import numpy as np
import pandas as pd
import datetime
from jqdata import *
from jqlib.technical_analysis import *

# 记录成交历史
trade_history = {}
# 指定股票池，这里默认选择了创业板
stocks_pool = get_index_stocks('399006.XSHE')

# 获取ma_min值
def get_ma(stock, ma_value, end_dt):
    price = get_price(security=stock, 
                      end_date=end_dt, 
                      frequency='daily', 
                      fields=['close'], 
                      skip_paused=False, 
                      fq='pre', 
                      count=ma_value+10)['close']
    ma = price[-ma_value:].mean()
    return ma

# 获取ema值
def get_ema(stock, ma_value, end_dt):
    ema = EMA(stock, check_date=end_dt, timeperiod=ma_value)
    return ema[stock]


# 判断是否出现买入信息
def is_buy(stock, yesterday, before_yesterday, *ema):
    ma_min = ema[0]
    ma_med = ema[1]
    ma_max = ema[2]
    
    # 求出上一个交易日的ma_min，ma_med,ma_max的值
    y_ma_min_value = get_ema(stock, ma_min, yesterday)
    y_ma_med_value = get_ema(stock, ma_med, yesterday)
    y_ma_max_value = get_ema(stock, ma_max, yesterday)

    # 求出上上个交易日的ma_min，ma_med,ma_max的值
    by_ma_min_value = get_ema(stock, ma_min, before_yesterday)
    by_ma_med_value = get_ema(stock, ma_med, before_yesterday)
    by_ma_max_value = get_ema(stock, ma_max, before_yesterday)

    if (y_ma_min_value > y_ma_max_value) and (by_ma_min_value < by_ma_max_value) and (y_ma_med_value < y_ma_max_value):
        return True
    else:
        return False
    
    
# 判断是否有卖出信息
def is_sell(stock, yesterday, before_yesterday, *ema):
    ma_min = ema[0]
    ma_med = ema[1]
    ma_max = ema[2]
    
    # 求出上一个交易日的ma_min，ma_med,ma_max的值
    y_ma_min_value = get_ema(stock, ma_min, yesterday)
    y_ma_med_value = get_ema(stock, ma_med, yesterday)
    y_ma_max_value = get_ema(stock, ma_max, yesterday)

    # 求出上上个交易日的ma_min，ma_med,ma_max的值
    by_ma_min_value = get_ema(stock, ma_min, before_yesterday)
    by_ma_med_value = get_ema(stock, ma_med, before_yesterday)
    by_ma_max_value = get_ema(stock, ma_max, before_yesterday)

    if (y_ma_min_value > y_ma_med_value) and (by_ma_min_value < by_ma_med_value) and (y_ma_med_value > y_ma_max_value):
        return True
    else:
        return False
    
    
# 判断是否有卖出信息
def is_loss(stock, yesterday, *ema):
    ma_min = ema[0]
    ma_med = ema[1]
    ma_max = ema[2]
    
    # 昨日收盘价
    close = get_price(security=stock, 
                          end_date=yesterday,
                          frequency='daily', 
                          fields=['open','close'], 
                          skip_paused=False, 
                          fq='pre', 
                          count=10)['close'][-1] 
    
    
    # 求出上一个交易日的ma_min，ma_med,ma_max的值
    y_ma_min_value = get_ema(stock, ma_min, yesterday)
    y_ma_med_value = get_ema(stock, ma_med, yesterday)
    y_ma_max_value = get_ema(stock, ma_max, yesterday)
    
    if (close < y_ma_med_value) and (y_ma_med_value < y_ma_max_value):
        return True
    else:
        return False

    
# 过滤掉有止影线和小实体阳线的时刻
def is_high_line(stock, end_dt):
    price = get_price(security=stock, 
                          end_date=end_dt, 
                          frequency='daily', 
                          fields=['open', 'close', 'high', 'low', 'volume', 'money'], 
                          skip_paused=False, 
                          fq='pre', 
                          count=1)
    open = price['open'][0]
    close = price['close'][0]
    high = price['high'][0]
    low = price['low'][0]

    o_c_ratio = (open-close)/close
    h_c_ratio = (high-close)/close

    if o_c_ratio > 0 and h_c_ratio < 0.02:
        return True
    else:
        return False

def trade(stock,ma_min,ma_med,ma_max,trade_days):
    # 记录盈利
    wine_loss_history = []
    # 持仓
    hold_list = {}
    # 权重
    weight = 0
    # 总权重
    last_weihgt = len(trade_days)
    
    # 因为回测的是历史
    for trade_day in trade_days:
        # 权重累加
        weight += 1
        # 将要被使用的日期集合
        the_days = get_trade_days(end_date=trade_day, count=5)
        # 回测当天
        today = the_days[-1]
        # 上一个交易日
        yesterday = the_days[-2]
        # 上上个交易日
        before_yesterday = the_days[-4]

        # ========================卖出操作========================
        sell_list = []
        for stock,info in hold_list.items():
            trade_day = info[0]
            buy_price = info[1]

            # 判断是否有卖出信号 
            re_value = is_sell(stock, yesterday, before_yesterday, ma_min,ma_med,ma_max)
            # 判断是否触发止损信号
            loss = is_loss(stock, yesterday, ma_min,ma_med,ma_max)
            
            # 进行卖出操作
            if re_value or loss:
                sell_list.append(stock)
                price = get_price(security=stock, 
                          end_date=today,  # 现实中成交按当天的开盘价交易
                          frequency='daily', 
                          fields=['open','close'], 
                          skip_paused=False, 
                          fq='pre', 
                          count=10)['open'][-1] 
                
                # 进行记录
                trade_dic = {'stock':stock,
                             'buy_date':trade_day,
                             'buy_price':buy_price,
                             'sell_date':today,
                             'sell_price':price,
                             'ratio':(price-buy_price)/buy_price,
                             'MA':(ma_min,ma_med,ma_max),
                             'weight':weight,
                             'count':last_weihgt}
                wine_loss_history.append(trade_dic)
        
        # 从持仓中删除已经卖出的股票
        for stock in sell_list:
            del hold_list[stock]
        # ========================卖出操作========================

            
        # ========================买入操作========================
        # 判断是否有买入信号
        re_value = is_buy(stock, yesterday, before_yesterday, ma_min,ma_med,ma_max)
        # 如果有买入信号，则买入
        if re_value:
            # 在今天的开盘时买入，参考的买入价格是昨天的收盘价
            price = get_price(security=stock, 
                          end_date=today, # 现实中按当天的开盘价交易 
                          frequency='daily', 
                          fields=['open','close'], 
                          skip_paused=False, 
                          fq='pre', 
                          count=10)['open'][-1]
            hold_list[stock] = [today, price]
        # ========================买入操作========================
    
    #========================将最后一次未卖出的也记录==========================
    if stock in hold_list.keys():
        sell_list.append(stock)
        price = get_price(security=stock, 
                  end_date=today,  # 现实中成交按当天的开盘价交易
                  frequency='daily', 
                  fields=['open','close'], 
                  skip_paused=False, 
                  fq='pre', 
                  count=10)['open'][-1] 

        # 进行记录
        trade_dic = {'stock':stock,
                     'buy_date':trade_day,
                     'buy_price':buy_price,
                     'sell_date':today,
                     'sell_price':price,
                     'ratio':(price-buy_price)/buy_price,
                     'MA':(ma_min,ma_med,ma_max),
                     'weight':weight,
                     'count':last_weihgt}
        wine_loss_history.append(trade_dic)
    #========================将最后一次未卖出的也记录==========================
        
    # 返回交易记录
    return wine_loss_history

# 获利近三年的交易日期
days = get_trade_days(end_date=datetime.datetime.now(), count=250*2+100)
train_days = days[:-100]
trade_days = days[-100:]

s_time = datetime.datetime.now()
# 回测股票
trade_stock = '600600.XSHG'
# 保存回测记录
all_list = []

# 三进兵的三个值集合
ma_min1 = [3, 5, 7]
ma_med1 = [10, 20, 30]
ma_max1 = [40, 50, 60]

# 回测不同的三进兵组合 
for ma1 in ma_min1:
    for ma2 in ma_med1:
        for ma3 in ma_max1:
            re_dic = trade(trade_stock,ma1,ma2,ma3,train_days)
            all_list = all_list + re_dic

# 输出各三进兵组合的值
df = pd.DataFrame(all_list,columns=['stock', 
                                    'buy_date', 
                                    'buy_price', 
                                    'sell_date', 
                                    'sell_price', 
                                    'ratio', 
                                    'MA', 
                                    'weight', 
                                    'count'])
e_time = datetime.datetime.now()
print(e_time-s_time)
df

group = df.groupby(by=['MA'])

print('组合收益之和')
max_sum = group.sum()
max_sum = max_sum.sort_values(by=['ratio'],ascending=False).loc[:,['ratio']].head()
value = {'stock':trade_stock, 'MA':max_sum.index[0]}
print(value)
max_sum

print('各组合总收益对比图')
import matplotlib.pylab as plt
max_sum.T.plot(kind='bar')
plt.show()

print('组合收益标准差')
min_std = group.std()
min_std = min_std.loc[max_sum.index,['ratio']]
min_std = min_std.sort_values(by=['ratio'])
value = {'stock':trade_stock, 'MA':min_std.index[0]}
print(value)
min_std

print('各组合收益拆线图')
for name in max_sum.index:
    table = df[df['MA']==name][['ratio']]
    x = range(len(table.index))
    plt.plot(x,table)
plt.legend(max_sum.index)
plt.show()

print('各组合收益对比图')
series = []
for name in max_sum.index:
    table = df[df['MA']==name]['ratio'].values
    series.append(table) 

bar_df = pd.DataFrame(series,max_sum.index).T
bar_df.plot(kind='bar')
plt.show()

print('交易次数表')
bar_df.T

print('求加权后的表现-平均到每一次交易')
dic = []

for name, g in group:
    weight_df = df[df['MA']==name]
    w = weight_df['weight']
    r = weight_df['ratio']
    c = weight_df['weight'].sum()
    v = w*r/c
    dic.append({'ma':name, 'ratio':sum(v)})
    
w_df = pd.DataFrame(dic)
w_df = w_df.sort_values(by=['ratio'], ascending=False)
w_df.head()

print('求加权后的表现-平均到每天')
dic = []
count = int(df['count'].mean())
l = list(range(1,count))

for name, g in group:
    weight_df = df[df['MA']==name]
    w = weight_df['weight']
    r = weight_df['ratio']
    v = w*r/sum(l)
    dic.append({'ma':name, 'ratio':sum(v)})
    
w_avge_df = pd.DataFrame(dic)
w_avge_df = w_avge_df.sort_values(by=['ratio'], ascending=False)
w_avge_df.head()

result_dic = []

print('最大收益组合回测本年',max_sum.index[0])
ma1,ma2,ma3 = max_sum.index[0]
re_dic = trade(trade_stock,ma1,ma2,ma3,trade_days)
re_dic = pd.DataFrame(re_dic)
print('总盈利', re_dic.sum()['ratio'])
result_dic.append({'收益':re_dic.sum()['ratio']})
re_dic

print('最小标准差组合回测本年',min_std.index[0])
ma1,ma2,ma3 = min_std.index[0]
re_dic = trade(trade_stock,ma1,ma2,ma3,trade_days)
re_dic = pd.DataFrame(re_dic)
print('总盈利', re_dic.sum()['ratio'])
result_dic.append({'收益':re_dic.sum()['ratio']})
re_dic

print('最优加权组合回测本年',w_df['ma'].iloc[0])
ma1,ma2,ma3 = w_df['ma'].iloc[0]
re_dic = trade(trade_stock,ma1,ma2,ma3,trade_days)
re_dic = pd.DataFrame(re_dic)
print('总盈利', re_dic.sum()['ratio'])
result_dic.append({'收益':re_dic.sum()['ratio']})
re_dic

df = pd.DataFrame(result_dic,index=['最大收益组合'+str(max_sum.index[0]),
                                    '最小标准差组合'+str(min_std.index[0]),
                                    '最优加权组合'+str(w_df['ma'].iloc[0])])
df.T.plot(kind='bar')
plt.show()
df