mirror of
https://github.com/shy3130/tick-stock-panel.git
synced 2026-11-02 10:26:13 +00:00
kevin9327andClaude Opus 5
88e600e1c2
_build_live_agg 的 ema5~ema60、macd_dea、kdj、atr 状态取自约 300 个自然日的历史 缓存, 与盘后全量同源; _ema12/_ema26 与 RSI 平均涨跌幅却在 90 个自然日的窗口切片 (约 60 根) 上重新 ewm_mean, 初值残留权重 RSI24 约 8%、RSI14/EMA26 约 1%。盘中 macd_dif 与同一 live_agg 的 macd_dea 状态暖机不一致, rsi_14/rsi_24 与盘后 (或盘中 重启后) 数值不同, 金叉/死叉与 RSI 阈值判断跟着漂移。 历史缓存路径下这两组状态改在缓存的收盘价上递推; 降级路径 (所有状态都由窗口切片 compute_indicators 算出, 暖机本身一致) 不变。live_agg 构建多约 50ms (5500 只 x 210 个交易日实测), 只在刷新/跨日时执行, 不在行情热路径。 Closes #335 Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
73 lines
2.9 KiB
Python
73 lines
2.9 KiB
Python
"""盘中 MACD / RSI 的递推状态与盘后全量同一暖机起点。
|
|
|
|
_build_live_agg 的 ema5~ema60、macd_dea、kdj、atr 状态取自历史缓存 (约 300 个自然日
|
|
暖机, 与盘后全量同源), 但 _ema12/_ema26 与 RSI 平均涨跌幅若只在 90 个自然日窗口切片上
|
|
重新递推, 暖机只有约 60 根: RSI24 初值残留权重 (23/24)^60 ≈ 8%, EMA26 约 1%。
|
|
盘中 macd_dif (EMA12 - EMA26) 与同一 live_agg 里的 macd_dea 状态口径不一致,
|
|
rsi_14/rsi_24 与盘后 (或盘中重启后) 数值不同, 金叉/死叉判断跟着漂移。
|
|
"""
|
|
from __future__ import annotations
|
|
|
|
import math
|
|
from datetime import date, timedelta
|
|
|
|
import polars as pl
|
|
import pytest
|
|
|
|
from app.indicators.pipeline import compute_enriched_today, compute_indicators
|
|
from app.tickflow.repository import DataStore, KlineRepository
|
|
|
|
TODAY = date(2026, 8, 3)
|
|
COLUMNS = ("macd_dif", "macd_dea", "macd_hist", "rsi_6", "rsi_14", "rsi_24")
|
|
|
|
|
|
def _bars() -> pl.DataFrame:
|
|
days: list[date] = []
|
|
d = TODAY - timedelta(days=299)
|
|
while d < TODAY:
|
|
if d.weekday() < 5:
|
|
days.append(d)
|
|
d += timedelta(days=1)
|
|
|
|
rows = []
|
|
for n, symbol in enumerate(("600001.SH", "000002.SZ")):
|
|
for i, day in enumerate([*days, TODAY]):
|
|
# 前段单边上涨/下跌、后段震荡: 暖机起点与近期状态差异大, 截断暖机的误差可见
|
|
trend = (0.03 if n == 0 else -0.02) * min(i, 120)
|
|
close = round(10.0 + n * 5 + trend + 0.5 * math.sin(i * 0.45 + n), 2)
|
|
rows.append((symbol, day, close, close + 0.1, close - 0.1, close))
|
|
return pl.DataFrame(
|
|
rows,
|
|
schema=["symbol", "date", "open", "high", "low", "close"],
|
|
orient="row",
|
|
).with_columns(
|
|
pl.lit(1000.0).alias("volume"),
|
|
(pl.col("close") * 1000.0).alias("amount"),
|
|
pl.col("close").alias("raw_close"),
|
|
pl.col("high").alias("raw_high"),
|
|
pl.col("low").alias("raw_low"),
|
|
).sort(["symbol", "date"])
|
|
|
|
|
|
@pytest.mark.parametrize("column", COLUMNS)
|
|
def test_intraday_macd_rsi_warmup_matches_full(tmp_path, column):
|
|
bars = _bars()
|
|
history = bars.filter(pl.col("date") < TODAY)
|
|
history_indicators = compute_indicators(history)
|
|
latest = history["date"].max()
|
|
|
|
repo = KlineRepository(DataStore(tmp_path))
|
|
repo._enriched_history_cache = history_indicators
|
|
repo._build_live_agg(latest)
|
|
|
|
incremental = compute_enriched_today(
|
|
repo.get_live_agg(),
|
|
history_indicators.filter(pl.col("date") == latest),
|
|
bars.filter(pl.col("date") == TODAY).drop("raw_close", "raw_high", "raw_low"),
|
|
None,
|
|
).sort("symbol")
|
|
full = compute_indicators(bars).filter(pl.col("date") == TODAY).sort("symbol")
|
|
|
|
assert incremental["symbol"].to_list() == full["symbol"].to_list()
|
|
assert incremental[column].to_list() == pytest.approx(full[column].to_list(), rel=1e-9, abs=1e-12)
|