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#!/usr/local/bin/python
#coding=utf-8

#回测均线策略

import pandas as pd
import matplotlib.pyplot as plt
import os
import numpy as np

AVR_SHORT = 12
AVR_LONG = 40
 
SIGNAL_BUY = 1  #买 
SIGNAL_SALE = -1 #卖
SIGNAL_DEFAULT = 0
    
    
# 导入csv股票数据，写5日、20日、60日移动平均线
def processEMA(stockCsvPath, stockCsvNewPath):
    #导入数据，stockCsvPath为在电脑中的路径
    stock_data = pd.read_csv(stockCsvPath)
    
    # 将数据按照交易日期从远到近排序
    stock_data.sort('Date', inplace=True)
    
    #=====================计算移动平均线
    
    # 分别计算5日、20日、60日移动平均线
    ma_list = [5, 20, 60]
    
    # 计算简单算术移动平均线MA - 注意：stock_data['close']为股票每条的收盘价
    for ma in ma_list:
        stock_data['MA_' + str(ma)] = pd.rolling_mean(stock_data['Adj Close'], ma)
        
    # 计算指数平滑移动平均线EMA
    for ma in ma_list:
        stock_data['EMA_' + str(ma)] = pd.ewma(stock_data['Adj Close'], span=ma)
        
    # 将数据按照交易日期从近到远排序
    stock_data.sort('Date', ascending=False, inplace=True)
    
    stock_data['DIF'] = stock_data['EMA_'+str(ma_list[0])] - stock_data['EMA_'+str(ma_list[-1])]
    stock_data['DEA_' + str(10)] = pd.ewma(stock_data['DIF'], span=10)
    
    # =================================== 将算好的数据输出到csv文件，这里请填写输出文件在您电脑的路径
    stock_data.to_csv(stockCsvNewPath, index=False)   
    
# 自适应均线    
def self_adaptive_ma(stock_data):
     # 将数据按照交易日期从远到近排序
    stock_data.sort('Date', inplace=True)
    
    close_price = stock_data['Adj Close'].get_values()
    high_price = stock_data['High'].get_values()
    low_price = stock_data['Low'].get_values()
    longDay = 100
    
    print len(close_price)
    if len(close_price) < 100:
        return
    
    print close_price[-10:-1]
    direction = abs(close_price[-1] - close_price[-10])
    volatility = sum(abs(close_price[i+1]-close_price[i]) for i in range(-9,0))
    ER = abs(direction/volatility)
    fastSC = 2.0/(2.0+1)
    slowSC = 2.0/(30.0+1)
    sSC = ER * (fastSC-slowSC) + slowSC
    constaint = sSC*sSC
    
    #EMA 100
    ema_close_100 = pd.ewma(close_price, span=longDay)
    ema_high_100 = pd.ewma(high_price, span=longDay)
    ema_low_100 = pd.ewma(low_price, span=longDay)
    
    amaClose = ema_close_100[-1] + constaint * (close_price[-1] - ema_close_100[-1])
    amaHigh = ema_high_100[-1] + constaint * (high_price[-1] - ema_high_100[-1])
    amaLow = ema_low_100[-1] + constaint * (low_price[-1] - ema_low_100[-1])
    print ema_close_100[-1], ema_high_100[-1], ema_low_100[-1]
    
    BKPRICE = 0.0
    SKPRICE = float('Inf')
    status = SIGNAL_DEFAULT
    print high_price[-1], low_price[-1], close_price[-1]
    if low_price[-1] > amaHigh:
        status = SIGNAL_BUY
    elif close_price[-1] < amaClose or close_price[-1] <= 0.995 * BKPRICE:
        status = SIGNAL_BUY
    elif high_price[-1] < amaLow:
        status = SIGNAL_SALE    
    elif close_price[-1] > amaClose or close_price[-1] >= 1.005 * SKPRICE:
        status = SIGNAL_SALE    
        
    return status   
     

# MA指标择时  (回测）
def select_Time_MA(stock_data, stockName):
    
    start_money = 100000000
    now_count = 0
    now_money = start_money
    
    # 将数据按照交易日期从远到近排序
    stock_data.sort('Date', inplace=True)
    
    close_price = stock_data['Adj Close'].get_values()
    
    #EMA 
    ma_list = [AVR_SHORT, AVR_LONG]
    ema_close_short = pd.ewma(close_price, span=ma_list[0])
    ema_close_long = pd.ewma(close_price, span=ma_list[1])
    
    signals = [0]*(ma_list[1]+1)
    tradeTimes = 0
    bBuySignal = True 
    for t in range(ma_list[1]+1, len(close_price)):
        
        signal = SIGNAL_DEFAULT
        
        if ema_close_short[t] > ema_close_short[t-1] and ema_close_short[t] > ema_close_long[t] \
                        and ema_close_short[t-1] < ema_close_long[t-1]:
            if bBuySignal:
                signal = SIGNAL_BUY
                now_count = (int)(start_money / close_price[t] /100)*100
                now_money = start_money - now_count * close_price[t]
                #print u"股票价格/持股数/剩余金额：",close_price[t], '/',  now_count, '/', now_money
                bBuySignal = False  
        elif ema_close_long[t] < ema_close_long[t-1] and ema_close_short[t] < ema_close_long[t] \
                        and ema_close_short[t-1] > ema_close_long[t-1]:
            if bBuySignal == False:
                signal = SIGNAL_SALE
                now_money += now_count * close_price[t]
                now_count = 0
                #print u"股票价格/持股数/剩余金额：",close_price[t], '/',  now_count, '/', now_money
                bBuySignal = True    
        signals.append(signal)
        if signal != 0:
            #print 't:', t, '  signal:', signal
            tradeTimes += 1

    
    print stockName, u"收益率：", (now_money+now_count * close_price[-1]-start_money)/start_money*100, '%\t' \
        u"交易次数", tradeTimes, u" 最新市值：", now_money+now_count * close_price[-1]  
    stock_data['SIGNAL_MA'] = signals
    
#     fig = plt.figure(facecolor='white')
#     left, width = 0.1, 0.8
#     rect1 = [left, 0.5, width, 0.4]
#     rect2 = [left, 0.1, width, 0.3]
#     
#     axescolor  = '#f6f6f6'  # the axes background color
#     ax1 = fig.add_axes(rect1, axisbg=axescolor)  #left, bottom, width, height
#     ax2 = fig.add_axes(rect2, axisbg=axescolor, sharex=ax1)
#     
#     ax1.plot(range(len(close_price)), close_price, color="black", linewidth=1)
#     ax1.grid()
#     ax2.plot(range(len(ema_close_long)), ema_close_long,label='', color="red", linewidth=1)
#     ax2.plot(range(len(ema_close_short)), ema_close_short,label='', color="blue", linewidth=1)
#     ax2.grid()
    #plt.show()
    
    # 将数据按照交易日期从近到远排序
    #stock_data.sort('date', ascending=False, inplace=True)
    
# MACD指标择时 (回测）
def select_Time_MACD(stock_data, stockName):
    
    start_money = 100000000
    now_count = 0
    now_money = start_money
    
    # 将数据按照交易日期从远到近排序
    stock_data.sort('Date', inplace=True)
    
    close_price = stock_data['Adj Close'].get_values()
    
    #EMA 
    ma_list = [AVR_SHORT, AVR_LONG]
    ma_dea = 10
    ema_close_short = pd.ewma(close_price, span=ma_list[0])
    ema_close_long = pd.ewma(close_price, span=ma_list[1])
    
    
    dif_price = ema_close_short - ema_close_long
    dea_price = pd.ewma(dif_price, span=ma_dea)
    macd_price = 2 * (dif_price - dea_price)
    
    signals = [0]*(ma_list[1]+1)
    tradeTimes = 0
    bBuySignal = True 
    for t in range(ma_list[1]+1, len(close_price)):
        
        signal = SIGNAL_DEFAULT
        
        if dif_price[t] > dif_price[t-1] and dif_price[t] > dea_price[t] \
                                        and dif_price[t-1] < dea_price[t-1] and dea_price[t] > 0:
            if bBuySignal:
                signal = SIGNAL_BUY
                now_count = (int)(now_money / close_price[t] /100)*100
                now_money = start_money - now_count * close_price[t]
                #print u"股票价格/持股数/剩余金额：",close_price[t], '/',  now_count, '/', now_money
                bBuySignal = False 
        elif dif_price[t] < dif_price[t-1] and dif_price[t] < dea_price[t] \
                        and dif_price[t-1] > dea_price[t-1] and dif_price[t] < 0:
            if bBuySignal == False:
                signal = SIGNAL_SALE
                now_money += now_count * close_price[t]
                now_count = 0
                #print u"股票价格/持股数/剩余金额：",close_price[t], '/',  now_count, '/', now_money
                bBuySignal = True      
        signals.append(signal)
        if signal != 0:
            #print 't:', t, '  signal:', signal
            tradeTimes += 1

    
    
    print stockName, u"收益率：", (now_money+now_count * close_price[-1]-start_money)/start_money*100, '%\t' \
        u"交易次数", tradeTimes, u" 最新市值：", now_money+now_count * close_price[-1]  
    stock_data['SIGNAL_MACD'] = signals
    
#     fig = plt.figure(facecolor='white')
#     left, width = 0.1, 0.8
#     rect1 = [left, 0.5, width, 0.4]
#     rect2 = [left, 0.1, width, 0.3]
#     
#     axescolor  = '#f6f6f6'  # the axes background color
#     ax1 = fig.add_axes(rect1, axisbg=axescolor)  #left, bottom, width, height
#     ax2 = fig.add_axes(rect2, axisbg=axescolor, sharex=ax1)
#     
#     ax1.plot(range(len(close_price)), close_price, color="black", linewidth=1)
#     ax1.grid()
#     ax2.plot(range(len(dif_price)), ema_close_long,label='', color="red", linewidth=1)
#     ax2.plot(range(len(dea_price)), ema_close_short,label='', color="blue", linewidth=1)
#     ax2.grid()
    #plt.show()
    
    # 将数据按照交易日期从近到远排序
    #stock_data.sort('date', ascending=False, inplace=True)
 
    return dif_price, dea_price, macd_price

# DMA指标择时 (回测）
def select_Time_DMA(stock_data, stockName):
    
    start_money = 100000000
    now_count = 0
    now_money = start_money
    
    # 将数据按照交易日期从远到近排序
    stock_data.sort('Date', inplace=True)
    
    close_price = stock_data['Adj Close'].get_values()
    
    #MA 
    ma_list = [AVR_SHORT, AVR_LONG]
    ma_dea = 10
    ma_close_short = pd.rolling_mean(close_price, ma_list[0])
    ma_close_long = pd.rolling_mean(close_price, ma_list[1])
    
    
    dma_price = ma_close_short - ma_close_long
    ama_price = pd.rolling_mean(dma_price, ma_dea)
    
    signals = [0]*(ma_list[1]+1)
    tradeTimes = 0
    bBuySignal = True 
    for t in range(ma_list[1]+1, len(close_price)):
        
        signal = SIGNAL_DEFAULT
        
        if dma_price[t] > dma_price[t-1] and dma_price[t] > ama_price[t] \
                                        and dma_price[t-1] < ama_price[t-1]:
            if bBuySignal:
                signal = SIGNAL_BUY
                now_count = (int)(now_money / close_price[t] /100)*100
                now_money = start_money - now_count * close_price[t]
                #print u"股票价格/持股数/剩余金额：",close_price[t], '/',  now_count, '/', now_money
                bBuySignal = False 
        elif dma_price[t] < dma_price[t-1] and dma_price[t] < ama_price[t] \
                        and dma_price[t-1] > ama_price[t-1]:
            if bBuySignal == False:
                signal = SIGNAL_SALE
                now_money += now_count * close_price[t]
                now_count = 0
                #print u"股票价格/持股数/剩余金额：",close_price[t], '/',  now_count, '/', now_money
                bBuySignal = True      
        signals.append(signal)
        if signal != 0:
            #print 't:', t, '  signal:', signal
            tradeTimes += 1

    
    
    print stockName, u"收益率：", (now_money+now_count * close_price[-1]-start_money)/start_money*100, '%\t' \
        u"交易次数", tradeTimes, u" 最新市值：", now_money+now_count * close_price[-1]  
    stock_data['SIGNAL_DMA'] = signals
    
#     fig = plt.figure(facecolor='white')
#     left, width = 0.1, 0.8
#     rect1 = [left, 0.5, width, 0.4]
#     rect2 = [left, 0.1, width, 0.3]
#     
#     axescolor  = '#f6f6f6'  # the axes background color
#     ax1 = fig.add_axes(rect1, axisbg=axescolor)  #left, bottom, width, height
#     ax2 = fig.add_axes(rect2, axisbg=axescolor, sharex=ax1)
#     
#     ax1.plot(range(len(close_price)), close_price, color="black", linewidth=1)
#     ax1.grid()
#     ax2.plot(range(len(dma_price)), dma_price,label='', color="red", linewidth=1)
#     ax2.plot(range(len(ama_price)), ama_price,label='', color="blue", linewidth=1)
#     ax2.grid()
    #plt.show()
    
    # 将数据按照交易日期从近到远排序
    #stock_data.sort('date', ascending=False, inplace=True)
 
# DMA指标择时 (回测）
def select_Time_TRIX(stock_data, stockName):
    
    start_money = 100000000
    now_count = 0
    now_money = start_money
    
    # 将数据按照交易日期从远到近排序
    stock_data.sort('Date', inplace=True)
    
    close_price = stock_data['Adj Close'].get_values()
    
    #EMA 
    ma_list = [AVR_SHORT, AVR_SHORT] #N,M
    
    ema_close = pd.ewma(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, ma_list[1])
    
    
    signals = [0]*(ma_list[1]+1)
    tradeTimes = 0
    bBuySignal = True 
    for t in range(ma_list[1]+1, len(close_price)):
        
        signal = SIGNAL_DEFAULT
        
        if trixs[t] > trixs[t-1] and trixs[t] > maxtrix[t] \
                                        and trixs[t-1] < maxtrix[t-1]:
            if bBuySignal:
                signal = SIGNAL_BUY
                now_count = (int)(now_money / close_price[t] /100)*100
                now_money = start_money - now_count * close_price[t]
                #print u"股票价格/持股数/剩余金额：",close_price[t], '/',  now_count, '/', now_money
                bBuySignal = False 
        elif trixs[t] < trixs[t-1] and trixs[t] < maxtrix[t] \
                        and trixs[t-1] > maxtrix[t-1]:
            if bBuySignal == False:
                signal = SIGNAL_SALE
                now_money += now_count * close_price[t]
                now_count = 0
                #print u"股票价格/持股数/剩余金额：",close_price[t], '/',  now_count, '/', now_money
                bBuySignal = True      
        signals.append(signal)
        if signal != 0:
            #print 't:', t, '  signal:', signal
            tradeTimes += 1

    
    
    print stockName, u"收益率：", (now_money+now_count * close_price[-1]-start_money)/start_money*100, '%\t' \
        u"交易次数", tradeTimes, u" 最新市值：", now_money+now_count * close_price[-1]  
    stock_data['SIGNAL_TRIX'] = signals


# 组合择时指标 (回测）
def select_Time_Mix(stock_data, stockName):
    
    start_money = 100000000
    now_count = 0
    now_money = start_money
    
    # 综合策略
    signals = []
    tradeTimes = 0
    bBuySignal = True 
    
    close_price = stock_data['Adj Close'].get_values()
    s_ma = stock_data['SIGNAL_MA'].get_values()
    s_macd = stock_data['SIGNAL_MACD'].get_values()
    s_dma = stock_data['SIGNAL_DMA'].get_values()
    s_trix = stock_data['SIGNAL_TRIX'].get_values()
    
    for i in range(len(s_ma)):
        
        signal = SIGNAL_DEFAULT
        
        up = 0; 
        down = 0;
        if s_ma[i] == 1:
            up += 1
        elif s_ma[i] == -1:
            down += 1
        
        if s_macd[i] == 1:
            up += 1
        elif s_macd[i] == -1:
            down += 1
        
        if s_dma[i] == 1:
            up += 1
        elif s_dma[i] == -1:
            down += 1
        
        if s_trix[i] == 1:
            up += 1
        elif s_trix[i] == -1:
            down += 1            
        
        
        if up >= 3:
            if bBuySignal:
                signal = SIGNAL_BUY
                now_count = (int)(now_money / close_price[i] /100)*100
                now_money = start_money - now_count * close_price[i]
                #print u"股票价格/持股数/剩余金额：",close_price[t], '/',  now_count, '/', now_money
                bBuySignal = False 
        elif down <= -3:   
            if bBuySignal == False:
                signal = SIGNAL_SALE
                now_money += now_count * close_price[i]
                now_count = 0
                #print u"股票价格/持股数/剩余金额：",close_price[t], '/',  now_count, '/', now_money
                bBuySignal = True 
        
        signals.append(signal)
        if signal != 0:
            #print 't:', t, '  signal:', signal
            tradeTimes += 1
                
    print stockName, u"收益率：", (now_money+now_count * close_price[-1]-start_money)/start_money*100, '%\t' \
        u"交易次数", tradeTimes, u" 最新市值：", now_money+now_count * close_price[-1]  
    stock_data['SIGNAL_MIX'] = signals

# AMA指标择时
def select_Time_AMA(stock_data, stockName):
    
    percentage = 0.1
    
    start_money = 100000000
    now_count = 0
    now_money = 0
    one_hand = 10000
    
    # 将数据按照交易日期从远到近排序
    stock_data.sort('Date', inplace=True)
    
    close_price = stock_data['Adj 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 = getConstaint(sub_price)
        containts.append(constaint);
        
    ama_price = [close_price[0]]    
    for i in range(1, len(close_price)):
        ama = containts[i-1] * close_price[i-1] + (1-containts[i-1])*ama_price[i-1]
        ama_price.append(ama)
        
    signals = [0]*21
    tradeTimes = 0
    
    record_buy = 0
    record_sale = []
        
    # 从20天以后开始判断买卖点
    for i in range(21, len(ama_price)):    
        
        signal = SIGNAL_DEFAULT
        
        #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
        
        if ama_price[i] - np.min(ama_price[i-5:i]) > threshold: 
            signal = SIGNAL_BUY
            now_count += one_hand
            record_buy += one_hand * close_price[i]
            #print u"股票价格/持股数/剩余金额：",close_price[t], '/',  now_count, '/', now_money
        elif np.max(ama_price[i-5:i]) - ama_price[i] > threshold:    
            signal = SIGNAL_SALE
            if now_count > one_hand:
                now_count -= one_hand
                record_sale = one_hand * close_price[i]
            #print u"股票价格/持股数/剩余金额：",close_price[t], '/',  now_count, '/', now_money
        signals.append(signal)
        if signal != 0:
            #print 't:', t, '  signal:', signal
            tradeTimes += 1           

    # 成本
    print u'盈利', record_sale + now_count * close_price[-1] - record_buy
    
#     print stockName, u"收益率：", (now_money+now_count * close_price[-1]-start_money)/start_money*100, '%\t' \
#         u"交易次数", tradeTimes, u" 最新市值：", now_money+now_count * close_price[-1]  
    stock_data['SIGNAL_AMA'] = signals
    
    return ama_price

# 获取平方平滑系数
def getConstaint(prices):
    direction = abs(prices[-1] - prices[0])
    volatility = sum(abs(prices[i+1]-prices[i]) for i in range(len(prices)-1))
    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

# 选择一种均线策略
def getMAStrategy(stockCsvPath, stockName, strategyName='MACD'):
    if os.path.exists(stockCsvPath) == False:
        return
    stock_data = pd.read_csv(stockCsvPath)
    if strategyName == 'MACD':
        return select_Time_MACD(stock_data, stockName)
    elif strategyName == 'MA':
        return select_Time_MA(stock_data, stockName)
    elif strategyName == 'DMA':
        return select_Time_DMA(stock_data, stockName)
    elif strategyName == 'TRIX':
        return select_Time_TRIX(stock_data, stockName)
    elif strategyName == 'AMA':
        return select_Time_AMA(stock_data, stockName)
    
# 执行策略 
def run(stockCsvPath, stockName):
    if os.path.exists(stockCsvPath) == False:
        return
    #stockCsvNewPath = stockName + '_macd.csv'
    #processEMA(stockCsvPath, stockCsvNewPath)
    stock_data = pd.read_csv(stockCsvPath)
    #self_adaptive_ma(stock_data)
    
    print u'>>>>>>>>>>>>> MA 策略 >>>>>>>>>>>>>>>>>>>>>>>>>>'
    select_Time_MA(stock_data, stockName)
       
    print u'>>>>>>>>>>>>> MACD 策略 >>>>>>>>>>>>>>>>>>>>>>>>>>'
    select_Time_MACD(stock_data, stockName)
   
    print u'>>>>>>>>>>>>> DMA 策略 >>>>>>>>>>>>>>>>>>>>>>>>>>'
    select_Time_DMA(stock_data, stockName)
       
    print u'>>>>>>>>>>>>> TRIX 策略 >>>>>>>>>>>>>>>>>>>>>>>>>>'
    select_Time_TRIX(stock_data, stockName)
      
    print u'>>>>>>>>>>>>> 组合策略 >>>>>>>>>>>>>>>>>>>>>>>>>>'
    select_Time_Mix(stock_data, stockName)
   
    print u'>>>>>>>>>>>>> AMA策略 >>>>>>>>>>>>>>>>>>>>>>>>>>'
    select_Time_AMA(stock_data, stockName)
            
    print '\n'
                               
if __name__ == "__main__":
    print "main begin"
    stockList=['000725','000783','002167','002505','002600','300315','600000','600011','600048','601001']
    #stockList = ['000725']
    for stockName in stockList:
#         if stockName != '002600':
#             continue
        stockCsvPath = os.path.pardir +"\\stockdata\\" + stockName + '.csv'
        #stockCsvPath = stockName + '.csv'
        if os.path.exists(stockCsvPath) == False:
            continue
        #stockCsvNewPath = stockName + '_macd.csv'
        #processEMA(stockCsvPath, stockCsvNewPath)
        stock_data = pd.read_csv(stockCsvPath)
        #self_adaptive_ma(stock_data)
        
        print u'>>>>>>>>>>>>> MA 策略 >>>>>>>>>>>>>>>>>>>>>>>>>>'
        select_Time_MA(stock_data, stockName)
           
        print u'>>>>>>>>>>>>> MACD 策略 >>>>>>>>>>>>>>>>>>>>>>>>>>'
        select_Time_MACD(stock_data, stockName)
       
        print u'>>>>>>>>>>>>> DMA 策略 >>>>>>>>>>>>>>>>>>>>>>>>>>'
        select_Time_DMA(stock_data, stockName)
           
        print u'>>>>>>>>>>>>> TRIX 策略 >>>>>>>>>>>>>>>>>>>>>>>>>>'
        select_Time_TRIX(stock_data, stockName)
          
        print u'>>>>>>>>>>>>> 组合策略 >>>>>>>>>>>>>>>>>>>>>>>>>>'
        select_Time_Mix(stock_data, stockName)
       
        print u'>>>>>>>>>>>>> AMA策略 >>>>>>>>>>>>>>>>>>>>>>>>>>'
        select_Time_AMA(stock_data, stockName)
                
        print '\n'
    print "main end"