mirror of
https://github.com/shy3130/tick-stock-panel.git
synced 2026-11-02 10:26:13 +00:00
shy3130
918c7ade7e
- 策略: 引擎只加载 data/strategies/{custom,ai,composite} 与研究模板目录,
原 25 个内置策略一次性迁移为用户自定义文件(ID 不变, 引用零迁移);
研究模板移至 app/strategy/research, 打包 spec 同步; 新装环境策略库为空
- 信号: 管线不再硬编码任何信号表达式; 涨跌停判定价成为运行时列
limit_up_price/limit_down_price(生效价∓0.005 容差), 20 个信号转为
data/user_data/custom_signals 定义(显式 column 沿用 signal_* 原列名);
依赖解析/注入顺序/当日路径 prev-join 全部定义驱动; 语义统一为历史路径
口径(顺带修正当日路径 BOLL 比较符与翘板/炸板空值两处既有偏差)
- 前端: 信号库单列表(全部可编辑), SignalPicker/监控选项/信号标签全走
定义接口; 回测页新增免责声明(模拟性质/数据源准确性不作保证/不构成
投资建议)
- 测试: 25 个策略夹具与 20 个信号种子入库 tests/fixtures, 受影响测试
全部自包含化(不依赖运行目录用户数据); 77 万行 MA 死叉新旧表达式对拍
逐行一致; 回测实跑 45 笔卖点信号出场验证矩阵链路
- 同批包含工作区既有进度: 策略结果 ext 列同日投影修复(含测试)、数据源插件、fuyao/auth/api_gateway/data_integrity 调整、README 信号库
描述与 strategy.md 导入路径补充
验证: 后端 2592 passed(空数据目录 CI 模拟 2588 passed, 4 例为模拟环境
伪影复验排除); 前端 vitest 125 passed + tsc 0 错 + vite build 通过
315 lines
11 KiB
Python
315 lines
11 KiB
Python
from __future__ import annotations
|
|
|
|
from datetime import date, timedelta
|
|
|
|
import polars as pl
|
|
import pytest
|
|
|
|
from app.indicators.pipeline import (
|
|
ENRICHED_COLUMNS_BY_CATEGORY,
|
|
compute_enriched_today,
|
|
compute_indicators,
|
|
)
|
|
from app.strategy import custom_signals
|
|
from app.tickflow.repository import DataStore, KlineRepository
|
|
|
|
|
|
@pytest.fixture(autouse=True)
|
|
def _isolated_limit_signal(monkeypatch):
|
|
"""信号列已定义驱动: 注入最小 limit_up 定义, 不依赖运行目录的用户数据。
|
|
|
|
无涨跌幅限制日 limit_up_price 为 null → 信号为 null (非 False),
|
|
消费端按假值处理; 断言用 ``is not True`` 口径。
|
|
"""
|
|
from app.indicators import pipeline
|
|
|
|
sigs = [{
|
|
"id": "limit_up", "name": "涨停", "kind": "both", "enabled": True,
|
|
"column": "signal_limit_up",
|
|
"conditions": [
|
|
{"left": "raw_close", "op": ">", "right": "0", "leftDays": 0, "rightDays": 0},
|
|
{"left": "raw_close", "op": ">=", "right": "field:limit_up_price",
|
|
"leftDays": 0, "rightDays": 0},
|
|
],
|
|
}]
|
|
prev_fields = custom_signals.prev_day_fields(sigs)
|
|
monkeypatch.setattr(
|
|
pipeline, "_custom_signal_exprs_today",
|
|
custom_signals.build_expressions_prev(sigs, prev_fields),
|
|
)
|
|
monkeypatch.setattr(pipeline, "_custom_prev_fields_cache", prev_fields)
|
|
|
|
|
|
def _historical_cache(latest: date) -> pl.DataFrame:
|
|
rows = []
|
|
for symbol, days in (("600001.SH", 100), ("000820.SZ", 98)):
|
|
first = latest - timedelta(days=99)
|
|
for offset in range(days):
|
|
trade_date = first + timedelta(days=offset)
|
|
close = 10.0 + offset * 0.01
|
|
rows.append({
|
|
"symbol": symbol,
|
|
"date": trade_date,
|
|
"open": close,
|
|
"high": close + 0.1,
|
|
"low": close - 0.1,
|
|
"close": close,
|
|
"raw_close": close,
|
|
"raw_high": close + 0.1,
|
|
"raw_low": close - 0.1,
|
|
"volume": 1000.0 + offset,
|
|
"amount": close * (1000.0 + offset),
|
|
})
|
|
return compute_indicators(pl.DataFrame(rows).sort(["symbol", "date"]))
|
|
|
|
|
|
def test_live_agg_uses_each_symbols_last_available_trading_state(tmp_path):
|
|
latest = date(2026, 7, 29)
|
|
repo = KlineRepository(DataStore(tmp_path))
|
|
repo._enriched_history_cache = _historical_cache(latest)
|
|
|
|
partition = tmp_path / "kline_daily_enriched" / f"date={latest.isoformat()}"
|
|
partition.mkdir(parents=True)
|
|
pl.DataFrame({
|
|
"symbol": ["600001.SH"],
|
|
"date": [latest],
|
|
"open": [10.99],
|
|
"high": [11.09],
|
|
"low": [10.89],
|
|
"close": [10.99],
|
|
"volume": [1099.0],
|
|
"amount": [12078.01],
|
|
"raw_close": [10.99],
|
|
"raw_high": [11.09],
|
|
"raw_low": [10.89],
|
|
"turnover_rate": [1.0],
|
|
"consecutive_limit_ups": pl.Series([0], dtype=pl.UInt32),
|
|
"consecutive_limit_downs": pl.Series([0], dtype=pl.UInt32),
|
|
}).write_parquet(partition / "part.parquet")
|
|
resumed_date = latest - timedelta(days=2)
|
|
resumed_partition = (
|
|
tmp_path / "kline_daily_enriched" / f"date={resumed_date.isoformat()}"
|
|
)
|
|
resumed_partition.mkdir(parents=True)
|
|
pl.DataFrame({
|
|
"symbol": ["000820.SZ"],
|
|
"date": [resumed_date],
|
|
"open": [10.97],
|
|
"high": [11.07],
|
|
"low": [10.87],
|
|
"close": [10.97],
|
|
"volume": [1097.0],
|
|
"amount": [12034.09],
|
|
"raw_close": [10.97],
|
|
"raw_high": [11.07],
|
|
"raw_low": [10.87],
|
|
"turnover_rate": [1.0],
|
|
"consecutive_limit_ups": pl.Series([2], dtype=pl.UInt32),
|
|
"consecutive_limit_downs": pl.Series([0], dtype=pl.UInt32),
|
|
}).write_parquet(resumed_partition / "part.parquet")
|
|
|
|
repo._build_live_agg(latest)
|
|
|
|
states = repo.get_live_agg().sort("symbol")
|
|
assert states["symbol"].to_list() == ["000820.SZ", "600001.SH"]
|
|
resumed = states.filter(pl.col("symbol") == "000820.SZ").row(0, named=True)
|
|
assert resumed["_prev_consec_up"] == 2
|
|
|
|
|
|
def _live_state() -> pl.DataFrame:
|
|
return pl.DataFrame({
|
|
"symbol": ["600001.SH"],
|
|
"ema5": [10.0],
|
|
"ema10": [10.0],
|
|
"ema20": [10.0],
|
|
"ema30": [10.0],
|
|
"ema60": [10.0],
|
|
"macd_dea": [0.0],
|
|
"kdj_k": [50.0],
|
|
"kdj_d": [50.0],
|
|
"atr_14": [0.2],
|
|
"close": [10.0],
|
|
"high": [10.1],
|
|
"low": [9.9],
|
|
"annual_vol_20d": [0.1],
|
|
"_ema12": [10.0],
|
|
"_ema26": [10.0],
|
|
"_adj_factor": [1.0],
|
|
"_vol_19d_pct_sum": [0.0],
|
|
"_vol_19d_pct_sq_sum": [0.0],
|
|
"_prev_consec_up": pl.Series([0], dtype=pl.UInt32),
|
|
"_prev_consec_down": pl.Series([0], dtype=pl.UInt32),
|
|
"_ma5_partial_sum": [40.0],
|
|
"_ma10_partial_sum": [90.0],
|
|
"_ma20_partial_sum": [190.0],
|
|
"_ma30_partial_sum": [290.0],
|
|
"_ma60_partial_sum": [590.0],
|
|
"_boll_partial_sum": [190.0],
|
|
"_boll_partial_sq_sum": [1900.0],
|
|
"_high_59d": [10.1],
|
|
"_low_59d": [9.9],
|
|
"_close_5d_ago": [10.0],
|
|
"_close_10d_ago": [10.0],
|
|
"_close_20d_ago": [10.0],
|
|
"_close_30d_ago": [10.0],
|
|
"_close_60d_ago": [10.0],
|
|
"_vol_ma5_partial_sum": [4000.0],
|
|
"_vol_ma10_partial_sum": [9000.0],
|
|
"_vol_ma5_prev_sum": [5000.0],
|
|
"_kdj_8d_low": [9.9],
|
|
"_kdj_8d_high": [10.1],
|
|
"_window_len": [59],
|
|
"_rsi_avg_gain_6": [0.01],
|
|
"_rsi_avg_loss_6": [0.01],
|
|
"_rsi_avg_gain_14": [0.01],
|
|
"_rsi_avg_loss_14": [0.01],
|
|
"_rsi_avg_gain_24": [0.01],
|
|
"_rsi_avg_loss_24": [0.01],
|
|
})
|
|
|
|
|
|
def _previous_enriched() -> pl.DataFrame:
|
|
return pl.DataFrame({
|
|
"symbol": ["600001.SH"],
|
|
"ma5": [10.0],
|
|
"ma10": [10.0],
|
|
"ma20": [10.0],
|
|
"ma60": [10.0],
|
|
"macd_dif": [0.0],
|
|
"macd_dea": [0.0],
|
|
"boll_upper": [10.2],
|
|
"boll_lower": [9.8],
|
|
"close": [10.0],
|
|
})
|
|
|
|
|
|
def test_realtime_enriched_keeps_rows_without_history_and_limits_technical_fields():
|
|
today = date(2026, 7, 30)
|
|
today_rows = pl.DataFrame({
|
|
"symbol": ["600001.SH", "000820.SZ", "001000.SZ", "600002.SH"],
|
|
"date": [today] * 4,
|
|
"open": [10.1, 11.0, 11.0, 0.0],
|
|
"high": [10.2, 11.0, 11.0, 0.0],
|
|
"low": [10.0, 11.0, 11.0, 0.0],
|
|
"close": [10.2, 11.0, 11.0, 0.0],
|
|
"volume": [1200.0, 3000.0, 2000.0, 0.0],
|
|
"amount": [12240.0, 33000.0, 22000.0, 0.0],
|
|
"prev_close": [10.0, None, 10.0, 10.0],
|
|
})
|
|
instruments = pl.DataFrame({
|
|
"symbol": ["600001.SH", "000820.SZ", "001000.SZ", "600002.SH"],
|
|
"name": ["已有历史", "复牌股票", "上市新股", "停牌股票"],
|
|
"float_shares": [1_000_000.0] * 4,
|
|
"limit_up": [11.0, 11.0, 100000.0, 11.0],
|
|
"limit_down": [9.0, 9.0, 0.0, 9.0],
|
|
"as_of": [today] * 4,
|
|
})
|
|
|
|
result = compute_enriched_today(
|
|
_live_state(),
|
|
_previous_enriched(),
|
|
today_rows,
|
|
instruments,
|
|
)
|
|
|
|
assert result["symbol"].to_list() == today_rows["symbol"].to_list()
|
|
existing = result.filter(pl.col("symbol") == "600001.SH").row(0, named=True)
|
|
resumed = result.filter(pl.col("symbol") == "000820.SZ").row(0, named=True)
|
|
ipo = result.filter(pl.col("symbol") == "001000.SZ").row(0, named=True)
|
|
halted = result.filter(pl.col("symbol") == "600002.SH").row(0, named=True)
|
|
|
|
assert existing["ma5"] is not None
|
|
assert resumed["raw_close"] == 11.0
|
|
assert resumed["signal_limit_up"] is True
|
|
assert resumed["consecutive_limit_ups"] == 1
|
|
technical_columns = {
|
|
column
|
|
for category in (
|
|
"ma", "ema", "macd", "boll", "kdj", "atr", "volume",
|
|
"extremes", "momentum", "volatility", "rsi",
|
|
)
|
|
for column in ENRICHED_COLUMNS_BY_CATEGORY[category]
|
|
}
|
|
assert all(resumed[column] is None for column in technical_columns)
|
|
assert ipo["signal_limit_up"] is not True # 无涨跌幅日判定价为 null
|
|
assert halted["signal_limit_up"] is not True
|
|
assert "_has_history_state" not in result.columns
|
|
|
|
|
|
def test_realtime_enriched_missing_quote_extra_fields_fall_back_to_recompute():
|
|
"""quote_extra 未提供换手/振幅 (fuyao 等源) 时, 当日值必须从 float_shares/价格回退计算。
|
|
|
|
实时链路 _build_quote_extra 丢弃全空列后, today_ohlcv 不带这两列,
|
|
_compute_limit_signals_today 从 float_shares 算换手、compute_enriched_today
|
|
从价格算振幅 —— 不允许再出现「全空列进管道 → 列存在即跳过重算 → 当日永远为空」。
|
|
"""
|
|
today = date(2026, 7, 30)
|
|
today_rows = pl.DataFrame({
|
|
"symbol": ["600001.SH", "000820.SZ", "001000.SZ"],
|
|
"date": [today] * 3,
|
|
"open": [10.1, 11.0, 11.0],
|
|
"high": [10.2, 11.2, 11.1],
|
|
"low": [10.0, 10.8, 10.9],
|
|
"close": [10.2, 11.0, 11.0],
|
|
"volume": [1200.0, 3000.0, 2000.0],
|
|
"amount": [12240.0, 33000.0, 22000.0],
|
|
"prev_close": [10.0, 10.5, 10.5],
|
|
})
|
|
instruments = pl.DataFrame({
|
|
"symbol": ["600001.SH", "000820.SZ", "001000.SZ"],
|
|
"name": ["已有历史", "复牌股票", "上市新股"],
|
|
"float_shares": [1_000_000.0] * 3,
|
|
"limit_up": [11.0, 11.0, 100000.0],
|
|
"limit_down": [9.0, 9.0, 0.0],
|
|
"as_of": [today] * 3,
|
|
})
|
|
|
|
result = compute_enriched_today(_live_state(), _previous_enriched(), today_rows, instruments)
|
|
|
|
# 换手 = volume(手) * 10000 / float_shares(股)
|
|
assert result["turnover_rate"].to_list() == [12.0, 30.0, 20.0]
|
|
# 振幅 = (high - low) / prev_close
|
|
assert result["amplitude"].is_not_null().all()
|
|
|
|
|
|
def test_repository_restores_active_rows_missing_from_latest_enriched(tmp_path):
|
|
today = date(2026, 7, 30)
|
|
repo = KlineRepository(DataStore(tmp_path))
|
|
repo._live_agg_cache = _live_state()
|
|
repo._instruments_cache = pl.DataFrame({
|
|
"symbol": ["600001.SH", "000820.SZ", "600002.SH"],
|
|
"name": ["已有历史", "复牌股票", "停牌股票"],
|
|
"float_shares": [1_000_000.0] * 3,
|
|
"limit_up": [11.0, 11.0, 11.0],
|
|
"limit_down": [9.0, 9.0, 9.0],
|
|
"as_of": [today] * 3,
|
|
})
|
|
daily_partition = tmp_path / "kline_daily" / f"date={today.isoformat()}"
|
|
daily_partition.mkdir(parents=True)
|
|
pl.DataFrame({
|
|
"symbol": ["600001.SH", "000820.SZ", "600002.SH"],
|
|
"date": [today] * 3,
|
|
"open": [10.2, 11.0, 0.0],
|
|
"high": [10.2, 11.0, 0.0],
|
|
"low": [10.2, 11.0, 0.0],
|
|
"close": [10.2, 11.0, 0.0],
|
|
"volume": [1200.0, 3000.0, 0.0],
|
|
"amount": [12240.0, 33000.0, 0.0],
|
|
}).write_parquet(daily_partition / "part.parquet")
|
|
existing_today = compute_enriched_today(
|
|
_live_state(),
|
|
_previous_enriched(),
|
|
pl.read_parquet(daily_partition / "part.parquet").head(1),
|
|
repo._instruments_cache,
|
|
)
|
|
history = _previous_enriched().with_columns(
|
|
pl.lit(today - timedelta(days=1)).alias("date"),
|
|
)
|
|
|
|
restored = repo._restore_missing_latest_rows(today, existing_today, history)
|
|
|
|
assert restored["symbol"].to_list() == ["000820.SZ", "600001.SH"]
|
|
resumed = restored.filter(pl.col("symbol") == "000820.SZ").row(0, named=True)
|
|
assert resumed["signal_limit_up"] is True
|
|
assert resumed["ma5"] is None
|