def test_select_types():
c1 = bt.Security('c1')
c2 = bt.CouponPayingSecurity('c2')
c3 = bt.HedgeSecurity('c3')
c4 = bt.CouponPayingHedgeSecurity('c4')
c5 = bt.FixedIncomeSecurity('c5')
s = bt.Strategy('p', children = [c1, c2, c3, c4, c5])
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2', 'c3', 'c4', 'c5'], data=100.)
coupons = pd.DataFrame(index=dts, columns=['c2', 'c4'], data=0.)
s.setup(data, coupons = coupons)
i = 0
s.update(dts[i])
algo = algos.SelectTypes(include_types=(bt.Security, bt.HedgeSecurity), exclude_types=())
assert algo(s)
assert set(s.temp.pop('selected')) == set(['c1', 'c3'])
algo = algos.SelectTypes(include_types=(bt.core.SecurityBase,), exclude_types=(bt.CouponPayingSecurity,))
assert algo(s)
assert set(s.temp.pop('selected')) == set(['c1', 'c3', 'c5'])
s.temp['selected'] = ['c1', 'c2', 'c3']
algo = algos.SelectTypes(include_types=(bt.core.SecurityBase,))
assert algo(s)
assert set(s.temp.pop('selected')) == set(['c1', 'c2', 'c3'])
def test_rebalance_fixedincome():
algo = algos.Rebalance()
c1 = bt.Security('c1')
c2 = bt.CouponPayingSecurity('c2')
s = bt.FixedIncomeStrategy('s', children = [c1, c2])
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
coupons = pd.DataFrame(index=dts, columns=['c2'], data=0)
s.setup(data, coupons=coupons)
s.update(dts[0])
s.temp['notional_value'] = 1000
s.temp['weights'] = {'c1': 1}
assert algo(s)
assert s.value == 0.
assert s.notional_value == 1000
assert s.capital == -1000
c1 = s['c1']
assert c1.value == 1000
assert c1.notional_value == 1000
assert c1.position == 10
assert c1.weight == 1.
s.temp['weights'] = {'c2': 1}
assert algo(s)
assert s.value == 0.
assert s.notional_value == 1000
assert s.capital == -1000*100
c2 = s['c2']
assert c1.value == 0
assert c1.notional_value == 0
assert c1.position == 0
assert c1.weight == 0
assert c2.value == 1000*100
assert c2.notional_value == 1000
assert c2.position == 1000
assert c2.weight == 1.
def benchmark_2():
x = np.random.randn(10000, 1000) * 0.01
idx = pd.date_range('1990-01-01', freq='B', periods=x.shape[0])
data = np.exp(pd.DataFrame(x, index=idx).cumsum())
bidoffer = data * 0.01
coupons = data * 0.
s = bt.FixedIncomeStrategy(
's',
algos=[
bt.algos.RunMonthly(),
bt.algos.SelectRandomly(len(data.columns) / 2),
bt.algos.WeighRandomly(),
bt.algos.Rebalance()
],
children=[bt.CouponPayingSecurity(c) for c in data])
t = bt.Backtest(s,
data,
additional_data={
'bidoffer': bidoffer,
'coupons': coupons
})
return bt.run(t)
def test_RenomalizedFixedIncomeResult():
dts = pd.date_range('2010-01-01', periods=5)
data = pd.DataFrame(index=dts, columns=['a'], data=1.)
data['a'][dts[0]] = 0.99
data['a'][dts[1]] = 1.01
data['a'][dts[2]] = 0.99
data['a'][dts[3]] = 1.01
data['a'][dts[4]] = 0.99
weights = pd.DataFrame(index=dts, columns=['a'], data=1.)
weights['a'][dts[0]] = 1.
weights['a'][dts[1]] = 2.
weights['a'][dts[2]] = 1.
weights['a'][dts[3]] = 2.
weights['a'][dts[4]] = 1.
coupons = pd.DataFrame(index=dts, columns=['a'], data=0.)
algos = [
bt.algos.SelectAll(),
bt.algos.WeighTarget(weights),
bt.algos.SetNotional('notional'),
bt.algos.Rebalance()
]
children = [bt.CouponPayingSecurity('a')]
s = bt.FixedIncomeStrategy('s', algos, children=children)
t = bt.Backtest(s,
data,
initial_capital=0,
additional_data={
'notional': pd.Series(1e6, dts),
'coupons': coupons
},
progress_bar=False)
res = bt.run(t)
t = res.backtests['s']
# Due to the relationship between the time varying notional and the prices,
# the strategy has lost money, but price == 100, so "total return" is zero
assert t.strategy.value < 0.
assert t.strategy.price == 100.
assert res.stats['s'].total_return == 0
# Renormalizing results to a constant size "fixes" this
norm_res = bt.backtest.RenormalizedFixedIncomeResult(
1e6, *res.backtest_list)
aae(norm_res.stats['s'].total_return, t.strategy.value / 1e6, 16)
# Check that using the lagged notional value series leads to the same results
# as the original calculation. This proves that we can re-derive the price
# series from the other data available on the strategy
notl_values = t.strategy.notional_values.shift(1)
notl_values[
dts[0]] = 1e6 # The notional value *before* any trades are put on
norm_res = bt.backtest.RenormalizedFixedIncomeResult(
notl_values, *res.backtest_list)
assert norm_res.stats['s'].total_return == res.stats['s'].total_return
assert norm_res.prices.equals(res.prices)