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Removes chained indexing. #449

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Aug 5, 2024
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Removes chained indexing. #449

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116 changes: 58 additions & 58 deletions tests/test_algos.py
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Original file line number Diff line number Diff line change
Expand Up @@ -651,9 +651,9 @@ def test_select_all():

dts = pd.date_range("2010-01-01", periods=3)
data = pd.DataFrame(index=dts, columns=["c1", "c2"], data=100.0)
data["c1"][dts[1]] = np.nan
data["c2"][dts[1]] = 95
data["c1"][dts[2]] = -5
data.loc[dts[1], "c1"] = np.nan
data.loc[dts[1], "c2"] = 95
data.loc[dts[2], "c1"] = -5

s.setup(data)
s.update(dts[0])
Expand Down Expand Up @@ -705,9 +705,9 @@ def test_select_randomly_n_none():

dts = pd.date_range("2010-01-01", periods=3)
data = pd.DataFrame(index=dts, columns=["c1", "c2"], data=100.0)
data["c1"][dts[1]] = np.nan
data["c2"][dts[1]] = 95
data["c1"][dts[2]] = -5
data.loc[dts[1], "c1"] = np.nan
data.loc[dts[1], "c2"] = 95
data.loc[dts[2], "c1"] = -5

s.setup(data)
s.update(dts[0])
Expand Down Expand Up @@ -758,9 +758,9 @@ def test_select_randomly():

dts = pd.date_range("2010-01-01", periods=3)
data = pd.DataFrame(index=dts, columns=["c1", "c2", "c3"], data=100.0)
data["c1"][dts[0]] = np.nan
data["c2"][dts[0]] = 95
data["c3"][dts[0]] = -5
data.loc[dts[0], "c1"] = np.nan
data.loc[dts[0], "c2"] = 95
data.loc[dts[0], "c3"] = -5

s.setup(data)
s.update(dts[0])
Expand Down Expand Up @@ -792,9 +792,9 @@ def test_select_these():

dts = pd.date_range("2010-01-01", periods=3)
data = pd.DataFrame(index=dts, columns=["c1", "c2"], data=100.0)
data["c1"][dts[1]] = np.nan
data["c2"][dts[1]] = 95
data["c1"][dts[2]] = -5
data.loc[dts[1], "c1"] = np.nan
data.loc[dts[1], "c2"] = 95
data.loc[dts[2], "c1"] = -5

s.setup(data)
s.update(dts[0])
Expand Down Expand Up @@ -852,9 +852,9 @@ def test_select_where_all():

dts = pd.date_range("2010-01-01", periods=3)
data = pd.DataFrame(index=dts, columns=["c1", "c2"], data=100.0)
data["c1"][dts[1]] = np.nan
data["c2"][dts[1]] = 95
data["c1"][dts[2]] = -5
data.loc[dts[1], "c1"] = np.nan
data.loc[dts[1], "c2"] = 95
data.loc[dts[2], "c1"] = -5

where = pd.DataFrame(index=dts, columns=["c1", "c2"], data=True)

Expand Down Expand Up @@ -911,7 +911,7 @@ def test_select_where():

where = pd.DataFrame(index=dts, columns=["c1", "c2"], data=True)
where.loc[dts[1]] = False
where["c1"].loc[dts[2]] = False
where.loc[dts[2], "c1"] = False

algo = algos.SelectWhere("where")

Expand Down Expand Up @@ -941,7 +941,7 @@ def test_select_where_legacy():

where = pd.DataFrame(index=dts, columns=["c1", "c2"], data=True)
where.loc[dts[1]] = False
where["c1"].loc[dts[2]] = False
where.loc[dts[2], "c1"] = False

algo = algos.SelectWhere(where)

Expand Down Expand Up @@ -980,9 +980,9 @@ def test_resolve_on_the_run():
s = bt.Strategy("s")
dts = pd.date_range("2010-01-01", periods=3)
data = pd.DataFrame(index=dts, columns=["c1", "c2", "b1"], data=100.0)
data["c1"][dts[1]] = np.nan
data["c2"][dts[1]] = 95
data["c2"][dts[2]] = -5
data.loc[dts[1], "c1"] = np.nan
data.loc[dts[1], "c2"] = 95
data.loc[dts[2], "c2"] = -5

on_the_run = pd.DataFrame(index=dts, columns=["c"], data="c1")
on_the_run.loc[dts[2], "c"] = "c2"
Expand Down Expand Up @@ -1097,8 +1097,8 @@ def test_weight_specified():

dts = pd.date_range("2010-01-01", periods=3)
data = pd.DataFrame(index=dts, columns=["c1", "c2"], data=100)
data["c1"][dts[1]] = 105
data["c2"][dts[1]] = 95
data.loc[dts[1], "c1"] = 105
data.loc[dts[1], "c2"] = 95

s.setup(data)
s.update(dts[0])
Expand Down Expand Up @@ -1128,8 +1128,8 @@ def test_select_has_data():

dts = pd.date_range("2010-01-01", periods=10)
data = pd.DataFrame(index=dts, columns=["c1", "c2"], data=100.0)
data["c1"].loc[dts[0]] = np.nan
data["c1"].loc[dts[1]] = np.nan
data.loc[dts[0], "c1"] = np.nan
data.loc[dts[1], "c1"] = np.nan

s.setup(data)
s.update(dts[2])
Expand All @@ -1147,8 +1147,8 @@ def test_select_has_data_preselected():

dts = pd.date_range("2010-01-01", periods=3)
data = pd.DataFrame(index=dts, columns=["c1", "c2"], data=100.0)
data["c1"].loc[dts[0]] = np.nan
data["c1"].loc[dts[1]] = np.nan
data.loc[dts[0], "c1"] = np.nan
data.loc[dts[1], "c1"] = np.nan

s.setup(data)
s.update(dts[2])
Expand Down Expand Up @@ -1195,8 +1195,8 @@ def test_weigh_target():
dts = pd.date_range("2010-01-01", periods=3)
data = pd.DataFrame(index=dts, columns=["c1", "c2"], data=100.0)
target = pd.DataFrame(index=dts[:2], columns=["c1", "c2"], data=0.5)
target["c1"].loc[dts[1]] = 1.0
target["c2"].loc[dts[1]] = 0.0
target.loc[dts[1], "c1"] = 1.0
target.loc[dts[1], "c2"] = 0.0

s.setup(data, target=target)

Expand Down Expand Up @@ -1227,16 +1227,16 @@ def test_weigh_inv_vol():
data = pd.DataFrame(index=dts, columns=["c1", "c2"], data=100.0)

# high vol c1
data["c1"].loc[dts[1]] = 105
data["c1"].loc[dts[2]] = 95
data["c1"].loc[dts[3]] = 105
data["c1"].loc[dts[4]] = 95
data.loc[dts[1], "c1"] = 105
data.loc[dts[2], "c1"] = 95
data.loc[dts[3], "c1"] = 105
data.loc[dts[4], "c1"] = 95

# low vol c2
data["c2"].loc[dts[1]] = 100.1
data["c2"].loc[dts[2]] = 99.9
data["c2"].loc[dts[3]] = 100.1
data["c2"].loc[dts[4]] = 99.9
data.loc[dts[1], "c2"] = 100.1
data.loc[dts[2], "c2"] = 99.9
data.loc[dts[3], "c2"] = 100.1
data.loc[dts[4], "c2"] = 99.9

s.setup(data)
s.update(dts[4])
Expand Down Expand Up @@ -1303,12 +1303,12 @@ def test_set_stat():

dts = pd.date_range("2010-01-01", periods=3)
data = pd.DataFrame(index=dts, columns=["c1", "c2"], data=100.0)
data["c1"].loc[dts[1]] = 105
data["c2"].loc[dts[1]] = 95
data.loc[dts[1], "c1"] = 105
data.loc[dts[1], "c2"] = 95

stat = pd.DataFrame(index=dts, columns=["c1", "c2"], data=4.0)
stat["c1"].loc[dts[1]] = 5.0
stat["c2"].loc[dts[1]] = 6.0
stat.loc[dts[1], "c1"] = 5.0
stat.loc[dts[1], "c2"] = 6.0

algo = algos.SetStat("test_stat")

Expand All @@ -1333,12 +1333,12 @@ def test_set_stat_legacy():

dts = pd.date_range("2010-01-01", periods=3)
data = pd.DataFrame(index=dts, columns=["c1", "c2"], data=100.0)
data["c1"].loc[dts[1]] = 105
data["c2"].loc[dts[1]] = 95
data.loc[dts[1], "c1"] = 105
data.loc[dts[1], "c2"] = 95

stat = pd.DataFrame(index=dts, columns=["c1", "c2"], data=4.0)
stat["c1"].loc[dts[1]] = 5.0
stat["c2"].loc[dts[1]] = 6.0
stat.loc[dts[1], "c1"] = 5.0
stat.loc[dts[1], "c2"] = 6.0

algo = algos.SetStat(stat)

Expand All @@ -1363,8 +1363,8 @@ def test_stat_total_return():

dts = pd.date_range("2010-01-01", periods=3)
data = pd.DataFrame(index=dts, columns=["c1", "c2"], data=100.0)
data["c1"].loc[dts[2]] = 105
data["c2"].loc[dts[2]] = 95
data.loc[dts[2], "c1"] = 105
data.loc[dts[2], "c2"] = 95

s.setup(data)
s.update(dts[2])
Expand All @@ -1384,8 +1384,8 @@ def test_select_n():

dts = pd.date_range("2010-01-01", periods=3)
data = pd.DataFrame(index=dts, columns=["c1", "c2"], data=100.0)
data["c1"].loc[dts[2]] = 105
data["c2"].loc[dts[2]] = 95
data.loc[dts[2], "c1"] = 105
data.loc[dts[2], "c2"] = 95

s.setup(data)
s.update(dts[2])
Expand Down Expand Up @@ -1424,8 +1424,8 @@ def test_select_n_perc():

dts = pd.date_range("2010-01-01", periods=3)
data = pd.DataFrame(index=dts, columns=["c1", "c2"], data=100.0)
data["c1"].loc[dts[2]] = 105
data["c2"].loc[dts[2]] = 95
data.loc[dts[2], "c1"] = 105
data.loc[dts[2], "c2"] = 95

s.setup(data)
s.update(dts[2])
Expand All @@ -1444,8 +1444,8 @@ def test_select_momentum():

dts = pd.date_range("2010-01-01", periods=3)
data = pd.DataFrame(index=dts, columns=["c1", "c2"], data=100.0)
data["c1"].loc[dts[2]] = 105
data["c2"].loc[dts[2]] = 95
data.loc[dts[2], "c1"] = 105
data.loc[dts[2], "c2"] = 95

s.setup(data)
s.update(dts[2])
Expand Down Expand Up @@ -2024,8 +2024,8 @@ def test_update_risk():
s = bt.Strategy("s", children=[c1, c2])
dts = pd.date_range("2010-01-01", periods=3)
data = pd.DataFrame(index=dts, columns=["c1", "c2"], data=100)
data["c1"].loc[dts[1]] = 105
data["c2"].loc[dts[1]] = 95
data.loc[dts[1], "c1"] = 105
data.loc[dts[1], "c2"] = 95
c1 = s["c1"]
c2 = s["c2"]

Expand Down Expand Up @@ -2064,8 +2064,8 @@ def test_update_risk_history_1():
s = bt.Strategy("s", children=[c1, c2])
dts = pd.date_range("2010-01-01", periods=3)
data = pd.DataFrame(index=dts, columns=["c1", "c2"], data=100)
data["c1"].loc[dts[1]] = 105
data["c2"].loc[dts[1]] = 95
data.loc[dts[1], "c1"] = 105
data.loc[dts[1], "c2"] = 95
c1 = s["c1"]
c2 = s["c2"]

Expand Down Expand Up @@ -2098,8 +2098,8 @@ def test_update_risk_history_2():
s = bt.Strategy("s", children=[c1, c2])
dts = pd.date_range("2010-01-01", periods=3)
data = pd.DataFrame(index=dts, columns=["c1", "c2"], data=100)
data["c1"].loc[dts[1]] = 105
data["c2"].loc[dts[1]] = 95
data.loc[dts[1], "c1"] = 105
data.loc[dts[1], "c2"] = 95
c1 = s["c1"]
c2 = s["c2"]

Expand Down
32 changes: 16 additions & 16 deletions tests/test_backtest.py
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Original file line number Diff line number Diff line change
Expand Up @@ -83,14 +83,14 @@ def test_turnover():
dts = pd.date_range("2010-01-01", periods=5)
data = pd.DataFrame(index=dts, columns=["a", "b"], data=100)

data["a"][dts[1]] = 105
data["b"][dts[1]] = 95
data.loc[dts[1], "a"] = 105
data.loc[dts[1], "b"] = 95

data["a"][dts[2]] = 110
data["b"][dts[2]] = 90
data.loc[dts[2], "a"] = 110
data.loc[dts[2], "b"] = 90

data["a"][dts[3]] = 115
data["b"][dts[3]] = 85
data.loc[dts[3], "a"] = 115
data.loc[dts[3], "b"] = 85

s = bt.Strategy(
"s", [bt.algos.SelectAll(), bt.algos.WeighEqually(), bt.algos.Rebalance()]
Expand Down Expand Up @@ -265,18 +265,18 @@ def test_30_min_data():
def test_RenomalizedFixedIncomeResult():
dts = pd.date_range("2010-01-01", periods=5)
data = pd.DataFrame(index=dts, columns=["a"], data=1.0)
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
data.loc[dts[0], "a"] = 0.99
data.loc[dts[1], "a"] = 1.01
data.loc[dts[2], "a"] = 0.99
data.loc[dts[3], "a"] = 1.01
data.loc[dts[4], "a"] = 0.99

weights = pd.DataFrame(index=dts, columns=["a"], data=1.0)
weights["a"][dts[0]] = 1.0
weights["a"][dts[1]] = 2.0
weights["a"][dts[2]] = 1.0
weights["a"][dts[3]] = 2.0
weights["a"][dts[4]] = 1.0
weights.loc[dts[0], "a"] = 1.0
weights.loc[dts[1], "a"] = 2.0
weights.loc[dts[2], "a"] = 1.0
weights.loc[dts[3], "a"] = 2.0
weights.loc[dts[4], "a"] = 1.0

coupons = pd.DataFrame(index=dts, columns=["a"], data=0.0)

Expand Down
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