def update_by_industry(index_name, indCode, intraday, type='full'):
'''
update by industry code;
full: all data
compact: today only
'''
data = get_index_table(index_name)
df = indCode_to_tickers(data,
indCode) # ticers in that Industry in DF format
if (type == 'compact' and intraday == 'none'):
df_dic = get_df_bySec(df,
intraday='none',
size='compact',
today_only=True,
sleep_timer=15)
elif (type == 'full' and intraday == 'none'):
df_dic = get_df_bySec(df,
intraday='none',
size='full',
today_only=False,
sleep_timer=15)
elif (type == 'compact' and intraday != 'none'):
df_dic = get_df_bySec(df,
intraday,
size='compact',
today_only=True,
sleep_timer=15)
elif (type == 'full' and intraday != 'none'):
df_dic = get_df_bySec(df,
intraday,
size='full',
today_only=False,
sleep_timer=15)
else:
pass
def report(index_name):
'''
report hub function
'''
# index table in df
indexT = get_index_table(index_name)
# index df to list of symbols
tickerL = indexT['Symbol'].tolist()
# daily db engine
engine_dailydb = create_dbengine(db="tsxci_daily_db")
# report db engine
engine_report = create_dbengine(db="tsxci_report")
# temp df for report with predefined columns
columns = [
'ticker', '52w high', '52w low', 'downtrend', 'uptrend', 'pattern',
'high volume', 'low volume', 'Industry'
]
dtypes = ['str', 'int', 'int', 'int', 'int', 'str', 'int', 'int', 'str']
report_df = df_empty(columns, dtypes)
for ticker in tickerL:
# equity name
equity = ticker
# ticker + '.to'
ticker_to = (ticker + '.to').lower()
# print(ticker)
# industry name & ticker
ind_df = indexT[(indexT['Symbol'] == equity)][['Industry', 'Symbol']]
ind_df.columns = ['Industry', 'ticker']
# read daily db return df
df = read_table_df_Engine(ticker_to, engine_dailydb)
# unusual volume stickers append to df
report_df = report_df.append(unusual_volume(equity, df),
ignore_index=True)
# unusual trend stickers append to df
report_df = report_df.append(trend_potential(equity, df),
ignore_index=True)
# 52w high/low/trending append to df
report_df = report_df.append(fiftytwo_week(equity, df),
ignore_index=True)
# decide if known pattern append to df
report_df = report_df.append(find_pattern(equity, df),
ignore_index=True)
# added industry name to each ticker, if ticker in index otherwise not add industry
if not report_df.empty:
if (equity in report_df['ticker'].unique()):
report_df = report_df.append(ind_df, ignore_index=True)
# grouby using first() and NaN to Zero
report_df = groupby_na_to_zero(report_df, 'ticker')
# pass columns don't want to be type(int), industry must exist after the loop above
# report_df = type_to_int(report_df,['pattern','Industry'])
# tname is today's date
tname = dt.datetime.today().strftime("%m-%d-%Y")
# print(report_df.columns)
# write df into db
report_df_to_sql(tname, report_df, engine_report)
def update_all(index_name, intraday, type='compact'):
'''
update all
full: all data
compact: today only
'''
data = get_index_table(index_name) # All tickers in DF format
# Use old function 'get_df_byTicker' ) NO index_name
if (index_name == 'tsxci'):
if (type == 'compact' and intraday == 'none'):
df_dic = get_df_byTicker(data,
intraday='none',
size='compact',
today_only=True,
sleep_timer=15)
elif (type == 'full' and intraday == 'none'):
df_dic = get_df_byTicker(data,
intraday='none',
size='full',
today_only=False,
sleep_timer=15)
elif (type == 'compact' and intraday != 'none'):
df_dic = get_df_byTicker(data,
intraday,
size='compact',
today_only=True,
sleep_timer=15)
elif (type == 'full' and intraday != 'none'):
df_dic = get_df_byTicker(data,
intraday,
size='full',
today_only=False,
sleep_timer=15)
else:
pass
# New function is called "fetch_write" for ca_etf
elif (index_name == 'ca_etf'):
if (type == 'compact' and intraday == 'none'):
df_dic = fetch_write(index_name,
data,
intraday='none',
size='compact',
today_only=True,
sleep_timer=15)
elif (type == 'full' and intraday == 'none'):
df_dic = fetch_write(index_name,
data,
intraday='none',
size='full',
today_only=False,
sleep_timer=15)
else:
pass
def update_all(index_name,intraday,type='compact'):
'''
update all
full: all data
compact: today only
'''
data = get_index_table(index_name) # All tickers in DF format
if (type=='compact' and intraday=='none'):
df_dic = get_df_byTicker(data,intraday='none',size='compact',today_only=True,sleep_timer=15)
elif (type=='full' and intraday=='none'):
df_dic = get_df_byTicker(data,intraday='none',size='full',today_only=False,sleep_timer=15)
elif (type=='compact' and intraday !='none'):
df_dic = get_df_byTicker(data,intraday,size='compact',today_only=True,sleep_timer=15)
elif (type=='full' and intraday !='none'):
df_dic = get_df_byTicker(data,intraday,size='full',today_only=False,sleep_timer=15)
else:
pass
def update_all(index_name, type='compact'):
'''
update all
full: all data
compact: today only
'''
data = get_index_table(index_name)
if (type == 'compact'):
df_dic = get_df_byTicker(data,
size='compact',
today_only=True,
sleep_timer=15)
else:
df_dic = get_df_byTicker(data,
size='full',
today_only=False,
sleep_timer=15)
def update_by_industry(index_name, indCode, type='full'):
'''
update by industry code;
full: all data
compact: today only
'''
data = get_index_table(index_name)
df = indCode_to_tickers(data, indCode)
if (type == 'compact'):
df_dic = get_df_bySec(df,
size='compact',
today_only=True,
sleep_timer=15)
else:
df_dic = get_df_bySec(df,
size='full',
today_only=False,
sleep_timer=15)