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backtestsma.py
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backtestsma.py
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#v1.0
#v0.9 - All research graph via menu & mouse click
#v0.8 - Candlestick graphs
#v0.7 - Base version with all graphs and bug fixes. Test
#v0.6
#v0.5
#v0.4
""" Class - cBackTestSMA
will accept
Alpha vantage key,
Script Name
start date,
end date,
timeperiod1 = (example 20 days,
timeperiod2 = (example 40 days)
Note timeperiod1 < timeperiod2,
function to buildBackTest():
It will create Alpha Vantage object
will get the daily adjusted time series
"""
from tkinter import *
from tkinter import ttk
from tkinter import messagebox as msgbx
from alpha_vantage.timeseries import TimeSeries
from alpha_vantage.techindicators import TechIndicators
import pandas as pd
from pandas import DataFrame
import datetime
from datetime import date
#from matplotlib.finance import candlestick2_ohlc
import matplotlib.pyplot as plt
from matplotlib.figure import Figure
import matplotlib.dates as mdates
from matplotlib.pyplot import Figure
from matplotlib import interactive
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2Tk
from scripttree import ScriptTreeView
from testdata import *
class BackTestSMA(Toplevel):
def __init__(self, master=None, argkey=None, argscript=None, argscripttree=None,
argavgsmall=None, argavglarge=None, arglookbackyears=1, argIsTest=False, argDataFolder='./scriptdata'):
Toplevel.__init__(self, master=master)
self.key = argkey
self.script = argscript
self.graphctr=1
self.datafolderpath = argDataFolder
#self.wm_state(newstate='zoomed') #maximize window, this works only for Win OS
self.wm_state(newstate='normal') #maximize window, this works only for Win OS
self.wm_title("Performance graphs: " + self.script)
self.wm_protocol("WM_DELETE_WINDOW", self.OnClose)
#numbers for SMA
self.avgsmall = argavgsmall
self.avglarge = argavglarge
self.lookbackyears=arglookbackyears
self.bool_test = argIsTest
if(self.bool_test == False):
self.ts = TimeSeries(self.key, output_format='pandas')
self.ti = TechIndicators(self.key, output_format='pandas')
self.treeofscripts = argscripttree
self.dfholdingvalues = DataFrame()
self.dfScript = DataFrame()
#self.dfSMAShort = DataFrame()
#self.dfSMALong = DataFrame()
#self.f = Figure(figsize=(12.8,9.5), dpi=100, facecolor='w', edgecolor='k', tight_layout=True, linewidth=0.9)
self.f = Figure(figsize=(12.8,4.5), dpi=100, facecolor='w', edgecolor='k', tight_layout=True, linewidth=0.9)
self.output_canvas=FigureCanvasTkAgg(self.f, master=self)
self.toolbar_frame=Frame(master=self)
self.toolbar = NavigationToolbar2Tk(self.output_canvas, self.toolbar_frame)
self.output_canvas.get_tk_widget().grid(row=0, column=0, columnspan=17, sticky=(N, E, W, S))
self.toolbar_frame.grid(row=1, column=0, columnspan=17, rowspan=1, sticky=(N, E, W, S))
self.toolbar.grid(row=0, column=2, sticky=(N, W))
self.grid_columnconfigure(0, weight=1)
self.grid_rowconfigure(0, weight=1)
def show(self):
self.wm_deiconify()
self.wait_window()
def OnClose(self):
self.destroy()
""" method - getScriptDataFromTree
builds a DataFrame for all the portfolio holding values extracted from Tree
HOLDING Values are stored in following order:
[0] = value of 'Purchase Price'
[1] = value of 'Purchase Date'
[2] = value of 'Purchase Qty'
[3] = value of 'Commission Paid'
[4] = value of 'Cost of Investment' of buying Purchase QTY of scripts on Purchase Date
[5] = value of 'Current Value' of the QTY at today's closing price
[6] = value of 'Status' this is either '↑' or '↓' or '↔'
what is needed is the self transaction information stored in Tree under HOLDINFVAL
Assumming there are multiple buy transactions for the current script, we need to plot
valuation in steps.
For example:
on 2020-02-24 there was a buy transacation for 2 shares at cost of 1000 + X%commission
we need to plot the graph with daily price starting from 2020-02-24
There was one more buy transaction for the same script for 2 shares on 2020-03-10.
Then from this date we need to show cumulative performance graph of all shares.
Whar we will do is we will build a DataFrame with all such rows for the specific script
then merge this DataFrame with the DataFrame from Alpha
"""
def getScriptDataFromTree(self):
#get HOLDINGVAL of current script from tree
allchildrows = self.treeofscripts.get_children(self.script)
row_val = list()
scriptQty = 0
self.dfholdingvalues = DataFrame()
for child in allchildrows:
if(str(child).upper().find(self.treeofscripts.HOLDINGVAL) >= 0):
row_val=self.treeofscripts.item(child, 'values')
#scriptQty += int(row_val[2])
d = {'PurchaseDate': [row_val[1]], 'PurchasePrice':[row_val[0]],
'PurchaseQTY':[row_val[2]], 'Commission':[row_val[3]],
'CostofInvestment':[row_val[4]], 'CurrentValue':[row_val[5]],
'Status':[row_val[6]], 'CumulativeQTY':['0.00']}
#tempDF = DataFrame.from_dict(data=d, orient='index')
tempDF = DataFrame(d)
#tempDF = tempDF.transpose()
self.dfholdingvalues=self.dfholdingvalues.append(tempDF, ignore_index=True)
#self.dfholdingvalues.set_index('PurchaseDate')
convert_type={'PurchaseQTY':float}
self.dfholdingvalues = self.dfholdingvalues.astype(convert_type)
convert_type={'CumulativeQTY':float}
self.dfholdingvalues = self.dfholdingvalues.astype(convert_type)
convert_type={'Commission':float}
self.dfholdingvalues = self.dfholdingvalues.astype(convert_type)
convert_type={'PurchasePrice':float}
self.dfholdingvalues = self.dfholdingvalues.astype(convert_type)
self.dfholdingvalues.sort_values('PurchaseDate', axis=0, inplace=True, ignore_index=True)
sumoflastrows=0
imax = self.dfholdingvalues.shape[0]
for i in range(imax):
#self.dfholdingvalues['PurchaseQTY'][i]=(self.dfholdingvalues['PurchaseQTY'][i])+sumoflastrows
#sumoflastrows=self.dfholdingvalues['PurchaseQTY'][i]
self.dfholdingvalues.loc[i, 'CumulativeQTY'] = self.dfholdingvalues.loc[i, 'PurchaseQTY']+sumoflastrows
sumoflastrows=self.dfholdingvalues.loc[i, 'CumulativeQTY']
""" setCurrentValInMarketDF
In this method we will need to add three columns to the Alpha returned DF
1. Purchase QTY, which is cumulative of QTY as of date
2. Current Value
3. Commission Paid
now we need to add values in these columns in the Alpha DF
i = 0 start of self.dfholdingvalues
imax = self.dfholdingvalues.shape()[0] //returns tuple (no of rows, no of cols)
for each row in Alpha DF
first check if i < imax
if( alpha.date >= self.dfholdingvalues[i].purchasedate AND
alpha.date < self.dfholdingvalues[i+1].purchasedate)
calculate the Alpha.CurretnValue = self.dfholdingvalues[i].QTY * Alpha.ClosePrice
set Alpha.QTY = self.dfholdingvalues[i].QTY
set Alpha.Commision = self.dfholdingvalues[i].commission
else if alpha.date < self.dfholdingvalues[i].purchasedate
do nothing (we must have all rows sortd by Date in Alpha)
else if alpha.date < self.dfholdingvalues[i+1].purchasedate
i += 1
calculate the Alpha.CurretnValue = self.dfholdingvalues[i].QTY * Alpha.ClosePrice
set Alpha.QTY = self.dfholdingvalues[i].QTY
set Alpha.Commision = self.dfholdingvalues[i].commission
else if i == imax
calculate the Alpha.CurretnValue = self.dfholdingvalues[i].QTY * Alpha.ClosePrice
set Alpha.QTY = self.dfholdingvalues[i].QTY
set Alpha.Commision = self.dfholdingvalues[i].commission """
def setCurrentValInMarketDF(self):
#find the shape of self.dfholdingvalues. shape returns tuple (no of rows, no of cols)
imax = self.dfholdingvalues.shape[0]
i = 0
# we will only use data from the date of first purchase
#self.dfScript = self.dfScript.loc[(self.dfScript.index[:] >= self.dfholdingvalues['PurchaseDate'][0])]
#self.dfScript = self.dfScript.loc[(self.dfScript.index[:] >= self.dfholdingvalues['PurchaseDate'][imax-1])]
#for i in range(imax-1, imax):
for i in range(imax):
if(i < imax-1): #we have still not last row
self.dfScript.loc[((self.dfScript.index[:] >= self.dfholdingvalues['PurchaseDate'][i]) &
(self.dfScript.index[:] < self.dfholdingvalues['PurchaseDate'][i+1])), 'PurchaseDate']=self.dfholdingvalues['PurchaseDate'][i]
self.dfScript.loc[((self.dfScript.index[:] >= self.dfholdingvalues['PurchaseDate'][i]) &
(self.dfScript.index[:] < self.dfholdingvalues['PurchaseDate'][i+1])), 'CumulativeQTY']=self.dfholdingvalues['CumulativeQTY'][i]
self.dfScript.loc[((self.dfScript.index[:] >= self.dfholdingvalues['PurchaseDate'][i]) &
(self.dfScript.index[:] < self.dfholdingvalues['PurchaseDate'][i+1])), 'PurchaseQTY']=self.dfholdingvalues['PurchaseQTY'][i]
self.dfScript.loc[((self.dfScript.index[:] >= self.dfholdingvalues['PurchaseDate'][i]) &
(self.dfScript.index[:] < self.dfholdingvalues['PurchaseDate'][i+1])), 'Commission']=self.dfholdingvalues['Commission'][i]
#self.dfScript.loc[((self.dfScript.index[:] >= self.dfholdingvalues['PurchaseDate'][i]) &
# (self.dfScript.index[:] < self.dfholdingvalues['PurchaseDate'][i])), 'CurrentVal'] = self.dfScript.loc[((self.dfScript.index[:] >= self.dfholdingvalues['PurchaseDate'][i]) & (self.dfScript.index[:] < self.dfholdingvalues['PurchaseDate'][i+1])), 'PurchaseQTY'] * self.dfScript.loc[((self.dfScript.index[:] >= self.dfholdingvalues['PurchaseDate'][i]) & (self.dfScript.index[:] < self.dfholdingvalues['PurchaseDate'][i+1])), 'Close']
else:
self.dfScript.loc[(self.dfScript.index[:] >= self.dfholdingvalues['PurchaseDate'][i]), 'PurchaseDate']=self.dfholdingvalues['PurchaseDate'][i]
self.dfScript.loc[(self.dfScript.index[:] >= self.dfholdingvalues['PurchaseDate'][i]), 'CumulativeQTY']=self.dfholdingvalues['CumulativeQTY'][i]
self.dfScript.loc[(self.dfScript.index[:] >= self.dfholdingvalues['PurchaseDate'][i]), 'PurchaseQTY']=self.dfholdingvalues['PurchaseQTY'][i]
self.dfScript.loc[(self.dfScript.index[:] >= self.dfholdingvalues['PurchaseDate'][i]), 'Commission']=self.dfholdingvalues['Commission'][i]
#self.dfScript.loc[(self.dfScript.index[:] >= self.dfholdingvalues['PurchaseDate'][i]), 'CurrentVal'] = self.dfScript.loc[(self.dfScript.index[:] >= self.dfholdingvalues['PurchaseDate'][i]), 'PurchaseQTY'] * self.dfScript.loc[(self.dfScript.index[:] >= self.dfholdingvalues['PurchaseDate'][i]), 'Close']
self.dfScript.loc[(self.dfScript.index[:] == self.dfholdingvalues['PurchaseDate'][i]), 'Status']=self.dfholdingvalues['Status'][i]
self.dfScript.loc[(self.dfScript.index[:] == self.dfholdingvalues['PurchaseDate'][i]), 'PurchasePrice']=self.dfholdingvalues['PurchasePrice'][i]
self.dfScript['CurrentVal'] = self.dfScript['CumulativeQTY'] * self.dfScript['Close']
""" changeColNameTypeofDailyTS
d
"""
def changeColNameTypeofDailyTS(self):
#rename columns
self.dfScript=self.dfScript.rename(columns={'1. open':'Open', '2. high':'High', '3. low':'Low', '4. close':'Close', '5. volume':'Volume'})
#Add new columns
#self.dfSMAShort=self.dfSMAShort.rename(columns={'SMA':'Short_Mean'})
#self.dfSMALong=self.dfSMALong.rename(columns={'SMA':'Long_Mean'})
#self.dfScript = pd.concat([self.dfScript, self.dfSMAShort, self.dfSMALong], axis=1)
#self.dfScript.sort_index(axis=0, ascending=False, inplace=True)
self.dfScript['PurchaseDate'] = ""
self.dfScript['PurchasePrice'] = 0.00
self.dfScript['PurchaseQTY'] = 0.00
self.dfScript['CumulativeQTY'] = 0.00
self.dfScript['CurrentVal'] = 0.00
self.dfScript['Commission'] = 0.00
self.dfScript['Status'] = ""
self.dfScript['Short_Mean']=0.00
self.dfScript['Long_Mean']=0.00
self.dfScript['Order'] = 0
self.dfScript['Returns'] = 0.00
self.dfScript['CumReturns'] = 0.00
convert_type={'Close':float}
self.dfScript = self.dfScript.astype(convert_type)
convert_type={'PurchasePrice':float}
self.dfScript = self.dfScript.astype(convert_type)
convert_type={'PurchaseQTY':float}
self.dfScript = self.dfScript.astype(convert_type)
convert_type={'CumulativeQTY':float}
self.dfScript = self.dfScript.astype(convert_type)
convert_type={'Commission':float}
self.dfScript = self.dfScript.astype(convert_type)
convert_type={'CurrentVal':float}
self.dfScript = self.dfScript.astype(convert_type)
convert_type={'Short_Mean':float}
self.dfScript = self.dfScript.astype(convert_type)
convert_type={'Long_Mean':float}
self.dfScript = self.dfScript.astype(convert_type)
convert_type={'Returns':float}
self.dfScript = self.dfScript.astype(convert_type)
convert_type={'CumReturns':float}
self.dfScript = self.dfScript.astype(convert_type)
def addPerformance(self):
#find the moving average as per the num of days specified by user onadjusted close
# OLD self.dfScript['Short_Mean']=self.dfScript.rolling(self.avgsmall).mean()['Close']
self.dfScript.loc[self.dfScript.index[:], 'Short_Mean']=self.dfScript.rolling(self.avgsmall).mean()['Close']
# OLD self.dfScript['Long_Mean']=self.dfScript.rolling(self.avglarge).mean()['Close']
self.dfScript.loc[self.dfScript.index[:], 'Long_Mean']=self.dfScript.rolling(self.avglarge).mean()['Close']
#If the short moving average is now above the long moving average,
# then we are on a short-term upwards trend. At least that is the theory.
# That means, that we will buy. If the averages cross the other way around, we sell.
# Otherwise we do nothing.
#This means that we make a new column which consists of a one if the short moving
# average is above the long moving average, otherwise it contains a zero.
# The shift in the last line is, because we compute on the closing prices.
# That means that we will then buy/sell on the next day, so we shift the buying
# signal one day.
self.dfScript.loc[self.dfScript['Short_Mean'] > self.dfScript['Long_Mean'], 'Order']=1
#self.dfScript['Order']=self.dfScript['Order'].shift(1)
self.dfScript.loc[self.dfScript.index[:], 'Order']=self.dfScript['Order'].shift(1)
#now we calculare relative returns for each day
#rt=(pt−(pt−1))/(pt−1)=(pt/(pt−1))−1
self.dfScript['Returns']=(self.dfScript['Close']/((self.dfScript['Close']).shift(1))) - 1
#self.dfScript.loc[self.dfScript.index[:], 'Returns']=(self.dfScript['Close']/((self.dfScript['Close']).shift(1))) - 1
#We have only returns when we trade. That means that we multiply the returns with
# the buying signal.
self.dfScript['Returns']=self.dfScript['Returns']*self.dfScript['Order']
#self.dfScript.loc[self.dfScript.index[:], 'Returns']=self.dfScript['Returns']*self.dfScript['Order']
#Since we reinvest all returns, we need to take a cumulative product
# over the last column.
# it=((it−1)+(it−1)*rt)=(1+rt)*(it−1),i0=1
self.dfScript['CumReturns']=(1+self.dfScript.Returns).cumprod()
def setAxesCommonConfig(self, argAxes, argTitle, argYlabel):
argAxes.set_ylabel(argYlabel, fontsize = 'xx-small', color='black')
argAxes.tick_params(direction='out', length=6, width=2, colors='black',
grid_color='black', grid_alpha=0.5, labelsize='xx-small')
argAxes.tick_params(axis='x', labelrotation=30)
argAxes.grid(True)
argAxes.set_title(argTitle, size='small')
argAxes.legend(fontsize='xx-small')
# argLookbackYears - is the no of years we want to go back from today
# if today is 2020-03-23 & argLookbackYears = 1, return will be 2019-03-23
# the expetion takes care of leap year
def getPastDateFromDate(self, argFromDate=date.today()):
try:
dt = argFromDate
dt = dt.replace(year=dt.year-self.lookbackyears)
except ValueError:
dt = dt.replace(year=dt.year-self.lookbackyears, day=dt.day-1)
return str(dt)
"""getDateAfter(self, argFromDate=str(date.today()), argNoOfDays=1)
argFromDate = date in string in YYYY-MM-DD format
argNoOfDays = Integer that indicates number of days ahead or back
Returns - new string in STR in yyyy-mm-dd format """
def getDateAfter(self, argFromDate=str(date.today()), argNoOfDays=1):
try:
ssincedate = datetime.datetime.strptime(argFromDate, "%Y-%m-%d")
dt = date(ssincedate.year, ssincedate.month, ssincedate.day)
dt += datetime.timedelta(days=argNoOfDays)
except Exception as e:
dt = date.today()
return str(dt)
def plotPortfolioPerformanceAX(self, argrows, argcols, argindex):
ax1 = self.f.add_subplot(argrows, argcols, argindex, label='Portfolio performance')
ax1.plot(self.dfScript.loc[self.dfScript.index[:] >=
self.dfholdingvalues['PurchaseDate'][self.dfholdingvalues.shape[0]-1], 'CurrentVal'],
label='Portfolio price')
buys= self.dfScript.loc[self.dfScript.index[:] >=
self.dfholdingvalues['PurchaseDate'][self.dfholdingvalues.shape[0]-1]]
buys = buys[buys['Status'] != '']
ax1.plot(buys.index, self.dfScript['CurrentVal'].loc[buys.index],
marker="*", markersize=10, color='b', label='Total QTY', linestyle='None')
for i in range(len(buys.index)):
ax1.annotate('Total Qty='+ str(buys['CumulativeQTY'][i]) + " "+buys['Status'][i],
(mdates.datestr2num(buys['PurchaseDate'][i]), buys['CurrentVal'][i]),
xycoords='data',
#xytext=(mdates.datestr2num(self.getDateAfter(buys['PurchaseDate'][i])), buys['CurrentVal'][i]+2), textcoords='data',
xytext=(1, 1), textcoords='offset points',
#arrowprops=dict(arrowstyle='-|>'),
horizontalalignment="left", bbox=dict(boxstyle="round", facecolor="w", edgecolor="0.5", alpha=0.9),
fontsize='xx-small')
self.setAxesCommonConfig(ax1, 'Portfolio performance - ' + self.script, "Portfolio Value")
def showCandelAnnotation(self, argAxis, argTextToShow, argX, argY, argXYcoords,
argXText, argYText, argTextcoords, argHA, argVA, argFontsize):
argAxis.annotate(argTextToShow,
xy=(argX, argY),
xycoords=argXYcoords,
#xytext=(argXText, argYText), textcoords=argTextcoords,
xytext=(1, 1), textcoords='offset points',
ha=argHA, va=argVA, fontsize=argFontsize, color='red', annotation_clip=True)
def plotMarketDataCandleSticks(self, argrows, argcols, argindex):
ax2 = self.f.add_subplot(argrows, argcols, argindex, label='Open High Low Close')
ssincedate = datetime.datetime.strptime(self.dfholdingvalues['PurchaseDate'][0], "%Y-%m-%d")
ssincedate = date(ssincedate.year, ssincedate.month, ssincedate.day)
syearpastfirst = self.getPastDateFromDate(argFromDate=ssincedate)
width=0.5
width2=0.1
#'1. open':'Open', '2. high':'High', '3. low':'Low', '4. close':'Close', '5. volume':'Volume
#pricesup = self.dfScript.loc[self.dfScript.index[:] >= syearpastfirst]
pricesup = self.dfScript.loc[self.dfScript.index[:] >= self.dfholdingvalues['PurchaseDate'][self.dfholdingvalues.shape[0]-1]]
pricesup = pricesup[pricesup.Close >= pricesup.Open]
#pricesdown = self.dfScript.loc[self.dfScript.index[:] >= syearpastfirst]
pricesdown = self.dfScript.loc[self.dfScript.index[:] >= self.dfholdingvalues['PurchaseDate'][self.dfholdingvalues.shape[0]-1]]
pricesdown = pricesdown[pricesdown.Close < pricesdown.Open]
#Axes.bar(self, x, height, width=0.8, bottom=None, *, align='center', data=None, **kwargs)[source]
rect1 = ax2.bar(pricesup.index,pricesup.Close-pricesup.Open,width,bottom=pricesup.Open,color='g', label='Closed higher')
i = 0
for eachrec in rect1:
amttext=''
if(pricesup.Low[i] != pricesup.Open[i]):
amttext = 'O=' + '{:.2f}'.format(pricesup.Open[i])
else:
amttext='L=O=' + '{:.2f}'.format(pricesup.Open[i])
self.showCandelAnnotation(ax2, amttext, eachrec.xy[0], pricesup.Open[i], 'data',
eachrec.xy[0], pricesup.Open[i], 'data', 'right', 'bottom', 'xx-small')
"""ax2.annotate(amttext,
xy=(eachrec.xy[0], pricesup.Open[i]),
xycoords='data',
xytext=(eachrec.xy[0], pricesup.Open[i]),
textcoords='data', ha='right', va='bottom', fontsize='xx-small', annotation_clip=True)"""
amttext = ''
if(pricesup.High[i] != pricesup.Close[i]):
amttext = 'C=' + '{:.2f}'.format(pricesup.Close[i])
else:
amttext = 'H=C=' + '{:.2f}'.format(pricesup.Close[i])
self.showCandelAnnotation(ax2, amttext, eachrec.xy[0], pricesup.Close[i], 'data',
eachrec.xy[0], pricesup.Close[i], 'data', 'right', 'top', 'xx-small')
"""ax2.annotate(amttext,
xy=(eachrec.xy[0], pricesup.Close[i]),
xycoords='data',
xytext=(eachrec.xy[0], pricesup.Close[i]),
textcoords='data', ha='right', va='top', fontsize='xx-small', annotation_clip=True)"""
i += 1
rect2 = ax2.bar(pricesup.index,pricesup.High-pricesup.Close,width2,bottom=pricesup.Close,color='g')
i = 0
for eachrec in rect2:
if(pricesup.High[i] != pricesup.Close[i]):
self.showCandelAnnotation(ax2, 'H:' + '{:.2f}'.format(pricesup.High[i]),
eachrec.xy[0], pricesup.High[i], 'data',
eachrec.xy[0], pricesup.High[i], 'data', 'right', 'top', 'xx-small')
"""ax2.annotate('H:' + '{:.2f}'.format(pricesup.High[i]),
xy=(eachrec.xy[0], pricesup.High[i]),
xycoords='data',
xytext=(eachrec.xy[0], pricesup.High[i]),
textcoords='data', ha='right', va='top', fontsize='xx-small', annotation_clip=True)"""
i += 1
rect3 = ax2.bar(pricesup.index,pricesup.Low-pricesup.Open,width2,bottom=pricesup.Open,color='g')
i = 0
for eachrec in rect3:
if(pricesup.Low[i] != pricesup.Open[i]):
self.showCandelAnnotation(ax2, 'L:' + '{:.2f}'.format(pricesup.Low[i]),
eachrec.xy[0], pricesup.Low[i], 'data',
eachrec.xy[0], pricesup.Low[i], 'data', 'right', 'bottom', 'xx-small')
"""ax2.annotate('L:' + '{:.2f}'.format(pricesup.Low[i]),
xy=(eachrec.xy[0], pricesup.Low[i]),
xycoords='data',
xytext=(eachrec.xy[0], pricesup.Low[i]),
textcoords='data', ha='right', va='bottom', fontsize='xx-small', annotation_clip=True)"""
i += 1
rect4 = ax2.bar(pricesdown.index,pricesdown.Close-pricesdown.Open,width,bottom=pricesdown.Open,color='black', label='Closed lower')
i = 0
for eachrec in rect4:
amttext=''
if(pricesdown.High[i] != pricesdown.Open[i]):
amttext = 'O=' + '{:.2f}'.format(pricesdown.Open[i])
else:
amttext='H=O=' + '{:.2f}'.format(pricesdown.Open[i])
self.showCandelAnnotation(ax2, amttext, eachrec.xy[0], pricesdown.Open[i], 'data',
eachrec.xy[0], pricesdown.Open[i], 'data', 'right', 'top', 'xx-small')
amttext = ''
if(pricesdown.Low[i] != pricesdown.Close[i]):
amttext = 'C=' + '{:.2f}'.format(pricesdown.Close[i])
else:
amttext = 'L=C=' + '{:.2f}'.format(pricesdown.Close[i])
self.showCandelAnnotation(ax2, amttext, eachrec.xy[0], pricesdown.Close[i], 'data',
eachrec.xy[0], pricesdown.Close[i], 'data', 'right', 'bottom', 'xx-small')
i += 1
rect5 = ax2.bar(pricesdown.index,pricesdown.High-pricesdown.Open,width2,bottom=pricesdown.Open,color='black')
i = 0
for eachrec in rect5:
if(pricesdown.High[i] != pricesdown.Open[i]):
self.showCandelAnnotation(ax2, 'H:' + '{:.2f}'.format(pricesdown.High[i]),
eachrec.xy[0], pricesdown.High[i], 'data',
eachrec.xy[0], pricesdown.High[i], 'data', 'right', 'top', 'xx-small')
i+=1
rect6 = ax2.bar(pricesdown.index,pricesdown.Low-pricesdown.Close,width2, bottom=pricesdown.Close,color='black')
i = 0
for eachrec in rect6:
if(pricesdown.Low[i] != pricesdown.Close[i]):
self.showCandelAnnotation(ax2, 'L:' + '{:.2f}'.format(pricesdown.Low[i]),
eachrec.xy[0], pricesdown.Low[i], 'data',
eachrec.xy[0], pricesdown.Low[i], 'data', 'right', 'top', 'xx-small')
i+=1
#ax2.set_yticks(list(pricesdown.Open) + list(pricesup.Close))
self.setAxesCommonConfig(ax2, 'Candlestick - ' + self.script, 'Prices')
#plots market data year from the first purchase date
def plotMarketData(self, argrows, argcols, argindex):
ax3 = self.f.add_subplot(argrows, argcols, argindex, label='Market Data')
ssincedate = datetime.datetime.strptime(self.dfholdingvalues['PurchaseDate'][0], "%Y-%m-%d")
ssincedate = date(ssincedate.year, ssincedate.month, ssincedate.day)
syearpastfirst = self.getPastDateFromDate(argFromDate=ssincedate)
#ax2.plot(self.dfScript.loc[self.dfScript.index[:] >= self.dfholdingvalues['PurchaseDate'][0], 'Close'], label='Daily Close from first purchase')
ax3.plot(self.dfScript.loc[self.dfScript.index[:] >= syearpastfirst, 'Close'],
label='Close - Year from first purchase')
#buys= self.dfScript.loc[self.dfScript.index[:] >= self.dfholdingvalues['PurchaseDate'][0]]
buys= self.dfScript.loc[self.dfScript.index[:] >= syearpastfirst]
buys = buys[buys['Status'] != '']
ax3.plot(buys.index, self.dfScript['Close'].loc[buys.index],
marker="*", markersize=10, color='b', label='QTY purchased', linestyle='None')
for i in range(len(buys.index)):
ax3.annotate('Qty='+ str(buys['PurchaseQTY'][i]) + " @ "+ str(buys['PurchasePrice'][i]),
(mdates.datestr2num(buys['PurchaseDate'][i]), float(buys['Close'][i])),
xycoords='data',
xytext=(1, 1), textcoords='offset points',
#xytext=(mdates.datestr2num(self.getDateAfter(buys['PurchaseDate'][i])), float(buys['Close'][i])+2), textcoords='data',
#arrowprops=dict(arrowstyle='-|>'),
horizontalalignment="left", bbox=dict(boxstyle="round", facecolor="w", edgecolor="0.5", alpha=0.9),
fontsize='xx-small')
#ax2.plot(self.dfScript.loc[self.dfScript.index[:] >= self.dfholdingvalues['PurchaseDate'][0], 'Short_Mean'], label='Short Mean')
ax3.plot(self.dfScript.loc[self.dfScript.index[:] >= syearpastfirst, 'Short_Mean'], color = 'r', label='Short Mean')
#ax2.plot(self.dfScript.loc[self.dfScript.index[:] >= self.dfholdingvalues['PurchaseDate'][0], 'Long_Mean'], label='Long Mean')
ax3.plot(self.dfScript.loc[self.dfScript.index[:] >= syearpastfirst, 'Long_Mean'], color = 'g', label='Long Mean')
#buys_suggested= self.dfScript.loc[self.dfScript.index[:] >= self.dfholdingvalues['PurchaseDate'][0]]
buys_suggested= self.dfScript.loc[self.dfScript.index[:] >= syearpastfirst]
buys_suggested = buys_suggested[buys_suggested['Order'] == 1]
ax3.plot(buys_suggested.index, self.dfScript['Close'].loc[buys_suggested.index],
marker=6, markersize=5, color='b', label='Buy', linestyle='None')
#sells_suggested=self.dfScript.loc[self.dfScript['Order'] == 0, 'Order']
#sells_suggested= self.dfScript.loc[self.dfScript.index[:] >= self.dfholdingvalues['PurchaseDate'][0]]
sells_suggested= self.dfScript.loc[self.dfScript.index[:] >= syearpastfirst]
sells_suggested = sells_suggested[sells_suggested['Order'] == 0]
ax3.plot(sells_suggested.index, self.dfScript['Close'].loc[sells_suggested.index],
marker=7, markersize=5, color='r', label='Sell', linestyle='None')
self.setAxesCommonConfig(ax3, 'Market Data - ' + self.script, 'Price')
def plotScriptReturns(self, argrows, argcols, argindex):
ax4 = self.f.add_subplot(argrows, argcols, argindex, label='Market Data')
ssincedate = datetime.datetime.strptime(self.dfholdingvalues['PurchaseDate'][0], "%Y-%m-%d")
ssincedate = date(ssincedate.year, ssincedate.month, ssincedate.day)
syearpastfirst = self.getPastDateFromDate(argFromDate=ssincedate)
ax4.plot(self.dfScript.loc[self.dfScript.index[:] >= syearpastfirst, 'CumReturns'],
label='Cumulative Returns - Year from first purchase')
ax4.plot(self.dfScript.loc[self.dfScript.index[:] >= syearpastfirst, 'Returns'],
label='Daily Returns - Year from first purchase')
self.setAxesCommonConfig(ax4, 'Returns - ' + self.script, 'Returns')
""" findScriptPerformance
This method will execute Alpha
"""
def findScriptPerformance(self, argShowPerformance=True, argShowCandlestick=True,
argShowMarketData=True, argShowReturns=True):
self.getScriptDataFromTree()
if(self.dfholdingvalues.shape[0] < 1):
msgbx.showwarning("Script Performance", "No script data found. Please add your purchased scripts before doing performance calculations")
return
try:
if(self.bool_test):
testobj = PrepareTestData(argFolder=self.datafolderpath, argOutputSize='full')
self.dfScript = testobj.loadDaily(self.script)
#self.dfSMAShort = testobj.loadSMA(self.script, self.avgsmall)
#self.dfSMAShort = testobj.loadSMA(self.script, self.avglarge)
else:
self.dfScript, meta_data = self.ts.get_daily(symbol=self.script, outputsize='full')
#self.dfSMAShort, meta_data = self.ti.get_sma(self.script, interval='daily', time_period=self.avgsmall, series_type='close')
#self.dfSMALong, meta_data = self.ti.get_sma(self.script, interval='daily', time_period=self.avglarge, series_type='close')
self.dfScript.sort_index(axis=0, ascending=False, inplace=True)
#self.dfSMAShort.sort_index(axis=0, ascending=False, inplace=True)
#self.dfSMALong.sort_index(axis=0, ascending=False, inplace=True)
except Exception as error:
msgbx.showerror("Error in findScriptPerformance()", str(error))
return
self.changeColNameTypeofDailyTS()
self.setCurrentValInMarketDF()
self.addPerformance()
#self.plotPerformanceGraphTS()
sumofgraphs = int(argShowPerformance) + int(argShowCandlestick) + int(argShowMarketData) + int(argShowReturns)
nrows=1
ncols=1
nindex=1
if(sumofgraphs == 4):
nrows=ncols=2
nindex=1
elif(sumofgraphs == 3):
nrows=1
ncols=3
nindex=1
elif(sumofgraphs == 2):
nrows=2
ncols=1
nindex=1
elif(sumofgraphs == 1):
nrows=1
ncols=1
nindex=1
if(argShowPerformance):
self.plotPortfolioPerformanceAX(nrows, ncols, nindex)
nindex +=1
if(argShowCandlestick):
self.plotMarketDataCandleSticks(nrows, ncols, nindex)
nindex +=1
if(argShowMarketData):
self.plotMarketData(nrows, ncols, nindex)
nindex +=1
if(argShowReturns):
self.plotScriptReturns(nrows, ncols, nindex)
nindex +=1
self.f.set_tight_layout(True)
self.output_canvas.draw()
self.toolbar.update()
def NOTUSED_plotPerformanceGraphTS(self):
#first 3 & 1 means we want to show 3 graphs in 1 column
#last 1 indicates the sequence number of the current graph
ax1 = plt.subplot(221)
ax1.set_label('Portfolio Performance')
# first plot the self portfolio performance using CurrentVal columns in dfScript
ax1.plot(self.dfScript.loc[self.dfScript.index[:] >=
self.dfholdingvalues['PurchaseDate'][self.dfholdingvalues.shape[0]-1], 'CurrentVal'], label='Portfolio price')
#ax1.plot(self.dfScript['CurrentVal'], label='Portfolio price')
# now we will put markers where the user has bought the scripts and show cumulative qty
#buys=self.dfScript.loc[(self.dfScript['Status'] != ''), ['PurchaseDate', 'PurchaseQTY', 'Status']]
#buys=self.dfScript.loc[(self.dfScript['Status'] != ''), :]
buys= self.dfScript.loc[self.dfScript.index[:] >=
self.dfholdingvalues['PurchaseDate'][self.dfholdingvalues.shape[0]-1]]
buys = buys[buys['Status'] != '']
ax1.plot(buys.index, self.dfScript['CurrentVal'].loc[buys.index], marker="*", markersize=5, color='b', label='Buy transaction', linestyle='None')
for i in range(len(buys.index)):
plt.annotate('Total Qty='+ str(buys['PurchaseQTY'][i]) + " "+buys['Status'][i],
(mdates.datestr2num(buys['PurchaseDate'][i]), buys['CurrentVal'][i]),
xycoords='data',
xytext=(mdates.datestr2num(buys['PurchaseDate'][i]) + 1, buys['CurrentVal'][i]),
textcoords='data', arrowprops=dict(arrowstyle='-|>'),
horizontalalignment="left", bbox=dict(boxstyle="round", facecolor="w", edgecolor="0.5", alpha=0.9),
fontsize='xx-small')
#ax1.set_ylabel("Portfolio Value")
self.setAxesCommonConfig(ax1, 'Portfolio performance - ' + self.script, 'Portfolio Value')
# now plot 2nd set of graph
#ax2 = plt.subplot(312, sharex=ax1)
ax2 = plt.subplot(222)
ax2.set_label('One year performance')
sdateyearback = self.getPastDateFromDate(argFromDate=date.today())
buys= self.dfScript.loc[self.dfScript.index[:] >= self.dfholdingvalues['PurchaseDate'][0]]
buys = buys[buys['Status'] != '']
ax2.plot(self.dfScript.loc[self.dfScript.index[:] >=
self.dfholdingvalues['PurchaseDate'][0], 'Close'], label='Daily Close Price for last year')
#self.dfholdingvalues['PurchaseDate'][self.dfholdingvalues.shape[0]-1], 'Close'], label='Daily Close Price for last year')
ax2.plot(buys.index, self.dfScript['Close'].loc[buys.index], marker="*", markersize=5, color='b', label='Buy transaction', linestyle='None')
prevqty = 0
for i in range(len(self.dfholdingvalues.index)):
prevqty = self.dfholdingvalues['PurchaseQTY'][i] - prevqty
plt.annotate('Qty='+ str(prevqty) + " @ "+self.dfholdingvalues['PurchasePrice'][i],
(mdates.datestr2num(self.dfholdingvalues['PurchaseDate'][i]), float(self.dfholdingvalues['PurchasePrice'][i])),
xycoords='data',
xytext=(mdates.datestr2num(self.dfholdingvalues['PurchaseDate'][i]) + 1, float(self.dfholdingvalues['PurchasePrice'][i])),
textcoords='data', arrowprops=dict(arrowstyle='-|>'),
horizontalalignment="left", bbox=dict(boxstyle="round", facecolor="w", edgecolor="0.5", alpha=0.9),
fontsize='small')
ax2.plot(self.dfScript.loc[self.dfScript.index[:] >=
self.dfholdingvalues['PurchaseDate'][0], 'Short_Mean'], label='Short Mean')
ax2.plot(self.dfScript.loc[self.dfScript.index[:] >=
self.dfholdingvalues['PurchaseDate'][0], 'Long_Mean'], label='Long Mean')
#buys_suggested=self.dfScript.loc[self.dfScript['Order'] == 1, 'Order']
buys_suggested= self.dfScript.loc[self.dfScript.index[:] >=
self.dfholdingvalues['PurchaseDate'][0]]
buys_suggested = buys_suggested[buys_suggested['Order'] == 1]
#plt.plot(buys.index, self.dfScript.columns['Adj Close'].loc[buys.index], marker=6, markersize=10, color='g', label='buy', linestyle='None')
ax2.plot(buys_suggested.index, self.dfScript['Close'].loc[buys_suggested.index], marker=6, markersize=5, color='b', label='Suggested buy', linestyle='None')
#sells_suggested=self.dfScript.loc[self.dfScript['Order'] == 0, 'Order']
sells_suggested= self.dfScript.loc[self.dfScript.index[:] >=
self.dfholdingvalues['PurchaseDate'][0]]
sells_suggested = buys_suggested[buys_suggested['Order'] == 0]
#plt.plot(sells.index, self.dfScript.column['Adj Close'].loc[sells.index], marker=7, markersize=10, color='r', label='sell', linestyle='None')
ax2.plot(sells_suggested.index, self.dfScript['Close'].loc[sells_suggested.index], marker=7, markersize=5, color='r', label='Suggested sell', linestyle='None')
#ax2.set_ylabel('Price')
self.setAxesCommonConfig(ax2, 'Market comparison - ' + self.script, 'Price')
# Now plot 3rd set of graph for cum returns
#ax3 = plt.subplot(313, sharex=ax1)
ax3 = plt.subplot(223)
ax3.set_label('Cumulative Returns')
ax3.plot(self.dfScript['CumReturns'], label='Cumulative Returns')
#ax3.set_ylabel('Cumulative Returns')
self.setAxesCommonConfig(ax3, 'Cumulative returns - ' + self.script, 'Cumulative Returns')
# Now plot 3rd set of graph for cum returns
#ax3 = plt.subplot(313, sharex=ax1)
ax4 = plt.subplot(224)
ax4.set_label('Daily Returns')
ax4.plot(self.dfScript['Returns'], label='Daily Returns')
#ax4.set_ylabel('Daily Returns')
self.setAxesCommonConfig(ax4, 'Daily returns - ' + self.script, 'Daily Returns')
plt.show()
""" Method - getData(self): Not used
get_daily_adjusted returns data and metadata in DF
data format example: "Time Series (Daily)": {
"2020-03-03": {
"1. open": "173.8000",
"2. high": "175.0000",
"3. low": "162.2600",
"4. close": "164.5100",
"5. adjusted close": "164.5100",
"6. volume": "71033645",
"7. dividend amount": "0.0000",
"8. split coefficient": "1.0000"
},
"2020-03-02": {
"1. open": "165.3100",
"2. high": "172.9200",
"3. low": "162.3100",
"4. close": "172.7900",
"5. adjusted close": "172.7900",
"6. volume": "71030810",
"7. dividend amount": "0.0000",
"8. split coefficient": "1.0000"
}}
"""
def getDataNotUsed(self):
self.dfScript, meta_data = self.ts.get_daily_adjusted(self.script)
records = {
"2020-03-03": {
"1. open": "173.8000",
"2. high": "175.0000",
"3. low": "162.2600",
"4. close": "164.5100",
"5. adjusted close": "164.5100",
"6. volume": "71033645",
"7. dividend amount": "0.0000",
"8. split coefficient": "1.0000"
},
"2020-03-02": {
"1. open": "165.3100",
"2. high": "172.9200",
"3. low": "162.3100",
"4. close": "172.7900",
"5. adjusted close": "172.7900",
"6. volume": "71030810",
"7. dividend amount": "0.0000",
"8. split coefficient": "1.0000"
},
"2020-03-01": {
"1. open": "111.3100",
"2. high": "172.9200",
"3. low": "162.3100",
"4. close": "172.7900",
"5. adjusted close": "111.7900",
"6. volume": "71030810",
"7. dividend amount": "0.0000",
"8. split coefficient": "1.0000"
},
"2020-02-29": {
"1. open": "165.3100",
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