def plot_statistics_sizes(self, type='order', bins=10):
"""
Sizes statistics
:param self:
:param type:
:return:
"""
if self.day is None:
raise NameError('Montly data are not loaded')
if type == 'execution':
fig = plt.figure(figsize=(12, 8))
ax = sns.distplot(capped_prices(
self.statistics['sell']['executionsize']),
kde=False,
bins=bins,
hist=True,
color='r',
label='Sell')
ax = sns.distplot(capped_prices(
self.statistics['buy']['executionsize']),
kde=False,
bins=bins,
hist=True,
color='g',
label='Buy')
plt.title(
f'Sell/Buy execution sizes distribution {self.itchday} {self.stock}',
fontsize=20)
plt.legend()
elif type == 'order':
fig = plt.figure(figsize=(12, 8))
ax = sns.distplot(capped_prices(
self.statistics['sell']['ordersize']),
kde=False,
bins=bins,
hist=True,
color='r',
label='Sell')
ax = sns.distplot(capped_prices(
self.statistics['buy']['ordersize']),
kde=False,
bins=bins,
hist=True,
color='g',
label='Buy')
plt.title(
f'Sell/Buy orders sizes distribution {self.itchday} {self.stock}',
fontsize=20)
plt.legend()
if cpny.get_company(stock) is not None:
print(cpny.get_company(stock))
print('N Buy/Sell orders:', norders)
if len(lexecutionsS) != 0:
print('N Order Executions Sell:', len(lexecutionsS))
print('Mean time to execution:',
nanoseconds_to_time(np.mean(lexecutionsS)))
print('Max time to execution:',
nanoseconds_to_time(np.max(lexecutionsS)))
print('Min time to execution:',
nanoseconds_to_time(np.min(lexecutionsS)))
ax = sns.distplot(np.log10(lexecutionsS), kde=True, norm_hist=True)
plt.title('Log plot of Sell execution time ' + day)
plt.show()
plt.close()
ax = sns.distplot(capped_prices(lpriceOS), kde=True, norm_hist=True)
plt.title('Orders Sell price ' + day)
plt.show()
plt.close()
if len(lexecutionsB) != 0:
print('N Order Executions Buy:', len(lexecutionsB))
print('Mean time to execution:',
nanoseconds_to_time(np.mean(lexecutionsB)))
print('Max time to execution:',
nanoseconds_to_time(np.max(lexecutionsB)))
print('Min time to execution:',
nanoseconds_to_time(np.min(lexecutionsB)))
print('N Hiden Executions:', len(ltimeEP))
ax = sns.distplot(np.log10(lexecutionsB), kde=True, norm_hist=True)
plt.title('Log plot of Buy execution time ' + day)
for stock in sorted(sstock.get_list_stocks()):
for day in ITCH_days[year]:
if os.path.isfile(datapath + '/Results/' + day + '-' + stock + '-MessageAnalysis.pkl'):
print(day, stock)
wfile = open(datapath + '/Results/' + day + '-' + stock + '-MessageAnalysis.pkl', 'rb')
ddata = pickle.load(wfile)
wfile.close()
fig = plt.figure(figsize=(12,8))
ax = sn.distplot(np.log10(ddata['DeltaTimeExecSell']), kde=True, norm_hist=True)
plt.title('Log plot of Sell execution delta time ' + day)
plt.show()
plt.close()
fig = plt.figure(figsize=(12,8))
ax = sn.distplot(capped_prices(ddata['PriceOrderSell']), kde=True, norm_hist=True)
plt.title('Orders Sell price ' + day)
plt.show()
plt.close()
fig = plt.figure(figsize=(12,8))
ax = sn.distplot(np.log10(ddata['DeltaTimeExecBuy']), kde=True, norm_hist=True)
plt.title('Log plot of Buy execution time ' + day)
plt.show()
plt.close()
fig = plt.figure(figsize=(12,8))
ax = sn.distplot(capped_prices(ddata['PriceOrderBuy']), kde=True, norm_hist=True)
plt.title('Orders Buy price ' + day)
plt.show()
plt.close()
def plot_statistics(statistics):
"""
Some plot of the results
:param statistics:
:return:
"""
fig = plt.figure(figsize=(12, 8))
ax = sns.distplot(np.log10(statistics['sell']['executiondeltatime']),
kde=True,
norm_hist=True)
plot_tscale()
plt.title(f'Log plot of Sell execution delta time {itchday} {args.stock}',
fontsize=20)
plt.show()
fig = plt.figure(figsize=(12, 8))
ax = sns.distplot(capped_prices(statistics['sell']['executionprice']),
kde=True,
norm_hist=True)
plt.title(f'Orders Sell price {itchday} {args.stock}', fontsize=20)
plt.show()
fig = plt.figure(figsize=(12, 8))
ax = sns.distplot(np.log10(statistics['buy']['executiondeltatime']),
kde=True,
norm_hist=True)
plot_tscale()
plt.title(f'Log plot of Buy execution time {itchday} {args.stock}',
fontsize=20)
plt.show()
fig = plt.figure(figsize=(12, 8))
ax = sns.distplot(capped_prices(statistics['buy']['executionprice']),
kde=True,
norm_hist=True)
plt.title(f'Orders Buy price {itchday} {args.stock}', fontsize=20)
plt.show()
fig = plt.figure(figsize=(12, 8))
ax = sns.distplot(np.log10(statistics['buy']['deletedeltatime']),
kde=True,
norm_hist=True,
label='Buy')
ax = sns.distplot(np.log10(statistics['sell']['deletedeltatime']),
kde=True,
norm_hist=True,
label='Sell')
plot_tscale()
plt.legend()
plt.title(f'Log plot of deletion time (Buy/Sell) {itchday} {args.stock}',
fontsize=20)
plt.show()
fig = plt.figure(figsize=(12, 8))
ax = sns.distplot(np.log10(statistics['sell']['executiondeltatime']),
kde=True,
hist=False,
color='r',
label='Exec Buy')
ax = sns.distplot(np.log10(statistics['buy']['executiondeltatime']),
kde=True,
hist=False,
color='g',
label='Exec Sell')
ax = sns.distplot(np.log10(statistics['buy']['deletedeltatime']),
kde=True,
hist=False,
color='b',
label='Del Buy')
ax = sns.distplot(np.log10(statistics['sell']['deletedeltatime']),
kde=True,
hist=False,
color='k',
label='Del Sell')
plt.legend()
plot_tscale()
plt.title(f'Sell/Buy/Deletion time {itchday} {args.stock}', fontsize=20)
plt.show()
fig = plt.figure(figsize=(12, 8))
ax = sns.distplot(capped_prices(statistics['sell']['orderprice']),
kde=True,
hist=False,
color='r',
label='Sell')
ax = sns.distplot(capped_prices(statistics['buy']['orderprice']),
kde=True,
hist=False,
color='g',
label='Buy')
plt.legend()
plt.title(f'Sell/Buy orders prices distribution {itchday} {args.stock}',
fontsize=20)
plt.show()
fig = plt.figure(figsize=(12, 8))
ax = sns.distplot(capped_prices(statistics['sell']['ordersize']),
kde=False,
hist=True,
color='r',
label='Sell')
ax = sns.distplot(capped_prices(statistics['buy']['ordersize']),
kde=False,
hist=True,
color='g',
label='Buy')
plt.title(f'Sell/Buy orders sizes distribution {itchday} {args.stock}',
fontsize=20)
plt.legend()
plt.show()
fig = plt.figure(figsize=(12, 8))
ax = sns.distplot(capped_prices(statistics['sell']['executionprice']),
kde=True,
hist=False,
color='r',
label='Sell')
ax = sns.distplot(capped_prices(statistics['buy']['executionprice']),
kde=True,
hist=False,
color='g',
label='Buy')
plt.legend()
plt.title(f'Sell/Buy execution prices distribution {itchday} {args.stock}',
fontsize=20)
plt.show()
fig = plt.figure(figsize=(12, 8))
ax = sns.distplot(capped_prices(statistics['sell']['executionsize']),
kde=False,
hist=True,
color='r',
label='Sell')
ax = sns.distplot(capped_prices(statistics['buy']['executionsize']),
kde=False,
hist=True,
color='g',
label='Buy')
plt.title(f'Sell/Buy execution sizes distribution {itchday} {args.stock}',
fontsize=20)
plt.legend()
plt.show()
fig = plt.figure(figsize=(14, 8))
plt.plot(statistics['sell']['ordertime'],
statistics['sell']['orderprice'],
color='r')
plt.plot(statistics['buy']['ordertime'],
statistics['buy']['orderprice'],
color='g')
plt.scatter(statistics['sell']['executiontime'],
statistics['sell']['executionprice'],
marker='o',
color='y',
s=statistics['sell']['executionsize'])
plt.scatter(statistics['buy']['executiontime'],
statistics['buy']['executionprice'],
marker='o',
color='b',
s=statistics['buy']['executionsize'])
plt.title(f'Order Sell/Buy price evolution ', fontsize=20)
plt.show()