Basic Gradient Boosting Model Training

Hello and welcome! Today, we'll be diving into training a basic Gradient Boosting Model using financial data, specifically focusing on Tesla ($TSLA) stock prices. By the end of this lesson, you will understand how to implement gradient boosting for predictive analysis in stock trading within a Python framework.

Let's go!

First, let's quickly revise how to load data and prepare it for machine learning:

import pandas as pd
from sklearn.preprocessing import StandardScaler
import datasets

# Load dataset
tesla = datasets.load_dataset('codesignal/tsla-historic-prices')
tesla_df = pd.DataFrame(tesla['train'])

# Convert the column to `datetime`
tesla_df['Date'] = pd.to_datetime(tesla_df['Date'])

tesla_df['SMA_5'] = tesla_df['Adj Close'].rolling(window=5).mean()
tesla_df['SMA_10'] = tesla_df['Adj Close'].rolling(window=10).mean()
tesla_df['EMA_5'] = tesla_df['Adj Close'].ewm(span=5, adjust=False).mean()
tesla_df['EMA_10'] = tesla_df['Adj Close'].ewm(span=10, adjust=False).mean()

# Drop NaN values created by moving averages
tesla_df.dropna(inplace=True)

# Features and target selection
features = tesla_df[['Open', 'High', 'Low', 'Close', 'Volume', 'SMA_5', 'SMA_10', 'EMA_5', 'EMA_10']].values
target = tesla_df['Adj Close'].values

# Standardizing features
scaler = StandardScaler()
features_scaled = scaler.fit_transform(features)

In this code we:

• Convert the 'Date' column to datetime format.
• Calculate SMA with windows of 5 and 10 days.
• Calculate EMA with spans of 5 and 10 days.
• Handle missing values resulting from moving averages.
• Select relevant features and the target variable.
• Standardize the feature values for better model performance.

Gradient Boosting is a powerful machine learning technique used for predictive modeling tasks. Gradient Boosting Regressor is a specific application of this technique for regression tasks, where we aim to predict a continuous target variable like stock prices.

In simple terms, Gradient Boosting works by creating an ensemble (a group) of weak prediction models, which are typically simple models. It combines these weak models in a sequential manner to build a robust predictive model. Here's a simplified explanation of how it works:

1. Initial Prediction: Start with an initial prediction, which is often the average of the target values.
2. Calculate Residuals: Calculate the residuals, which are the differences between the actual target values and the current predictions.
3. Train Weak Learners: Train a weak learner (a simple model) on the residuals to predict these errors.
4. Update Predictions: Update the overall predictions by adding the predictions of the weak learner to the current predictions.
5. Iterate: Repeat steps 2-4 multiple times, each time using a new weak learner to correct the errors of the previous model.

Through this iterative process, the gradient boosting regressor minimizes the errors and produces a strong predictive model.