def calc_stocks_feat(dreturn, dstocks, dquote, assets, industry):
# join dfs
dfull = dreturn.join(dstocks.set_index('Ticker'), on='Ticker', how='inner')
dfull['Price'] = dquote[str(dt.date.today().year)]
# distribution
dfull['Geo'] = dfull[assets].idxmax(axis=1)
dfull['Domain'] = dfull[industry].idxmax(axis=1)
dfull['Asset'] = dfull[assets].idxmax(axis=1).apply(lambda x: x if (
x == 'Bonds' or x == 'Commod' or x == 'Cash') else 'Equity')
# risk and return
dfull['Risk'] = dfull['Volatility'].apply(lambda x: d.int2pct(x / 100))
dfull['Tax'] = dfull['TER'].apply(lambda x: d.int2pct(x / 100))
dfull['Return'] = dfull['Predicted'].apply(d.int2pct)
dfull['YTD'] = dfull[str(dt.date.today().year)].apply(d.int2pct)
# score
weights = [0.05, 0.5, 0.35, 0.1]
dfull['Score'] = (10 + (30 - dfull['Price']) / 27) * weights[0] + dfull[
'Predicted'] / 0.3 * 10 * weights[1] + (
10 + (7 - dfull['Volatility']) / 2.3) * weights[2] + (
10 - dfull['TER'] / 0.2) * weights[3]
return dfull
def calc_flows(df):
# read values
open_val = df.head(1)['Valor'].item()
final_val = df.tail(1)['Valor'].item()
open_date = df.head(1)['Data'].item()
final_date = df.tail(1)['Data'].item()
status = df.tail(1)['Status'].item()
itype = df.tail(1)['Tipo'].item()
# compute metrics
active = False if status == 'Fechado' else True
gain = final_val - open_val
duration = (final_date - open_date).days
total_yield = gain / open_val
daily_yield = (1 + total_yield)**(1 / duration) - 1 if duration > 0 else 0
monthly_yield = (1 + daily_yield)**30.5 - 1
annual_yield = (1 + daily_yield)**365 - 1
year_profit = final_val * annual_yield
# structure dataframe
dr = pd.DataFrame({
'Valor inicial': [open_val],
'Ganho': [gain],
'Meses': [duration / 30.5],
'Rendimento': [d.int2pct(total_yield)],
'Rendimento mensal': [d.int2pct(monthly_yield)],
'Rendimento anual': [d.int2pct(annual_yield)],
'Ganho em 1 ano': [year_profit],
'Ativo': [active],
'Tipo': [itype]
})
return dr
def calc_port_perfo(dport):
dport['Return'] = dport['Predicted'] * dport['Value'] / dport['Value'].sum(
)
dport['Risk'] = dport['Volatility'] * dport['Value'] / dport['Value'].sum()
dcurrent = pd.DataFrame({
'Value': [dport['Value'].sum()],
'Profit': [dport['Profit'].sum()],
'Return': [d.int2pct(dport['Return'].sum())],
'Risk': [d.int2pct(dport['Risk'].sum())],
})
dcurrent['Yield'] = (dcurrent['Profit'] / dcurrent['Value']).apply(
d.int2pct)
return dcurrent
def calc_flows_overview(df):
gain = df['Ganho'].sum()
value = df[df['Ativo']]['Valor inicial'].sum() + df[
df['Ativo']]['Ganho'].sum()
total_yield = gain / df['Valor inicial'].sum()
year_profit = df[df['Ativo']]['Ganho em 1 ano'].sum()
annual_yield = year_profit / value
doverview = pd.DataFrame([[
value, gain,
d.int2pct(total_yield), year_profit,
d.int2pct(annual_yield)
]],
columns=[
'Valor base', 'Ganho atual',
'Rendimento atual', 'Ganho em 1 ano',
'Rendimento anual'
])
return doverview
def calc_bonds_portfolio(dflow, assets):
profit = dflow['Ganho'].sum()
value = dflow[dflow['Ativo']]['Valor inicial'].sum() + dflow[
dflow['Ativo']]['Ganho'].sum()
yields = profit / dflow['Valor inicial'].sum()
di = pd.DataFrame([[
'NU', value, 1, value, 0, 0.03, profit, yields,
d.int2pct(yields), 0, 0, 0, 0, 100, 0, 0
]],
columns=[
'Ticker', 'Buy', 'Shares', 'Value', 'Volatility',
'Predicted', 'Profit', 'Yield', 'Yield %'
] + assets)
return di