data_frame.ix[data_frame.date <= split_date, 'low'] *= s.get_multiplier() data_frame.ix[data_frame.date <= split_date, 'close'] *= s.get_multiplier() return data_frame for un in list_of_all_unique_names: inner_timer.start_timer() print('Processing stock index: ' + str(index) + ' out of ' + str(len(list_of_all_unique_names)) + '\tLength of all data: ' + str(len(all_data))) index += 1 single_data_frame = all_data[all_data.symbol == un] single_data_frame['date'] = pd.to_datetime(single_data_frame['date']) single_data_frame = single_data_frame.sort_values(by='date') split_list = splits.get_splits_for_stock(un.replace('&', '').replace('.csv', '')) if len(split_list) != 0: single_data_frame = perform_splits_on_stock(split_list, single_data_frame) inner_earnings = simulation.run_simulation_for_single_stock(single_data_frame) if inner_earnings.made_trades(): all_earnings.append(inner_earnings) else: bad_stocks.append(un) # We are finished using the information for that particular stock so we will remove it from the combined data_xv frame. all_data = all_data[all_data.symbol != un] inner_timer.end_timer() inner_timer.print_time()
timer = Timer() #gc.disable() for single_stock in dm.get_all_files_in_directory(dm.individual_data_path): timer.start_timer() single_stock_path = dm.individual_data_path + single_stock print(single_stock_path) df = pd.read_csv(single_stock_path, header=None, usecols=[0, 1, 2, 3, 4]) df.columns = ['date', 'open', 'high', 'low', 'close'] df['date'] = pd.to_datetime(df['date']) split_list = splits.get_splits_for_stock(single_stock.replace('&', '').replace('.csv', '')) if len(split_list) != 0: df = perform_splits_on_stock(split_list, df) profits.append(sim.run_simple_simulation_for_single_stock(single_stock, df)) timer.end_timer() timer.print_time() #gc.enable() profits.sort(key=lambda x: x.total_profit, reverse=False) for p in profits: print(p)