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def market_value(self):
"""每日每个股票持仓市值表
Returns:
pd.DataFrame -- 市值表
"""
if self.account.daily_hold is not None:
if self.if_fq:
return (
self.market_data.to_qfq().pivot('close').fillna(
method='ffill'
... |
def max_dropback(self):
"""最大回撤
"""
return round(
float(
max(
[
(self.assets.iloc[idx] - self.assets.iloc[idx::].min())
/ self.assets.iloc[idx]
for idx in range(len(self.assets... |
def total_commission(self):
"""总手续费
"""
return float(
-abs(round(self.account.history_table.commission.sum(),
2))
) |
def total_tax(self):
"""总印花税
"""
return float(-abs(round(self.account.history_table.tax.sum(), 2))) |
def profit_construct(self):
"""利润构成
Returns:
dict -- 利润构成表
"""
return {
'total_buyandsell':
round(
self.profit_money - self.total_commission - self.total_tax,
2
),
'total_tax':
self.... |
def profit_money(self):
"""盈利额
Returns:
[type] -- [description]
"""
return float(round(self.assets.iloc[-1] - self.assets.iloc[0], 2)) |
def annualize_return(self):
"""年化收益
Returns:
[type] -- [description]
"""
return round(
float(self.calc_annualize_return(self.assets,
self.time_gap)),
2
) |
def benchmark_data(self):
"""
基准组合的行情数据(一般是组合,可以调整)
"""
return self.fetch[self.benchmark_type](
self.benchmark_code,
self.account.start_date,
self.account.end_date
) |
def benchmark_assets(self):
"""
基准组合的账户资产队列
"""
return (
self.benchmark_data.close /
float(self.benchmark_data.close.iloc[0])
* float(self.assets[0])
) |
def benchmark_annualize_return(self):
"""基准组合的年化收益
Returns:
[type] -- [description]
"""
return round(
float(
self.calc_annualize_return(
self.benchmark_assets,
self.time_gap
)
),... |
def beta(self):
"""
beta比率 组合的系统性风险
"""
try:
res = round(
float(
self.calc_beta(
self.profit_pct.dropna(),
self.benchmark_profitpct.dropna()
)
),
... |
def alpha(self):
"""
alpha比率 与市场基准收益无关的超额收益率
"""
return round(
float(
self.calc_alpha(
self.annualize_return,
self.benchmark_annualize_return,
self.beta,
0.05
)
... |
def sharpe(self):
"""
夏普比率
"""
return round(
float(
self.calc_sharpe(self.annualize_return,
self.volatility,
0.05)
),
2
) |
def plot_assets_curve(self, length=14, height=12):
"""
资金曲线叠加图
@Roy T.Burns 2018/05/29 修改百分比显示错误
"""
plt.style.use('ggplot')
plt.figure(figsize=(length, height))
plt.subplot(211)
plt.title('BASIC INFO', fontsize=12)
plt.axis([0, length, 0, 0.6])
... |
def plot_signal(self, start=None, end=None):
"""
使用热力图画出买卖信号
"""
start = self.account.start_date if start is None else start
end = self.account.end_date if end is None else end
_, ax = plt.subplots(figsize=(20, 18))
sns.heatmap(
self.account.trade.rese... |
def pnl_lifo(self):
"""
使用后进先出法配对成交记录
"""
X = dict(
zip(
self.target.code,
[LifoQueue() for i in range(len(self.target.code))]
)
)
pair_table = []
for _, data in self.target.history_table_min.iterrows():
... |
def plot_pnlratio(self):
"""
画出pnl比率散点图
"""
plt.scatter(x=self.pnl.sell_date.apply(str), y=self.pnl.pnl_ratio)
plt.gcf().autofmt_xdate()
return plt |
def plot_pnlmoney(self):
"""
画出pnl盈亏额散点图
"""
plt.scatter(x=self.pnl.sell_date.apply(str), y=self.pnl.pnl_money)
plt.gcf().autofmt_xdate()
return plt |
def win_rate(self):
"""胜率
胜率
盈利次数/总次数
"""
data = self.pnl
try:
return round(len(data.query('pnl_money>0')) / len(data), 2)
except ZeroDivisionError:
return 0 |
def next_time(self, asc=False):
"""Get the local time of the next schedule time this job will run.
:param bool asc: Format the result with ``time.asctime()``
:returns: The epoch time or string representation of the epoch time that
the job should be run next
"""
_time ... |
def QA_fetch_get_future_transaction_realtime(package, code):
"""
期货实时tick
"""
Engine = use(package)
if package in ['tdx', 'pytdx']:
return Engine.QA_fetch_get_future_transaction_realtime(code)
else:
return 'Unsupport packages' |
def QA_indicator_MA(DataFrame,*args,**kwargs):
"""MA
Arguments:
DataFrame {[type]} -- [description]
Returns:
[type] -- [description]
"""
CLOSE = DataFrame['close']
return pd.DataFrame({'MA{}'.format(N): MA(CLOSE, N) for N in list(args)}) |
def QA_indicator_MACD(DataFrame, short=12, long=26, mid=9):
"""
MACD CALC
"""
CLOSE = DataFrame['close']
DIF = EMA(CLOSE, short)-EMA(CLOSE, long)
DEA = EMA(DIF, mid)
MACD = (DIF-DEA)*2
return pd.DataFrame({'DIF': DIF, 'DEA': DEA, 'MACD': MACD}) |
def QA_indicator_DMI(DataFrame, M1=14, M2=6):
"""
趋向指标 DMI
"""
HIGH = DataFrame.high
LOW = DataFrame.low
CLOSE = DataFrame.close
OPEN = DataFrame.open
TR = SUM(MAX(MAX(HIGH-LOW, ABS(HIGH-REF(CLOSE, 1))),
ABS(LOW-REF(CLOSE, 1))), M1)
HD = HIGH-REF(HIGH, 1)
LD = R... |
def QA_indicator_PBX(DataFrame, N1=3, N2=5, N3=8, N4=13, N5=18, N6=24):
'瀑布线'
C = DataFrame['close']
PBX1 = (EMA(C, N1) + EMA(C, 2 * N1) + EMA(C, 4 * N1)) / 3
PBX2 = (EMA(C, N2) + EMA(C, 2 * N2) + EMA(C, 4 * N2)) / 3
PBX3 = (EMA(C, N3) + EMA(C, 2 * N3) + EMA(C, 4 * N3)) / 3
PBX4 = (EMA(C, N4) + ... |
def QA_indicator_DMA(DataFrame, M1=10, M2=50, M3=10):
"""
平均线差 DMA
"""
CLOSE = DataFrame.close
DDD = MA(CLOSE, M1) - MA(CLOSE, M2)
AMA = MA(DDD, M3)
return pd.DataFrame({
'DDD': DDD, 'AMA': AMA
}) |
def QA_indicator_MTM(DataFrame, N=12, M=6):
'动量线'
C = DataFrame.close
mtm = C - REF(C, N)
MTMMA = MA(mtm, M)
DICT = {'MTM': mtm, 'MTMMA': MTMMA}
return pd.DataFrame(DICT) |
def QA_indicator_EXPMA(DataFrame, P1=5, P2=10, P3=20, P4=60):
""" 指数平均线 EXPMA"""
CLOSE = DataFrame.close
MA1 = EMA(CLOSE, P1)
MA2 = EMA(CLOSE, P2)
MA3 = EMA(CLOSE, P3)
MA4 = EMA(CLOSE, P4)
return pd.DataFrame({
'MA1': MA1, 'MA2': MA2, 'MA3': MA3, 'MA4': MA4
}) |
def QA_indicator_CHO(DataFrame, N1=10, N2=20, M=6):
"""
佳庆指标 CHO
"""
HIGH = DataFrame.high
LOW = DataFrame.low
CLOSE = DataFrame.close
VOL = DataFrame.volume
MID = SUM(VOL*(2*CLOSE-HIGH-LOW)/(HIGH+LOW), 0)
CHO = MA(MID, N1)-MA(MID, N2)
MACHO = MA(CHO, M)
return pd.DataFrame({... |
def QA_indicator_BIAS(DataFrame, N1, N2, N3):
'乖离率'
CLOSE = DataFrame['close']
BIAS1 = (CLOSE - MA(CLOSE, N1)) / MA(CLOSE, N1) * 100
BIAS2 = (CLOSE - MA(CLOSE, N2)) / MA(CLOSE, N2) * 100
BIAS3 = (CLOSE - MA(CLOSE, N3)) / MA(CLOSE, N3) * 100
DICT = {'BIAS1': BIAS1, 'BIAS2': BIAS2, 'BIAS3': BIAS3}... |
def QA_indicator_ROC(DataFrame, N=12, M=6):
'变动率指标'
C = DataFrame['close']
roc = 100 * (C - REF(C, N)) / REF(C, N)
ROCMA = MA(roc, M)
DICT = {'ROC': roc, 'ROCMA': ROCMA}
return pd.DataFrame(DICT) |
def QA_indicator_CCI(DataFrame, N=14):
"""
TYP:=(HIGH+LOW+CLOSE)/3;
CCI:(TYP-MA(TYP,N))/(0.015*AVEDEV(TYP,N));
"""
typ = (DataFrame['high'] + DataFrame['low'] + DataFrame['close']) / 3
cci = ((typ - MA(typ, N)) / (0.015 * AVEDEV(typ, N)))
a = 100
b = -100
return pd.DataFrame({
... |
def QA_indicator_WR(DataFrame, N, N1):
'威廉指标'
HIGH = DataFrame['high']
LOW = DataFrame['low']
CLOSE = DataFrame['close']
WR1 = 100 * (HHV(HIGH, N) - CLOSE) / (HHV(HIGH, N) - LLV(LOW, N))
WR2 = 100 * (HHV(HIGH, N1) - CLOSE) / (HHV(HIGH, N1) - LLV(LOW, N1))
DICT = {'WR1': WR1, 'WR2': WR2}
... |
def QA_indicator_OSC(DataFrame, N=20, M=6):
"""变动速率线
震荡量指标OSC，也叫变动速率线。属于超买超卖类指标,是从移动平均线原理派生出来的一种分析指标。
它反应当日收盘价与一段时间内平均收盘价的差离值,从而测出股价的震荡幅度。
按照移动平均线原理，根据OSC的值可推断价格的趋势，如果远离平均线，就很可能向平均线回归。
"""
C = DataFrame['close']
OS = (C - MA(C, N)) * 100
MAOSC = EMA(OS, M)
DICT = {'OSC': OS, 'MAOS... |
def QA_indicator_RSI(DataFrame, N1=12, N2=26, N3=9):
'相对强弱指标RSI1:SMA(MAX(CLOSE-LC,0),N1,1)/SMA(ABS(CLOSE-LC),N1,1)*100;'
CLOSE = DataFrame['close']
LC = REF(CLOSE, 1)
RSI1 = SMA(MAX(CLOSE - LC, 0), N1) / SMA(ABS(CLOSE - LC), N1) * 100
RSI2 = SMA(MAX(CLOSE - LC, 0), N2) / SMA(ABS(CLOSE - LC), N2) * 1... |
def QA_indicator_ADTM(DataFrame, N=23, M=8):
'动态买卖气指标'
HIGH = DataFrame.high
LOW = DataFrame.low
OPEN = DataFrame.open
DTM = IF(OPEN > REF(OPEN, 1), MAX((HIGH - OPEN), (OPEN - REF(OPEN, 1))), 0)
DBM = IF(OPEN < REF(OPEN, 1), MAX((OPEN - LOW), (OPEN - REF(OPEN, 1))), 0)
STM = SUM(DTM, N)
... |
def QA_indicator_ASI(DataFrame, M1=26, M2=10):
"""
LC=REF(CLOSE,1);
AA=ABS(HIGH-LC);
BB=ABS(LOW-LC);
CC=ABS(HIGH-REF(LOW,1));
DD=ABS(LC-REF(OPEN,1));
R=IF(AA>BB AND AA>CC,AA+BB/2+DD/4,IF(BB>CC AND BB>AA,BB+AA/2+DD/4,CC+DD/4));
X=(CLOSE-LC+(CLOSE-OPEN)/2+LC-REF(OPEN,1));
SI=16*X/R*MAX... |
def QA_indicator_OBV(DataFrame):
"""能量潮"""
VOL = DataFrame.volume
CLOSE = DataFrame.close
return pd.DataFrame({
'OBV': np.cumsum(IF(CLOSE > REF(CLOSE, 1), VOL, IF(CLOSE < REF(CLOSE, 1), -VOL, 0)))/10000
}) |
def QA_indicator_BOLL(DataFrame, N=20, P=2):
'布林线'
C = DataFrame['close']
boll = MA(C, N)
UB = boll + P * STD(C, N)
LB = boll - P * STD(C, N)
DICT = {'BOLL': boll, 'UB': UB, 'LB': LB}
return pd.DataFrame(DICT) |
def QA_indicator_MIKE(DataFrame, N=12):
"""
MIKE指标
指标说明
MIKE是另外一种形式的路径指标。
买卖原则
1 WEAK-S，MEDIUM-S，STRONG-S三条线代表初级、中级、强力支撑。
2 WEAK-R，MEDIUM-R，STRONG-R三条线代表初级、中级、强力压力。
"""
HIGH = DataFrame.high
LOW = DataFrame.low
CLOSE = DataFrame.close
TYP = (HIGH+LOW+CLOSE)/3
LL = ... |
def QA_indicator_BBI(DataFrame, N1=3, N2=6, N3=12, N4=24):
'多空指标'
C = DataFrame['close']
bbi = (MA(C, N1) + MA(C, N2) + MA(C, N3) + MA(C, N4)) / 4
DICT = {'BBI': bbi}
return pd.DataFrame(DICT) |
def QA_indicator_MFI(DataFrame, N=14):
"""
资金指标
TYP := (HIGH + LOW + CLOSE)/3;
V1:=SUM(IF(TYP>REF(TYP,1),TYP*VOL,0),N)/SUM(IF(TYP<REF(TYP,1),TYP*VOL,0),N);
MFI:100-(100/(1+V1));
赋值: (最高价 + 最低价 + 收盘价)/3
V1赋值:如果TYP>1日前的TYP,返回TYP*成交量(手),否则返回0的N日累和/如果TYP<1日前的TYP,返回TYP*成交量(手),否则返回0的N日累和
输出资金流... |
def QA_indicator_ATR(DataFrame, N=14):
"""
输出TR:(最高价-最低价)和昨收-最高价的绝对值的较大值和昨收-最低价的绝对值的较大值
输出真实波幅:TR的N日简单移动平均
算法：今日振幅、今日最高与昨收差价、今日最低与昨收差价中的最大值，为真实波幅，求真实波幅的N日移动平均
参数：N 天数，一般取14
"""
C = DataFrame['close']
H = DataFrame['high']
L = DataFrame['low']
TR = MAX(MAX((H - L), ABS(REF(C, 1)... |
def QA_indicator_SKDJ(DataFrame, N=9, M=3):
"""
1.指标>80 时，回档机率大；指标<20 时，反弹机率大；
2.K在20左右向上交叉D时，视为买进信号参考；
3.K在80左右向下交叉D时，视为卖出信号参考；
4.SKDJ波动于50左右的任何讯号，其作用不大。
"""
CLOSE = DataFrame['close']
LOWV = LLV(DataFrame['low'], N)
HIGHV = HHV(DataFrame['high'], N)
RSV = EMA((CLOSE - LOWV) /... |
def QA_indicator_DDI(DataFrame, N=13, N1=26, M=1, M1=5):
"""
'方向标准离差指数'
分析DDI柱状线，由红变绿(正变负)，卖出信号参考；由绿变红，买入信号参考。
"""
H = DataFrame['high']
L = DataFrame['low']
DMZ = IF((H + L) > (REF(H, 1) + REF(L, 1)),
MAX(ABS(H - REF(H, 1)), ABS(L - REF(L, 1))), 0)
DMF = IF((H + L) < (REF... |
def QA_indicator_shadow(DataFrame):
"""
上下影线指标
"""
return {
'LOW': lower_shadow(DataFrame), 'UP': upper_shadow(DataFrame),
'BODY': body(DataFrame), 'BODY_ABS': body_abs(DataFrame), 'PRICE_PCG': price_pcg(DataFrame)
} |
def run(self, series, exponent=None):
'''
:type series: List
:type exponent: int
:rtype: float
'''
try:
return self.calculateHurst(series, exponent)
except Exception as e:
print(" Error: %s" % e) |
def bestExponent(self, seriesLenght):
'''
:type seriesLenght: int
:rtype: int
'''
i = 0
cont = True
while(cont):
if(int(seriesLenght/int(math.pow(2, i))) <= 1):
cont = False
else:
i += 1
return int(i-... |
def mean(self, series, start, limit):
'''
:type start: int
:type limit: int
:rtype: float
'''
return float(np.mean(series[start:limit])) |
def deviation(self, series, start, limit, mean):
'''
:type start: int
:type limit: int
:type mean: int
:rtype: list()
'''
d = []
for x in range(start, limit):
d.append(float(series[x] - mean))
return d |
def standartDeviation(self, series, start, limit):
'''
:type start: int
:type limit: int
:rtype: float
'''
return float(np.std(series[start:limit])) |
def calculateHurst(self, series, exponent=None):
'''
:type series: List
:type exponent: int
:rtype: float
'''
rescaledRange = list()
sizeRange = list()
rescaledRangeMean = list()
if(exponent is None):
exponent = self.bestExponent(len(s... |
def QA_util_send_mail(msg, title, from_user, from_password, to_addr, smtp):
"""邮件发送
Arguments:
msg {[type]} -- [description]
title {[type]} -- [description]
from_user {[type]} -- [description]
from_password {[type]} -- [description]
to_addr {[type]} -- [description]
... |
def QA_fetch_get_stock_analysis(code):
"""
'zyfw', 主营范围 'jyps'#经营评述 'zygcfx' 主营构成分析
date 主营构成 主营收入(元) 收入比例cbbl 主营成本(元) 成本比例 主营利润(元) 利润比例 毛利率(%)
行业 /产品/ 区域 hq cp qy
"""
market = 'sh' if _select_market_code(code) == 1 else 'sz'
null = 'none'
data = eval(requests.get(BusinessAnalysis_url.f... |
def send_order(self, code, price, amount, towards, order_model, market=None):
"""下单
Arguments:
code {[type]} -- [description]
price {[type]} -- [description]
amount {[type]} -- [description]
towards {[type]} -- [description]
order_model {[typ... |
def QA_util_getBetweenMonth(from_date, to_date):
"""
#返回所有月份，以及每月的起始日期、结束日期，字典格式
"""
date_list = {}
begin_date = datetime.datetime.strptime(from_date, "%Y-%m-%d")
end_date = datetime.datetime.strptime(to_date, "%Y-%m-%d")
while begin_date <= end_date:
date_str = begin_date.strftime("... |
def QA_util_add_months(dt, months):
"""
#返回dt隔months个月后的日期，months相当于步长
"""
dt = datetime.datetime.strptime(
dt, "%Y-%m-%d") + relativedelta(months=months)
return(dt) |
def QA_util_get_1st_of_next_month(dt):
"""
获取下个月第一天的日期
:return: 返回日期
"""
year = dt.year
month = dt.month
if month == 12:
month = 1
year += 1
else:
month += 1
res = datetime.datetime(year, month, 1)
return res |
def QA_util_getBetweenQuarter(begin_date, end_date):
"""
#加上每季度的起始日期、结束日期
"""
quarter_list = {}
month_list = QA_util_getBetweenMonth(begin_date, end_date)
for value in month_list:
tempvalue = value.split("-")
year = tempvalue[0]
if tempvalue[1] in ['01', '02', '03']:
... |
def save_account(message, collection=DATABASE.account):
"""save account
Arguments:
message {[type]} -- [description]
Keyword Arguments:
collection {[type]} -- [description] (default: {DATABASE})
"""
try:
collection.create_index(
[("account_cookie", ASCENDING), (... |
def QA_SU_save_financial_files():
"""本地存储financialdata
"""
download_financialzip()
coll = DATABASE.financial
coll.create_index(
[("code", ASCENDING), ("report_date", ASCENDING)], unique=True)
for item in os.listdir(download_path):
if item[0:4] != 'gpcw':
print(
... |
def QA_util_log_info(
logs,
ui_log=None,
ui_progress=None,
ui_progress_int_value=None,
):
"""
QUANTAXIS Log Module
@yutiansut
QA_util_log_x is under [QAStandard#0.0.2@602-x] Protocol
"""
logging.warning(logs)
# 给GUI使用，更新当前任务到日志和进度
if ui_log is not None:
... |
def QA_save_tdx_to_mongo(file_dir, client=DATABASE):
"""save file
Arguments:
file_dir {str:direction} -- 文件的地址
Keyword Arguments:
client {Mongodb:Connection} -- Mongo Connection (default: {DATABASE})
"""
reader = TdxMinBarReader()
__coll = client.stock_min_five
for... |
def exclude_from_stock_ip_list(exclude_ip_list):
""" 从stock_ip_list删除列表exclude_ip_list中的ip
从stock_ip_list删除列表future_ip_list中的ip
:param exclude_ip_list: 需要删除的ip_list
:return: None
"""
for exc in exclude_ip_list:
if exc in stock_ip_list:
stock_ip_list.remove(exc)
# 扩展市场
... |
def get_config(
self,
section='MONGODB',
option='uri',
default_value=DEFAULT_DB_URI
):
"""[summary]
Keyword Arguments:
section {str} -- [description] (default: {'MONGODB'})
option {str} -- [description] (default: {'uri'})
... |
def set_config(
self,
section='MONGODB',
option='uri',
default_value=DEFAULT_DB_URI
):
"""[summary]
Keyword Arguments:
section {str} -- [description] (default: {'MONGODB'})
option {str} -- [description] (default: {'uri'})
... |
def get_or_set_section(
self,
config,
section,
option,
DEFAULT_VALUE,
method='get'
):
"""[summary]
Arguments:
config {[type]} -- [description]
section {[type]} -- [description]
option {[type]... |
def QA_util_date_str2int(date):
"""
日期字符串 '2011-09-11' 变换成 整数 20110911
日期字符串 '2018-12-01' 变换成 整数 20181201
:param date: str日期字符串
:return: 类型int
"""
# return int(str(date)[0:4] + str(date)[5:7] + str(date)[8:10])
if isinstance(date, str):
return int(str().join(date.split('-')))
... |
def QA_util_date_int2str(int_date):
"""
类型datetime.datatime
:param date: int 8位整数
:return: 类型str
"""
date = str(int_date)
if len(date) == 8:
return str(date[0:4] + '-' + date[4:6] + '-' + date[6:8])
elif len(date) == 10:
return date |
def QA_util_to_datetime(time):
"""
字符串 '2018-01-01' 转变成 datatime 类型
:param time: 字符串str -- 格式必须是 2018-01-01 ，长度10
:return: 类型datetime.datatime
"""
if len(str(time)) == 10:
_time = '{} 00:00:00'.format(time)
elif len(str(time)) == 19:
_time = str(time)
else:
QA_ut... |
def QA_util_datetime_to_strdate(dt):
"""
:param dt: pythone datetime.datetime
:return: 1999-02-01 string type
"""
strdate = "%04d-%02d-%02d" % (dt.year, dt.month, dt.day)
return strdate |
def QA_util_datetime_to_strdatetime(dt):
"""
:param dt: pythone datetime.datetime
:return: 1999-02-01 09:30:91 string type
"""
strdatetime = "%04d-%02d-%02d %02d:%02d:%02d" % (
dt.year,
dt.month,
dt.day,
dt.hour,
dt.minute,
dt.second
)
return... |
def QA_util_date_stamp(date):
"""
字符串 '2018-01-01' 转变成 float 类型时间 类似 time.time() 返回的类型
:param date: 字符串str -- 格式必须是 2018-01-01 ，长度10
:return: 类型float
"""
datestr = str(date)[0:10]
date = time.mktime(time.strptime(datestr, '%Y-%m-%d'))
return date |
def QA_util_time_stamp(time_):
"""
字符串 '2018-01-01 00:00:00' 转变成 float 类型时间 类似 time.time() 返回的类型
:param time_: 字符串str -- 数据格式 最好是%Y-%m-%d %H:%M:%S 中间要有空格
:return: 类型float
"""
if len(str(time_)) == 10:
# yyyy-mm-dd格式
return time.mktime(time.strptime(time_, '%Y-%m-%d'))
elif l... |
def QA_util_stamp2datetime(timestamp):
"""
datestamp转datetime
pandas转出来的timestamp是13位整数 要/1000
It’s common for this to be restricted to years from 1970 through 2038.
从1970年开始的纳秒到当前的计数 转变成 float 类型时间 类似 time.time() 返回的类型
:param timestamp: long类型
:return: 类型float
"""
try:
retur... |
def QA_util_realtime(strtime, client):
"""
查询数据库中的数据
:param strtime: strtime str字符串 -- 1999-12-11 这种格式
:param client: client pymongo.MongoClient类型 -- mongodb 数据库 从 QA_util_sql_mongo_setting 中 QA_util_sql_mongo_setting 获取
:return: Dictionary -- {'time_real': 时间,'id': id}
"""... |
def QA_util_id2date(idx, client):
"""
从数据库中查询 通达信时间
:param idx: 字符串 -- 数据库index
:param client: pymongo.MongoClient类型 -- mongodb 数据库 从 QA_util_sql_mongo_setting 中 QA_util_sql_mongo_setting 获取
:return: Str -- 通达信数据库时间
"""
coll = client.quantaxis.trade_date
temp_str = coll.find_o... |
def QA_util_is_trade(date, code, client):
"""
判断是否是交易日
从数据库中查询
:param date: str类型 -- 1999-12-11 这种格式 10位字符串
:param code: str类型 -- 股票代码 例如 603658 ， 6位字符串
:param client: pymongo.MongoClient类型 -- mongodb 数据库 从 QA_util_sql_mongo_setting 中 QA_util_sql_mongo_setting 获取
:return: Boolean -- 是... |
def QA_util_select_hours(time=None, gt=None, lt=None, gte=None, lte=None):
'quantaxis的时间选择函数,约定时间的范围,比如早上9点到11点'
if time is None:
__realtime = datetime.datetime.now()
else:
__realtime = time
fun_list = []
if gt != None:
fun_list.append('>')
if lt != None:
fun_lis... |
def QA_util_calc_time(func, *args, **kwargs):
"""
'耗时长度的装饰器'
:param func:
:param args:
:param kwargs:
:return:
"""
_time = datetime.datetime.now()
func(*args, **kwargs)
print(datetime.datetime.now() - _time) |
def high_limit(self):
'涨停价'
return self.groupby(level=1).close.apply(lambda x: round((x.shift(1) + 0.0002)*1.1, 2)).sort_index() |
def next_day_low_limit(self):
"明日跌停价"
return self.groupby(level=1).close.apply(lambda x: round((x + 0.0002)*0.9, 2)).sort_index() |
def get_medium_order(self, lower=200000, higher=1000000):
"""return medium
Keyword Arguments:
lower {[type]} -- [description] (default: {200000})
higher {[type]} -- [description] (default: {1000000})
Returns:
[type] -- [description]
"""
retu... |
def shadow_calc(data):
"""计算上下影线
Arguments:
data {DataStruct.slice} -- 输入的是一个行情切片
Returns:
up_shadow {float} -- 上影线
down_shdow {float} -- 下影线
entity {float} -- 实体部分
date {str} -- 时间
code {str} -- 代码
"""
up_shadow = abs(data.high - (max(data.open, da... |
def query_data(self, code, start, end, frequence, market_type=None):
"""
标准格式是numpy
"""
try:
return self.fetcher[(market_type, frequence)](
code, start, end, frequence=frequence)
except:
pass |
def QA_SU_save_stock_min(client=DATABASE, ui_log=None, ui_progress=None):
"""
掘金实现方式
save current day's stock_min data
"""
# 导入掘金模块且进行登录
try:
from gm.api import set_token
from gm.api import history
# 请自行将掘金量化的 TOKEN 替换掉 GMTOKEN
set_token("9c5601171e97994686b47b5cb... |
def datetime(self):
'分钟线结构返回datetime 日线结构返回date'
index = self.data.index.remove_unused_levels()
return pd.to_datetime(index.levels[0]) |
def price_diff(self):
'返回DataStruct.price的一阶差分'
res = self.price.groupby(level=1).apply(lambda x: x.diff(1))
res.name = 'price_diff'
return res |
def pvariance(self):
'返回DataStruct.price的方差 variance'
res = self.price.groupby(level=1
).apply(lambda x: statistics.pvariance(x))
res.name = 'pvariance'
return res |
def bar_pct_change(self):
'返回bar的涨跌幅'
res = (self.close - self.open) / self.open
res.name = 'bar_pct_change'
return res |
def bar_amplitude(self):
"返回bar振幅"
res = (self.high - self.low) / self.low
res.name = 'bar_amplitude'
return res |
def mean_harmonic(self):
'返回DataStruct.price的调和平均数'
res = self.price.groupby(level=1
).apply(lambda x: statistics.harmonic_mean(x))
res.name = 'mean_harmonic'
return res |
def amplitude(self):
'返回DataStruct.price的百分比变化'
res = self.price.groupby(
level=1
).apply(lambda x: (x.max() - x.min()) / x.min())
res.name = 'amplitude'
return res |
def close_pct_change(self):
'返回DataStruct.close的百分比变化'
res = self.close.groupby(level=1).apply(lambda x: x.pct_change())
res.name = 'close_pct_change'
return res |
def normalized(self):
'归一化'
res = self.groupby('code').apply(lambda x: x / x.iloc[0])
return res |
def security_gen(self):
'返回一个基于代码的迭代器'
for item in self.index.levels[1]:
yield self.new(
self.data.xs(item,
level=1,
drop_level=False),
dtype=self.type,
if_fq=self.if_fq
) |
def get_dict(self, time, code):
'''
'give the time,code tuple and turn the dict'
:param time:
:param code:
:return: 字典dict 类型
'''
try:
return self.dicts[(QA_util_to_datetime(time), str(code))]
except Exception as e:
raise e |
def kline_echarts(self, code=None):
def kline_formater(param):
return param.name + ':' + vars(param)
"""plot the market_data"""
if code is None:
path_name = '.' + os.sep + 'QA_' + self.type + \
'_codepackage_' + self.if_fq + '.html'
kline = K... |
def query(self, context):
"""
查询data
"""
try:
return self.data.query(context)
except pd.core.computation.ops.UndefinedVariableError:
print('QA CANNOT QUERY THIS {}'.format(context))
pass |
def groupby(
self,
by=None,
axis=0,
level=None,
as_index=True,
sort=False,
group_keys=False,
squeeze=False,
**kwargs
):
"""仿dataframe的groupby写法,但控制了by的code和datetime
Keyword Arguments:
... |
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