def Run(codes, task_id=0):
from pypublish import publish
pl = publish.Publish()
df_year = stock.THS().df_year
df_jll = stock.THS().df_jll
#myredis.delKeys(myredis.enum.KEY_HISDAT_NO_FUQUAN)
for code in codes:
df = df_year[df_year['code'] == code]
df_hisdat = stock.getHisdatDfRedis(code)
history_syl = calc_history_syl(df, df_hisdat, 'year')
ui.DrawTs(pl, history_syl, title=stock.GetCodeName(code).decode('utf8'))
df = df_jll[df_jll['code'] == code]
history_syl = calc_history_syl(df, df_hisdat, period='quarter')
ui.DrawTs(pl, history_syl, title=stock.GetCodeName(code).decode('utf8'))
def _test1(self):
t = stock.THS()
df = t.df_jll
code = '300033'
df = df[df['code'] == code][df.columns[2]]
#series
df = df[df.map(lambda x: x.strip() != '')]
df = df.astype(float)
predict_jll(df)
def SelectCodes():
df_year = stock.THS().df_year
df_year = df_year.ix['2010':'2016']
#历史平均市盈率小于25且净利润逐年上升
codes = stock.get_codes()
codes = jbm.find_avg_syl(codes, df_year, 25)
df_year = df_year[df_year['code'].map(lambda x: x in codes)]
codes = jbm.find_jll_increase(df_year)
#codes = agl.array_shuffle(codes)
#max_row = min(len(codes)-1, 5)
return codes
def test_find_avg_syl(self):
#统计历史平均市盈率小于某值的个数
scopre = [10, 15, 20, 30]
nums = []
codes = stock.get_codes()
#codes = ['300033']
df_jll = stock.THS().df_year
for v in scopre:
cur_codes = find_avg_syl(codes, df_jll, syl=v)
print len(cur_codes)
nums.append(len(cur_codes))
ui.bar(pl, scopre, nums)
#-*- coding:utf-8 -*-
"""boll分仓, 择时策略"""
import numpy as np
import pandas as pd, pylab as pl
import boll_pramid
import backtest_policy, stock, myenum, agl, account as ac, help, myredis, sign_observation as so, ui,jbm, stock_pinyin as jx
import os,warnings
agl.tic()
#ths = stock.createThs()
g_ths = stock.THS()
class BollFenCangKline(boll_pramid.Strategy_Boll_Pre):
"""基于日线的boll分仓, 基本指标使用日线Boll以及日线Four"""
def setParams(self, *args, **kwargs):
self.base_num = 10000 #第一次买卖的数量
self.base_num_ratio = 0.1 #总资金的10%作为初始建仓, 与上一个变量两者可选， 注释该行使用绝对数量
self.base_ratio = 0.05 #盈亏区间的比率
self.base_pramid_ratio = 3 #底部增仓比率
self.base_rhombus_mid_ratio = 0.3 #仓位形态菱形的下部占比, 当资金不够时， 尽量保留一些资金
self.base_four = [-0.5, 0.3] #第一次买卖的技术指标阀值
for k, v in kwargs.iteritems():
setattr(self, k, v)
def OnFirstRun(self):
self.key_sell_avg_price = 'BollFenCangKline.SellAvgPrice'+ str(os.getpid())
self.key_sell_num = 'BollFenCangKline.SellNum'+ str(os.getpid())
myredis.delKeys('BollFenCangKline')
#只需要计算一次的数值