# Imports
import pandas as pd
import numpy as np
from pathlib import Path
import hvplot.pandas
import matplotlib.pyplot as plt
from sklearn import svm
from sklearn.preprocessing import StandardScaler
from pandas.tseries.offsets import DateOffset
from sklearn.metrics import classification_report
- Establish a Baseline Performance
- Tune the Baseline Trading Algorithm: Support Vector Machine Learning Algorithm
- Evaluate the New Machine Learning Classifier: AdaBoost Boosting Alogrithm
- Evaluation Report
Looking at all three plots, and comparing the two machine learning algorithms, we can see that the Adaboost does better with the training data with 94% accuracy but when utilizing the testing data it seems to underperform the SVM algorithm.
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What impact resulted from increasing or decreasing the training window?
The training window changed to 12 months actually decreased the performance of both algorithms.
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What impact resulted from increasing or decreasing either or both of the SMA windows?
By increaseing the window for both ML Algorithms, the Adaboost Algo peformed poorly compared to the SVM algorithm.
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