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4 changes: 2 additions & 2 deletions docs/_navbar.md
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<!-- _navbar.md -->

* [En](/)
* [chinese](/zh-cn/)
* [中文](/)
* [english](/en/)
28 changes: 28 additions & 0 deletions docs/en/README.md
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[![github](https://img.shields.io/github/stars/zvtvz/zvt.svg)](https://github.com/zvtvz/zvt)
[![image](https://img.shields.io/pypi/l/zvt.svg)](https://pypi.org/project/zvt/)
[![Build Status](https://api.travis-ci.org/zvtvz/zvt.svg?branch=master)](https://travis-ci.org/zvtvz/zvt)
[![codecov.io](https://codecov.io/github/zvtvz/zvt/coverage.svg?branch=master)](https://codecov.io/github/zvtvz/zvt)
## what's zvt?

ZVT is a quant trading platform written after rethinking about [fooltrader] (https://github.com/foolcage/fooltrader), which includes scalable data recorder, api, factor calculation, stock picking, backtesting, trading and focus on **low frequency**, **multi-level**, **multi-factors** **multi-targets** full market analysis and trading framework.

## what else can ZVT be?

- From the text, zero vector trader, meaning the market is the result of a variety of vector synergy, you could only be a zero vector to see the market clearly.
- From the perspective of shape, Z V T itself coincides with the typical form of the market, implying the importance of market geometry.
- The meaning of the zvt icon, you can interpret it yourself

<p align="center"><img src='./imgs/zvt-ok.gif'/></p>

## features

- china market data: stock meta, financial statements, major shareholder behavior, executive transactions, dividend financing details, stock market capital flow, margin financing, dragon and tiger charts, etc.
- crypto currency data
- Standardization of data, multi-data source (provider) cross-validation, completion
- Data recorder is very easy to expand
- Uniform and simple API, support sql query, support pandas
- Scalable factor, abstracting a unified calculation for single- and multi-targets operations
- Provides a unified way of visualizing the factor
- Support multi-targets, multi-factor, multi-level backtesting
- Real-time visualization of factors, trading signals and strategies
- Support a variety of real trading (implementation)
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- preface
- [introduction](en/README.md "zvt guide")
- [design philosophy](en/design-philosophy.md "zvt design philosophy")

- usage

- [quick start](en/quickstart.md "zvt quick start")
- [data usage](en/data_usage.md "zvt data usage")
- [factor usage](en/factor_usage.md "zvt factor usage")
- [selector usage](en/selector_usage.md "zvt selector usage")
- [trader usage](en/trader_usage.md "zvt trader usage")

- extending
- [extend data](en/data_extending.md "extend zvt data")
- [extend factor](en/factor_extending.md "extend zvt factor")
- [extend selector](en/selector_extending.md "extend zvt selector")
- [extend factor](en/trader_extending.md "extend zvt trader")

- [Changelog](changelog.md)
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## 目前支持的数据

### 股票数据
|名称|dataschema|provider|comments| download|
|-----------|--------|--------|-----|-----|
|个股资料|Stock|eastmoney,sina|个股和板块为多对多的关系|
|板块资料|Index|eastmoney,sina|板块有行业,概念,区域三个分类的维度,不同的provider分类会有所不同,个股和板块为多对多的关系|
|个股行情|Stock{level}Kdata|joinquant,netease,eastmoney|支持1,5,15,30,60分钟, 日线,周线级别|
|指数日线行情|Index1DKdata|eastmoney,sina,joinquant|指数本质上也是一种板块,指数对应板块资料中的标的|
|个股资金流|MoneyFlow|eastmoney,sina,joinquant||
|板块资金流|MoneyFlow|eastmoney,sina,joinquant|对应板块资料里面的标的|
|分红融资数据|DividendFinancing|eastmoney|企业最后的底线就是能不能给投资者赚钱,此为年度统计信息|
|分红明细|DividendDetail|eastmoney||
|融资明细|SPODetail|eastmoney||
|配股明细|RightsIssueDetail|eastmoney||
|主要财务指标|FinanceFactor|eastmoney||
|资产负债表|BalanceSheet|eastmoney||
|利润表|IncomeStatement|eastmoney||
|现金流量表|CashFlowStatement|eastmoney||
|十大股东|TopTenHolder|eastmoney||
|十大流通股东|TopTenTradableHolder|eastmoney||
|机构持股|InstitutionalInvestorHolder|eastmoney||
|高管交易|ManagerTrading|eastmoney||
|大股东交易|HolderTrading|eastmoney||
|大宗交易|BigDealTrading|eastmoney||
|融资融券|MarginTrading|eastmoney||
|龙虎榜数据|DragonAndTiger|eastmoney||

### 数字货币数据

|名称|dataschema|provider|comments| download|
|-----------|--------|--------|-----|-----|
|货币资料|Coin|ccxt||
|行情|Coin{level}Kdata|ccxt|支持tick,1,5,15,30,60分钟, 日线,周线级别|

### 期货数据
> 待支持

## 使用示例

### 个股K线
```
In [5]: from zvt.api import technical
In [6]: df= technical.get_kdata(security_id='stock_sz_000338',provider='joinquant')
In [7]: df.tail()
Out[7]:
id provider timestamp security_id code name level open hfq_open qfq_open close hfq_close qfq_close high hfq_high qfq_high low hfq_low qfq_low volume turnover change_pct turnover_rate factor
timestamp
2019-06-25 stock_sz_000338_2019-06-25 joinquant 2019-06-25 stock_sz_000338 000338 潍柴动力 1d 12.55 235.41 12.549845 12.28 230.35 12.280094 12.56 235.60 12.559974 12.08 226.60 12.080179 75627481.0 9.256614e+08 None None 18.758
2019-06-26 stock_sz_000338_2019-06-26 joinquant 2019-06-26 stock_sz_000338 000338 潍柴动力 1d 12.20 228.85 12.200128 12.25 229.79 12.250240 12.38 232.22 12.379785 12.12 227.35 12.120162 39932435.0 4.891142e+08 None None 18.758
2019-06-27 stock_sz_000338_2019-06-27 joinquant 2019-06-27 stock_sz_000338 000338 潍柴动力 1d 12.25 229.79 12.250240 12.25 229.79 12.250240 12.25 229.79 12.250240 12.25 229.79 12.250240 0.0 0.000000e+00 None None 18.758
2019-06-28 stock_sz_000338_2019-06-28 joinquant 2019-06-28 stock_sz_000338 000338 潍柴动力 1d 12.23 229.41 12.229982 12.29 230.54 12.290223 12.44 233.35 12.440026 12.23 229.41 12.229982 43280844.0 5.325563e+08 None None 18.758
2019-07-01 stock_sz_000338_2019-07-01 joinquant 2019-07-01 stock_sz_000338 000338 潍柴动力 1d 12.50 234.48 12.500267 12.89 241.79 12.889967 12.95 242.92 12.950208 12.41 232.79 12.410172 101787878.0 1.291295e+09 None None 18.758
```

### 数字货币k线
```
In [10]: df= technical.get_kdata(security_id='coin_binance_EOS/USDT',provider='ccxt')
In [10]: df.tail()
Out[10]:
id provider timestamp security_id code name level open close high low volume turnover
timestamp
2019-06-26 coin_binance_EOS/USDT_2019-06-26 ccxt 2019-06-26 coin_binance_EOS/USDT EOS/USDT EOS/USDT 1d 7.1736 6.8096 7.4475 6.1000 16934720.29 None
2019-06-27 coin_binance_EOS/USDT_2019-06-27 ccxt 2019-06-27 coin_binance_EOS/USDT EOS/USDT EOS/USDT 1d 6.8082 5.9663 6.8557 5.6329 20215677.51 None
2019-06-28 coin_binance_EOS/USDT_2019-06-28 ccxt 2019-06-28 coin_binance_EOS/USDT EOS/USDT EOS/USDT 1d 5.9742 6.2182 6.2918 5.7625 12172080.98 None
2019-06-29 coin_binance_EOS/USDT_2019-06-29 ccxt 2019-06-29 coin_binance_EOS/USDT EOS/USDT EOS/USDT 1d 6.2206 6.3302 6.3915 5.9566 7403462.75 None
2019-06-30 coin_binance_EOS/USDT_2019-06-30 ccxt 2019-06-30 coin_binance_EOS/USDT EOS/USDT EOS/USDT 1d 6.3282 5.7926 6.3966 5.6894 8043978.96 None
```

### 社保持仓
```
In [11]: from zvt.domain import *
from zvt.api import fundamental
In [12]: df = fundamental.get_top_ten_tradable_holder(start_timestamp='2018-09-30',filters=[TopTenTradableHolder.holder_name.like('%社保%')],order=TopTenTradableHolder.shareholding_ratio.desc())
In [18]: df.tail()
Out[18]:
id provider timestamp security_id code report_period report_date holder_code holder_name shareholding_numbers shareholding_ratio change change_ratio
timestamp
2019-03-31 stock_sz_000778_2019-03-31 00:00:00_全国社保基金五零三组合 None 2019-03-31 stock_sz_000778 000778 ReportPeriod.season1 2019-03-31 70010503 全国社保基金五零三组合 60000000.0 0.0153 1000000.0 0.0169
2019-03-31 stock_sz_002572_2019-03-31 00:00:00_全国社保基金一零九组合 None 2019-03-31 stock_sz_002572 002572 ReportPeriod.season1 2019-03-31 70010109 全国社保基金一零九组合 7520000.0 0.0118 -8013000.0 -0.5159
2019-03-31 stock_sz_000338_2019-03-31 00:00:00_全国社保基金一零二组合 None 2019-03-31 stock_sz_000338 000338 ReportPeriod.season1 2019-03-31 70010102 全国社保基金一零二组合 44000000.0 0.0071 -6000000.0 -0.1200
2019-03-31 stock_sz_000338_2019-03-31 00:00:00_全国社保基金一零一组合 None 2019-03-31 stock_sz_000338 000338 ReportPeriod.season1 2019-03-31 70010101 全国社保基金一零一组合 36850000.0 0.0060 NaN NaN
2019-03-31 stock_sz_000778_2019-03-31 00:00:00_全国社保基金四一三组合 None 2019-03-31 stock_sz_000778 000778 ReportPeriod.season1 2019-03-31 70010413 全国社保基金四一三组合 17800000.0 0.0045 NaN NaN
```

### 马云持仓 ###
```bash
In [26]: df = get_top_ten_tradable_holder(filters=[TopTenTradableHolder.holder_name=='马云'])
Out[27]:
holder_name code shareholding_numbers shareholding_ratio change change_ratio
0 马云 002204 460800.0 0.0085 NaN NaN
1 马云 300027 3912000.0 0.0205 NaN NaN
2 马云 300027 8319000.0 0.0230 NaN NaN
3 马云 300027 8319000.0 0.0230 NaN NaN

22 马云 300027 99780000.0 0.0520 NaN NaN
23 马云 300027 99780000.0 0.0520 NaN NaN
24 马云 300027 99780000.0 0.0451 NaN NaN
```
### 2018年报eps前50
```bash
In [30]: df = get_finance_factor(start_timestamp='2018-12-31',order=FinanceFactor.basic_eps.desc(),limit=50,columns=[FinanceFactor.code,FinanceFactor.timestamp,FinanceFactor.basic_eps])
Out[31]:
code timestamp basic_eps
0 600519 2018-12-31 28.0200
1 603444 2018-12-31 10.1200
2 601318 2018-12-31 6.0200
3 000661 2018-12-31 5.9200

47 603393 2018-12-31 2.0900
48 601869 2018-12-31 2.0900
49 600507 2018-12-31 2.0800

```

更多api和相应的数据，可查看代码:
[*data schema*](https://github.com/zvtvz/zvt/tree/master/zvt/domain)
[*data api*](https://github.com/zvtvz/zvt/tree/master/zvt/api)

filters参数的使用请参考[*sqlalchemy*](https://docs.sqlalchemy.org/en/13/orm/query.html),SQL能做的查询都能做

## SQL查询
你也可以直接使用项目中的sqlite数据库,利用你熟悉的工具,语言来进行研究

比如:查看某段时间整个市场的高管增持减持
```
select * from manager_trading where volume < 0 and timestamp > '2018-01-01';
select count(id) from manager_trading where volume < 0 and timestamp > '2018-01-01';
select * from manager_trading where volume > 0 and timestamp > '2018-01-01';
select count(id) from manager_trading where volume > 0 and timestamp > '2018-01-01'
```
<p align="center"><img src='./imgs/sql-usage.gif'/></p>

库都给你了,SQL大神,请开始你的表演
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## 统一性(Unity)
统一性使你能够愉快的思考

比如投资标的的唯一标识,zvt里面定义如下
```
{security_type}_{exchange}_{code}
```
很自然的,你就知道stock_sz_000338,coin_binance_EOS/USDT代表什么.

比如Recorder,其对所有标的的记录提供了统一的抽象.
比如get_kdata,其对所有的标的的使用方式都是一致的.
比如TechnicalFactor,其对所有标的,所有级别的操作都是一致的.
比如回测和实时交易,Trader提供了一致的处理方式.

## 分层(Layer)

分层的核心在于不同层次之间的协议,整个系统的稳定性在于协议的稳定性.
<p align="center"><img src='./imgs/architecture.png'/></p>

## 扩展性(Scalable)

- 很容易的在系统中添加数据,并自动获得其他模块的能力.
- 很容易实现自己的factor
- 很容易的扩展自己的trader
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## 什么是factor

一般来说,人们常把macd,kdj,roe,pe等当作指标,zvt中把他们叫做indicator,甚至什么也不是,就只是普通的value;factor在zvt中有更高层次的抽象.这个会在如何扩展factor里面会做详细说明,你现在只需要知道,zvt里面的factor分为三类:FilterFactor,ScoreFacor,StateFactor.

## TechnicalFactor

> 该factor为计算各种技术指标的算法模板类,基于它可以构造出用于选股和交易的Factor
```
factor = TechnicalFactor(codes=['000338'], start_timestamp='2018-01-01', end_timestamp='2019-02-01',
indicators=['ma', 'ma'],
indicators_param=[{'window': 5}, {'window': 10}])
factor.draw_with_indicators()
```

factor本身是可以draw的,并且可以在notebook中使用:
<p align="center"><img src='./imgs/factor-in-notebook.gif'/></p>

你甚至可以把所有标的的factor都draw成一致的图片,然后用机器学习来对其进行分析,需要做的也只是把codes遍历一遍.

## 技术买卖指标
基于TechnicalFactor你可以构造自己的FilterFactor,比如,均线交叉买卖:
```
class CrossMaFactor(TechnicalFactor):
def __init__(self,
security_list: List[str] = None,
security_type: Union[str, SecurityType] = SecurityType.stock,
exchanges: List[str] = ['sh', 'sz'],
codes: List[str] = None,
the_timestamp: Union[str, pd.Timestamp] = None,
start_timestamp: Union[str, pd.Timestamp] = None,
end_timestamp: Union[str, pd.Timestamp] = None,
columns: List = None, filters: List = None,
provider: Union[str, Provider] = 'netease',
level: TradingLevel = TradingLevel.LEVEL_1DAY,
real_time: bool = False,
refresh_interval: int = 10,
category_field: str = 'security_id',
# child added arguments
short_window=5,
long_window=10) -> None:
self.short_window = short_window
self.long_window = long_window
super().__init__(security_list, security_type, exchanges, codes, the_timestamp, start_timestamp, end_timestamp,
columns, filters, provider, level, real_time, refresh_interval, category_field,
indicators=['ma', 'ma'],
indicators_param=[{'window': short_window}, {'window': long_window}], valid_window=long_window)
def compute(self):
super().compute()
s = self.depth_df['ma{}'.format(self.short_window)] > self.depth_df['ma{}'.format(self.long_window)]
self.result_df = s.to_frame(name='score')
def on_category_data_added(self, category, added_data: pd.DataFrame):
super().on_category_data_added(category, added_data)
# TODO:improve it to just computing the added data
self.compute()
```

其本身也是可以draw的:
```
cross = CrossMaFactor(codes=['000338'], start_timestamp='2018-01-01', end_timestamp='2019-02-01',provider='joinquant')
cross.draw_result(render='notebook')
```
<p align="center"><img src='./imgs/factor-result-in-notebook.gif'/></p>

## ScoreFactor

[**ScoreFactor**](https://github.com/zvtvz/zvt/blob/master/zvt/factors/factor.py#L138)内置了分位数算法(quantile),你可以非常方便的对其进行扩展.

下面展示一个例子:对个股的**营收,利润增速,资金,净资产收益率**进行评分
```
class FinanceGrowthFactor(ScoreFactor):
def __init__(self,
security_list: List[str] = None,
security_type: Union[str, SecurityType] = SecurityType.stock,
exchanges: List[str] = ['sh', 'sz'],
codes: List[str] = None,
the_timestamp: Union[str, pd.Timestamp] = None,
start_timestamp: Union[str, pd.Timestamp] = None,
end_timestamp: Union[str, pd.Timestamp] = None,
columns: List = [FinanceFactor.op_income_growth_yoy, FinanceFactor.net_profit_growth_yoy,
FinanceFactor.rota,
FinanceFactor.roe],
filters: List = None,
provider: Union[str, Provider] = 'eastmoney',
level: TradingLevel = TradingLevel.LEVEL_1DAY,
real_time: bool = False,
refresh_interval: int = 10,
category_field: str = 'security_id',
keep_all_timestamp: bool = True,
fill_method: str = 'ffill',
effective_number: int = None,
depth_computing_method='ma',
depth_computing_param={'window': '365D', 'on': 'timestamp'},
breadth_computing_method='quantile',
breadth_computing_param={'score_levels': [0.1, 0.3, 0.5, 0.7, 0.9]}) -> None:
super().__init__(FinanceFactor, security_list, security_type, exchanges, codes, the_timestamp, start_timestamp,
end_timestamp, columns, filters, provider, level, real_time, refresh_interval, category_field,
keep_all_timestamp, fill_method, effective_number, depth_computing_method,
depth_computing_param, breadth_computing_method, breadth_computing_param)
```

## StateFactor

> 可在该类Factor中实现趋势,震荡,缠论笔,段,中枢之类的状态机
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