Files
xiaobaifupan/app/backend/features/screener/tracking.py
T

135 lines
5.3 KiB
Python

from __future__ import annotations
from typing import Any
from backend.database.repositories import StrategyTrackingRepository
class StrategyTrackingService:
def __init__(self, repository: StrategyTrackingRepository) -> None:
self.repository = repository
def record_run(
self,
user_id: int,
run_id: int,
selection_date: str,
strategy_name: str,
candidates: list[dict[str, Any]],
) -> int:
return self.repository.save_strategy_tracks(
user_id, run_id, selection_date, strategy_name, candidates
)
def add_candidate(self, user_id: int, run_id: int, code: str) -> dict[str, Any]:
run = self.repository.get_screener_run(user_id, run_id)
if not run:
run = self.repository.get_screener_run(0, run_id)
if not run:
raise ValueError("选股结果不存在或不属于当前账号。")
normalized_code = str(code or "").strip().split(".")[0]
candidate = next(
(
item for item in run.get("candidates", [])
if str(item.get("code") or item.get("ts_code") or "").split(".")[0]
== normalized_code
),
None,
)
if not candidate:
raise ValueError("该股票不在本次选股结果中。")
added = self.record_run(
user_id,
run_id,
str(run.get("meta", {}).get("trade_date") or ""),
str(run.get("strategy_name") or "未命名策略"),
[candidate],
)
return {"added": added, "tracking": self.list_tracking(user_id)}
def remove_candidate(self, user_id: int, track_id: int) -> dict[str, Any]:
deleted = self.repository.delete_strategy_track(user_id, track_id)
return {"deleted": deleted, "tracking": self.list_tracking(user_id)}
def list_tracking(self, user_id: int, limit_batches: int = 12) -> dict[str, Any]:
tracks = self.repository.list_strategy_tracks(user_id, limit_batches)
if not tracks:
return {"batches": [], "summary": self._summary([])}
bars = self.repository.load_tracking_bars(
[(item["ts_code"], item["selection_date"]) for item in tracks], 5
)
batches: dict[int, dict[str, Any]] = {}
all_items: list[dict[str, Any]] = []
for track in tracks:
key = (track["ts_code"], track["selection_date"])
metrics = self.calculate_metrics(float(track["entry_price"]), bars.get(key, []))
item = {
"id": track["id"],
"code": track["code"],
"name": track["name"],
"sector": track["sector"],
"entry_price": round(float(track["entry_price"]), 2),
**metrics,
}
all_items.append(item)
batch = batches.setdefault(
int(track["run_id"]),
{
"run_id": int(track["run_id"]),
"selection_date": track["selection_date"],
"strategy_name": track["strategy_name"],
"items": [],
},
)
batch["items"].append(item)
ordered = list(batches.values())
for batch in ordered:
batch["summary"] = self._summary(batch["items"])
return {"batches": ordered, "summary": self._summary(all_items)}
@staticmethod
def calculate_metrics(entry_price: float, bars: list[dict[str, Any]]) -> dict[str, Any]:
valid = [row for row in bars[:5] if float(row.get("close") or 0) > 0]
if entry_price <= 0 or not valid:
return {
"observed_days": 0,
"status": "等待 T+1",
"t1_open": None,
"t1_close": None,
"t3_close": None,
"t5_close": None,
"max_gain": None,
"max_drawdown": None,
}
def change(price: Any) -> float:
return round((float(price or 0) / entry_price - 1) * 100, 2)
observed = len(valid)
return {
"observed_days": observed,
"status": "已完成" if observed >= 5 else f"跟踪中 {observed}/5",
"t1_open": change(valid[0]["open"]),
"t1_close": change(valid[0]["close"]),
"t3_close": change(valid[2]["close"]) if observed >= 3 else None,
"t5_close": change(valid[4]["close"]) if observed >= 5 else None,
"max_gain": max(change(row["high"]) for row in valid),
"max_drawdown": min(change(row["low"]) for row in valid),
}
@staticmethod
def _summary(items: list[dict[str, Any]]) -> dict[str, Any]:
completed = [item for item in items if item.get("t5_close") is not None]
t1 = [float(item["t1_close"]) for item in items if item.get("t1_close") is not None]
t5 = [float(item["t5_close"]) for item in completed]
return {
"total": len(items),
"observed": len(t1),
"completed": len(completed),
"t1_win_rate": round(sum(value > 0 for value in t1) / len(t1) * 100, 1) if t1 else None,
"t5_win_rate": round(sum(value > 0 for value in t5) / len(t5) * 100, 1) if t5 else None,
"average_t5": round(sum(t5) / len(t5), 2) if t5 else None,
}