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，可以托管平台建立和执行交易策略。为直接使用A股数据进行回测，小幅度进行了改动（当前不支持分时回测）。
- 仅在Windows10操作系统下完成测试。参考环境
- Windows10 64位
- Anaconda3 64位
- python 3.6.3
- Visual Studio Community 2017
- 安装本频道的cswd odo blaze empyrical
- 转移至`setup.py`所在的安装目录
- python setup.py install
- 从公共网站提取并更新A股数据（使用Windows任务计划程序）
- 添加基础数据
fundamentals模块 - 增加
bulitin模块 - 整合talib，增加
quantalib模块
- 股票日线交易数据
- 指数日线交易数据
- 融资融券
- 证监会行业、国证行业
- 财务数据及指标
- 股票概念
注 使用Windows计划任务管理，在指定时段自动采集更新数据
Fundamentals是一个容器类，类似Quantopian Fundamental Data，包含pipeline所需的基本数据。如资产负债表、利润表、现金流量表、财务指标及行业分类等等，暂不包含估值部分。其属性或是单个绑定列，或是数据集，以此生成pipeline中常用的自定义因子(Factor)，过滤器(Filter)或是分类器(Classifier)。
此模块包含常用的自定义因子、过滤器、分类器，以及通用的总体筛选函数。
整合适用于pipeline的talib
%load_ext zipline
%%zipline --start 2017-1-1 --end 2017-4-20 --capital-base 100000
from six import viewkeys
from zipline.api import (
attach_pipeline,
date_rules,
order_target_percent,
pipeline_output,
record,
schedule_function,
)
from zipline.finance import commission
from zipline.pipeline import Pipeline
from zipline.pipeline.factors import RSI
def make_pipeline():
rsi = RSI()
return Pipeline(
columns={
'longs': rsi.top(3),
'shorts': rsi.bottom(3),
},
)
def rebalance(context, data):
# Pipeline data will be a dataframe with boolean columns named 'longs' and
# 'shorts'.
pipeline_data = context.pipeline_data
all_assets = pipeline_data.index
longs = all_assets[pipeline_data.longs]
shorts = all_assets[pipeline_data.shorts]
record(universe_size=len(all_assets))
# Build a 2x-leveraged, equal-weight, long-short portfolio.
one_third = 1.0 / 3.0
for asset in longs:
order_target_percent(asset, one_third)
for asset in shorts:
order_target_percent(asset, -one_third)
# Remove any assets that should no longer be in our portfolio.
portfolio_assets = longs | shorts
positions = context.portfolio.positions
for asset in viewkeys(positions) - set(portfolio_assets):
# This will fail if the asset was removed from our portfolio because it
# was delisted.
if data.can_trade(asset):
order_target_percent(asset, 0)
def initialize(context):
attach_pipeline(make_pipeline(), 'my_pipeline')
# Rebalance each day. In daily mode, this is equivalent to putting
# `rebalance` in our handle_data, but in minute mode, it's equivalent to
# running at the start of the day each day.
schedule_function(rebalance, date_rules.every_day())
# Explicitly set the commission to the "old" value until we can
# rebuild example data.
# github.com/quantopian/zipline/blob/master/tests/resources/
# rebuild_example_data#L105
context.set_commission(commission.PerShare(cost=.0075, min_trade_cost=1.0))
def before_trading_start(context, data):
context.pipeline_data = pipeline_output('my_pipeline')
[2017-12-09 20:29:33.920809] INFO: Loader: Read benchmark and treasury data for 000300 from 1990-10-31 to 2017-12-08 [2017-12-09 20:29:49.959577] INFO: Performance: after split: asset: Equity(002836 [新宏泽]), amount: 1494.0, cost_basis: 30.03, last_sale_price: 62.300000000000004 [2017-12-09 20:29:49.959577] INFO: Performance: returning cash: 0.0 [2017-12-09 20:29:50.462507] INFO: Performance: after split: asset: Equity(300213 [佳讯飞鸿]), amount: -726.0, cost_basis: 11.61, last_sale_price: 22.830000000000002 [2017-12-09 20:29:50.463506] INFO: Performance: returning cash: 0.0 [2017-12-09 20:29:50.903947] INFO: Performance: after split: asset: Equity(000711 [京蓝科技]), amount: -402.0, cost_basis: 15.25, last_sale_price: 31.11 [2017-12-09 20:29:50.903947] INFO: Performance: returning cash: 0.0 [2017-12-09 20:29:52.262802] INFO: Performance: Simulated 71 trading days out of 71. [2017-12-09 20:29:52.262802] INFO: Performance: first open: 2017-01-03 01:31:00+00:00 [2017-12-09 20:29:52.262802] INFO: Performance: last close: 2017-04-20 07:00:00+00:00
- 修正补充
- 进一步完善
TensorBoard - 整合使用
tensorflow