Zipline是QUANTOPIAN开发的算法交易库。这是一个事件驱动，支持回测和实时交易的系统。 Zipline目前有一个免费的回测平台，可以很方便编写交易策略和执行回测。为处理A股数据，增加或修改基础数据相关部分，用于本机策略回测分析。
prev_close前收盘turnover换手率amount成交额tmv总市值cmv流通市值circulating_share流通股本total_share总股本
注意 以上数据不会因股票分红派息而调整
Fundamentals是用于pipeline的数据集容器，包括偶发性变化及固定信息数据，如上市日期、行业分类、所属概念、财务数据等信息。
参考quantopian公司帮助文档，完成optimize模块，实现在限定条件下的MaximizeAlpha及TargetWeights投资组合优化。
AnalysisFigure绘制OHLCV及常用MACD等技术指标图，使用买入卖出信号标注来辅助策略改进。
通过定时后台计划任务，自动提取回测所需网络数据，并转换为符合zipline需求的数据格式，可以更加专注于编写策略及相关分析。
$ git clone https://github.com/zhangshoug/czipline.git$ conda create -n zipline python=3.6 # 环境名称随意，python版本要求3.6以下安装需要进入环境
$ source activate zipline # 进入刚才建立的环境
$ activate zipline # windows 进入环境下载并安装ta-lib包以及数据处理包、统计分析包。转移至项目安装文件所在目录后执行：
参考talib安装方法
$ # 以下需确保在zipline项目安装目录下执行
$ pip install -r ./etc/requirements.txt
$ pip install -r ./etc/requirements_add.txt$ python setup.py build_ext --inplace$ python setup.py install
$ # 如需开发安装
$ python setup.py develop成功安装zipline后，cswd包也已经成功安装。在相应环境下，执行以下命令：
$ init-stock-data # 初始化基础数据。耗时大约24小时(主要下载日线及最近一个月的分时交易数据。对数据量小，但抓取网页数据耗时长的，整理好的数据存放在github，初始化时会从该处提取，节约初始化时间。)
$ zipline ingest # 转换日线数据，耗时约10分钟
$ sql-to-bcolz # `Fundamentals`数据，耗时约1.5分钟
# 如需要进行回测分析，请运行以下指令，生成ff因子数据(empyrical包使用)
$ gen-ff-factors初始化数据后，参考如何设置后台数据自动处理，设置后台计划任务。后台在盘后自动完成数据导入及转换。网络数据的采集可能因各种原失败，请注意查阅日志文档。文档默认路径为"～/stockdata/logs"。
如能正常运行以下代码，证明已经安装成功。(注意，由于版本兼容问题，安装过程中会有大量警告信息，只要不是错误，可忽略)
from zipline import get_calendar
c = get_calendar('SZSH')
c.first_session
# Timestamp('1990-12-19 00:00:00+0000', tz='UTC', freq='C')计算Fama-French三因子案例涉及到：
- 如何在
Notebook运行回测 - 选择基准收益率指数代码
- 计划函数用法
Fundamentals及财务数据pipeline及自定义因子用法- 回测速度
比较适合作为演示材料
%load_ext zipline%%zipline --start 2017-1-1 --end 2018-1-1 --bm-symbol 399001
from zipline.api import symbol, sid, get_datetime
import pandas as pd
import numpy as np
from zipline.api import (attach_pipeline, pipeline_output, get_datetime,
calendars, schedule_function, date_rules)
from zipline.pipeline import Pipeline
from zipline.pipeline import CustomFactor
from zipline.pipeline.data import USEquityPricing
from zipline.pipeline.fundamentals import Fundamentals
# time frame on which we want to compute Fama-French
normal_days = 31
# approximate the number of trading days in that period
# this is the number of trading days we'll look back on,
# on every trading day.
business_days = int(0.69 * normal_days)
# 以下自定义因子选取期初数
class Returns(CustomFactor):
"""
每个交易日每个股票窗口长度"business_days"期间收益率
"""
window_length = business_days
inputs = [USEquityPricing.close]
def compute(self, today, assets, out, price):
out[:] = (price[-1] - price[0]) / price[0] * 100
class MarketEquity(CustomFactor):
"""
每个交易日每只股票所对应的总市值
"""
window_length = business_days
inputs = [USEquityPricing.tmv]
def compute(self, today, assets, out, mcap):
out[:] = mcap[0]
class BookEquity(CustomFactor):
"""
每个交易日每只股票所对应的账面价值（所有者权益）
"""
window_length = business_days
inputs = [Fundamentals.balance_sheet.A107]
def compute(self, today, assets, out, book):
out[:] = book[0]
def initialize(context):
"""
use our factors to add our pipes and screens.
"""
pipe = Pipeline()
mkt_cap = MarketEquity()
pipe.add(mkt_cap, 'market_cap')
book_equity = BookEquity()
# book equity over market equity
be_me = book_equity / mkt_cap
pipe.add(be_me, 'be_me')
returns = Returns()
pipe.add(returns, 'returns')
attach_pipeline(pipe, 'ff_example')
schedule_function(
func=myfunc,
date_rule=date_rules.month_end())
def before_trading_start(context, data):
"""
every trading day, we use our pipes to construct the Fama-French
portfolios, and then calculate the Fama-French factors appropriately.
"""
factors = pipeline_output('ff_example')
# get the data we're going to use
returns = factors['returns']
mkt_cap = factors.sort_values(['market_cap'], ascending=True)
be_me = factors.sort_values(['be_me'], ascending=True)
# to compose the six portfolios, split our universe into portions
half = int(len(mkt_cap) * 0.5)
small_caps = mkt_cap[:half]
big_caps = mkt_cap[half:]
thirty = int(len(be_me) * 0.3)
seventy = int(len(be_me) * 0.7)
growth = be_me[:thirty]
neutral = be_me[thirty:seventy]
value = be_me[seventy:]
# now use the portions to construct the portfolios.
# note: these portfolios are just lists (indices) of equities
small_value = small_caps.index.intersection(value.index)
small_neutral = small_caps.index.intersection(neutral.index)
small_growth = small_caps.index.intersection(growth.index)
big_value = big_caps.index.intersection(value.index)
big_neutral = big_caps.index.intersection(neutral.index)
big_growth = big_caps.index.intersection(growth.index)
# take the mean to get the portfolio return, assuming uniform
# allocation to its constituent equities.
sv = returns[small_value].mean()
sn = returns[small_neutral].mean()
sg = returns[small_growth].mean()
bv = returns[big_value].mean()
bn = returns[big_neutral].mean()
bg = returns[big_growth].mean()
# computing SMB
context.smb = (sv + sn + sg) / 3 - (bv + bn + bg) / 3
# computing HML
context.hml = (sv + bv) / 2 - (sg + bg) / 2
def myfunc(context, data):
d = get_datetime('Asia/Shanghai')
print(d, context.smb, context.hml)2017-01-26 15:00:00+08:00 0.014014289806335789 6.605843892342312
2017-02-28 15:00:00+08:00 4.1169182374497195 7.690119769984805
2017-03-31 15:00:00+08:00 0.35808304923773615 2.7492806758694215
2017-04-28 15:00:00+08:00 -4.318408584890385 5.414312699826368
2017-05-31 15:00:00+08:00 -0.4828317045367072 3.0869028143557147
2017-06-30 15:00:00+08:00 0.8640245866550513 0.09803178533289003
2017-07-31 15:00:00+08:00 -2.3024594948720227 6.2829537294457145
2017-08-31 15:00:00+08:00 3.2003154621799155 2.269609384481118
2017-09-29 15:00:00+08:00 1.1669055941862554 -0.6079568594636064
2017-10-31 15:00:00+08:00 -1.6233534895267374 -0.795885505339075
2017-11-30 15:00:00+08:00 -2.965097825507776 4.4434701009908615
2017-12-29 15:00:00+08:00 -1.1942883365086068 -0.38062423581176485
[2018-05-02 02:07:02.681398] INFO: zipline.finance.metrics.tracker: Simulated 244 trading days
first open: 2017-01-03 01:31:00+00:00
last close: 2017-12-29 07:00:00+00:00
运行时长10-12秒
- 有关如何使用，请参考quantopian使用手册。
- 有关本项目的说明，请参阅介绍材料
- 有关后台自动数据处理，请参考脚本
特别说明：个人当前使用Ubuntu18.04操作系统
- Ubuntu 18.04
- Anaconda
- python 3.6
该项目纯属个人爱好，水平有限，欢迎加入来一起完善。
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