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feat(selection): 集成 B1 FastDTW 图形评分

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yuxuanhui
2026-08-31 16:14:16 +08:00
parent 86762c0d9a
commit 6ce291e242
51 changed files with 2917 additions and 41 deletions
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zhixing-server/src/zhixing_server/modules/selection/infrastructure/postgres_runs.py
+113 -12
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@@ -15,6 +15,11 @@ import psycopg
from psycopg.types.json import Jsonb
from ..domain.models import SelectionSignal, ZhixingB1Category
from ..domain.pattern_scoring import (
ZHIXING_B1_PATTERN_CASES,
PatternScore,
PatternScoreBreakdown,
)
from ..domain.runs import (
SelectionExecutionSource,
SelectionRerunRequired,
@@ -44,17 +49,37 @@ _SIGNAL_ORDER_SQL = (
)
+ f" ELSE {len(ZHIXING_B1_SIGNAL_ORDER)} END"
)
_PATTERN_CASES_BY_ID = {definition.id: definition for definition in ZHIXING_B1_PATTERN_CASES}
_STOCK_ORDER_SQL = {
"code": "item.ts_code ASC",
"score_desc": "item.score_value DESC NULLS LAST, item.ts_code ASC",
"score_asc": "item.score_value ASC NULLS LAST, item.ts_code ASC",
}
_ITEM_UPSERT = """
INSERT INTO selection_run_item
(run_id, ts_code, name, status, signal_count, reason)
VALUES (%s, %s, %s, %s, %s, %s)
(
run_id, ts_code, name, status, signal_count, reason,
score_status, score_value, score_threshold, score_version,
match_case_id, match_case_name, match_case_breakout_date,
match_breakdown, score_reason
)
VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
ON CONFLICT (run_id, ts_code) DO UPDATE SET
name = EXCLUDED.name,
status = EXCLUDED.status,
signal_count = EXCLUDED.signal_count,
reason = EXCLUDED.reason
reason = EXCLUDED.reason,
score_status = EXCLUDED.score_status,
score_value = EXCLUDED.score_value,
score_threshold = EXCLUDED.score_threshold,
score_version = EXCLUDED.score_version,
match_case_id = EXCLUDED.match_case_id,
match_case_name = EXCLUDED.match_case_name,
match_case_breakout_date = EXCLUDED.match_case_breakout_date,
match_breakdown = EXCLUDED.match_breakdown,
score_reason = EXCLUDED.score_reason
"""
_SIGNAL_UPSERT = """
INSERT INTO selection_signal
@@ -200,6 +225,19 @@ class PostgresSelectionRunRepository(SelectionRunStore):
item.status,
item.signal_count,
item.reason,
item.pattern_score.status,
item.pattern_score.value,
item.pattern_score.threshold,
item.pattern_score.version,
item.pattern_score.case.id if item.pattern_score.case else None,
item.pattern_score.case.name if item.pattern_score.case else None,
item.pattern_score.case.breakout_date if item.pattern_score.case else None,
(
Jsonb(item.pattern_score.breakdown.as_dict())
if item.pattern_score.breakdown
else None
),
item.pattern_score.reason,
)
for item in items
)
@@ -354,7 +392,11 @@ class PostgresSelectionRunRepository(SelectionRunStore):
return None
item_rows = connection.execute(
"""
SELECT ts_code, name, status, signal_count, reason
SELECT
ts_code, name, status, signal_count, reason,
score_status, score_value, score_threshold, score_version,
match_case_id, match_case_name, match_case_breakout_date,
match_breakdown, score_reason
FROM selection_run_item
WHERE run_id = %s
ORDER BY ts_code
@@ -363,7 +405,7 @@ class PostgresSelectionRunRepository(SelectionRunStore):
).fetchall()
stock_filter, stock_parameters = _stock_filter(query, run_id)
stock_total_row = connection.execute(
f"SELECT COUNT(DISTINCT ts_code) FROM selection_signal WHERE {stock_filter}",
f"SELECT COUNT(*) FROM selection_run_item AS item WHERE {stock_filter}",
tuple(stock_parameters),
).fetchone()
stock_total = int(stock_total_row[0] or 0) if stock_total_row else 0
@@ -372,10 +414,10 @@ class PostgresSelectionRunRepository(SelectionRunStore):
list[tuple[object, ...]],
connection.execute(
f"""
SELECT DISTINCT ts_code
FROM selection_signal
SELECT item.ts_code
FROM selection_run_item AS item
WHERE {stock_filter}
ORDER BY ts_code
ORDER BY {_STOCK_ORDER_SQL[query.sort]}
LIMIT %s OFFSET %s
""",
tuple((*stock_parameters, query.page_size, offset)),
@@ -403,7 +445,7 @@ class PostgresSelectionRunRepository(SelectionRunStore):
sorted(
(_signal_from_row(value) for value in signal_rows),
key=lambda signal: (
signal.ts_code,
stock_codes.index(signal.ts_code),
_SIGNAL_PRIORITY.get(signal.category, len(_SIGNAL_PRIORITY)),
),
)
@@ -427,6 +469,7 @@ class PostgresSelectionRunRepository(SelectionRunStore):
),
signal_count=int(value[3] or 0),
reason=str(value[4]) if value[4] is not None else None,
pattern_score=_pattern_score_from_row(value[5:14]),
signals=tuple(signals_by_stock.get(str(value[0]), ())),
)
for value in item_rows
@@ -509,18 +552,76 @@ def _stock_filter(query: SelectionResultQuery, run_id: str) -> tuple[str, list[o
can present all independently persisted categories together.
"""
clauses = ["run_id = %s"]
clauses = ["item.run_id = %s", "item.status = 'selected'", "item.signal_count > 0"]
parameters: list[object] = [run_id]
if query.search:
pattern = f"%{_escape_like(query.search)}%"
clauses.append("(name ILIKE %s ESCAPE '\\' OR ts_code ILIKE %s ESCAPE '\\')")
clauses.append("(item.name ILIKE %s ESCAPE '\\' OR item.ts_code ILIKE %s ESCAPE '\\')")
parameters.extend((pattern, pattern))
if query.category:
clauses.append("category LIKE %s")
clauses.append(
"EXISTS ("
"SELECT 1 FROM selection_signal AS signal "
"WHERE signal.run_id = item.run_id "
"AND signal.ts_code = item.ts_code "
"AND signal.category LIKE %s"
")"
)
parameters.append(f"{_CATEGORY_PREFIXES[query.category]}%")
return " AND ".join(clauses), parameters
def _pattern_score_from_row(row: Sequence[object]) -> PatternScore:
"""Reconstruct a validated stock-level score from nullable item columns."""
if len(row) < 9:
return PatternScore()
status = str(row[0] or "not_executed")
if status == "not_executed":
return PatternScore()
if status == "failed":
return PatternScore.failed(str(row[8] or "pattern scoring failed"))
if status not in {"matched", "below_threshold"}:
return PatternScore.failed("persisted pattern score status is invalid")
definition = _PATTERN_CASES_BY_ID.get(str(row[4]))
breakdown = _pattern_breakdown(row[7])
if definition is None or breakdown is None:
return PatternScore.failed("persisted pattern score is incomplete")
try:
return PatternScore(
status=cast(Literal["matched", "below_threshold"], status),
value=float(str(row[1])),
threshold=float(str(row[2])),
version=str(row[3]),
case=definition,
breakdown=breakdown,
)
except (TypeError, ValueError):
return PatternScore.failed("persisted pattern score is invalid")
def _pattern_breakdown(value: object) -> PatternScoreBreakdown | None:
"""Parse the four finite JSONB score dimensions."""
if isinstance(value, str):
try:
value = json.loads(value)
except json.JSONDecodeError:
return None
if not isinstance(value, Mapping):
return None
values = cast(Mapping[object, object], value)
try:
return PatternScoreBreakdown(
trend_structure=float(str(values["trend_structure"])),
kdj_state=float(str(values["kdj_state"])),
volume_pattern=float(str(values["volume_pattern"])),
price_shape=float(str(values["price_shape"])),
)
except (KeyError, TypeError, ValueError):
return None
def _escape_like(value: str) -> str:
"""Escape user wildcards before placing text inside a SQL LIKE pattern."""