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chanlun.rs/chanlun-py/tests/test_all.py
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完善 测试类
2026-05-29 23:26:15 +08:00
#!/usr/bin/env python3
"""chanlun PyO3 综合测试 — 所有测试类统一入口。
使用了 pyo3_test_helpers 中的泛化 Mixin:
- RcIdentityMixin: Rc/Arc 指针身份一致性
- PyO3SubclassMixin: PyO3 子类化兼容性
- ApiConsistencyMixin: chan/chanlun API 描述符类型一致性
- assert_type_shape: 返回值类型形状验证
运行方式:
python -m pytest tests/test_all.py -v
python tests/test_all.py # 全部测试
python tests/test_all.py Test观察者子类化 # 指定测试类
python tests/test_all.py Test观察者子类化.test_subclass_basic # 单个测试
"""
import unittest
import sys
import os
import struct
import tempfile
import math
from datetime import datetime
sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "tests"))
import chanlun
import chanlun.chan # noqa # 用于 API 一致性测试
from helpers import ApiConsistencyMixin, RcIdentityMixin, PyO3SubclassMixin, assert_type_shape
# ---- 路径 ----
_PROJECT_ROOT = os.environ.get(
"CHANLUN_PROJECT_ROOT",
os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))),
)
观察者 线段分析/扩展分析 支持无限递归，保留之前序列为@property 形式
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NB_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "btcusd-300-1777649100-1778398800.nb")
_PY_REF_DIR = os.path.join(_PROJECT_ROOT, "Py_btcusd:300_1777649100_1778398800")
_RUST_REF_DIR = os.path.join(_PROJECT_ROOT, "chanlun", "Rust_btcusd:300_1777649100_1778398800")
完善 测试类
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# ---- 辅助函数 ----
def read_nb_bars(path, max_bars=None):
"""从 .nb 文件读取 K 线数据 (48 字节: 6 × f64 大端)."""
bars = []
with open(path, "rb") as f:
i = 0
while True:
data = f.read(48)
if not data:
break
ts, o, h, l, c, v = struct.unpack(">6d", data)
bars.append((int(ts), o, h, l, c, v))
i += 1
if max_bars and i >= max_bars:
break
return bars
def _has_nb():
"""检查 .nb 数据文件是否存在."""
return os.path.isfile(NB_PATH)
def create_observer(symbol="btcusd", period=14400, n_bars=500):
"""创建观察者并喂入模拟K线数据."""
cfg = chanlun.缠论配置()
obs = chanlun.观察者(symbol, period, cfg)
for i in range(n_bars):
trend = i * 3
wave = math.sin(i * 0.05) * 2000
mid = 68000.0 + trend + wave
high = mid + abs(math.cos(i * 0.3)) * 400 + 100
low = mid - abs(math.sin(i * 0.5)) * 400 - 100
k = chanlun.K线(
标识=symbol,
周期=period,
时间戳=1771675200 + i * period,
开盘价=mid - 50,
高=high,
低=low,
收盘价=mid + 50,
成交量=abs(math.sin(i)) * 1000,
)
obs.增加原始K线(k)
return obs
def _load_observer(max_bars=None):
"""从 .nb 文件加载数据并创建观察者."""
cfg = chanlun.缠论配置()
obs = chanlun.观察者("btcusd", 300, cfg)
bars = read_nb_bars(NB_PATH, max_bars=max_bars)
for i, (ts, o, h, l, c, v) in enumerate(bars):
k = chanlun.K线.创建普K(f"k{i}", ts, o, h, l, c, v, i, 300)
obs.增加原始K线(k)
return obs
# ============================================================
# 观察者 子类化 / 方法重写 测试
# ============================================================
class Test观察者子类化(PyO3SubclassMixin, unittest.TestCase):
"""观察者 子类化/方法重写 测试 — 使用 PyO3SubclassMixin 泛化基类."""
base_class = chanlun.观察者
constructor_args = ("btcusd", 300)
constructor_kwargs = {}
sequence_getter_names = [
"普通K线序列",
"缠论K线序列",
"分型序列",
"笔序列",
"笔_中枢序列",
"线段序列",
"中枢序列",
"扩展线段序列",
"扩展中枢序列",
"扩展线段序列_线段",
"扩展中枢序列_线段",
"线段_线段序列",
"线段_中枢序列",
"扩展线段序列_扩展线段",
"扩展中枢序列_扩展线段",
]
feed_method_name = "增加原始K线"
# ---- Mixin hooks: 使用预加载数据，避免每个测试重复读取 .nb ----
@staticmethod
def make_data_item():
return chanlun.K线.创建普K("test", 1761327300, 100.0, 105.0, 99.0, 103.0, 1000.0, 0, 300)
@classmethod
def make_target_with_data(cls):
obs = chanlun.观察者("btcusd", 300)
for i, (ts, o, h, l, c, v) in enumerate(cls._bars):
k = chanlun.K线.创建普K(f"base_{i}", ts, o, h, l, c, v, i, 300)
obs.增加原始K线(k)
return obs
@classmethod
def make_sub_with_data(cls):
class Sub(chanlun.观察者):
pass
obs = Sub("btcusd", 300)
for i, (ts, o, h, l, c, v) in enumerate(cls._bars):
k = chanlun.K线.创建普K(f"sub_{i}", ts, o, h, l, c, v, i, 300)
obs.增加原始K线(k)
return obs
@classmethod
def setUpClass(cls):
"""预加载 .nb 数据和预暖观察者，所有测试共享."""
if not _has_nb():
return # setUPClass 中不能 skip，各测试自行检查
cls._bars = read_nb_bars(NB_PATH, max_bars=500)
cls._base_obs = chanlun.观察者("btcusd", 300)
for i, (ts, o, h, l, c, v) in enumerate(cls._bars):
k = chanlun.K线.创建普K(f"b_{i}", ts, o, h, l, c, v, i, 300)
cls._base_obs.增加原始K线(k)
def _require_bars(self):
if not hasattr(type(self), "_bars"):
self.skipTest("需要 .nb 数据文件")
# ---- 以下为 chanlun 专项测试 ----
def test_subclass_new_pass_config(self):
"""__new__ 透传 配置 参数到父类."""
cfg = chanlun.缠论配置()
class Sub(chanlun.观察者):
def __new__(cls, 符号, 周期, 配置=None, *, tag="", **kwargs):
return super().__new__(cls, 符号, 周期, 配置=配置)
def __init__(self, 符号, 周期, 配置=None, *, tag="", **kwargs):
self.tag = tag
obs = Sub("btcusd", 300, cfg, tag="test-tag")
self.assertEqual(obs.标识, "btcusd:300")
self.assertEqual(obs.tag, "test-tag")
def test_override_method_super_call(self):
"""重写 增加原始K线，super() 调用父类，全线管线运行."""
self._require_bars()
class Sub(chanlun.观察者):
def __init__(self, 符号, 周期):
self.intercept_count = 0
self.intercept_timestamps = []
def 增加原始K线(self, 普K):
self.intercept_count += 1
self.intercept_timestamps.append(普K.时间戳)
super().增加原始K线(普K)
sub_obs = Sub("btcusd", 300)
for i, (ts, o, h, l, c, v) in enumerate(self._bars):
k = chanlun.K线.创建普K(f"s_{i}", ts, o, h, l, c, v, i, 300)
sub_obs.增加原始K线(k)
self.assertEqual(sub_obs.intercept_count, len(self._bars))
self.assertEqual(len(sub_obs.intercept_timestamps), len(self._bars))
for attr in ["普通K线序列", "缠论K线序列", "分型序列", "笔序列", "线段序列", "中枢序列"]:
base_len = len(getattr(self._base_obs, attr))
sub_len = len(getattr(sub_obs, attr))
self.assertEqual(base_len, sub_len, f"{attr}: base={base_len}, sub={sub_len}")
for j, (bp, sp) in enumerate(zip(self._base_obs.笔序列, sub_obs.笔序列)):
self.assertEqual(bp.文.中.时间戳, sp.文.中.时间戳, f"笔[{j}] 时间戳不一致")
def test_override_getter(self):
"""重写 @property getter，super() 取基类值."""
class Sub(chanlun.观察者):
@property
def 标识(self):
return f"[MOCKED] {super().标识}"
obs = Sub("btcusd", 300)
self.assertEqual(obs.标识, "[MOCKED] btcusd:300")
self.assertEqual(obs.周期, 300)
def test_override_str_repr(self):
"""重写 __str__ / __repr__."""
class Sub(chanlun.观察者):
def __str__(self):
return f"Custom({self.标识})"
def __repr__(self):
return self.__str__()
obs = Sub("btcusd", 300)
self.assertEqual(str(obs), "Custom(btcusd:300)")
self.assertEqual(repr(obs), "Custom(btcusd:300)")
def test_multi_level_inheritance(self):
"""多层继承，MRO 调用链完整."""
k = self.make_data_item()
class Level1(chanlun.观察者):
def 增加原始K线(self, 普K):
self.l1_log = getattr(self, "l1_log", [])
self.l1_log.append("L1")
super().增加原始K线(普K)
class Level2(Level1):
def 增加原始K线(self, 普K):
self.l2_log = getattr(self, "l2_log", [])
self.l2_log.append("L2")
super().增加原始K线(普K)
obs = Level2("btcusd", 300)
obs.增加原始K线(k)
self.assertEqual(obs.l2_log, ["L2"])
self.assertEqual(obs.l1_log, ["L1"])
self.assertEqual(len(obs.普通K线序列), 1)
def test_unoverridden_method_inherited(self):
"""未重写的方法从基类直接继承."""
k = self.make_data_item()
class Sub(chanlun.观察者):
pass
obs = Sub("btcusd", 120)
obs.增加原始K线(k)
self.assertEqual(obs.标识, "btcusd:120")
self.assertEqual(obs.周期, 120)
self.assertEqual(len(obs.普通K线序列), 1)
self.assertEqual(len(obs.缠论K线序列), 1)
obs.静态重新分析()
def test_override_reset(self):
"""重写 重置基础序列，子类状态也重置."""
k = self.make_data_item()
class Sub(chanlun.观察者):
def __init__(self, 符号, 周期):
self.my_log = []
def 重置基础序列(self):
self.my_log.clear()
super().重置基础序列()
obs = Sub("btcusd", 300)
obs.增加原始K线(k)
obs.my_log.append("test")
self.assertEqual(len(obs.普通K线序列), 1)
obs.重置基础序列()
self.assertEqual(len(obs.普通K线序列), 0)
self.assertEqual(obs.my_log, [])
# --- Property getter 逐一覆盖 ---
def test_override_getter_符号(self):
class Sub(chanlun.观察者):
@property
def 符号(self):
return f"[WRAPPED] {super().符号}"
obs = Sub("btcusd", 300)
self.assertEqual(obs.符号, "[WRAPPED] btcusd")
def test_override_getter_周期(self):
class Sub(chanlun.观察者):
@property
def 周期(self):
return super().周期 * 60
obs = Sub("btcusd", 5)
self.assertEqual(obs.周期, 300)
def test_override_getter_配置(self):
class Sub(chanlun.观察者):
@property
def 配置(self):
self._cfg_accessed = True
return super().配置
obs = Sub("btcusd", 300)
cfg = obs.配置
self.assertIsNotNone(cfg)
self.assertTrue(obs._cfg_accessed)
def test_override_getter_当前K线(self):
k = self.make_data_item()
class Sub(chanlun.观察者):
def __init__(self, 符号, 周期):
self._cur_k_accessed = False
@property
def 当前K线(self):
self._cur_k_accessed = True
return super().当前K线
obs = Sub("btcusd", 300)
obs.增加原始K线(k)
cur = obs.当前K线
self.assertIsNotNone(cur)
self.assertTrue(obs._cur_k_accessed)
def test_override_getter_当前缠K(self):
k = self.make_data_item()
class Sub(chanlun.观察者):
def __init__(self, 符号, 周期):
self._cur_ck_accessed = False
@property
def 当前缠K(self):
self._cur_ck_accessed = True
return super().当前缠K
obs = Sub("btcusd", 300)
obs.增加原始K线(k)
cur = obs.当前缠K
self.assertIsNotNone(cur)
self.assertTrue(obs._cur_ck_accessed)
def test_override_getter_观察员(self):
class Sub(chanlun.观察者):
@property
def 观察员(self):
self._obs_accessed = True
return self
obs = Sub("btcusd", 300)
self.assertIs(obs.观察员, obs)
self.assertTrue(obs._obs_accessed)
def test_override_sequence_getter(self):
k = self.make_data_item()
class Sub(chanlun.观察者):
def __init__(self, 符号, 周期):
self._seq_accessed = set()
@property
def 普通K线序列(self):
self._seq_accessed.add("普通K线序列")
return super().普通K线序列
@property
def 缠论K线序列(self):
self._seq_accessed.add("缠论K线序列")
return super().缠论K线序列
@property
def 笔序列(self):
self._seq_accessed.add("笔序列")
return super().笔序列
@property
def 线段序列(self):
self._seq_accessed.add("线段序列")
return super().线段序列
obs = Sub("btcusd", 300)
obs.增加原始K线(k)
self.assertEqual(len(obs.普通K线序列), 1)
self.assertIn("普通K线序列", obs._seq_accessed)
self.assertEqual(len(obs.缠论K线序列), 1)
self.assertIn("缠论K线序列", obs._seq_accessed)
self.assertIsInstance(obs.笔序列, list)
self.assertIn("笔序列", obs._seq_accessed)
self.assertIsInstance(obs.线段序列, list)
self.assertIn("线段序列", obs._seq_accessed)
def test_override_all_sequence_getters(self):
k = self.make_data_item()
all_seqs = [
"普通K线序列",
"缠论K线序列",
"分型序列",
"笔序列",
"笔_中枢序列",
"线段序列",
"中枢序列",
"扩展线段序列",
"扩展中枢序列",
"扩展线段序列_线段",
"扩展中枢序列_线段",
"线段_线段序列",
"线段_中枢序列",
"扩展线段序列_扩展线段",
"扩展中枢序列_扩展线段",
]
def _make_getter(name):
@property
def getter(self, _name=name):
self._all_seq_accessed.add(_name)
for cls in type(self).__mro__[1:]:
if hasattr(cls, _name) and isinstance(getattr(cls, _name, None), property):
return getattr(cls, _name).fget(self)
return []
return getter
class Sub(chanlun.观察者):
def __init__(self, 符号, 周期):
self._all_seq_accessed = set()
for seq_name in all_seqs:
setattr(Sub, seq_name, _make_getter(seq_name))
obs = Sub("btcusd", 300)
obs.增加原始K线(k)
for seq_name in all_seqs:
seq = getattr(obs, seq_name)
self.assertIn(seq_name, obs._all_seq_accessed, f"{seq_name} 未被拦截")
self.assertIsInstance(seq, list, f"{seq_name} 不是 list，而是 {type(seq)}")
# --- 方法重写（使用预加载数据） ---
def test_override_加载保存读取重分析(self):
"""串行验证 加载本地数据 / 保存数据 / 读取数据文件 / 静态重新分析 四个重写."""
self._require_bars()
class Sub(chanlun.观察者):
def __init__(self, 符号, 周期, 配置=None):
self._loaded = False
self._load_count = 0
self._reanalyzed = 0
self._saved = False
self._save_root = None
self._reload = 0
super().__init__(符号, 周期, 配置)
def 测试_保存数据(self, root=None):
self._saved = True
self._save_root = root
super().测试_保存数据(root)
def 静态重新分析(self):
self._reanalyzed += 1
super().静态重新分析()
def 加载本地数据(self, 文件路径):
self._loaded = True
self._load_count += 1
super().加载本地数据(文件路径)
def 重置基础序列(self):
self._reload += 1
super().重置基础序列()
@classmethod
def 读取数据文件(cls, 文件路径, 配置=None):
观察者 线段分析/扩展分析 支持无限递归，保留之前序列为@property 形式
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obs = cls("", 0) # 创建子类实例，父类方法会覆盖符号/周期
chanlun.观察者.读取数据文件(文件路径, 配置, 观察员=obs)
完善 测试类
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obs._custom_classmethod_flag = True
return obs
# 4. 读取数据文件（classmethod 重写
obs = Sub.读取数据文件(NB_PATH)
self.assertIsInstance(obs, Sub)
self.assertTrue(obs._custom_classmethod_flag)
self.assertGreater(len(obs.普通K线序列), 0)
# 1. 加载本地数据
obs.重置基础序列()
obs.加载本地数据(NB_PATH)
self.assertTrue(obs._loaded)
观察者 线段分析/扩展分析 支持无限递归，保留之前序列为@property 形式
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self.assertEqual(obs._load_count, 2)
完善 测试类
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self.assertGreater(len(obs.普通K线序列), 0)
# 2. 静态重新分析（复用已加载数据的 obs）
obs.重置基础序列()
obs.加载本地数据(NB_PATH)
obs.静态重新分析()
self.assertEqual(obs._reanalyzed, 1)
self.assertGreaterEqual(len(obs.笔序列), 0)
# 3. 保存数据
obs.重置基础序列()
obs.增加原始K线(self.make_data_item())
with tempfile.TemporaryDirectory() as tmpdir:
obs.测试_保存数据(tmpdir)
self.assertTrue(obs._saved)
self.assertEqual(obs._save_root, tmpdir)
# 5. 重置次数
观察者 线段分析/扩展分析 支持无限递归，保留之前序列为@property 形式
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self.assertEqual(obs._reload, 6)
完善 测试类
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def test_override_completely_no_super(self):
k = self.make_data_item()
class Sub(chanlun.观察者):
def 增加原始K线(self, 普K):
self._custom = getattr(self, "_custom", [])
self._custom.append(普K.时间戳)
obs = Sub("btcusd", 300)
obs.增加原始K线(k)
self.assertEqual(obs._custom, [1761327300])
self.assertEqual(len(obs.普通K线序列), 0)
def test_override_identical_subclass(self):
self._require_bars()
class Sub(chanlun.观察者):
pass
sub = Sub("btcusd", 300)
for i, (ts, o, h, l, c, v) in enumerate(self._bars):
sk = chanlun.K线.创建普K(f"s_{i}", ts, o, h, l, c, v, i, 300)
sub.增加原始K线(sk)
for attr in ["普通K线序列", "笔序列", "线段序列", "中枢序列"]:
base_len = len(getattr(self._base_obs, attr))
sub_len = len(getattr(sub, attr))
self.assertEqual(base_len, sub_len, f"{attr}: base={base_len}, sub={sub_len}")
def test_override_three_level_mixed(self):
k = self.make_data_item()
class L1(chanlun.观察者):
def __init__(self, 符号, 周期):
self._l1_feed = 0
def 增加原始K线(self, 普K):
self._l1_feed += 1
super().增加原始K线(普K)
class L2(L1):
def __init__(self, 符号, 周期):
super().__init__(符号, 周期)
self._l2_标识 = 0
@property
def 标识(self):
self._l2_标识 += 1
return f"[L2] {super().标识}"
class L3(L2):
def __init__(self, 符号, 周期):
super().__init__(符号, 周期)
self._l3_reset = 0
def 重置基础序列(self):
self._l3_reset += 1
super().重置基础序列()
obs = L3("btcusd", 300)
obs.增加原始K线(k)
self.assertEqual(obs._l1_feed, 1)
self.assertEqual(obs.标识, "[L2] btcusd:300")
self.assertEqual(obs._l2_标识, 1)
obs.重置基础序列()
self.assertEqual(obs._l3_reset, 1)
self.assertEqual(len(obs.普通K线序列), 0)
def test_override_cross_method_dispatch(self):
k = self.make_data_item()
class Sub(chanlun.观察者):
def __init__(self, 符号, 周期):
self._feed_called = False
self._reset_called = False
def 增加原始K线(self, 普K):
self._feed_called = True
super().增加原始K线(普K)
def 重置基础序列(self):
self._reset_called = True
super().重置基础序列()
def compound_operation(self, 普K):
self.增加原始K线(普K)
self.重置基础序列()
obs = Sub("btcusd", 300)
obs.compound_operation(k)
self.assertTrue(obs._feed_called)
self.assertTrue(obs._reset_called)
self.assertEqual(len(obs.普通K线序列), 0)
# ============================================================
# K线.截取 测试
# ============================================================
class TestK线截取(unittest.TestCase):
"""K线.截取 测试."""
def test_jiequ_basic(self):
k1 = chanlun.K线.创建普K("k1", 1000000000, 100.0, 105.0, 98.0, 102.0, 1000.0, 0, 300)
k2 = chanlun.K线.创建普K("k2", 1000000060, 102.0, 108.0, 101.0, 106.0, 1200.0, 1, 300)
k3 = chanlun.K线.创建普K("k3", 1000000120, 106.0, 110.0, 104.0, 108.0, 900.0, 2, 300)
k4 = chanlun.K线.创建普K("k4", 1000000180, 108.0, 112.0, 107.0, 110.0, 1100.0, 3, 300)
ks = [k1, k2, k3, k4]
r = chanlun.K线.截取(ks, k2, k4)
self.assertEqual(len(r), 3)
self.assertEqual(r[0].时间戳, k2.时间戳)
self.assertEqual(r[-1].时间戳, k4.时间戳)
r = chanlun.K线.截取(ks, k1, k4)
self.assertEqual(len(r), 4)
r = chanlun.K线.截取(ks, k3, k3)
self.assertEqual(len(r), 1)
self.assertEqual(r[0].时间戳, k3.时间戳)
def test_jiequ_error(self):
k1 = chanlun.K线.创建普K("k1", 1000000000, 100.0, 105.0, 98.0, 102.0, 1000.0, 0, 300)
k2 = chanlun.K线.创建普K("k2", 1000000060, 102.0, 108.0, 101.0, 106.0, 1200.0, 1, 300)
k3 = chanlun.K线.创建普K("k3", 1000000120, 106.0, 110.0, 104.0, 108.0, 900.0, 2, 300)
ks = [k1, k2, k3]
with self.assertRaises(ValueError):
chanlun.K线.截取([], k1, k1)
kx = chanlun.K线.创建普K("kx", 9999999999, 1.0, 1.0, 1.0, 1.0, 1.0, 99, 300)
with self.assertRaises(ValueError):
chanlun.K线.截取(ks, kx, k3)
with self.assertRaises(ValueError):
chanlun.K线.截取(ks, k3, k1)
@unittest.skipUnless(_has_nb(), "需要 .nb 数据文件")
def test_jiequ_from_observer(self):
cfg = chanlun.缠论配置()
obs = chanlun.观察者.读取数据文件(NB_PATH, cfg)
self.assertGreater(len(obs.笔序列), 0, "笔序列为空，数据可能不够")
bi = obs.笔序列[0]
pk_seq = bi.获取普K序列(obs)
self.assertGreater(len(pk_seq), 0)
r = chanlun.K线.截取(obs.普通K线序列, bi.文.中.标的K线, bi.武.中.标的K线)
self.assertGreater(len(r), 0)
self.assertEqual(r[0].时间戳, bi.文.中.标的K线.时间戳)
self.assertEqual(r[-1].时间戳, bi.武.中.标的K线.时间戳)
mid = len(pk_seq) // 2
if mid > 0:
r = chanlun.K线.截取(pk_seq, pk_seq[0], pk_seq[mid])
self.assertEqual(len(r), mid + 1)
self.assertEqual(r[0].时间戳, pk_seq[0].时间戳)
self.assertEqual(r[-1].时间戳, pk_seq[mid].时间戳)
@unittest.skipUnless(_has_nb(), "需要 .nb 数据文件")
def test_jiequ_multi_bi(self):
cfg = chanlun.缠论配置()
obs = chanlun.观察者.读取数据文件(NB_PATH, cfg)
n_checked = 0
for i, bi in enumerate(obs.笔序列[:10]):
pk_seq = bi.获取普K序列(obs)
if len(pk_seq) < 2:
continue
r = chanlun.K线.截取(pk_seq, pk_seq[0], pk_seq[-1])
self.assertEqual(len(r), len(pk_seq), f"笔[{i}] 截取长度不一致: {len(r)} vs {len(pk_seq)}")
n_checked += 1
self.assertGreater(n_checked, 0)
# ============================================================
# Rc 指针身份 / list.index 测试
# ============================================================
class TestRc身份列表索引(unittest.TestCase):
"""Rc 指针身份 / list.index 测试."""
def setUp(self):
if not _has_nb():
self.skipTest("需要 .nb 数据文件")
self.obs = _load_observer()
def test_same_seq_index(self):
obs = self.obs
fx_list = obs.分型序列
if len(fx_list) >= 2:
self.assertEqual(fx_list.index(fx_list[0]), 0)
self.assertEqual(fx_list.index(fx_list[1]), 1)
ck_list = obs.缠论K线序列
if len(ck_list) >= 2:
self.assertEqual(ck_list.index(ck_list[0]), 0)
self.assertEqual(ck_list.index(ck_list[-1]), len(ck_list) - 1)
bi_list = obs.笔序列
if len(bi_list) >= 2:
self.assertEqual(bi_list.index(bi_list[0]), 0)
self.assertEqual(bi_list.index(bi_list[-1]), len(bi_list) - 1)
if len(obs.线段序列) > 0:
for 段 in obs.线段序列:
笔列表 = 段.笔序列
if len(笔列表) >= 2:
self.assertEqual(笔列表.index(笔列表[0]), 0)
self.assertEqual(笔列表.index(笔列表[-1]), len(笔列表) - 1)
break
zs_list = obs.中枢序列
if len(zs_list) >= 2:
self.assertEqual(zs_list.index(zs_list[0]), 0)
self.assertEqual(zs_list.index(zs_list[-1]), len(zs_list) - 1)
def test_cross_seq_index(self):
obs = self.obs
n_checked = 0
for 段 in obs.线段序列:
zs_list = 段.合_中枢序列
if len(zs_list) == 0:
continue
zs = zs_list[-1]
笔列表 = 段.笔序列
for j, elem in enumerate(zs.基础序列):
idx = 笔列表.index(elem)
self.assertGreaterEqual(idx, 0)
self.assertEqual(笔列表[idx], elem)
n_checked += 1
break
self.assertGreaterEqual(n_checked, 3, f"应至少检查3个中枢元素，实际 {n_checked}")
def test_zhaofenxing_cross_ref(self):
obs = self.obs
fx_list = obs.分型序列
if len(fx_list) >= 2 and len(obs.笔序列) > 0:
bi = obs.笔序列[-1]
try:
idx = fx_list.index(bi.文)
self.assertGreaterEqual(idx, 0)
except ValueError:
pass
def test_user_pattern(self):
obs = self.obs
n_segments_with_zs = 0
for 段 in obs.线段序列:
zs_list = 段.合_中枢序列
if len(zs_list) == 0:
continue
n_segments_with_zs += 1
zs = zs_list[-1]
笔列表 = 段.笔序列
idx = 笔列表.index(zs.基础序列[0])
found = False
for bi in reversed(笔列表[:idx]):
if bi.方向 == zs.基础序列[0].方向:
found = True
break
self.assertGreater(n_segments_with_zs, 0, "应至少有一个段包含中枢")
def test_tongji_macd_behavior_types(self):
obs = _load_observer(max_bars=5000)
if len(obs.笔序列) == 0:
self.skipTest("无笔")
笔 = obs.笔序列[-1]
普K序列 = 笔.获取普K序列(obs)
if len(普K序列) < 2:
self.skipTest("K线不够")
result = chanlun.虚线.统计MACD行为(普K序列, 8, 3)
# 使用泛化工具验证类型形状
assert_type_shape(
result,
{
"DIF上穿0": int,
"DIF下穿0": int,
"DEA上穿0": int,
"DEA下穿0": int,
"金叉次数": int,
"死叉次数": int,
"密集交叉区域": [(int, int, int)],
},
)
def test_xiangduifangxiang_methods(self):
fx = chanlun.相对方向.分析(2.0, 0.5, 1.0, 0.8)
assert_type_shape(fx.是否向上, callable)
assert_type_shape(fx.是否向下, callable)
assert_type_shape(fx.是否包含, callable)
assert_type_shape(fx.是否缺口, callable)
assert_type_shape(fx.是否衔接, callable)
self.assertIsInstance(fx.是否向上(), bool)
self.assertIsInstance(fx.是否向下(), bool)
# ============================================================
# Rc 身份测试 — 使用 RcIdentityMixin
# ============================================================
class _Base身份:
"""身份测试基类：通过 setUpClass 预缓存 target 供 Mixin 使用."""
@classmethod
def setUpClass(cls):
cls._cached_target = cls.target_factory()
class Test缠K身份(_Base身份, RcIdentityMixin, unittest.TestCase):
"""缠论K线: 从序列、分型、笔端点、中枢等不同路径访问."""
@staticmethod
def target_factory():
return create_observer()
sequence_getters = {
"缠论K线序列": lambda t: t.缠论K线序列,
}
stable_getters = {
"分型[0].中": lambda t: t.分型序列[0].中 if t.分型序列 else None,
}
# 专项测试保留
def test_分型中K(self):
t = self._get_target()
seq = t.缠论K线序列
分序 = t.分型序列
for fx in 分序[:10]:
中 = fx.中
for ck in seq:
if ck.时间戳 == 中.时间戳:
self.assertIs(ck, 中, f"分型.中 (ts={中.时间戳}) 与序列中元素不匹配")
break
def test_笔端点钟K(self):
t = self._get_target()
seq = t.缠论K线序列
for bi in t.笔序列:
for nm, getter in [("文", lambda b=bi: b.文), ("武", lambda b=bi: b.武)]:
ep = getter()
if ep is None:
continue
中 = ep.中
for ck in seq:
if ck.时间戳 == 中.时间戳:
self.assertIs(ck, 中, f"笔.{nm}.中 (ts={中.时间戳}) 与序列中元素不匹配")
break
class Test分型身份(_Base身份, RcIdentityMixin, unittest.TestCase):
"""分型: 从分型序列、笔/线段端点等不同路径访问."""
@staticmethod
def target_factory():
return create_observer()
sequence_getters = {
"分型序列": lambda t: t.分型序列,
}
stable_getters = {
"笔[0].文": lambda t: t.笔序列[0].文 if t.笔序列 else None,
"笔[0].武": lambda t: t.笔序列[0].武 if t.笔序列 else None,
}
def test_笔端点与序列(self):
t = self._get_target()
分序 = t.分型序列
for bi in t.笔序列:
for nm in ["文", "武"]:
ep = getattr(bi, nm)
if ep is None:
continue
matched = False
for fx in 分序:
if fx.时间戳 == ep.时间戳 and fx.结构 == ep.结构:
self.assertIs(fx, ep, f"笔.{nm} (ts={ep.时间戳}) 与分型序列中元素不匹配")
matched = True
break
self.assertTrue(matched, f"笔.{nm} (ts={ep.时间戳}) 在分型序列中未找到")
def test_段端点与序列(self):
t = self._get_target()
分序 = t.分型序列
for duan in t.线段序列:
for nm in ["文", "武"]:
ep = getattr(duan, nm)
if ep is None:
continue
matched = False
for fx in 分序:
if fx.时间戳 == ep.时间戳 and fx.结构 == ep.结构:
self.assertIs(fx, ep, f"段.{nm} (ts={ep.时间戳}) 与分型序列中元素不匹配")
matched = True
break
self.assertTrue(matched, f"段.{nm} (ts={ep.时间戳}) 在分型序列中未找到")
class Test虚线身份(_Base身份, RcIdentityMixin, unittest.TestCase):
"""虚线(笔/线段): 从笔序列、线段序列、中枢内部序列等不同路径访问."""
@staticmethod
def target_factory():
return create_observer()
sequence_getters = {
"笔序列": lambda t: t.笔序列,
"线段序列": lambda t: t.线段序列,
}
def test_多个扩展序列(self):
t = self._get_target()
s1 = t.扩展线段序列
s2 = t.扩展线段序列_线段
for d1 in s1:
for d2 in s2:
if d1.序号 == d2.序号:
self.assertIs(d1, d2, f"扩展线段序列[{d1.序号}] 跨序列身份不一致")
break
class TestK线身份(_Base身份, RcIdentityMixin, unittest.TestCase):
"""原始K线: 从序列、买卖点、缠K标的等不同路径访问."""
@staticmethod
def target_factory():
return create_observer()
sequence_getters = {
"普通K线序列": lambda t: t.普通K线序列,
}
class Test中枢身份(_Base身份, RcIdentityMixin, unittest.TestCase):
"""中枢: 从中枢序列、分型关联、笔中枢/线段中枢等不同路径访问."""
@staticmethod
def target_factory():
return create_observer(period=3600, n_bars=800)
sequence_getters = {
"中枢序列": lambda t: t.中枢序列,
}
stable_getters = {
"笔中枢[0].文": lambda t: t.笔_中枢序列[0].文 if t.笔_中枢序列 else None,
"段中枢[0].文": lambda t: t.线段_中枢序列[0].文 if t.线段_中枢序列 else None,
}
class Test整体身份(_Base身份, unittest.TestCase):
"""跨类型综合身份测试."""
@classmethod
def setUpClass(cls):
cls.obs = create_observer(period=3600, n_bars=800)
def test_买卖点分型(self):
分序 = self.obs.分型序列
笔序 = self.obs.笔序列
self.assertGreaterEqual(len(分序), 0)
self.assertGreaterEqual(len(笔序), 0)
def test_全链路一致性(self):
obs = create_observer()
seq = obs.缠论K线序列
for bi in obs.笔序列:
for nm, getter in [("文", lambda b=bi: b.文), ("武", lambda b=bi: b.武)]:
ep = getter()
if ep is None:
continue
中 = ep.中
for ck in seq:
if ck.时间戳 == 中.时间戳:
self.assertIs(ck, 中)
break
左 = ep.左
if 左 is not None:
for ck in seq:
if ck.时间戳 == 左.时间戳:
self.assertIs(ck, 左)
break
右 = ep.右
if 右 is not None:
for ck in seq:
if ck.时间戳 == 右.时间戳:
self.assertIs(ck, 右)
break
观察者 线段分析/扩展分析 支持无限递归，保留之前序列为@property 形式
2026-06-07 13:08:19 +08:00
# ============================================================
# 跨线程身份测试 — 验证全局缓存（非 thread_local）的跨线程一致性
# ============================================================
class Test跨线程身份(unittest.TestCase):
"""跨线程 RC 身份一致性：全局缓存应在不同线程间共享同一 Python 对象."""
@classmethod
def setUpClass(cls):
cls.obs = create_observer(period=3600, n_bars=800)
def _run_in_thread(self, fn):
"""在子线程中执行 fn，通过 queue 收集结果和异常."""
import threading
result = []
err = []
def wrapper():
try:
result.append(fn())
except Exception as e:
err.append(e)
t = threading.Thread(target=wrapper)
t.start()
t.join()
if err:
raise err[0]
return result[0]
# ---- 序列级别 ----
def test_缠K序列跨线程重复获取_is一致(self):
"""缠论K线序列：从子线程重复获取，元素 is 一致."""
obs = self.obs
def check():
s1 = obs.缠论K线序列
s2 = obs.缠论K线序列
return [(s1[i] is s2[i], len(s1), len(s2)) for i in range(min(len(s1), len(s2), 20))]
results = self._run_in_thread(check)
for i, (ok, l1, l2) in enumerate(results):
self.assertTrue(ok, f"缠K序列[{i}] 跨线程 is 不一致")
def test_分型序列跨线程重复获取_is一致(self):
"""分型序列：从子线程重复获取，元素 is 一致."""
obs = self.obs
def check():
s1 = obs.分型序列
s2 = obs.分型序列
return [(s1[i] is s2[i], len(s1)) for i in range(min(len(s1), len(s2), 20))]
results = self._run_in_thread(check)
for i, (ok, _) in enumerate(results):
self.assertTrue(ok, f"分型序列[{i}] 跨线程 is 不一致")
def test_笔序列跨线程重复获取_is一致(self):
"""笔序列：从子线程重复获取，元素 is 一致."""
obs = self.obs
def check():
s1 = obs.笔序列
s2 = obs.笔序列
return [(s1[i] is s2[i], len(s1)) for i in range(min(len(s1), len(s2), 20))]
results = self._run_in_thread(check)
for i, (ok, _) in enumerate(results):
self.assertTrue(ok, f"笔序列[{i}] 跨线程 is 不一致")
def test_线段序列跨线程重复获取_is一致(self):
"""线段序列：从子线程重复获取，元素 is 一致."""
obs = self.obs
def check():
s1 = obs.线段序列
s2 = obs.线段序列
return [(s1[i] is s2[i], len(s1)) for i in range(min(len(s1), len(s2), 20))]
results = self._run_in_thread(check)
for i, (ok, _) in enumerate(results):
self.assertTrue(ok, f"线段序列[{i}] 跨线程 is 不一致")
def test_中枢序列跨线程重复获取_is一致(self):
"""中枢序列：从子线程重复获取，元素 is 一致."""
obs = self.obs
def check():
s1 = obs.中枢序列
s2 = obs.中枢序列
return [(s1[i] is s2[i], len(s1)) for i in range(min(len(s1), len(s2), 20))]
results = self._run_in_thread(check)
for i, (ok, _) in enumerate(results):
self.assertTrue(ok, f"中枢序列[{i}] 跨线程 is 不一致")
def test_普K序列跨线程重复获取_is一致(self):
"""普通K线序列：从子线程重复获取，元素 is 一致."""
obs = self.obs
def check():
s1 = obs.普通K线序列
s2 = obs.普通K线序列
return [(s1[i] is s2[i], len(s1)) for i in range(min(len(s1), len(s2), 20))]
results = self._run_in_thread(check)
for i, (ok, _) in enumerate(results):
self.assertTrue(ok, f"普K序列[{i}] 跨线程 is 不一致")
# ---- 跨路径 ----
def test_跨线程分型中K线_is一致(self):
"""子线程中 分型.中 is 缠论K线序列[同时间戳]."""
obs = self.obs
def check():
results = []
seq = obs.缠论K线序列
for fx in obs.分型序列[:10]:
中 = fx.中
found = False
for ck in seq:
if ck.时间戳 == 中.时间戳:
results.append((ck is 中, ck.时间戳))
found = True
break
if not found:
results.append((None, 中.时间戳))
return results
results = self._run_in_thread(check)
for ok, ts in results:
self.assertIsNotNone(ok, f"分型.中 ts={ts} 在缠K序列中未找到")
self.assertTrue(ok, f"跨线程 分型.中 ts={ts} is 不一致")
def test_跨线程笔端点钟K_is一致(self):
"""子线程中 笔.文中 is 缠论K线序列[同时间戳]."""
obs = self.obs
def check():
results = []
seq = obs.缠论K线序列
for bi in obs.笔序列[:10]:
for nm, ep in [("文", bi.文), ("武", bi.武)]:
if ep is None:
continue
中 = ep.中
for ck in seq:
if ck.时间戳 == 中.时间戳:
results.append((ck is 中, nm, ck.时间戳))
break
return results
results = self._run_in_thread(check)
for ok, nm, ts in results:
self.assertTrue(ok, f"跨线程 笔.{nm}.中 ts={ts} is 不一致")
def test_跨线程中枢元件_is一致(self):
"""子线程中 中枢.元件 中的虚线对象 is 线段序列[同索引]."""
obs = self.obs
def check():
results = []
for zs in obs.中枢序列[:5]:
for elem in zs.元件[:3]:
results.append(elem is elem) # 自我 is
results.append(elem is not None)
return results
results = self._run_in_thread(check)
for ok in results:
self.assertTrue(ok)
# ---- list.index 基于 is ----
def test_跨线程list_index基于身份(self):
"""子线程中 list.index(elem) 正常工作（依赖 __eq__ 基于 is）."""
obs = self.obs
def check():
results = []
for name, getter in [
("缠论K线序列", lambda o: o.缠论K线序列),
("分型序列", lambda o: o.分型序列),
("笔序列", lambda o: o.笔序列),
("线段序列", lambda o: o.线段序列),
("中枢序列", lambda o: o.中枢序列),
]:
seq = getter(obs)
if len(seq) >= 2:
r0 = seq.index(seq[0]) == 0
r1 = seq.index(seq[-1]) == len(seq) - 1
results.append((name, r0, r1, len(seq)))
return results
results = self._run_in_thread(check)
for name, r0, r1, length in results:
self.assertTrue(r0, f"跨线程 {name}(len={length}): index(seq[0]) != 0")
self.assertTrue(r1, f"跨线程 {name}(len={length}): index(seq[-1]) != {length - 1}")
# ---- 主线程-子线程之间 ----
def test_主线程与子线程对象_is一致(self):
"""主线程获取的对象与子线程获取的对象 is 相同."""
import threading
obs = self.obs
# 主线程先获取
seq_main = obs.缠论K线序列
fx_main = obs.分型序列
bi_main = obs.笔序列
err = []
def check():
try:
seq_thread = obs.缠论K线序列
for i in range(min(len(seq_main), 10)):
if seq_main[i] is not seq_thread[i]:
raise AssertionError(f"缠K[{i}] 主线程与子线程 is 不一致")
fx_thread = obs.分型序列
for i in range(min(len(fx_main), 10)):
if fx_main[i] is not fx_thread[i]:
raise AssertionError(f"分型[{i}] 主线程与子线程 is 不一致")
bi_thread = obs.笔序列
for i in range(min(len(bi_main), 10)):
if bi_main[i] is not bi_thread[i]:
raise AssertionError(f"笔[{i}] 主线程与子线程 is 不一致")
except Exception as e:
err.append(e)
t = threading.Thread(target=check)
t.start()
t.join()
if err:
raise err[0]
def test_跨线程getter稳定性(self):
"""子线程中同一 getter 多次调用返回同一对象（如 分型.结构 等）."""
obs = self.obs
def check():
results = []
if obs.分型序列:
fx = obs.分型序列[0]
for name, getter in [
("分型.结构", lambda f: f.结构),
("分型.左", lambda f: f.左),
("分型.中", lambda f: f.中),
("分型.右", lambda f: f.右),
]:
v1 = getter(fx)
v2 = getter(fx)
results.append((name, v1 is v2 if v1 is not None else True))
return results
results = self._run_in_thread(check)
for name, ok in results:
self.assertTrue(ok, f"跨线程 {name}: 两次调用 is 不一致")
def test_多线程并发访问_is一致(self):
"""两个子线程同时访问，各自拿到的对象与主线程 is 一致."""
import threading
obs = self.obs
errors = []
seq_main = obs.笔序列
def worker(thread_id):
try:
seq = obs.笔序列
for i in range(min(len(seq), 10)):
if seq[i] is not seq_main[i]:
errors.append(f"线程{thread_id} 笔[{i}] is 不一致")
if seq[i] is not seq[i]:
errors.append(f"线程{thread_id} 笔[{i}] 自我 is 失败")
except Exception as e:
errors.append(f"线程{thread_id}: {e}")
t1 = threading.Thread(target=worker, args=(1,))
t2 = threading.Thread(target=worker, args=(2,))
t1.start()
t2.start()
t1.join()
t2.join()
self.assertEqual(len(errors), 0, "\n".join(errors))
# ============================================================
# 买卖意义 双端一致性测试
# ============================================================
class Test买卖意义双端对比(unittest.TestCase):
"""运行时对比 Rust 绑定层 与 chan.py 的 虚线.买卖意义() 结果."""
@classmethod
def setUpClass(cls):
if not _has_nb():
raise unittest.SkipTest("需要 .nb 数据文件")
cls.bars = read_nb_bars(NB_PATH)
def _build_observers(self, n_bars=2000):
"""构建双端观察者并喂入相同数据."""
import chanlun
from chanlun import chan
cfg_rs = chanlun.缠论配置()
obs_rs = chanlun.观察者("btcusd", 300, cfg_rs)
cfg_py = chan.缠论配置()
obs_py = chan.观察者("btcusd", 300, cfg_py)
for i, (ts, o, h, l, c, v) in enumerate(self.bars[:n_bars]):
k_rs = chanlun.K线.创建普K(f"k{i}", ts, o, h, l, c, v, i, 300)
k_py = chan.K线.创建普K(f"k{i}", ts, o, h, l, c, v, i, 300)
obs_rs.增加原始K线(k_rs)
obs_py.增加原始K线(k_py)
return obs_rs, obs_py
def test_笔买卖意义双端一致(self):
"""笔序列的 买卖意义() 双端结果完全一致."""
import chanlun
from chanlun import chan
obs_rs, obs_py = self._build_observers()
n = min(len(obs_rs.笔序列), len(obs_py.笔序列))
self.assertGreater(n, 0, "笔序列为空")
mismatches = []
for i in range(n):
r = chanlun.虚线.买卖意义(obs_rs.笔序列[i], obs_rs)
p = chan.虚线.买卖意义(obs_py.笔序列[i], obs_py)
if r != p:
mismatches.append((i, r, p, obs_rs.笔序列[i].获取数据文本(), obs_py.笔序列[i].获取数据文本()))
self.assertEqual(len(mismatches), 0, f"笔买卖意义 不一致 ({len(mismatches)}/{n}):\n" + "\n".join(f" [{i}] R={r} P={p}\n R文本={rt}\n P文本={pt}" for i, r, p, rt, pt in mismatches[:3]))
def test_线段买卖意义双端一致(self):
"""线段序列的 买卖意义() 双端结果完全一致."""
import chanlun
from chanlun import chan
obs_rs, obs_py = self._build_observers()
n = min(len(obs_rs.线段序列), len(obs_py.线段序列))
self.assertGreater(n, 0, "线段序列为空")
mismatches = []
for i in range(n):
r = chanlun.虚线.买卖意义(obs_rs.线段序列[i], obs_rs)
p = chan.虚线.买卖意义(obs_py.线段序列[i], obs_py)
if r != p:
mismatches.append((i, r, p, obs_rs.线段序列[i].获取数据文本(), obs_py.线段序列[i].获取数据文本()))
self.assertEqual(len(mismatches), 0, f"线段买卖意义 不一致 ({len(mismatches)}/{n}):\n" + "\n".join(f" [{i}] R={r} P={p}\n R文本={rt}\n P文本={pt}" for i, r, p, rt, pt in mismatches[:3]))
def test_笔序列长度一致(self):
"""双端笔序列数量一致."""
obs_rs, obs_py = self._build_observers()
self.assertGreater(len(obs_rs.笔序列), 0)
self.assertEqual(len(obs_rs.笔序列), len(obs_py.笔序列), f"笔数量: Rust={len(obs_rs.笔序列)} Py={len(obs_py.笔序列)}")
def test_线段序列长度一致(self):
"""双端线段序列数量一致."""
obs_rs, obs_py = self._build_observers()
self.assertGreater(len(obs_rs.线段序列), 0)
self.assertEqual(len(obs_rs.线段序列), len(obs_py.线段序列), f"线段数量: Rust={len(obs_rs.线段序列)} Py={len(obs_py.线段序列)}")
def test_中枢序列长度一致(self):
"""双端中枢序列数量一致."""
obs_rs, obs_py = self._build_observers()
self.assertGreater(len(obs_rs.中枢序列), 0)
self.assertEqual(len(obs_rs.中枢序列), len(obs_py.中枢序列), f"中枢数量: Rust={len(obs_rs.中枢序列)} Py={len(obs_py.中枢序列)}")
# ---- MACD趋向背驰 ----
def test_笔MACD趋向背驰双端一致(self):
"""笔的 K线序列 MACD趋向背驰 双端结果完全一致."""
import chanlun
from chanlun import chan
obs_rs, obs_py = self._build_observers()
mismatches = []
for bi_rs, bi_py in zip(obs_rs.笔序列, obs_py.笔序列):
k_seq_rs = chanlun.K线.截取(
obs_rs.普通K线序列,
bi_rs.文.中.标的K线,
bi_rs.武.中.标的K线,
)
k_seq_py = chan.K线.截取(
obs_py.普通K线序列,
bi_py.文.中.标的K线,
bi_py.武.中.标的K线,
)
r = chanlun.虚线.计算K线序列MACD趋向背驰(k_seq_rs, bi_rs.方向)
p = chan.虚线.计算K线序列MACD趋向背驰(k_seq_py, bi_py.方向)
if r != list(p):
mismatches.append((bi_rs.文.时间戳, bi_rs.武.时间戳, list(r), list(p)))
self.assertEqual(len(mismatches), 0, f"笔MACD趋向背驰 不一致 ({len(mismatches)}):\n" + "\n".join(f" [{ts_w},{ts_wu}] R={r} P={p}" for ts_w, ts_wu, r, p in mismatches[:5]))
def test_线段MACD趋向背驰双端一致(self):
"""线段的 K线序列 MACD趋向背驰 双端结果完全一致."""
import chanlun
from chanlun import chan
obs_rs, obs_py = self._build_observers()
mismatches = []
for seg_rs, seg_py in zip(obs_rs.线段序列, obs_py.线段序列):
k_seq_rs = chanlun.K线.截取(
obs_rs.普通K线序列,
seg_rs.文.中.标的K线,
seg_rs.武.中.标的K线,
)
k_seq_py = chan.K线.截取(
obs_py.普通K线序列,
seg_py.文.中.标的K线,
seg_py.武.中.标的K线,
)
r = chanlun.虚线.计算K线序列MACD趋向背驰(k_seq_rs, seg_rs.方向)
p = chan.虚线.计算K线序列MACD趋向背驰(k_seq_py, seg_py.方向)
if r != list(p):
mismatches.append((seg_rs.文.时间戳, seg_rs.武.时间戳, list(r), list(p)))
self.assertEqual(len(mismatches), 0, f"线段MACD趋向背驰 不一致 ({len(mismatches)}):\n" + "\n".join(f" [{ts_w},{ts_wu}] R={r} P={p}" for ts_w, ts_wu, r, p in mismatches[:5]))
# ---- 统计MACD行为 ----
def test_笔统计MACD行为双端一致(self):
"""笔的 统计MACD行为() 双端结果完全一致."""
import chanlun
from chanlun import chan
obs_rs, obs_py = self._build_observers()
mismatches = []
for bi_rs, bi_py in zip(obs_rs.笔序列, obs_py.笔序列):
k_seq_rs = chanlun.K线.截取(
obs_rs.普通K线序列,
bi_rs.文.中.标的K线,
bi_rs.武.中.标的K线,
)
k_seq_py = chan.K线.截取(
obs_py.普通K线序列,
bi_py.文.中.标的K线,
bi_py.武.中.标的K线,
)
r = chanlun.虚线.统计MACD行为(k_seq_rs)
p = chan.虚线.统计MACD行为(k_seq_py)
if r != p:
mismatches.append((bi_rs.文.时间戳, bi_rs.武.时间戳, r, p))
self.assertEqual(len(mismatches), 0, f"笔统计MACD行为 不一致 ({len(mismatches)}):\n" + "\n".join(f" [{ts_w},{ts_wu}] R={r} P={p}" for ts_w, ts_wu, r, p in mismatches[:5]))
def test_线段统计MACD行为双端一致(self):
"""线段的 统计MACD行为() 双端结果完全一致."""
import chanlun
from chanlun import chan
obs_rs, obs_py = self._build_observers()
mismatches = []
for seg_rs, seg_py in zip(obs_rs.线段序列, obs_py.线段序列):
k_seq_rs = chanlun.K线.截取(
obs_rs.普通K线序列,
seg_rs.文.中.标的K线,
seg_rs.武.中.标的K线,
)
k_seq_py = chan.K线.截取(
obs_py.普通K线序列,
seg_py.文.中.标的K线,
seg_py.武.中.标的K线,
)
r = chanlun.虚线.统计MACD行为(k_seq_rs)
p = chan.虚线.统计MACD行为(k_seq_py)
if r != p:
mismatches.append((seg_rs.文.时间戳, seg_rs.武.时间戳, r, p))
self.assertEqual(len(mismatches), 0, f"线段统计MACD行为 不一致 ({len(mismatches)}):\n" + "\n".join(f" [{ts_w},{ts_wu}] R={r} P={p}" for ts_w, ts_wu, r, p in mismatches[:5]))
# ---- 获取所有停顿位置 ----
def test_笔获取所有停顿位置双端一致(self):
"""笔的 获取所有停顿位置() 双端结果一致（通过虚线相等 逐项比对）."""
import chanlun
from chanlun import chan
obs_rs, obs_py = self._build_observers()
mismatches = []
for bi_rs, bi_py in zip(obs_rs.笔序列, obs_py.笔序列):
r = chanlun.笔.获取所有停顿位置(bi_rs, obs_rs)
p = chan.笔.获取所有停顿位置(bi_py, obs_py)
if len(r) != len(p):
mismatches.append((f"len R={len(r)} P={len(p)}", [x.获取数据文本() for x in r], [x.获取数据文本() for x in p]))
else:
for a, b in zip(r, p):
eq, msg = chanlun.虚线相等(a, b)
if not eq:
mismatches.append((msg, [x.获取数据文本() for x in r], [x.获取数据文本() for x in p]))
break
self.assertEqual(len(mismatches), 0, f"笔获取所有停顿位置 不一致 ({len(mismatches)}):\n" + "\n".join(f" {tag}\n R={rl}\n P={pl}" for tag, rl, pl in mismatches[:3]))
def test_线段获取所有停顿位置双端一致(self):
"""线段的 获取所有停顿位置() 双端结果一致（通过虚线相等 逐项比对）."""
import chanlun
from chanlun import chan
obs_rs, obs_py = self._build_observers()
mismatches = []
for seg_rs, seg_py in zip(obs_rs.线段序列, obs_py.线段序列):
r = chanlun.线段.获取所有停顿位置(seg_rs, obs_rs)
p = chan.线段.获取所有停顿位置(seg_py, obs_py)
if len(r) != len(p):
mismatches.append((f"len R={len(r)} P={len(p)}", [x.获取数据文本() for x in r], [x.获取数据文本() for x in p]))
else:
for a, b in zip(r, p):
eq, msg = chanlun.虚线相等(a, b)
if not eq:
mismatches.append((msg, [x.获取数据文本() for x in r], [x.获取数据文本() for x in p]))
break
self.assertEqual(len(mismatches), 0, f"线段获取所有停顿位置 不一致 ({len(mismatches)}):\n" + "\n".join(f" {tag}\n R={rl}\n P={pl}" for tag, rl, pl in mismatches[:3]))
# ---- 判断线段内部是否背驰 ----
def test_判断线段内部是否背驰双端一致(self):
"""线段的 判断线段内部是否背驰() 双端结果完全一致."""
import chanlun
from chanlun import chan
obs_rs, obs_py = self._build_observers()
n = min(len(obs_rs.线段序列), len(obs_py.线段序列))
self.assertGreater(n, 0, "线段序列为空")
mismatches = []
for i in range(n):
r = chanlun.线段.判断线段内部是否背驰(obs_rs.线段序列[i], obs_rs)
p = chan.线段.判断线段内部是否背驰(obs_py.线段序列[i], obs_py)
if r != p:
mismatches.append((i, r, p, obs_rs.线段序列[i].获取数据文本(), obs_py.线段序列[i].获取数据文本()))
self.assertEqual(len(mismatches), 0, f"判断线段内部是否背驰 不一致 ({len(mismatches)}/{n}):\n" + "\n".join(f" [{i}] R={r} P={p}\n R文本={rt}\n P文本={pt}" for i, r, p, rt, pt in mismatches[:3]))
# ---- 是否背驰过 ----
# ---- 获取内部中枢序列 ----
def test_线段获取内部中枢序列双端一致(self):
"""线段的 获取内部中枢序列() 双端结果完全一致（通过 中枢相等 逐项比对）."""
import chanlun
from chanlun import chan
obs_rs, obs_py = self._build_observers()
n = min(len(obs_rs.线段序列), len(obs_py.线段序列))
self.assertGreater(n, 0, "线段序列为空")
mismatches = []
for i in range(n):
seg_rs = obs_rs.线段序列[i]
seg_py = obs_py.线段序列[i]
r = chanlun.线段.获取内部中枢序列(seg_rs, obs_rs.配置)
p = chan.线段.获取内部中枢序列(seg_py, obs_py.配置)
if len(r) != len(p):
mismatches.append((i, f"tuple len R={len(r)} P={len(p)}"))
else:
for k, (hr, hp) in enumerate(zip(r, p)):
if len(hr) != len(hp):
mismatches.append((i, f"{['实', '虚', '合'][k]} len R={len(hr)} P={len(hp)}"))
break
for j, (ha, hb) in enumerate(zip(hr, hp)):
eq, msg = chanlun.中枢相等(ha, hb)
if not eq:
mismatches.append((i, f"{['实', '虚', '合'][k]}[{j}]: {msg}"))
break
self.assertEqual(len(mismatches), 0, f"线段获取内部中枢序列 不一致 ({len(mismatches)}/{n}):\n" + "\n".join(f" Seg[{i}]: {detail}" for i, detail in mismatches[:5]))
# ---- 是否背驰过 ----
def test_线段是否背驰过双端一致(self):
"""线段的 是否背驰过() 双端结果一致（通过 缠论K线相等 逐项比对）."""
import chanlun
from chanlun import chan
obs_rs, obs_py = self._build_observers()
n = min(len(obs_rs.线段序列), len(obs_py.线段序列))
self.assertGreater(n, 0, "线段序列为空")
mismatches = []
for i in range(n):
r = chanlun.线段.是否背驰过(obs_rs.线段序列[i], obs_rs)
p = chan.线段.是否背驰过(obs_py.线段序列[i], obs_py)
if len(r) != len(p):
mismatches.append((i, f"len R={len(r)} P={len(p)}", obs_rs.线段序列[i].获取数据文本()))
else:
for a, b in zip(r, p):
eq, msg = chanlun.缠论K线相等(a, b)
if not eq:
mismatches.append((i, msg, obs_rs.线段序列[i].获取数据文本()))
break
self.assertEqual(len(mismatches), 0, f"线段是否背驰过 不一致 ({len(mismatches)}/{n}):\n" + "\n".join(f" [{i}] {detail}\n 段={txt[:120]}" for i, detail, txt in mismatches[:3]))
# ============================================================
# 指标挂载测试
# ============================================================
class Test指标挂载(unittest.TestCase):
"""指标计算与动态挂载回填测试."""
@staticmethod
def _make_k(i: int, ts_base: int = 1771675200, period: int = 300):
"""创建一根模拟K线."""
return chanlun.K线.创建普K(
"btcusd",
ts_base + i * period,
50000.0 + i,
51000.0 + i,
49000.0 + i,
50500.0 + i,
100.0 + i,
i,
period,
)
def test_基本指标计算(self):
"""每根K线都应有默认指标值."""
cfg = chanlun.缠论配置()
obs = chanlun.观察者("btcusd", 300, cfg)
for i in range(50):
obs.增加原始K线(self._make_k(i))
for k in obs.普通K线序列:
self.assertIn("macd", k.指标)
self.assertIn("rsi", k.指标)
self.assertIn("kdj", k.指标)
def test_动态MACD参数回填(self):
"""中途修改 obs.配置 添加 MACD 变体后，历史K线应被回填."""
cfg = chanlun.缠论配置()
obs = chanlun.观察者("btcusd", 300, cfg)
for i in range(100):
if i == 50:
obs.配置.MACD_参数列表 = [
信号 版本一
2026-06-16 12:01:58 +08:00
("macd", "收", 12, 26, 9),
("macd_10_20_7", "收", 10, 20, 7),
观察者 线段分析/扩展分析 支持无限递归，保留之前序列为@property 形式
2026-06-07 13:08:19 +08:00
]
obs.增加原始K线(self._make_k(i))
for k in obs.普通K线序列:
self.assertIn("macd_10_20_7", k.指标)
def test_多指标同时回填(self):
"""同时修改 MACD + RSI + KDJ 参数，验证全部回填."""
cfg = chanlun.缠论配置()
obs = chanlun.观察者("btcusd", 300, cfg)
for i in range(80):
if i == 40:
信号 版本一
2026-06-16 12:01:58 +08:00
obs.配置.MACD_参数列表 = [("macd", "收", 12, 26, 9), ("macd_fast", "收", 5, 13, 5)]
obs.配置.RSI_周期列表 = [("rsi", "收", 14, 13, 75.0, 25.0), ("rsi_7", "收", 7, 6, 75.0, 25.0)]
obs.配置.KDJ_参数列表 = [("kdj", "收", 9, 3, 3, 80.0, 20.0), ("kdj_5", "收", 5, 2, 2, 80.0, 20.0)]
观察者 线段分析/扩展分析 支持无限递归，保留之前序列为@property 形式
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obs.增加原始K线(self._make_k(i))
for k in obs.普通K线序列:
self.assertIn("macd_fast", k.指标)
self.assertIn("rsi_7", k.指标)
self.assertIn("kdj_5", k.指标)
def test_回填后增量计算一致(self):
"""回填后的指标值应与从头计算一致."""
cfg_full = chanlun.缠论配置()
信号 版本一
2026-06-16 12:01:58 +08:00
cfg_full.MACD_参数列表 = [("macd", "收", 12, 26, 9), ("macd_extra", "收", 8, 16, 6)]
观察者 线段分析/扩展分析 支持无限递归，保留之前序列为@property 形式
2026-06-07 13:08:19 +08:00
obs_full = chanlun.观察者("btcusd", 300, cfg_full)
cfg_late = chanlun.缠论配置()
obs_late = chanlun.观察者("btcusd", 300, cfg_late)
for i in range(100):
if i == 50:
信号 版本一
2026-06-16 12:01:58 +08:00
obs_late.配置.MACD_参数列表 = [("macd", "收", 12, 26, 9), ("macd_extra", "收", 8, 16, 6)]
观察者 线段分析/扩展分析 支持无限递归，保留之前序列为@property 形式
2026-06-07 13:08:19 +08:00
obs_full.增加原始K线(self._make_k(i))
obs_late.增加原始K线(self._make_k(i))
seq_full = obs_full.普通K线序列
seq_late = obs_late.普通K线序列
self.assertEqual(len(seq_full), len(seq_late))
for i in range(len(seq_full)):
macd_full = seq_full[i].指标["macd_extra"]
macd_late = seq_late[i].指标["macd_extra"]
self.assertEqual(macd_full.DIF, macd_late.DIF)
self.assertEqual(macd_full.DEA, macd_late.DEA)
def test_同时间戳更新后指标重算(self):
"""同时间戳K线更新后，指标应基于新值重新计算."""
cfg = chanlun.缠论配置()
obs = chanlun.观察者("btcusd", 300, cfg)
ts = 1771675200
k1 = chanlun.K线.创建普K("btcusd", ts, 50000, 51000, 49000, 50500, 100, 0, 300)
obs.增加原始K线(k1)
k2 = chanlun.K线.创建普K("btcusd", ts + 300, 50500, 52000, 50000, 51500, 200, 1, 300)
obs.增加原始K线(k2)
macd_before = obs.普通K线序列[-1].指标["macd"].DIF
# 同时间戳，不同收盘价
k2_upd = chanlun.K线.创建普K("btcusd", ts + 300, 50500, 53000, 49000, 52500, 300, 1, 300)
obs.增加原始K线(k2_upd)
macd_after = obs.普通K线序列[-1].指标["macd"].DIF
self.assertNotEqual(macd_before, macd_after)
修复 观察者混合扩展边界问题
2026-06-07 20:45:09 +08:00
# ============================================================
# 线段分析层次 = 0 时不崩溃
# ============================================================
class Test线段分析层次为零(unittest.TestCase):
"""验证 线段分析层次=0 时，各处理方法不会越界崩溃."""
@classmethod
def setUpClass(cls):
if not _has_nb():
raise unittest.SkipTest("需要 .nb 数据文件")
def test_处理数据_不崩溃(self):
"""投喂K线时 线段分析层次=0 → 跳过所有线段/扩展线段/混合扩展线段分析，不应崩溃."""
import chanlun
from chanlun import chan
# — Rust 侧 —
cfg_rs = chanlun.缠论配置()
obs_rs = chanlun.观察者("btcusd", 300, cfg_rs)
obs_rs.线段分析层次 = 0
obs_rs.重置基础序列()
for ts, o, h, l, c, v in read_nb_bars(NB_PATH)[:500]:
obs_rs.投喂原始数据(ts, o, h, l, c, v)
self.assertGreater(len(obs_rs.缠论K线序列), 0, "缠K序列应有数据")
self.assertGreater(len(obs_rs.分型序列), 0, "分型序列应有数据")
self.assertEqual(len(obs_rs.线段序列组), 0, "线段序列组应为空")
self.assertTrue(all(len(s) == 0 for s in obs_rs.混合扩展线段序列组), "混合扩展线段序列组所有条目应为空")
# — Python 侧 (chan.py) —
cfg_py = chan.缠论配置()
obs_py = chan.观察者("btcusd", 300, cfg_py)
obs_py.线段分析层次 = 0
obs_py.重置基础序列()
for ts, o, h, l, c, v in read_nb_bars(NB_PATH)[:500]:
obs_py.投喂原始数据(ts, o, h, l, c, v)
self.assertGreater(len(obs_py.缠论K线序列), 0, "chan.py 缠K序列应有数据")
self.assertGreater(len(obs_py.分型序列), 0, "chan.py 分型序列应有数据")
self.assertEqual(len(obs_py.线段序列组), 0, "chan.py 线段序列组应为空")
def test_静态重新分析_不崩溃(self):
"""静态重新分析时 线段分析层次=0 → 跳过所有线段分析，不应崩溃."""
import chanlun
from chanlun import chan
# — Rust 侧 —
cfg_rs = chanlun.缠论配置()
obs_rs = chanlun.观察者("btcusd", 300, cfg_rs)
for ts, o, h, l, c, v in read_nb_bars(NB_PATH)[:300]:
obs_rs.投喂原始数据(ts, o, h, l, c, v)
self.assertGreater(len(obs_rs.线段序列组), 0, "正常初始化后应有线段")
obs_rs.线段分析层次 = 0
obs_rs.静态重新分析()
self.assertEqual(len(obs_rs.线段序列组), 0, "静态重新分析后线段序列组应为空")
self.assertGreater(len(obs_rs.分型序列), 0, "静态重新分析后分型序列应有数据")
# — Python 侧 (chan.py) —
cfg_py = chan.缠论配置()
obs_py = chan.观察者("btcusd", 300, cfg_py)
for ts, o, h, l, c, v in read_nb_bars(NB_PATH)[:300]:
obs_py.投喂原始数据(ts, o, h, l, c, v)
self.assertGreater(len(obs_py.线段序列组), 0, "chan.py 正常初始化后应有线段")
obs_py.线段分析层次 = 0
obs_py.静态重新分析()
self.assertEqual(len(obs_py.线段序列组), 0, "chan.py 静态重新分析后线段序列组应为空")
self.assertGreater(len(obs_py.分型序列), 0, "chan.py 静态重新分析后分型序列应有数据")
完善 测试类
2026-05-29 23:26:15 +08:00
# ============================================================
# 集成对比测试
# ============================================================
class Test集成对比(unittest.TestCase):
"""全量集成测试：喂入 .nb 数据，与 Python 参考输出对比."""
@classmethod
def setUpClass(cls):
if not _has_nb():
raise unittest.SkipTest("需要 .nb 数据文件")
def test_full_integration(self):
out_dir = os.path.join(tempfile.gettempdir(), "chanlun_py_test_output")
bars = read_nb_bars(NB_PATH)
self.assertGreater(len(bars), 0, "未读取到任何 K 线")
obs = chanlun.观察者("btcusd", 300)
self.assertEqual(obs.标识, "btcusd:300")
self.assertEqual(obs.周期, 300)
for i, (ts, o, h, l, c, v) in enumerate(bars):
k = chanlun.K线.创建普K(f"btcusd_{i}", ts, o, h, l, c, v, i, 300)
obs.增加原始K线(k)
self.assertGreater(len(obs.普通K线序列), 0)
self.assertGreater(len(obs.缠论K线序列), 0)
os.makedirs(out_dir, exist_ok=True)
obs.测试_保存数据(out_dir)
subdirs = [d for d in os.listdir(out_dir) if os.path.isdir(os.path.join(out_dir, d))]
self.assertGreater(len(subdirs), 0, "No output subdirectory found!")
actual_out_dir = os.path.join(out_dir, subdirs[0])
out_files = sorted(os.listdir(actual_out_dir))
self.assertGreaterEqual(len(out_files), 14, f"Expected >= 14 output files, got {len(out_files)}")
if not os.path.isdir(_PY_REF_DIR):
self.skipTest(f"Python reference dir not found: {_PY_REF_DIR}")
ref_files = sorted(os.listdir(_PY_REF_DIR))
match_count = 0
diff_count = 0
for fname in ref_files:
ref_path = os.path.join(_PY_REF_DIR, fname)
out_path = os.path.join(actual_out_dir, fname)
if not os.path.exists(out_path):
self.fail(f"MISSING: {fname}")
continue
with open(ref_path) as f:
ref_lines = f.readlines()
with open(out_path) as f:
out_lines = f.readlines()
if ref_lines == out_lines:
match_count += 1
else:
diff_count += 1
for j, (rl, ol) in enumerate(zip(ref_lines, out_lines)):
if rl != ol:
print(f" DIFF {fname} line {j}:")
print(f" REF: {rl.rstrip()}")
print(f" OUT: {ol.rstrip()}")
break
if len(ref_lines) != len(out_lines):
print(f" DIFF {fname}: line count {len(ref_lines)} vs {len(out_lines)}")
self.assertEqual(diff_count, 0, f"{diff_count} files differ, {match_count} match")
print(f" All {match_count} Python reference files match!")
# ============================================================
# API 一致性测试 — chan.py vs chanlun
# ============================================================
class TestApi一致性(ApiConsistencyMixin, unittest.TestCase):
"""chan.py (Python参考) 与 chanlun (Rust/PyO3) 的 API 描述符类型一致性.
验证: 同名类的同名成员在两边有相同的描述符类型:
property / classmethod / staticmethod / regular_method
"""
reference_module = chanlun.chan
target_module = chanlun
# pydantic 模型方法 + Python list/str 继承方法 → 噪音过滤
noise_filters = [
# pydantic v1/v2
"construct",
"copy",
"dict",
"json",
"schema",
"schema_json",
"validate",
"parse_file",
"parse_obj",
"parse_raw",
"from_orm",
"update_forward_refs",
"model_",
"bool_parse_fallback_default",
# Python str (Enum 继承)
"capitalize",
"casefold",
"center",
"count",
"encode",
"endswith",
"expandtabs",
"find",
"format",
"format_map",
"index",
"isalnum",
"isalpha",
"isascii",
"isdecimal",
"isdigit",
"isidentifier",
"islower",
"isnumeric",
"isprintable",
"isspace",
"istitle",
"isupper",
"join",
"ljust",
"lower",
"lstrip",
"maketrans",
"partition",
"removeprefix",
"removesuffix",
"replace",
"rfind",
"rindex",
"rjust",
"rpartition",
"rsplit",
"rstrip",
"split",
"splitlines",
"startswith",
"strip",
"swapcase",
"title",
"translate",
"upper",
"zfill",
# Python list
"append",
"clear",
"extend",
"insert",
"pop",
"remove",
"reverse",
"sort",
]
# 已知 target 中缺失的成员（有意未移植或用户自行实现）
known_missing_in_target = {
"K线合成器": {"设置事件回调"},
"观察者": {"识别买卖点"}, # 用户子类化时重写
"缠论配置": {"兼容旧版本配置"}, # pydantic v1 兼容
}
# ============================================================
# main
# ============================================================
第十版
2026-06-07 17:02:10 +08:00
class Test导出函数双端等效(unittest.TestCase):
"""验证: 序列修改类导出函数与 chan.py 行为一致 (就地修改 + 返回值等效)"""
_TEST_COUNT = 300 # 投喂 K 线数
@classmethod
def setUpClass(cls):
if not _has_nb():
raise unittest.SkipTest("需要 .nb 数据文件")
cls.bars = read_nb_bars(NB_PATH)
# ---- 辅助 ----
def _make_observers(self):
import chanlun
from chanlun import chan
cfg_rs = chanlun.缠论配置()
cfg_rs.计算指标 = True
cfg_py = chan.缠论配置()
cfg_py.计算指标 = True
obs_rs = chanlun.观察者("btcusd", 300, cfg_rs)
obs_py = chan.观察者("btcusd", 300, cfg_py)
for i, (ts, o, h, l, c, v) in enumerate(self.bars[: self._TEST_COUNT]):
obs_rs.投喂原始数据(ts, o, h, l, c, v)
obs_py.投喂原始数据(ts, o, h, l, c, v)
return obs_rs, obs_py
# ================================================================
# 缠论K线.分析
# ================================================================
def test_缠论K线分析_等效(self):
"""缠论K线.分析 双端行为一致 (返回值 + 序列长度)."""
import chanlun
from chanlun import chan
obs_rs, obs_py = self._make_observers()
ck_rs: list = []
bar_rs: list = []
ck_py: list = []
bar_py: list = []
mismatches = []
for k_rs, k_py in zip(obs_rs.普通K线序列[100:200], obs_py.普通K线序列[100:200]):
st_rs, fx_rs = chanlun.缠论K线.分析(k_rs, ck_rs, bar_rs, obs_rs.配置)
st_py, fx_py = chan.缠论K线.分析(k_py, ck_py, bar_py, obs_py.配置)
if st_rs != st_py:
mismatches.append(f"状态: R={st_rs} P={st_py}")
if (fx_rs is None) != (fx_py is None):
mismatches.append(f"分型None: R={fx_rs is None} P={fx_py is None}")
self.assertEqual(len(mismatches), 0, f"缠论K线.分析 不一致 ({len(mismatches)}):\n" + "\n".join(mismatches[:5]))
self.assertEqual(len(ck_rs), len(ck_py), f"缠K序列长度: R={len(ck_rs)} P={len(ck_py)}")
self.assertEqual(len(bar_rs), len(bar_py), f"普K序列长度: R={len(bar_rs)} P={len(bar_py)}")
# ================================================================
# 笔.分析
# ================================================================
def test_笔分析_等效(self):
"""笔.分析 双端行为一致 (返回值 + 序列修改)."""
import chanlun
from chanlun import chan
obs_rs, obs_py = self._make_observers()
ck_rs = list(obs_rs.缠论K线序列)
ck_py = list(obs_py.缠论K线序列)
bar_rs = list(obs_rs.普通K线序列)
bar_py = list(obs_py.普通K线序列)
fr_rs: list = []
bi_rs: list = []
fr_py: list = []
bi_py: list = []
mismatches = []
for fx_rs, fx_py in zip(obs_rs.分型序列[2:], obs_py.分型序列[2:]):
d_rs = chanlun.笔.分析(fx_rs, fr_rs, bi_rs, ck_rs, bar_rs, 0, obs_rs.配置)
d_py = chan.笔.分析(fx_py, fr_py, bi_py, ck_py, bar_py, 0, obs_py.配置)
if d_rs != d_py:
mismatches.append(f"递归层次: R={d_rs} P={d_py}")
self.assertEqual(len(mismatches), 0, f"笔.分析 不一致 ({len(mismatches)}):\n" + "\n".join(mismatches[:3]))
self.assertEqual(len(fr_rs), len(fr_py), f"分型序列长度: R={len(fr_rs)} P={len(fr_py)}")
self.assertGreater(len(bi_rs), 0, "笔序列为空 (Rust)")
self.assertEqual(len(bi_rs), len(bi_py), f"笔序列长度: R={len(bi_rs)} P={len(bi_py)}")
# ================================================================
# 线段.分析
# ================================================================
def test_线段分析_等效(self):
"""线段.分析 双端行为一致 (序列修改)."""
import chanlun
from chanlun import chan
obs_rs, obs_py = self._make_observers()
seg_rs = list(obs_rs.线段序列)
seg_py = list(obs_py.线段序列)
# 先用 chanlun.线段.分析 重新分析
bi_list_rs = list(obs_rs.笔序列)
bi_list_py = list(obs_py.笔序列)
# 重置线段序列
seg_rs.clear()
seg_py.clear()
chanlun.线段.分析(bi_list_rs, seg_rs, obs_rs.配置)
chan.线段.分析(bi_list_py, seg_py, obs_py.配置)
self.assertGreater(len(seg_rs), 0, "线段序列为空 (Rust)")
self.assertEqual(len(seg_rs), len(seg_py), f"线段序列长度: R={len(seg_rs)} P={len(seg_py)}")
# ================================================================
# 线段.扩展分析
# ================================================================
def test_线段扩展分析_等效(self):
"""线段.扩展分析 双端行为一致."""
import chanlun
from chanlun import chan
obs_rs, obs_py = self._make_observers()
ext_rs = list(obs_rs.扩展线段序列)
ext_py = list(obs_py.扩展线段序列)
ext_rs.clear()
ext_py.clear()
bi_list_rs = list(obs_rs.笔序列)
bi_list_py = list(obs_py.笔序列)
chanlun.线段.扩展分析(bi_list_rs, ext_rs, obs_rs.配置)
chan.线段.扩展分析(bi_list_py, ext_py, obs_py.配置)
self.assertGreater(len(ext_rs), 0, "扩展线段序列为空 (Rust)")
self.assertEqual(len(ext_rs), len(ext_py), f"扩展线段: R={len(ext_rs)} P={len(ext_py)}")
# ================================================================
# 中枢.分析
# ================================================================
def test_中枢分析_等效(self):
"""中枢.分析 双端行为一致."""
import chanlun
from chanlun import chan
obs_rs, obs_py = self._make_observers()
hub_rs: list = []
hub_py: list = []
bi_list_rs = list(obs_rs.笔序列)
bi_list_py = list(obs_py.笔序列)
chanlun.中枢.分析(bi_list_rs, hub_rs, True, "", 0)
chan.中枢.分析(bi_list_py, hub_py, True, "", 0)
self.assertGreater(len(hub_rs), 0, "笔中枢序列为空 (Rust)")
self.assertEqual(len(hub_rs), len(hub_py), f"笔中枢: R={len(hub_rs)} P={len(hub_py)}")
# ================================================================
# 中枢.获取扩展中枢
# ================================================================
def test_获取扩展中枢_等效(self):
"""中枢.获取扩展中枢 双端行为一致."""
import chanlun
from chanlun import chan
obs_rs, obs_py = self._make_observers()
ext_hub_rs = list(obs_rs.扩展中枢序列)
ext_hub_py = list(obs_py.扩展中枢序列)
ext_hub_rs.clear()
ext_hub_py.clear()
# init with 笔中枢
bi_list_rs = list(obs_rs.笔序列)
bi_list_py = list(obs_py.笔序列)
chanlun.中枢.分析(bi_list_rs, ext_hub_rs, True, "", 0)
chan.中枢.分析(bi_list_py, ext_hub_py, True, "", 0)
# 若基础序列≥9，调用获取扩展中枢
for hub_rs, hub_py in zip(ext_hub_rs, ext_hub_py):
sub_rs: list = []
sub_py: list = []
hub_rs.获取扩展中枢(sub_rs, obs_rs.配置)
hub_py.获取扩展中枢(sub_py, obs_py.配置)
if len(sub_rs) != len(sub_py):
self.fail(f"获取扩展中枢 长度不一致: R={len(sub_rs)} P={len(sub_py)}")
# all passed (or vacuously true if no hubs with >=9 segments)
修复 异常BUG
2026-06-08 23:17:21 +08:00
class TestK线合成器(unittest.TestCase):
"""K线合成器 模块测试."""
def test_构造(self):
"""K线合成器 初始状态."""
import chanlun
s = chanlun.K线合成器("btcusd", [60, 300])
self.assertEqual(s.标识, "btcusd")
self.assertEqual(s.周期组, [60, 300])
self.assertIsNone(s.获取当前K线(60))
self.assertIsNone(s.获取当前K线(300))
def test_投喂单周期(self):
"""投喂单周期K线."""
import chanlun
s = chanlun.K线合成器("btcusd", [300])
bar = chanlun.K线.创建普K("btcusd", 300, 100, 110, 90, 105, 1000, 0, 60)
s.投喂K线(bar)
cur = s.获取当前K线(300)
self.assertIsNotNone(cur)
self.assertEqual(cur.周期, 300)
self.assertAlmostEqual(cur.高, 110)
def test_投喂多周期(self):
"""投喂生成多周期K线."""
import chanlun
s = chanlun.K线合成器("btcusd", [60, 300])
bar = chanlun.K线.创建普K("btcusd", 60, 100, 110, 90, 105, 1000, 0, 60)
s.投喂K线(bar)
self.assertIsNotNone(s.获取当前K线(60))
self.assertIsNotNone(s.获取当前K线(300))
def test_便捷投喂(self):
"""便捷投喂方法."""
import chanlun
s = chanlun.K线合成器("btcusd", [300])
s.投喂(1218124800, 100, 110, 90, 105, 1000)
cur = s.获取当前K线(300)
self.assertIsNotNone(cur)
class TestK线合成器双端一致(unittest.TestCase):
"""K线合成器 Rust vs chan.py 运行时行为一致 — 每步对比."""
_TEST_COUNT = 200
@classmethod
def setUpClass(cls):
if not _has_nb():
raise unittest.SkipTest("需要 .nb 数据文件")
cls.bars = read_nb_bars(NB_PATH, cls._TEST_COUNT)
def _make_synthesizers(self):
import chanlun
from chanlun import chan
return chanlun.K线合成器("btcusd", [60, 300]), chan.K线合成器("btcusd", [60, 300])
def test_合成K线逐笔OHLC一致(self):
"""每投喂一根K线后，大周期当前K线OHLC双端一致."""
import chanlun
from chanlun import chan
s_rs, s_py = self._make_synthesizers()
mismatches = []
for i, (ts, o, h, l, c, v) in enumerate(self.bars):
bar_rs = chanlun.K线.创建普K("btcusd", ts, o, h, l, c, v, 0, 60)
bar_py = chan.K线.创建普K("btcusd", ts, o, h, l, c, v, 0, 60)
s_rs.投喂K线(bar_rs)
s_py.投喂K线(bar_py)
cur_rs = s_rs.获取当前K线(300)
cur_py = s_py.获取当前K线(300)
if cur_rs is None and cur_py is None:
continue
if (cur_rs is None) != (cur_py is None):
mismatches.append(f"#{i} ts={ts}: R={cur_rs} P={cur_py}")
continue
if abs(cur_rs.高 - cur_py.高) > 1e-6 or abs(cur_rs.低 - cur_py.低) > 1e-6 or abs(cur_rs.开盘价 - cur_py.开盘价) > 1e-6 or abs(cur_rs.收盘价 - cur_py.收盘价) > 1e-6:
mismatches.append(f"#{i} ts={ts}: R(o={cur_rs.开盘价} h={cur_rs.高} l={cur_rs.低} c={cur_rs.收盘价}) P(o={cur_py.开盘价} h={cur_py.高} l={cur_py.低} c={cur_py.收盘价})")
self.assertEqual(len(mismatches), 0, f"合成K线不一致 ({len(mismatches)}处):\n" + "\n".join(mismatches[:10]))
def test_合成K线逐笔时间戳一致(self):
"""每投喂一根K线后，大周期当前K线时间戳双端一致."""
import chanlun
from chanlun import chan
s_rs, s_py = self._make_synthesizers()
mismatches = []
for i, (ts, o, h, l, c, v) in enumerate(self.bars):
bar_rs = chanlun.K线.创建普K("btcusd", ts, o, h, l, c, v, 0, 60)
bar_py = chan.K线.创建普K("btcusd", ts, o, h, l, c, v, 0, 60)
s_rs.投喂K线(bar_rs)
s_py.投喂K线(bar_py)
cur_rs = s_rs.获取当前K线(300)
cur_py = s_py.获取当前K线(300)
if cur_rs is None and cur_py is None:
continue
if (cur_rs is None) != (cur_py is None):
mismatches.append(f"#{i} ts={ts}: R={cur_rs} P={cur_py}")
continue
if int(cur_rs.时间戳) != int(cur_py.时间戳):
mismatches.append(f"#{i}: R={int(cur_rs.时间戳)} P={int(cur_py.时间戳)}")
self.assertEqual(len(mismatches), 0, f"时间戳不一致 ({len(mismatches)}处):\n" + "\n".join(mismatches[:10]))
class Test立体分析器(unittest.TestCase):
"""立体分析器 模块测试."""
@classmethod
def setUpClass(cls):
if not _has_nb():
raise unittest.SkipTest("需要 .nb 数据文件")
cls.bars = read_nb_bars(NB_PATH) # [:300]
def test_构造(self):
"""立体分析器 构造."""
import chanlun
cfg = chanlun.缠论配置()
ma = chanlun.立体分析器("btcusd", [60, 300], cfg)
self.assertEqual(ma.周期组, [60, 300])
def test_单体分析器(self):
"""单体分析器字典包含各周期观察者."""
import chanlun
cfg = chanlun.缠论配置()
ma = chanlun.立体分析器("btcusd", [60, 300], cfg)
d = ma._单体分析器
self.assertIn(60, d)
self.assertIn(300, d)
self.assertEqual(d[60].周期, 60)
self.assertEqual(d[300].周期, 300)
def test_投喂K线生成各级别数据(self):
"""投喂K线后各周期有分析数据."""
import chanlun
cfg = chanlun.缠论配置()
ma = chanlun.立体分析器("btcusd", [300, 300 * 5], cfg)
for ts, o, h, l, c, v in self.bars:
bar = chanlun.K线.创建普K("btcusd", ts, o, h, l, c, v, 0, 300)
ma.投喂K线(bar)
obs_300 = ma._单体分析器[300]
self.assertGreater(len(obs_300.缠论K线序列), 0, "300周期无缠K")
self.assertGreater(len(obs_300.普通K线序列), 0, "300周期无普K")
class Test立体分析器双端一致(unittest.TestCase):
"""立体分析器 Rust vs chan.py 运行时行为一致 — 每步对比 + 数据内容对比."""
_TEST_COUNT = 500
@classmethod
def setUpClass(cls):
if not _has_nb():
raise unittest.SkipTest("需要 .nb 数据文件")
cls.bars = read_nb_bars(NB_PATH, cls._TEST_COUNT)
def _make_analyzers(self):
import chanlun
from chanlun import chan
cfg_rs = chanlun.缠论配置()
cfg_py = chan.缠论配置()
return (chanlun.立体分析器("btcusd", [300, 300 * 5], cfg_rs), chan.立体分析器("btcusd", [300, 300 * 5], cfg_py))
def test_立体分析逐笔笔序列增长一致(self):
"""每投喂K线后，各周期笔序列长度双端一致."""
import chanlun
from chanlun import chan
ma_rs, ma_py = self._make_analyzers()
mismatches = []
for i, (ts, o, h, l, c, v) in enumerate(self.bars):
bar_rs = chanlun.K线.创建普K("btcusd", ts, o, h, l, c, v, 0, 300)
bar_py = chan.K线.创建普K("btcusd", ts, o, h, l, c, v, 0, 300)
ma_rs.投喂K线(bar_rs)
ma_py.投喂K线(bar_py)
for period in [300, 300 * 5]:
obs_rs = ma_rs._单体分析器[period]
obs_py = ma_py._单体分析器[period]
if len(obs_rs.笔序列) != len(obs_py.笔序列):
mismatches.append(f"#{i} ts={ts} 周期{period}: R笔={len(obs_rs.笔序列)} P笔={len(obs_py.笔序列)}")
if len(obs_rs.分型序列) != len(obs_py.分型序列):
mismatches.append(f"#{i} ts={ts} 周期{period}: R分型={len(obs_rs.分型序列)} P分型={len(obs_py.分型序列)}")
eq, msg = chan.观察者相等(obs_py, obs_rs)
self.assertTrue(eq, msg)
self.assertEqual(len(mismatches), 0, f"立体分析不一致 ({len(mismatches)}处):\n" + "\n".join(mismatches[:10]))
def test_立体分析逐笔缠K序列增长一致(self):
"""每投喂K线后，各周期缠论K线序列长度双端一致."""
import chanlun
from chanlun import chan
ma_rs, ma_py = self._make_analyzers()
mismatches = []
for i, (ts, o, h, l, c, v) in enumerate(self.bars):
bar_rs = chanlun.K线.创建普K("btcusd", ts, o, h, l, c, v, 0, 300)
bar_py = chan.K线.创建普K("btcusd", ts, o, h, l, c, v, 0, 300)
ma_rs.投喂K线(bar_rs)
ma_py.投喂K线(bar_py)
for period in [300, 300 * 5]:
obs_rs = ma_rs._单体分析器[period]
obs_py = ma_py._单体分析器[period]
if len(obs_rs.缠论K线序列) != len(obs_py.缠论K线序列):
mismatches.append(f"#{i} ts={ts} 周期{period}: R缠K={len(obs_rs.缠论K线序列)} P缠K={len(obs_py.缠论K线序列)}")
self.assertEqual(len(mismatches), 0, f"缠K序列不一致 ({len(mismatches)}处):\n" + "\n".join(mismatches[:10]))
def test_立体分析逐笔线段序列增长一致(self):
"""每投喂K线后，显示周期线段序列长度双端一致."""
import chanlun
from chanlun import chan
ma_rs, ma_py = self._make_analyzers()
mismatches = []
for i, (ts, o, h, l, c, v) in enumerate(self.bars):
bar_rs = chanlun.K线.创建普K("btcusd", ts, o, h, l, c, v, 0, 300)
bar_py = chan.K线.创建普K("btcusd", ts, o, h, l, c, v, 0, 300)
ma_rs.投喂K线(bar_rs)
ma_py.投喂K线(bar_py)
obs_rs = ma_rs._单体分析器[300 * 5]
obs_py = ma_py._单体分析器[300 * 5]
if len(obs_rs.线段序列) != len(obs_py.线段序列):
mismatches.append(f"#{i} ts={ts}: R线段={len(obs_rs.线段序列)} P线段={len(obs_py.线段序列)}")
self.assertEqual(len(mismatches), 0, f"线段序列不一致 ({len(mismatches)}处):\n" + "\n".join(mismatches[:10]))
def test_立体分析器相等(self):
"""立体分析后 chan.立体分析器相等 全量数据对比一致."""
import chanlun
from chanlun import chan
ma_rs, ma_py = self._make_analyzers()
for ts, o, h, l, c, v in self.bars:
bar_rs = chanlun.K线.创建普K("btcusd", ts, o, h, l, c, v, 0, 300)
bar_py = chan.K线.创建普K("btcusd", ts, o, h, l, c, v, 0, 300)
ma_rs.投喂K线(bar_rs)
ma_py.投喂K线(bar_py)
eq, msg = chan.立体分析器相等(ma_rs, ma_py)
self.assertTrue(eq, msg)
def test_立体分析观察者相等(self):
"""立体分析后主周期 chan.观察者相等 全量数据对比一致."""
import chanlun
from chanlun import chan
ma_rs, ma_py = self._make_analyzers()
for ts, o, h, l, c, v in self.bars:
bar_rs = chanlun.K线.创建普K("btcusd", ts, o, h, l, c, v, 0, 300)
bar_py = chan.K线.创建普K("btcusd", ts, o, h, l, c, v, 0, 300)
ma_rs.投喂K线(bar_rs)
ma_py.投喂K线(bar_py)
eq, msg = chan.观察者相等(ma_rs._单体分析器[300 * 5], ma_py._单体分析器[300 * 5])
self.assertTrue(eq, msg)
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class Test缠论配置双端一致(unittest.TestCase):
"""缠论配置 to_dict / from_dict / model_copy 双端输出一致."""
def _make_configs(self):
import chanlun
from chanlun import chan
cfg_rs = chanlun.缠论配置()
cfg_py = chan.缠论配置()
return cfg_rs, cfg_py
def test_to_dict_keys_一致(self):
"""to_dict 字段名集合双端一致."""
import chanlun
from chanlun import chan
cfg_rs, cfg_py = self._make_configs()
d_rs = cfg_rs.to_dict()
d_py = cfg_py.to_dict()
self.assertEqual(set(d_rs.keys()), set(d_py.keys()), f"to_dict 字段不一致: R extra={set(d_rs.keys()) - set(d_py.keys())} P extra={set(d_py.keys()) - set(d_rs.keys())}")
def test_to_dict_values_一致(self):
"""to_dict 值双端一致."""
import chanlun
from chanlun import chan
cfg_rs, cfg_py = self._make_configs()
d_rs = cfg_rs.to_dict()
d_py = cfg_py.to_dict()
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# Rust (serde_json) 产 list-of-list，Python 产 list-of-tuple，统一为 list 比较
def _normalize(v):
if isinstance(v, list):
return [_normalize(x) for x in v]
if isinstance(v, tuple):
return [_normalize(x) for x in v]
return v
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mismatches = []
for k in d_rs:
v_rs = d_rs[k]
v_py = d_py.get(k)
if v_rs is None and v_py is None:
continue
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if _normalize(v_rs) != _normalize(v_py):
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mismatches.append(f" {k}: R={v_rs!r} P={v_py!r}")
self.assertEqual(len(mismatches), 0, f"to_dict 值不一致 ({len(mismatches)}处):\n" + "\n".join(mismatches[:10]))
def test_to_json_content_一致(self):
"""to_json 内容一致（JSON 解析后对比）."""
import chanlun
from chanlun import chan
import json
cfg_rs, cfg_py = self._make_configs()
j_rs = json.loads(cfg_rs.to_json())
j_py = json.loads(cfg_py.to_json())
self.assertEqual(j_rs, j_py, f"to_json 内容不一致")
def test_from_dict_roundtrip_一致(self):
"""from_dict → to_dict 往返双端一致."""
import chanlun
from chanlun import chan
cfg_rs, cfg_py = self._make_configs()
d = cfg_rs.to_dict()
cfg2_rs = chanlun.缠论配置.from_dict(d)
cfg2_py = chan.缠论配置.from_dict(d)
d2_rs = cfg2_rs.to_dict()
d2_py = cfg2_py.to_dict()
for k in d2_rs:
self.assertEqual(d2_rs[k], d2_py.get(k), f"from_dict 往返不一致: {k}")
def test_from_json_roundtrip_一致(self):
"""from_json → to_dict 往返双端一致."""
import chanlun
from chanlun import chan
cfg_rs, cfg_py = self._make_configs()
j = cfg_rs.to_json()
cfg2_rs = chanlun.缠论配置.from_json(j)
cfg2_py = chan.缠论配置.from_json(j)
# 验证标识和关键字段一致
self.assertEqual(cfg2_rs.标识, cfg2_py.标识)
self.assertEqual(cfg2_rs.笔内元素数量, cfg2_py.笔内元素数量)
self.assertEqual(cfg2_rs.买卖点偏移, cfg2_py.买卖点偏移)
self.assertEqual(cfg2_rs.指标计算方式, cfg2_py.指标计算方式)
def test_custom_values_from_dict_一致(self):
"""自定义字段 from_dict 双端一致."""
import chanlun
from chanlun import chan
data = {
"标识": "custom_test",
"缠K合并替换": True,
"笔内元素数量": 8,
"笔弱化": True,
"计算指标": False,
"指标计算方式": "高低均值",
"平滑异同移动平均线_快线周期": 12,
"买卖点偏移": 3,
"买卖点激进识别": True,
}
cfg_rs = chanlun.缠论配置.from_dict(data)
cfg_py = chan.缠论配置.from_dict(data)
d_rs = cfg_rs.to_dict()
d_py = cfg_py.to_dict()
for k in data:
self.assertEqual(d_rs.get(k), d_py.get(k), f"自定义字段 {k}: R={d_rs.get(k)} P={d_py.get(k)}")
def test_model_copy_一致(self):
"""model_copy 双端输出一致."""
import chanlun
from chanlun import chan
cfg_rs, cfg_py = self._make_configs()
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update = {"标识": "copied", "买卖点偏移": 5, "笔内元素数量": 10}
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copy_rs = cfg_rs.model_copy(update)
copy_py = cfg_py.model_copy(update)
self.assertEqual(copy_rs.标识, copy_py.标识)
self.assertEqual(copy_rs.笔内元素数量, copy_py.笔内元素数量)
# 未更新字段保持原值一致
self.assertEqual(copy_rs.买卖点偏移, copy_py.买卖点偏移)
def test_from_dict_过滤未知字段_一致(self):
"""from_dict 过滤未知字段（兼容旧版本配置）双端一致."""
import chanlun
from chanlun import chan
data = {"标识": "test", "笔内元素数量": 7, "废弃字段_已删除": 999, "另一个旧字段": "xxx"}
cfg_rs = chanlun.缠论配置.from_dict(data)
cfg_py = chan.缠论配置.from_dict(data)
self.assertEqual(cfg_rs.标识, cfg_py.标识)
self.assertEqual(cfg_rs.笔内元素数量, cfg_py.笔内元素数量)
# 未知字段应被忽略，不影响构造
d_rs = cfg_rs.to_dict()
self.assertNotIn("废弃字段_已删除", d_rs)
def test_不推送_一致(self):
"""不推送 静态方法双端一致."""
import chanlun
from chanlun import chan
cfg_rs = chanlun.缠论配置.不推送()
cfg_py = chan.缠论配置.不推送()
self.assertFalse(cfg_rs.图表展示)
self.assertFalse(cfg_py.图表展示)
self.assertEqual(cfg_rs.笔内元素数量, cfg_py.笔内元素数量)
def test_对比_默认一致(self):
"""默认配置 self 对比应无差异."""
import chanlun
from chanlun import chan
cfg_rs_a, _ = self._make_configs()
cfg_rs_b = chanlun.缠论配置()
diff_rs = cfg_rs_a.对比(cfg_rs_b)
self.assertIsInstance(diff_rs, dict)
self.assertEqual(len(diff_rs), 0, "默认一致配置不应有差异")
# Python side
cfg_py_a = chan.缠论配置()
cfg_py_b = chan.缠论配置()
diff_py = cfg_py_a.对比(cfg_py_b)
self.assertEqual(len(diff_py), 0)
def test_对比_有差异字段一致(self):
"""修改字段后 对比 双端输出一致."""
import chanlun
from chanlun import chan
cfg_rs_a, cfg_py_a = self._make_configs()
# 构造有差异的配置
update = {"标识": "changed", "笔内元素数量": 99, "推送K线": False}
cfg_rs_b = cfg_rs_a.model_copy(update)
cfg_py_b = cfg_py_a.model_copy(update)
diff_rs = cfg_rs_a.对比(cfg_rs_b)
diff_py = cfg_py_a.对比(cfg_py_b)
self.assertEqual(set(diff_rs.keys()), set(diff_py.keys()), f"对比字段不一致: R={set(diff_rs.keys())} P={set(diff_py.keys())}")
for k in diff_rs:
self.assertEqual(diff_rs[k], diff_py[k], f"对比[{k}] 值不一致: R={diff_rs[k]!r} P={diff_py[k]!r}")
def test_对比_往返一致(self):
"""to_dict → from_dict → 对比 应无差异."""
import chanlun
from chanlun import chan
cfg_rs, _ = self._make_configs()
d = cfg_rs.to_dict()
cfg2_rs = chanlun.缠论配置.from_dict(d)
diff = cfg_rs.对比(cfg2_rs)
self.assertEqual(len(diff), 0, f"Rust往返后对比不应有差异: {diff}")
# Python side
cfg_py = chan.缠论配置()
d_py = cfg_py.to_dict()
cfg2_py = chan.缠论配置.from_dict(d_py)
diff_py = cfg_py.对比(cfg2_py)
self.assertEqual(len(diff_py), 0)
def test_对比_与chan输出一致(self):
"""对比 输出与 chan.对比 逐项一致."""
import chanlun
from chanlun import chan
cfg_rs, cfg_py = self._make_configs()
update = {"标识": "test_x", "缠K合并替换": True, "笔内元素数量": 7, "计算指标": False, "买卖点激进识别": True, "线段_修正": True}
alt_rs = cfg_rs.model_copy(update)
alt_py = cfg_py.model_copy(update)
# Rust binding: cfg_rs.对比(alt_rs)
diff_rs = cfg_rs.对比(alt_rs)
# chan.py: cfg_py.对比(alt_py)
diff_py = cfg_py.对比(alt_py)
self.assertEqual(diff_rs, diff_py, f"对比输出不一致:\n R={diff_rs}\n P={diff_py}")
def test_对比_only_model_fields(self):
"""对比 仅比较 model_fields 字段."""
import chanlun
from chanlun import chan
cfg_rs, cfg_py = self._make_configs()
update = {"标识": "only_test"}
alt_rs = cfg_rs.model_copy(update)
alt_py = cfg_py.model_copy(update)
diff_rs = cfg_rs.对比(alt_rs)
diff_py = cfg_py.对比(alt_py)
self.assertEqual(len(diff_rs), 1)
self.assertEqual(len(diff_py), 1)
self.assertIn("标识", diff_rs)
self.assertIn("标识", diff_py)
self.assertEqual(diff_rs["标识"], "only_test")
self.assertEqual(diff_py["标识"], "only_test")
def test_对比_不推送_一致(self):
"""不推送 配置与默认配置 对比 双端一致."""
import chanlun
from chanlun import chan
cfg_rs, cfg_py = self._make_configs()
muted_rs = chanlun.缠论配置.不推送()
muted_py = chan.缠论配置.不推送()
diff_rs = cfg_rs.对比(muted_rs)
diff_py = cfg_py.对比(muted_py)
self.assertEqual(set(diff_rs.keys()), set(diff_py.keys()))
# 不推送应关闭所有推送/图表字段
for k in diff_rs:
self.assertFalse(diff_rs[k], f"不推送差异字段 {k} 应为 False")
self.assertFalse(diff_py[k], f"不推送差异字段 {k} 应为 False")
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class Test生成K线双端一致(unittest.TestCase):
"""根据当前K线生成新K线 Rust vs chan.py 输出一致."""
def test_生成K线_居中各方向双端一致(self):
"""居中模式下各方向生成K线双端OHLC一致（居中=确定性输出）"""
import chanlun
from chanlun import chan
bar_rs = chanlun.K线.创建普K("btcusd", 1000000000, 50000, 50200, 49800, 50100, 100, 0, 300)
bar_py = chan.K线.创建普K("btcusd", chan.转化为时间戳(1000000000), 50000, 50200, 49800, 50100, 100, 0, 300)
directions = {
"向上": 0,
"向下": 1,
"向上缺口": 2,
"向下缺口": 3,
"衔接向上": 4,
"衔接向下": 5,
}
py_dirs = {
"向上": chan.相对方向.向上,
"向下": chan.相对方向.向下,
"向上缺口": chan.相对方向.向上缺口,
"向下缺口": chan.相对方向.向下缺口,
"衔接向上": chan.相对方向.衔接向上,
"衔接向下": chan.相对方向.衔接向下,
}
for name in directions:
new_rs = bar_rs.根据当前K线生成新K线(directions[name], 居中=True)
new_py = bar_py.根据当前K线生成新K线(py_dirs[name], 居中=True)
# 居中模式下，高和低是确定性的（偏移=高低差*0.5）
# 开盘价/收盘价/成交量含随机，不比较
tol = 1.0 + abs(new_py.高) * 1e-6
self.assertAlmostEqual(new_rs.高, new_py.高, delta=tol, msg=f"{name}: 最高价不一致 (R={new_rs.高}, P={new_py.高})")
self.assertAlmostEqual(new_rs.低, new_py.低, delta=tol, msg=f"{name}: 最低价不一致 (R={new_rs.低}, P={new_py.低})")
# 时间戳和序号
self.assertEqual(new_rs.序号, new_py.序号)
self.assertEqual(int(new_rs.时间戳), int(chan.转化为时间戳_数字(new_py.时间戳)))
def test_生成K线_居中外推验证(self):
"""居中向上生成：新K线的高/低应整体高于原K线."""
import chanlun
bar = chanlun.K线.创建普K("btcusd", 1000000000, 50000, 50200, 49800, 50100, 100, 0, 300)
new = bar.根据当前K线生成新K线(0, 居中=True)
self.assertGreater(new.高, bar.高, "向上：新高应高于原高")
self.assertGreater(new.低, bar.低, "向上：新低应高于原低")
new_down = bar.根据当前K线生成新K线(1, 居中=True)
self.assertLess(new_down.高, bar.高, "向下：新高应低于原高")
self.assertLess(new_down.低, bar.低, "向下：新低应低于原低")
def test_生成K线_衔接验证(self):
"""衔接向上：新K线的低 = 原K线的高（无缝衔接）."""
import chanlun
bar = chanlun.K线.创建普K("btcusd", 1000000000, 50000, 50200, 49800, 50100, 100, 0, 300)
new = bar.根据当前K线生成新K线(4, 居中=True)
self.assertAlmostEqual(new.低, bar.高, delta=1e-6, msg="衔接向上：新低应=原高")
new_down = bar.根据当前K线生成新K线(5, 居中=True)
self.assertAlmostEqual(new_down.高, bar.低, delta=1e-6, msg="衔接向下：新高应=原低")
完善 测试类
2026-05-29 23:26:15 +08:00
if __name__ == "__main__":
unittest.main()
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