Codex
558 lines
25 KiB
Python
558 lines
25 KiB
Python
from __future__ import annotations
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import sys
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import tempfile
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import unittest
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from argparse import Namespace
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from pathlib import Path
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import numpy as np
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import pandas as pd
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TRAINING_ROOT = Path(__file__).resolve().parents[1]
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if str(TRAINING_ROOT) not in sys.path:
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sys.path.insert(0, str(TRAINING_ROOT))
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from trader_training.onnx_export import LinearHead, export_heads
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from trader_training.dynamic_exit_search import search_dynamic_exit_plans
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from trader_training.entry_feature_screen import _bucket_edges, _screen_edge_column
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from trader_training.io_utils import read_json, write_json
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from trader_training.labels import ENTRY_LABEL_METHOD, _path_stats_for_group, build_entry_labels
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from trader_training.ofi_feature_experiment import _load_entry_dataset, l1_snapshot_diff_ofi_quote
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from trader_training.promote import promote_artifact_bundle
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from trader_training.replay import build_splits
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from trader_training.schemas import FEATURE_ORDER, LATEST_STRESS_SPLIT, MODEL_OUTPUTS, OUTPUT_MAPPING, TRAINING_SPLITS, VALIDATION_LOCKED_SPLIT
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class TrainingContractTest(unittest.TestCase):
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def test_feature_order_is_v4_contract_size(self) -> None:
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self.assertEqual(54, len(FEATURE_ORDER))
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self.assertEqual(len(FEATURE_ORDER), len(set(FEATURE_ORDER)))
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self.assertEqual("ret_1m_bps", FEATURE_ORDER[0])
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self.assertEqual("book_pressure_reversal_15m", FEATURE_ORDER[-1])
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def test_output_mapping_matches_model_outputs(self) -> None:
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for model_name, fields in MODEL_OUTPUTS.items():
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self.assertEqual(set(fields), set(OUTPUT_MAPPING[model_name]))
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self.assertEqual([f"prediction[{idx}]" for idx in range(len(fields))], [OUTPUT_MAPPING[model_name][field] for field in fields])
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def test_entry_feature_screen_prefers_actual_plan_edge(self) -> None:
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dataset = pd.DataFrame(
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{
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"long_expected_net_edge_bps": [20.0],
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"short_expected_net_edge_bps": [15.0],
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"long_actual_plan_net_edge_bps": [-3.0],
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"short_actual_plan_net_edge_bps": [4.0],
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}
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)
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self.assertEqual("long_actual_plan_net_edge_bps", _screen_edge_column(dataset, "LONG"))
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self.assertEqual("short_actual_plan_net_edge_bps", _screen_edge_column(dataset, "SHORT"))
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def test_entry_feature_screen_keeps_zero_inflated_event_features(self) -> None:
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values = np.concatenate((np.zeros(5000), np.linspace(1.0, 100.0, 500)))
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edges = _bucket_edges(values)
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self.assertGreaterEqual(len(edges), 3)
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self.assertEqual(-np.inf, edges[0])
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self.assertEqual(np.inf, edges[-1])
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def test_dynamic_exit_search_writes_plan_diagnostics(self) -> None:
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with tempfile.TemporaryDirectory() as tmp:
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data_root = Path(tmp)
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run_root = data_root / "trader-v4" / "runs" / "unit-dynamic-exit"
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feature_path = run_root / "feature" / "feature_frame.parquet"
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replay_path = run_root / "replay" / "replay_1m.parquet"
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config_path = data_root / "label_config.json"
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feature_path.parent.mkdir(parents=True)
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replay_path.parent.mkdir(parents=True)
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times = pd.date_range("2026-01-01", periods=7, freq="min", tz="UTC")
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pd.DataFrame(
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{
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"sample_id": [f"s{i}" for i in range(4)],
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"symbol": ["BTC-USDT-PERP"] * 4,
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"event_time": times[:4],
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"open_time_ms": [0, 60_000, 120_000, 180_000],
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"split_id": ["tune_inner", "validation_locked", "latest_stress", "fit_inner"],
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"walk_forward_fold": [0, 0, 0, 0],
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"data_quality_flag": ["OK", "OK", "OK", "OK"],
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}
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).to_parquet(feature_path, index=False)
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pd.DataFrame(
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{
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"event_time": times,
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"open_time_ms": [0, 60_000, 120_000, 180_000, 240_000, 300_000, 360_000],
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"symbol": ["BTC-USDT-PERP"] * 7,
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"open": [100.0] * 7,
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"high": [100.0, 100.12, 100.22, 100.24, 100.24, 100.24, 100.24],
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"low": [100.0, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00],
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"close": [100.0, 100.10, 100.18, 100.20, 100.20, 100.20, 100.20],
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"spread_bps": [1.0] * 7,
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}
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).to_parquet(replay_path, index=False)
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write_json(config_path, {"entry": {"min_expected_net_edge_bps": 3.0}})
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search_dynamic_exit_plans(
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Namespace(
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data_root=data_root,
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run_id="unit-dynamic-exit",
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feature_path=feature_path,
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replay_path=replay_path,
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label_config_path=config_path,
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cost_config_path=None,
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horizons=(3,),
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targets=(10.0,),
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stops=(5.0,),
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trailing_stops=(4.0,),
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second_target_multipliers=(2.0,),
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take1_ratios=(0.5,),
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take2_ratios=(0.25,),
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output_dir_name="dynamic-exit-search",
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)
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)
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result = read_json(run_root / "dynamic-exit-search" / "dynamic_exit_search_result.json")
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self.assertEqual("DYNAMIC_TRAILING_V1", result["best_plan"]["plan_method"])
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self.assertEqual(1, result["candidate_count"])
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self.assertTrue((run_root / "dynamic-exit-search" / "dynamic_exit_search_report.md").is_file())
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def test_ofi_entry_dataset_uses_actual_plan_edge(self) -> None:
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with tempfile.TemporaryDirectory() as tmp:
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baseline_root = Path(tmp)
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dataset_path = baseline_root / "dataset" / "entry_train.parquet"
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dataset_path.parent.mkdir(parents=True)
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pd.DataFrame(
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{
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"sample_id": ["s1"],
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"long_entry_target": [1],
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"short_entry_target": [0],
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"long_actual_plan_net_edge_bps": [4.0],
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"short_actual_plan_net_edge_bps": [-7.0],
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}
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).to_parquet(dataset_path, index=False)
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feature = pd.DataFrame(
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{
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"sample_id": ["s1"],
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"symbol": ["BTC-USDT-PERP"],
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"event_time": pd.to_datetime(["2026-01-01T00:00:00Z"]),
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"open_time_ms": [0],
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"split_id": ["fit_inner"],
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"walk_forward_fold": [0],
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"data_quality_flag": ["OK"],
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}
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)
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dataset = _load_entry_dataset(baseline_root, feature)
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self.assertIn("long_actual_plan_net_edge_bps", dataset.columns)
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self.assertNotIn("long_expected_net_edge_bps", dataset.columns)
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self.assertEqual(4.0, float(dataset.loc[0, "long_actual_plan_net_edge_bps"]))
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def test_split_builder_uses_locked_validation_contract(self) -> None:
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with tempfile.TemporaryDirectory() as tmp:
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data_root = Path(tmp)
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replay_path = data_root / "replay_1m.parquet"
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frame = pd.DataFrame(
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{
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"event_time": pd.date_range("2025-06-20", "2026-06-19", freq="D", tz="UTC"),
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"symbol": "BTC-USDT-PERP",
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}
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)
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frame.to_parquet(replay_path, index=False)
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build_splits(
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Namespace(
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data_root=data_root,
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run_id="unit-split",
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replay_path=replay_path,
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fit_inner_start="2025-06-20",
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fit_inner_end="2026-01-15",
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tune_inner_start="2026-01-16",
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tune_inner_end="2026-02-28",
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validation_locked_start="2026-03-01",
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validation_locked_end="2026-04-30",
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latest_stress_start="2026-05-01",
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latest_stress_end="2026-06-19",
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gap_minutes=0,
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fold_count=2,
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)
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)
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manifest = read_json(data_root / "trader-v4" / "runs" / "unit-split" / "split" / "split_manifest.json")
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self.assertEqual(set(TRAINING_SPLITS), {item["split_id"] for item in manifest["splits"]})
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self.assertEqual([VALIDATION_LOCKED_SPLIT, LATEST_STRESS_SPLIT], manifest["sealed_splits"])
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self.assertEqual("FINAL_GATE_ONLY", manifest["latest_stress_policy"])
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def test_path_stats_keeps_same_bar_target_stop_as_stop_first(self) -> None:
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frame = pd.DataFrame(
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{
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"event_time": pd.date_range("2026-01-01", periods=6, freq="min", tz="UTC"),
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"open_time_ms": np.arange(6, dtype=np.int64) * 60_000,
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"symbol": "BTC-USDT-PERP",
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"close": [100.0, 100.0, 100.0, 100.0, 100.0, 100.0],
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"high": [100.0, 100.05, 100.20, 100.0, 100.0, 100.0],
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"low": [100.0, 99.95, 99.70, 100.0, 100.0, 100.0],
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"spread_bps": [1.0, 1.1, 1.2, 1.3, 1.4, 1.5],
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}
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)
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stats = _path_stats_for_group(frame, "LONG", horizon=3, target_bps=10.0, stop_bps=8.0)
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first = stats.loc[stats["open_time_ms"].eq(0)].iloc[0]
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self.assertEqual(0, first["target_hit"])
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self.assertEqual(1, first["stop_hit"])
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self.assertEqual(1, first["ambiguous_hit"])
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self.assertEqual(120_000, first["time_to_stop_ms"])
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self.assertAlmostEqual(-8.0, first["gross_edge_bps"])
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def test_entry_label_uses_price_plan_outcome_not_max_future_edge(self) -> None:
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with tempfile.TemporaryDirectory() as tmp:
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data_root = Path(tmp)
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run_root = data_root / "trader-v4" / "runs" / "unit-entry"
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feature_path = run_root / "feature" / "feature_frame.parquet"
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replay_path = run_root / "replay" / "replay_1m.parquet"
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plan_path = run_root / "label" / "price_plan_context.json"
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config_path = data_root / "label_config.json"
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feature_path.parent.mkdir(parents=True)
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replay_path.parent.mkdir(parents=True)
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times = pd.date_range("2026-01-01", periods=5, freq="min", tz="UTC")
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pd.DataFrame(
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{
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"sample_id": ["s0", "s1"],
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"symbol": "BTC-USDT-PERP",
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"event_time": times[:2],
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"open_time_ms": [0, 60_000],
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"split_id": "fit_inner",
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"walk_forward_fold": 0,
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"data_quality_flag": "OK",
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"spread_bps": 1.0,
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"spread_rank_24h_pct": 0.1,
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"realized_vol_15m_bps": 2.0,
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}
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).to_parquet(feature_path, index=False)
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pd.DataFrame(
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{
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"event_time": times,
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"open_time_ms": np.arange(5, dtype=np.int64) * 60_000,
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"symbol": "BTC-USDT-PERP",
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"open": [100.0, 100.0, 100.0, 100.0, 100.0],
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"high": [100.0, 100.05, 100.19, 100.20, 100.0],
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"low": [100.0, 99.99, 99.98, 99.97, 100.0],
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"close": [100.0, 100.0, 100.0, 100.0, 100.0],
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"spread_bps": 1.0,
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}
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).to_parquet(replay_path, index=False)
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write_json(
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config_path,
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{
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"entry": {
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"max_hold_minutes": 3,
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"target_bps": 50.0,
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"stop_bps": 50.0,
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"min_expected_net_edge_bps": 3.0,
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}
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},
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)
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write_json(
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plan_path,
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{
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"pricePlanId": "unit-plan",
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"pricePlanConfigHash": "unit-hash",
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"targetDistanceBps": 50.0,
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"stopDistanceBps": 50.0,
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"maxHoldMinutes": 3,
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"costBps": 6.5,
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"entryLabelMethod": ENTRY_LABEL_METHOD,
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},
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)
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build_entry_labels(
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Namespace(
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data_root=data_root,
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run_id="unit-entry",
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feature_path=feature_path,
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replay_path=replay_path,
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label_config_path=config_path,
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cost_config_path=None,
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price_plan_context_path=plan_path,
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)
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)
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labels = pd.read_parquet(run_root / "label" / "entry_labels.parquet")
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row = labels[labels["sample_id"].eq("s0") & labels["side"].eq("LONG")].iloc[0]
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self.assertEqual(0, row["target_hit"])
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self.assertEqual(0, row["entry_target"])
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self.assertEqual(ENTRY_LABEL_METHOD, row["label_method"])
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self.assertAlmostEqual(-6.5, row["expected_net_edge_bps"], places=6)
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self.assertAlmostEqual(row["gross_edge_bps"] - row["cost_bps"], row["expected_net_edge_bps"], places=6)
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self.assertAlmostEqual(row["mfe_bps"] - row["cost_bps"], row["max_achievable_net_edge_bps"], places=6)
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def test_entry_opportunity_label_keeps_plan_outcome_for_pm(self) -> None:
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with tempfile.TemporaryDirectory() as tmp:
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data_root = Path(tmp)
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run_root = data_root / "trader-v4" / "runs" / "unit-entry-opportunity"
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feature_path = run_root / "feature" / "feature_frame.parquet"
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replay_path = run_root / "replay" / "replay_1m.parquet"
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plan_path = run_root / "label" / "price_plan_context.json"
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config_path = data_root / "label_config.json"
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feature_path.parent.mkdir(parents=True)
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replay_path.parent.mkdir(parents=True)
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times = pd.date_range("2026-01-01", periods=5, freq="min", tz="UTC")
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pd.DataFrame(
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{
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"sample_id": ["s0"],
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"symbol": "BTC-USDT-PERP",
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"event_time": [times[0]],
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"open_time_ms": [0],
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"split_id": "fit_inner",
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"walk_forward_fold": 0,
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"data_quality_flag": "OK",
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"spread_bps": 1.0,
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"spread_rank_24h_pct": 0.1,
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"realized_vol_15m_bps": 2.0,
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}
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).to_parquet(feature_path, index=False)
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pd.DataFrame(
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{
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"event_time": times,
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"open_time_ms": np.arange(5, dtype=np.int64) * 60_000,
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"symbol": "BTC-USDT-PERP",
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"open": [100.0] * 5,
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"high": [100.0, 100.05, 100.19, 100.20, 100.0],
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"low": [100.0, 99.99, 99.98, 99.97, 100.0],
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"close": [100.0] * 5,
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"spread_bps": 1.0,
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}
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).to_parquet(replay_path, index=False)
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write_json(
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config_path,
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{
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"entry": {
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"max_hold_minutes": 3,
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"target_bps": 50.0,
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"stop_bps": 50.0,
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"min_expected_net_edge_bps": 3.0,
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"target_method": "OPPORTUNITY_MFE_V1",
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}
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},
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)
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write_json(
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plan_path,
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{
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"pricePlanId": "unit-plan",
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"pricePlanConfigHash": "unit-hash",
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"targetDistanceBps": 50.0,
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"stopDistanceBps": 50.0,
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"maxHoldMinutes": 3,
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"costBps": 6.5,
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"entryLabelMethod": ENTRY_LABEL_METHOD,
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"entryTargetMethod": "OPPORTUNITY_MFE_V1",
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},
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)
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build_entry_labels(
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Namespace(
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data_root=data_root,
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run_id="unit-entry-opportunity",
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feature_path=feature_path,
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replay_path=replay_path,
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label_config_path=config_path,
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cost_config_path=None,
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price_plan_context_path=plan_path,
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)
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)
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labels = pd.read_parquet(run_root / "label" / "entry_labels.parquet")
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row = labels[labels["sample_id"].eq("s0") & labels["side"].eq("LONG")].iloc[0]
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self.assertEqual(0, row["target_hit"])
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self.assertEqual(1, row["entry_target"])
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self.assertEqual("OPPORTUNITY_MFE_V1", row["label_method"])
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self.assertAlmostEqual(row["mfe_bps"] - row["cost_bps"], row["expected_net_edge_bps"], places=6)
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self.assertAlmostEqual(-6.5, row["gross_edge_bps"] - row["cost_bps"], places=6)
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def test_entry_quality_label_rejects_untradable_opportunity(self) -> None:
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with tempfile.TemporaryDirectory() as tmp:
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data_root = Path(tmp)
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run_root = data_root / "trader-v4" / "runs" / "unit-entry-quality"
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feature_path = run_root / "feature" / "feature_frame.parquet"
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replay_path = run_root / "replay" / "replay_1m.parquet"
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plan_path = run_root / "label" / "price_plan_context.json"
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config_path = data_root / "label_config.json"
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feature_path.parent.mkdir(parents=True)
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replay_path.parent.mkdir(parents=True)
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times = pd.date_range("2026-01-01", periods=5, freq="min", tz="UTC")
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pd.DataFrame(
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{
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"sample_id": ["s0"],
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"symbol": "BTC-USDT-PERP",
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"event_time": [times[0]],
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"open_time_ms": [0],
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"split_id": "fit_inner",
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"walk_forward_fold": 0,
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"data_quality_flag": "OK",
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"spread_bps": 1.0,
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"spread_rank_24h_pct": 0.1,
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"realized_vol_15m_bps": 2.0,
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}
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).to_parquet(feature_path, index=False)
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pd.DataFrame(
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{
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"event_time": times,
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"open_time_ms": np.arange(5, dtype=np.int64) * 60_000,
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"symbol": "BTC-USDT-PERP",
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"open": [100.0] * 5,
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"high": [100.0, 100.05, 100.19, 100.20, 100.0],
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"low": [100.0, 99.99, 99.98, 99.97, 100.0],
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"close": [100.0] * 5,
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"spread_bps": 1.0,
|
|
}
|
|
).to_parquet(replay_path, index=False)
|
|
write_json(
|
|
config_path,
|
|
{
|
|
"entry": {
|
|
"max_hold_minutes": 3,
|
|
"target_bps": 50.0,
|
|
"stop_bps": 50.0,
|
|
"min_expected_net_edge_bps": 3.0,
|
|
"min_plan_net_edge_bps": 0.0,
|
|
"max_entry_mae_bps": 12.0,
|
|
"target_method": "OPPORTUNITY_QUALITY_V1",
|
|
}
|
|
},
|
|
)
|
|
write_json(
|
|
plan_path,
|
|
{
|
|
"pricePlanId": "unit-plan",
|
|
"pricePlanConfigHash": "unit-hash",
|
|
"targetDistanceBps": 50.0,
|
|
"stopDistanceBps": 50.0,
|
|
"maxHoldMinutes": 3,
|
|
"costBps": 6.5,
|
|
"entryLabelMethod": ENTRY_LABEL_METHOD,
|
|
"entryTargetMethod": "OPPORTUNITY_QUALITY_V1",
|
|
},
|
|
)
|
|
|
|
build_entry_labels(
|
|
Namespace(
|
|
data_root=data_root,
|
|
run_id="unit-entry-quality",
|
|
feature_path=feature_path,
|
|
replay_path=replay_path,
|
|
label_config_path=config_path,
|
|
cost_config_path=None,
|
|
price_plan_context_path=plan_path,
|
|
)
|
|
)
|
|
|
|
labels = pd.read_parquet(run_root / "label" / "entry_labels.parquet")
|
|
row = labels[labels["sample_id"].eq("s0") & labels["side"].eq("LONG")].iloc[0]
|
|
self.assertEqual("OPPORTUNITY_QUALITY_V1", row["label_method"])
|
|
self.assertGreater(row["expected_net_edge_bps"], 3.0)
|
|
self.assertLess(row["actual_plan_net_edge_bps"], 0.0)
|
|
self.assertEqual(0, row["entry_target"])
|
|
|
|
def test_l1_snapshot_diff_ofi_uses_quote_notional_and_signed_ask_side(self) -> None:
|
|
bid_part, ask_part = l1_snapshot_diff_ofi_quote(
|
|
pd.Series([101.0, 101.0, 100.5]),
|
|
pd.Series([2.0, 3.0, 4.0]),
|
|
pd.Series([102.0, 101.5, 102.5]),
|
|
pd.Series([5.0, 6.0, 7.0]),
|
|
pd.Series([100.0, 101.0, 101.0]),
|
|
pd.Series([1.5, 2.0, 3.0]),
|
|
pd.Series([102.0, 102.0, 101.5]),
|
|
pd.Series([4.0, 5.0, 6.0]),
|
|
)
|
|
|
|
self.assertAlmostEqual(202.0, bid_part.iloc[0])
|
|
self.assertAlmostEqual(-102.0, ask_part.iloc[0])
|
|
self.assertAlmostEqual(101.0, bid_part.iloc[1])
|
|
self.assertAlmostEqual(-609.0, ask_part.iloc[1])
|
|
self.assertAlmostEqual(-303.0, bid_part.iloc[2])
|
|
self.assertAlmostEqual(609.0, ask_part.iloc[2])
|
|
|
|
def test_exported_onnx_accepts_java_feature_shape(self) -> None:
|
|
import onnxruntime as ort
|
|
|
|
with tempfile.TemporaryDirectory() as tmp:
|
|
path = Path(tmp) / "direction.onnx"
|
|
export_heads(
|
|
path,
|
|
[
|
|
LinearHead(
|
|
"direction",
|
|
"softmax",
|
|
np.zeros((len(FEATURE_ORDER), 3), dtype=np.float32),
|
|
np.array([0.1, 0.2, 0.3], dtype=np.float32),
|
|
)
|
|
],
|
|
feature_count=len(FEATURE_ORDER),
|
|
)
|
|
session = ort.InferenceSession(str(path))
|
|
output = session.run(None, {"features": np.zeros((1, len(FEATURE_ORDER)), dtype=np.float32)})[0]
|
|
self.assertEqual((1, 3), output.shape)
|
|
self.assertAlmostEqual(1.0, float(output.sum()), places=6)
|
|
|
|
def test_promotion_requires_passed_validation_and_marks_all_manifests_active(self) -> None:
|
|
with tempfile.TemporaryDirectory() as tmp:
|
|
root = Path(tmp) / "artifact_bundle"
|
|
manifest_dir = root / "manifests"
|
|
manifest_dir.mkdir(parents=True)
|
|
write_json(root.parent / "artifact_validation_result.json", {"status": "PASS", "release_gate_status": "PASS", "release_gate_reasons": []})
|
|
write_json(manifest_dir / "model_bundle_manifest.json", {"status": "CANDIDATE"})
|
|
write_json(manifest_dir / "model_manifest.json", [{"model_name": "DIRECTION", "status": "CANDIDATE"}])
|
|
write_json(manifest_dir / "calibration_manifest.json", [{"model_name": "DIRECTION", "status": "CANDIDATE"}])
|
|
write_json(manifest_dir / "position_manager_manifest.json", {"status": "CANDIDATE"})
|
|
write_json(manifest_dir / "training_export_manifest.json", {"status": "CANDIDATE"})
|
|
|
|
promote_artifact_bundle(Namespace(artifact_root=root, reason="unit test"))
|
|
|
|
self.assertEqual("ACTIVE", read_json(manifest_dir / "model_bundle_manifest.json")["status"])
|
|
self.assertEqual("ACTIVE", read_json(manifest_dir / "model_manifest.json")[0]["status"])
|
|
self.assertEqual("ACTIVE", read_json(manifest_dir / "calibration_manifest.json")[0]["status"])
|
|
self.assertEqual("ACTIVE", read_json(manifest_dir / "position_manager_manifest.json")["status"])
|
|
self.assertEqual("ACTIVE", read_json(manifest_dir / "training_export_manifest.json")["status"])
|
|
|
|
def test_promotion_refuses_failed_validation(self) -> None:
|
|
with tempfile.TemporaryDirectory() as tmp:
|
|
root = Path(tmp) / "artifact_bundle"
|
|
(root / "manifests").mkdir(parents=True)
|
|
write_json(root.parent / "artifact_validation_result.json", {"status": "FAIL"})
|
|
with self.assertRaises(SystemExit):
|
|
promote_artifact_bundle(Namespace(artifact_root=root, reason="unit test"))
|
|
result = read_json(root.parent / "artifact_promotion_result.json")
|
|
self.assertEqual("REFUSED", result["status"])
|
|
self.assertEqual("validation result is not PASS", result["message"])
|
|
|
|
def test_promotion_refuses_failed_release_gate_and_overwrites_stale_result(self) -> None:
|
|
with tempfile.TemporaryDirectory() as tmp:
|
|
root = Path(tmp) / "artifact_bundle"
|
|
(root / "manifests").mkdir(parents=True)
|
|
write_json(root.parent / "artifact_promotion_result.json", {"status": "ACTIVE"})
|
|
write_json(
|
|
root.parent / "artifact_validation_result.json",
|
|
{
|
|
"status": "PASS",
|
|
"release_gate_status": "REJECTED",
|
|
"release_gate_reasons": ["backtest_status=REJECTED"],
|
|
},
|
|
)
|
|
|
|
with self.assertRaises(SystemExit):
|
|
promote_artifact_bundle(Namespace(artifact_root=root, reason="unit test"))
|
|
|
|
result = read_json(root.parent / "artifact_promotion_result.json")
|
|
self.assertEqual("REFUSED", result["status"])
|
|
self.assertEqual("release gate is not PASS", result["message"])
|
|
self.assertEqual(["backtest_status=REJECTED"], result["release_gate_reasons"])
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|