mirror of
https://ghfast.top/https://github.com/aeroxw/tick-stock-panel.git
synced 2026-09-12 17:54:15 +08:00
shy3130
697c27bb02
- 市场环境: 新增情绪周期6阶段(冰点/启动/主升/高潮/退潮/修复, 连板梯队驱动, EMA平滑+2日确认+弱档否决, 平均段长9.7天)与概念/行业主线排名(涨停梯队聚合, 可配置宽基/风格标签过滤); 市场环境页重构, regime 透明加列, 与5档state并存 - 挖掘: 因子与策略挖掘全链路(API/worker/进程锁/候选库/前端工作台/文档), 周度调度默认关闭且永不自动发布 - 回测: 财务快照因子(点时口径), 批量回测预计算共享下期收益, 信号路径矩阵列依赖展开修复(consecutive_limit_ups 缺列报错) - 数据/性能: enriched 生成与预热治理, 重任务限流, 行情/K线缓存复用, 时区修复 - 测试: 后端全量 914 通过; GUI 黑盒验证截图存证 gui-test-screenshots/
76 lines
2.7 KiB
Python
76 lines
2.7 KiB
Python
from __future__ import annotations
|
|
|
|
from datetime import date, timedelta
|
|
|
|
import polars as pl
|
|
|
|
from app.indicators.pipeline import compute_indicators, compute_limit_signals, compute_signals
|
|
|
|
|
|
def _bars(n: int = 90) -> pl.DataFrame:
|
|
rows = []
|
|
for symbol, offset in (("600000", 0.0), ("300001", 2.0)):
|
|
for i in range(n):
|
|
close = 10.0 + offset + i * 0.03 + ((i % 7) - 3) * 0.04
|
|
rows.append({
|
|
"symbol": symbol,
|
|
"date": date(2024, 1, 1) + timedelta(days=i),
|
|
"open": close - 0.02,
|
|
"high": close + 0.10,
|
|
"low": close - 0.10,
|
|
"close": close,
|
|
"volume": 1000 + i * 10,
|
|
"amount": close * (1000 + i * 10),
|
|
"raw_close": close,
|
|
"raw_high": close + 0.10,
|
|
"raw_low": close - 0.10,
|
|
})
|
|
return pl.DataFrame(rows)
|
|
|
|
|
|
def test_compute_indicators_assume_sorted_matches_default_values():
|
|
bars = _bars().sort(["symbol", "date"])
|
|
|
|
default = compute_indicators(bars, needed={"macd_hist", "rsi_14", "momentum_20d"})
|
|
fast = compute_indicators(
|
|
bars,
|
|
needed={"macd_hist", "rsi_14", "momentum_20d"},
|
|
assume_sorted=True,
|
|
)
|
|
|
|
assert fast.equals(default)
|
|
|
|
|
|
def test_compute_signals_subset_matches_full_values():
|
|
indicators = compute_indicators(_bars())
|
|
full = compute_signals(indicators)
|
|
subset = compute_signals(indicators, needed={"signal_macd_golden", "signal_volume_surge"})
|
|
|
|
assert "signal_macd_golden" in subset.columns
|
|
assert "signal_volume_surge" in subset.columns
|
|
assert "signal_ma20_breakout" not in subset.columns
|
|
assert subset["signal_macd_golden"].equals(full["signal_macd_golden"])
|
|
assert subset["signal_volume_surge"].equals(full["signal_volume_surge"])
|
|
|
|
|
|
def test_compute_signals_empty_needed_adds_no_signals():
|
|
indicators = compute_indicators(_bars(), needed={"ma20"})
|
|
result = compute_signals(indicators, needed=set())
|
|
assert not any(col.startswith(("signal_", "csg_")) for col in result.columns)
|
|
|
|
|
|
def test_compute_limit_signals_subset_matches_full_and_prunes_other_outputs():
|
|
bars = compute_indicators(_bars(), needed={"change_pct"})
|
|
instruments = pl.DataFrame({
|
|
"symbol": ["600000", "300001"],
|
|
"name": ["浦发银行", "测试股份"],
|
|
"float_shares": [1_000_000_000.0, 500_000_000.0],
|
|
})
|
|
full = compute_limit_signals(bars, instruments)
|
|
subset = compute_limit_signals(bars, instruments, needed={"signal_limit_up"})
|
|
|
|
assert "signal_limit_up" in subset.columns
|
|
assert "signal_limit_down" not in subset.columns
|
|
assert "signal_broken_limit_up" not in subset.columns
|
|
assert subset["signal_limit_up"].equals(full["signal_limit_up"])
|