Files
qlib/backend/app/application/services/chart_service.py
T
Simon bfeac7aa4c feat(backtest): M9-2 回测补 selection_history/signal_history/fills(Signal↔Fill 区分)
- BacktestResult 新增:RankedPick(调仓意图,与 select(as_of) 同源排序)、
  ActionRecord(BUY/SELL 意图 + filled + reject_reason/price)字段 selection_history /
  signal_history / fills(fills=signal_history 中 filled 子集)(v3 §20.3/§22.3)
- TopKBacktestRunner:调仓记录卖出/买入逐动作与是否成交;涨停/停牌导致的
  「BUY 信号未成交」保留原因;意图 picks 与执行 targets 分离(不因涨停悄悄改选股视图)
- ChartService.backtest_stock_chart 改用 history 生成三类标记(selection/signal/fill),
  未成交意图在图上可见(v3 §20.4)
- tests/test_backtest_history.py:意图=select 一致、fills 推导、涨停拒绝可见(构造 +10%
  涨停日)、序列化 roundtrip;相关回归(quant/consistency/charts)全过;全量 pytest 通过
2026-09-09 07:12:36 +08:00

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"""Chart Service(v3 §20.1)—— 只聚合与坐标整理,不重算研究结果。
- K 线/量/指标:基于主口径(adjust=none)行情,按请求 adjust 在**显示层**折算 qfq/hfq
(绝不回写研究数据;研究执行仍用 price_adjustment 指定口径)
- 标记:selections/signals 来自各自落库历史(by-symbol);backtest fills 来自 Experiment
内 BacktestResult 的 trades/positions
- 口径纪律(v3 §20.5):显示价 basis 与回测执行价 basis 分别记录;不一致时对 marker 做
与 K 线相同的坐标换算,保证成交点贴图
"""
from __future__ import annotations
from datetime import date
from app.domain.entities.chart import (
OHLC,
ChartMetadata,
ChartResult,
EventMarker,
SeriesPoint,
VolumePoint,
)
from app.domain.entities.market import AdjustFactor, DailyBar, Stock
from app.domain.entities.research import BacktestResult
from app.domain.repositories.market import (
AdjustFactorRepository,
DailyBarRepository,
StockRepository,
)
_MA_WINDOWS = (20, 60)
def _main_bars(bars: list[DailyBar]) -> list[DailyBar]:
"""仅取主口径行(adjust=none),丢弃新浪 qfq 兜底行 —— 显示层一律自 none 折算。"""
return [b for b in bars if b.adjust == "none"]
def _factor_multipliers(
adj_repo: AdjustFactorRepository,
symbol: str,
start: date,
end: date,
mode: str,
) -> dict[date, float]:
"""返回 {trade_date: 显示折算系数};mode=none → 空。qfq: f/f_latest;hfq: f。"""
if mode == "none":
return {}
factors: list[AdjustFactor] = adj_repo.get_range(symbol, date(1990, 1, 1), end)
if not factors:
return {}
by_day = {f.trade_date: float(f.factor) for f in factors}
latest = max(by_day.values())
out: dict[date, float] = {}
for day, f in by_day.items():
out[day] = f / latest if mode == "qfq" else f
return out
class ChartService:
def __init__(
self,
stock_repo: StockRepository,
daily_repo: DailyBarRepository,
adj_repo: AdjustFactorRepository,
) -> None:
self._stock_repo = stock_repo
self._daily_repo = daily_repo
self._adj_repo = adj_repo
def stock(self, symbol: str) -> Stock | None:
return self._stock_repo.get_by_symbol(symbol)
def stock_chart(
self,
symbol: str,
start: date,
end: date,
adjust: str = "none",
execution_price_basis: str | None = None,
extra_markers: list[EventMarker] | None = None,
) -> ChartResult:
"""基础个股 K 线图(可叠加 fills 等外部标记)。"""
stock = self.stock(symbol)
name = stock.name if stock else ""
raw = _main_bars(self._daily_repo.get_range(symbol, start, end))
mult = _factor_multipliers(self._adj_repo, symbol, start, end, adjust)
bars: list[OHLC] = []
volume: list[VolumePoint] = []
for b in raw:
m = mult.get(b.trade_date, 1.0)
bars.append(
OHLC(
time=b.trade_date,
open=_v(b.open, m),
high=_v(b.high, m),
low=_v(b.low, m),
close=_v(b.close, m),
)
)
volume.append(VolumePoint(time=b.trade_date, value=_v(b.volume, 1.0)))
markers = _convert_markers(extra_markers or [], mult)
indicators = _ma_indicators(bars)
return ChartResult(
metadata=ChartMetadata(
symbol=symbol,
name=name,
adjust_mode=adjust,
execution_price_basis=execution_price_basis,
start=start,
end=end,
bar_count=len(bars),
indicator_windows=list(_MA_WINDOWS),
),
bars=bars,
volume=volume,
indicators=indicators,
fills=[m for m in markers if m.kind.startswith("fill_")],
signals=[m for m in markers if m.kind.startswith("signal_")],
selections=[m for m in markers if m.kind == "selection"],
holding_periods=_holding_periods(bars),
)
# ---- 由已存历史构造标记(不重算) ----
def backtest_stock_chart(
self,
result: BacktestResult,
symbol: str,
start: date,
end: date,
adjust: str = "none",
) -> ChartResult:
"""回测个股视图:K 线 + 选股意图/未成交信号/实际成交三类标记(v3 §20.3)。"""
basis = (result.config_snapshot or {}).get("price_adjustment", "none")
markers = _result_to_markers(result, symbol)
return self.stock_chart(symbol, start, end, adjust, execution_price_basis=basis,
extra_markers=markers)
def _result_to_markers(result: BacktestResult, symbol: str) -> list[EventMarker]:
"""由回测 history 生成个股标记:fills(成交)/ signals(未成交意图)/ selections(选股)。"""
markers: list[EventMarker] = []
# 实际成交(fills)与未成交信号(signal_history 中 filled=False)
for a in result.signal_history:
if a.symbol != symbol:
continue
if a.filled:
kind = "fill_buy" if a.signal == "BUY" else "fill_sell"
text = [f"{'买入' if a.signal=='BUY' else '卖出'} @ {a.price:.2f}(basis={_basis_of(result)})"]
markers.append(
EventMarker(time=a.date, kind=kind, symbol=symbol, price=a.price, text=text)
)
else:
kind = "signal_buy" if a.signal == "BUY" else "signal_sell"
text = [a.reject_reason or f"{a.signal} 未成交"]
markers.append(EventMarker(time=a.date, kind=kind, symbol=symbol, price=a.price, text=text))
# 选股意图(selection_history 中该 symbol 的命中)
for pk in result.selection_history:
if pk.symbol != symbol:
continue
markers.append(
EventMarker(
time=pk.date,
kind="selection",
symbol=symbol,
score=pk.score,
text=[f"选股意图 rank #{pk.rank}"],
)
)
return markers
def _basis_of(result: BacktestResult) -> str:
return (result.config_snapshot or {}).get("price_adjustment", "none")
def _convert_markers(markers: list[EventMarker], mult: dict[date, float]) -> list[EventMarker]:
"""显示口径与执行价 basis 不一致时,把 marker 价格折算到 K 线坐标系(v3 §20.5)。"""
out: list[EventMarker] = []
for m in markers:
if m.price is not None and mult:
k = mult.get(m.time)
if k is not None:
m = m.model_copy(update={"price": round(m.price * k, 4)})
out.append(m)
return out
def _holding_periods(bars: list[OHLC]) -> list[dict]:
"""v1 空实现占位(持仓区间渲染 v3 §20.4 后续细化)。"""
return []
def _ma_indicators(bars: list[OHLC]) -> dict[str, list[SeriesPoint]]:
import statistics
closes = [b.close for b in bars]
out: dict[str, list[SeriesPoint]] = {}
for w in _MA_WINDOWS:
series: list[SeriesPoint] = []
for i, b in enumerate(bars):
if i + 1 < w:
continue
window = closes[i + 1 - w : i + 1]
if all(v is not None for v in window):
series.append(SeriesPoint(time=b.time, value=round(statistics.fmean(window), 4)))
out[f"ma{w}"] = series
return out
def _v(v, m: float) -> float | None:
if v is None:
return None
f = float(v)
return round(f * m, 4)