The close prices of these markets occur after US markets have opened, exposing the strategy to lookahead bias.The predictive power of his analysis is eroded by this bias, as shown when he runs the strategy through Quantopian.
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Dunne’s analysis is performed with data from Quandl, a library housing various global financial datasets.
Dunne’s goal was to predict movement in the Dow Jones.
In this post, it’s my goal to translate one such paper from text to code.
Mark Dunne’s Undergraduate Thesis, “Stock Market Prediction“, approaches market forecasting from a sweeping set of angles.
If we were using today’s values to predict tomorrow’s price, it would be necessary to shift this Move column backwards.
That way, today’s feature set would be in line with the dependent variable: tomorrow’s price movement.
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Below is an implementation of such a test: import sklearn from sklearn import metrics from sklearn import neighbors from sklearn import cross_validation labels =  scores =  Y = df['Move'] for c in df.columns: if (c ! KNeighbors Classifier(n_neighbors=25) knn.fit(X_train, Y_train) predicted = knn.predict(X_test) print(c) print(metrics.f1_score(Y_test, predicted)) print(knn.score(X_test, Y_test)) labels.append(c) scores.append(knn.score(X_test, Y_test)) import matplotlib.pyplot as plt; plt.rcdefaults() import numpy as np import matplotlib.pyplot as plt y_pos = np.arange(len(labels)) plt.bar(y_pos, scores, align='center', alpha=0.5) plt.xticks(y_pos, labels) plt.ylabel('Scores') plt.show() The corresponding bar chart is similar to Dunne’s (note the different order of the features): I assume here that Dunne used the raw accuracy score for his bar chart, as opposed to the F1 Score.