kevin3

library('quantmod')
 
#loadSymbols of the stock codes you are interested in 
loadSymbols(c('K17.SI','BN4.SI'))
 
#retrieves the values for a single date
BN4.SI['2013-01-25']
#K17.SI['2013-03-04']
 
#Convert the matrix retrieved as a data.frame for easier data usage

kevin3

#Sample Contract Value
vol=1000
price=10
volprice=vol*price

def commSCB(volprice):
  "SCB commission is fixed 0.2%"
  brokerage=volprice*0.2/100
  clearingfee=0.04/100*volprice
  #no SGX trading fee

kevin3

#!/usr/bin/python
"""simple python script to join every 4 lines into one from a plain text file"""
data = open("SCBtransactions2013-03-19.orig.csv").readlines()
data = [ i.strip() for i in data ] #get rid of newlines
data = [ i.replace("\t \t","\t") for i in data ] #get rid of double tabs
data = [ i.replace("SGD","") for i in data ] #get rid of SGD
fourlines = range(0,len(data),4) 
for num,line in enumerate(data):
 	if num in fourlines:
 		print ' '.join(data[num:num+4])

kevin3

Create Table "main"."transactions"
CREATE  TABLE  IF NOT EXISTS "main"."transactions" ("tid" INTEGER PRIMARY KEY  AUTOINCREMENT  NOT NULL ,
"stkcode" TEXT NOT NULL ,
"stkname" TEXT, "qty" INTEGER NOT NULL ,
"broker" TEXT NOT NULL ,
"price" REAL NOT NULL ,
"targetprice" REAL,
"stoploss" REAL,
"longshort" TEXT NOT NULL ,
"date" TEXT NOT NULL ,

kevin3

import plotly
from plotly.tools import FigureFactory as FF

import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/gantt_example.csv')

fig = FF.create_gantt(df, colors=['#333F44', '#93e4c1'], index_col='Complete', show_colorbar=True,
                      bar_width=0.2, showgrid_x=True, showgrid_y=True)
plotly.offline.plot(fig, filename='gantt-use-a-pandas-dataframe')

kevin3

rsync -ahP --times --owner --group /mnt/external/exportedReports/Auto_user_S5-00333-1 /media/USB

kevin3

The following tricks I find pretty useful in my daily Python work. I also added a few I stumbled upon lately.

1. Use collections

This really makes your code more elegant and less verbose, a few examples I absorbed this week:

Named tuples:

>>> Point = collections.namedtuple('Point', ['x', 'y'])
>>> p = Point(x=1.0, y=2.0)

kevin3

#!/bin/bash
samtools view -b -f 4 $1 > $1.unmapped.bam
samtools index $1.unmapped.bam
java -Xmx8g -jar /opt/picard/picard-tools-current/picard.jar SamToFastq I=$1.unmapped.bam F=$1.unmapped.bam.fastq
/home/ionadmin/bin/bbmap/reformat.sh in=$1.unmapped.bam.fastq out=$1.unmapped.bam.fastq.fasta
gzip $1.unmapped.bam.fastq.fasta

kevin3

~/bin/kraken2/kraken2 --db  ~/k2_standard_16gb_20210517 --use-names --gzip-compressed $1 --output $1.kraken --report $1.kraken.report
cut -f2,3 $1.kraken > $1.kraken.kronainput
ktImportTaxonomy -tax /home/kev/Krona-master/KronaTools/taxonomy $1.kraken.kronainput -o $1.kraken.kronainput.html

kevin3

def read_date(date):
    if isinstance(date, int):
        return pd.to_datetime(date,unit='D', origin='1899-12-30')
        #return xlrd.xldate.xldate_as_datetime(date, 0)
    else:
        return(pd.to_datetime(date))


print("Converting 42985  to")
print(read_date(42985 ))