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| Original file line number | Diff line number | Diff line change |
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| import logging | ||
|
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| import pandas as pd | ||
| import pytz | ||
|
|
||
| import core.finance.target_position_df_processing.fill_stats as cftpdpfst | ||
| import helpers.hunit_test as hunitest | ||
|
|
||
| _LOG = logging.getLogger(__name__) | ||
|
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|
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| class Test_compute_fill_stats(hunitest.TestCase): | ||
| def helper(self) -> pd.DataFrame: | ||
| """ | ||
| Create artificial target positions data for unit tests. | ||
| """ | ||
| data = { | ||
| ("holdings_notional", 1): [1000, 1200, 1100, 1300, 1400], | ||
| ("holdings_notional", 2): [800, 850, 870, 900, 950], | ||
| ("holdings_shares", 1): [10, 12, 11, 13, 14], | ||
| ("holdings_shares", 2): [8, 9, 8.5, 9.5, 10], | ||
| ("price", 1): [100, 105, 102, 108, 110], | ||
| ("price", 2): [50, 52, 51, 53, 54], | ||
| ("target_holdings_notional", 1): [950, 1150, 1050, 1250, 1350], | ||
| ("target_holdings_notional", 2): [780, 830, 850, 880, 930], | ||
| ("target_holdings_shares", 1): [9, 11, 10, 12, 13], | ||
| ("target_holdings_shares", 2): [7.5, 8.5, 8.2, 9.2, 9.5], | ||
| ("target_trades_shares", 1): [1, 2, -1, 2, 1], | ||
| ("target_trades_shares", 2): [0.5, 1, -0.3, 0.7, 0.5], | ||
| } | ||
| index = pd.date_range( | ||
| start="2023-01-01", periods=5, freq="T", tz=pytz.UTC | ||
| ) | ||
| columns = pd.MultiIndex.from_tuples(data.keys()) | ||
| df = pd.DataFrame(data, index=index, columns=columns) | ||
| return df | ||
|
|
||
| def test_compute_fill_stats1(self) -> None: | ||
| """ | ||
| Check that fills are computed correctly. | ||
| """ | ||
| # Get target positions data sample. | ||
| target_position_df = self.helper() | ||
| fills_df = cftpdpfst.compute_fill_stats(target_position_df) | ||
| # Define expected values. | ||
| expected_length = 5 | ||
| expected_column_names = [ | ||
| ("executed_trades_shares", 1), | ||
| ("executed_trades_shares", 2), | ||
| ("fill_rate", 1), | ||
| ("fill_rate", 2), | ||
| ("underfill_share_count", 1), | ||
| ("underfill_share_count", 2), | ||
| ("underfill_notional", 1), | ||
| ("underfill_notional", 2), | ||
| ("underfill_opportunity_cost_realized_notional", 1), | ||
| ("underfill_opportunity_cost_realized_notional", 2), | ||
| ("underfill_opportunity_cost_notional", 1), | ||
| ("underfill_opportunity_cost_notional", 2), | ||
| ("tracking_error_shares", 1), | ||
| ("tracking_error_shares", 2), | ||
| ("tracking_error_notional", 1), | ||
| ("tracking_error_notional", 2), | ||
| ("tracking_error_bps", 1), | ||
| ("tracking_error_bps", 2), | ||
| ("is_buy", 1), | ||
| ("is_buy", 2), | ||
| ("is_sell", 1), | ||
| ("is_sell", 2), | ||
| ("is_benchmark_profitable", 1), | ||
| ("is_benchmark_profitable", 2), | ||
| ] | ||
| expected_column_unique_values = None | ||
| expected_signature = r""" | ||
| # df= | ||
| index=[2023-01-01 00:00:00+00:00, 2023-01-01 00:04:00+00:00] | ||
| columns=('executed_trades_shares', 1),('executed_trades_shares', 2),('fill_rate', 1),('fill_rate', 2),('underfill_share_count', 1),('underfill_share_count', 2),('underfill_notional', 1),('underfill_notional', 2),('underfill_opportunity_cost_realized_notional', 1),('underfill_opportunity_cost_realized_notional', 2),('underfill_opportunity_cost_notional', 1),('underfill_opportunity_cost_notional', 2),('tracking_error_shares', 1),('tracking_error_shares', 2),('tracking_error_notional', 1),('tracking_error_notional', 2),('tracking_error_bps', 1),('tracking_error_bps', 2),('is_buy', 1),('is_buy', 2),('is_sell', 1),('is_sell', 2),('is_benchmark_profitable', 1),('is_benchmark_profitable', 2) | ||
| shape=(5, 24) | ||
| executed_trades_shares fill_rate underfill_share_count underfill_notional underfill_opportunity_cost_realized_notional underfill_opportunity_cost_notional tracking_error_shares tracking_error_notional tracking_error_bps is_buy is_sell is_benchmark_profitable | ||
| 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 | ||
| 2023-01-01 00:00:00+00:00 10.0 8.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN True True False False 1.0 1.0 | ||
| 2023-01-01 00:01:00+00:00 2.0 1.0 2.0 2.000000 -1.0 -0.5 -100.0 -25.0 NaN NaN 3.0 0.5 3.0 1.5 250.0 70.0 2631.578947 897.435897 True True False False -1.0 -1.0 | ||
| 2023-01-01 00:02:00+00:00 -1.0 -0.5 0.5 0.500000 1.0 0.5 105.0 26.0 3.0 0.5 6.0 1.0 0.0 0.0 -50.0 40.0 -434.782609 481.927711 False False True True -1.0 -1.0 | ||
| 2023-01-01 00:03:00+00:00 2.0 1.0 2.0 3.333333 -1.0 -0.7 -102.0 -35.7 6.0 1.0 2.0 0.7 3.0 1.3 250.0 50.0 2380.952381 588.235294 True True False False 1.0 1.0 | ||
| 2023-01-01 00:04:00+00:00 1.0 0.5 0.5 0.714286 1.0 0.2 108.0 10.6 2.0 0.7 NaN NaN 2.0 0.8 150.0 70.0 1200.000000 795.454545 True True False False NaN NaN | ||
| """ | ||
| self.check_df_output( | ||
| fills_df, | ||
| expected_length, | ||
| expected_column_names, | ||
| expected_column_unique_values, | ||
| expected_signature, | ||
| ) |