def sell_real_time(self, data):
"""
# 各种情况。。。。。卖出要排序
# 计算延时时间，如果可以获取当前卖二价，则挂单卖，如果有挂最新卖价按钮，再好不过
:return: DataFrame({'price':,'num':})。。。。
"""
data_sell0 = pd.DataFrame([])
# 涨停止盈,9%以上卖出什么的10%
if float(data.price[0]) >= self.df.close[0] * 1.09:
price = float(data.a3_p[0])
df = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
num = self.obj_5.num_buy_process(coe=1, data=df)
data_sell0 = pd.DataFrame({'price': price, 'num': num}, index=[0])
# 长上影线止盈,卖3价，1/10
data_sell1 = pd.DataFrame([])
close_ave5 = create_data.ave_data_create(self.df.close, 5)
open_ave5 = create_data.ave_data_create(self.df.open, 5)
high_ave5 = create_data.ave_data_create(self.df.high, 5)
low_ave5 = create_data.ave_data_create(self.df.low, 5)
if ((float(data.high[0]) - max(float(data.open[0]), float(data.price[0]))) / (float(data.high[0]) - float(data.low[0])) >= 0.5) or \
((high_ave5[0] - max(open_ave5[0], close_ave5[0])) / (high_ave5[0] - low_ave5[0]) >= 0.5):
if float(data.volume[0]) > self.df.volume[0:5].mean():
price = float(data.a3_p[0])
df = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
num = self.obj_5.num_buy_process(coe=1, data=df)
data_sell1 = pd.DataFrame({'price': price, 'num': num}, index=[0])
# 跳空止盈,卖3价，1/10
data_sell2 = pd.DataFrame([])
if float(data.low[0]) > self.ave_price_5[0]:
price = float(data.a3_p[0])
df = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
num = self.obj_5.num_buy_process(coe=1, data=df)
data_sell2 = pd.DataFrame({'price': price, 'num': num}, index=[0])
data_sell_ = pd.concat([data_sell0, data_sell1, data_sell2])
# 因为目前买价和买量都是一样的，所以不需要分类
# 可能会有一个小bug，当一次性全部卖完的时候，也就是说，当vs的情况发生一个事件以上的时候，存量比卖出的数量要少。。。。
if data_sell_.empty is False:
num = data_sell_.num.sum()
price = data_sell_.price.drop_duplicates()[0]
data_sell_ = pd.DataFrame({'price': price, 'num': num}, index=[0])
return data_sell_
def __init__(self,
stock_id,
date=time.strftime('%Y%m%d', time.localtime(time.time()))):
self.date = date
self.stock_id = stock_id
self.df = data_process.Get_DataFrame(self.stock_id, self.date)
if len(self.df.high) >= 196:
dif = max(self.df.high[:196]) - min(self.df.low[:196])
# 根据196个交易日内，长宽比等于价格差与天数乘以系数（ef）比，为了计算自变量，来替换时间序列，好得出想要的角度
# 切记，要个股196至200天的最高与最低的数据
ef = round((36 / 12.5) * dif / 196, 4)
else:
# 如果数据不够的情况，为使角度变大
ef = 0.0000001
self.ef = ef
# 不考虑数据不够的情况，比较少
self.ave_price_60 = create_data.ave_data_create(self.df.close, 60)
self.ave_price_5 = create_data.ave_data_create(self.df.close, 5)
def angle_data(self, ave=60, stop_dot=50):
"""
:return: DataFrame (for create line_model below,60 for angle enough)
"""
date = []
for i in range(1, stop_dot + 1):
date.append(self.ef * i)
ave_price = create_data.ave_data_create(self.df.close, ave)[:stop_dot]
ave_price.reverse()
data = pd.DataFrame({'date': date, 'price': np.array(ave_price)})
# df = pd.DataFrame({'square_feet':[150,200,250,300,350,400,600],'price':[150,200,250,300,350,400,600]})
return data
def candle_k(data):
"""
一定要更改均线值，不然均线会错误
存在的问题：1 x轴日期包含非交易日和停牌日，如果遇到节假日，或者分钟级数据遇到收市,时间就不会连续；
2 图为静态图；
3 不能交互
4 必须限定x轴日期范围
:param data: 某只股票的数据集，不包括停牌数据
:return: K线和成交量图
"""
# drop the date index from the dateframe & make a copy
datareshape = data.reset_index()
# convert the datetime64 column in the dataframe to 'float days'
datareshape['date'] = mdates.date2num(datareshape['date'].astype(dt.date))
# clean day data for candle view
datareshape.drop(['volume', 'amount', 'factor', 'code'],
axis=1,
inplace=True)
# 填缺失值的行为，是停牌缺失还是列缺失
datareshape = datareshape.reindex(
columns=['date', 'open', 'high', 'low', 'close'])
# 考虑不满足210天的股票
# av1,av2长度为150
av1 = create_data.ave_data_create(datareshape.close.values,
MA1,
date=len(datareshape))
# av1.reverse()
av2 = create_data.ave_data_create(datareshape.close.values,
MA2,
date=len(datareshape))
# av2.reverse()
sp = len(datareshape.date.values[MA2 - 1:])
fig = plt.figure(facecolor='#07000d', figsize=(15, 10))
ax1 = plt.subplot2grid((6, 4), (1, 0),
rowspan=4,
colspan=4,
axisbg='#07000d')
candlestick_ohlc(ax1,
datareshape.values[-sp:],
width=.6,
colorup='#ff1717',
colordown='#53c156')
Label1 = str(MA1) + ' SMA'
Label2 = str(MA2) + ' SMA'
ax1.plot(datareshape.date.values[-sp:],
av1[-sp:],
'#e1edf9',
label=Label1,
linewidth=1.5)
ax1.plot(datareshape.date.values[-sp:],
av2[-sp:],
'#4ee6fd',
label=Label2,
linewidth=1.5)
ax1.grid(True, color='w')
ax1.xaxis.set_major_locator(mticker.MaxNLocator(10))
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
ax1.yaxis.label.set_color("w")
ax1.spines['bottom'].set_color("#5998ff")
ax1.spines['top'].set_color("#5998ff")
ax1.spines['left'].set_color("#5998ff")
ax1.spines['right'].set_color("#5998ff")
ax1.tick_params(axis='y', colors='w')
plt.gca().yaxis.set_major_locator(mticker.MaxNLocator(prune='upper'))
ax1.tick_params(axis='x', colors='w')
plt.ylabel('Stock price and Volume')
# 绘制成交量
volume_min = 0
ax1v = ax1.twinx()
ax1v.fill_between(datareshape.date.values[-sp:],
volume_min,
data.volume.values[-sp:],
facecolor='#00ffe8',
alpha=.4)
ax1v.axes.yaxis.set_ticklabels([])
ax1v.grid(False)
# Edit this to 3, so it's a bit larger
ax1v.set_ylim(0, 3 * data.volume.values.max())
ax1v.spines['bottom'].set_color("#5998ff")
ax1v.spines['top'].set_color("#5998ff")
ax1v.spines['left'].set_color("#5998ff")
ax1v.spines['right'].set_color("#5998ff")
ax1v.tick_params(axis='x', colors='w')
ax1v.tick_params(axis='y', colors='w')
# 修饰 添加股票代码以及指定位置添加文字与箭头
plt.suptitle(stock, color='w')
# plt.setp(ax0.get_xticklabels(), visible=False)
plt.setp(ax1.get_xticklabels(), visible=False)
# Mark big event
# TODO: Make a real case here
ax1.annotate('BreakNews!', (datareshape.date.values[90], av1[90]),
xytext=(0.8, 0.9),
textcoords='axes fraction',
arrowprops=dict(facecolor='white', shrink=0.05),
fontsize=10,
color='w',
horizontalalignment='right',
verticalalignment='bottom')
plt.subplots_adjust(left=.09,
bottom=.14,
right=.94,
top=.95,
wspace=.20,
hspace=0)
plt.show()
def selling_3(self, first_sell_up, second_sell_up, third_sell_up,
first_sell_down):
global running_box
# 支撑线压力线，波谷波峰数据集，变异系数，支撑点压力点，以及原因的获取
# df_s, cv_s, price_s = self.support_line()
e_support, reason_s = self.support_dot()
# df_p, cv_p, price_p = self.pressure_line()
e_pressure, reason_p = self.pressure_dot()
obj1 = Deal(self.stock_id)
price_buy = 0
price_sell = 0
cash = 0
num = 0
# 涨停止盈
if self.df.low[0] <= self.df.close[1] * 1.09 <= self.df.high[0]:
price_buy = 0
price_sell = self.df.close[1] * 1.09
num0 = list(running_box.loc[running_box.stock_id == self.stock_id,
'bought_num'])[0] / 5
num = math.floor(num0 * 100) * 100
commission_buy, commission_sell = obj1.order_cost(
num,
price_sell,
close_tax=0.001,
open_commission=0.0003,
close_commission=0.0003,
min_commission=5)
cash = num * price_sell - commission_sell
if num >= 200:
print 'situation3_5'
return price_buy, price_sell, cash, num
else:
num = 200
commission_buy, commission_sell = obj1.order_cost(
num,
price_sell,
close_tax=0.001,
open_commission=0.0003,
close_commission=0.0003,
min_commission=5)
cash = num * price_sell - commission_sell
print 'situation3_6'
return price_buy, price_sell, cash, num
# 长上影线止盈
close_ave5 = create_data.ave_data_create(self.df.close, 5)
open_ave5 = create_data.ave_data_create(self.df.open, 5)
high_ave5 = create_data.ave_data_create(self.df.high, 5)
low_ave5 = create_data.ave_data_create(self.df.low, 5)
if ((self.df.high[0] - max(self.df.close[0], self.df.open[0])) / (self.df.high[0] - self.df.low[0]) >= 0.5) or \
((high_ave5[0] - max(open_ave5[0], close_ave5[0])) / (high_ave5[0] - low_ave5[0]) >= 0.5):
if self.df.volume[0] > self.df.volums[0:5].mean():
price_buy = 0
price_sell = self.df.close[0]
num0 = list(
running_box.loc[running_box.stock_id == self.stock_id,
'bought_num'])[0] / 5
num = math.floor(num0 * 100) * 100
commission_buy, commission_sell = obj1.order_cost(
num,
price_sell,
close_tax=0.001,
open_commission=0.0003,
close_commission=0.0003,
min_commission=5)
cash = num * price_sell - commission_sell
if num >= 200:
print 'situation3_7'
return price_buy, price_sell, cash, num
else:
num = 200
commission_buy, commission_sell = obj1.order_cost(
num,
price_sell,
close_tax=0.001,
open_commission=0.0003,
close_commission=0.0003,
min_commission=5)
cash = num * price_sell - commission_sell
print 'situation3_8'
return price_buy, price_sell, cash, num
if self.df.high[0] < self.ave_price_60[0]:
price_sell = min(e_support, first_sell_down) * 0.99
# 支撑位止损
if self.df.close[0] <= price_sell:
num = list(
running_box.loc[running_box.stock_id == self.stock_id,
'bought_num'])[0]
commission_buy, commission_sell = obj1.order_cost(
num,
price_sell,
close_tax=0.001,
open_commission=0.0003,
close_commission=0.0003,
min_commission=5)
cash = num * price_sell - commission_sell
print 'situation3_9'
return price_buy, price_sell, cash, num
else:
# 跳空止盈
if self.df.low[0] > self.ave_price_5[0]:
price_buy = 0
price_sell = self.df.close[0]
num0 = list(
running_box.loc[running_box.stock_id == self.stock_id,
'bought_num'])[0] / 5
num = math.floor(num0 * 100) * 100
commission_buy, commission_sell = obj1.order_cost(
num,
price_sell,
close_tax=0.001,
open_commission=0.0003,
close_commission=0.0003,
min_commission=5)
cash = num * price_sell - commission_sell
if num >= 200:
print 'situation3_10'
return price_buy, price_sell, cash, num
else:
num = 200
commission_buy, commission_sell = obj1.order_cost(
num,
price_sell,
close_tax=0.001,
open_commission=0.0003,
close_commission=0.0003,
min_commission=5)
cash = num * price_sell - commission_sell
print 'situation3_11'
return price_buy, price_sell, cash, num
# 超过压力位止盈
# 对于cv_p没做限定，如果存在负角度的压力线，是否会低于60日线,已改pressure角度0到30
elif self.df.low[0] <= first_sell_up <= self.df.high[0]:
price_buy = 0
price_sell = self.df.close[0]
num0 = list(
running_box.loc[running_box.stock_id == self.stock_id,
'bought_num'])[0] / 5
num = math.floor(num0 * 100) * 100
commission_buy, commission_sell = obj1.order_cost(
num,
price_sell,
close_tax=0.001,
open_commission=0.0003,
close_commission=0.0003,
min_commission=5)
cash = num * price_sell - commission_sell
if num >= 200:
print 'situation3_12'
return price_buy, price_sell, cash, num
else:
num = 200
commission_buy, commission_sell = obj1.order_cost(
num,
price_sell,
close_tax=0.001,
open_commission=0.0003,
close_commission=0.0003,
min_commission=5)
cash = num * price_sell - commission_sell
print 'situation3_13'
return price_buy, price_sell, cash, num
elif self.df.low[0] <= second_sell_up <= self.df.high[0]:
price_buy = 0
price_sell = self.df.close[0]
num0 = list(
running_box.loc[running_box.stock_id == self.stock_id,
'bought_num'])[0] / 5
num = math.floor(num0 * 100) * 100
commission_buy, commission_sell = obj1.order_cost(
num,
price_sell,
close_tax=0.001,
open_commission=0.0003,
close_commission=0.0003,
min_commission=5)
cash = num * price_sell - commission_sell
if num >= 200:
print 'situation3_14'
return price_buy, price_sell, cash, num
else:
num = 200
commission_buy, commission_sell = obj1.order_cost(
num,
price_sell,
close_tax=0.001,
open_commission=0.0003,
close_commission=0.0003,
min_commission=5)
cash = num * price_sell - commission_sell
print 'situation3_15'
return price_buy, price_sell, cash, num
elif self.df.low[0] <= third_sell_up <= self.df.high[0]:
price_buy = 0
price_sell = self.df.close[0]
num0 = list(
running_box.loc[running_box.stock_id == self.stock_id,
'bought_num'])[0] / 5
num = math.floor(num0 * 100) * 100
commission_buy, commission_sell = obj1.order_cost(
num,
price_sell,
close_tax=0.001,
open_commission=0.0003,
close_commission=0.0003,
min_commission=5)
cash = num * price_sell - commission_sell
if num >= 200:
print 'situation3_16'
return price_buy, price_sell, cash, num
else:
num = 200
commission_buy, commission_sell = obj1.order_cost(
num,
price_sell,
close_tax=0.001,
open_commission=0.0003,
close_commission=0.0003,
min_commission=5)
cash = num * price_sell - commission_sell
print 'situation3_17'
return price_buy, price_sell, cash, num
return price_buy, price_sell, cash, num
overlap.add(line)
if render_path:
overlap.render(render_path)
return overlap # .render('k-line.html')
if __name__ == '__main__':
# 数据导入
name = '300317'
period = '1'
if period.isdigit():
period += 'day'
data_raw = data_process.Get_DataFrame(name, '20171210')
data_raw.sort_index(inplace=True)
ma5 = create_data.ave_data_create(data_raw.close.values, 5)[:150]
ma5.reverse()
ma60 = create_data.ave_data_create(data_raw.close.values, 60)[:150]
ma60.reverse()
# 均线维度必须一致，data不能有空值
data_new = pd.DataFrame(data_raw,
columns=['open', 'high', 'low', 'close', 'volume'])
open = list(data_new.open)[:150]
open.reverse()
close = list(data_new.close)[:150]
close.reverse()
high = list(data_new.high)[:150]
high.reverse()
low = list(data_new.low)[:150]
low.reverse()
volume = list(data_new.volume)[:150]