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Calculate.py
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Calculate.py
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# import math
import statistics
from Utility import numerify
# Percentage
# unit %
def Cal_percentage(previous, new):
previous = numerify(previous, "Float")
new = numerify(new, "Float")
per = ((new - previous) / previous) * 100
return per
# Moving Average (MA)
# unit $
def Cal_MA_mean(import_list):
import_list = numerify(import_list, "Float")
return statistics.mean(import_list)
def Cal_MA(Total_import_list, samples=5):
Total_import_list = numerify(Total_import_list, "Float")
length = len(Total_import_list)
MA = []
for i in range(length):
if i + 1 < samples:
ma = None
else:
ma = Cal_MA_mean(Total_import_list[i - samples + 1 : i + 1])
MA.append(ma)
return MA
def Cal_MA_with_desirelist(Total_import_list, desire_ma_list):
MA_list = []
for i in range(len(desire_ma_list)):
tmp_list = Cal_MA(
Total_import_list, numerify(desire_ma_list[i], "Int")
)
MA_list.append(tmp_list)
return MA_list
# Bias Rate (BIAS, BR)
# unit %
def Cal_BR_pr(import_list):
import_list = numerify(import_list, "Float")
last_close_price = import_list[-1]
# sample = len(import_list)
MA = Cal_MA_mean(import_list)
return (last_close_price - MA) / MA * 100
def Cal_BR(Total_import_list, samples=10):
Total_import_list = numerify(Total_import_list, "Float")
length = len(Total_import_list)
BR = []
for i in range(length):
if i + 1 < samples:
br = None
else:
br = Cal_BR_pr(Total_import_list[i - samples + 1 : i + 1])
BR.append(br)
return BR
# Stochastic Oscillator (KD)
# unit %
def Cal_KD_rsv(import_list):
import_list = numerify(import_list, "Float")
last_close_price = import_list[-1]
Max_price = max(import_list)
Min_price = min(import_list)
if (Max_price - Min_price) <= 0:
return 0
else:
return (last_close_price - Min_price) / (Max_price - Min_price) * 100
def Cal_KDJ(Total_import_list, samples=10):
Total_import_list = numerify(Total_import_list, "Float")
length = len(Total_import_list)
K, D, J = [], [], []
J_shift = 50
for i in range(length):
if i + 1 < samples:
k, d, j = None, None, None
else:
RSV = Cal_KD_rsv(Total_import_list[i - samples + 1 : i + 1])
if K[-1] is None:
k = RSV
d = k
else:
k = 2 / 3 * K[-1] + 1 / 3 * RSV
d = 2 / 3 * D[-1] + 1 / 3 * k
j = k - d + J_shift
K.append(k)
D.append(d)
J.append(j)
return K, D, J
# Bolllinger Bands (BBands)
# unit $
def Cal_STD(import_list):
return statistics.stdev(import_list)
def Cal_BBands(Total_import_list, numofSTD=2.1, samples=20):
Total_import_list = numerify(Total_import_list, "Float")
length = len(Total_import_list)
Center = Cal_MA(Total_import_list, samples)
STD = []
for i in range(length):
if i + 1 < samples:
std = None
else:
std = Cal_STD(Total_import_list[i - samples + 1 : i + 1])
STD.append(std)
UL, LL, UL_s, LL_s = [], [], [], []
for i in range(length):
if i + 1 < samples:
ul, ll, ul_s, ll_s = None, None, None, None
else:
ul = Center[i] + numofSTD * STD[i]
ll = Center[i] - numofSTD * STD[i]
ul_s = Center[i] + 0.1 * STD[i]
ll_s = Center[i] - 0.1 * STD[i]
UL.append(ul)
LL.append(ll)
UL_s.append(ul_s)
LL_s.append(ll_s)
return Center, UL, LL, UL_s, LL_s
# Low Pass Filter (LPF)
# unit $
# notice: according to the sample rate is 1 (everday get the data once)
def Cal_lowpassfilter(Total_import_list, sample_rate=1, Cutoff_freq=0.5):
RC = 1 / (Cutoff_freq * 2 * 3.1415)
dt = 1 / sample_rate
alpha = dt / (RC + dt)
output = []
output = output + [Total_import_list[0]]
for i in range(1, len(Total_import_list)):
output = output + [
output[i - 1] + (alpha * (Total_import_list[i] - output[i - 1]))
]
return output
# Volume and Price
def Cal_Vol_Price(Total_import_list_volume, Total_import_list_price):
output = []
volume_len = len(Total_import_list_volume)
price_len = len(Total_import_list_price)
if volume_len == price_len:
isSameLen = True
else:
isSameLen = False
if isSameLen:
for i in range(volume_len):
volume_price = (
Total_import_list_volume[i] * Total_import_list_price[i]
)
output.append(volume_price)
return output
# Moving Average Convergence Divergence (MACD)
def Cal_EMA(
Total_import_list, Total_import_list_High, Total_import_list_Low, samples
):
length = len(Total_import_list)
EMA = []
DI = 0
for i in range(length):
if i + 1 < samples:
ema = None
DI = (
DI
+ (
Total_import_list_High[i]
+ Total_import_list_Low[i]
+ Total_import_list[i] * 2
)
/ 4
)
elif i + 1 == samples:
ema = DI / samples
else:
ema = (EMA[-1] * (samples - 1) + Total_import_list[i] * 2) / (
samples + 1
)
EMA.append(ema)
return EMA
def Cal_MACD(
Total_import_list,
Total_import_list_High,
Total_import_list_Low,
Sample_1=12,
Sample_2=26,
Sample_Dif=9,
):
MACD, DIF, DIF_MACD = [], [], []
if Sample_1 > Sample_2:
temp = Sample_2
Sample_2 = Sample_1
Sample_1 = temp
if Sample_1 != Sample_2:
EMA_1 = Cal_EMA(
Total_import_list,
Total_import_list_High,
Total_import_list_Low,
Sample_1,
)
EMA_2 = Cal_EMA(
Total_import_list,
Total_import_list_High,
Total_import_list_Low,
Sample_2,
)
for i in range(len(EMA_1)):
if EMA_1[i] and EMA_2[i]:
DIF.append(EMA_1[i] - EMA_2[i])
else:
DIF.append(None)
length = len(DIF)
for j in range(length):
if j + 1 < Sample_2 + Sample_Dif:
macd = None
elif j + 1 == Sample_2 + Sample_Dif:
macd = (
sum(DIF[Sample_2 : (Sample_2 + Sample_Dif)]) / Sample_Dif
)
else:
macd = (MACD[-1] * (Sample_Dif - 1) + DIF[j] * 2) / (
Sample_Dif + 1
)
MACD.append(macd)
for k in range(length):
if DIF[k] is None or MACD[k] is None:
dif_macd = None
else:
dif_macd = DIF[k] - MACD[k]
DIF_MACD.append(dif_macd)
return DIF, MACD, DIF_MACD
# Exponential Moving Average (EMA)
# EMA_now = EMA_prev + alpa * (Rawdata_now - EMA_prev)
# alpa = 2/(N+1)
def Cal_Tr(
Total_import_list, Total_import_list_High, Total_import_list_Low
): # True range
length = len(Total_import_list)
Tr = []
for i in range(length):
if i == 0:
tr = 0.0
else:
tr = max(
(Total_import_list_High[i] - Total_import_list_Low[i]),
abs(Total_import_list_High[i] - Total_import_list[i - 1]),
abs(Total_import_list_Low[i] - Total_import_list[i - 1]),
)
Tr.append(tr)
return Tr
def Cal_EMA_pos(
Total_import_list, Total_import_list_High, Total_import_list_Low, samples
):
length = len(Total_import_list)
EMA_pos = []
Tr = Cal_Tr(
Total_import_list, Total_import_list_High, Total_import_list_Low
)
for i in range(length):
if i + 1 < samples:
ema_pos = None
elif i + 1 == samples:
ema_pos = sum(Tr[0:samples]) / samples
else:
ema_pos = (EMA_pos[-1] * (samples - 1) + Tr[i] * 2) / (samples + 1)
EMA_pos.append(ema_pos)
return EMA_pos
# Average True Range (ATR)
def Cal_ATR(
Total_import_list,
Total_import_list_High,
Total_import_list_Low,
samples=21,
):
ATR = Cal_EMA_pos(
Total_import_list,
Total_import_list_High,
Total_import_list_Low,
samples,
)
return ATR