Method get_classifier

history/models.py:278–388

(self, train=True, test=True)

                                             type='mock')

    def get_classifier(self, train=True, test=True):

        all_output = ""
        h = .02  # step size in the mesh
        self.names = ["Nearest Neighbors", "Linear SVM", "RBF SVM", "Decision Tree",
                      "Random Forest", "AdaBoost", "Naive Bayes", "Linear Discriminant Analysis",
                      "Quadratic Discriminant Analysis"]
        classifiers = [
            KNeighborsClassifier(3),
            SVC(kernel="linear", C=0.025),
            SVC(gamma=2, C=1),
            DecisionTreeClassifier(max_depth=5),
            RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
            AdaBoostClassifier(),
            GaussianNB(),
            LinearDiscriminantAnalysis(),
            QuadraticDiscriminantAnalysis()]

        for i in range(0, len(self.names)):
            if self.names[i] == self.name:
                clf = classifiers[i]

        if train:
            start_time = int(time.time())
            data = self.get_latest_prices(normalize=False)
            price_datasets = [[], []]
            for i, val in enumerate(data):
                try:
                    # get classifier projection
                    sample = create_sample_row(data, i, self.datasetinputs)
                    last_price = data[i + self.datasetinputs - 1]
                    next_price = data[i + self.datasetinputs]
                    change = next_price - last_price
                    pct_change = change / last_price
                    fee_pct = get_fee_amount()
                    fee_pct = fee_pct * 2  # fee x 2 since we'd need to clear both buy and sell fees to be profitable
                    fee_pct = fee_pct * settings.FEE_MANAGEMENT_STRATEGY  # see desc in settings.py
                    do_buy = ClassifierTest.HOLD if abs(pct_change) < fee_pct else (
                        ClassifierTest.BUY if change > 0 else ClassifierTest.SELL)
                    price_datasets[0].append(sample)
                    price_datasets[1].append(do_buy)
                except Exception:
                    pass

            data = price_datasets
            if self.timedelta_back_in_granularity_increments == 0:
                train_data = data
                test_data = [[], []]
            else:
                train_data = [data[0][0:(-1 * self.timedelta_back_in_granularity_increments)],
                              data[1][0:(-1 * self.timedelta_back_in_granularity_increments)]]
                test_data = [data[0][len(train_data[0]):], data[1][len(train_data[1]):]]
            self.datasets = train_data

            X, y = train_data
            X = StandardScaler().fit_transform(X)
            self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(X, y, test_size=.4)