Challenge: Predict Market Direction with Logistic Regression
To put your understanding of machine learning in trading to the test, you'll tackle a hands-on challenge: predicting the next day's market direction using logistic regression. You'll begin with a hardcoded DataFrame of closing prices, engineer lagged return features, and use scikit-learn's logistic regression to classify whether the next day's return is positive or negative. You'll then evaluate the model's accuracy and display a confusion matrix to summarize its predictive performance.
Start by creating a DataFrame with daily closing prices. From these prices, calculate daily returns and then generate lagged versions of these returns to use as input features for your model. The target variable will be whether the following day's return is positive (market up) or not (market down or unchanged).
You can experiment by adjusting the number of lagged features or changing the train-test split to see how it affects the model's performance. For more on logistic regression and confusion matrices, consult the scikit-learn documentation.
Swipe to start coding
You are given a DataFrame of daily closing prices for a hypothetical asset. Your task is to build a logistic regression model to predict whether the next day's return will be positive (market up) or not (market down or unchanged) using lagged return features.
- Calculate daily returns from closing prices and add them as a new column named
Return. - Create two new columns
Lag1andLag2that contain the previous day's and the day-before-previous's return, respectively. - Define a new column
Directionas your target variable: set to 1 if the next day's return is greater than 0, otherwise 0. - Remove any rows containing missing values caused by shifting.
- Use
Lag1andLag2as features (X) andDirectionas the target (y). - Use all but the last 5 observations for training, and the last 5 for testing.
- Fit a
LogisticRegressionmodel using scikit-learn on the training data. - Use the model to make predictions on the test set.
- Calculate and print the test accuracy.
- Calculate and print the confusion matrix for the test predictions.
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Can you show me how to create the DataFrame with closing prices?
How do I calculate lagged return features from the closing prices?
Can you explain how to set up the logistic regression model for this task?
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Completion rate forbedret til 4.76Challenge: Predict Market Direction with Logistic Regression
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To put your understanding of machine learning in trading to the test, you'll tackle a hands-on challenge: predicting the next day's market direction using logistic regression. You'll begin with a hardcoded DataFrame of closing prices, engineer lagged return features, and use scikit-learn's logistic regression to classify whether the next day's return is positive or negative. You'll then evaluate the model's accuracy and display a confusion matrix to summarize its predictive performance.
Start by creating a DataFrame with daily closing prices. From these prices, calculate daily returns and then generate lagged versions of these returns to use as input features for your model. The target variable will be whether the following day's return is positive (market up) or not (market down or unchanged).
You can experiment by adjusting the number of lagged features or changing the train-test split to see how it affects the model's performance. For more on logistic regression and confusion matrices, consult the scikit-learn documentation.
Swipe to start coding
You are given a DataFrame of daily closing prices for a hypothetical asset. Your task is to build a logistic regression model to predict whether the next day's return will be positive (market up) or not (market down or unchanged) using lagged return features.
- Calculate daily returns from closing prices and add them as a new column named
Return. - Create two new columns
Lag1andLag2that contain the previous day's and the day-before-previous's return, respectively. - Define a new column
Directionas your target variable: set to 1 if the next day's return is greater than 0, otherwise 0. - Remove any rows containing missing values caused by shifting.
- Use
Lag1andLag2as features (X) andDirectionas the target (y). - Use all but the last 5 observations for training, and the last 5 for testing.
- Fit a
LogisticRegressionmodel using scikit-learn on the training data. - Use the model to make predictions on the test set.
- Calculate and print the test accuracy.
- Calculate and print the confusion matrix for the test predictions.
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