def ditto_plus(cand_,st,ct,d,mdl,cpu=0):
if cpu ==0:
cpu = None
cand = cand_.copy()
counter = 0
used = []
while True:
found = False
args = ((x,st,ct,d,mdl) for x in cand)
with mp.Pool(cpu) as pool:
for i, pattern in enumerate(pool.imap(plus_,args)):
if pattern:
used.append(pattern)
counter += i
print( f'{round(100*(counter/len(cand_)),2)}%' ,end='\r')
ct = add(pattern,ct,d)
mdl = mdl_calc(ct,d,st)
cand = cand[i+1:]
#print(f'Current MDL: {mdl}')
found = True
break
if not found:
print(f'After adding codes:\t\t{mdl}')
break
return ct, used
from add_rem import add
from cover import cov_order
from import_data import import_dat
from output_gen import load_dictionary, painter
from pattern_finder import finder
#%%
st, d = import_dat('./output/UNHMCD/UNH_MCD_5y_close.dat')
d_og = d
ct = st.copy()
#index patterns
ct = add(((0, (4, )), (1, (4, ))), ct, d)
ct = add(((0, (4, )), (1, (3, ))), ct, d)
ct = add(((0, (2, )), (1, (4, ))), ct, d)
ct = add(((0, (5, 4)), (1, (4, ))), ct, d)
ct = add(((0, (4, )), (1, (5, 3))), ct, d)
#stock pattens
ct = add(((0, (3, )), (1, (3, ))), ct, d)
ct = add(((0, (3, )), (1, (4, ))), ct, d)
ct = add(((0, (3, )), (1, (2, ))), ct, d)
ct = add(((0, (2, )), (1, (2, ))), ct, d)
patterns = {}
for key, value in ct.items():
if value[1] > 1:
ct_df['Time'] = [ct_df.iloc[i,1][2] for i in range(len(ct_df))]
del ct_df['value']
ct_df = ct_df.set_index('index')
#Now we sort it based on support
ct_df = ct_df.sort_values('Support')[::-1]
#%%
st, d = import_dat('./output/UNHMCD/UNH_MCD_5y_close.dat')
d_og = d
ct = st.copy()
#this gives 30% coverage of line 0 and 1
ct = add(list(ct_df.index)[0],ct,d)
ct = add(list(ct_df.index)[1],ct,d)
ct = add(list(ct_df.index)[2],ct,d)
ct = add(list(ct_df.index)[3],ct,d)
ct = add(list(ct_df.index)[4],ct,d)
patterns = {}
for key, value in ct.items():
if value[1]>1:
patterns[key]=value
ordered_p = cov_order(patterns)
val_d ={}
sign = -100
d = load_dictionary('./output/dowj_5y_close.dict')
ct = {k: v for k, v in d.items() if v[1] > 1}
ct_df = pd.DataFrame.from_dict(ct,
orient='index',
columns=['Support', 'Length', 'Time'])
#%%
st, d = import_dat('./output/dowj_5y_close.dat')
#%%
d_og = d
ct = st.copy()
#this gives 42% coverage of line 20 and 27`
ct = add(((20, (4, )), (27, (2, ))), ct, d)
ct = add(((20, (3, )), (27, (3, ))), ct, d)
ct = add(((20, (3, )), (27, (2, ))), ct, d)
ct = add(((20, (2, )), (27, (2, ))), ct, d)
patterns = {}
for key, value in ct.items():
if value[1] > 1:
patterns[key] = value
ordered_p = cov_order(patterns)
val_d = {}
sign = -100
for x in ordered_p:
# 'Paint' the dataset with 0's where covered and return p amount
def make_plot():
# Load the dictionary to see the patterns
btc_dict = load_dictionary('./bitcoin2016.dict')
# Remove the singletons
btc_dict = {k: v for k, v in btc_dict.items() if v[1] > 1}
# Turn it into a dataframe
df = pd.DataFrame.from_dict(btc_dict,
orient='Index',
columns=['Support', 'Length', 'Time'])
# Sort them by support
df = df.sort_values(by='Support')[::-1]
st, d = import_dat('./bitcoin2016.dat')
ct = st.copy()
for key in list(df.index[:10]):
ct = add(key, ct, d)
#patterns[key]=value
patterns = {}
for key, value in ct.items():
if value[1] > 1:
patterns[key] = value
ordered_p = cov_order(patterns)
val_d = {}
sign = -100
for x in ordered_p:
# 'Paint' the dataset with 0's where covered and return p amount
d = painter(x, d, sign)
val_d[sign] = x
sign *= 2
d2 = [[val_d[x] if x in val_d else 'None' for x in row] for row in d]
df2 = pd.read_excel('./bitcoin2016.xlsx')
df2['Date'] = pd.to_datetime(df2['<DATE>'])
df2['ToolTipDates'] = df2.Date.map(lambda x: x.strftime("%d %b %y"))
colors = [
'#e6194B', '#3cb44b', '#ffe119', '#4363d8', '#f58231', '#42d4f4',
'#f032e6', '#e6beff', '#9A6324', '#800000', '#000075'
]
P_TO_COLOR = {x: colors[i] for i, x in enumerate(patterns)}
P_TO_COLOR['None'] = '#f1f1f1'
for i, x in enumerate(('<OPEN>', '<HIGH>', '<LOW>', '<CLOSE>', '<VOL>')):
df2[f'pattern{x}'] = [str(x) for x in d2[i]]
df2[f'color{x}'] = [P_TO_COLOR[x] for x in d2[i]]
#for every column we generate the line we need
open_ = xyc(df2, 'Date', 'og<OPEN>', 'color<OPEN>')
high_ = xyc(df2, 'Date', 'og<HIGH>', 'color<HIGH>')
low_ = xyc(df2, 'Date', 'og<LOW>', 'color<LOW>')
close_ = xyc(df2, 'Date', 'og<CLOSE>', 'color<CLOSE>')
vol_ = xyc(df2, 'Date', 'og<VOL>', 'color<VOL>')
df2['labOPEN'] = df2['pattern<OPEN>']
df2['labHIGH'] = df2['pattern<HIGH>']
df2['labLOW'] = df2['pattern<LOW>']
df2['labCLOSE'] = df2['pattern<CLOSE>']
df2['labVOL'] = df2['pattern<VOL>']
source2 = ColumnDataSource(df2)
output_file('bitcoindaily.html')
p = figure(x_axis_type='datetime',
plot_width=1440,
plot_height=600,
title="Bitcoin Stock Price")
p.circle(x='Date',
y='og<OPEN>',
name='open',
alpha=0,
source=source2,
size=3)
p.circle(x='Date',
y='og<CLOSE>',
name='close',
alpha=0,
source=source2,
size=3)
p.circle(x='Date',
y='og<HIGH>',
name='high',
alpha=0,
source=source2,
size=3)
p.circle(x='Date',
y='og<LOW>',
name='low',
alpha=0,
source=source2,
size=3)
p.multi_line(name='q',
xs=open_[0],
ys=open_[1],
color=open_[2],
line_width=3)
p.multi_line(name='e',
xs=high_[0],
ys=high_[1],
color=high_[2],
line_width=3)
p.multi_line(name='ee',
xs=low_[0],
ys=low_[1],
color=low_[2],
line_width=3)
p.multi_line(name='w',
xs=close_[0],
ys=close_[1],
color=close_[2],
line_width=3)
q = figure(x_range=p.x_range,
x_axis_type='datetime',
plot_width=1440,
plot_height=200,
title="Stock Volume",
y_axis_type='linear')
q.circle(x='Date',
y='og<VOL>',
name='VOL',
alpha=0,
source=source2,
size=3)
p.circle(x='Date',
y='og<LOW>',
name='low',
alpha=0,
source=source2,
size=3)
q.multi_line(name='qw',
xs=vol_[0],
ys=vol_[1],
color=vol_[2],
line_width=3)
p.add_tools(
HoverTool(names=['low'],
mode="vline",
line_policy='nearest',
point_policy='snap_to_data',
tooltips=[
('Date : ', '@ToolTipDates'),
('Low Price : ', '@{og<LOW>}{0.2f}'),
('Low Pattern : ', '@labLOW'),
('High Price : ', '@{og<HIGH>}{0.2f}'),
('High Pattern : ', '@labHIGH'),
('Open Price : ', '@{og<OPEN>}{0.2f}'),
('Open Pattern : ', '@labOPEN'),
('Close Price : ', '@{og<CLOSE>}{0.2f}'),
('Close Pattern : ', '@labCLOSE'),
]))
p.add_tools(
HoverTool(names=['open'],
mode="vline",
line_policy='nearest',
point_policy='snap_to_data',
tooltips=[
('Name', 'Open'),
]))
p.add_tools(
HoverTool(names=['close'],
mode="vline",
line_policy='nearest',
point_policy='snap_to_data',
tooltips=[
('Name', 'Close'),
]))
p.add_tools(
HoverTool(names=['high'],
mode="vline",
line_policy='nearest',
point_policy='snap_to_data',
tooltips=[
('Name', 'High'),
]))
p.add_tools(
HoverTool(names=['low'],
mode="vline",
line_policy='nearest',
point_policy='snap_to_data',
tooltips=[
('Name', 'Low'),
]))
q.add_tools(
HoverTool(names=['VOL'],
mode="vline",
line_policy='nearest',
point_policy='snap_to_data',
tooltips=[
('Date : ', '@ToolTipDates'),
('High Price : ', '@{og<VOL>}{0.2f}'),
('High Pattern : ', '@labVOL'),
]))
show(column(p, q))
def plus_(args):
x,st,ct,d,mdl = args
if mdl_calc(add(x,ct,d),d,st) < mdl:
return x
x.append(row[date])
xs.append(x)
ys.append(y)
return xs, ys, c
# %%
#We now do the usual for making a graph
d_og = d
ct = st.copy()
#this gives 16% coverage of line 1
# and 23% of line 6`
ct = add(((1, (1, )), (6, (1, ))), ct, d)
ct = add(((1, (2, )), (6, (4, ))), ct, d)
ct = add(((1, (5, )), (6, (4, ))), ct, d)
ct = add(((1, (3, )), (6, (3, 3))), ct, d)
patterns = {}
for key, value in ct.items():
if value[1] > 1:
patterns[key] = value
ordered_p = cov_order(patterns)
val_d = {}
sign = -100
for x in ordered_p:
# 'Paint' the dataset with 0's where covered and return p amount