def price_section(df, period: str):
"""
获取某个周期的boll上下轨的值 以及 收敛情况
:param df: 包含boll数据 的DateFrame
:param period: 自定义周期
:return: 返回boll上下轨的值 及 收敛情况
"""
last_df = df.iloc[-1, :]
boll_up = last_df['boll_up']
boll_low = last_df['boll_low']
boll_sd_df = df['boll_SD']
sd_mean = boll_sd_df[-10:].mean()
# print(sd_mean)
boll_sd = '正常'
if boll_sd_df.iloc[-1] > sd_mean:
boll_sd = '发散'
if boll_sd_df.iloc[-1] < sd_mean:
boll_sd = '收敛'
return {
f'{period}_boll_up': get_float(boll_up),
f'{period}_boll_low': get_float(boll_low),
f'{period}_boll_sd': boll_sd,
f'{period}_close': get_float(last_df['close']),
f'{period}_high': get_float(last_df['high']),
f'{period}_low': get_float(last_df['low']),
}
def trade_day(stock_code, asset='E'):
start_day = datetime.datetime.strptime(str(
datetime.date.today()), '%Y-%m-%d') + datetime.timedelta(days=-200)
start_date = start_day.date().strftime('%Y%m%d')
day_df = ts.pro_bar(ts_code=stock_code,
start_date=start_date,
asset=asset,
freq='D',
adj='qfq')
day_df.sort_values(by='trade_date', inplace=True)
my_BOLL(day_df)
my_MA(day_df)
my_MA(day_df, item='vol', timeperiod=(10, ))
my_swing(day_df)
my_MACD(day_df)
my_KDJ(day_df)
# day_df.to_csv('kdj.csv')
price_res = price_section(day_df, period='day')
trend_res = trend(day_df)
probab_res = probability(day_df)
deviation_res = deviation(day_df)
res_day = dict(**price_res, **probab_res, **trend_res, **deviation_res)
advice = give_advice(res_day)
# trend_res.get('ma_trend')
# trend_res.get('trend_go_on')
section = ''
if '买入' in advice:
section_low = get_float(float(price_res.get('day_boll_low')) * 0.98)
section_up = get_float(float(price_res.get('day_boll_low')) * 1.02)
section += f'交易区间：({section_low},{section_up})'
elif '卖出' in advice:
section_low = get_float(float(price_res.get('day_boll_up')) * 0.98)
section_up = get_float(float(price_res.get('day_boll_up')) * 1.02)
section += f'交易区间：({section_low},{section_up})'
else:
pass
html_day = f"""
<div>
<div style="color: magenta;height: 10px"><b>日参考：</b></div>
<ul>
<li>趋势：<b style="color: blue">{' ++ '.join(trend_res.values())}</b></li>
<li>背离：<b style="color: red">{' ++ '.join(deviation_res.values())}</b></li>
<li>KDJ：【{probab_res.get('buy_sell')}】 快值K:{probab_res.get('value').get('kdj_k')} 慢值D:{probab_res.get('value').get('kdj_D')}</li>
<li>BOLL：<b>{price_res.get('day_boll_sd')}</b> 区间：【{price_res.get('day_boll_low')},{price_res.get('day_boll_up')}】</li>
<li>交易参考：<b style="color: blue">{advice}</b> {section}</li>
</ul>
</div>
"""
# return report_body
return html_day
def fromMillimeters(self, value):
"""Scale a value from an equivalent distance in millimeters.
fromMillimeters(value)
"""
_v = util.get_float(value)
if self.__unit == Unit.MILLIMETERS:
_sv = _v
elif self.__unit == Unit.MICROMETERS:
_sv = _v * 1e3
elif self.__unit == Unit.METERS:
_sv = _v * 1e-3
elif self.__unit == Unit.KILOMETERS:
_sv = _v * 1e-6
elif self.__unit == Unit.INCHES:
_sv = _v / 25.4
elif self.__unit == Unit.FEET:
_sv = _v / 304.8
elif self.__unit == Unit.YARDS:
_sv = _v / 914.4
elif self.__unit == Unit.MILES:
_sv = _v / 1609344.4
else:
raise ValueError, "Undefined unit value! " + str(self.__unit)
return _sv
def fromMillimeters(self, value):
"""Scale a value from an equivalent distance in millimeters.
fromMillimeters(value)
"""
_v = util.get_float(value)
if self.__unit == Unit.MILLIMETERS:
_sv = _v
elif self.__unit == Unit.MICROMETERS:
_sv = _v * 1e3
elif self.__unit == Unit.METERS:
_sv = _v * 1e-3
elif self.__unit == Unit.KILOMETERS:
_sv = _v * 1e-6
elif self.__unit == Unit.INCHES:
_sv = _v / 25.4
elif self.__unit == Unit.FEET:
_sv = _v / 304.8
elif self.__unit == Unit.YARDS:
_sv = _v / 914.4
elif self.__unit == Unit.MILES:
_sv = _v / 1609344.4
else:
raise ValueError, "Undefined unit value! " + str(self.__unit)
return _sv
def probability(df):
# 1. kdj指标 K、D的值与20 80 比较 给出超买 超卖结论
last_kdj = df.iloc[-1, :]
k_value = last_kdj['kdj_K']
d_value = last_kdj['kdj_D']
buy_sell = '合理'
if d_value > 80 or k_value > 80:
buy_sell = '超买'
if d_value < 20 or k_value < 20:
buy_sell = '超卖'
res = {
'buy_sell': buy_sell,
'value': {
'kdj_k': get_float(k_value, 2),
'kdj_D': get_float(d_value, 2)
}
}
return res
def get_market():
#util.clean_market(0)
url = 'http://pregao-online.bmfbovespa.com.br/Cotacoes.aspx'
soup = BeautifulSoup(urlfetch.fetch(url, deadline=50).content, 'lxml')
rate = util.get_exchange()
#dt = get_datetime()
dt = datetime.datetime.now(tz.tzstr('EBST3EBDT'))
market = Market(ref=0, date=dt.date(), time=dt.time(), exchange_rate=rate)
market.put()
table = soup('table', attrs={'id': 'ctl00_DefaultContent_GrdCarteiraIndice'})[0]
for tr in table('tr')[1:]:
tds = tr('td')
code = str(tds[0].string)
name = util.clean_string(tds[1].string)
value = util.get_float(tds[2].string)
diff = util.get_float(tds[3].text.strip())
stock = Stock(name=util.get_or_create_name(0, code, name),
value=value, diff=diff, market=market.key())
stock.put()
def week_price_section(df_month, df_week):
"""
根据boll来判断价格区间
:param df_month:
:param df_week:
:return:
"""
# 1. 月线boll线上下轨
last_month_df = df_month.iloc[-1, :]
second_month_df = df_month.iloc[-2, :]
next_month_boll_up = last_month_df['boll_up'] * 2 - second_month_df[
'boll_up']
next_month_boll_low = last_month_df['boll_low'] * 2 - second_month_df[
'boll_low']
# 2. 周线boll线上下轨 标准差收敛情况
week_boll_up_df = df_week['boll_up']
week_boll_low_df = df_week['boll_low']
week_boll_mid_df = df_week['boll_mid']
# 计算收敛情况
week_boll_sd_df = df_week['boll_SD']
sd_mean = week_boll_sd_df[-10:].mean()
# print(sd_mean)
week_boll_sd = '正常'
if week_boll_sd_df.iloc[-1] > sd_mean:
week_boll_sd = '发散'
if week_boll_sd_df.iloc[-1] < sd_mean:
week_boll_sd = '收敛'
# 计算下一周的boll上下轨价格
next_week_up = week_boll_up_df.iloc[-1] * 2 - week_boll_up_df.iloc[-2]
next_week_low = week_boll_low_df.iloc[-1] * 2 - week_boll_low_df.iloc[-2]
next_week_mid = week_boll_mid_df.iloc[-1] * 2 - week_boll_mid_df.iloc[-2]
# 3. 计算周线boll线上轨附近卖出价格区间 下轨附近买入区间
last_week_df = df_week.iloc[-1, :]
last_close = last_week_df['close']
risk_rate_income, section = 0, 0
# 进入观察区的价格浮动比率
look_k = 0.05
# 买入价格浮动比率
k = 0.025
# 回报风险比
income_rate_risk = 0
# boll线，上轨附近，卖出 给出价格区间
# boll线上轨附近
if abs(last_close - next_week_up) < next_week_mid * look_k:
section = (get_float(next_week_up - k * week_boll_mid_df.iloc[-1]),
get_float(next_week_up + k * week_boll_mid_df.iloc[-1]))
# 卖出 收益风险比很小
income_rate_risk = 0.01
# boll线下轨 买入 给出买入区间
if abs(last_close - next_week_low) < week_boll_mid_df.iloc[-1] * look_k:
section = (get_float(next_week_low - k * week_boll_mid_df.iloc[-1]),
get_float(next_week_low + k * week_boll_mid_df.iloc[-1]))
try:
income_rate_risk = (next_week_mid -
last_close) / (last_close - next_week_low) - 1
except:
income_rate_risk = 6
if income_rate_risk > 5 or -2 < income_rate_risk < -1:
income_rate_risk = 5
res = {
# 本月boll上轨
'month_boll_up': get_float(next_month_boll_up),
'month_boll_low': get_float(next_month_boll_low),
# 下周boll
'next_week_up': get_float(next_week_up),
'next_week_mid': get_float(next_week_mid),
'next_week_low': get_float(next_week_low),
# 'last_week_high':last_week_df['high'],
# 'last_week_low':last_week_df['low'],
'week_boll_sd': week_boll_sd,
# 风险回报比
'income_risk': get_float(income_rate_risk),
# 价格参考区间
'section': section,
}
return res