def write_hourly_volume_index_to_csv(print_components=0):
bitstamp, time_list_bitstampM, prices_bitstampM, volumes_bitstampM = di.get_list(exc="bitstampusd")
coinbase, time_list_coinbaseM, prices_coinbaseM, volumes_coinbaseM = di.get_list(exc="coinbaseusd")
korbit, time_list_korbitM, prices_korbitM, volumes_korbitM = di.get_list(exc="korbitkrw")
krak, time_list_krakM, prices_krakM, volumes_krakM = di.get_list(exc="krakeneur")
cc, time_list_ccM, prices_ccM, volumes_ccM = di.get_list(exc="coincheckjpy")
bitstamp_hour_list, prices, volumes_bitstampH = dis.convert_to_hour(time_list_bitstampM, prices_bitstampM,volumes_bitstampM)
coinbase_hour_list, prices, volumes_coinbaseH = dis.convert_to_hour(time_list_coinbaseM, prices_coinbaseM, volumes_coinbaseM)
korbit_hour_list, prices, volumes_korbitH = dis.convert_to_hour(time_list_korbitM, prices_korbitM, volumes_korbitM)
krak_hour_list, prices, volumes_krakH = dis.convert_to_hour(time_list_krakM, prices_krakM, volumes_krakM)
cc_hour_list, prices, volumes_ccH = dis.convert_to_hour(time_list_ccM, prices_ccM, volumes_ccM)
time_list_combined, volumes_combined = dis.add_two_series_w_different_times(coinbase_hour_list, volumes_coinbaseH, bitstamp_hour_list, volumes_bitstampH)
time_list_combined, volumes_combined = dis.add_two_series_w_different_times(time_list_combined, volumes_combined, korbit_hour_list, volumes_korbitH)
time_list_combined, volumes_combined = dis.add_two_series_w_different_times(time_list_combined, volumes_combined, krak_hour_list, volumes_krakH)
time_list_combined, volumes_combined = dis.add_two_series_w_different_times(cc_hour_list, volumes_ccH, time_list_combined, volumes_combined)
if print_components == 1:
total = sum(volumes_bitstampH) + sum(volumes_coinbaseH) + sum(volumes_korbitH) + sum(volumes_krakH) + sum(volumes_ccH)
# bitstamp_perc = 100 * float(sum(volumes_bitstampH) / total)
# coinbase_perc = 100 * float(sum(volumes_coinbaseH) / total)
# korbit_perc = 100 * float(sum(volumes_korbitH) / total)
# krak_perc = 100 * float(sum(volumes_krakH) / total)
# cc_perc = 100 * float(sum(volumes_ccH) / total)
#
# print("%s approximate share of the total volume")
location = "data/export_csv/"
file_name = location + "global_hourly_volume_index.csv"
with open(file_name, 'w', newline='') as csvfile:
writ = csv.writer(csvfile, delimiter=';', quotechar='|', quoting=csv.QUOTE_MINIMAL)
print("\033[0;32;0m Writing to file '%s'...\033[0;0;0m" % file_name)
header2 = ["Time"]
header2.append("Volume")
writ.writerow(header2)
for i in range(len(volumes_combined)):
rowdata = [time_list_combined[i]]
rowdata.append(volumes_combined[i])
writ.writerow(rowdata)
intraweek = 0
plots = 0
print_table = 0
time_list_minutes, prices, volumes = data_import.fetch_aggregate_csv(file_name, n_exc)
y, mo, d, h, mi = supp.fix_time_list(time_list_minutes, move_n_hours=0)
time_list_minutes = supp.make_time_list(y, mo, d, h, mi )
time_listH, returnsH, spreadH, log_volumesH, illiqH, log_illiqH, rvolH, log_rvolH = dis.clean_series_hour(time_list_minutes, prices, volumes)
prices_minutes = prices[0, :]
volumes_minutes = volumes[0, :]
returns_minutes = jake_supp.logreturn(prices_minutes)
time_list_hours, prices_hours, volumes_hours = dis.convert_to_hour(time_list_minutes, prices_minutes, volumes_minutes)
spread_hours = rolls.rolls(prices_minutes, time_list_minutes, calc_basis="h", kill_output=1)[1]
illiq_hours_time, illiq_hours = ILLIQ.illiq(time_list_minutes, returns_minutes, volumes_minutes, hourly_or_daily="h", threshold=0)
rvol_hours, time_list_hours_rvol = realized_volatility.RVol(time_list_minutes, prices_minutes, daily=0, annualize=1)
if intraday == 1:
time_list_removed = []
time_list_hours, time_list_removed, volumes_hours, prices_hours, rvol_hours, spread_hours = supp.remove_list1_zeros_from_all_lists(time_list_hours, time_list_removed, volumes_hours, prices_hours, rvol_hours, spread_hours)
time_list_hours, time_list_removed, rvol_hours, volumes_hours, prices_hours, spread_hours, illiq_hours = supp.remove_list1_zeros_from_all_lists(time_list_hours, time_list_removed, rvol_hours, volumes_hours, prices_hours, spread_hours, illiq_hours)
time_list_hours, time_list_removed, spread_hours, rvol_hours, volumes_hours, prices_hours, illiq_hours = supp.remove_list1_zeros_from_all_lists(time_list_hours, time_list_removed, spread_hours, rvol_hours, volumes_hours, prices_hours, illiq_hours)
time_list_hours, time_list_removed, illiq_hours, spread_hours, rvol_hours, volumes_hours, prices_hours = supp.remove_list1_zeros_from_all_lists(time_list_hours, time_list_removed, illiq_hours, spread_hours, rvol_hours, volumes_hours, prices_hours)
print()
print("removed: ")
for i in range(len(time_list_removed)):
# plt.plot(volumes_actualD)
# plt.figure()
# plt.plot(volume_indexD)
# plt.show()
#
#
# corr = np.corrcoef(volumes_actualD, volume_indexD)
# print("Our index accounts for %0.1f%% of the volume and has a correlation of %0.1f%% with the actual volumes" % (100*sum(volume_indexD)/sum(volumes_actualD), 100*corr[0,1]))
#
# plot.time_series_single(time_list_indexD,volume_indexD,"global_volumes_index")
# plot.time_series_single(time_listD,volumes_actualD,"actual_global_volumes")
if spread_vs_global_volume_regression_daily:
exc_name, time_listM, priceM, volumeM = di.get_list(exc="korbit", freq="m")
time_list_nativeH, priceH, volume_nativeH = dis.convert_to_hour(time_listM, priceM, volumeM)
spread_abs, spreadH, time_list_spread, count_value_error = rolls.rolls(priceM, time_listM, calc_basis="h", kill_output=1)
start_i = time_list_spread.index(time_list_indexH[0])
end_i = time_list_spread.index(time_list_indexH[-1]) + 1
time_list_spread = time_list_spread[start_i:end_i]
spreadH = spreadH[start_i:end_i]
volume_nativeH = volume_nativeH[start_i:end_i]
print("It is %s that the time lists are equal" % (time_list_spread == time_list_indexH))
time_listH = time_list_spread
time_list_removed = []
time_listH, time_list_removed, spreadH, volume_indexH, volume_nativeH = supp.remove_list1_zeros_from_all_lists(time_listH, time_list_removed, spreadH, volume_indexH, volume_nativeH)
print("Time: ", time_listH[start+i], "Index: ", i+start)
"""
"""
#"""
"""
timestamps = time_listH[305370:]
highs = hi[0][305370:]
lows = lo[0][305370:]
prices = prices[0][305370:]
"""
#"""
#"""
hour_time, hi_hour, lo_hour = dis.convert_to_hour(time_list,
hi,
lo,
list2_basis=0)
hour_time_closing, closing_prices, closing_prices_dummy = dis.convert_to_hour(
time_list1, prices, prices, list2_basis=0)
"""
#Index-finder
start = 4000
end = 6000
for i in range(len(hour_time[start:end])):
print("Time: ", hour_time[start+i], "Index: ", i+start)
"""
#"""
timestamps = hour_time[1827:]
highs = hi_hour[0][1827:]
lows = lo_hour[0][1827:]
prices = closing_prices[0][1827:]
import numpy as np
import data_import as di
import legacy
from Jacob import jacob_support as jake_supp
import data_import_support as dis
from matplotlib import pyplot as plt
import os
os.chdir("/Users/sondre/Documents/GitHub/krypto") # Blir ikke likt på deres pc
exchanges = ["bitstampusd", "btceusd", "coinbaseusd", "krakenusd"]
# Importing data for all minutes of the day
exchanges, time_list, prices, volumes = legacy.get_lists_legacy(opening_hours="n", make_totals="n")
# Converting to hourly data
time_list, prices, volumes = dis.convert_to_hour(time_list, prices, volumes)
full_returns = jake_supp.logreturn(prices[0, :])
# Only extracting opening hours
time_list_open, prices_open, volumes_opeen = legacy.opening_hours_w_weekends(time_list, prices, volumes)
bitstamp_price_open = np.transpose(prices_open[0, :])
open_returns = jake_supp.logreturn(bitstamp_price_open)
# Finding average for entire day
full_day_time, full_day_avg_returns = dis.cyclical_average_legacy(time_list, full_returns)
# Finding average for opening hours
open_time, open_avg_returns = dis.cyclical_average_legacy(time_list_open, open_returns)
# Want to compare the two in a single graph
x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]
opening_y = np.zeros(24)
for i in range(0, 7):
remove_extremes = 1
remove_zero_volume = 1
remove_zero_bas = 1
print_cyclical = 1
print_results = 0
sum_volumes = 1
if sum_volumes == 1:
summed_volume = []
bitstamp, time_list_bitstampM, prices_bitstampM, volumes_bitstampM = di.get_list(
0)
coinbase, time_list_coinbaseM, prices_coinbaseM, volumes_coinbaseM = di.get_list(
3)
kraken, time_list_krakenM, prices_krakenM, volumes_krakenM = di.get_list(5)
bitstamp_hour_list, prices, volumes_bitstampH = dis.convert_to_hour(
time_list_bitstampM, prices_bitstampM, volumes_bitstampM)
coinbase_hour_list, prices, volumes_coinbaseH = dis.convert_to_hour(
time_list_coinbaseM, prices_coinbaseM, volumes_coinbaseM)
kraken_hour_list, prices, volumes_krakenH = dis.convert_to_hour(
time_list_krakenM, prices_krakenM, volumes_krakenM)
j = 0 #coinbase counter
t = 0 #kraken counter
partsum = 0
for i in range(len(bitstamp_hour_list)):
partsum = volumes_bitstampH[i]
if not supp.A_before_B(
bitstamp_hour_list[i],
kraken_hour_list[t]) and t < len(kraken_hour_list) - 1:
if bitstamp_hour_list[i] == kraken_hour_list[t]: