def tur10_dataPre(qx,xnam0,ksgn0):
''' 策略.数据预处理函数 说明
Args:
qx (zwQuantX): zwQuantX数据包
xnam0 (str)：函数标签
ksgn0 (str): 价格列名称，一般是'adj close'
'''
zwx.sta_dataPre0xtim(qx,xnam0);
#----对各只股票数据，进行预处理，提高后期运算速度
ksgn,qx.priceCalc=ksgn0,ksgn0; #'adj close';
for xcod in zw.stkLibCode:
d20=zw.stkLib[xcod];
# 计算交易价格kprice和策略分析采用的价格dprice,kprice一般采用次日的开盘价
#d20['dprice']=d20['open']*d20[ksgn]/d20['close']
d20['dprice']=d20['close']
#d20['kprice']=d20['dprice'].shift(-1)
d20['kprice']=d20['dprice']
#
d=qx.staVars[0];d20=zwta.MA(d20,d,ksgn);
d=qx.staVars[1];d20=zwta.MA(d20,d,ksgn);
#
zw.stkLib[xcod]=d20;
if qx.debugMod>0:
print(d20.tail())
#---
fss='tmp\\'+qx.prjName+'_'+xcod+'.csv'
d20.to_csv(fss)
def SMA_dataPre(qx, xnam0, ksgn0):
''' 簡單均線策略資料預處理函數
Args:
qx (zwQuantX): zwQuantX數據包
xnam0 (str)：函數標籤
ksgn0 (str): 價格列名稱，一般是'adj close'
:ivar xcod (int): 股票代碼
'''
zwx.sta_dataPre0xtim(qx, xnam0)
#----對各檔股票資料，進行預處理，提高後期運算速度
ksgn, qx.priceCalc = ksgn0, ksgn0
#'adj close';
for xcod in zw.stkLibCode:
d20 = zw.stkLib[xcod]
# 計算交易價格kprice和策略分析採用的價格dprice,kprice一般採用次日的開盤價
d20['dprice'] = d20['open'] * d20[ksgn] / d20['close']
d20['kprice'] = d20['dprice'].shift(-1)
#d20['kprice']=d20['dprice']
#
d = qx.staVars[0]
d20 = zwta.MA(d20, d, ksgn)
d = qx.staVars[1]
d20 = zwta.MA(d20, d, ksgn)
#
zw.stkLib[xcod] = d20
if qx.debugMod > 0:
print(d20.tail())
#---
fss = 'tmp\\' + qx.prjName + '_' + xcod + '.csv'
d20.to_csv(fss)
def SMA_dataPre(qx, xnam0, ksgn0):
''' 简单均线策略数据预处理函数
Args:
qx (zwQuantX): zwQuantX数据包
xnam0 (str)：函数标签
ksgn0 (str): 价格列名称，一般是'adj close'
:ivar xcod (int): 股票代码
'''
zwx.sta_dataPre0xtim(qx, xnam0)
#----对各只股票数据，进行预处理，提高后期运算速度
ksgn, qx.priceCalc = ksgn0, ksgn0
#'adj close';
for xcod in zw.stkLibCode:
d20 = zw.stkLib[xcod]
# 计算交易价格kprice和策略分析采用的价格dprice,kprice一般采用次日的开盘价
d20['dprice'] = d20['open'] * d20[ksgn] / d20['close']
d20['kprice'] = d20['dprice'].shift(-1)
#d20['kprice']=d20['dprice']
#
d = qx.staVars[0]
d20 = zwta.MA(d20, d, ksgn)
d = qx.staVars[1]
d20 = zwta.MA(d20, d, ksgn)
#
zw.stkLib[xcod] = d20
if qx.debugMod > 0:
print(d20.tail())
#---
fss = 'tmp\\' + qx.prjName + '_' + xcod + '.csv'
d20.to_csv(fss)
def tim0Trade_dataPre(qx, xnam0):
# 设置当前策略的变量参数，此处是5、30日的MA日均线数据
qx.staName = xnam0
qx.rfRate = 0.05; # 无风险年收益，一般为0.05(5%)，计算夏普指数等需要
# qx.stkNum9=20000; #每手交易，最多20000股
#
# 按指定的时间周期，裁剪数据源
# xtim0=parse('9999-01-01');xtim9=parse('1000-01-01');
# xtim0=xtim0.strftime('%Y-%m-%d');xtim9=xtim9.strftime('%Y-%m-%d')
# qx.qxTimSet(xtim0,xtim9)
# zwx.stkLibSet8XTim(qx.xtim0,qx.xtim9);# print('zw.stkLibCode',zw.stkLibCode)
# ============
# ---设置qxUsr用户数据
qx.qxUsr = zwx.qxObjSet(qx.xtim0, 0, qx.money, 0);
# ----对各只股票数据，进行预处理，提高后期运算速度
ksgn = 'adj close';
for xcod in zw.stkLibCode:
d20 = zw.stkLib[xcod];
# 计算交易价格kprice和策略分析采用的价格dprice,kprice一般采用次日的开盘价
# d20['dprice']=d20['open']*d20[ksgn]/d20['close']
d20['dprice'] = d20[ksgn]
# d20['kprice']=d20['dprice'].shift(-1)
d20['kprice'] = d20['dprice']
#
d = qx.staVars[0];
d20 = zwta.MA(d20, d, ksgn);
d = qx.staVars[1];
d20 = zwta.MA(d20, d, ksgn);
#
zw.stkLib[xcod] = d20;
print(d20.tail())
# ---
fss = 'tmp\\' + qx.prjName + '_' + xcod + '.csv'
d20.to_csv(fss)
def CMA_dataPre(qx,xnam0,ksgn0):
''' 均线交叉策略数据预处理函数
Args:
qx (zwQuantX): zwQuantX数据包
xnam0 (str)：函数标签
ksgn0 (str): 价格列名称，一般是'adj close'
'''
zwx.sta_dataPre0xtim(qx,xnam0);
#----对各只股票数据，进行预处理，提高后期运算速度
ksgn,qx.priceCalc=ksgn0,ksgn0; #'adj close';
for xcod in zw.stkLibCode:
d20=zw.stkLib[xcod];
#---------------dprice,kprice
#d20['dprice']=d20['open']*d20['adj close']/d20['close']
d20['dprice']=d20[ksgn]
d20['kprice']=d20['dprice']
#d20['kprice']=d20['dprice'].shift(-1)
#
d=qx.staVars[0];d20=zwta.MA(d20,d,ksgn);k0ma='ma_%d' %qx.staVars[0]
#d=qx.staVars[1];d20=zwta.MA(d20,d,ksgn);k1ma='ma_%d' %qx.staVars[1]
#
#d20['ma1n']=d20[k0ma].shift(1)
d20['ma2n']=d20[k0ma].shift(2)
#d20['dp1n']=d20['dprice'].shift(1)
d20['dp2n']=d20['dprice'].shift(2)
#---
d20=np.round(d20,3);
zw.stkLib[xcod]=d20;
if qx.debugMod>0:
print(d20.tail())
fss='tmp\\'+qx.prjName+'_'+xcod+'.csv'
d20.to_csv(fss)
def tim0Trade_dataPre(qx,xnam0,ksgn0):
zwx.sta_dataPre0xtim(qx,xnam0);
#-------
ksgn,qx.priceCalc=ksgn0,ksgn0; #'adj close';
for xcod in zw.stkLibCode:
d20=zw.stkLib[xcod];
# 计算交易价格kprice和策略分析采用的价格dprice,kprice一般采用次日的开盘价
#d20['dprice']=d20['open']*d20[ksgn]/d20['close']
d20['dprice']=d20[ksgn]
#d20['kprice']=d20['dprice'].shift(-1)
d20['kprice']=d20['dprice']
#
d=qx.staVars[0];d20=zwta.MA(d20,d,ksgn);
d=qx.staVars[1];d20=zwta.MA(d20,d,ksgn);
#
zw.stkLib[xcod]=d20;
if qx.debugMod>0:
print(d20.tail())
fss='tmp\\'+qx.prjName+'_'+xcod+'.csv'
d20.to_csv(fss)
def CMA_dataPre(qx, xnam0, ksgn0):
''' 均線交叉策略資料預處理函數
Args:
qx (zwQuantX): zwQuantX數據包
xnam0 (str)：函數標籤
ksgn0 (str): 價格列名稱，一般是'adj close'
'''
zwx.sta_dataPre0xtim(qx, xnam0)
#----對各檔股票資料，進行預處理，提高後期運算速度
ksgn, qx.priceCalc = ksgn0, ksgn0
#'adj close';
for xcod in zw.stkLibCode:
d20 = zw.stkLib[xcod]
#---------------dprice,kprice
#d20['dprice']=d20['open']*d20['adj close']/d20['close']
d20['dprice'] = d20[ksgn]
d20['kprice'] = d20['dprice']
#d20['kprice']=d20['dprice'].shift(-1)
#
d = qx.staVars[0]
d20 = zwta.MA(d20, d, ksgn)
k0ma = 'ma_%d' % qx.staVars[0]
#d=qx.staVars[1];d20=zwta.MA(d20,d,ksgn);k1ma='ma_%d' %qx.staVars[1]
#
#d20['ma1n']=d20[k0ma].shift(1)
d20['ma2n'] = d20[k0ma].shift(2)
#d20['dp1n']=d20['dprice'].shift(1)
d20['dp2n'] = d20['dprice'].shift(2)
#---
d20 = np.round(d20, 3)
zw.stkLib[xcod] = d20
if qx.debugMod > 0:
print(d20.tail())
fss = 'tmp\\' + qx.prjName + '_' + xcod + '.csv'
d20.to_csv(fss)
# -*- coding: utf-8 -*-
import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
import zw_talib as ta
#
#----------code.init
mpl.style.use('seaborn-whitegrid')
#============main
fss = "dat\\AAPL-201x.csv"
df = pd.read_csv(fss, encoding='gbk')
#df=ta.MA(df,5);
df = ta.MA(df, 10, 'Close')
df = ta.MA(df, 30, 'Close')
df = ta.MA(df, 50, 'Close')
print(df.tail())
df['Close'].plot(figsize=(15, 5))
#df['ma_5'].plot();
df['ma_10'].plot()
df['ma_30'].plot()
df['ma_50'].plot()
plt.legend(ncol=5)
