claude_dev
a68cf4905e
把聚宽"全天候轮动"(post48819)搬到 BulletTrade。融合=pip+扩展点注入 (SanguoMiniQmtProvider 继承 MiniQMTProvider 只 override get_fundamentals, set_data_provider 公开 API 注入, BulletTrade 源码 0 改动)。 - providers: SanguoMiniQmtProvider 补 get_fundamentals(PershareIndex+自算PE/PS/PB/PCF/市值/ROIC) - strategies/all_weather: 4选股函数+大小盘轮动+ETF兜底+涨停止损(聚宽风格翻译) - factors(估值/ROIC自算) + filters(ST/涨跌停/次新/停牌) - 88/88 测试 Mac+VPS 双过; VPS 回测 pipeline 跑通(修9bug:Capital单位/日期格式/百分数口径/11字段alias) - 实盘 runner_live+runbook 就绪等交易日; DEFAULT_DATA_PROVIDER=miniqmt env 不装 jqdatasdk - 文档: sanguo_portfolio_plan / portfolio_backtest_result / portfolio_live_runbook
366 lines
14 KiB
Python
366 lines
14 KiB
Python
"""AllWeatherStrategy 单元测试(mock provider + mock broker)。
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策略层只测**逻辑分支正确**(选股 / 轮动决策 / 调仓),不测真实数据。
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真实数据回测在 VPS 跑,这里只保证策略翻译等价。
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"""
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from __future__ import annotations
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from datetime import datetime
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from typing import Any, Dict, List
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from unittest.mock import MagicMock
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import numpy as np
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import pandas as pd
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import pytest
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from sanguo_portfolio import AllWeatherConfig, AllWeatherStrategy, BrokerFacade
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# ------------------------ 测试 helper:构造策略实例 ------------------------
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def make_strategy(
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*,
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fund_df: pd.DataFrame | None = None,
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index_stocks_map: Dict[str, List[str]] | None = None,
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price_df_map: Dict[str, pd.DataFrame] | None = None,
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) -> AllWeatherStrategy:
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"""构造一个 mock provider + mock broker 驱动的策略。
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- fund_df: 默认 get_fundamentals_df 返回
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- index_stocks_map: get_index_stocks 返回,dict[index] -> List[code]
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- price_df_map: get_price 按 (code, fields) 缓存的返回
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"""
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provider = MagicMock(name="provider")
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# 默认 fundamentals:空表,测试里覆盖
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if fund_df is None:
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fund_df = pd.DataFrame(columns=["code"])
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provider.get_fundamentals_df.return_value = fund_df
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# get_index_stocks
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index_stocks_map = index_stocks_map or {}
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def _get_index_stocks(index_symbol, date=None):
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return list(index_stocks_map.get(index_symbol, []))
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provider.get_index_stocks.side_effect = _get_index_stocks
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# get_security_info(filter_st/filter_new 默认放过)
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provider.get_security_info.return_value = {
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"display_name": "NORMAL",
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"name": "600519",
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"start_date": datetime(2000, 1, 1),
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}
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# get_live_current:不停牌不涨跌停
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provider.get_live_current.return_value = {
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"paused": False, "last_price": 10.0,
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"high_limit": 11.0, "low_limit": 9.0,
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}
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# get_price 按 key 缓存
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price_df_map = price_df_map or {}
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def _get_price(security, **kwargs):
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# 构造 cache key:不严格,按 security+fields+count 取
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fields = tuple(kwargs.get("fields") or [])
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count = kwargs.get("count", 1)
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key = (str(security), fields, count)
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return price_df_map.get(key, pd.DataFrame())
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provider.get_price.side_effect = _get_price
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broker = BrokerFacade()
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broker.order_target_value = MagicMock(return_value=MagicMock(filled=100))
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broker.order_value = MagicMock(return_value=MagicMock(filled=100))
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broker.set_benchmark = MagicMock()
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broker.set_option = MagicMock()
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broker.run_daily = MagicMock()
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broker.run_monthly = MagicMock()
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return AllWeatherStrategy(provider=provider, broker=broker)
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def make_fund_df(rows: List[Dict[str, Any]]) -> pd.DataFrame:
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"""构造 fundamentals DataFrame(带 index = code)。"""
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if not rows:
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return pd.DataFrame(columns=["code"])
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df = pd.DataFrame(rows)
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df["code"] = df.get("code", df.index.astype(str))
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df = df.set_index("code", drop=False)
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return df
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# =================== initialize ===================
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class TestInitialize:
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def test_initialize_registers_scheduled_tasks(self, fake_context):
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# Arrange
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s = make_strategy()
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# Act
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s.initialize(fake_context)
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# Assert:run_daily / run_monthly 各被调一次(至少)
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assert s.broker.run_daily.called
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assert s.broker.run_monthly.called
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assert s.broker.set_benchmark.called
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def test_initialize_sets_benchmark_from_config(self, fake_context):
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cfg = AllWeatherConfig(benchmark="000300.XSHG")
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s = make_strategy()
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s.config = cfg
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s.initialize(fake_context)
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s.broker.set_benchmark.assert_called_with("000300.XSHG")
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# =================== prepare_stock_list ===================
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class TestPrepareStockList:
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def test_empty_positions_clears_lists(self):
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# Arrange
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s = make_strategy()
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ctx = MagicMock()
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ctx.portfolio.positions = {}
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ctx.previous_date = "2024-09-30"
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# Act
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s.prepare_stock_list(ctx)
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# Assert
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assert s.hold_list == []
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assert s.yesterday_hl_list == []
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def test_populates_hold_list_from_positions(self):
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s = make_strategy()
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pos = MagicMock(); pos.security = "600519.XSHG"
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ctx = MagicMock()
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ctx.portfolio.positions = {"600519.XSHG": pos}
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ctx.previous_date = "2024-09-30"
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# get_price 返回空(不报错即可)
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s.provider.get_price.return_value = pd.DataFrame()
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s.prepare_stock_list(ctx)
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assert s.hold_list == ["600519.XSHG"]
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def test_records_yesterday_limit_up(self):
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s = make_strategy()
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# 清掉 make_strategy 设置的 side_effect,直接用 return_value
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s.provider.get_price.side_effect = None
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pos = MagicMock(); pos.security = "600519.XSHG"
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ctx = MagicMock()
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ctx.portfolio.positions = {"600519.XSHG": pos}
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ctx.previous_date = "2024-09-30"
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# close == high_limit 视为涨停
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s.provider.get_price.return_value = pd.DataFrame({
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"code": ["600519.XSHG"],
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"close": [10.0],
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"high_limit": [10.0],
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})
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s.prepare_stock_list(ctx)
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assert "600519.XSHG" in s.yesterday_hl_list
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# =================== stop_loss ===================
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class TestStopLoss:
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def test_stop_loss_triggers_when_price_drops_8pct(self):
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"""avg_cost=100, price=91 (< 100*0.92=92) → 止损。"""
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from tests.portfolio.conftest import FakePosition, FakeContext
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s = make_strategy()
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pos = FakePosition("600519.XSHG", avg_cost=100.0, price=91.0)
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ctx = FakeContext(positions={"600519.XSHG": pos})
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s.yesterday_hl_list = [] # 跳过昨日涨停分支
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s.stop_loss(ctx)
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s.broker.order_target_value.assert_called_with("600519.XSHG", 0)
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def test_stop_loss_skipped_when_price_above_threshold(self):
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from tests.portfolio.conftest import FakePosition, FakeContext
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s = make_strategy()
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pos = FakePosition("600519.XSHG", avg_cost=100.0, price=95.0) # > 92
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ctx = FakeContext(positions={"600519.XSHG": pos})
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s.yesterday_hl_list = []
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s.stop_loss(ctx)
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# 不调 order_target_value(code, 0)
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sell_calls = [c for c in s.broker.order_target_value.call_args_list if c.args[1] == 0]
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assert sell_calls == []
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# =================== monthly_adjustment:轮动决策分支 ===================
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class TestMonthlyAdjustmentDecision:
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def test_foreign_etf_branch_when_both_trends_negative(self):
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"""b_mean < 0 且 s_mean < 0 → 开外盘(海外 ETF)。"""
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# Arrange
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s = make_strategy(
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index_stocks_map={
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"000300.XSHG": ["600519.XSHG"],
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"399101.XSHE": ["000001.XSHE"],
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},
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price_df_map={
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# trend window = 10, 但 close 都跌
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("['600519.XSHG']", ("close",), 10): pd.DataFrame({
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"time": pd.to_datetime(["2024-09-20", "2024-09-30"]),
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"code": ["600519.XSHG"] * 2,
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"close": [15.0, 10.0], # 跌
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}),
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("['000001.XSHE']", ("close",), 10): pd.DataFrame({
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"time": pd.to_datetime(["2024-09-20", "2024-09-30"]),
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"code": ["000001.XSHE"] * 2,
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"close": [15.0, 10.0],
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}),
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},
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)
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# 流通市值 top/bottom 的 fund_df:让 _market_cap_top 仍能跑
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s.provider.get_fundamentals_df.return_value = make_fund_df([
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{"code": "600519.XSHG", "circulating_market_cap": 20000, "market_cap": 20000},
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{"code": "000001.XSHE", "circulating_market_cap": 500, "market_cap": 500},
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])
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ctx = MagicMock()
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ctx.current_dt = datetime(2024, 10, 8, 9, 30)
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ctx.previous_date = "2024-09-30"
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ctx.portfolio.positions = {}
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ctx.portfolio.available_cash = 1_000_000
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# Act
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s.monthly_adjustment(ctx)
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# Assert:海外 ETF 在 order_target_value 入参里
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called_codes = [c.args[0] for c in s.broker.order_target_value.call_args_list]
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for etf in s.config.foreign_etf:
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assert etf in called_codes, f"未触发海外 ETF 下单: {etf}"
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def test_big_market_branch_when_b_trend_dominant(self):
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"""b_mean > s_mean 且 b_mean > 0 → 开大(选 B_stocks)。"""
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s = make_strategy(
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index_stocks_map={
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"000300.XSHG": ["600519.XSHG"],
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"399101.XSHE": ["000001.XSHE"],
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},
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price_df_map={
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("['600519.XSHG']", ("close",), 10): pd.DataFrame({
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"time": pd.to_datetime(["2024-09-20", "2024-09-30"]),
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"code": ["600519.XSHG"] * 2,
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"close": [10.0, 15.0], # 涨 50%
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}),
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("['000001.XSHE']", ("close",), 10): pd.DataFrame({
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"time": pd.to_datetime(["2024-09-20", "2024-09-30"]),
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"code": ["000001.XSHE"] * 2,
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"close": [10.0, 11.0], # 涨 10%
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}),
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},
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)
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# 选股函数返回的 fund_df:让 big 路径选到 1 只
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big_fund = make_fund_df([{
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"code": "600519.XSHG", "market_cap": 20000,
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"circulating_market_cap": 20000,
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"pe_ratio": 10.0, "ps_ratio": 2.0, "pcf_ratio": 2.0,
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"eps": 1.0, "roe": 0.2, "roa": 0.15,
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"net_profit_margin": 0.2, "gross_profit_margin": 0.5,
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"inc_revenue_year_on_year": 0.3,
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"inc_operation_profit_year_on_year": 0.2,
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"inc_total_revenue_year_on_year": 0.4,
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"total_liability": 1e9, "total_sheet_owner_equities": 1e10,
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"retained_profit": 5e9, "roic": 0.15, "pb_ratio": 2.0,
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}])
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s.provider.get_fundamentals_df.return_value = big_fund
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ctx = MagicMock()
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ctx.current_dt = datetime(2024, 10, 8, 9, 30)
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ctx.previous_date = "2024-09-30"
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ctx.portfolio.positions = {}
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ctx.portfolio.available_cash = 1_000_000
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s.monthly_adjustment(ctx)
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# 600519 应被买入(开大 + 多个选股函数都会选它)
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buy_calls = [
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c.args[0] for c in s.broker.order_target_value.call_args_list
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if c.args[1] != 0
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]
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assert "600519.XSHG" in buy_calls
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# =================== 选股函数直接测试 ===================
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class TestStockPickers:
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def test_small_filters_by_roe_roa(self):
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"""roe>0.15 & roa>0.10 → 仅保留合格股,按 market_cap asc。"""
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df = make_fund_df([
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{"code": "A.XSHG", "roe": 0.20, "roa": 0.15, "market_cap": 500},
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{"code": "B.XSHG", "roe": 0.10, "roa": 0.20, "market_cap": 300}, # roe 不够
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{"code": "C.XSHG", "roe": 0.30, "roa": 0.05, "market_cap": 200}, # roa 不够
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{"code": "D.XSHG", "roe": 0.25, "roa": 0.12, "market_cap": 100},
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])
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s = make_strategy()
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s.provider.get_fundamentals_df.return_value = df
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out = s.small(["A", "B", "C", "D"], current_dt=None, previous_date="2024-09-30")
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# A 和 D 合格,D 市值小排前
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assert out == ["D.XSHG", "A.XSHG"]
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def test_big_applies_full_multi_factor_filter(self):
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df = make_fund_df([{
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# 全部满足
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"code": "PASS.XSHG",
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"market_cap": 500,
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"pe_ratio": 15.0, "ps_ratio": 3.0, "pcf_ratio": 5.0,
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"eps": 1.0, "roe": 0.2, "net_profit_margin": 0.2,
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"gross_profit_margin": 0.5, "inc_revenue_year_on_year": 0.3,
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}, {
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"code": "FAIL.XSHG",
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"market_cap": 800,
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"pe_ratio": 50.0, # pe 不在 [0,30]
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"ps_ratio": 3.0, "pcf_ratio": 5.0,
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"eps": 1.0, "roe": 0.2, "net_profit_margin": 0.2,
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"gross_profit_margin": 0.5, "inc_revenue_year_on_year": 0.3,
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}])
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s = make_strategy()
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s.provider.get_fundamentals_df.return_value = df
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out = s.big(["PASS", "FAIL"], current_dt=None, previous_date="2024-09-30")
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assert out == ["PASS.XSHG"]
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def test_roic_big_filters_by_roic_threshold(self):
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"""ROIC > 0.08 才保留。"""
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df = make_fund_df([
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{"code": "HIGH.XSHG", "market_cap": 500, "pe_ratio": 20,
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"eps": 0.5, "roa": 0.20, "total_liability": 1e8,
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"total_sheet_owner_equities": 1e10, "retained_profit": 5e9,
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"inc_total_revenue_year_on_year": 0.4,
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"inc_revenue_year_on_year": 0.3, "roic": 0.15},
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{"code": "LOW.XSHG", "market_cap": 500, "pe_ratio": 20,
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"eps": 0.5, "roa": 0.20, "total_liability": 1e8,
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"total_sheet_owner_equities": 1e10, "retained_profit": 5e9,
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"inc_total_revenue_year_on_year": 0.4,
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"inc_revenue_year_on_year": 0.3, "roic": 0.05}, # ROIC 不够
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])
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s = make_strategy()
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s.provider.get_fundamentals_df.return_value = df
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out = s.roic_big(["HIGH", "LOW"], current_dt=None, previous_date="2024-09-30")
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assert "HIGH.XSHG" in out
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assert "LOW.XSHG" not in out
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def test_bm_uses_mid_cap_value_filters(self):
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df = make_fund_df([{
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"code": "GOOD.XSHG",
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"market_cap": 500, "pb_ratio": 2.0, "pcf_ratio": 2.0,
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"eps": 1.0, "roe": 0.3, "net_profit_margin": 0.2,
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"inc_revenue_year_on_year": 0.3,
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"inc_operation_profit_year_on_year": 0.2,
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}, {
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"code": "BIG.XSHG",
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"market_cap": 1000, # 不在 [100, 900]
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"pb_ratio": 2.0, "pcf_ratio": 2.0,
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"eps": 1.0, "roe": 0.3, "net_profit_margin": 0.2,
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"inc_revenue_year_on_year": 0.3,
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"inc_operation_profit_year_on_year": 0.2,
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}])
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s = make_strategy()
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s.provider.get_fundamentals_df.return_value = df
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out = s.bm(["GOOD", "BIG"], current_dt=None, previous_date="2024-09-30")
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assert out == ["GOOD.XSHG"]
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# =================== filter_roic ===================
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class TestFilterRoic:
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def test_filters_below_threshold(self):
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df = make_fund_df([{"code": "A.XSHG", "roic": 0.15}])
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s = make_strategy()
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s.provider.get_fundamentals_df.return_value = df
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out = s.filter_roic(["A.XSHG", "B.XSHG"], previous_date="2024-09-30")
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# 第 2 次调用 fund_df 也是同一个 mock,所以 B 也算 roic=0.15 → 都保留
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assert "A.XSHG" in out
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def test_empty_input_returns_empty(self):
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s = make_strategy()
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out = s.filter_roic([], previous_date="2024-09-30")
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assert out == []
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