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shallow_logic.py
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shallow_logic.py
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#!/usr/bin/python3
import urllib
import urllib.request
from bs4 import BeautifulSoup
import pandas as pd
import numpy as np
import re
import logging
import argparse
import time
import threading
# logging setup
logging.basicConfig(level=logging.INFO)
# my private classes & methods
from nasdaq_uvoljs import un_volumes
from nasdaq_quotes import nquote
from bigcharts_md import bc_quote
from marketwatch_md import mw_quote
from y_techevents import y_techevents
from nasdaq_wrangler import nq_wrangler
#####################################################
# CLASS
class combo_logic:
"""
Do deeper logic data cleaning & thinking across multiple dataframes.
Although this is not considerd DEEP Logic, Machine Learning, or AI.
"""
deep_1 = ""
deep_2 = ""
deep_3 = ""
inst_uid = 0
combo_df = ""
combo_dupes = ""
min_price = {} # Heler: DICT to help find cheapest ***HOT stock
args = [] # class dict to hold global args being passed in from main() methods
rx = [] # hottest stock with lowest price overall
cx = { 'LT': 'Mega cap + % gainer', \
'LB': 'L cap + % gainer', \
'LM': 'M cap + % gainer', \
'LZ': 'Zero L cap + % gainer', \
'SB': 'B/S cap + % gainer', \
'SM': 'S cap + % gainer', \
'SZ': 'Zero S cap + % gainer', \
'EF': 'ETF Fund Trust + % gainer', \
'UZ': '? cap + % gainer', \
'TM': 'T cap + % gainer',
}
def __init__(self, yti, d1, d2, d3, global_args):
cmi_debug = __name__+"::"+self.__init__.__name__
logging.info('%s - INST_class' % cmi_debug )
self.inst_uid = yti
self.deep_1 = d1.tg_df1.drop(columns=[ 'ERank', 'Time' ]).sort_values(by='Pct_change', ascending=False )
self.deep_2 = d2.dg1_df1.drop(columns=[ 'Row', 'Time' ] )
self.deep_3 = d3.up_df0.drop(columns=[ 'Row', 'Time', 'Vol', 'Vol_pct']).sort_values(by='Pct_change', ascending=False )
self.args = global_args
return
def __repr__(self):
return ( f'{self.__class__.__name__}(' f'{self.inst_uid!r})' )
#############################################################################
# method #0
def prepare_combo_df(self):
"""
combo_df is the **Single Source of Truth** dataset.
It is used & referenced by a lot of methods, functions and stuff.
"""
cmi_debug = __name__+"::"+self.prepare_combo_df.__name__+".#"+str(self.inst_uid)
logging.info('%s - IN' % cmi_debug )
temp_df = pd.concat( [ self.deep_1, self.deep_2, self.deep_3], sort=False, ignore_index=True ).sort_values(by=['Pct_change', 'M_B', 'Mkt_cap'], ascending=False, na_position='last')
temp_df.reset_index(inplace=True, drop=True) # reset index each time so its guaranteed sequential
self.combo_df = temp_df.sort_values(by=['Pct_change'], ascending=False ) # ensure sorted combo DF is avail as class global attr
self.combo_dupes = self.combo_df.duplicated(['Symbol']).to_frame() # convert Bool SERIES > DF & make avail as class global attr DF
return
###################################################################################
# method 1
def polish_combo_df(self, me):
"""
Clean, Polish & Wax the main Combo DataFrame.
We do a lot of heavy DF data generation/manipulation/insertion here.
Fill-out key collumn data thats missing, incomplete and/or not reliable due to errors in initial data extraction
"""
cmi_debug = __name__+"::"+self.polish_combo_df.__name__+".#"+str(self.inst_uid)+"."+str(me)
logging.info( f"%s - CALLED" % cmi_debug )
self.prepare_combo_df() # FIRST, merge Small_cap + med + large + mega into a single DF
# Look into the main combo_df at the Unsual Vol columns
# Find/fix missing data in nasdaq.com unusual volume DF - i.e. market_cap info
#self.tag_dupes()
#print ( f"{self.combo_dupes_only_listall(2)}" )
uvol_badata = self.combo_df[self.combo_df['Mkt_cap'].isna()] # Non and NaN = True
uvol_badsymbols = uvol_badata['Symbol'].tolist() # make list of bad symbols from the DF
nq = nquote(4, self.args) # setup an nasdaq.com quote instance to get live data from
nq.init_dummy_session() # nasdaq.com session setup
self.total_wrangle_errors = 0
self.unfixable_errors = 0
self.cleansed_errors = 0
self.wrangle_errors = 0
self.loop_count = 1
self.fixchars = 0
self.cols = 1
############################### get quote Setup #################################
# Get missing data from nasdaq.com. Rewrite in into combo_df.
# This is network expensive - do a live network quote get for each stock
logging.info( f"%s - Get quote data from nasdaq.com for: {len(uvol_badsymbols)} symbols" % cmi_debug )
print ( f"========== ask nasdaq.com for missing quote data =====================================================" )
for qsymbol in uvol_badsymbols:
xsymbol = qsymbol
qsymbol = qsymbol.rstrip() # cleand/striped of trailing spaces
logging.info( f"%s - get quote: {qsymbol} : {self.loop_count}" % cmi_debug )
nq.update_headers(qsymbol, "stocks") # nasdaq.com session - set path: header object
nq.form_api_endpoint(qsymbol, "stocks") # nasdaq.com set API endpoint - default GUESS asset_class=stocks
ac = nq.learn_aclass(qsymbol) # nasdaq.com lead what the real asset class is
if ac != "stocks":
logging.info( f"%s - re-shape asset class endpoint to: {ac}" % cmi_debug )
nq.form_api_endpoint(qsymbol, ac) # re-form API endpoint if default asset_class guess was wrong
nq.get_nquote(qsymbol.upper()) # get a live quote
wq = nq_wrangler(4, self.args) # instantiate a class for Quote Data Wrangeling
wq.asset_class = ac
wq.setup_zones(4, nq.quote_json1, nq.quote_json2, nq.quote_json3)
wq.do_wrangle()
wq.clean_cast()
wq.build_data_sets()
print ( f"{qsymbol:5}...", end="", flush=True ) # >> pretty printer <<
else:
nq.get_nquote(qsymbol.upper()) # get a live quote
wq = nq_wrangler(3, self.args) # instantiate a class for Quote Data Wrangeling
wq.asset_class = ac
wq.setup_zones(3, nq.quote_json1, nq.quote_json2, nq.quote_json3)
wq.do_wrangle()
wq.clean_cast()
wq.build_data_sets()
print ( f"{qsymbol:5}...", end="", flush=True ) # >> pretty printer <<
############################### Phase 1 ###########################################
# Evaluate Asset Class = an Exchnage Traded Fund (ETF)
logging.info( f"{cmi_debug} - Begin market cap/scale logic cycle... {nq.asset_class}")
if wq.asset_class == "etf": # Global attribute - Cant get STOCK-type data for 'etf'
logging.info( f"{cmi_debug} - {qsymbol} asset class is ETF" )
self.wrangle_errors += 1
self.unfixable_errors += 1
print ( f"E!", end="" ) # >> pretty printer <<
self.fixchars += 2
z_float = round(float(0), 2)
if self.args['bool_xray'] is True:
print ( f"=xray=========================== {self.inst_uid} ================================begin=" )
print ( f"z_float: {z_float}" )
print ( f"combo_df: {self.combo_df}" )
print ( f"=xray=========================== {self.inst_uid} ==================================end=" )
row_index = self.combo_df.loc[self.combo_df['Symbol'] == xsymbol].index[0]
self.combo_df.at[row_index, 'Mkt_cap'] = z_float # set Market cap to 0 for ETF
row_index = self.combo_df.loc[self.combo_df['Symbol'] == xsymbol].index[0]
self.combo_df.at[row_index, 'M_B'] = 'EF'
else:
logging.info( f"{cmi_debug} - {qsymbol} asset class is {wq.asset_class}" )
pass
############################### Phase 2 ###########################################
# Evaluate Market Cap data field - quality of data
logging.info( f"{cmi_debug} - Test {wq.asset_class} Mkt_cap for BAD data..." )
z_float = round(float(0), 3) # 0.000
try:
null_tester = wq.qd_quote['mkt_cap'] # some ETF/Funds have a market cap - but data is inconsistent
except TypeError:
logging.info( f"{cmi_debug} - {wq.asset_class} Mkt_cap data is NULL / setting to: 0" )
if self.args['bool_xray'] is True:
print ( f"=xray=TypeError================= {self.inst_uid} ================================begin=" )
print ( f"quote: {wq.qd_quote.items()}" )
print ( f"combo_df: {self.combo_df}" )
print ( f"=xray=========================== {self.inst_uid} ==================================end=" )
self.combo_df.at[self.combo_df[self.combo_df['Symbol'] == xsymbol].index, 'Mkt_cap'] = 'UZ' # make is a real number = 0
print ( f"^^", end="" ) # >> pretty printer <<
self.cleansed_errors += 2
self.fixchars += 2
y = 0
except KeyError:
logging.info( f"{cmi_debug} - {wq.asset_class} Mkt_cap key is NULL / setting to: 0" )
if self.args['bool_xray'] is True:
print ( f"=xray=KeyError================== {self.inst_uid} ================================begin=" )
print ( f"quote: {wq.qd_quote.items()}" )
print ( f"combo_df: {self.combo_df}" )
print ( f"=xray=========================== {self.inst_uid} ==================================end=" )
self.combo_df.at[self.combo_df[self.combo_df['Symbol'] == xsymbol].index, 'Mkt_cap'] = 'UZ' # make is a real number = 0
self.cleansed_errors += 1
print ( f"!", end="" ) # >> pretty printer <<
self.fixchars += 1
y = 0
else:
logging.info( f"{cmi_debug} - Set {wq.asset_class} Mkt_cap to: {wq.qd_quote['mkt_cap']}" )
z_float = (float(wq.qd_quote['mkt_cap']))
row_index = self.combo_df.loc[self.combo_df['Symbol'] == xsymbol].index[0]
self.combo_df.at[row_index, 'Mkt_cap'] = round(z_float, 3) # set Market cap to real/live num from nasdaq.com
print ( f"$", end="" ) # >> pretty printer <<
self.fixchars += 1
self.cleansed_errors += 1
############################### Phase 3 ###########################################
# Set the Market Cap scale tag (M_B col)
if wq.asset_class == "stocks":
logging.info( f"{cmi_debug} - Compute Mkt_cap scale tag: [ {wq.qd_quote['mkt_cap']} ]..." )
for i in (("MT", 999999), ("LB", 10000), ("SB", 2000), ("LM", 500), ("SM", 50), ("TM", 10), ("UZ", 0)):
if wq.qd_quote['mkt_cap'] == float(0):
row_index = self.combo_df.loc[self.combo_df['Symbol'] == xsymbol].index[0]
self.combo_df.at[row_index, 'M_B'] = "UZ"
logging.info( f"{cmi_debug} - Bad Market cap: [ {wq.qd_quote['mkt_cap']} ] scale set to: UZ" )
print ( f"0", end="" )
self.fixchars += 1
break
elif i[1] >= wq.qd_quote['mkt_cap']:
pass
else:
row_index = self.combo_df.loc[self.combo_df['Symbol'] == xsymbol].index[0]
self.combo_df.at[row_index, 'M_B'] = i[0]
logging.info( f"{cmi_debug} - Market cap: [ {wq.qd_quote['mkt_cap']} ] scale set to: {i[0]}" )
self.wrangle_errors += 1 # insert market cap scale into DF @ column M_B for this symbol
self.cleansed_errors += 1
print ( f"+", end="" )
self.fixchars += 1
break
# nice column/rows status bar to show the hard work we are grinding on...
finally:
if self.fixchars == 1: print ( f" ", end="" )
if self.fixchars == 2: print ( f" ", end="" )
if self.fixchars == 3: print ( f" ", end="" )
self.fixchars = 0
self.cols += 1
if self.cols == 8: # 8 symbols per row
print ( f" " ) # onlhy print 8 symbols per row
self.cols = 1
else:
print ( f"/ ", end="" )
logging.info( f"{cmi_debug} ================ end quote: {qsymbol} : {self.loop_count} ====================" )
self.total_wrangle_errors = self.total_wrangle_errors + self.wrangle_errors
self.wrangle_errors = 0
self.loop_count += 1
print ( " " )
print ( " " )
print ( f"Symbols scanned: {self.loop_count-1} / Issues: {self.cleansed_errors} / Repaired: {self.total_wrangle_errors} / Unfixbale: {self.unfixable_errors}" )
return
#############################################################################
# method #2
def tag_dupes(self):
"""
Find & Tag the *duplicate* entries in the combo_df matrix dataset, which is important b/c dupes
here means these stocks are HOT and appearing across multiple dataframes.
"""
cmi_debug = __name__+"::"+self.tag_dupes.__name__+".#"+str(self.inst_uid)
logging.info('%s - IN' % cmi_debug )
self.combo_df = self.combo_df.assign(Hot="", Insights="" ) # pre-insert 2 new columns
self.mpt = () # Helper: Internal DICT(tuple) element to find cheapest ***HOT stock
for ds in self.combo_df[self.combo_df.duplicated(['Symbol'])].Symbol.values: # ONLY work on dupes in DF !!!
for row_idx in iter( self.combo_df.loc[self.combo_df['Symbol'] == ds ].index ):
sym = self.combo_df.loc[row_idx].Symbol
cap = self.combo_df.loc[row_idx].Mkt_cap
scale = self.combo_df.loc[row_idx].M_B
price = self.combo_df.loc[row_idx].Cur_price
if pd.isna(self.combo_df.loc[row_idx].Mkt_cap) == False and pd.isna(self.combo_df.loc[row_idx].M_B) == False:
self.combo_df.loc[row_idx,'Hot'] = "*Hot*" # Tag as a **HOT** stock
self.combo_df.loc[row_idx,'Insights'] = self.cx.get(scale) + " + ^Un vol" # Annotate why...
self.mpt = ( row_idx, sym.rstrip(), round(float(price), 2) ) # pack a tuple - for min_price analysis later
self.min_price.update({row_idx: self.mpt}) # load helpder DICT e.g. {1: (7, 'IBM', 120.51), 7: (24, 'TSLA', 138.21)}
elif pd.isna(self.combo_df.loc[row_idx].Mkt_cap) == True and pd.isna(self.combo_df.loc[row_idx].M_B) == True:
self.combo_df.drop([row_idx], inplace=True) # drop this row from DF
else:
print ( f"WARNING: Don't know what to do for: {sym} - Mkt_cap: {cap} / M_B: {scale}" )
break
return
#####################################################################################
# method #2.1
# find and tag the lowest priced stock within the list of Hottest stocks
# must call tag_dupes() first as that tags hottest with *Hot* / this method relies on that
def find_hottest(self):
print ( f"\n========== Hot stock analysis ====================================================" )
if self.min_price: # not empty, We have some **HOT stocks to evaluate
print ( f"\nLocating...", end="" )
mptv = min(( td[2] for td in self.min_price.values() )) # td[2] = iterator of 3rd elment of min_price{}
for v in self.min_price.values(): # v = tuple structured like: (0, IBM, 28.42)
if v[2] == mptv: # v[2] = 3rd element = price symbol
row_idx = int(v[0]) # v[0] = 1st emelent = DF index of symbol
self.rx = [row_idx, v[1].rstrip()] # add hottest stock with lowest price / 1 entry in list[]
self.combo_df.loc[row_idx,'Hot'] = "*Hot*" # Tag as a **HOT** stock in DataFrame
found_sym = self.combo_df.loc[row_idx, 'Symbol']
print ( f" [ {found_sym.rstrip()} ] @ #{row_idx}" )
print ( f"========== complete ==============================================================" )
break
else:
print ( f".", end="" )
# TODO: ** This logic can fail @ Market open when many things are empty & unpopulated...
if not bool(self.min_price): # is empty?
print ( f"NO **HOT tagged stocks located yet" )
return
#####################################################################################
# method #3
def tag_uniques(self):
"""
Find & Tag unique untagged entries in the combo_df dataset.
ONLY do this after the tag_dupes, because its cleaner to eliminate & tage dupes first
When you get to this phase, combo_df should now contain UNQIUES only
"""
cmi_debug = __name__+"::"+self.tag_uniques.__name__+".#"+str(self.inst_uid)
logging.info('%s - IN' % cmi_debug )
for row_idx in self.combo_df.loc[self.combo_df['Insights'] == "" ].index:
logging.info('%s - Cycle over list of symbols' % cmi_debug )
sym = self.combo_df.loc[row_idx].Symbol
cap = self.combo_df.loc[row_idx].Mkt_cap
scale = self.combo_df.loc[row_idx].M_B
if pd.isna(self.combo_df.loc[row_idx].Mkt_cap) == False and pd.isna(self.combo_df.loc[row_idx].M_B) == False:
self.combo_df.loc[row_idx,'Insights'] = self.cx.get(scale)
elif pd.isna(self.combo_df.loc[row_idx].Mkt_cap) == True and pd.isna(self.combo_df.loc[row_idx].M_B) == True:
logging.info('%s - Apply NaN/NaN inferrence logic' % cmi_debug )
self.combo_df.loc[row_idx,'Insights'] = "^ Un vol only"
else:
logging.info('%s - Unknown logic discovered' % cmi_debug )
self.combo_df.loc[row_idx,'Insights'] = "!No logic!"
logging.info('%s - Exit tag uniques cycle' % cmi_debug )
return
#################################################################################
# method 4
# a safety catch-all to scan for any NaaN's lying arround that wern't caught
def tag_naans(self):
"""
Hunt down and loose NaaN entries left lying arround
"""
cmi_debug = __name__+"::"+self.tag_naans.__name__+".#"+str(self.inst_uid)
logging.info('%s - IN' % cmi_debug )
print ( f"{self.combo_df[self.combo_df.isna().any(axis=1)]}" )
return
"""
for row_idx in self.combo_df.loc[self.combo_df['Insights'] == "" ].index:
logging.info('%s - Cycle over list of symbols' % cmi_debug )
sym = self.combo_df.loc[row_idx].Symbol
cap = self.combo_df.loc[row_idx].Mkt_cap
scale = self.combo_df.loc[row_idx].M_B
if pd.isna(self.combo_df.loc[row_idx].Mkt_cap) == False and pd.isna(self.combo_df.loc[row_idx].M_B) == False:
self.combo_df.loc[row_idx,'Insights'] = self.cx.get(scale)
#
elif pd.isna(self.combo_df.loc[row_idx].Mkt_cap) == True and pd.isna(self.combo_df.loc[row_idx].M_B) == True:
self.combo_df.loc[row_idx].Mkt_cap = 0
self.combo_df.loc[row_idx].M_B = 'UZ'
logging.info('%s - Apply NaN/NaN inferrence logic' % cmi_debug )
self.combo_df.loc[row_idx,'Insights'] = "^ Unusual vol only"
else:
logging.info('%s - Unknown logic discovered' % cmi_debug )
self.combo_df.loc[row_idx,'Insights'] = "!No logic!"
logging.info('%s - Exit tag uniques cycle' % cmi_debug )
"""
############################################################################
# method 5 Hottest
def rank_hot(self):
"""
isolate all *Hot* tagged stocks and rank them by price, lowest=1 to highest=n
Since these stocks are the most active all-round, tag_rank them with 1xx (e.g., 100, 101, 102, 103)
"""
cmi_debug = __name__+"::"+self.rank_hot.__name__+".#"+str(self.inst_uid)
logging.info('%s - IN' % cmi_debug )
self.combo_df = self.combo_df.assign(rank="" ) # pre-insert the new Tag_Rank column
# get a list of rows that meet the critera and find each row's index ID's.
# Pack the list of index ID's into a list [] & pass it as an indexer inside a Loop
# use rank column to hold a tag/ranking of cheapest *Hot* stock to most expensive *Hot* stock
#rh_df0 = self.combo_df.sort_values(by=['Cur_price'], ascending=True)
#rh_list = rh_df0.loc[self.combo_df['Hot'] == "*Hot*"]
z = list(self.combo_df.sort_values(by=['Cur_price'], ascending=True).loc[self.combo_df['Hot'] == "*Hot*"].index)
y = 100 # HOT stocks ranking starts at 100
for i in z: # cycle thru the sorted DF
self.combo_df.loc[i, 'rank'] = y # rank each Hot entry
# DEBUG: print ( "i: ", i, "x:", y )
y += 1
return self.combo_df
###################################################################################
# method 6
def rank_unvol(self):
"""
Isolate all Unusual Vol stocks only.
tag_rank them with code: 3xx (e.g. 300, 301, 302)
"""
z = list(self.combo_df.sort_values(by=['Cur_price'], ascending=True).loc[self.combo_df['Insights'] == "^ Un vol only"].index)
y = 300 # Unusual Vol stocks ranking starts at 300
for i in z: # cycle thru the sorted DF
self.combo_df.loc[i, 'rank'] = y # rank each Unique entry
# DEBUG: print ( "i: ", i, "x:", y )
y += 1
return self.combo_df
###################################################################################
# method 7
def rank_caps(self):
"""
isolate any stock NOT tagged (i.e not Hot or Unusual Vol tagged).
tag_rank them with code: 2xx (e.g. 200, 201, 202, 203)
WARNING:
This is a cheap way to find/select criteria.
MUST call this ranking method last for this to work correctly.
"""
z = list(self.combo_df.sort_values(by=['Cur_price'], ascending=True).loc[self.combo_df['rank'] == "" ].index)
y = 200 # Non tagged average unknown stocks ranking starts at 200
for i in z: # cycle thru the sorted DF
self.combo_df.loc[i, 'rank'] = y # rank each entry
# DEBUG : print ( "i: ", i, "x:", y )
y += 1
return self.combo_df
###################################################################################
# method 8
def combo_listall(self):
"""
Print the full contents of the combo DataFrame. All comumns. Not sorted.
WARNING:
DF contains DUPLICATE rows b/c **Hot** stocks appear multiple times.
"""
cmi_debug = __name__+"::"+self.combo_listall.__name__+".#"+str(self.inst_uid)
logging.info('%s - IN' % cmi_debug )
pd.set_option('display.max_rows', None)
pd.set_option('max_colwidth', 40)
return self.combo_df
###################################################################################
# method 9
def combo_listall_ranked(self):
"""
Print the full contents of the combo DataFrame. Sorted by % Change
WARNING:
DF contains DUPLICATE rows b/c **Hot** stocks appear multiple times.
"""
cmi_debug = __name__+"::"+self.combo_listall_ranked.__name__+".#"+str(self.inst_uid)
logging.info('%s - IN' % cmi_debug )
pd.set_option('display.max_rows', None)
pd.set_option('max_colwidth', 40)
# DEBUG: print ( self.combo_df.sort_values(by=['Pct_change'], ascending=False) )
return self.combo_df.sort_values(by=['Pct_change'], ascending=False)
###################################################################################
# method 10
def combo_listall_nodupes(self):
"""
Print the entire combo DataFrame.
DUPES REMOVED. Sorted by % Change
"""
cmi_debug = __name__+"::"+self.combo_listall_nodupes.__name__+".#"+str(self.inst_uid)
logging.info('%s - IN' % cmi_debug )
pd.set_option('display.max_rows', None)
pd.set_option('max_colwidth', 40)
c = self.combo_df.drop_duplicates(subset=['Symbol'], keep='first') # only look at dupes in symbol colum
# return c.sort_values(by=['Pct_change'], ascending=False)
return c # raw df list. DO NOT sort by anything
###################################################################################
# method 11
def list_uniques(self):
"""
Print the full contents of the combo DataFrame with DUPES removed
NOT sorted
note: method can be deleted. It is replcaed by unique_symbols
"""
cmi_debug = __name__+"::"+self.list_uniques.__name__+".#"+str(self.inst_uid)
logging.info('%s - IN' % cmi_debug )
pd.set_option('display.max_rows', None)
pd.set_option('max_colwidth', 40)
return self.combo_df.drop_duplicates(subset=['Symbol'], keep='first') # only look at dupes in symbol colum
###################################################################################
# method 12
def unique_symbols(self):
"""
Build a DF of UNIQUE symbols from the combo DataFrame with DUPES remove
(keep the FIRST instance of each dupe discovered. Sort by Symbol
"""
cmi_debug = __name__+"::"+self.unique_symbols.__name__+".#"+str(self.inst_uid)
logging.info('%s - IN' % cmi_debug )
pd.set_option('display.max_rows', None)
pd.set_option('max_colwidth', 40)
unique_s = self.combo_df.drop_duplicates(subset=['Symbol'], keep='first') # only look at dupes in symbol colum
return unique_s.sort_values(by=['Symbol'])
###################################################################################
# method 13
def combo_grouped(self, opt):
"""
Print a set of insights like Agerages and Mean etc
Sorted by % Change & grouped by Insights
"""
cmi_debug = __name__+"::"+self.combo_grouped.__name__+".#"+str(self.inst_uid)
self.gopt = opt
logging.info('%s - IN' % cmi_debug )
pd.set_option('display.max_rows', None)
pd.set_option('max_colwidth', 40)
if self.gopt == 1: # pct grouping
g_df = pd.DataFrame(self.combo_df.sort_values(by=['rank'], ascending=True).groupby(['Insights'])['Pct_change'].mean() )
if self.gopt == 2: # prc grouping
g_df = pd.DataFrame(self.combo_df.sort_values(by=['rank'], ascending=True).groupby(['Insights'])['Prc_change'].mean() )
#g_df = pd.DataFrame(self.combo_df.sort_values(by=['rank'], ascending=True).groupby(['Insights']) )
g_df.loc['Average_overall'] = g_df.mean()
return g_df
###################################################################################
# method 14
def combo_dupes_only_listall(self, opt):
"""
returns ad DF
Print the full contents of the combo DataFrame with the DUPES tagged & sorted by % Change.
Will only list the dupes unless you have called tag_dupes() first, and then youll get the full DF
"""
cmi_debug = __name__+"::"+self.combo_dupes_only_listall.__name__+".#"+str(self.inst_uid)
self.opt = opt
logging.info('%s - IN' % cmi_debug )
pd.set_option('display.max_rows', None)
pd.set_option('max_colwidth', 40)
if self.opt == 1:
# dupe symbols show up b/c they are very active & all over the data. They are tagged as HOT stocks
temp_1 = self.combo_df.sort_values(by=['Pct_change'], ascending=False)
return (temp_1[temp_1.duplicated(['Symbol'])] )
if self.opt == 2:
return ( self.combo_dupes[self.combo_dupes[0] == True] )
return
###################################################################################
# method 15
def reindex_combo_df(self):
"""
Make combo_df index numbering linear; starting from 0, 1, 2, 3, 4...
WANRING:
This will write the new index INTO the exiting combo DF. Its a permenant change.
Only do this if you are very sure you must do this NOW...!
"""
cmi_debug = __name__+"::"+self.reindex_combo_df.__name__+".#"+str(self.inst_uid)
logging.info('%s - IN' % cmi_debug )
self.combo_df.reset_index(inplace=True, drop=True) # reset index each time so its guaranteed sequential
return