KDJ策略——金叉死叉

策略分享
标签: #<Tag:0x00007fcf625290c0>

(Jessie_zhang) #1
克隆策略

KDJ策略(2)金叉死叉

因为很多量化在线平台目前还不支持期货交易,且KD指标对大盘和热门大盘股有着较高的准确性,故此策略选取'0700.HKEX'为标的股票,HSI.HKEX为参考标准。
策略逻辑:
当kt-1>80,dt-2>80, jt>100时,kt-2>dt-2,kt-1<dt-1,卖出
当kt-1<20,dt-2<20, jt<0 时,kt-2<dt-2,kt-1>dt-1,买入
为了防止指数过于敏感,产生错判,故带宽设置为1%
来源:王劲松. 股市常用技术分析方法的有效性实证研究[D]. 西南财经大学, 2010.

In [13]:
import numpy as np
import pandas as pd
from pandas import DataFrame
import talib as ta

1. 主要参数

In [14]:
# 选取腾讯控股
instruments = ['0700.HKEX']  
# 开始时间
start_date = '2011-11-08'  
# 结束时间
end_date = '2017-11-08'
# 策略比较参考标准,恒生指数
benchmark = 'HSI.HKEX'

2. 策略回测主体

In [15]:
# 初始化账户
def initialize(context):
    context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5)) # 设置手续费,买入成本为万分之三,卖出为千分之1.3

def handle_data(context, data):
    k = instruments[0] # 标的为字符串格式
    sid = context.symbol(k) # 将标的转化为equity格式
    price = data.current(sid, 'price') # 最新价格
    cash = context.portfolio.cash  # 现金
    cur_position = context.portfolio.positions[sid].amount # 持仓
    curr= data.current(sid,'price')
    indicators={}      #指标
    hp=data.history(sid, 'high', 50, '1d')
    lp=data.history(sid, 'low', 50, '1d')
    cp=data.history(sid, 'close', 50, '1d')
    indicators['k'],indicators['d']=ta.STOCH(np.array(hp,dtype='f8'),np.array(lp,dtype='f8'),np.array(cp,dtype='f8'),fastk_period=9,slowk_period=3,slowk_matype=0,slowd_period=3,slowd_matype=0)
    indicators['j']=np.array(indicators['k'])*3-np.array(indicators['d'])*2
    indicators['closePrice']=cp
    indicators=pd.DataFrame(indicators)#将字典形式转化为dataframe格式
    # 交易逻辑
    if indicators.iloc[-1]['k'] > 80 and indicators.iloc[-2]['d'] > 80 :
        if indicators.iloc[-1]['k'] < indicators.iloc[-1]['d'] and indicators.iloc[-2]['k'] > indicators.iloc[-2]['d']:
            if float((indicators.iloc[-2]['k']-indicators.iloc[-2]['d'])/indicators.iloc[-2]['k'])>0.01 and float((indicators.iloc[-1]['d']-indicators.iloc[-1]['k'])/indicators.iloc[-1]['d'])>0.01:
                if cur_position >= 0:
                    context.order_target_percent(sid, 0)
    elif indicators.iloc[-1]['k'] < 20 and indicators.iloc[-2]['d'] < 20 :
        if indicators.iloc[-1]['k'] > indicators.iloc[-1]['d'] and indicators.iloc[-2]['k'] < indicators.iloc[-2]['d']:
            if float((indicators.iloc[-2]['d']-indicators.iloc[-2]['k'])/indicators.iloc[-2]['d'])>0.01 and float((indicators.iloc[-1]['k']-indicators.iloc[-1]['d'])/indicators.iloc[-1]['k'])>0.01:
                if cash >= 0:
                    number = (int((cash / curr) / 100)) * 100
                    if number > 0:
                        context.order_target_percent(sid, number)   

3.回测接口

In [16]:
m=M.trade.v3(
    instruments=instruments,
    start_date=start_date,
    end_date=end_date,
    initialize=initialize,
    handle_data=handle_data,
    order_price_field_buy='open', # 以开盘价买入
    order_price_field_sell='open', # 以开盘价卖出
    capital_base=1000000, # 本金
    benchmark=benchmark,
    )
[2018-01-12 08:15:41.122410] INFO: bigquant: backtest.v7 开始运行..
[2018-01-12 08:15:46.574174] INFO: algo: set price type:backward_adjusted
[2018-01-12 08:15:48.296755] INFO: Blotter: 2011-11-16 cancel order Equity(0 [0700.HKEX]) 
[2018-01-12 08:15:48.331014] INFO: Blotter: 2011-11-22 cancel order Equity(0 [0700.HKEX]) 
[2018-01-12 08:15:49.193923] INFO: Blotter: 2012-05-15 cancel order Equity(0 [0700.HKEX]) 
[2018-01-12 08:15:49.461233] INFO: Blotter: 2012-06-28 cancel order Equity(0 [0700.HKEX]) 
[2018-01-12 08:15:50.514614] INFO: Blotter: 2012-11-21 cancel order Equity(0 [0700.HKEX]) 
[2018-01-12 08:15:51.470119] INFO: Blotter: 2013-03-22 cancel order Equity(0 [0700.HKEX]) 
[2018-01-12 08:15:51.524258] INFO: Blotter: 2013-03-28 cancel order Equity(0 [0700.HKEX]) 
[2018-01-12 08:15:51.591223] INFO: Blotter: 2013-04-09 cancel order Equity(0 [0700.HKEX]) 
[2018-01-12 08:16:12.464274] INFO: Performance: Simulated 1478 trading days out of 1478.
[2018-01-12 08:16:12.465566] INFO: Performance: first open: 2011-11-08 01:30:00+00:00
[2018-01-12 08:16:12.466720] INFO: Performance: last close: 2017-11-08 08:00:00+00:00
  • 收益率51.1%
  • 年化收益率7.29%
  • 基准收益率46.9%
  • 阿尔法0.02
  • 贝塔0.27
  • 夏普比率0.28
  • 胜率1.0
  • 盈亏比--
  • 收益波动率10.13%
  • 信息比率0.03
  • 最大回撤13.14%
[2018-01-12 08:16:17.892174] INFO: bigquant: backtest.v7 运行完成[36.769734s].