from get_data import GetData getData = GetData() fields = ['Open', 'High', 'Low', 'Close', 'Adj_Close'] accuracy = {} features = getData.getAllFeatures() symbols = getData.getAllSymbols() # get_data_block_end for symbol in symbols: accuracy[symbol] = [] for field in range(1, 5): labels = getData.getSymbolCLFLabels(symbol, field) ######################## # now the real MA work # ######################## # create train and test data set X_test, X_train, y_test, y_train = train_test_split(features, labels, test_size=.5) # create classifier my_classifier = tree.DecisionTreeClassifier() # train the classifier my_classifier.fit(X_train, y_train) # do prediction
import time from datetime import datetime, date, time, timedelta # get_data_block_start from get_data import GetData from save_data import SaveData getData = GetData() saveData = SaveData() symbols = getData.getAllSymbols() for symbol in symbols: # we just predict up/down of close price # result = getData.getSymbolCLFLabels(symbol, 4) features = getData.getSymbolFeaturesWithoutDate(symbol) allFeatures = getData.getSymbolFeatures(symbol) dates = [] for feature in allFeatures: dates.append(feature[0]) # create train and test data set # high = len(features) mid = high - 100 low = 0 X_train = features[low:mid] y_train = result[low:mid] X_test = features[mid+1:] y_test = result[mid+1:]