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TPTBusiness
2de7275b8f
EUR/USD synthetic data has \$volume=0 for all rows, causing any VWAP or volume-weighted factor to produce all-NaN output. Insert a guard after pd.read_hdf() that replaces zero volume with (\$high - \$low) range proxy so volume-dependent factors produce meaningful signals. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
250 lines
10 KiB
Python
250 lines
10 KiB
Python
"""Tests for FactorAutoFixer — the pre-execution code patcher."""
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import pytest
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from rdagent.components.coder.factor_coder.auto_fixer import FactorAutoFixer
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@pytest.fixture()
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def fixer():
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return FactorAutoFixer()
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class TestResetIndexGroupby:
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def test_replaces_level_groupby_on_reset_var(self, fixer):
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code = "df_r = df.reset_index()\ndf_r['x'] = df_r.groupby(level=1)['$close'].mean()"
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result = fixer.fix(code)
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assert "groupby('instrument')" in result
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def test_does_not_touch_normal_multiindex_groupby(self, fixer):
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code = "df['x'] = df.groupby(level=1)['$close'].mean()"
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result = fixer.fix(code)
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assert "groupby(level=1)" in result
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class TestGroupbyMixedLevels:
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def test_strips_string_from_mixed_list(self, fixer):
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result = fixer.fix("df.groupby(level=[1, 'date']).apply(fn)")
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assert "groupby(level=1)" in result
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def test_multiple_ints_kept(self, fixer):
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result = fixer.fix("df.groupby(level=[0, 1, 'x']).apply(fn)")
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assert "groupby(level=[0, 1])" in result
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class TestGroupbyColumnOnMultiindex:
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def test_instrument_date_becomes_two_level(self, fixer):
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code = "df['v'] = df.groupby(['instrument', 'date'])['$volume'].cumsum()"
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result = fixer.fix(code)
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assert "get_level_values(1)" in result
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assert "normalize()" in result
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assert "level=1)" not in result.split("get_level_values")[0]
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def test_date_instrument_becomes_two_level(self, fixer):
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code = "df['v'] = df.groupby(['date', 'instrument'])['$volume'].cumsum()"
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result = fixer.fix(code)
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assert "get_level_values(0).normalize()" in result
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assert "get_level_values(1)" in result
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def test_single_instrument_becomes_level1(self, fixer):
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result = fixer.fix("df.groupby(['instrument'])['x'].mean()")
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assert "groupby(level=1)" in result
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def test_reset_index_not_double_fixed(self, fixer):
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# After reset_index fix emits groupby('instrument'), this fixer must NOT
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# convert that to groupby(level=1).
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code = "df_r = df.reset_index()\ndf_r['x'] = df_r.groupby(level=1)['p'].mean()"
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result = fixer.fix(code)
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assert "groupby('instrument')" in result
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class TestChainedGroupby:
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def test_chained_groupby_level_then_date(self, fixer):
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code = "df.groupby(level=1).groupby('date')['price_volume'].transform('cumsum')"
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result = fixer.fix(code)
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assert "get_level_values(1)" in result
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assert "get_level_values(0).normalize()" in result
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assert ".groupby('date')" not in result
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def test_chained_groupby_with_double_quotes(self, fixer):
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code = 'df.groupby(level=0).groupby("date")["col"].sum()'
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result = fixer.fix(code)
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assert "get_level_values" in result
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assert '.groupby("date")' not in result
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def test_list_with_level_keyword_syntax_error(self, fixer):
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# groupby([level=1, 'date']) is a SyntaxError — must be fixed before execution
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code = "asian_vol = df[mask].groupby([level=1, 'date'])['log_return'].std()"
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result = fixer.fix(code)
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assert "get_level_values(1)" in result
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assert "normalize()" in result
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assert "level=1," not in result
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def test_list_with_level_keyword_reversed(self, fixer):
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code = "df.groupby(['date', level=1])['x'].mean()"
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result = fixer.fix(code)
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assert "get_level_values" in result
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assert "level=1" not in result
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class TestMinPeriodsNotTouched:
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def test_small_min_periods_preserved(self, fixer):
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# _fix_min_periods is disabled — LLM-set min_periods must not be changed.
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# window=60, min_periods=1 should stay as-is (was wrongly raised to 60 before).
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result = fixer.fix("df.groupby(level=1)['x'].transform(lambda x: x.rolling(window=60, min_periods=1).mean())")
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assert "min_periods=1" in result
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def test_large_window_min_periods_preserved(self, fixer):
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# window=240 > 96 bars/day: if min_periods were set to 240 the output would be
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# all-NaN for intraday data. Verify we leave it untouched.
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result = fixer.fix("df['x'] = df.groupby(level=1)['y'].transform(lambda x: x.rolling(240, min_periods=10).std())")
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assert "min_periods=10" in result
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class TestInstrumentColumnAccess:
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def test_instrument_column_replaced(self, fixer):
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code = "df['group_key'] = df['instrument'] + '_' + df['day_id'].astype(str)"
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result = fixer.fix(code)
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assert "df.index.get_level_values(1)" in result
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assert "df['instrument']" not in result
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def test_reset_index_var_not_touched(self, fixer):
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# After reset_index, 'instrument' IS a real column — must not be replaced
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code = "df_r = df.reset_index()\nval = df_r['instrument'].unique()"
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result = fixer.fix(code)
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assert "df_r['instrument']" in result
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assert "get_level_values" not in result
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def test_groupby_after_instrument_fix(self, fixer):
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# Combined: df['instrument'] in a groupby context
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code = "df['key'] = df['instrument']\nout = df.groupby(df['key'])[['$close']].mean()"
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result = fixer.fix(code)
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assert "df['instrument']" not in result
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def test_assignment_target_not_touched(self, fixer):
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# df['instrument'] = <expr> is an assignment — must NOT be converted to
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# df.index.get_level_values(1) = <expr> (SyntaxError)
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code = "df['instrument'] = df.index.get_level_values('instrument')"
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result = fixer.fix(code)
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assert "df['instrument'] =" in result
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class TestInstrumentLocMultiindex:
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def test_loc_replaced_with_xs(self, fixer):
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code = (
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"for instrument in df.index.get_level_values('instrument').unique():\n"
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" inst_df = df.loc[instrument].copy()\n"
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)
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result = fixer.fix(code)
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assert "df.xs(instrument, level=1)" in result
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assert "df.loc[instrument]" not in result
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def test_loc_replaced_with_level1_int(self, fixer):
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code = (
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"for inst in df.index.get_level_values(1).unique():\n"
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" data = df.loc[inst]\n"
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)
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result = fixer.fix(code)
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assert "df.xs(inst, level=1)" in result
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def test_loc_assignment_not_touched(self, fixer):
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# Write-back df.loc[instrument] = ... must not be changed
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code = (
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"for instrument in df.index.get_level_values('instrument').unique():\n"
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" df.loc[instrument] = modified\n"
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)
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result = fixer.fix(code)
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assert "df.loc[instrument] = modified" in result
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def test_non_instrument_loop_not_touched(self, fixer):
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# for-loop not related to instrument levels must not be changed
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code = "for date in dates:\n sub = df.loc[date]\n"
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result = fixer.fix(code)
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assert "df.loc[date]" in result
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class TestGroupbyLevelStringNames:
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def test_level_instrument_date_replaced(self, fixer):
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code = "df.groupby(level=['instrument', 'date'])['col'].transform('sum')"
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result = fixer.fix(code)
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assert "get_level_values(1)" in result
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assert "get_level_values(0).normalize()" in result
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assert "level=['instrument', 'date']" not in result
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def test_level_date_instrument_replaced(self, fixer):
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code = "data.groupby(level=['date', 'instrument'])['x'].mean()"
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result = fixer.fix(code)
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assert "get_level_values(0).normalize()" in result
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assert "get_level_values(1)" in result
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def test_level_instrument_single_replaced(self, fixer):
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code = "df.groupby(level=['instrument'])['vol'].sum()"
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result = fixer.fix(code)
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assert "groupby(level=1)" in result
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assert "level=['instrument']" not in result
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class TestGroupbyApplyToTransform:
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def test_col_apply_lambda_replaced(self, fixer):
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code = "df_overlap.groupby(level=1)['$close'].apply(lambda x: np.log(x / x.shift(1)))"
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result = fixer.fix(code)
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assert ".transform(" in result
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assert ".apply(" not in result
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def test_col_apply_lambda_preserves_lambda_body(self, fixer):
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code = "series.groupby(level=1)['ret'].apply(lambda x: x.cumsum())"
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result = fixer.fix(code)
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assert "lambda x: x.cumsum()" in result
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assert ".transform(" in result
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def test_transform_reset_index_stripped(self, fixer):
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# .transform() already preserves index — .reset_index() after it is wrong
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code = "df['v'] = df.groupby(level=1)['x'].transform(lambda x: x.rolling(20).mean()).reset_index(level=0, drop=True)"
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result = fixer.fix(code)
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assert ".reset_index(level=0, drop=True)" not in result
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assert ".transform(" in result
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class TestZeroVolumeProxy:
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def test_injects_proxy_when_volume_used(self, fixer):
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code = (
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"def calc():\n"
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" df = pd.read_hdf('data.h5', key='data')\n"
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" df['pv'] = df['$close'] * df['$volume']\n"
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" return df[['pv']]\n"
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)
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result = fixer.fix(code)
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assert "volume proxy" in result
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assert "df['$volume'] = df['$high'] - df['$low']" in result
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# Proxy must come right after read_hdf line
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lines = result.splitlines()
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hdf_idx = next(i for i, l in enumerate(lines) if "read_hdf" in l)
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assert "volume proxy" in lines[hdf_idx + 1]
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def test_no_injection_when_volume_absent(self, fixer):
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code = "df = pd.read_hdf('data.h5', key='data')\ndf['x'] = df['$close'].pct_change()\n"
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result = fixer.fix(code)
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assert "volume proxy" not in result
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def test_no_double_injection(self, fixer):
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code = (
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"def calc():\n"
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" df = pd.read_hdf('data.h5', key='data')\n"
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" # volume proxy: $volume is always 0 in FX data — use price-range as proxy\n"
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" if (df['$volume'] == 0).all():\n"
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" df['$volume'] = df['$high'] - df['$low']\n"
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" df['pv'] = df['$close'] * df['$volume']\n"
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)
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result = fixer.fix(code)
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assert result.count("volume proxy") == 1
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class TestRollingDdof:
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def test_removes_ddof_from_rolling_args(self, fixer):
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result = fixer.fix("df.rolling(20, min_periods=1, ddof=1).std()")
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assert "ddof" not in result
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def test_removes_ddof_from_std_args(self, fixer):
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result = fixer.fix("df.rolling(20).std(ddof=1)")
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assert "ddof" not in result
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