czsc - 缠中说禅技术分析工具

项目文档 | 投研数据共享 | 信号函数编写规范 | DEVIN生成的文档

1.0.X 版本开始，缠论核心算法（分型、笔、中枢等）已全部迁移到 Rust 实现，通过 PyO3 扩展（czsc._native）暴露给 Python。 需要了解旧 Python 实现逻辑的，可查看 0.9.X 版本。

czsc_skills

源于缠中说缠博客，原始博客中的内容不太完整，且没有评论，以下是网友整理的原文备份

备份网址1：http://www.fxgan.com
备份网址2：https://chzhshch.blog
已经开始用czsc库进行量化研究的朋友，欢迎加入飞书群，快点击加入吧！
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架构概览

CZSC 1.0 采用 Rust + Python 混合架构：

czsc (Python 包)
├── czsc._native          ← Rust 扩展（PyO3），缠论核心
│   ├── CZSC / FX / BI / ZS / RawBar / NewBar / BarGenerator
│   ├── Freq / Mark / Direction / Signal / Event / Position / Operate
│   ├── CzscTrader / CzscSignals / generate_czsc_signals
│   ├── signals.*         ← 220+ 信号函数（Python 端 7 个子模块；详见 crates/czsc-signals/src/）
│   └── ta.*              ← Rust TA 算子 (ema/sma/boll 等，本次清理 起仅内部使用)
├── czsc.traders          ← Python 门面，汇聚 Rust 交易 API
├── czsc.utils            ← 工具函数（绘图/缓存/统计/交易工具）
├── czsc.connectors       ← 数据源连接器（天勤/Tushare/CCXT/本地缓存）
├── czsc.strategies       ← 策略门面（CzscStrategyBase/CzscJsonStrategy）
├── czsc.fsa              ← 飞书自动化工具
├── czsc.mock             ← 测试用模拟数据（转发自 wbt）
└── czsc.envs             ← 环境变量管理

底层 Rust workspace 包含 9 个 crate：czsc / czsc-core / czsc-derive / czsc-signals / czsc-trader / czsc-utils / czsc-ta / czsc-signal-macros / czsc-python（PyO3 绑定入口）。

项目贡献

缠论的 分型、笔 的自动识别，由 Rust 实现并通过 czsc._native 暴露
定义并实现 信号-事件-交易 量化交易逻辑体系，事件通过 signals_all/signals_any/signals_not 实现信号的逻辑组合
定义并实现了 220+ 信号函数（Rust 实现），见 czsc._native.signals
缠论多级别联立决策分析交易，详见 CzscTrader
HTML 可视化（plotly + lightweight-charts）：czsc.utils.plotting.{kline,weight,lightweight}.*

安装使用

注意： Python 版本必须 ≥ 3.10

从 PyPI 安装预编译版本（推荐）：

pip install czsc -U

使用 uv 安装（推荐开发环境）：

uv pip install czsc

从源码构建（需要 Rust 工具链和 maturin）：

# 安装 Rust：https://rustup.rs/
# 安装 maturin
pip install maturin

# 克隆并构建
git clone https://github.com/waditu/czsc.git
cd czsc
maturin develop --release

Rust 构建环境约束：底层依赖 pyo3 / pyo3-stub-gen 0.22 要求 Python ≥ 3.10。当系统默认 Python 低于 3.10 时，请通过环境变量显式指定：
export PYO3_PYTHON=$(which python3.12)   # 或任意 3.10+ 的解释器
否则 cargo build / cargo test 会在 crates/czsc-python/build.rs 提前 panic 并给出修复建议。用 uv sync --extra dev 走 UV 流程时，UV 会自动选择项目声明的 Python，不需要额外设置。

快速开始

核心缠论分析

import czsc
from czsc import CZSC, Freq, format_standard_kline
from czsc.mock import generate_symbol_kines

# 生成模拟 K 线数据
df = generate_symbol_kines('000001', '30分钟', '20240101', '20240601')

# 转换为 RawBar 对象列表
bars = format_standard_kline(df, freq=Freq.F30)

# 创建 CZSC 分析对象（自动识别分型、笔、中枢）
czsc_obj = CZSC(bars)
print(f"笔数量：{len(czsc_obj.bi_list)}")
print(f"中枢数量：{len(czsc_obj.zs_list)}")

K 线合成与多级别分析

from czsc import BarGenerator, Freq

# 使用 BarGenerator 进行 K 线合成
bg = BarGenerator(base_freq='1分钟', freqs=['5分钟', '30分钟', '日线'])
for bar in raw_bars:
    bg.update(bar)

# 获取各周期 K 线
bars_5m = bg.bars['5分钟']
bars_30m = bg.bars['30分钟']

信号生成

from czsc import generate_czsc_signals, get_signals_config, get_signals_freqs

# 配置信号序列（使用 Rust 实现的信号函数）
signals_seq = [
    "czsc._native.signals.bar.bar_end_V230331",
    "czsc._native.signals.cxt.cxt_bi_status_V230101",
]

# 解析信号所需的周期配置
freqs = get_signals_freqs(signals_seq)
config = get_signals_config(signals_seq)

# 生成信号序列
results = generate_czsc_signals(bars, signals_seq)

权重回测

from czsc import WeightBacktest
from czsc.mock import generate_klines_with_weights

# 生成带权重的模拟数据
dfw = generate_klines_with_weights(seed=42)

# 运行权重回测
wb = WeightBacktest(dfw, fee_rate=0.0002)
print(wb.stats)  # 回测统计汇总

策略研究

from czsc import run_research, run_replay

# 单品种回放
run_replay(bars, signals_seq, pos_seq, res_path='./results/')

# 批量品种研究
run_research(symbols, signals_seq, pos_seq, res_path='./results/')

缠论可视化与回测报告

# 缠论 K 线（多周期联立，自包含 HTML，离线即可打开）
from czsc.utils.plotting.lightweight import plot_czsc, plot_czsc_trader

html = plot_czsc(c, output="html")  # 单周期
plot_czsc_trader(ct, output="html", path="trader.html")  # 多周期

# 回测 HTML 报告：用 wbt.generate_backtest_report 一键产出
from wbt import generate_backtest_report
generate_backtest_report(df=dfw, output_path="report.html", weight_type="ts")

使用案例

更多端到端案例汇总见飞书 wiki：CZSC 1.0.X 版本使用说明和案例。

序号	案例	一句话介绍
1	基于 Event 的策略回测 + wbt HTML 报告	5 行命令跑通 Event → Position → 回测 → 可交互 HTML 报告的完整闭环。
2	lightweight_charts 缠论可视化	几行代码把缠论结构（分型/笔/线段/中枢）导出为自包含离线 HTML，便于分享与嵌入。
3	把信号函数画到 K 线主图（plot_czsc_signals）	把信号触发点叠在 K 线主图上，直观调试 signals_config 与多周期联立。
4	性能基准测试：在本地复现 CZSC / CzscTrader 吞吐量	一行命令本地评估吞吐量，配套关键指标速查表，用于容量规划与加速效果核对。
5	信号函数模块深入介绍	系统讲解 czsc-signals 架构与 246 个信号函数的模块划分、参数模板、信号表达示例。

核心 API 一览

类型	符号	说明
缠论对象	`CZSC`, `FX`, `BI`, `ZS`	缠论核心数据结构（Rust）
K线对象	`RawBar`, `NewBar`, `BarGenerator`	K线与合成器（Rust）
枚举	`Freq`, `Mark`, `Direction`, `Operate`	方向/频率等枚举（Rust）
信号/事件	`Signal`, `Event`, `Position`	信号与持仓逻辑（Rust）
分析工具	`check_fx`, `check_bi`, `remove_include`	分型/笔校验工具（Rust）
TA算子顶层别名	`czsc.ema`, `czsc.sma`, `czsc.rolling_rank`, `czsc.boll_positions`, `czsc.ultimate_smoother`	技术指标算子顶层别名（Rust）
交易器	`CzscTrader`, `CzscSignals`	多级别交易决策（Rust）
信号生成	`generate_czsc_signals`	批量信号生成（Rust）
权重回测	`WeightBacktest`	权重序列回测（来自 wbt）
策略	`CzscStrategyBase`, `CzscJsonStrategy`	策略封装（Python）
模拟数据	`generate_symbol_kines`	测试用 K线数据（来自 wbt）
格式转换	`format_standard_kline`	DataFrame → RawBar 列表

数据源连接器

czsc.connectors 提供多个数据源适配器：

模块	数据源	说明
`tq_connector.py`	天勤（TQSdk）	期货实时/历史行情
`ts_connector.py`	Tushare	A股历史数据
`ccxt_connector.py`	CCXT	数字货币交易所
`local_data.py`	投研数据本地缓存	CZSC 共享数据本地读取入口

可视化（HTML 输出）

可视化统一以 plotly + lightweight-charts 输出 HTML：

模块	功能
`czsc.utils.plotting.kline`	单周期 K 线 + 缠论结构（plotly Figure）
`czsc.utils.plotting.weight`	权重时序图（plotly Figure）
`czsc.utils.plotting.lightweight`	lightweight-charts 自包含 HTML，多周期联立 + 信号叠加
累计收益 / 回撤 / 月度热力图 / 综合回测概览	改用 `wbt.generate_backtest_report` 或直接 plotly，见 `docs/migration/cleanup-non-czsc-core.md`

使用 plot_czsc、plot_czsc_trader 或 plot_czsc_signals 生成自包含 HTML 后，可直接在浏览器中打开。

项目	GitHub	角色	与 czsc 的关系
wbt	https://github.com/zengbin93/wbt	策略持仓权重回测引擎（Weight Back Test）	硬依赖。提供 `WeightBacktest / daily_performance / top_drawdowns / generate_backtest_report` 等；`czsc.mock` 也转发自 `wbt.mock`。czsc 顶层 `from czsc import WeightBacktest` 即来自此包。
wmr	https://github.com/zengbin93/wmr	策略持仓权重管理系统（Weight Manager，DuckDB / ClickHouse 双后端）	下游配套（非硬依赖）。wbt 负责"离线回测权重"，wmr 负责"实盘 / 投研环境下权重的持久化、版本管理与查询"。czsc 产出信号 / 持仓 → wbt 跑回测 → wmr 落库供下游消费。
talib-rs	https://github.com/0xcjun/talib-rs	纯 Rust 实现的 TA-Lib 替代库	测试可选依赖。仅 `tests/unit/test_ta_parity.py` 用它对 `czsc._native.ta`（EMA / SMA 等）做数值 parity 校验，确保 czsc 自研 TA 算子与 TA-Lib 行为一致。运行时代码不依赖。

开发环境搭建

# 使用 UV 管理依赖（推荐）
uv sync --extra dev

# 构建 Rust 扩展（开发模式）
maturin develop

# 运行测试
uv run pytest tests/ -v

# 代码格式化
uv run ruff format czsc/ tests/
uv run ruff check czsc/ tests/

关键环境变量

变量	说明	默认值
`CZSC_MIN_BI_LEN`	最小笔长度	6
`CZSC_MAX_BI_NUM`	最大笔数量	50
`CZSC_VERBOSE`	是否输出详细日志	False

使用前必看

1.0.X 版本核心算法已迁移到 Rust，与 0.9.X 版本 不兼容；旧代码需按新 API 迁移；
免责声明：项目开源仅用于技术交流！
如果你发现了项目中的 Bug，可以先读一下《如何有效地报告 Bug》，然后在 issues 中报告 Bug

缠论精华

学了本ID的理论，去再看其他的理论，就可以更清楚地看到其缺陷与毛病，因此，广泛地去看不同的理论，不仅不影响本ID理论的学习，更能明白本ID理论之所以与其他理论不同的根本之处。

为什么要去了解其他理论，就是这些理论操作者的行为模式，将构成以后我们猎杀的对象，他们操作模式的缺陷，就是以后猎杀他们的最好武器，这就如同学独孤九剑，必须学会发现所有派别招数的缺陷，这也是本ID理论学习中一个极为关键的步骤。

真正的预测，就是不测而测。所有预测的基础，就是分类，把所有可能的情况进行完全分类。有人可能说，分类以后，把不可能的排除，最后一个结果就是精确的。这是脑子锈了的想法，任何的排除，等价于一次预测，每排除一个分类，按概率的乘法原则，就使得最后的所谓精确变得越不精确，最后还是逃不掉概率的套子。对于预测分类的唯一正确原则就是不进行任何排除，而是要严格分清每种情况的边界条件。任何的分类，其实都等价于一个分段函数，就是要把这分段函数的边界条件确定清楚。边界条件分段后，就要确定一旦发生哪种情况就如何操作，也就是把操作也同样给分段化了。然后，把所有情况交给市场本身，让市场自己去当下选择。所有的操作，其实都是根据不同分段边界的一个结果，只是每个人的分段边界不同而已。因此，问题不是去预测什么，而是确定分段边界。

原文整理

资料分享

链接：https://pan.baidu.com/s/1RXkP3188F0qu8Yk6CjbxRQ
提取码：vhue

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The following attestation bundles were made for czsc-1.0.1-cp310-abi3-macosx_10_12_x86_64.whl:

Publisher: python-publish.yml on waditu/czsc

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- Runner Environment: github-hosted
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Release history Release notifications | RSS feed

This release

1.0.1

Aug 9, 2026

1.0.0rc8 pre-release

May 29, 2026

1.0.0rc7 pre-release

May 20, 2026

1.0.0rc5 pre-release

May 18, 2026

0.10.12

Mar 9, 2026

0.10.11

Feb 24, 2026

0.10.10

Feb 12, 2026

0.10.9

Jan 21, 2026

0.10.8

Dec 31, 2025

0.10.7

Dec 26, 2025

0.10.6

Dec 16, 2025

0.10.5

Dec 9, 2025

0.10.4

Dec 8, 2025

0.10.3

Oct 3, 2025

0.10.2

Sep 27, 2025

0.10.1

Sep 14, 2025

0.9.69

Aug 2, 2025

0.9.68

Jun 12, 2025

0.9.67

May 14, 2025

0.9.66

May 2, 2025

0.9.65

Apr 5, 2025

0.9.64

Mar 15, 2025

0.9.63

Feb 19, 2025

0.9.62

Jan 1, 2025

0.9.61

Dec 6, 2024

0.9.60

Oct 31, 2024

0.9.59

Sep 19, 2024

0.9.58

Aug 31, 2024

0.9.57

Aug 4, 2024

0.9.56

Jul 21, 2024

0.9.55

Jul 12, 2024

0.9.54

Jun 29, 2024

0.9.53

Jun 16, 2024

0.9.52

Jun 2, 2024

0.9.51

May 26, 2024

0.9.50

May 12, 2024

0.9.49

May 4, 2024

0.9.48

Apr 19, 2024

0.9.47

Apr 10, 2024

0.9.46

Mar 29, 2024

0.9.45

Mar 17, 2024

0.9.44

Mar 8, 2024

0.9.43

Mar 2, 2024

0.9.42

Feb 17, 2024

0.9.41

Jan 21, 2024

0.9.40

Jan 14, 2024

0.9.39

Dec 17, 2023

0.9.38

Dec 3, 2023

0.9.37

Nov 26, 2023

0.9.36

Nov 19, 2023

0.9.35

Nov 11, 2023

0.9.34

Nov 4, 2023

0.9.33

Oct 22, 2023

0.9.32

Oct 16, 2023

0.9.31

Oct 13, 2023

0.9.30

Oct 7, 2023

0.9.29

Sep 25, 2023

0.9.28

Sep 4, 2023

0.9.27

Aug 19, 2023

0.9.26

Aug 11, 2023

0.9.25

Jul 24, 2023

0.9.24

Jul 16, 2023

0.9.23

Jun 30, 2023

0.9.22

Jun 25, 2023

0.9.21

Jun 16, 2023

0.9.20

May 28, 2023

0.9.19

May 13, 2023

0.9.18

Apr 25, 2023

0.9.17

Apr 16, 2023

0.9.16

Apr 10, 2023

0.9.15

Apr 3, 2023

0.9.14

Mar 31, 2023

0.9.13

Mar 23, 2023

0.9.12

Mar 14, 2023

0.9.11

Mar 12, 2023

0.9.10

Mar 4, 2023

0.9.9

Feb 26, 2023

0.9.8

Feb 19, 2023

0.9.7

Feb 10, 2023

0.9.6

Jan 1, 2023

0.9.5

Dec 18, 2022

0.9.4

Dec 12, 2022

0.9.3

Nov 27, 2022

0.9.2

Nov 20, 2022

0.9.1

Nov 12, 2022

0.8.30

Oct 23, 2022

0.8.29

Sep 8, 2022

0.8.28

Jul 4, 2022

0.8.27

Jun 3, 2022

0.8.26

May 14, 2022

0.8.25

May 7, 2022

0.8.24

Apr 25, 2022

0.8.23

Apr 18, 2022

0.8.22

Apr 13, 2022

0.8.21

Apr 4, 2022

0.8.20

Apr 2, 2022

0.8.19

Mar 25, 2022

0.8.18

Mar 19, 2022

0.8.17

Mar 10, 2022

0.8.16

Feb 18, 2022

0.8.15

Feb 7, 2022

0.8.14

Jan 22, 2022

0.8.13

Jan 16, 2022

0.8.12

Jan 10, 2022

0.8.11

Jan 3, 2022

0.8.10

Dec 23, 2021

0.8.9

Dec 13, 2021

0.8.8

Dec 9, 2021

0.8.7

Nov 26, 2021

0.8.6

Nov 21, 2021

0.8.5

Nov 17, 2021

0.8.4

Nov 14, 2021

0.8.3

Nov 12, 2021

0.8.2

Nov 8, 2021

0.8.1

Nov 4, 2021

0.7.10

Oct 26, 2021

0.7.9

Oct 6, 2021

0.7.8

Sep 29, 2021

0.7.7

Sep 23, 2021

0.7.6

Sep 16, 2021

0.7.5

Sep 8, 2021

0.7.4

Aug 22, 2021

0.7.3

Aug 3, 2021

0.7.2

May 8, 2021

0.7.1

Apr 30, 2021

0.7.1a0 pre-release

Apr 25, 2021

0.6.10

Apr 11, 2021

0.6.9

Mar 20, 2021

0.6.8

Mar 14, 2021

0.6.7

Mar 5, 2021

0.6.6

Feb 20, 2021

0.6.5

Feb 18, 2021

0.6.4

Feb 6, 2021

0.6.3

Jan 31, 2021

0.6.2

Jan 29, 2021

0.6.1

Jan 23, 2021

0.5.9

Dec 26, 2020

0.5.8

Dec 12, 2020

0.5.7

Oct 12, 2020

0.5.6

Sep 28, 2020

0.5.5

Sep 20, 2020

0.5.4

Sep 13, 2020

0.5.3

Sep 5, 2020

0.5.2

Aug 13, 2020

0.5.1

Jul 28, 2020

0.4.3

Jul 17, 2020

0.4.2

Jul 12, 2020

0.4.1

Jun 19, 2020

0.3.9

Jun 17, 2020

0.3.8

Jun 17, 2020

0.3.7

Jun 5, 2020

0.3.5

Jun 2, 2020

0.3.4

Jun 2, 2020

0.0.1

Dec 17, 2022

czsc 1.0.1

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