# configure logger Log.init_instance(config().logfile(), config().loglevel(), config().logformat()) assets = {} correlations = {} if __name__ == "__main__": for path in config().input_path(): Log.info("Searching in %s", path) for filename in os.listdir(path): if fnmatch.fnmatch(filename, "*.csv"): Log.info("File %s", filename) asset = Asset() asset.read_csv(os.path.join(path, filename)) if len(asset.data["Volume"][ asset.data["Volume"] > 10000]) < args.min_data_points: Log.info( "Ignoring data set since it has too few entries ({} instead of {})" .format( len(asset.data["Volume"][ asset.data["Volume"] > 10000]), args.min_data_points)) continue if asset.exchange is None or asset.symbol is None: Log.info( "Ignoring data set since exchange or symbol is missing" ) continue assets[asset.exchange + "/" + asset.symbol] = asset
help="Log level: DEBUG, INFO, WARN, ERROR") parser.add_argument("--logfile", action="store", metavar="PATH", help="Path to log file.") parser.add_argument("-o", "--output", metavar="FILE", required=True, action="store", help="Output file") parser.add_argument("filename", metavar="FILE", help="files to process") args = parser.parse_args() # configuration files Configuration.init_instance(args.config, args) # configure logger Log.init_instance(config().logfile(), config().loglevel(), config().logformat()) if __name__ == "__main__": Log.info("Generating indicators for %s", args.filename) asset = Asset() asset.read_csv(args.filename) asset = gen_indicators(asset) asset.write_csv(args.output) Log.info("Output written to %s", args.output)
"svm=SVM regressor, " "knn=KNN regressor, " "nn=neural network regressor") parser.add_argument("filename", metavar="FILE", nargs='*', help="Stock data input files to process") args = parser.parse_args() # configuration files Configuration.init_instance(args.config, args) # configure logger Log.init_instance(config().logfile(), config().loglevel(), config().logformat()) if __name__ == "__main__": for filename in config().input_filenames(): Log.info("Running models for %s", filename) asset = Asset() asset.read_csv(filename) for model in args.models.split(","): if model == "lstm": lstm_model.fit_LSTM_models(asset) elif model == "clf": models.fit_classifiers(asset, classifiers=args.clf.split(",")) elif model == "reg": models.fit_regressors(asset, regressors=args.reg.split(","))