三线打击K线形态：
三线打击K线形态，是给我们一种上涨行情的信号，那三线打击K线形态是怎么样的走势?这种形态的形成蕴含着怎么样的心理?今天赢家财富网的主编人员将为大家带来相关的图形剖析内容。
三线打击K线形态是怎么样的?下图中的胜利精密的走势图中就有该形态，从下图我们可以知道，三线打击K线形态是有四根K线所组成的，前三根伟上升的“红三兵”形态，有的时候也被称为是三个百姓，也有的被叫做笨拙三战士，它是当前上涨趋势的延续。
理论引用自：http://www.yingjia360.com/kxtj/2017-02-11/36403.html
照着上面的理论， 笔者得出策略：
三线打击会上涨信号，全仓买入，后续涨了三天，全仓卖出
上代码：
#出现三线打击K线形态，全仓买入，三日定律连涨卖出
import tushare as ts
import pandas as pd
import datetime # For datetime objects
import os.path # To manage paths
import sys # To find out the script name (in argv[0])
# Import the backtrader platform
import backtrader as bt
import talib as talib
import numpy as np
class MyStrategy(bt.Strategy):
# 策略参数
params = dict(
printlog=False
)
def __init__(self):
self.star = dict()
self.cdl3inside = dict()
self.cdl3linestrike = dict()
# 定义全局变量
self.count = 0
for data in self.datas:
# 转为tabib要求的数据格式
opens = np.array(data.open.array)
highs = np.array(data.high.array)
lows = np.array(data.low.array)
closes = np.array(data.close.array)
# 三日定律形态
cdl3insideRes = talib.CDL3INSIDE(opens, highs, lows, closes)
# 三线打击K线形态
cdl3linestrikeRes = talib.CDL3LINESTRIKE(opens, highs, lows, closes)
# 数据放入self中
print('三日定律，100是三天连续上涨，-100是三天连续下跌')
print(cdl3insideRes)
print('三线打击K线形态')
print(cdl3linestrikeRes)
self.cdl3inside[data._id] = cdl3insideRes
self.cdl3linestrike[data._id] = cdl3linestrikeRes
def next(self):
# 得到当前的账户价值
total_value = self.broker.getcash()
for data in self.datas:
pos = self.getposition(data).size
# 三线打击K线形态
if total_value > 0 and (self.cdl3linestrike[data._id][self.count] == 100 or \
self.cdl3linestrike[data._id][self.count] == -100):
p_value = total_value * 0.9 / 10
size = ((int(total_value / self.data.close[0]))) - ((int(total_value / self.data.close[0])) % 100) - 100
if(size > 100 ):
self.buy(data=data, size=size)
print('三线打击K线形态，全仓买入,买入数量' + str(size) )
#三日连涨
if pos > 0 and self.cdl3inside[data._id][self.count] == 100:
# 全部卖出
# 跟踪订单避免重复
self.sell(data=data, size=pos)
print('出现三日连涨定律，卖出数量' + str(pos))
#自增处理
self.count = self.count + 1
def log(self, txt, dt=None, doprint=False):
if self.params.printlog or doprint:
dt = dt or self.datas[0].datetime.date(0)
print(f'{dt.isoformat()},{txt}')
# 记录交易执行情况（可省略，默认不输出结果）
def notify_order(self, order):
# 如果order为submitted/accepted,返回空
if order.status in [order.Submitted, order.Accepted]:
return
# 如果order为buy/sell executed,报告价格结果
if order.status in [order.Completed]:
if order.isbuy():
self.log(f'买入:\n价格:{order.executed.price:.2f},\
成本:{order.executed.value:.2f},\
数量:{order.executed.size:.2f},\
手续费:{order.executed.comm:.2f}')
self.buyprice = order.executed.price
self.buycomm = order.executed.comm
else:
self.log(f'卖出:\n价格：{order.executed.price:.2f},\
成本: {order.executed.value:.2f},\
数量:{order.executed.size:.2f},\
手续费{order.executed.comm:.2f}')
self.bar_executed = len(self)
# 如果指令取消/交易失败, 报告结果
elif order.status in [order.Canceled, order.Margin, order.Rejected]:
self.log('交易失败')
self.order = None
# 记录交易收益情况（可省略，默认不输出结果）
def notify_trade(self, trade):
if not trade.isclosed:
return
self.log(f'策略收益：\n毛收益 {trade.pnl:.2f}, 净收益 {trade.pnlcomm:.2f}')
pro = ts.pro_api('cbb257058b7cb228769b4949437c27c27e5132e882747dc80f01a5a5')
def ts_get_daily_stock(code, start_dt, end_dt):
start_dt = start_dt.replace("'", "", 3);
end_dt = end_dt.replace("'", "", 3);
# start_dt = '20190101'
# end_dt=''
print(code, start_dt, end_dt)
data = pro.daily(ts_code=code, start_date=start_dt, end_date=end_dt)
data['trade_date'] = pd.to_datetime(data['trade_date'])
data['trade_date'] = pd.to_datetime(data['trade_date'])
data = data.sort_values(by='trade_date')
data.index = data['trade_date']
data['openinterest'] = 0
data['volume'] = data['vol']
data = data[
['open', 'close', 'high', 'low', 'volume']
]
return data
# 读取选股的结果
df = pd.read_csv('stock_alpha.csv')
df.columns = ['ts_code', 'name', 'alpha', 'start_dt', 'end_dt']
min_a = df.sort_values(by='alpha')
min_a = min_a.iloc[:10, :]
code = []
code = min_a['ts_code'] # 股票代码
start_dts = []
start_dts = min_a['start_dt'] # 股票代码起始时间
end_dts = []
end_dts = min_a['end_dt'] # 股票代码结束时间
for i in range(len(code)):
data = ts_get_daily_stock(code.iloc[i], start_dts.iloc[i], end_dts.iloc[i]) # 字段分别为股票代码、开始日期、结束日期
data.to_csv(code.iloc[i] + '.csv')
cerebro = bt.Cerebro()
for i in range(len(code)): # 循环获取股票历史数据
dataframe = pd.read_csv(code.iloc[i] + '.csv', index_col=0, parse_dates=True)
dataframe['openinterest'] = 0
data = bt.feeds.PandasData(dataname=dataframe,
fromdate=datetime.datetime(2010, 2, 20),
todate=datetime.datetime(2022, 4, 5)
)
cerebro.adddata(data)
# 回测设置
startcash = 100000.0
cerebro.broker.setcash(startcash)
# 设置佣金为千分之一
cerebro.broker.setcommission(commission=0.001)
# 添加策略
cerebro.addstrategy(MyStrategy, printlog=True)
cerebro.run()
# 获取回测结束后的总资金
portvalue = cerebro.broker.getvalue()
pnl = portvalue - startcash
# 打印结果
print(f'总资金: {round(portvalue,2)}')
print(f'净收益: {round(pnl,2)}')
cerebro.plot()
执行结果：
总资金: 157630.77
净收益: 57630.77
talib.CDL3LINESTRIKE(opens, highs, lows, closes)
100出现三线打击，0不是