def __init__(self, name, price_series, degrees):
self.stat_object = statistics.Calculate_Stats()
self.helper = helper_methods.HelperMethods()
# name and data
self.name = name
self.price_series = price_series
self.degrees = degrees
# price
self.price_change = self.stat_object.helper.turn_prices_into_changes(
self.price_series, permille=True)
# statistical variables
self.is_stationary, self.stat_sigma, self.stat_critical_value = self.stat_object.calculate_stationarity(
self.price_change)
self.is_random, self.rand_sigma, self.rand_critical_value = self.stat_object.calculate_randomness(
self.stat_object.helper.remove_outliers(self.price_change))
self.is_independent, self.ind_sigma, self.ind_critical_value, self.dep_object = \
self.stat_object.calculate_relative_price_change_independence(
self.stat_object.helper.remove_outliers(self.price_change), self.degrees)
def setUp(self):
self.helper_methods_object = helper_methods.HelperMethods()
self.stat_object = statistics.Calculate_Stats()
self.test_data = self.helper_methods_object.parse_csv("test-data.csv")
self.test_data_delta = self.helper_methods_object.turn_prices_into_changes(self.test_data, False)
self.first_half_delta, self.second_half_delta = self.helper_methods_object.split_in_two(self.test_data_delta)
self.first_half, self.second_half = self.helper_methods_object.split_in_two(self.test_data)
self.first_freq = self.helper_methods_object.make_frequency_histogram(self.first_half)
self.second_freq = self.helper_methods_object.make_frequency_histogram(self.second_half)
self.first_cum = self.helper_methods_object.make_cumulative_probability_density_function(self.first_freq)
self.second_cum = self.helper_methods_object.make_cumulative_probability_density_function(self.second_freq)
self.is_stationary, self.stat_sigma, self.stat_critical_value = self.stat_object.calculate_stationarity(self.test_data)
self.is_diff_independent, self.di_sigma, self.di_critical_value = self.stat_object.calculate_differential_spectrum_independence(
self.test_data_delta)
self.is_rel_independent, self.ri_sigma, self.ri_critical_value, self.dep_object= self.stat_object.calculate_relative_price_change_independence(
self.test_data_delta, 2)
self.is_rel_independent3, self.ri_sigma3, self.ri_critical_value3, self.dep_object = self.stat_object.calculate_relative_price_change_independence(
self.test_data_delta, 3)
self.is_random, self.rand_sigma, self.rand_critical_value = self.stat_object.calculate_randomness(self.test_data)
def main():
stat_object = statistics.Calculate_Stats()
now = datetime.datetime.now()
scrape_obj = scraper.Scraper()
# stat_object.fourier_analysis(scrape_obj.get_price_history("ethereum"))
coin_names = [
"ripple", "ethereum", "monero", "stellar", "nav-coin", "dash",
"ethereum-classic", "lisk", "verge", "zcash", "stratis", "bitcoin",
"litecoin"
]
coins = []
for coin in coin_names:
coins.append(
crypto_object.CryptoObject(coin,
scrape_obj.get_price_history(coin), 4))
# print(quick_report(coins, now))
OFFICIAL_BOA(coins, now)
def back_test(series, name):
stat_object = statistics.Calculate_Stats()
current_series = series
current_name = name
print(current_series.size)
price_change = stat_object.helper.turn_prices_into_changes(current_series,
permille=True)
import time
start_time = time.time()
days_to_test = 150
for j in range(2, 8):
boa_balance = 10000
inverse_boa_balance = 10000
likelihood_boa_balance = 10000
inverse_likelihood_balance = 10000
hodl_balance = 10000
boa_accuracy = 0
inv_boa_accuracy = 0
likeli_accuracy = 0
inv_likeli_accuracy = 0
hodl_accuracy = 0
order = j
hodl_result = []
boa_result = []
for i in range(1, days_to_test + 1):
if i % 30 == 0:
print(i)
up_to = current_series.size - (days_to_test - i + 1)
coin = crypto_object.CryptoObject(current_name,
current_series[:up_to], order)
# calculate boa values up to current point
prediction = coin.get_prediction(coin.get_most_recent_pattern())
num_prediction = coin.get_odds_of_increase(
coin.get_most_recent_pattern())
current_change = (
1.0 +
price_change.values[(days_to_test - i + 1) * -1] / 1000.0)
# decide buy or sell
boa_balance *= current_change if (prediction is "Buy") else 1.0
inverse_boa_balance *= current_change if (
prediction is "Sell") else 1.0
likelihood_boa_balance *= current_change if (
num_prediction >= 50.0) else 1.0
inverse_likelihood_balance *= current_change if (
num_prediction < 50.0) else 1.0
hodl_balance *= current_change
# add to the arrays
hodl_result.append(hodl_balance)
boa_result.append(boa_balance)
# record accuracy
boa_accuracy += 1 if (
(coin.get_numeric_prediction(coin.get_most_recent_pattern()) <
1.0 and current_change < 1.0) or
(coin.get_numeric_prediction(coin.get_most_recent_pattern()) >
1.0 and current_change > 1.0)) else 0
inv_boa_accuracy += 1 if (
(coin.get_numeric_prediction(coin.get_most_recent_pattern()) >
1.0 and current_change < 1.0) or
(coin.get_numeric_prediction(coin.get_most_recent_pattern()) <
1.0 and current_change > 1.0)) else 0
likeli_accuracy += 1 if (
num_prediction > .5 and current_change > 1.0) or (
num_prediction < .5 and current_change < 1.0) else 0
inv_likeli_accuracy += 1 if (
num_prediction < .5 and current_change > 1.0) or (
num_prediction > .5 and current_change < 1.0) else 0
hodl_accuracy += 1 if current_change > 1.0 else 0
print(prediction, "BOA: ", '%.2f' % boa_balance, "INV BOA: ",
'%.2f' % inverse_boa_balance, "Likeli: ",
'%.2f' % likelihood_boa_balance, "Inv Likeli: ",
'%.2f' % inverse_likelihood_balance,
"Hodl: ", '%.2f' % hodl_balance,
coin.get_stationarity_as_string(), coin.get_stat_sigma(),
coin.get_stat_critical_value(), " boa accuracy ",
boa_accuracy, " inv boa accuracy ", inv_boa_accuracy,
" likeli accuracy ", likeli_accuracy,
" inv likeli accuracy ", inv_likeli_accuracy,
" hodl accuracy ", hodl_accuracy)
# Timer block
m, s = divmod(time.time() - start_time, 60)
h, m = divmod(m, 60)
results = "The " + coin.get_name()+" results for " + str(days_to_test)+ " days and " + str(order) + " degrees are: BOA Final Balance: " + \
'%.2f' % boa_balance + "INV BOA: " + '%.2f' % inverse_boa_balance + "Likeli: " + '%.2f' % likelihood_boa_balance + "Inv Likeli: " + '%.2f' % inverse_likelihood_balance + " Hodl Final Balance: " + '%.2f' %(hodl_balance) + \
" Boa Accuracy: " + str(boa_accuracy) + " inv boa accuracy " + str(inv_boa_accuracy) + " likeli accuracy " + str(likeli_accuracy) + " inv likeli accuracy " + str(inv_likeli_accuracy) + " Hodl accuracy: " + str(hodl_accuracy) + "|| Series Size "+ str(current_series.size) + " Elapsed time: " + str("%d:%02d:%02d" % (h, m, s)) + "\n"
f = open('backtest.txt', 'a+')
f.write(results)
f.close()
import winsound
frequency = 2500 # Set Frequency To 2500 Hertz
duration = 500 # Set Duration To 1000 ms == 1 second
winsound.Beep(frequency, duration)
# pyplot.plot(hodl_result)
# pyplot.plot(boa_result)
# pyplot.show()
print(results)
def backtest_alpha():
stat_object = statistics.Calculate_Stats()
now = datetime.datetime.now()
scraper_obj = scraper.Scraper()
current_name = [
"ripple", "ethereum", "monero", "stellar", "nav-coin", "dash",
"ethereum-classic", "lisk", "verge", "zcash","stratis", "bitcoin", "litecoin"
]
current_degree = []
for i in range(current_name.__len__()):
current_degree.append(4)
current_series = []
price_change = []
for name in current_name:
new_name = scraper_obj.get_price_history(name)
current_series.append(new_name)
price_change.append(stat_object.helper.turn_prices_into_changes(new_name, permille=True))
import time
start_time = time.time()
today = datetime.date.today()
begin = datetime.date(2018, 1, 1)
days_to_test = (today - begin).days
print(days_to_test)
for j in range(4, 5):
boa_balance = 9999
classic_boa_balance = []
hodl_balance = []
for i in range(current_name.__len__()):
classic_boa_balance.append(9999/current_name.__len__())
hodl_balance.append(9999/current_name.__len__())
random_balance = 9999
boa_accuracy = 0
random_accuracy = 0
hodl_result = []
random_result = []
boa_result = []
boa_classic_result = []
fee = 0.001
for i in range(1, days_to_test + 1-26):
coins = []
for k in range(current_name.__len__()):
up_to = current_series[k].size - (days_to_test - i + 1)
coin = crypto_object.CryptoObject(current_name[k], current_series[k][:up_to], current_degree[k])
coins.append(coin)
# calculate boa values up to current point
prediction_array = []
for coin in coins:
if coin.get_stationarity():
prediction_array.append(coin.get_numeric_prediction(coin.get_most_recent_pattern()))
else:
prediction_array.append(0.0)
current_change = []
for change in price_change:
current_change.append((
1.0 + change.values[(days_to_test - i + 1) * -1] / 1000.0))
# decide buy or sell
#boa_pick = current_change[prediction_array.index(max(prediction_array))]-fee
boa_pick = 0
counter = 0
for val in range(prediction_array.__len__()):
if prediction_array[val] >= 1.0:
boa_pick += current_change[val]
counter += 1
boa_balance *= boa_pick/counter if max(prediction_array) > 1.0 else 1.0
print(boa_pick/counter)
for k in range(current_name.__len__()):
hodl_balance[k] *= current_change[k]
classic_boa_balance[k] *= current_change[k] if prediction_array[k] > 1.0 else 1.0
random_pick = randint(0, current_name.__len__()-1)
random_value = current_change[random_pick]
random_balance *= random_value
# add to the arrays
hodl_result.append(sum(hodl_balance))
boa_classic_result.append(sum(classic_boa_balance))
random_result.append(random_balance)
boa_result.append(boa_balance)
# record accuracy
boa_accuracy += 1 if (
boa_pick + fee >= max(current_change)) else 0
random_accuracy += 1 if (
random_value >= max(current_change)) else 0
print_val = str(i) + ". \n"
print_val2 = ""
for k in range(current_name.__len__()):
print_val += '%.2f' % prediction_array[k] + "(" + str(current_change[k]) + ")"
print_val2 += coins[k].get_name() + ": " + coins[k].get_stationarity_as_string() + coins[k].get_stat_sigma() + coins[k].get_stat_critical_value()
print_val += "BOA: " + '%.2f' % boa_balance + "BOA Classic: " + '%.2f' % (sum(classic_boa_balance)) + "Hodl: " + \
'%.2f' % (sum(hodl_balance)) + "Random: " + '%.2f' % random_balance + print_val2
print(print_val)
# Timer block
m, s = divmod(time.time() - start_time, 60)
h, m = divmod(m, 60)
results = "\n=======BACK TEST RESULTS " + now.strftime("%Y-%m-%d") + "=======\n\n" \
"The results for a portfolio containing " + str(current_name) + " with degrees " + str(current_degree) + " are" \
" BOA Final Balance: " + '%.2f' % boa_balance + " Random Final Balance: " + '%.2f' % random_balance + " Hodl Final Balance: " + \
'%.2f' % sum(hodl_balance) + " Boa Accuracy: " + str(boa_accuracy) + " Random accuracy: " + str(random_accuracy) + \
" Elapsed time: " + str("%d:%02d:%02d" % (h, m, s)) + "\n"
f = open('backtest_alpha.txt', 'a+')
f.write(results)
f.close()
import winsound
frequency = 2500 # Set Frequency To 2500 Hertz
duration = 1000 # Set Duration To 1000 ms == 1 second
winsound.Beep(frequency, duration)
Hold= go.Scatter(x=current_series[0].index.values[current_series[0].size-days_to_test:], y=hodl_result, name= "Buy and Hold")
BOA = go.Scatter(x=current_series[0].index.values[current_series[0].size-days_to_test:], y=boa_result, name="BOA")
layout = go.Layout(
title='BOA Results 2017',
xaxis=dict(
title='Time',
titlefont=dict(
family='Courier New, monospace',
size=18,
color='#7f7f7f'
)
),
yaxis=dict(
title='Portfolio Value',
titlefont=dict(
family='Courier New, monospace',
size=18,
color='#7f7f7f'
)
)
)
figure = go.Figure(data=[Hold, BOA],layout=layout)
url = py.offline.plot(figure, image_height=400, image_width=400, auto_open=False,
filename='results2018.html', )
# pyplot.plot(hodl_result)
# pyplot.plot(hodl_card_result)
# pyplot.plot(boa_result)
# pyplot.show()
print(results)