding more features (Year, Month, Day)
data['Year'] = data['Date'].dt.year
data['Month'] = data['Date'].dt.month
data['Day'] = data['Date'].dt.day
Next, we select the relevant features and the target variable. The target variable is the ‘Close’ price, which we want to predict.
Python
# Selecting features and target
features = ['Open', 'High', 'Low', 'Volume', 'Year', 'Month', 'Day']
target = 'Close'
# Splitting the data into features (X) and target (y)
X = data[features]
y = data[target]
# Splitting the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
Step 3: Training the CatBoost ModelExplanation:
- CatBoost is a machine learning algorithm that handles categorical data efficiently and often provides better performance with less tuning compared to other algorithms.
- Training the model involves feeding it data so it can learn patterns and relationships within the data.
Let’s create and train a CatBoost model using the training data.
Python
# Creating the CatBoostRegressor model
model = CatBoostRegressor(iterations=1000, learning_rate=0.1, depth=6, verbose=0)
# Training the model
model.fit(X_train, y_train)
# Making predictions on the test data
y_pred = model.predict(X_test)
Step 4: Evaluating the ModelExplanation:
- Evaluation helps us understand how well our model is performing. We can use metrics like Mean Absolute Error (MAE) to measure the accuracy of the predictions.
- Visualization is useful for comparing predicted values against actual values to see how well the model is performing.
First, we calculate the Mean Absolute Error.
Python
from sklearn.metrics import mean_absolute_error
# Calculating Mean Absolute Error
mae = mean_absolute_error(y_test, y_pred)
print(f'Mean Absolute Error: {mae}')
Output:
Mean Absolute Error: 0.5031276691916505 Next, we visualize the predicted vs. actual prices using a scatter plot.
Python
# Visualizing the predicted vs actual prices
plt.figure(figsize=(10, 6))
plt.scatter(y_test, y_pred, alpha=0.5)
plt.xlabel("Actual Prices")
plt.ylabel("Predicted Prices")
plt.title("Actual vs Predicted Prices")
plt.show()
Output:
Step 5: Interactive GUI for Real-Time TestingExplanation:
- Interactive GUIs allow users to input values and see real-time predictions. This can be done using ipywidgets in Jupyter notebooks.
Let’s create a simple interactive GUI where users can input feature values and see the predicted stock price.
Python
# Function to make predictions based on user input
def predict_price(open_price, high_price, low_price, volume, year, month, day):
input_data = np.array([[open_price, high_price, low_price, volume, year, month, day]])
prediction = model.predict(input_data)
print(f'Predicted Stock Price: {prediction[0]:.2f}')
# Creating the interactive widgets
open_price_slider = widgets.FloatSlider(value=150, min=100, max=200, step=1, description='Open Price:')
high_price_slider = widgets.FloatSlider(value=155, min=105, max=205, step=1, description='High Price:')
low_price_slider = widgets.FloatSlider(value=145, min=95, max=195, step=1, description='Low Price:')
volume_slider = widgets.FloatSlider(value=50000000, min=10000000, max=100000000, step=1000000, description='Volume:')
year_slider = widgets.IntSlider(value=2020, min=2015, max=2025, step=1, description='Year:')
month_slider = widgets.IntSlider(value=6, min=1, max=12, step=1, description='Month:')
day_slider = widgets.IntSlider(value=15, min=1, max=31, step=1, description='Day:')
# Displaying the widgets and connecting them to the prediction function
interactive_plot = widgets.interactive(predict_price,
open_price=open_price_slider,
high_price=high_price_slider,
low_price=low_price_slider,
volume=volume_slider,
year=year_slider,
month=month_slider,
day=day_slider)
display(interactive_plot)
Output:
We have gone over how to use CatBoost to anticipate stock values. We discussed:
- Importing Libraries: Essential tools for data handling and machine learning.
- Loading the Dataset: Fetching stock price data from Yahoo Finance.
- Data Preparation: Cleaning and organizing data for the model.
- Training the Model: Using CatBoost to learn from the data.
- Evaluating the Model: Checking the model’s performance.
- Interactive GUI: Allowing users to input data and see predictions.
By following these steps, you can create your own stock price prediction model and gain insights into the stock market. Happy Boosting!
Can CatBoost Really Predict the Future?Not precisely! Numerous factors, some of which are unanticipated, affect the stock market. Precise forecasts cannot be guaranteed by even the most intelligent cat (or CatBoost). However, CatBoost can help you make better selections by indicating which direction the wind may be blowing.
Frequently Asked Questions (FAQs)How does CatBoost vary from other types of algorithms?When processing categorical data CatBoost performs particularly well when compared to other algorithms and often produces higher accuracy with less time and effort.
Can CatBoost predict prices for any stock?Indeed, with sufficient data, CatBoost can be trained to forecast stock values for any company.
Do I need a powerful computer to use CatBoost?Not necessarily. While more powerful computers can handle larger datasets and more complex models faster, CatBoost is optimized to run efficiently even on regular computers.
ConclusionCatBoost stock price prediction is a useful tool for investors. Even novices, can begin generating forecasts by comprehending the fundamental ideas and adhering to the aforementioned instructions. These forecasts can become more accurate with experience and additional data enabling investors to make better-informed choices
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