---
title: "XGBoost"
description: "Learn how to create, train, and evaluate machine learning models in the research environment in QuantConnect with XGBoost library."
canonical: https://www.quantconnect.com/docs/v2/research-environment/machine-learning/popular-libraries/xgboost
markdown: https://www.quantconnect.com/docs/v2/research-environment/machine-learning/popular-libraries/xgboost.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/04%20Research%20Environment/08%20Machine%20Learning/03%20Popular%20Libraries/10%20XGBoost
modified: 2026-10-30
index: https://www.quantconnect.com/llms.txt
---

# XGBoost

[Documentation](https://www.quantconnect.com/docs/v2.md) > [Research Environment](https://www.quantconnect.com/docs/v2/research-environment.md) > Machine Learning > [Popular Libraries](https://www.quantconnect.com/docs/v2/research-environment/machine-learning/popular-libraries.md) > XGBoost

> Code examples are shown in Python. Append `?lang=csharp` to this URL for the C# version of this page.

## Introduction

This page explains how to build, train, test, and store `XGBoost` models.

## Import Libraries

Import the `xgboost`, `sklearn`, and `joblib` libraries.

```python
import xgboost as xgb
from sklearn.model_selection import train_test_split
import joblib
```

You need the `sklearn` library to prepare the data and the `joblib` library to save models.

## Get Historical Data

[Get some historical market data](https://www.quantconnect.com/docs/v2/research-environment/datasets/us-equity.md#04-Get-Historical-Data) to train and test the model. For example, to get data for the SPY ETF during 2020 and 2021, run:

```python
qb = QuantBook()
symbol = qb.add_equity("SPY", Resolution.DAILY).symbol
history = qb.history(symbol, datetime(2020, 1, 1), datetime(2022, 1, 1)).loc[symbol]
```

## Prepare Data

You need some [historical data](https://www.quantconnect.com/docs/v2/research-environment/machine-learning/popular-libraries/pytorch.md#03-Get-Historical-Data) to prepare the data for the model. If you have historical data, manipulate it to train and test the model. In this example, use the following features and labels:

| Data Category | Description |
| --- | --- |
| Features | The last 5 closing prices |
| Labels | The following day's closing price |

The following image shows the time difference between the features and labels:

![Features and labels for training](https://cdn.quantconnect.com/i/tu/ml-keras-function.png)

Follow these steps to prepare the data:

1. Perform fractional differencing on the historical data.
   ```python
   df = (history['close'] * 0.5 + history['close'].diff() * 0.5)[1:]
   ```
   Fractional differencing helps make the data stationary yet retains the variance information.
2. Loop through the `df` DataFrame and collect the features and labels.
   ```python
   n_steps = 5
   features = []
   labels = []
   for i in range(len(df)-n_steps):
       features.append(df.iloc[i:i+n_steps].values)
       labels.append(df.iloc[i+n_steps])
   ```
3. Convert the lists of features and labels into `numpy` arrays.
   ```python
   features = np.array(features)
   labels = np.array(labels)
   ```
4. Standardize the features and labels
   ```python
   X = (features - features.mean()) / features.std()
   y = (labels - labels.mean()) / labels.std()
   ```
5. Split the data into training and testing periods.
   ```python
   X_train, X_test, y_train, y_test = train_test_split(X, y)
   ```

## Train Models

You need to [prepare the historical data](https://www.quantconnect.com/docs/v2/research-environment/machine-learning/popular-libraries/xgboost.md#04-Prepare-Data) for training before you train the model. If you have prepared the data, build and train the model. In this example, train a gradient-boosted random forest for future price prediction. Follow these steps to create the model:

1. Split the data for training and testing to evaluate the model.
   ```python
   X_train, X_test, y_train, y_test = train_test_split(X, y)
   ```
2. Format the training set into an XGBoost matrix.
   ```python
   dtrain = xgb.DMatrix(X_train, label=y_train)
   ```
3. Train the model with parameters.
   ```python
   params = {
     'booster': 'gbtree',
     'colsample_bynode': 0.8,
     'learning_rate': 0.1,
     'lambda': 0.1,
     'max_depth': 5,
     'num_parallel_tree': 100,
     'objective': 'reg:squarederror',
     'subsample': 0.8,
   }
   model = xgb.train(params, dtrain, num_boost_round=10)
   ```

## Test Models

You need to [build and train the model](https://www.quantconnect.com/docs/v2/research-environment/machine-learning/popular-libraries/xgboost.md#05-Train-Models) before you test its performance. If you have trained the model, test it on the out-of-sample data. Follow these steps to test the model:

1. Format the testing set into an XGBoost matrix.
   ```python
   dtest = xgb.DMatrix(X_test, label=y_test)
   ```
2. Call the `predict` method with the testing dataset.
   ```python
   y_predict = model.predict(dtest)
   ```
3. Plot the predicted values against the actual values to assess the model's predictive power.
   ```python
   df = pd.DataFrame({'Real': y_test.flatten(), 'Predicted': y_predict.flatten()})
   df.plot(title='Model Performance: predicted vs actual closing price', figsize=(15, 10))
   plt.show()
   ```
   ![Xgboost model performance](https://cdn.quantconnect.com/i/tu/xgboost-plot-2.png)

## Store Models

You can save and load `xgboost` models using the Object Store.

### Save Models

Follow these steps to save models in the Object Store:

1. Set the key name of the model to be stored in the Object Store.
   ```python
   model_key = "model"
   ```
2. Call the `get_file_path` method with the key.
   ```python
   file_name = qb.object_store.get_file_path(model_key)
   ```
   This method returns the file path where the model will be stored.
3. Call the `dump` method with the model and file path.
   ```python
   joblib.dump(model, file_name)
   ```
   If you dump the model using the `joblib` module before you save the model, you don't need to retrain the model.

### Load Models

You must save a model into the Object Store before you can load it from the Object Store. If you saved a model, follow these steps to load it:

1. Call the `contains_key` method.
   ```python
   qb.object_store.contains_key(model_key)
   ```
   This method returns a boolean that represents if the `model_key` is in the Object Store. If the Object Store does not contain the `model_key`, save the model using the `model_key` before you proceed.
2. Call the `get_file_path` method with the key.
   ```python
   file_name = qb.object_store.get_file_path(model_key)
   ```
   This method returns the path where the model is stored.
3. Call the `load` method with the file path.
   ```python
   loaded_model = joblib.load(file_name)
   ```
   This method returns the saved model.

To verify loading the model was successful, test the model.

```python
y_pred = loaded_model.predict(dtest)
df = pd.DataFrame({'Real': y_test.flatten(), 'Predicted': y_pred.flatten()})
df.plot(title='Model Performance: predicted vs actual closing price', figsize=(15, 10))
```

![Restored xgboost model performance](https://cdn.quantconnect.com/i/tu/xgboost-plot-2.png)

## Examples

The following examples demonstrate some common practices for using the xgboost library.

### Example 1: Predict Next Price

The following research notebook uses an `xgboost` machine learning model to predict the next day's close price by the previous 5 days' daily closes.

```python
# Import the xgboost library and others.
import xgboost as xgb
from sklearn.model_selection import train_test_split
import joblib

# Instantiate the QuantBook for researching.
qb = QuantBook()
# Request the daily SPY history with the date range to be studied.
symbol = qb.add_equity("SPY", Resolution.DAILY).symbol
history = qb.history(symbol, datetime(2020, 1, 1), datetime(2022, 1, 1)).loc[symbol]

# Obtain the daily fractional differencing in close price to be the features and labels.
df = (history['close'] * 0.5 + history['close'].diff() * 0.5)[1:]
# Use the previous 5 day returns as the features.
# Get the 1-day forward return as the labels.
n_steps = 5
features = []
labels = []
for i in range(len(df)-n_steps):
    features.append(df.iloc[i:i+n_steps].values)
    labels.append(df.iloc[i+n_steps])

# Clean up and process the data for faster convergence.
features = np.array(features)
labels = np.array(labels)
X = (features - features.mean()) / features.std()
y = (labels - labels.mean()) / labels.std()

# Split the data into a training set and test set for validation.
X_train, X_test, y_train, y_test = train_test_split(X, y)

# Format the training set into an XGBoost matrix.
dtrain = xgb.DMatrix(X_train, label=y_train)
# Train the model with parameters.
params = {
  'booster': 'gbtree',
  'colsample_bynode': 0.8,
  'learning_rate': 0.1,
  'lambda': 0.1,
  'max_depth': 5,
  'num_parallel_tree': 100,
  'objective': 'reg:squarederror',
  'subsample': 0.8,
}
model = xgb.train(params, dtrain, num_boost_round=10)

# Format the testing set into an XGBoost matrix.
dtest = xgb.DMatrix(X_test, label=y_test)
# Call the predict method with the testing dataset.
y_predict = model.predict(dtest)
# Plot the predicted values against the actual values.
df = pd.DataFrame({'Real': y_test.flatten(), 'Predicted': y_predict.flatten()})
df.plot(title='Model Performance: predicted vs actual closing price', figsize=(15, 10))
plt.show()

# Store the model in the object store to allow accessing the model in the next research session or in the algorithm for trading.
model_key = "model"
file_name = qb.object_store.get_file_path(model_key)
joblib.dump(model, file_name)
```