def test_or():
target = mock.MagicMock()
target.temp = {}
#run on the 1/2/18
runOnDateAlgo = algos.RunOnDate(pd.to_datetime('2018-01-02'))
runOnDateAlgo2 = algos.RunOnDate(pd.to_datetime('2018-01-03'))
runOnDateAlgo3 = algos.RunOnDate(pd.to_datetime('2018-01-04'))
runOnDateAlgo4 = algos.RunOnDate(pd.to_datetime('2018-01-04'))
orAlgo = algos.Or(
[runOnDateAlgo, runOnDateAlgo2, runOnDateAlgo3, runOnDateAlgo4])
#verify it returns false when neither is true
target.now = pd.to_datetime('2018-01-01')
assert not orAlgo(target)
# verify it returns true when the first is true
target.now = pd.to_datetime('2018-01-02')
assert orAlgo(target)
# verify it returns true when the second is true
target.now = pd.to_datetime('2018-01-03')
assert orAlgo(target)
# verify it returns true when both algos return true
target.now = pd.to_datetime('2018-01-04')
assert orAlgo(target)
def test_run_on_date():
target = mock.MagicMock()
target.now = pd.to_datetime('2010-01-01')
algo = algos.RunOnDate('2010-01-01', '2010-01-02')
assert algo(target)
target.now = pd.to_datetime('2010-01-02')
assert algo(target)
target.now = pd.to_datetime('2010-01-03')
assert not algo(target)
def bt_strategy(self, data, cats, graph_path):
rsmpl = {
'days': 'B',
'weeks': 'W-Fri',
'months': 'BM',
'years': 'BY'
}[self.roll_ptype]
first_date = data.index[0] + relativedelta(**{self.est_ptype: self.est_plen})
run_dates = asfreq_actual(data[data.columns[0]], str(self.roll_plen) + rsmpl)
run_dates = data.loc[run_dates.index]
run_dates = run_dates.loc[run_dates.index > first_date]
run_dates = run_dates.iloc[:-1]
run_dates.loc[data.index[-1]] = data.iloc[-1]
fee_func_parial = partial(fee_func, ff=self.fix_fee, pf=self.prc_fee)
algo_stack = [
algos.RunOnDate(*run_dates.index.tolist()),
algos.SelectAll(),
SaveWeights(),
WeightHRP(plen=self.est_plen, ptype=self.est_ptype, robust=self.robust, ddev=self.ddev, cats=cats, graph_path=graph_path),
GapWeights(self.reb_gap),
]
if self.is_tvol:
algo_stack.append(WeightTargetVol(self.tvol, plen=self.est_plen, ptype=self.est_ptype, kmax=self.leverage))
algo_stack.extend([
WeightAdjust(self.leverage, self.weight_round),
LimitWeights(self.min_weight, self.max_weight, self.leverage),
WeightsToPerm(),
CheckFeeBankrupt(fee_func_parial),
algos.Rebalance()
])
strategy = bt.Strategy(self.name(), algo_stack)
return bt.Backtest(strategy, data.copy(), initial_capital=self.init_balance,
commissions=fee_func_parial, integer_positions=self.int_pos, progress_bar=True)