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Functions.py
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Functions.py
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from sklearn.impute import KNNImputer
from datetime import datetime
import pandas as pd
import numpy as np
import requests
import time
def join_fundamentals(stock_list: np.array, Alphavantage_key: str, excel_name: str, error_safe = True, financial_statement = "INCOME_STATEMENT"):
""" Stock_list: cargar excel sp500.xslx con pandas y seleccionar columna de tickers, debe ser un array de numpy.
Alphavantage_key: Poner clave alphavantage.
Excel_name: nombre del output sin .xlsx
Output: DF con fundamentales.
Se guarda un Excel automaticamente
Error_safe: True / False. Prompts not only the resulting DF but the tickers at which an error was encountered.
If False it will only prompt the resulting DF
Financial_statement: BALANCE_SHEET, INCOME_STATEMENT or CASH_FLOW"""
raw_data = []
wrong_tickers = []
i = 0
for ticker in stock_list:
i = i + 1
url = f'https://www.alphavantage.co/query?function={financial_statement}&symbol={ticker}&apikey={Alphavantage_key}'
r = requests.get(url)
income_statement = r.json()
raw_data.append(income_statement)
if i ==5:
time.sleep(60)
i = 0
print(f"{int(round(list(stock_list).index(ticker)/len(stock_list)*100,0))}% complete")
for j in range(len(stock_list)):
try:
if j == 0:
idx = pd.MultiIndex.from_tuples([(stock_list[j],pd.DataFrame(raw_data[j]["quarterlyReports"]).columns[i])
for i in range(len(pd.DataFrame(raw_data[j]["quarterlyReports"]).columns))])
df = pd.DataFrame(raw_data[j]["quarterlyReports"])
df.columns = idx
else:
idx = pd.MultiIndex.from_tuples([(stock_list[j],pd.DataFrame(raw_data[j]["quarterlyReports"]).columns[i])
for i in range(len(pd.DataFrame(raw_data[j]["quarterlyReports"]).columns))])
df2 = pd.DataFrame(raw_data[j]["quarterlyReports"])
df2.columns = idx
df = pd.concat([df,df2], axis = 1)
except:
print('Problem encountered')
wrong_tickers.append(ticker)
continue
df.transpose().to_excel(f'{excel_name}.xlsx')
print(f"Finished! Go look at your file: {excel_name}.xlsx saved next to this jupyter file.")
if error_safe == True:
return df, wrong_tickers
else:
return df
def load_partial_excel(df: pd.DataFrame):
"""
For partial Excel files
"""
df[df.columns[0]] = df[df.columns[0]].ffill()
df = df.rename(
columns = {df.columns[0]:'Ticker', df.columns[1]:'Fundamental'}
)
df = df.set_index(
pd.MultiIndex.from_frame( df[df.columns[:2]] )
)[df.columns[2:]].transpose()
return df
def missing_tickers(downloaded_assets:list, full_asset_list:list):
compare = pd.Series([value in downloaded_assets for value in full_asset_list])
compare = compare[compare == False].index.values
return list(pd.Series(full_asset_list).iloc[list(compare)].values)
def in_both_lists(list_1:list, list_2:list):
"""" If one list is bigger assing it to list_2 """
compare = pd.Series([value in list_1 for value in list_2])
compare = compare[compare == True].index.values
return list(pd.Series(list_2).iloc[list(compare)].values)
def load_full_excel(path: str):
""""
For final Excel Files with all assets
Path/Name.xlsx """
df = pd.read_excel(path)
df[df.columns[0]] = df[df.columns[0]].ffill()
df = df.rename(
columns = {df.columns[0]:'Ticker', df.columns[1]:'Fundamental'}
)
df = df.set_index(
pd.MultiIndex.from_frame( df[df.columns[:2]] )
)[df.columns[2:]].transpose()
return df
def quarters(dates_data):
data = pd.Series(dates_data).dropna().values
dates = [datetime.strptime(value,'%Y-%m-%d') for value in data]
return np.array([date - pd.tseries.offsets.DateOffset(days=1) + pd.tseries.offsets.QuarterEnd() for date in dates])
def assets(income_statement: pd.DataFrame):
"""
income_statement: DataFrame with all the Income Statements. Functions.load_full_excel('Data/Income/Income_Statement.xlsx')
"""
tickers = list(np.unique(np.array([income_statement.columns[i][0] for i in range(len(income_statement.columns))])))
return tickers
def prices_date(balance_statement: pd.DataFrame, prices: pd.DataFrame, sp500: list):
"""
Function that cleans dates in prices in order for them to match with the last fiscal date, for prices in weekends last prices available is taken.
balance_statement: DataFrame with all the balance_statements. Functions.load_full_excel('Data/Balance/Balance_Statement.xlsx')
prices: Precio de los activos. yf.download(tickers=sp500, start='2018-09-01', progress=False)['Adj Close']
sp500: Lista de activos. Usar assets formula.
"""
fiscal_endings = quarters(balance_statement[sp500[0]]['fiscalDateEnding'].values)
# Filtrado Precios
comparisson_list = prices.index.values
dates_test = pd.to_datetime( [date if date in comparisson_list else comparisson_list[comparisson_list < date][-1] for date in fiscal_endings] )
prices_filtered = prices.loc[dates_test] # Con fechas más cercanas al día fiscal
prices_fiscal = prices_filtered.copy()
prices_fiscal['fiscalDateEnding'] = fiscal_endings
prices_fiscal = prices_fiscal.set_index('fiscalDateEnding')
prices_fiscal.columns = pd.MultiIndex.from_tuples( [(value,'Adj Close') for value in prices_fiscal.columns.values] )
return prices_fiscal
def clean_df(balance_statement: pd.DataFrame, income_statement: pd.DataFrame, sp500: list, prices_fiscal: pd.DataFrame):
""""
Return a Clean DataFrame with selected variables ready to calculate finantial ratios.
balance_statement: DataFrame with all the balance_statements. Functions.load_full_excel('Data/Balance/Balance_Statement.xlsx')
income_statement: DataFrame with all the income statements. Functions.load_full_excel('Data/Income/Income_Statement.xlsx')
sp500: Lista de activos. Usar assets formula.
prices_fiscal: Precio de los activos despues de usar función prices_date.
"""
# Datos necesarios de los Estados Financieros
balance_cols = ['fiscalDateEnding','currentDebt','inventory','totalAssets','totalCurrentAssets',
'currentAccountsPayable','currentNetReceivables','commonStockSharesOutstanding','totalLiabilities','totalShareholderEquity']
income_cols = ['totalRevenue','costofGoodsAndServicesSold','costOfRevenue','netIncome']
# Get Returns
returns = prices_fiscal.pct_change()
returns.columns = pd.MultiIndex.from_tuples([( returns.columns[i][0],'Return') for i in range(len(returns.columns))])
# Formatos de Fecha homogeneos
balance = balance_statement[sp500[0]][balance_cols]
balance['fiscalDateEnding'] = quarters(balance['fiscalDateEnding'].values)
balance = balance.set_index('fiscalDateEnding')
income = income_statement[sp500[0]][income_cols]
income['fiscalDateEnding'] = balance.index.values
income = income.set_index('fiscalDateEnding')
# Unir columnas
company = pd.concat([balance,income,prices_fiscal[sp500[0]],returns[sp500[0]]], axis = 1)
company.columns = pd.MultiIndex.from_tuples( [(sp500[0],value) for value in company.columns.values] )
companies = company.copy()
for ticker in sp500[1:]:
# Formatos de Fecha homogeneos
balance = balance_statement[sp500[0]][balance_cols]
balance['fiscalDateEnding'] = quarters(balance['fiscalDateEnding'].values)
balance = balance.set_index('fiscalDateEnding')
income = income_statement[ticker][income_cols]
income['fiscalDateEnding'] = balance.index.values
income = income.set_index('fiscalDateEnding')
# Unir columnas
company = pd.concat([balance,income,prices_fiscal[ticker],returns[ticker]], axis = 1)
company.columns = pd.MultiIndex.from_tuples( [(ticker,value) for value in company.columns.values] )
companies = pd.concat([companies,company], axis=1)
companies = companies.iloc[1:]
for ticker in sp500:
companies[(ticker,'Return')] = companies[(ticker,'Return')].apply(lambda x: 1 if x > 0 else 0)
return companies
def tabular_df(financial_info: pd.DataFrame, sp500: list):
"""
financial_info: DataFrame resultant from using function clean_df.
sp500: Lista de activos. Usar assets formula.
"""
table = []
for ticker in sp500:
partial = financial_info[ticker].reset_index()
partial['Stock'] = ticker
table.append(partial)
table = pd.concat(table, axis=0)
order = ['Stock'] + list(table.columns.values[:-1])
table = table.reindex(columns=order)
table = table.fillna(0)
for column in table.columns[2:-1]:
table[column] = table[column].apply(float)
return table
def PER(data_table: pd.DataFrame):
""""
data_table: DataFrame resultant from using tabular_df function.
"""
ratio = data_table['Adj Close'] / (data_table['netIncome'] / data_table['commonStockSharesOutstanding'])
return ratio
def PBV(data_table: pd.DataFrame):
""""
data_table: DataFrame resultant from using tabular_df function.
"""
ratio = ( data_table['commonStockSharesOutstanding'] * data_table['Adj Close'] ) / data_table['totalAssets']
return ratio
def Acid_test(data_table: pd.DataFrame):
""""
data_table: DataFrame resultant from using tabular_df function.
"""
ratio = ( data_table['totalCurrentAssets'] - data_table['inventory'] ) / data_table['currentDebt']
return ratio
def ATR(data_table: pd.DataFrame):
""""
data_table: DataFrame resultant from using tabular_df function.
"""
ratio = data_table['totalRevenue'] / data_table['totalAssets']
return ratio
def AR(data_table: pd.DataFrame):
""""
data_table: DataFrame resultant from using tabular_df function.
"""
ratio = ( data_table['totalCurrentAssets'] * data_table['inventory'] ) / data_table['currentDebt']
return ratio
def CCC(data_table: pd.DataFrame):
""""
data_table: DataFrame resultant from using tabular_df function.
"""
days_sales_of_inventory = 365 / ( data_table['costOfRevenue'] / data_table['inventory'] )
days_sales_outstanding = 365 / ( data_table['totalRevenue'] / data_table['currentNetReceivables'] )
days_payables_outstanding = 365 / ( data_table['costOfRevenue'] / data_table['currentAccountsPayable'] )
return days_sales_of_inventory + days_sales_outstanding - days_payables_outstanding
def ROA(data_table: pd.DataFrame):
""""
data_table: DataFrame resultant from using tabular_df function.
"""
ratio = data_table['netIncome'] / data_table['totalAssets']
return ratio
def DER(data_table: pd.DataFrame):
""""
data_table: DataFrame resultant from using tabular_df function.
"""
ratio = data_table['totalLiabilities'] / data_table['totalShareholderEquity']
return ratio
def NPM(data_table: pd.DataFrame):
""""
data_table: DataFrame resultant from using tabular_df function.
"""
ratio = data_table['netIncome'] / data_table['totalRevenue']
return ratio
def EM(data_table: pd.DataFrame):
""""
data_table: DataFrame resultant from using tabular_df function.
"""
ratio = data_table['totalAssets'] / data_table['totalShareholderEquity']
return ratio
def financial_ratios(data_table: pd.DataFrame):
""""
data_table: DataFrame resultant from using tabular_df function.
"""
financials = data_table.copy()
financials['PER'] = PER(data_table)
financials['PBV'] = PBV(data_table)
financials['Acid_test'] = Acid_test(data_table)
financials['ATR'] = ATR(data_table)
financials['CCC'] = CCC(data_table)
financials['ROA'] = ROA(data_table)
financials['DER'] = DER(data_table)
financials['NPM'] = NPM(data_table)
financials['EM'] = EM(data_table)
return financials[['Stock','fiscalDateEnding','PER','PBV','Acid_test','ATR','CCC','ROA','DER','NPM','EM','Return']]
def dqr(data):
# Lista de variables de la base de datos
columns = pd.DataFrame(list(data.columns.values), columns=['Nombres'], index=list(data.columns.values))
# Lista de tipos de datos
data_types = pd.DataFrame(data.dtypes, columns=['Data_Type'])
# Lista de valores perdidos (NaN)
missing_values = pd.DataFrame(data.isnull().sum(), columns=['Missing_Values'])
# Lista de valores presentes
present_values = pd.DataFrame(data.count(), columns=['Present_Values'])
# Número de valores únicos para cada variable
unique_values = pd.DataFrame(columns=['Num_Unique_Values'])
for col in list(data.columns.values):
unique_values.loc[col] = [data[col].nunique()]
# Lista de valores mínimos para cada variable
min_values = pd.DataFrame(columns=['Min'])
for col in list(data.columns.values):
try:
min_values.loc[col] = [data[col].min()]
except:
pass
# Lista de valores máximos para cada variable
max_values = pd.DataFrame(columns=['Max'])
for col in list(data.columns.values):
try:
max_values.loc[col] = [data[col].max()]
except:
pass
# Columna 'Categórica' que obtenga un valor booleano True cuando mi columna es una variable
# Categórica; y False, cuando sea Numérica
categorical = pd.DataFrame(columns=['Categorical'])
for col in list(data.columns.values):
if data[col].dtype == 'object':
categorical.loc[col] = True
else:
categorical.loc[col] = False
# Si es categórica no mayor a 20 elementos únicos, anexar sus valores
cat_values = pd.DataFrame(columns=['Categories'])
for col in list(data.columns.values):
if data[col].dtype == 'object' and data[col].nunique() < 21:
cat_values.loc[col] = [data[col].unique()]
elif data[col].dtype == 'object' and data[col].nunique() > 20:
cat_values.loc[col] = 'Category too large'
else:
cat_values.loc[col] = 'Not categorical'
# Unión de tablas / DataFrames
return columns.join(data_types).join(missing_values).join(present_values).join(unique_values).join(min_values).join(max_values).join(categorical).join(cat_values)
def clean_ratios_function(df: pd.DataFrame):
# Conservar solo ratios y reemplazo de infinito (y -infinito) por NaN
ratios_only = df.drop(['Stock','fiscalDateEnding','Return'],axis=1).replace(np.inf,np.nan).replace(-np.inf,np.nan)
# Imputación de valores faltantes
imputer = KNNImputer(n_neighbors=5)
imputed_ratios = imputer.fit_transform(ratios_only)
df[['PER','PBV','Acid_test','ATR','CCC','ROA','DER','NPM','EM']] = imputed_ratios
return df