def run(self):
for idx, factor in enumerate(self.indicators):
if factor == 'ma':
window = self.indicators_param[idx].get('window')
if self.security_type == SecurityType.stock:
self.data_df['ma{}'.format(window)] = ma(
self.data_df['qfq_close'], window=window)
else:
self.data_df['ma{}'.format(window)] = ma(
self.data_df['close'], window=window)
if factor == 'macd':
slow = self.indicators_param[idx].get('slow')
fast = self.indicators_param[idx].get('fast')
n = self.indicators_param[idx].get('n')
if self.security_type == SecurityType.stock:
diff, dea, m = macd(self.data_df['qfq_close'],
slow=slow,
fast=fast,
n=n)
else:
diff, dea, m = macd(self.data_df['close'],
slow=slow,
fast=fast,
n=n)
self.data_df['diff'] = diff
self.data_df['dea'] = dea
self.data_df['m'] = m
def on_category_data_added(self, category, added_data: pd.DataFrame):
size = len(added_data)
df = self.data_df.loc[category].iloc[-self.valid_window - size:]
for idx, indicator in enumerate(self.indicators):
if indicator == 'ma':
window = self.indicators_param[idx].get('window')
if self.security_type == SecurityType.stock:
df['ma{}'.format(window)] = ma(df['qfq_close'], window=window)
else:
df['ma{}'.format(window)] = ma(df['close'], window=window)
if indicator == 'macd':
slow = self.indicators_param[idx].get('slow')
fast = self.indicators_param[idx].get('fast')
n = self.indicators_param[idx].get('n')
if self.security_type == SecurityType.stock:
df['diff'], df['dea'], df['m'] = macd(df['qfq_close'], slow=slow, fast=fast, n=n)
else:
df['diff'], df['dea'], df['m'] = macd(df['close'], slow=slow, fast=fast, n=n)
df = df.iloc[-size:, ]
df = df.reset_index()
df[self.category_field] = category
df = index_df_with_security_time(df)
self.depth_df = self.depth_df.append(df)
self.depth_df = self.depth_df.sort_index(level=[0, 1])
def depth_computing(self):
self.depth_df = self.data_df.reset_index(level='timestamp')
for idx, indicator in enumerate(self.indicators):
if indicator == 'ma':
window = self.indicators_param[idx].get('window')
col = 'ma{}'.format(window)
self.indicator_cols.add(col)
for security_id, df in self.depth_df.groupby('security_id'):
if self.security_type == SecurityType.stock:
self.depth_df.loc[security_id, col] = ma(df['qfq_close'], window=window)
else:
self.depth_df.loc[security_id, col] = ma(df['close'], window=window)
if indicator == 'macd':
slow = self.indicators_param[idx].get('slow')
fast = self.indicators_param[idx].get('fast')
n = self.indicators_param[idx].get('n')
self.indicator_cols.add('diff')
self.indicator_cols.add('dea')
self.indicator_cols.add('macd')
for security_id, df in self.depth_df.groupby('security_id'):
if self.security_type == SecurityType.stock:
diff, dea, m = macd(df['qfq_close'], slow=slow, fast=fast, n=n)
else:
diff, dea, m = macd(df['close'], slow=slow, fast=fast, n=n)
self.depth_df.loc[security_id, 'diff'] = diff
self.depth_df.loc[security_id, 'dea'] = dea
self.depth_df.loc[security_id, 'macd'] = m
self.depth_df = self.depth_df.set_index('timestamp', append=True)
def do_compute(self):
"""
calculate tech indicators self.depth_df
"""
if df_is_not_null(self.depth_df) and self.indicators:
for idx, indicator in enumerate(self.indicators):
if indicator == 'ma':
window = self.indicators_param[idx].get('window')
col = 'ma{}'.format(window)
self.indicator_cols.add(col)
if self.entity_type == 'stock' and self.fq == 'qfq':
df = self.depth_df['qfq_close'].groupby(
level=0).rolling(window=window,
min_periods=window).mean()
else:
df = self.depth_df['close'].groupby(level=0).rolling(
window=window, min_periods=window).mean()
df = df.reset_index(level=0, drop=True)
self.depth_df[col] = df
# self.depth_df = pd.concat([self.depth_df, df], axis=1, sort=False)
if indicator == 'macd':
slow = self.indicators_param[idx].get('slow')
fast = self.indicators_param[idx].get('fast')
n = self.indicators_param[idx].get('n')
self.indicator_cols.add('diff')
self.indicator_cols.add('dea')
self.indicator_cols.add('macd')
# for entity_id, df in self.depth_df.groupby('entity_id'):
# if self.entity_type == 'stock' and self.fq == 'qfq':
# diff, dea, m = macd(df['qfq_close'], slow=slow, fast=fast, n=n)
# else:
# diff, dea, m = macd(df['close'], slow=slow, fast=fast, n=n)
#
# self.depth_df.loc[entity_id, 'diff'] = diff
# self.depth_df.loc[entity_id, 'dea'] = dea
# self.depth_df.loc[entity_id, 'macd'] = m
if self.entity_type == 'stock' and self.fq == 'qfq':
df = self.depth_df.groupby(
level=0)['qfq_close'].apply(lambda x: macd(
x, slow=slow, fast=fast, n=n, return_type='df')
)
else:
df = self.depth_df.groupby(
level=0)['close'].apply(lambda x: macd(
x, slow=slow, fast=fast, n=n, return_type='df')
)
self.depth_df = pd.concat([self.depth_df, df],
axis=1,
sort=False)
def on_data_added(self, security_id, size):
df = self.original_df.loc[security_id].iloc[-self.valid_window - size:]
for idx, indicator in enumerate(self.indicators):
if indicator == 'ma':
window = self.indicators_param[idx].get('window')
if self.security_type == SecurityType.stock:
df['ma{}'.format(window)] = ma(df['qfq_close'],
window=window)
else:
df['ma{}'.format(window)] = ma(df['close'], window=window)
if indicator == 'macd':
slow = self.indicators_param[idx].get('slow')
fast = self.indicators_param[idx].get('fast')
n = self.indicators_param[idx].get('n')
if self.security_type == SecurityType.stock:
df['diff'], df['dea'], df['m'] = macd(df['qfq_close'],
slow=slow,
fast=fast,
n=n)
else:
df['diff'], df['dea'], df['m'] = macd(df['close'],
slow=slow,
fast=fast,
n=n)
df = df.iloc[-size:, ]
df = df.reset_index()
df['security_id'] = security_id
df = index_df_with_security_time(df)
self.data_df = self.data_df.append(df)
self.data_df = self.data_df.sort_index(level=[0, 1])
def transform(self, input_df) -> pd.DataFrame:
macd_df = input_df.groupby(level=0)['close'].apply(lambda x: macd(
x, slow=self.slow, fast=self.fast, n=self.n, return_type='df'))
input_df = pd.concat([input_df, macd_df], axis=1, sort=False)
return input_df
