def test_batcher():
data = {
'col1CLS': [3, 3, 4, 5, 7, 8, 7, 6, 5, 4],
'col2CLS': [6, 5, 5, 6, 7, 6, 4, 3, 3, 8],
'col3CLS': [7, 6, 4, 6, 4, 2, 4, 5, 6, 5],
'col4CLS': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
'col5CLS': [3, 3, 3, 3, 3, 3, 3, 3, 3, 3],
'col1CHG': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
'col2CHG': [4, 4, 4, 4, 4, 4, 4, 4, 4, 4],
'col3CHG': [4, 4, 4, 4, 4, 4, 4, 4, 4, 4],
'col4CHG': [4, 4, 4, 4, 4, 4, 4, 4, 4, 4],
'col5CHG': [3, 3, 3, 3, 3, 3, 3, 3, 3, 3],
'col2slope_sum': [13, 8, -3, 3, 3, 3, 3, 3, 3, 3],
'col3slope_sum': [1, -3, 3, 5, 3, 3, 3, 3, 3, 3],
'col4slope_sum': [9, 3, 9, 3, -3, 3, 3, 3, 3, 3],
}
stock_data = pd.DataFrame(data=data)
print sample_slopes.generate_target_values(stock_data, 3, "col2CLS", 2)
assert len(
sample_slopes.create_batch_of_slopes(stock_data, 'col4slope_sum', 2,
6)) == 6
assert sample_slopes.create_batch_of_slopes(stock_data, 'col4slope_sum', 2,
6) == [
[9, 3],
[3, 9],
[9, 3],
[3, -3],
[-3, 3],
[3, 3],
]
def test_train_on_test_data():
data = {'col1CLS': [3, 3, 4, 5, 7, 8, 7, 6, 5, 4],
'col2CLS': [6, 5, 5, 6, 7, 6, 4, 3, 3, 8],
'col3CLS': [7, 6, 4, 6, 4, 2, 4, 5, 6, 5],
'col4CLS': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
'col5CLS': [3, 3, 3, 3, 3, 3, 3, 3, 3, 3],
'col1CHG': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
'col2CHG': [4, 4, 4, 4, 4, 4, 4, 4, 4, 4],
'col3CHG': [4, 4, 4, 4, 4, 4, 4, 4, 4, 4],
'col4CHG': [4, 4, 4, 4, 4, 4, 4, 4, 4, 4],
'col5CHG': [3, 3, 3, 3, 3, 3, 3, 3, 3, 3],
'col2slope_sum': [13, 8, -3, 3, 3, 3, 3, 3, 3, 3],
'col3slope_sum': [1, -3, 3, 5, 3, 3, 3, 3, 3, 3],
'col4slope_sum': [9, 3, 9, 3, -3, 7, -14, 3, 99, 3],
}
stock_data = pd.DataFrame(data=data)
# generate_target_values(df, batch_count, column_name, look_ahead)
y_values = sample_slopes.generate_target_values(
stock_data, 3, 'col2CLS', 2)
# create_batch_of_slopes(df, batch_count, cut_length)
x_vaules = sample_slopes.create_batch_of_slopes(
stock_data, 'col2slope_sum', 3, y_values[1])
print x_vaules, 'x values'
print y_values[0], ' yvalues'
sv = support_vector.Support_Vector(x_vaules, y_values[0])
sv.train()
assert sv.predict_out_put([[-0.8, -1, 0]]) == [-1] or [1]
print "it was warnred "
def test_different_lengths_of_objects():
"""
This test is used to try out the returns calculator on the stock market data
"""
ticker = 'FB'
main_df = pd.read_pickle(settings.settings_dict['stock_data_path'])
main_df = sample_slopes.create_slope_sum(main_df)
Back_Test = back_test.BackTest(main_df,
settings.settings_dict['model_path'])
y_values = sample_slopes.generate_target_values(main_df, 18,
ticker + "CLS", 2)
x_values = sample_slopes.create_batch_of_slopes(main_df, ticker + 'CLS',
18, y_values[1])
array_of_batches = Back_Test.create_batch_of_slopes(
main_df, ticker + 'slope_sum', 18, y_values[1])
print array_of_batches, ' here is len of array_of_batch'
print Back_Test.append_list_of_buy_sells(array_of_batches,
ticker + "slope_sum")
print "algorithm dfa ", sum(
Back_Test.take_bid_stream_calculate_profit(ticker + "bid_stream", 18,
2)), ' alogritmsss'
print "percent change", Back_Test.calculate_holding_profit(
ticker + "CLS", 18, 2),
def test_on_market_data_single_stock():
"""
This test is used to try out the returns calculator on the stock market data
"""
main_df = pd.read_pickle(settings.settings_dict['stock_data_path'])
main_df = sample_slopes.create_slope_sum(main_df)
Back_Test = back_test.BackTest(main_df,
settings.settings_dict['model_path'])
y_values = sample_slopes.generate_target_values(main_df, 18, 'FBCLS', 2)
x_values = sample_slopes.create_batch_of_slopes(main_df, 'FBCLS', 18,
y_values[1])
array_of_batches = Back_Test.create_batch_of_slopes(
main_df, 'FBslope_sum', 18, y_values[1])
print array_of_batches, ' here is lensss'
print Back_Test.append_list_of_buy_sells(array_of_batches, "FBslope_sum")
print "algorithm ", sum(
Back_Test.take_bid_stream_calculate_return("FBbid_stream", 18,
2)) * 100, '%'
print "log return ", sum(
Back_Test.test_calculate_holding_log_return('FBCLS')) * 100, '%'
print "percent change", Back_Test.calculate_holding_percent_change_return(
"FBCLS") * 100, '%'
def test_generate_target_values_and_sliding_window_lenth():
data = {
'col1CLS': [3, 3, 4, 5, 7, 8, 7, 6, 5, 4],
'col2CLS': [6, 5, 5, 6, 7, 6, 4, 3, 3, 8],
'col3CLS': [7, 6, 4, 6, 4, 2, 4, 5, 6, 5],
'col4CLS': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
'col5CLS': [3, 3, 3, 3, 3, 3, 3, 3, 3, 3],
'col1CHG': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
'col2CHG': [4, 4, 4, 4, 4, 4, 4, 4, 4, 4],
'col3CHG': [4, 4, 4, 4, 4, 4, 4, 4, 4, 4],
'col4CHG': [4, 4, 4, 4, 4, 4, 4, 4, 4, 4],
'col5CHG': [3, 3, 3, 3, 3, 3, 3, 3, 3, 3],
'col2slope_sum': [13, 8, -3, 3, 3, 3, 3, 3, 3, 3],
'col3slope_sum': [1, -3, 3, 5, 3, 3, 3, 3, 3, 3],
'col4slope_sum': [9, 3, 9, 3, -3, 3, 3, 3, 3, 3],
}
stock_data = pd.DataFrame(data=data)
# generate_target_values(df, batch_count, column_name, look_ahead)
y_values = sample_slopes.generate_target_values(stock_data, 2, 'col4CLS',
2)
# create_batch_of_slopes(df, batch_count, cut_length)
x_vaules = sample_slopes.create_batch_of_slopes(stock_data,
'col4slope_sum', 2,
y_values[1])
print(y_values[0]), 'len y ', len(y_values[0])
print(x_vaules), 'len x ', len(x_vaules)
assert len(y_values[0]) == len(x_vaules)
y_values = sample_slopes.generate_target_values(stock_data, 3, 'col4CLS',
3)
# create_batch_of_slopes(df, batch_count, cut_length)
x_vaules = sample_slopes.create_batch_of_slopes(stock_data,
'col4slope_sum', 3,
y_values[1])
print(y_values[0]), 'len y ', len(y_values[0])
print(x_vaules), 'len x ', len(x_vaules)
assert len(y_values[0]) == len(x_vaules)
def test_iterate_and_persist_slope_sums():
"""
used to make sure that i can make the logic to maintain the congruency between
featcures and targetvalues across all slope_sum_cols
"""
data = {'col1CLS': [3, 3, 4, 5, 7, 8, 7, 6, 5, 4],
'col2CLS': [6, 5, 5, 6, 7, 6, 4, 3, 3, 8],
'col3CLS': [7, 6, 4, 6, 4, 2, 4, 5, 6, 5],
'col4CLS': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
'col5CLS': [3, 3, 3, 3, 3, 3, 3, 3, 3, 3],
'col1CHG': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
'col2CHG': [4, 4, 4, 4, 4, 4, 4, 4, 4, 4],
'col3CHG': [4, 4, 4, 4, 4, 4, 4, 4, 4, 4],
'col4CHG': [4, 4, 4, 4, 4, 4, 4, 4, 4, 4],
'col5CHG': [3, 3, 3, 3, 3, 3, 3, 3, 3, 3],
'col2slope_sum': [13, 8, -3, 3, 3, 3, 3, 3, 3, 3],
'col3slope_sum': [1, -3, 3, 5, 3, 3, 3, 3, 3, 3],
'col4slope_sum': [9, 3, 9, 3, -3, 7, -14, 3, 99, 3],
}
stock_data = pd.DataFrame(data=data)
columns = list(stock_data)
columns_with_sample_slopes = sample_slopes.get_columns_with_slope_sum(
columns)
sv = support_vector.Support_Vector([], [])
for column in columns_with_sample_slopes:
y_values = sample_slopes.generate_target_values(
stock_data, 3, column.replace('slope_sum', 'CLS'), 2)
sv.Y = sv.Y + y_values[0]
# create_batch_of_slopes(df, batch_count, cut_length)
# y_values[1] bec thats used to tell create batch_of_slopes where to
# stop
x_values = sample_slopes.create_batch_of_slopes(
stock_data, column, 3, y_values[1])
sv.X = sv.X + x_values
print sv.X, 'xvalues'
print sv.Y, 'yvalues'
sv.train()
assert sv.predict_out_put([[9, 3, -3]]) == [1]
test_data = [[1.00893849384939849, 2, 3], [9, 3, -3], [-1, 5, 3],
[8, -3, 3], [-3, 3, 3], [3, 3, 3], [3, 3, 3], ]
for sample in test_data:
print sv.predict_out_put([sample])
def test_generate_target_values():
data = {
'col1CLS': [3, 3, 4, 5, 7, 8, 7, 6, 5, 4],
'col2CLS': [6, 5, 5, 6, 7, 6, 4, 3, 3, 8],
'col3CLS': [7, 6, 4, 6, 4, 2, 4, 5, 6, 5],
'col4CLS': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
'col5CLS': [3, 3, 3, 3, 3, 3, 3, 3, 3, 3],
'col1CHG': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
'col2CHG': [4, 4, 4, 4, 4, 4, 4, 4, 4, 4],
'col3CHG': [4, 4, 4, 4, 4, 4, 4, 4, 4, 4],
'col4CHG': [4, 4, 4, 4, 4, 4, 4, 4, 4, 4],
'col5CHG': [3, 3, 3, 3, 3, 3, 3, 3, 3, 3],
'col2slope_sum': [13, 8, -3, 3, 3, 3, 3, 3, 3, 3],
'col3slope_sum': [1, -3, 3, 5, 3, 3, 3, 3, 3, 3],
'col4slope_sum': [9, 3, 9, 3, -3, 3, 3, 3, 3, 3],
}
stock_data = pd.DataFrame(data=data)
assert sample_slopes.generate_target_values(stock_data, 3, 'col2CLS',
2) == ([1, 1, -1, -1, -1,
1], 6)
assert sample_slopes.generate_target_values(stock_data, 3, 'col3CLS',
2) == ([1, -1, 1, 1, 1, 1], 6)
def test_on_array_of_market():
"""
This test is used to try out the returns calculator on the stock market data
"""
tickers = [
"GOOG", "FB", "INTC", 'TSM', "CSCO", "ORCL", "NVDA", "SAP", "IBM",
"ADBE", "TXN", "AVGO", "CRM", "QCOM", "MU", "BIDU", "ADP", "VMW",
"ATVI", "AMAT", "INTU", "CTSH", "EA", "EA", "NXPI", "INFY"
]
# tickers = ["GOOG", "FB", "INTC", 'TSM', "CSCO"]
main_df = pd.read_pickle(settings.settings_dict['stock_data_path'])
main_df = sample_slopes.create_slope_sum(main_df)
Back_Test = back_test.BackTest(main_df,
settings.settings_dict['model_path'])
with open('files/testing_files/return_output.csv', 'w') as f:
for ticker in tickers:
y_values = sample_slopes.generate_target_values(
main_df, 18, ticker + "CLS", 2)
x_values = sample_slopes.create_batch_of_slopes(
main_df, ticker + 'CLS', 18, y_values[1])
array_of_batches = Back_Test.create_batch_of_slopes(
main_df, ticker + 'slope_sum', 18, y_values[1])
Back_Test.append_list_of_buy_sells(array_of_batches,
ticker + "slope_sum")
algorithm_return = sum(
Back_Test.take_bid_stream_calculate_return(
ticker + "bid_stream", 18, 2)) * 100
log_return = sum(
Back_Test.test_calculate_holding_log_return(ticker +
'CLS')) * 100
percent_change = Back_Test.calculate_holding_percent_change_return(
ticker + "CLS") * 100
print "algorithm ", algorithm_return, '%'
print "log return ", log_return, ' %'
print "percent change", percent_change, '%'
f.write(ticker + ',' + str(algorithm_return) + ',' +
str(log_return) + ',' + str(percent_change) + '\n')
def main(batch_size, look_ahead):
"""
da main function
"""
i = 0
main_df = pd.DataFrame()
ticker_data = Ticker_Data(main_df)
# NOTE ============start here to get new stock data=======================
# for ticker in tickers:
# print ticker
# time.sleep(.02)
# # print ticker
# df = web.DataReader(ticker, 'iex', start, end)
# df = df.reset_index(level='date')
# # print df.head()
# ticker_data.append_change_column(df, ticker)
# i = i + 50
# # remove the rows that contain any 0's or NA
# ticker_data.main_df.to_csv('before_NA_drop_stock_data_slope_sumNoNA' +
# str(start) + '--' + str(end) + '.csv')
# # ticker_data.drop_row_with_zeros()
# ticker_data.drop_row_with_NA()
# ticker_data.main_df.to_pickle(
# 'stock_data/df_without_NA_' + str(start) + '--' + str(end) + '.pkl')
# NOTE ============end================================================
ticker_data.main_df = pd.read_pickle('stock_data/df_without_NA_' +
str(start) + '--' + str(end) + '.pkl')
# add the slope sum values to the dataframe
# ticker_data.main_df = sample_slopes.create_slope_sum(ticker_data.main_df)
ticker_data.main_df = sample_slopes.create_slope_sum_market(
ticker_data.main_df)
# write the whole datarame to a csv if you want to
ticker_data.main_df.to_csv('stock_data_slope_sumNoNA' + str(start) + '--' +
str(end) + '.csv')
# get the names of all the column titles
columns = list(ticker_data.main_df)
# get the names of the columns that have a slope_sum
columns_with_sample_slopes = sample_slopes.get_columns_with_slope_sum(
columns)
# set up the ML package to hold the features and target values
sv = support_vector.Support_Vector([], [])
for column in columns_with_sample_slopes:
y_values = sample_slopes.generate_target_values(
ticker_data.main_df, batch_size,
column.replace('slope_sum', 'CLS'), look_ahead)
# keeps adding new target values to varable
sv.Y = sv.Y + y_values[0]
# create_batch_of_slopes(df, batch_count, cut_length)
# y_values[1] bec thats used to tell create batch_of_slopes where to
# stop
x_values = sample_slopes.create_batch_of_slopes(
ticker_data.main_df, column, batch_size, y_values[1])
# x_values = sample_slopes.create_batch_of_slopes_moving_av(
# ticker_data.main_df, column, batch_size, y_values[1], 15)
# keeps adding new feature values to varable
sv.X = sv.X + x_values
write_feature_and_targets(sv.X, sv.Y)
print(sv.Y, 'Yvalues')
print(sv.X[-1], ' Xvalues ')
# print sv.X, 'Xvalues'
print('training the model...')
sv.train()
def test_plot_stock():
"""
Makes sure we can just plot a stock
"""
batch_size = 18
look_ahead = 2
ticker = 'ADBE'
main_df = pd.read_pickle(settings.settings_dict['stock_data_path'])
main_df = sample_slopes.create_slope_sum_market(main_df)
Back_Test = back_test.BackTest(main_df,
settings.settings_dict['model_path'])
y_values = sample_slopes.generate_target_values(main_df, batch_size,
ticker + 'CLS', look_ahead)
x_values = sample_slopes.create_batch_of_slopes(main_df, ticker + 'CLS',
batch_size, y_values[1])
array_of_batches = Back_Test.create_batch_of_slopes(
main_df, ticker + 'slope_sum', batch_size, y_values[1])
Back_Test.append_list_of_buy_sells(array_of_batches, ticker + "slope_sum")
algorithm_return = Back_Test.take_bid_stream_calculate_profit(
ticker + "bid_stream", batch_size, look_ahead, for_graph=True)
array_of_bid_stream = Back_Test.main_df[ticker +
'bid_stream'].tolist()[batch_size +
look_ahead -
1:]
index_bid_stream = range(0, len(array_of_bid_stream))
array_of_bid_stream = np.array(array_of_bid_stream) * 5
runningTotal = []
total = 0
for n in algorithm_return:
total += n
runningTotal.append(total)
# list_of_bids = Back_Test.array_of_profits
# shift the graph to the left to account for the initial days there there
# inst enough info for
index = range(0, len(runningTotal))
index_stock = range(
0, len(main_df[ticker + 'CLS'].tolist()[batch_size + look_ahead - 1:]))
plt.plot(index_stock,
main_df[ticker + 'CLS'].tolist()[batch_size + look_ahead - 1:],
'r',
label=ticker + ' Close')
plt.plot(index, runningTotal, 'g', label='Algorithm Profits')
plt.title(ticker + ' Profit')
plt.plot(index_bid_stream, array_of_bid_stream, 'b', label='Bid Stream')
print len(main_df[ticker + 'CLS'].tolist()), 'length of the close valeus'
print len(algorithm_return), 'algo proffits'
print len(runningTotal), 'runnign total'
print len(array_of_bid_stream), 'bid stream len'
print array_of_bid_stream[:50], 'bid stream'
helper_turn_data_into_csv(
main_df[ticker + 'CLS'].tolist()[:len(runningTotal)], runningTotal,
array_of_bid_stream[:len(runningTotal)])
plt.legend(loc='best')
plt.show()
def test_generate_target_values_longer():
"""
used to tests really long CLS lengths
"""
data = {
'col2CLS': [
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8,
6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5,
5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6,
7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6,
4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3,
3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8, 6, 5, 5, 6, 7, 6, 4, 3, 3, 8
],
}
stock_data = pd.DataFrame(data=data)
sample_slopes.generate_target_values(stock_data, 18, 'col2CLS', 2)
def test_on_array_of_tickers_profit():
"""
This test is used to try out the returns calculator on the stock market data
"""
tickers = [
"GOOG", "FB", "INTC", 'TSM', "CSCO", "ORCL", "NVDA", "SAP", "IBM",
"ADBE", "TXN", "AVGO", "CRM", "QCOM", "MU", "BIDU", "ADP", "VMW",
"ATVI", "AMAT", "INTU", "CTSH", "EA", "NXPI", "INFY"
]
# tickers = ["GOOG", "FB", "INTC", 'TSM', "CSCO"]
main_df = pd.read_pickle(settings.settings_dict['stock_data_path'])
main_df = sample_slopes.create_slope_sum_market(main_df)
Back_Test = back_test.BackTest(main_df,
settings.settings_dict['model_path'])
with open('files/testing_files/return_output.csv', 'w') as f:
mean_array = []
std_array = []
returns_difference_array = []
for ticker in tickers:
slope_sums = main_df[ticker + "slope_sum"]
mean = np.mean(main_df[ticker + "slope_sum"])
std = np.std(main_df[ticker + "slope_sum"])
y_values = sample_slopes.generate_target_values(
main_df, 18, ticker + "CLS", 2)
x_values = sample_slopes.create_batch_of_slopes(
main_df, ticker + 'CLS', 18, y_values[1])
array_of_batches = Back_Test.create_batch_of_slopes(
main_df, ticker + 'slope_sum', 18, y_values[1])
Back_Test.append_list_of_buy_sells(array_of_batches,
ticker + "slope_sum")
algorithm_profit = sum(
Back_Test.take_bid_stream_calculate_profit(
ticker + "bid_stream", 18, 2))
print Back_Test.take_bid_stream_calculate_profit(
ticker + "bid_stream", 18, 2)
log_return = sum(
Back_Test.test_calculate_holding_log_return(ticker + 'CLS'))
holding_profit = Back_Test.calculate_holding_profit(
ticker + "CLS", 18, 2)
print "algorithm ", algorithm_profit
print "log return ", log_return, ' %'
print "percent change", holding_profit,
mean_array.append(mean)
std_array.append(std)
returns_difference_array.append(algorithm_profit - holding_profit)
f.write(ticker + ',' + str(algorithm_profit) + ',' +
str(log_return) + ',' + str(holding_profit) + ',' +
str(mean) + ',' + str(std) + '\n')
data = {
'mean': mean_array,
'std': std_array,
'returns_diff': returns_difference_array
}
meaningfull_stats = pd.DataFrame(data=data)
meaningfull_stats.to_pickle('files/meaningfull_stats.pkl')
