def cross_validation_gold(df, lower_bound, higher_bound, step_length,
leng_of_training, leng_of_test, type):
Interval = 10
error = []
for delta in np.linspace(lower_bound, higher_bound, step_length):
oneerror = 0
for count in range(1, 9):
x1 = df[(Interval * (count - 1)):(Interval * (count - 1) +
leng_of_training)]
if type == "L1T": filtered = l1tf(x1, (delta))
elif type == "L1C": filtered = l1ctf(x1, (delta))
else: filtered = hp(x1, (delta))
estimate = filtered[(leng_of_training -
leng_of_test):leng_of_training]
y1 = list(
df[(Interval * (count - 1) +
leng_of_training):(Interval * (count - 1) +
(leng_of_training + leng_of_test))])
newerror = [pow(y1[i] - estimate[i], 2) for i in range(0, len(y1))]
oneerror = oneerror + sum(newerror)
error.append(oneerror)
np.array(error)
plt.plot(np.linspace(lower_bound, higher_bound, step_length), error)
plt.legend([type], fontsize=20)
plt.xlabel("lambda")
plt.ylabel('Error')
plt.title('CrossValidation')
plt.show()
return np.linspace(lower_bound, higher_bound,
step_length)[error.index(min(error))]
def l1(pocetak, kraj):
# uvoz podataka iz datoteke
with open('NYSE_prices_daily.csv') as f:
lines = list(csv.reader(f))
f.close()
# broj_dionica = len(lines[0]) - 1
max_lambda = 1000
br = 1
firstCol = 501
for stupac in range(firstCol, 571): # drugi broj mora biti broj_dionica+1
x = pd.Series(np.asarray([float(e[stupac]) for e in lines[pocetak:kraj]]))
for delta in range(1, max_lambda+1): # drugi broj mora biti konacna_delta+1
filter_data = l1tf(x, delta)
nagib = filter_data[filter_data.size-1] / filter_data[filter_data.size-2]
if nagib > 1:
zeros[stupac-firstCol][delta - 1] = 1
# zeros[stupac-1][delta-1] = 1
elif nagib < 1:
zeros[stupac-firstCol][delta - 1] = -1
# zeros[stupac - 1][delta - 1] = -1
else:
zeros[stupac-firstCol][delta - 1] = 0
# zeros[stupac - 1][delta - 1] = 0
print(lines[0][stupac] + " obraden " + str(br))
br += 1
return zeros
def estimate_expected_returns(data):
max_lambda = 1000
stock_num = len(data[0])
days_num = len(data)
filtered_data = [[0] * max_lambda for _ in range(stock_num)
] # stock_num-1 ako zanemarujemo prvi stupac
for col in range(1, stock_num + 1): # promijeniti u 0?
x = pd.Series(np.asarray([float(e[col]) for e in data[1:days_num]]))
for delta in range(1, max_lambda + 1):
filtered_stock = l1tf(x, delta)
slope = filtered_stock[filtered_stock.size -
1] / filtered_stock[filter_stock.size - 2]
if slope > 1:
filtered_data[col - 1][delta - 1] = 1
elif slope < 1:
filtered_data[col - 1][delta - 1] = -1
else:
filtered_data[col - 1][delta - 1] = 0
returns = []
for row in filtered_data:
returns.append(np.mean(row))
del filtered_data
return returns
alltype = ["L1T", "L1C", "L2", "L1TC"]
allbestlambda = [best_lamda_L1T, best_lamda_L1C, best_lamda_L2]
result1 = calculate_return(df, alltype[0], allbestlambda[0], time)
result2 = calculate_return(df, alltype[1], allbestlambda[1], time)
result3 = calculate_return(df, alltype[2], allbestlambda[2], time)
result4 = calculate_return(df, alltype[3], allbestlambda[0], time,
allbestlambda[1])
newfilter1 = np.array(result1[3])
newfilter2 = np.array(result2[3])
newfilter3 = np.array(result3[3])
newfilter4 = np.array(result4[3])
close_15to17 = df['log Close'].loc["2019-01-01":"2019-04-04"]
filtered = l1tf(close_15to17, best_lamda_L1T)
filtered2 = l1ctf(close_15to17, best_lamda_L1C)
gc.collect()
filtered3 = hp(close_15to17, best_lamda_L2)
filtered4 = l1tccf(close_15to17, best_lamda_L1T, best_lamda_L1C)
df["trend_L1T"] = np.hstack((filtered, newfilter1))
df["trend_L1C"] = np.hstack((filtered2, newfilter2))
df["trend_L2"] = np.hstack((filtered3, newfilter3))
df["trend_L1TC"] = np.hstack((filtered4, newfilter4))
plt.subplot(221)
plt.plot(df["trend_L1T"], linewidth='2.5')
plt.plot(df["log Close"])
#plt.show()
plt.subplot(222)
def calculate_return(df, TYPE, best_lambda1, time, best_lambda2=0):
close_2015 = time[0]
close_2016_Actual = time[1]
close_2016 = np.log(close_2016_Actual)
close_2017_Actual = time[2]
close_2017 = np.log(close_2017_Actual)
close_2018_Actual = time[3]
close_2018 = np.log(close_2018_Actual)
close_2019_Actual = time[4]
close_2019 = np.log(close_2019_Actual)
start = close_2015
end = close_2016
close_all = np.hstack((start, end))
close_end_Actual = close_2016_Actual
inter = 10
if TYPE == "L1T":
filtered = l1tf(start, best_lambda1)
elif TYPE == "L1C":
filtered = l1ctf(start, best_lambda1)
elif TYPE == "L1TC":
filtered = l1tccf(start, best_lambda1, best_lambda2)
elif TYPE == "Wave":
filtered = wave(start)
else:
filtered = hp(start, best_lambda1)
filter_derivative = [(filtered[i] - filtered[i - 1])
for i in range(1, len(filtered))]
filter_derivative.insert(0, filter_derivative[0])
average_filtered_derivative = [
np.mean(filter_derivative[(i - inter):i])
for i in range(inter, len(filter_derivative))
]
temp_lis = np.array([0] * inter)
average_filtered_derivative = np.hstack(
(temp_lis, average_filtered_derivative))
size_start = len(start)
size_end = len(end)
temp_lis2 = np.array([0] * size_end)
average_filtered_derivative = np.hstack(
(average_filtered_derivative, temp_lis2))
for i in range(0, size_end):
if TYPE == "L1T":
filtered_updating = l1tf(close_all[0:size_start + i + 1],
best_lambda1)
elif TYPE == "L1C":
filtered_updating = l1ctf(close_all[0:size_start + i + 1],
best_lambda1)
elif TYPE == "L1TC":
filtered_updating = l1tccf(close_all[0:size_start + i + 1],
best_lambda1, best_lambda2)
elif TYPE == "Wave":
filtered_updating = wave(close_all[0:size_start + i + 1])
else:
filtered_updating = hp(close_all[0:size_start + i + 1],
best_lambda1)
filter_derivative = [(filtered_updating[i] - filtered_updating[i - 1])
for i in range(1, len(filtered_updating))]
target = np.mean(filter_derivative[-inter - 1:-1])
try:
average_filtered_derivative[size_start + i] = target
except:
continue
# updating zt
n = 60
size = len(close_all)
st = [0] * size
for date in range(n, size):
total = 0
for i in range(0, n - 1):
for j in range(i + 1, n):
value = judge(close_all[date - i], close_all[date - j])
total = total + value
st[date] = total
# st_normal = list(map(lambda num: num * 2 / (n * n + 1), st))
std = math.sqrt((n * (n - 1) * (2 * n + 5)) / 18)
zt = list(map(lambda num: num / std, st))
confidence = [0] * size
for i in range(len(zt)):
if (zt[i] > 1.96):
confidence[i] = 1
elif (zt[i] < -1.96):
confidence[i] = -1
average_filtered_derivative_end = average_filtered_derivative[-len(end):]
confidence_end = confidence[-len(end):]
bp11 = best_period(close_end_Actual, average_filtered_derivative_end,
confidence_end)
bp31 = best_period3(close_end_Actual, average_filtered_derivative_end)
# print(bp11,bp11_r,bp31,bp31_r)
# print(100000/close_2018_Actual[0]*close_2018_Actual[-1])
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Do for 2nd
start = close_2016
end = close_2017
close_all = np.hstack((start, end))
close_end_Actual = close_2017_Actual
if TYPE == "L1T":
filtered = l1tf(start, best_lambda1)
elif TYPE == "L1C":
filtered = l1ctf(start, best_lambda1)
elif TYPE == "L1TC":
filtered = l1tccf(start, best_lambda1, best_lambda2)
elif TYPE == "Wave":
filtered = wave(start)
else:
filtered = hp(start, best_lambda1)
filter_derivative = [(filtered[i] - filtered[i - 1])
for i in range(1, len(filtered))]
filter_derivative.insert(0, filter_derivative[0])
average_filtered_derivative = [
np.mean(filter_derivative[(i - inter):i])
for i in range(inter, len(filter_derivative))
]
temp_lis = np.array([0] * inter)
average_filtered_derivative = np.hstack(
(temp_lis, average_filtered_derivative))
size_start = len(start)
size_end = len(end)
temp_lis2 = np.array([0] * size_end)
average_filtered_derivative = np.hstack(
(average_filtered_derivative, temp_lis2))
for i in range(0, size_end):
if TYPE == "L1T":
filtered_updating = l1tf(close_all[0:size_start + i + 1],
best_lambda1)
elif TYPE == "L1C":
filtered_updating = l1ctf(close_all[0:size_start + i + 1],
best_lambda1)
elif TYPE == "L1TC":
filtered_updating = l1tccf(close_all[0:size_start + i + 1],
best_lambda1, best_lambda2)
elif TYPE == "Wave":
filtered_updating = wave(close_all[0:size_start + i + 1])
else:
filtered_updating = hp(close_all[0:size_start + i + 1],
best_lambda1)
filter_derivative = [(filtered_updating[i] - filtered_updating[i - 1])
for i in range(1, len(filtered_updating))]
target = np.mean(filter_derivative[-inter - 1:-1])
try:
average_filtered_derivative[size_start + i] = target
except:
continue
# updating zt
n = 60
size = len(close_all)
st = [0] * size
for date in range(n, size):
total = 0
for i in range(0, n - 1):
for j in range(i + 1, n):
value = judge(close_all[date - i], close_all[date - j])
total = total + value
st[date] = total
# st_normal = list(map(lambda num: num * 2 / (n * n + 1), st))
std = math.sqrt((n * (n - 1) * (2 * n + 5)) / 18)
zt = list(map(lambda num: num / std, st))
confidence = [0] * size
for i in range(len(zt)):
if (zt[i] > 1.96):
confidence[i] = 1
elif (zt[i] < -1.96):
confidence[i] = -1
average_filtered_derivative_end = average_filtered_derivative[-len(end):]
confidence_end = confidence[-len(end):]
bp12 = best_period(close_end_Actual, average_filtered_derivative_end,
confidence_end)
bp32 = best_period3(close_end_Actual, average_filtered_derivative_end)
# print(bp12,bp12_r,bp32,bp32_r)
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Do for 3rd
start = close_2017
end = close_2018
close_all = np.hstack((start, end))
close_end_Actual = close_2018_Actual
if TYPE == "L1T":
filtered = l1tf(start, best_lambda1)
elif TYPE == "L1C":
filtered = l1ctf(start, best_lambda1)
elif TYPE == "L1TC":
filtered = l1tccf(start, best_lambda1, best_lambda2)
elif TYPE == "Wave":
filtered = wave(start)
else:
filtered = hp(start, best_lambda1)
filter_derivative = [(filtered[i] - filtered[i - 1])
for i in range(1, len(filtered))]
filter_derivative.insert(0, filter_derivative[0])
average_filtered_derivative = [
np.mean(filter_derivative[(i - inter):i])
for i in range(inter, len(filter_derivative))
]
temp_lis = np.array([0] * inter)
average_filtered_derivative = np.hstack(
(temp_lis, average_filtered_derivative))
size_start = len(start)
size_end = len(end)
temp_lis2 = np.array([0] * size_end)
average_filtered_derivative = np.hstack(
(average_filtered_derivative, temp_lis2))
for i in range(0, size_end):
if TYPE == "L1T":
filtered_updating = l1tf(close_all[0:size_start + i + 1],
best_lambda1)
elif TYPE == "L1C":
filtered_updating = l1ctf(close_all[0:size_start + i + 1],
best_lambda1)
elif TYPE == "L1TC":
filtered_updating = l1tccf(close_all[0:size_start + i + 1],
best_lambda1, best_lambda2)
elif TYPE == "Wave":
filtered_updating = wave(close_all[0:size_start + i + 1])
else:
filtered_updating = hp(close_all[0:size_start + i + 1],
best_lambda1)
filter_derivative = [(filtered_updating[i] - filtered_updating[i - 1])
for i in range(1, len(filtered_updating))]
target = np.mean(filter_derivative[-inter - 1:-1])
try:
average_filtered_derivative[size_start + i] = target
except:
continue
# updating zt
n = 60
size = len(close_all)
st = [0] * size
for date in range(n, size):
total = 0
for i in range(0, n - 1):
for j in range(i + 1, n):
value = judge(close_all[date - i], close_all[date - j])
total = total + value
st[date] = total
# st_normal = list(map(lambda num: num * 2 / (n * n + 1), st))
std = math.sqrt((n * (n - 1) * (2 * n + 5)) / 18)
zt = list(map(lambda num: num / std, st))
confidence = [0] * size
for i in range(len(zt)):
if (zt[i] > 1.96):
confidence[i] = 1
elif (zt[i] < -1.96):
confidence[i] = -1
average_filtered_derivative_end = average_filtered_derivative[-len(end):]
confidence_end = confidence[-len(end):]
bp13 = best_period(close_end_Actual, average_filtered_derivative_end,
confidence_end)
bp33 = best_period3(close_end_Actual, average_filtered_derivative_end)
# print(bp13,bp13_r,bp33,bp33_r)
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Do for 4th
start = close_2018
end = close_2019
close_all = np.hstack((start, end))
close_end_Actual = close_2019_Actual
if TYPE == "L1T":
filtered = l1tf(start, best_lambda1)
elif TYPE == "L1C":
filtered = l1ctf(start, best_lambda1)
elif TYPE == "L1TC":
filtered = l1tccf(start, best_lambda1, best_lambda2)
elif TYPE == "Wave":
filtered = wave(start)
else:
filtered = hp(start, best_lambda1)
filter_derivative = [(filtered[i] - filtered[i - 1])
for i in range(1, len(filtered))]
filter_derivative.insert(0, filter_derivative[0])
average_filtered_derivative = [
np.mean(filter_derivative[(i - inter):i])
for i in range(inter, len(filter_derivative))
]
temp_lis = np.array([0] * inter)
average_filtered_derivative = np.hstack(
(temp_lis, average_filtered_derivative))
size_start = len(start)
size_end = len(end)
temp_lis2 = np.array([0] * size_end)
average_filtered_derivative = np.hstack(
(average_filtered_derivative, temp_lis2))
for i in range(0, size_end):
if TYPE == "L1T":
filtered_updating = l1tf(close_all[0:size_start + i + 1],
best_lambda1)
elif TYPE == "L1C":
filtered_updating = l1ctf(close_all[0:size_start + i + 1],
best_lambda1)
elif TYPE == "L1TC":
filtered_updating = l1tccf(close_all[0:size_start + i + 1],
best_lambda1, best_lambda2)
elif TYPE == "Wave":
filtered_updating = wave(close_all[0:size_start + i + 1])
else:
filtered_updating = hp(close_all[0:size_start + i + 1],
best_lambda1)
filter_derivative = [(filtered_updating[i] - filtered_updating[i - 1])
for i in range(1, len(filtered_updating))]
target = np.mean(filter_derivative[-inter - 1:-1])
try:
average_filtered_derivative[size_start + i] = target
except:
continue
# updating zt
n = 60
size = len(close_all)
st = [0] * size
for date in range(n, size):
total = 0
for i in range(0, n - 1):
for j in range(i + 1, n):
value = judge(close_all[date - i], close_all[date - j])
total = total + value
st[date] = total
std = math.sqrt((n * (n - 1) * (2 * n + 5)) / 18)
zt = list(map(lambda num: num / std, st))
confidence = [0] * size
for i in range(len(zt)):
if (zt[i] > 1.96):
confidence[i] = 1
elif (zt[i] < -1.96):
confidence[i] = -1
average_filtered_derivative_end = average_filtered_derivative[-len(end):]
confidence_end = confidence[-len(end):]
bp1 = int(np.mean([bp11, bp12, bp13]))
bp3 = int(np.mean([bp31, bp32, bp33]))
Ac_list1 = strategy(average_filtered_derivative_end, 0, bp1,
confidence_end)
Ac_list3 = strategy3(average_filtered_derivative_end, 0, bp3)
print(
"######################################################################"
)
print(bp11, bp12, bp13)
print(bp31, bp32, bp33)
print(bp1, bp3)
revenue1 = calculate_revenue(Ac_list1, close_2019_Actual, bp1)
revenue2 = calculate_revenue(Ac_list3, close_2019_Actual, bp3)
benchmark = 100000 / close_2019_Actual[0] * close_2019_Actual[-1]
print(calculate_revenue(Ac_list1, close_2019_Actual, bp1))
print(calculate_revenue(Ac_list3, close_2019_Actual, bp3))
print(100000 / close_2019_Actual[0] * close_2019_Actual[-1])
return (revenue1, revenue2, benchmark, filtered_updating)
plt.plot(trend)
plt.grid()
#Simulate Yt
walk = []
for i in range(steps):
step = np.random.normal(0, 2)
walk.append(trend[i] + step)
plt.subplot(322)
plt.plot(walk)
plt.grid()
plt.title("Real Value")
walk = np.array(walk)
delta = 5
#L1-T
filtered = l1tf(walk, delta)
plt.subplot(323)
plt.title("L1-T filter")
plt.plot(filtered)
plt.grid()
#L1_C
filtered = l1ctf(walk, delta)
plt.subplot(324)
plt.title("L1-C filter")
plt.plot(filtered)
plt.grid()
#L1-tc
filtered = l1tccf(walk, 5, 10)
plt.subplot(325)
plt.title("L1-TC filter")
plt.plot(filtered)
