def equal_rebalancing(self):
self.invest_amount_calculation()
self.equal_df = pd.DataFrame()
number_of_stocks = len(self.new_df.index)
invest_amount_per_stock = self.invest_amount/number_of_stocks
for i, price in enumerate(self.new_df['Current Price']):
temp_series = pd.Series([invest_amount_per_stock/price], index=['Equal Rebalance'])
temp_series = self.new_df.iloc[i].append(temp_series)
self.equal_df = self.equal_df.append(temp_series, ignore_index=True)
self.equal_df = self.equal_df[['Symbol', 'Description', 'Sector', 'Quantity', 'Equal Rebalance', 'Cost Basis Per Share', 'Market Cap', 'Current Price', 'P/E Ratio(PER)', 'P/B Ratio(PBR)']]
# self.pt.clearData()
self.pt = Table(self.display, dataframe=self.equal_df, showtoolbar=False, showstatusbar=False, editable=False, enable_menus=False)
options = config.load_options()
options = {'rowselectedcolor':None}
config.apply_options(options, self.pt)
self.pt.show()
# Add color to indicate 'add' or 'sub' quantity to user. Green color for add. Red color for Sub.
add_mask = self.equal_df['Quantity'] < self.equal_df['Equal Rebalance']
self.pt.setColorByMask('Equal Rebalance', add_mask, '#7FFF00')
sub_mask = self.equal_df['Quantity'] > self.equal_df['Equal Rebalance']
self.pt.setColorByMask('Equal Rebalance', sub_mask, '#FF6EB4')
# self.pt.redraw()
self.pt.autoResizeColumns()
def __init__(self, master, controller):
tk.Frame.__init__(self, master, borderwidth=10, bg="#363636")
self.table = pandastable.Table(self, dataframe=df, height=550, columns=4)
self.table.grid()
options = {'align': 'c',
'cellbackgr': '#303030',
'cellwidth': 200,
'colheadercolor': '#393939',
'entrybackgr': '#393939',
'floatprecision': 6,
'font': 'Consolas',
'fontsize': 12,
'grid_color': '#ABB1AD',
'linewidth': 1,
'rowheight': 22,
'rowselectedcolor': '#555555',
'textcolor': '#AAAAAA'
}
config.apply_options(options, self.table)
# self.table.autoResizeColumns()
self.table.show()
button_table_graph = tk.Button(self, text="Grafički prikaz", font=MED_FONT, bg="#363636", fg="#18d9cc",
command=lambda: controller.show_frame(GraphPage))
button_table_graph.grid(row=3, column=0, ipadx=10, ipady=5, padx=50, pady=20)
button_table_convert = tk.Button(self, text="Konverter valuta", font=MED_FONT, bg="#363636", fg="#18d9cc",
command=lambda: controller.show_frame(ConvertPage))
button_table_convert.grid(row=3, column=3, ipadx=10, ipady=5, padx=50, pady=20)
self.grid_rowconfigure(3, weight=1)
self.listenerT = self.after(refresh_rate, self.update_after)
def allYearsGDPPrediction(countries, predictionYear):
df = pd.DataFrame(columns=[
'Countries', 'GDP in 2015', 'Predicted GDP in ' + str(predictionYear)
])
for i in countries:
if dataframeCreation(i) is False:
print(i + "'s dataframe is empty!")
df = df.append(pd.Series([i, 'No Data To Make Prediction'],
index=df.columns),
ignore_index=True)
else:
pred1 = linearReg2(i, 2015, dataframeCreation(i))[1][0][0]
pred2 = linearReg2(i, predictionYear,
dataframeCreation(i))[1][0][0]
df = df.append(pd.Series([i, pred1, pred2], index=df.columns),
ignore_index=True)
df.index += 1
window = tk.Toplevel()
window.title("All Countries' GDP Prediction in the year: %s" %
predictionYear)
f = Frame(window)
f.pack(fill=BOTH, expand=1)
pt = Table(f, dataframe=df, showstatusbar=True, width=200, height=300)
options = {'cellwidth': 150, 'floatprecision': 4, 'align': 'center'}
config.apply_options(options, pt)
pt.showIndex()
pt.show()
return df
def __init__(self, master):
super(DTable, self).__init__(master)
self.mystyle = ttk.Style()
self.mystyle.configure("Vertical.TScrollbar",
troughcolor='black',
background='black',
bordercolor="black")
self.mystyle.configure("Horizontal.TScrollbar",
troughcolor='black',
background='black',
bordercolor="black")
apply_options(options, self)
def displayCorrTable(dict, userSelection):
corrDict = dict
df = pd.DataFrame(data=corrDict, index=[0]).T
df.columns = ['Correlation Value']
window = tk.Toplevel()
window.title('%s - GDP Factors correlation values' % userSelection)
f = Frame(window)
f.pack(fill=BOTH, expand=1)
pt = Table(f, dataframe=df, showstatusbar=True, width=200, height=300)
options = {'cellwidth': 150, 'floatprecision': 4, 'align': 'center'}
config.apply_options(options, pt)
pt.showIndex()
pt.show()
def update_data(self):
self.load_market_data()
for i, symbol in enumerate(self.df['Symbol']):
# Variable for display status of Progressive Bar
self.p_var.set(100/len(self.df.index)*(i+1))
self.progressbar.update()
if symbol in self.tickers['Symbol'].values:
# Loading live market data from yahoo finance
data_from_yahoo = get_quote_data(symbol)
# print(data_from_yahoo.keys())
temp = pd.Series(
{'Symbol': symbol,
'Description': self.tickers.loc[self.tickers['Symbol'] == symbol]['Security'].item().strip(),
'Sector': self.tickers.loc[self.tickers['Symbol'] == symbol]['GICS Sector'].item().strip(),
'Cost Basis Per Share': self.df.loc[self.df['Symbol'] == symbol]['Cost Basis Per Share'].item().strip(),
'Quantity': self.df.loc[self.df['Symbol'] == symbol]['Quantity'].item(),
'Market Cap': data_from_yahoo['marketCap'],
'Current Price': data_from_yahoo['regularMarketPrice'],
'P/E Ratio(PER)': data_from_yahoo['trailingPE'] if 'trailingPE' in data_from_yahoo.keys() else None,
'P/B Ratio(PBR)': data_from_yahoo['priceToBook'] if 'priceToBook' in data_from_yahoo.keys() else None}
)
self.new_df = self.new_df.append(temp, ignore_index=True)
# print("Loading is completed")
# Reorder Columns
self.new_df = self.new_df[['Symbol', 'Description', 'Sector', 'Quantity', 'Cost Basis Per Share', 'Market Cap', 'Current Price', 'P/E Ratio(PER)', 'P/B Ratio(PBR)']]
# first_column = self.new_df.pop('Symbol')
# self.new_df.insert(0, 'Symbol', first_column)
# self.pt.destroy()
# self.pt.clearData()
self.invested_temp_sum = 0
for i in range(len(self.new_df)):
self.invested_temp_sum += self.new_df['Quantity'][i] * self.new_df['Current Price'][i]
self.invested_temp_sum = round(self.invested_temp_sum, 3)
self.invested_value.set(str(self.invested_temp_sum))
self.pt = Table(self.display, dataframe=self.new_df, showtoolbar=False, showstatusbar=False, editable=False, enable_menus=False)
options = config.load_options()
options = {'rowselectedcolor':None}
config.apply_options(options, self.pt)
self.pt.show()
self.pt.autoResizeColumns()
# self.pt.redraw()
# Update menu status
self.menu.entryconfig("Strategies", state="normal")
# self.menu_file.entryconfig("Save to Excel", state="normal")
self.button_rebalancing.config(state="normal")
def displayFactor(country, dataframe, factor):
countryName = country
df = dataframe
if df is False:
tk.messagebox.showinfo("Error", "Insufficient data")
else:
dfFactor = df[factor]
df = pd.concat([dfFactor], axis=1)
window = tk.Toplevel()
window.title(countryName + " - " + factor + ' Data')
f = Frame(window)
f.pack(fill=BOTH, expand=1)
pt = Table(f, dataframe=df, showstatusbar=True, width=200, height=300)
options = {'cellwidth': 150, 'floatprecision': 4, 'align': 'center'}
config.apply_options(options, pt)
pt.showIndex()
pt.show()
def display_all(user_input):
"""
:param user_input: Fetch year value from user input
Generate DataFrame based on factor value and plot accordingly
"""
new_df = trim_column(df, user_input)
new_df.drop(['Series Name', 'Series Code', 'Country Code'],
axis=1,
inplace=True)
new_df.reset_index(drop=True, inplace=True)
window = tk.Toplevel()
window.title('GPD For All Countries In ' + user_input)
window.geometry("500x700")
f = Frame(window)
f.pack(fill=BOTH, expand=1)
pt = Table(f, dataframe=new_df, showstatusbar=True, width=200, height=300)
options = {'cellwidth': 150, 'floatprecision': 4, 'align': 'center'}
config.apply_options(options, pt)
pt.showIndex()
pt.show()
def load_user_data(self):
_file = filedialog.askopenfilename(title="Choose csv file...", \
filetypes=(("csv", "*.csv"),("All", "*.*")), initialdir=r"C:\Users\baekh\OneDrive\Desktop\Python")
try:
self.df = pd.read_csv(_file, index_col=False)
self.df = self.df.drop(columns=['Account Name/Number'])
self.df = self.df.rename(columns={"Current Value":"Current Price"})
# Keep only first row if there is duplications
# self.df.drop_duplicates(subset=['Symbol'], inplace=True)
# self.df = self.df.head()
self.pt = Table(self.display, dataframe=self.df, showtoolbar=False, showstatusbar=False, editable=False, enable_menus=False)
options = config.load_options()
options = {'rowselectedcolor':None}
config.apply_options(options, self.pt)
self.pt.show()
self.pt.autoResizeColumns()
# self.pt.redraw()
# Update Menu status
self.menu_file.entryconfig("Update Market Values", state="normal")
except:
pass
def market_cap_rebalancing(self):
self.invest_amount_calculation()
self.cap_df = pd.DataFrame()
total_market_cap = self.new_df['Market Cap'].sum()
for i, cap in enumerate(self.new_df['Market Cap']):
temp_series = pd.Series([self.invest_amount*cap/total_market_cap/self.new_df['Current Price'][i]], index=['Cap Rebalance'])
temp_series = self.new_df.iloc[i].append(temp_series)
self.cap_df = self.cap_df.append(temp_series, ignore_index=True)
self.cap_df = self.cap_df[['Symbol', 'Description', 'Sector', 'Quantity', 'Cap Rebalance', 'Cost Basis Per Share', 'Market Cap', 'Current Price', 'P/E Ratio(PER)', 'P/B Ratio(PBR)']]
self.pt = Table(self.display, dataframe=self.cap_df, showtoolbar=False, showstatusbar=False, editable=False, enable_menus=False)
options = config.load_options()
options = {'rowselectedcolor':None}
config.apply_options(options, self.pt)
self.pt.show()
# Add color to indicate 'add' or 'sub' quantity to user. Green color for add. Red color for Sub.
add_mask = self.cap_df['Quantity'] < self.cap_df['Cap Rebalance']
self.pt.setColorByMask('Cap Rebalance', add_mask, '#7FFF00')
sub_mask = self.cap_df['Quantity'] > self.cap_df['Cap Rebalance']
self.pt.setColorByMask('Cap Rebalance', sub_mask, '#FF6EB4')
# print(total_market_cap)
self.pt.autoResizeColumns()
def readData_createWindow(self):
try:
filename = filedialog.askopenfilename()
f = open(filename, "rb")
self.set_header(self.header_bool)
# f = 'http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'
header = self.header_bool.get() == 1
self.data = pd.read_csv(f, header=0 if header else None, sep=',')
if not header:
self.data.columns = [
'var' + str(i) for i in range(len(self.data.columns))
]
except AttributeError:
pass
except Exception as e:
return showerror("Error", "Data could not be loaded! Make sure the data is " \
+ "formated in a similar way as the sample data: {}.".format(e))
self.cols = self.data.columns
self.setUpData()
self.bools = [
] # variables for columns: first axis: which col; second axis: what type
height = min(5, self.data.shape[0])
width = len(self.cols)
# data frame
data_frame = LabelFrame(self.master, text="Data Summary", relief=RIDGE)
data_frame.grid(row=0,
column=0,
columnspan=5,
sticky="EWNS",
padx=15,
pady=7.5)
data_size = "Data size is {} kilobytes. " \
.format(np.round(self.data.memory_usage(deep=False).sum() / 1000, 2))
cols_rows = "The number of columns is {} and the number of rows is {}. " \
.format(self.data.shape[1], self.data.shape[0])
miss_data = "The data does have missing values." if self.data.isnull().values.any() \
else "The data does not have missing values."
Message(data_frame, text=data_size + cols_rows + miss_data, width=335) \
.grid(row=0, column=0, columnspan=width-1, rowspan=3, sticky='NW')
Button(data_frame, text="Data", width=13, command=self.show_data) \
.grid(row=0, column=4, columnspan=1, padx=4, pady=4)
Button(data_frame, text="Description", width=13, command=self.show_description) \
.grid(row=1, column=4, columnspan=1, padx=4, pady=0)
Button(data_frame, text="Pair Plot", width=13, command=self.pair_plot) \
.grid(row=2, column=4, columnspan=1, padx=4, pady=4)
# head frame
head_frame = LabelFrame(self.master, text="Head of data", relief=RIDGE)
head_frame.grid(row=5,
column=0,
columnspan=5,
sticky="EWNS",
padx=15,
pady=7.5)
for i in range(len(self.cols)):
self.bools.append(
self.initBools(5, trues=[0, 3] if i < width - 1 else [1, 3]))
table = CustomTable(main_window=self,
parent=head_frame,
dataframe=self.data.head(),
editable=False,
width=1,
height=120)
config.apply_options({'fontsize': 10}, table)
table.show()
# modeling frame
model_frame = LabelFrame(self.master, text="Modeling", relief=RIDGE)
model_frame.grid(row=height + 11,
column=0,
columnspan=5,
sticky="EWNS",
padx=15,
pady=7.5)
# train / test ratio
self.train_test_ratio = 1 / 3
self.train_test_ratio_str = StringVar(
self.master, value=str(np.round(self.train_test_ratio, 2)))
Button(model_frame, text="Shuffle Data", width=13, command=self.shuffle_data) \
.grid(row=0, column=0, columnspan=1)
Button(model_frame, text="TrainTest Ratio", width=13, command=self.set_traintest) \
.grid(row=0, column=2, columnspan=1)
Label(model_frame, textvariable=self.train_test_ratio_str) \
.grid(row=0, column=3, columnspan=1, sticky="E")
# model selection
ttk.Style().configure("TMenubutton", background="#E1E1E1")
self.model_btn = Menubutton(model_frame, text="Model", width=9)
self.set_model(self.model_btn)
self.model_btn.grid(row=1, column=0, columnspan=1, padx=0, pady=4)
Button(model_frame, text="Parameters", width=13, command=self.set_model_parameters) \
.grid(row=1, column=1, columnspan=1, padx=0, pady=4)
self.metric_btn = Menubutton(model_frame, text="Metric", width=9)
self.set_metric(self.metric_btn)
self.metric_btn.grid(row=1, column=2, columnspan=1, padx=0, pady=4)
# model training
self.score = -1
self.score_str = StringVar(self.master, value="")
Button(model_frame, text="Train Model", width=13, command=self.startModel) \
.grid(row=1, column=3, columnspan=width-1)
Label(model_frame, textvariable=self.score_str) \
.grid(row=3, columnspan=width, sticky='W')
# Export Frame
export_frame = LabelFrame(self.master, text="Save", relief=RIDGE)
export_frame.grid(row=height + 15,
column=0,
columnspan=5,
sticky="EWNS",
padx=15,
pady=7.5)
Button(export_frame, text="Export Data", width=13, command=self.export_data) \
.grid(row=0, column=0, columnspan=1, padx=4, pady=4)
Button(export_frame, text="Export Model", width=13, command=self.export_model) \
.grid(row=0, column=1, columnspan=1, padx=0, pady=4)