import pandas as pd
import numpy as np
import talib  ## 使用talib计算MACD的参数
import time
import sys
import json
from jqlib.technical_analysis import *

#this version to develop yellowline and every time focus time context#
def initialize(context):
    g.security = ['000030.XSHE', '000036.XSHE', '000069.XSHE', '000488.XSHE',
       '000501.XSHE', '000537.XSHE', '000581.XSHE', '000591.XSHE',
       '000636.XSHE', '000651.XSHE', '000717.XSHE', '000720.XSHE',
       '000825.XSHE', '000830.XSHE', '000877.XSHE', '000885.XSHE',
       '000898.XSHE', '000900.XSHE', '000921.XSHE', '000926.XSHE',
       '000932.XSHE', '000983.XSHE', '002068.XSHE', '002078.XSHE',
       '002092.XSHE', '002110.XSHE', '002128.XSHE', '002195.XSHE',
       '002462.XSHE', '002589.XSHE', '300145.XSHE']
    set_backtest()
    run_daily(begin,time='09:30')
    
    run_daily(trade,time='09:33')
    #run_daily(trade,time='09:34')
    #run_daily(trade,time='09:35')
    run_daily(trade,time='09:36')
    #run_daily(trade,time='09:37')
    #run_daily(trade,time='09:39')
    run_daily(trade,time='09:40')
    run_daily(trade,time='09:45')
    run_daily(trade,time='09:50')
    run_daily(trade,time='09:55')
    run_time(30)#每30分钟运行一次
    #run_daily(trade1,time='10:00')
  
def run_time(x):
    datas = get_price('000300.XSHG',count=200,frequency='1m').index.tolist()[:-1]#剔除15:00
    times = [str(t)[-8:] for t in datas]
    #print(times)
    #times.insert(0,'09:30:00')
    for t in times[::x]:
        run_daily(trade1, t)

def set_backtest():
    set_benchmark('000300.XSHG') 
    set_option('use_real_price', True)  
    log.set_level('order', 'error')

def order_dict(dicts, n):
    result = []
    result1 = []
    p = sorted([(k, v) for k, v in dicts.items()], reverse=True)
    s = set()
    for i in p:
        s.add(i[1])
    for i in sorted(s, reverse=True)[:n]:
        for j in p:
            if j[1] == i:
                result.append(j)
    for r in result:
        result1.append(r[0])
    #print (result1)
    return result1
    
def get_v_ratio(security_list,time):
    #'''计算某个时间点股票的量比,
    #输入:股票池/股票,时间(datetime)
    #返回:截至当天某一时间点的量比'''
    if isinstance(security_list, str):
        security_list = [security_list]
    d = {}
    time2= time-datetime.timedelta(days=1)
    start_time = datetime.datetime(time.year,time.month,time.day,9,30)
    for security in security_list:
        ma5 = get_price(security,end_date=time2,count=5,skip_paused=True,fields='volume').volume.sum()/1200
        n_ma = get_price(security,end_date=time,start_date =start_time ,skip_paused=True,fields='volume',
                          frequency='1m').volume.mean()
        d[security]=n_ma/ma5
    return d
#分时黄金线实际是当日平均价#
def get_yellowline(security_list,time):
    if isinstance(security_list, str):
        security_list = [security_list]
    d = {}
    time2= time-datetime.timedelta(days=1)
    start_time = datetime.datetime(time.year,time.month,time.day,9,30)
    for security in security_list:
        df1=get_price(security,end_date=time, start_date=start_time,frequency='1m', fields='avg', skip_paused=False, fq='none')
        series1=df1['avg'].mean()
        d[security]=series1
    return d

####1.盘前只留MACD day good####
def begin(context):
    stockpoolbegin = g.security
 
    long_list = []
    g.short_list = []
    result = []
    g.stockpool = []
    g.stockpoollist= []
    g.hold = []
    g.stockset = list(context.portfolio.positions.keys())
    #print('portfolio_value',context.portfolio.portfolio_value)
    for stock in stockpoolbegin:
        #prices = attribute_history(stock,300, '1d',['close'])
        prices = get_bars(stock, 50, '1d',include_now=True)
        price = array(prices['close'])
        #print(price)
        macd_tmp = talib.MACD(price, fastperiod=12, slowperiod=26, signalperiod=20)
        #print(macd_tmp)
        DIF = macd_tmp[0]
        DEA = macd_tmp[1]
        MACD = np.round(macd_tmp[2],3)
        #print(MACD)
        # 判断MACD走向
        if MACD[-1] > 0:
        #if MACD[-1] < 0 and MACD[-1] > MACD[-2] and MACD[-2] <MACD[-3]:
            print(MACD[-2],MACD[-1],'choose it',stock)
            r = (stock,MACD[-1])
            result.append(r)
            #g.stockpool.append(stock)
            
        elif MACD[-1] < 0:
        #elif MACD[-1] > 0 and MACD[-1] < MACD[-2] and MACD[-2] >MACD[-3]:
            print(MACD[-2],MACD[-1],'exclude it',stock)
            g.short_list.append(stock)
        #elif MACD[-1] > 0 and MACD[-1] > MACD[-2]*1.3:
            #long_list.append(stock)
        #elif MACD[-1] < MACD[-2] *0.8:
            #short_list.append(stock)
    result = np.array(result)
    columns = ['stock','macd-1']
    num_columns =['macd-1']
    g.stockpoollist=pd.DataFrame(data = result, columns=columns)
    #g.stockpoollist[num_columns] = g.stockpoollist[num_columns].apply(pd.to_numeric, errors='ingnore')
    g.stockpoollist=g.stockpoollist.sort_index(axis = 0,ascending =False,by = 'macd-1')
    g.stockpool=g.stockpoollist.head(50)['stock']
    #print(g.stockpoollist)
    print('short_list by day MACD',g.short_list)
    print('choosed by day MACD',g.stockpool)
    for stock in g.stockset:
        if stock in g.short_list:
            print('sell it right now with bad day MACD', stock)
            order_target_value(stock, 0) 
            #order_value(stock, -10000)
        else:
            g.hold.append(stock)#如果不在卖出列表里则持有
    print('temp hold for today following test',g.hold)


##2.Qucik handle the valume ratio good' stock to buy or not##   
def trade(context):
#按分钟回测/模拟, 在每分钟的第一秒运行, 每天执行240次,

    security_list =g.stockpool.tolist()
    #time = datetime.datetime(2019,3,14,15,00)
    time=datetime.datetime.now()
    #回测中: 
    time = context.current_dt
    volume_ratio=get_v_ratio(security_list,time)
    print ('volume_ratio',volume_ratio)
    stockpool=order_dict(volume_ratio, 3)
    print ('after filter',stockpool)
    g.stockset = list(context.portfolio.positions.keys())
    result = []
    long_list = []
    hold = []
    g.short_list = []
    
    for stock in stockpool:
        prices = get_bars(stock, 5, '1m',include_now=True)
        #print(prices)
        price = array(prices['close'])
        #print(price)
        #macd_tmp = talib.MACD(price, fastperiod=12, slowperiod=26, signalperiod=20)
        #DIF = macd_tmp[0]
        #DEA = macd_tmp[1]
        #MACD = np.round(macd_tmp[2],3)
        #print(stock,MACD[-1],MACD[-2],MACD[-3])
        time2= time-datetime.timedelta(days=1)
        start_time = datetime.datetime(time.year,time.month,time.day,9,30)
        df1=get_price(stock,end_date=time, start_date=start_time,frequency='1m', fields='avg', skip_paused=False, fq='none')
        yellowline=df1['avg'].mean()
        
        # 判断MACD走向？？need to judge the 1 minutes rate
        if price[-1] > yellowline:
        #if MACD[-1] > 0: #and MACD[-2] <0:
        #if MACD[-1] < 0 and MACD[-1] > MACD[-2]*1.002 and MACD[-2] <MACD[-3]*1.005:
            print(price[-1],yellowline,'buy it at',price[-1],stock)
            #print(MACD[-3],MACD[-2],MACD[-1],'buy it at',price[-1],stock)
            long_list.append(stock)
            
        elif price[-1] < yellowline: 
        #elif MACD[-1] > 0 and MACD[-1] < MACD[-2]*1.005 and MACD[-2] >MACD[-3]*1.002:
            print(price[-1],yellowline,'sell it at',price[-1],stock)
            #print(MACD[-3],MACD[-2],MACD[-1],'sell it at',price[-1],stock)
            g.short_list.append(stock)
        #elif MACD[-1] > 0 and MACD[-1] > MACD[-2]*1.3:
            #long_list.append(stock)
        #elif MACD[-1] < MACD[-2] *0.8:
            #short_list.append(stock)
    print('long_list',long_list)
    print('short_list',g.short_list)
    for stock in g.stockset:
        if stock in g.short_list:
            print('sell it right now with bad 1 minute data and high volume', stock)
            #order_target_value(stock, 0) 
            order_value(stock, -10000)
        else:
            hold.append(stock)#如果不在卖出列表里则持有
    #print(hold)
    buy_list = []
    for stock in long_list:
        #if stock not in hold:
        buy_list.append(stock)#新增的买入股票

    if len(buy_list)==0: 
        Cash = context.portfolio.available_cash
        #print(Cash)
    else:
        #Cash = context.portfolio.available_cash/10#len(buy_list)
        for stock in buy_list:
            #order_target_value(stock, 30000)   
             # 买入这么多现金的股票
            order_value(stock, 10000)
            print('buy right now with good 1 minute data and high volume',stock)


def trade1(context):
#5分钟for mid of day#
    #print(g.stockpoollist)
    security_list =g.stockpool.tolist()
    #time = datetime.datetime(2019,3,14,15,00)
    time=datetime.datetime.now()
    #回测中: 
    time = context.current_dt
    volume_ratio=get_v_ratio(security_list,time)
    print ('volume_ratio',volume_ratio)
    stockpool=order_dict(volume_ratio, 3)
    print ('after filter',stockpool)
    g.stockset = list(context.portfolio.positions.keys())
    stockpool=list(set(stockpool+g.stockset))
    #两个list比较去除重复元素，然后合并#
    print('filtered combined hold for total focus',stockpool)
    result = []
    long_list = []
    hold = []
    g.short_list = []
    
    for stock in stockpool:
        pricesd = get_bars(stock, 3, '1d',include_now=True)
        priced=array(pricesd['open'])
        print('open price',priced[-1])
        prices = get_bars(stock, 50, '5m',include_now=True)
        price = array(prices['close'])
        print('current price',price[-1])
        #macd_tmp = talib.MACD(price, fastperiod=12, slowperiod=26, signalperiod=20)
        #DIF = macd_tmp[0]
        #DEA = macd_tmp[1]
        #MACD = np.round(macd_tmp[2],3)
        #print(stock,MACD[-1],MACD[-2],MACD[-3])
        time2= time-datetime.timedelta(days=1)
        start_time = datetime.datetime(time.year,time.month,time.day,9,30)
        df1=get_price(stock,end_date=time, start_date=start_time,frequency='1m', fields='avg', skip_paused=False, fq='none')
        yellowline=df1['avg'].mean()

        # 判断MACD走向and if current price not lower than beginning price
        #if MACD[-1] > 0: #and MACD[-2] <0:
        if price[-1] > yellowline and price[-1]>priced[-1]:
        #if MACD[-1] > 0 and MACD[-1] > MACD[-2]*1 and MACD[-1] <MACD[-2]*1.05 and price[-1]>priced[-1]:
        #if MACD[-1] < 0 and MACD[-1] > MACD[-2]*1.002 and MACD[-2] <MACD[-3]*1.005 and price[-1]>priced[-1]:
            print(price[-1],yellowline,'buy it at',price[-1],stock) 
            #?if add ratio to avoid catch too high
            long_list.append(stock)
            
        #elif MACD[-1] < 0: #and MACD[-2] >0:
        #elif MACD[-1] > 0 and MACD[-1] < MACD[-2]*1.05 and MACD[-2] >MACD[-3]*1.02:
            #print(MACD[-1],MACD[-2],MACD[-3],'sell it at',price[-1],stock)
        else:
            g.short_list.append(stock)
    print('long_list',long_list)
    print('short_list',g.short_list)
    g.stockset = list(context.portfolio.positions.keys())
    for stock in g.stockset:
        if stock not in long_list: #g.short_list:
            print('sell', stock)
            #order_target_value(stock, 0) 
            order_value(stock, -5000*2)
        else:
            hold.append(stock)#如果不在卖出列表里则持有
    #print(hold)
    buy_list = []
    for stock in long_list:
        #if stock not in hold:
        buy_list.append(stock)#新增的买入股票

    if len(buy_list)==0: 
        Cash = context.portfolio.available_cash
        #print(Cash)
    else:
        Cash = context.portfolio.available_cash/len(buy_list)
        for stock in buy_list:
            #调整股票仓位到10000元价值
            #order_target_value(stock, 30000)   
             # 买入这么多现金的股票
            order_value(stock, 5000)
            print('buy')