#!/usr/local/bin/python
#coding=utf-8
#实时均线策略
import pandas as pd
import numpy as np
from data_process.data_calcute import calcute_ma
from init import *
from util.date_convert import GetNowDate
from copy import deepcopy
class MAStrategy:
# df: DataFrame
def __init__(self, code, trade, df_close):
"""
:param code: 股票代码
:param trade: 实时股价, float
:param df_close: 收盘价序列,已排序, columns=['date', 'close']
"""
self.AVR_SHORT = AVR_SHORT
self.AVR_LONG = AVR_LONG
self.MA_DEA = 10
self.COL_MA_S = 'ma_{}'.format(self.AVR_SHORT)
self.COL_MA_L = 'ma_{}'.format(self.AVR_LONG)
self.COL_EMA_S = 'ema_{}'.format(self.AVR_SHORT)
self.COL_EMA_L = 'ema_{}'.format(self.AVR_LONG)
#方式二
# 将数据按照交易日期从远到近排序
# df.sort(columns='date', inplace=True)
df_close = calcute_ma(df_close, AVR_SHORT, AVR_LONG)
if trade: #有实时股价
df_now = deepcopy(df_close.tail(1))
df_now['date'] = GetNowDate()
df_now['close'] = trade
df_close = pd.concat([df_close, df_now], ignore_index=True)
# print df_close.tail()
#计算当前日期的ma
lastest_ma_short = sum(df_close['close'][-self.AVR_SHORT:])/self.AVR_SHORT
lastest_ma_long = sum(df_close['close'][-self.AVR_LONG:])/self.AVR_LONG
df_last = df_close[-1:]
df_last[self.COL_MA_S] = lastest_ma_short
df_last[self.COL_MA_L] = lastest_ma_long
#计算当前的ema
lastest_ema_short = df_close[self.COL_EMA_S].get_values()[-2] * (self.AVR_SHORT-1)/(self.AVR_SHORT+1) + trade * 2 /(self.AVR_SHORT+1)
lastest_ema_long = df_close[self.COL_EMA_L].get_values()[-2] * (self.AVR_LONG-1)/(self.AVR_LONG+1) + trade * 2 /(self.AVR_LONG+1)
df_last[self.COL_EMA_S] = lastest_ema_short
df_last[self.COL_EMA_L] = lastest_ema_long
self.df_close = df_close
# print self.df_close.head()
# print self.df_close.tail()
# 组合择时指标 (实时）
def select_Time_Mix(self, conditionBuy = 2, conditonSale = 2):
# 综合策略
signalMA = self.select_Time_MA()
signalMACD = self.select_Time_MACD()
signalDMA = self.select_Time_DMA()
signalTRIX = self.select_Time_TRIX()
signalAMA = self.select_Time_AMA()
# 买入信号的总数
buyTotal = (abs(signalMA)+signalMA)/2 + (abs(signalMACD)+signalMACD)/2 + \
(abs(signalDMA)+signalDMA)/2 + (abs(signalTRIX)+signalTRIX)/2 + (abs(signalAMA)+signalAMA)/2
# 卖出信号的总数
saleTotal = (-abs(signalMA)+signalMA)/2 + (-abs(signalMACD)+signalMACD)/2 + \
(-abs(signalDMA)+signalDMA)/2 + (-abs(signalTRIX)+signalTRIX)/2 + (-abs(signalAMA)+signalAMA)/2
signal = SIGNAL_DEFAULT
if buyTotal+saleTotal >= conditionBuy:
signal = SIGNAL_BUY
elif buyTotal+saleTotal <= -conditonSale:
signal = SIGNAL_SALE
return signal
# MA指标择时
def select_Time_MA(self):
#DMA
dma_close_short = self.df_close[self.COL_MA_S].get_values()
dma_close_long = self.df_close[self.COL_MA_L].get_values()
signal = SIGNAL_DEFAULT
if dma_close_short[-1] > dma_close_short[-2] and dma_close_short[-1] > dma_close_long[-1] \
and dma_close_short[-2] < dma_close_long[-2]:
signal = SIGNAL_BUY
elif dma_close_long[-1] < dma_close_long[-2] and dma_close_short[-1] < dma_close_long[-1] \
and dma_close_short[-2] > dma_close_long[-2]:
signal = SIGNAL_SALE
return signal
# MACD指标择时
def select_Time_MACD(self):
#EMA
# print self.df_close.tail()
ema_close_short = self.df_close[self.COL_EMA_S].get_values()
ema_close_long = self.df_close[self.COL_EMA_L].get_values()
dif_price = ema_close_short - ema_close_long
dea_price = pd.ewma(dif_price, span=self.MA_DEA)
macd_price = 2 * (dif_price - dea_price)
signal = SIGNAL_DEFAULT
if dif_price[-1] > dif_price[-2] and dif_price[-1] > dea_price[-2] \
and dif_price[-2] < dea_price[-2] and dea_price[-1] > 0:
signal = SIGNAL_BUY
elif dif_price[-1] < dif_price[-2] and dif_price[-1] < dea_price[-1] \
and dif_price[-2] > dea_price[-2] and dif_price[-1] < 0:
signal = SIGNAL_SALE
return signal
# DMA指标择时 (回测）
def select_Time_DMA(self):
# DMA
ma_close_short = self.df_close[self.COL_MA_S].get_values()
ma_close_long = self.df_close[self.COL_MA_L].get_values()
# #MA
# ma_list = [self.AVR_SHORT, self.AVR_LONG]
# ma_dea = 10
#
# if ma_list[0] == self.AVR_SHORT and ma_list[1] == self.AVR_LONG:
# ma_close_short = self.ma_short
# ma_close_long = self.ma_long
# else:
# ma_close_short = pd.rolling_mean(self.close_price, ma_list[0])
# ma_close_long = pd.rolling_mean(self.close_price, ma_list[1])
dma_price = ma_close_short - ma_close_long
ama_price = pd.rolling_mean(dma_price, self.MA_DEA)
signal = SIGNAL_DEFAULT
if dma_price[-1] > dma_price[-2] and dma_price[-1] > ama_price[-1] \
and dma_price[-2] < ama_price[-2]:
signal = SIGNAL_BUY
elif dma_price[-1] < dma_price[-2] and dma_price[-1] < ama_price[-1] \
and dma_price[-2] > ama_price[-2]:
signal = SIGNAL_SALE
return signal
# TRIX指标择时 (回测）
def select_Time_TRIX(self):
#EMA
ema_close_short = self.df_close[self.COL_EMA_S].get_values()
ema_ema_close_short = pd.ewma(ema_close_short, span=self.AVR_SHORT)
tr_close = pd.ewma(ema_ema_close_short, span=self.AVR_SHORT)
# ma_list = [self.AVR_SHORT, self.AVR_SHORT] #N,M
#
# if ma_list[0] == self.AVR_SHORT:
# ema_close = self.ema_short
# else:
# ema_close = pd.ewma(self.close_price, span=ma_list[0])
# ema_close = pd.ewma(ema_close, span=ma_list[0])
# tr_close = pd.ewma(ema_close, span=ma_list[0])
trixsList = [0]
for i in range(1, len(tr_close)):
#print tr_close[i], tr_close[i-1]
trix = (tr_close[i]-tr_close[i-1])/tr_close[i-1]*100
trixsList.append(trix)
trixs = np.array(trixsList)
maxtrix = pd.rolling_mean(trixs, self.AVR_LONG)
signal = SIGNAL_DEFAULT
if trixs[-1] > trixs[-2] and trixs[-1] > maxtrix[-1] \
and trixs[-2] < maxtrix[-2]:
signal = SIGNAL_BUY
elif trixs[-1] < trixs[-2] and trixs[-1] < maxtrix[-1] \
and trixs[-2] > maxtrix[-2]:
signal = SIGNAL_SALE
return signal
# AMA指标择时
def select_Time_AMA(self):
percentage = 0.1
close_price = self.df_close['close'].get_values()
# 指数平滑序列
containts = [0]*10
for i in range(10, len(close_price)):
sub_price = close_price[i-10:i]
constaint = self._getConstaint(sub_price)
containts.append(constaint)
ama_price = [float(close_price[0])]
for i in range(1, len(close_price)):
ama = containts[i-1] * float(close_price[i-1]) + (1-containts[i-1])*ama_price[i-1]
ama_price.append(float(ama))
#print np.array(ama_price[i-19:i+1]) - np.array(ama_price[i-20:i])
threshold = percentage * np.std(np.array(ama_price[i-19:i+1]) - np.array(ama_price[i-20:i])) # 过滤器
signal = SIGNAL_DEFAULT
if ama_price[-1] - np.min(ama_price[-6:-1]) > threshold:
signal = SIGNAL_BUY
elif np.max(ama_price[-6:-1]) - ama_price[-1] > threshold:
signal = SIGNAL_SALE
return signal
# 获取平方平滑系数
def _getConstaint(self, prices):
direction = abs(float(prices[-1]) - float(prices[0]))
volatility = sum(abs(float(prices[i+1])-float(prices[i])) for i in range(len(prices)-1))
if volatility == 0.0:
return 0
ER = abs(direction/volatility)
fastSC = 2.0/(2.0+1)
slowSC = 2.0/(30.0+1)
sSC = ER * (fastSC-slowSC) + slowSC
constaint = sSC*sSC
return constaint
