README.md

EasyQuant

Introduction

This project implements a high-level quantitative toolkit based on open stock data platforms. It uses mysql as the backend database, and extracts, processes and integrates daily market data to support the research and development of quantitative trading strategies.

Data Source

Tushare is a free, open source python financial data interface package. It mainly realizes the processing of stock data, and provides financial analysts with fast, clean and diverse data interfaces, so that they can focus more on the research and implementation of strategies and models.

with code field name 'ts_code'
and format '60XXXX.SH', '00XXXX.SZ' for individual stock
and '000001.SH', '399001.SZ' for index of SSE(Shanghai Stock Exchange) and SZSE(Shenzhen Stock Exchange).

JoinQuant is a cloud platform tailored for quants, providing easy-to-use API documentation that allows quants to quickly implement and use their own quant trading strategies.

with code field name 'code' 
and format '60XXXX.XSHG', 00XXXX.XSHE for individual stock
and '000001.XSHG', '399001.XSHE' for index of SSE and SZSE.

Code Structure

> easy-quant
    > conf 
    > data 
        > demo 
        > html
    > learner 
    > plotter 
    > service 
    > tools 

The conf folder stores the configuration files required by the project, such as token information, database links, or parameters for strategy objects.

The data folder holds the .html file output from the plotting methods and the images of typical trend (under the demo subfolder). In addition, the two text files named ALL_SECURITIES and ALL_TRADE_DAYS are cached to make it faster when getting the trading calendar, stock names or codes without having to access a database or remote API.

The learner package realizes models based on machine learning and deep learning, which support for further analysis of stock data. We will use a series of popular tools such as sklearn, tensorflow and Keras. This folder is currently being extended on demand.

The plotter package provides a rich visualization implementation. It integrates matplot, plotly, cufflinks and other popular visualization modules, which is very powerful.

The service package provides the most important data processing capabilities and is built on top of Tushare and JoinQuant's remote python APIs. After the market closes, we will call the interfaces of these data platforms to append the latest trading data to our mysql database. This process ensures that the data in our database is up to date. Meanwhile, we provide advanced methods for calendar, market data, stock information and trading indicators. Based on these methods, we can easily write our own strategies, such as "look for stocks that suddenly rose by the daily limit", "Which stocks triggered our MA rule today". Of course, you can also write your strategy as a stored procedure and let the database process it periodically.

The tools package provides some basic functions, such as database services (on mysql), mathematical methods, and so on.

Robust Database

Let the database take over as much as possible. Based on this principle, we will build lightweight python applications. With the powerful functions of mysql database, we can not only define built-in functions, but also encapsulate all complex data processing and query operations into stored procedures or customized query statements.

Examples

User-defined functions

-- functionName: getTradeDateBack
-- description: Get trade date backward
CREATE DEFINER=`username`@`dbname` FUNCTION `getTradeDateBack`(TD char(8), N int) RETURNS char(8) CHARSET utf8mb3
    COMMENT 'TD: trade_date with format yyyymmdd; N: number of days;'
begin 
    return (
		select trade_date  
		from (select replace(cal_date,'-','') as trade_date
				,rank() over (order by cal_date desc) rk 
			from js_calendar jc 
			where replace(cal_date,'-','') < TD
		) t 
		where rk = N
	);
end

Custom query

SELECT t.statement FROM finance.sql_statement t WHERE name = 'getSuddenLimit' AND 'version=1.0';

-- queryName: getSuddenLimit 
-- description: Get sudden limit-up stocks using MA5 slope
-- inputs: trade_date
 set @TD1 = (select getTradeDateBackgetTradeDateBack(%s,1));
 set @TD2 = (select getTradeDateBack(%s,2));
 with checkSlope as 
 (select *  
 	from (select t1.ts_code
 		, @TD1 as td1 
 		, t1.ma5 as ma5_td1
 		, @TD2 as td2 
 		, t2.ma5 as ma5_td2
 		, round(abs(t1.ma5 - t2.ma5)/t2.ma5, 3) as slope
 		from 
 		(select ts_code, ma5 from pro_stock_kline where trade_date = @TD1) t1 
 		inner join 
 		(select ts_code , ma5 from pro_stock_kline where trade_date = @TD2) t2 
 		on t1.ts_code = t2.ts_code
 	) t 
 	where slope < 0.01 
 	order by ts_code
 ); -- check with ma5 slope

 select a.*, b.* from daily_limit a inner join checkSlope b
 on a.ts_code = b.ts_code and a.trade_date = %s order by a.ts_code;

*For complete database schema script, please contact konhay@163.com.

Powerful Visualization

Examples

Profit and Loss Distribution

from service.tushare_querier import get_distribution
from plotter.plotly_plotter import plot_distribution

df = get_distribution(trade_date="20240510")
plot_distribution(df)

Animation Effect

from service.tushare_querier import get_stock_daily
from plotter.plotly_plotter import plot_animations_px

df = get_stock_daily(ts_code="000001.SZ", trade_date="20240510", count=120)
plot_animations_px(df, y_name='close')

Candlestick

from service.tushare_querier import get_stock_daily
from plotter.cufflinks_plotter import plot_quantfig

df = get_stock_daily(ts_code="000001.SZ", trade_date="20240510", count=120)
df.set_index("trade_date", inplace=True)
plot_quantfig(df)

Flexible Strategy

Example 1

Strategies can be implemented through database query, e.g. "findKeyStand", a factor-based strategy.

from tools.mysql_service import MySQLUtil
from service.tushare_querier import get_query_statement

sql = get_query_statement("findKeyStand")
args = {"trade_date": "20240220"}
df = MySQLUtil.df_read(sql, args)

Result of strategy "findKeyStand" on 20240220

ts_code	trade_date	open	high	low	close	pct_chg	ma5	ma20
002775.SZ	20240220	2.27	2.31	2.18	2.31	1.3158	2.27	3.23

002775.SZ's experienced considerable gains on the next trading day (20240221)

Example 2

Strategies can also be based on mathematical methods, such as: To find historical tick of most similarity with a given end date "2019-12-30" for 000001.XSHG(SSE Index).

from tools.math_tools import get_similarity

df = get_df_tick(security="000001.XSHG", days=250, end_dt="2019-12-30")
# df['date'] = pd.to_datetime(df['date'])
groups = dict(list(df.groupby(df['date'].dt.date)['close']))
tick_edt = list(list(groups.values())[-1])
trade_days = list(groups.keys())[:-1]
result={}
for i in trade_days:
    tick_i = list(groups[i])
    # Calculate similarity by Frechet Distance
    similarity = get_similarity(tick_edt, tick_i)
    result.setdefault(str(i),similarity)
print(min(result.items(),key=lambda x:x[1])[0])

Output: 2019-11-01

TODO

AI methods have also been integrated in EeayQuaut, but currently, their performance in predicting stock returns is not ideal. We will continue to improve them and enhance other existing capabilities. Next, we will focus on the development of alpha trading strategies, as well as support for backtesting. If you have any good suggestions, please contact konhay@163.com.