import pandas as pd import numpy as np import pyaf.Bench.TS_datasets as tsds import pyaf.Bench.YahooStocks as ys symbol_lists = tsds.get_yahoo_symbol_lists(); y_keys = sorted(symbol_lists.keys()) print(y_keys) for k in y_keys: tester = ys.cYahoo_Tester(tsds.load_yahoo_stock_prices(k) , "YAHOO_STOCKS_" + k); tester.run_multiprocessed();
import pandas as pd import numpy as np import pyaf.ForecastEngine as autof import pyaf.Bench.TS_datasets as tsds import pyaf.CodeGen.TS_CodeGenerator as tscodegen stock = "BNP.PA" b1 = tsds.load_yahoo_stock_prices("cac40")[stock] df = b1.mPastData df.head() df.info() lEngine = autof.cForecastEngine() lEngine H = b1.mHorizon[stock] lEngine.train(df, b1.mTimeVar, b1.mSignalVar, H) lEngine.getModelInfo() print(lEngine.mSignalDecomposition.mTrPerfDetails.head()) dfapp_in = df.copy() dfapp_in.tail() dfapp_out = lEngine.forecast(dfapp_in, H) #dfapp_out.to_csv("outputs/ozone_apply_out.csv") dfapp_out.tail(2 * H) print("Forecast Columns ", dfapp_out.columns)
import pyaf.Bench.YahooStocks as ys import pyaf.Bench.TS_datasets as tsds tester7 = ys.cYahoo_Tester(tsds.load_yahoo_stock_prices("my_test"), "YAHOO_my_test") tester7.testAllSignals(12) # tester7.run_multiprocessed(18);
import pyaf.Bench.TS_datasets as tsds import pyaf.Bench.YahooStocks as ys tester7 = ys.cYahoo_Tester(tsds.load_yahoo_stock_prices("cac40"), "YAHOO_STOCKS") #tester7.testAllSignals(12); tester7.testSignals("ML.PA", 12) # tester7.run_multiprocessed(18);