def analyze_event():
# --------------------------------------------------------------------------------
# Step.1 load dataview
dv = DataView()
dv.load_dataview(dataview_folder)
# --------------------------------------------------------------------------------
# Step.2 calculate mask (to mask those ill data points)
mask_limit_reached = dv.get_ts('mask_limit_reached')
mask_index_member = dv.get_ts('mask_index_member')
mask_sus = dv.get_ts('mask_sus')
mask_all = np.logical_or(
mask_sus, np.logical_or(mask_index_member, mask_limit_reached))
# --------------------------------------------------------------------------------
# Step.3 get signal, benchmark and price data
price = dv.get_ts('close_adj')
price_bench = dv.data_benchmark
dv.add_formula('in_',
'(Delay(index_weight, 1) == 0) && (index_weight > 0)',
is_quarterly=False)
signal = dv.get_ts('in_').shift(1, axis=0) # avoid look-ahead bias
# Step.4 analyze!
obj = SignalDigger(output_folder='../../output', output_format='pdf')
obj.create_binary_event_report(signal,
price,
mask_all,
price_bench,
periods=[20, 60, 121, 242],
group_by=None)
def analyze_event():
# --------------------------------------------------------------------------------
# Step.1 load dataview
dv = DataView()
dv.load_dataview(dataview_folder)
# --------------------------------------------------------------------------------
# Step.2 calculate mask (to mask those ill data points)
mask_limit_reached = dv.get_ts('mask_limit_reached')
mask_index_member = dv.get_ts('mask_index_member')
mask_sus = dv.get_ts('mask_sus')
mask_all = np.logical_or(mask_sus, np.logical_or(mask_index_member, mask_limit_reached))
# --------------------------------------------------------------------------------
# Step.3 get signal, benchmark and price data
price = dv.get_ts('close_adj')
price_bench = dv.data_benchmark
dv.add_formula('in_', '(Delay(index_weight, 1) == 0) && (index_weight > 0)', is_quarterly=False)
signal = dv.get_ts('in_').shift(1, axis=0) # avoid look-ahead bias
# Step.4 analyze!
obj = SignalDigger(output_folder='../../output', output_format='pdf')
obj.create_binary_event_report(signal, price, mask_all, price_bench, periods=[20, 60, 121, 242], group_by=None)
def analyze_event():
# --------------------------------------------------------------------------------
# Step.1 load dataview
dv = DataView()
dv.load_dataview(dataview_folder)
# --------------------------------------------------------------------------------
# Step.2 calculate mask (to mask those ill data points)
trade_status = dv.get_ts('trade_status')
mask_sus = trade_status == u'停牌'.encode('utf-8')
df_index_member = dv.get_ts('index_member')
mask_index_member = ~(df_index_member > 0)
dv.add_formula('limit_reached',
'Abs((open - Delay(close, 1)) / Delay(close, 1)) > 0.095',
is_quarterly=False)
df_limit_reached = dv.get_ts('limit_reached')
mask_limit_reached = df_limit_reached > 0
mask_all = np.logical_or(
mask_sus, np.logical_or(mask_index_member, mask_limit_reached))
# --------------------------------------------------------------------------------
# Step.3 get signal, benchmark and price data
dv.add_formula('new_high',
'close_adj >= Ts_Max(close_adj, 300)',
is_quarterly=False)
dv.add_formula('new_high_delay',
'Delay(Ts_Max(new_high, 300), 1)',
is_quarterly=False)
dv.add_formula('sig', 'new_high && (! new_high_delay)', is_quarterly=False)
signal = dv.get_ts('sig').shift(0, axis=0) # avoid look-ahead bias
price = dv.get_ts('close_adj')
price_bench = dv.data_benchmark
# Step.4 analyze!
obj = SignalDigger(output_folder=jutil.join_relative_path('../output'),
output_format='pdf')
obj.create_binary_event_report(signal,
price,
mask_all,
5,
price_bench,
periods=[5, 20, 40])
