def calculate_sma(self, recalculate: bool = False):
df = tl.load_csv(self.signal_stocks)
if recalculate or 'SMA_' + str(self.sma_period) not in df.keys():
print(
f"Calculate SMA for {self.signal_stocks} with {self.sma_period} period"
)
df['SMA_' + str(self.sma_period)] = round(
df['Close'].rolling(self.sma_period).mean(), 2)
tl.save_csv(self.FOLDER_WITH_DATA, self.signal_stocks, df)
else:
return
def calculate_momentum(self, recalc: bool = False):
for ticker in set(self.tickers_for_mom):
df = tl.load_csv(ticker)
for mom in self.momentums.keys():
if recalc or 'Momentum_' + str(mom) not in df.keys():
print(
f"Calculate momentum for {ticker} with {mom} month-period"
)
mom_list = []
for day in range(len(df.Date)):
if day != len(df) - 1 and df.Date[
day].month != df.Date[day + 1].month:
find_month = df.Date[day] - rdelta(months=mom)
month_cls = df[
(df.Date.dt.year == find_month.year)
& (df.Date.dt.month == find_month.month)].Close
if len(month_cls) != 0:
mom_list.append(df.Close[day] /
month_cls.iloc[-1])
else:
mom_list.append(None)
elif day != 0 and df.Date[day].month != df.Date[
day - 1].month:
find_month = df.Date[day] - rdelta(months=mom)
month_opn = df[
(df.Date.dt.year == find_month.year)
& (df.Date.dt.month == find_month.month)].Open
if len(month_opn) != 0:
mom_list.append(df.Open[day] /
month_opn.iloc[0])
else:
mom_list.append(None)
else:
mom_list.append(None)
else:
continue
df['Momentum_' + str(mom)] = mom_list
tl.save_csv(self.FOLDER_WITH_DATA, ticker, df)
def calculate_momentum(self, asset: str, recalculate: bool = False):
if asset == 'stocks':
ticker, mom = self.signal_stocks, self.momentum_stocks
elif asset == 'bonds':
ticker, mom = self.signal_bonds, self.momentum_bonds
else:
raise ValueError(
"Incorrect value for 'asset' in 'calculate_momentum' func")
df = tl.load_csv(ticker)
if recalculate or 'Momentum_' + str(mom) not in df.keys():
print(f"Calculate momentum for {ticker} with {mom} month-period")
mom_list = []
for day in range(len(df.Date)):
if day != len(df) - 1 and df.Date[day].month != df.Date[
day + 1].month:
find_month = df.Date[day] - rdelta(months=mom)
month_cls = df[(df.Date.dt.year == find_month.year) & (
df.Date.dt.month == find_month.month)].Close
if len(month_cls) != 0:
mom_list.append(df.Close[day] / month_cls.iloc[-1])
else:
mom_list.append(None)
elif day != 0 and df.Date[day].month != df.Date[day - 1].month:
find_month = df.Date[day] - rdelta(months=mom)
month_opn = df[(df.Date.dt.year == find_month.year) &
(df.Date.dt.month == find_month.month)].Open
if len(month_opn) != 0:
mom_list.append(df.Open[day] / month_opn.iloc[0])
else:
mom_list.append(None)
else:
mom_list.append(None)
else:
return
df['Momentum_' + str(mom)] = mom_list
tl.save_csv(self.FOLDER_WITH_DATA, ticker, df)
'(' + f"Period_Range {test_port.vol_calc_period} {test_port.vol_calc_range}" + ') ' + \
'(' + f"Type_Leverage {test_port.vola_type} {test_port.use_margin}" + ') '
return df_strategy, df_yield_by_years, chart_name
if __name__ == "__main__":
portfolios = {'risk_on': {'QQQ': 1.0}, 'risk_off': {'IEF': 1.0}}
test_port = TargetVolatility(portfolios=portfolios,
date_start=datetime(1915, 1, 1),
benchmark='QQQ',
vol_target=6.0,
rebalance='monthly',
vol_calc_period='month',
vol_calc_range=1)
df_strategy, df_yield_by_years, chart_name = start(test_port)
tl.plot_capital_plotly(test_port.FOLDER_WITH_IMG + chart_name,
list(df_strategy.Date), list(df_strategy.Capital),
df_yield_by_years, portfolios)
tl.save_csv(test_port.FOLDER_TO_SAVE,
chart_name + str(test_port.ports_tickers()), df_strategy)
# portfolios = {'risk_on':
# {'DJIndex': 1.0},
# 'risk_off':
# {'Treasures': 1.0}
# }
both_strategies_df = df_strategy_vol.merge(df_strategy_mom,
left_on='Date',
right_on='Date')
both_strategies_df['Capital'] = both_strategies_df[
'Capital_x'] + both_strategies_df['Capital_y']
df_yield_by_years = core_port.df_yield_std_by_every_year(
both_strategies_df)
chart_name = 'CoreSattelitePort ' + \
'(' + rebalance + ') ' + \
'(' + 'by ' + trade_rebalance_at + ') ' + \
'(' + f"VolTarget {core_port.vol_target}" + ') ' + \
'(' + f"Period_Range {core_port.vol_calc_period} {core_port.vol_calc_range}" + ') ' + \
'(' + f"Mom {satelt_port.momentum_stocks} {satelt_port.momentum_bonds}" + ') ' + \
'(' + f"SMA {satelt_port.sma_period}" + ') '
tl.plot_capital_plotly(core_port.FOLDER_WITH_IMG + chart_name,
list(both_strategies_df.Date),
list(both_strategies_df.Capital), df_yield_by_years,
{
**core_ports,
**satelt_ports
})
tl.save_csv(core_port.FOLDER_TO_SAVE, chart_name, both_strategies_df)
# make_charts_for_site()
# make_json_for_site()
from datetime import datetime
from libs import trading_lib as tl
import pandas as pd
import SMA_and_Mom
port = {'high_risk':
{'VFINX': .5, 'VEIEX': .5}
}
port_class = SMA_and_Mom.SMAandMomentum(portfolios=port,
date_start=datetime(1980, 1, 1),
signal_stocks='VFINX',
signal_bonds='VEIEX',
benchmark='VFINX')
port_class.download_data()
start_date, end_date = port_class.find_oldest_newest_dates()
port_class.cut_data_by_dates(start_date, end_date)
port_class.all_tickers_data['VFINX_VEIEX'] = {}
port_class.all_tickers_data['VFINX_VEIEX']['Date'] = port_class.all_tickers_data['VFINX']['Date']
port_class.all_tickers_data['VFINX_VEIEX']['Close'] = \
port_class.all_tickers_data['VFINX']['Close'] / port_class.all_tickers_data['VEIEX']['Close']
sma = port_class.all_tickers_data['VFINX_VEIEX']['Close']
port_class.all_tickers_data['VFINX_VEIEX']['SMA_200'] = round(sma.rolling(200).mean(), 2)
df = pd.DataFrame.from_dict(port_class.all_tickers_data['VFINX_VEIEX'])
tl.save_csv(port_class.FOLDER_WITH_DATA, 'VFINX_VEIEX', df)