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每日每个股票持仓市值表
Returns:
pd.DataFrame -- 市值表
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'
) * self.account.daily_hold.apply(abs)
).fillna(method='ffill')
else:
return (
self.market_data.pivot('close').fillna(method='ffill') *
self.account.daily_hold.apply(abs)
).fillna(method='ffill')
else:
return None |
最大回撤
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))
]
)
),
2
) |
总手续费
def total_commission(self):
"""总手续费
"""
return float(
-abs(round(self.account.history_table.commission.sum(),
2))
) |
总印花税
def total_tax(self):
"""总印花税
"""
return float(-abs(round(self.account.history_table.tax.sum(), 2))) |
利润构成
Returns:
dict -- 利润构成表
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.total_tax,
'total_commission':
self.total_commission,
'total_profit':
self.profit_money
} |
盈利额
Returns:
[type] -- [description]
def profit_money(self):
"""盈利额
Returns:
[type] -- [description]
"""
return float(round(self.assets.iloc[-1] - self.assets.iloc[0], 2)) |
年化收益
Returns:
[type] -- [description]
def annualize_return(self):
"""年化收益
Returns:
[type] -- [description]
"""
return round(
float(self.calc_annualize_return(self.assets,
self.time_gap)),
2
) |
基准组合的行情数据(一般是组合,可以调整)
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
) |
基准组合的账户资产队列
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])
) |
基准组合的年化收益
Returns:
[type] -- [description]
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
)
),
2
) |
beta比率 组合的系统性风险
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()
)
),
2
)
except:
print('贝塔计算错误。。')
res = 0
return res |
alpha比率 与市场基准收益无关的超额收益率
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
)
),
2
) |
夏普比率
def sharpe(self):
"""
夏普比率
"""
return round(
float(
self.calc_sharpe(self.annualize_return,
self.volatility,
0.05)
),
2
) |
资金曲线叠加图
@Roy T.Burns 2018/05/29 修改百分比显示错误
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])
plt.axis('off')
i = 0
for item in ['account_cookie', 'portfolio_cookie', 'user_cookie']:
plt.text(
i,
0.5,
'{} : {}'.format(item,
self.message[item]),
fontsize=10,
rotation=0,
wrap=True
)
i += (length / 2.8)
i = 0
for item in ['benchmark_code', 'time_gap', 'max_dropback']:
plt.text(
i,
0.4,
'{} : {}'.format(item,
self.message[item]),
fontsize=10,
ha='left',
rotation=0,
wrap=True
)
i += (length / 2.8)
i = 0
for item in ['annualize_return', 'bm_annualizereturn', 'profit']:
plt.text(
i,
0.3,
'{} : {} %'.format(item,
self.message.get(item,
0) * 100),
fontsize=10,
ha='left',
rotation=0,
wrap=True
)
i += length / 2.8
i = 0
for item in ['init_cash', 'last_assets', 'volatility']:
plt.text(
i,
0.2,
'{} : {} '.format(item,
self.message[item]),
fontsize=10,
ha='left',
rotation=0,
wrap=True
)
i += length / 2.8
i = 0
for item in ['alpha', 'beta', 'sharpe']:
plt.text(
i,
0.1,
'{} : {}'.format(item,
self.message[item]),
ha='left',
fontsize=10,
rotation=0,
wrap=True
)
i += length / 2.8
plt.subplot(212)
self.assets.plot()
self.benchmark_assets.xs(self.benchmark_code, level=1).plot()
asset_p = mpatches.Patch(
color='red',
label='{}'.format(self.account.account_cookie)
)
asset_b = mpatches.Patch(
label='benchmark {}'.format(self.benchmark_code)
)
plt.legend(handles=[asset_p, asset_b], loc=0)
plt.title('ASSET AND BENCKMARK')
return plt |
使用热力图画出买卖信号
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.reset_index().drop(
'account_cookie',
axis=1
).set_index('datetime').loc[start:end],
cmap="YlGnBu",
linewidths=0.05,
ax=ax
)
ax.set_title(
'SIGNAL TABLE --ACCOUNT: {}'.format(self.account.account_cookie)
)
ax.set_xlabel('Code')
ax.set_ylabel('DATETIME')
return plt |
使用后进先出法配对成交记录
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():
while True:
if X[data.code].qsize() == 0:
X[data.code].put((data.datetime, data.amount, data.price))
break
else:
l = X[data.code].get()
if (l[1] * data.amount) < 0:
# 原有多仓/ 平仓 或者原有空仓/平仓
if abs(l[1]) > abs(data.amount):
temp = (l[0], l[1] + data.amount, l[2])
X[data.code].put_nowait(temp)
if data.amount < 0:
pair_table.append(
[
data.code,
data.datetime,
l[0],
abs(data.amount),
data.price,
l[2]
]
)
break
else:
pair_table.append(
[
data.code,
l[0],
data.datetime,
abs(data.amount),
l[2],
data.price
]
)
break
elif abs(l[1]) < abs(data.amount):
data.amount = data.amount + l[1]
if data.amount < 0:
pair_table.append(
[
data.code,
data.datetime,
l[0],
l[1],
data.price,
l[2]
]
)
else:
pair_table.append(
[
data.code,
l[0],
data.datetime,
l[1],
l[2],
data.price
]
)
else:
if data.amount < 0:
pair_table.append(
[
data.code,
data.datetime,
l[0],
abs(data.amount),
data.price,
l[2]
]
)
break
else:
pair_table.append(
[
data.code,
l[0],
data.datetime,
abs(data.amount),
l[2],
data.price
]
)
break
else:
X[data.code].put_nowait(l)
X[data.code].put_nowait(
(data.datetime,
data.amount,
data.price)
)
break
pair_title = [
'code',
'sell_date',
'buy_date',
'amount',
'sell_price',
'buy_price'
]
pnl = pd.DataFrame(pair_table, columns=pair_title).set_index('code')
pnl = pnl.assign(
unit=pnl.code.apply(lambda x: self.market_preset.get_unit(x)),
pnl_ratio=(pnl.sell_price / pnl.buy_price) - 1,
sell_date=pd.to_datetime(pnl.sell_date),
buy_date=pd.to_datetime(pnl.buy_date)
)
pnl = pnl.assign(
pnl_money=(pnl.sell_price - pnl.buy_price) * pnl.amount * pnl.unit,
hold_gap=abs(pnl.sell_date - pnl.buy_date),
if_buyopen=(pnl.sell_date -
pnl.buy_date) > datetime.timedelta(days=0)
)
pnl = pnl.assign(
openprice=pnl.if_buyopen.apply(
lambda pnl: 1 if pnl else 0) * pnl.buy_price + pnl.if_buyopen.apply(lambda pnl: 0 if pnl else 1) * pnl.sell_price,
opendate=pnl.if_buyopen.apply(
lambda pnl: 1 if pnl else 0) * pnl.buy_date.map(str) + pnl.if_buyopen.apply(lambda pnl: 0 if pnl else 1) * pnl.sell_date.map(str),
closeprice=pnl.if_buyopen.apply(
lambda pnl: 0 if pnl else 1) * pnl.buy_price + pnl.if_buyopen.apply(lambda pnl: 1 if pnl else 0) * pnl.sell_price,
closedate=pnl.if_buyopen.apply(
lambda pnl: 0 if pnl else 1) * pnl.buy_date.map(str) + pnl.if_buyopen.apply(lambda pnl: 1 if pnl else 0) * pnl.sell_date.map(str))
return pnl.set_index('code') |
画出pnl比率散点图
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 |
画出pnl盈亏额散点图
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 |
胜率
胜率
盈利次数/总次数
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 |
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
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 = time.localtime(time.time() + self.next())
if asc:
return time.asctime(_time)
return time.mktime(_time) |
期货实时tick
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' |
MA
Arguments:
DataFrame {[type]} -- [description]
Returns:
[type] -- [description]
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)}) |
MACD CALC
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}) |
趋向指标 DMI
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 = REF(LOW, 1)-LOW
DMP = SUM(IFAND(HD>0,HD>LD,HD,0), M1)
DMM = SUM(IFAND(LD>0,LD>HD,LD,0), M1)
DI1 = DMP*100/TR
DI2 = DMM*100/TR
ADX = MA(ABS(DI2-DI1)/(DI1+DI2)*100, M2)
ADXR = (ADX+REF(ADX, M2))/2
return pd.DataFrame({
'DI1': DI1, 'DI2': DI2,
'ADX': ADX, 'ADXR': ADXR
}) |
瀑布线
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) + EMA(C, 2 * N4) + EMA(C, 4 * N4)) / 3
PBX5 = (EMA(C, N5) + EMA(C, 2 * N5) + EMA(C, 4 * N5)) / 3
PBX6 = (EMA(C, N6) + EMA(C, 2 * N6) + EMA(C, 4 * N6)) / 3
DICT = {'PBX1': PBX1, 'PBX2': PBX2, 'PBX3': PBX3,
'PBX4': PBX4, 'PBX5': PBX5, 'PBX6': PBX6}
return pd.DataFrame(DICT) |
平均线差 DMA
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
}) |
动量线
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) |
指数平均线 EXPMA
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
}) |
佳庆指标 CHO
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({
'CHO': CHO, 'MACHO': MACHO
}) |
乖离率
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}
return pd.DataFrame(DICT) |
变动率指标
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) |
TYP:=(HIGH+LOW+CLOSE)/3;
CCI:(TYP-MA(TYP,N))/(0.015*AVEDEV(TYP,N));
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({
'CCI': cci, 'a': a, 'b': b
}) |
威廉指标
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}
return pd.DataFrame(DICT) |
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。
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, 'MAOSC': MAOSC}
return pd.DataFrame(DICT) |
相对强弱指标RSI1:SMA(MAX(CLOSE-LC,0),N1,1)/SMA(ABS(CLOSE-LC),N1,1)*100;
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) * 100
RSI3 = SMA(MAX(CLOSE - LC, 0), N3) / SMA(ABS(CLOSE - LC), N3) * 100
DICT = {'RSI1': RSI1, 'RSI2': RSI2, 'RSI3': RSI3}
return pd.DataFrame(DICT) |
动态买卖气指标
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)
SBM = SUM(DBM, N)
ADTM1 = IF(STM > SBM, (STM - SBM) / STM,
IF(STM != SBM, (STM - SBM) / SBM, 0))
MAADTM = MA(ADTM1, M)
DICT = {'ADTM': ADTM1, 'MAADTM': MAADTM}
return pd.DataFrame(DICT) |
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(AA,BB);
ASI:SUM(SI,M1);
ASIT:MA(ASI,M2);
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(AA,BB);
ASI:SUM(SI,M1);
ASIT:MA(ASI,M2);
"""
CLOSE = DataFrame['close']
HIGH = DataFrame['high']
LOW = DataFrame['low']
OPEN = DataFrame['open']
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 = IFAND(AA > BB, AA > CC, AA+BB/2+DD/4,
IFAND(BB > CC, 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(AA, BB)
ASI = SUM(SI, M1)
ASIT = MA(ASI, M2)
return pd.DataFrame({
'ASI': ASI, 'ASIT': ASIT
}) |
能量潮
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
}) |
布林线
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) |
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。
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 = LLV(LOW, N)
HH = HHV(HIGH, N)
WR = TYP+(TYP-LL)
MR = TYP+(HH-LL)
SR = 2*HH-LL
WS = TYP-(HH-TYP)
MS = TYP-(HH-LL)
SS = 2*LL-HH
return pd.DataFrame({
'WR': WR, 'MR': MR, 'SR': SR,
'WS': WS, 'MS': MS, 'SS': SS
}) |
多空指标
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) |
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
输出资金流量指标:100-(100/(1+V1))
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
输出资金流量指标:100-(100/(1+V1))
"""
C = DataFrame['close']
H = DataFrame['high']
L = DataFrame['low']
VOL = DataFrame['volume']
TYP = (C + H + L) / 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))
DICT = {'MFI': mfi}
return pd.DataFrame(DICT) |
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
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) - H)), ABS(REF(C, 1) - L))
atr = MA(TR, N)
return pd.DataFrame({'TR': TR, 'ATR': atr}) |
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。
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) / (HIGHV - LOWV) * 100, M)
K = EMA(RSV, M)
D = MA(K, M)
DICT = {'RSV': RSV, 'SKDJ_K': K, 'SKDJ_D': D}
return pd.DataFrame(DICT) |
'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。
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(H, 1) + REF(L, 1)),
MAX(ABS(H - REF(H, 1)), ABS(L - REF(L, 1))), 0)
DIZ = SUM(DMZ, N) / (SUM(DMZ, N) + SUM(DMF, N))
DIF = SUM(DMF, N) / (SUM(DMF, N) + SUM(DMZ, N))
ddi = DIZ - DIF
ADDI = SMA(ddi, N1, M)
AD = MA(ADDI, M1)
DICT = {'DDI': ddi, 'ADDI': ADDI, 'AD': AD}
return pd.DataFrame(DICT) |
上下影线指标
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)
} |
:type series: List
:type exponent: int
:rtype: float
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) |
:type seriesLenght: int
:rtype: int
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-1) |
:type start: int
:type limit: int
:rtype: float
def mean(self, series, start, limit):
'''
:type start: int
:type limit: int
:rtype: float
'''
return float(np.mean(series[start:limit])) |
:type start: int
:type limit: int
:type mean: int
:rtype: list()
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 |
:type start: int
:type limit: int
:rtype: float
def standartDeviation(self, series, start, limit):
'''
:type start: int
:type limit: int
:rtype: float
'''
return float(np.std(series[start:limit])) |
:type series: List
:type exponent: int
:rtype: float
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(series))
for i in range(0, exponent):
partsNumber = int(math.pow(2, i))
size = int(len(series)/partsNumber)
sizeRange.append(size)
rescaledRange.append(0)
rescaledRangeMean.append(0)
for x in range(0, partsNumber):
start = int(size*(x))
limit = int(size*(x+1))
deviationAcumulative = self.sumDeviation(self.deviation(
series, start, limit, self.mean(series, start, limit)))
deviationsDifference = float(
max(deviationAcumulative) - min(deviationAcumulative))
standartDeviation = self.standartDeviation(
series, start, limit)
if(deviationsDifference != 0 and standartDeviation != 0):
rescaledRange[i] += (deviationsDifference /
standartDeviation)
y = 0
for x in rescaledRange:
rescaledRangeMean[y] = x/int(math.pow(2, y))
y = y+1
# log calculation
rescaledRangeLog = list()
sizeRangeLog = list()
for i in range(0, exponent):
rescaledRangeLog.append(math.log(rescaledRangeMean[i], 10))
sizeRangeLog.append(math.log(sizeRange[i], 10))
slope, intercept = np.polyfit(sizeRangeLog, rescaledRangeLog, 1)
ablineValues = [slope * i + intercept for i in sizeRangeLog]
plt.plot(sizeRangeLog, rescaledRangeLog, '--')
plt.plot(sizeRangeLog, ablineValues, 'b')
plt.title(slope)
# graphic dimension settings
limitUp = 0
if(max(sizeRangeLog) > max(rescaledRangeLog)):
limitUp = max(sizeRangeLog)
else:
limitUp = max(rescaledRangeLog)
limitDown = 0
if(min(sizeRangeLog) > min(rescaledRangeLog)):
limitDown = min(rescaledRangeLog)
else:
limitDown = min(sizeRangeLog)
plt.gca().set_xlim(limitDown, limitUp)
plt.gca().set_ylim(limitDown, limitUp)
print("Hurst exponent: " + str(slope))
plt.show()
return slope |
邮件发送
Arguments:
msg {[type]} -- [description]
title {[type]} -- [description]
from_user {[type]} -- [description]
from_password {[type]} -- [description]
to_addr {[type]} -- [description]
smtp {[type]} -- [description]
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]
smtp {[type]} -- [description]
"""
msg = MIMEText(msg, 'plain', 'utf-8')
msg['Subject'] = Header(title, 'utf-8').encode()
server = smtplib.SMTP(smtp, 25) # SMTP协议默认端口是25
server.set_debuglevel(1)
server.login(from_user, from_password)
server.sendmail(from_user, [to_addr], msg.as_string()) |
'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
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.format(
market, code), headers=headers_em).text)
zyfw = pd.DataFrame(data.get('zyfw', None))
jyps = pd.DataFrame(data.get('jyps', None))
zygcfx = data.get('zygcfx', [])
temp = []
for item in zygcfx:
try:
data_ = pd.concat([pd.DataFrame(item['hy']).assign(date=item['rq']).assign(classify='hy'),
pd.DataFrame(item['cp']).assign(
date=item['rq']).assign(classify='cp'),
pd.DataFrame(item['qy']).assign(date=item['rq']).assign(classify='qy')])
temp.append(data_)
except:
pass
try:
res_zyfcfx = pd.concat(temp).set_index(
['date', 'classify'], drop=False)
except:
res_zyfcfx = None
return zyfw, jyps, res_zyfcfx |
下单
Arguments:
code {[type]} -- [description]
price {[type]} -- [description]
amount {[type]} -- [description]
towards {[type]} -- [description]
order_model {[type]} -- [description]
market:市场，SZ 深交所，SH 上交所
Returns:
[type] -- [description]
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 {[type]} -- [description]
market:市场，SZ 深交所，SH 上交所
Returns:
[type] -- [description]
"""
towards = 0 if towards == ORDER_DIRECTION.BUY else 1
if order_model == ORDER_MODEL.MARKET:
order_model = 4
elif order_model == ORDER_MODEL.LIMIT:
order_model = 0
if market is None:
market = QAFetch.base.get_stock_market(code)
if not isinstance(market, str):
raise Exception('%s不正确，请检查code和market参数' % market)
market = market.lower()
if market not in ['sh', 'sz']:
raise Exception('%s不支持，请检查code和market参数' % market)
return self.data_to_df(self.call("send_order", {
'client_id': self.client_id,
'category': towards,
'price_type': order_model,
'gddm': self.gddm_sh if market == 'sh' else self.gddm_sz,
'zqdm': code,
'price': price,
'quantity': amount
})) |
#返回所有月份，以及每月的起始日期、结束日期，字典格式
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("%Y-%m")
date_list[date_str] = ['%d-%d-01' % (begin_date.year, begin_date.month),
'%d-%d-%d' % (begin_date.year, begin_date.month,
calendar.monthrange(begin_date.year, begin_date.month)[1])]
begin_date = QA_util_get_1st_of_next_month(begin_date)
return(date_list) |
#返回dt隔months个月后的日期，months相当于步长
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) |
获取下个月第一天的日期
:return: 返回日期
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 |
#加上每季度的起始日期、结束日期
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']:
quarter_list[year + "Q1"] = ['%s-01-01' % year, '%s-03-31' % year]
elif tempvalue[1] in ['04', '05', '06']:
quarter_list[year + "Q2"] = ['%s-04-01' % year, '%s-06-30' % year]
elif tempvalue[1] in ['07', '08', '09']:
quarter_list[year + "Q3"] = ['%s-07-31' % year, '%s-09-30' % year]
elif tempvalue[1] in ['10', '11', '12']:
quarter_list[year + "Q4"] = ['%s-10-01' % year, '%s-12-31' % year]
return(quarter_list) |
save account
Arguments:
message {[type]} -- [description]
Keyword Arguments:
collection {[type]} -- [description] (default: {DATABASE})
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), ("user_cookie", ASCENDING), ("portfolio_cookie", ASCENDING)], unique=True)
except:
pass
collection.update(
{'account_cookie': message['account_cookie'], 'portfolio_cookie':
message['portfolio_cookie'], 'user_cookie': message['user_cookie']},
{'$set': message},
upsert=True
) |
本地存储financialdata
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(
"file ", item, " is not start with gpcw , seems not a financial file , ignore!")
continue
date = int(item.split('.')[0][-8:])
print('QUANTAXIS NOW SAVING {}'.format(date))
if coll.find({'report_date': date}).count() < 3600:
print(coll.find({'report_date': date}).count())
data = QA_util_to_json_from_pandas(parse_filelist([item]).reset_index(
).drop_duplicates(subset=['code', 'report_date']).sort_index())
# data["crawl_date"] = str(datetime.date.today())
try:
coll.insert_many(data, ordered=False)
except Exception as e:
if isinstance(e, MemoryError):
coll.insert_many(data, ordered=True)
elif isinstance(e, pymongo.bulk.BulkWriteError):
pass
else:
print('ALL READY IN DATABASE')
print('SUCCESSFULLY SAVE/UPDATE FINANCIAL DATA') |
QUANTAXIS Log Module
@yutiansut
QA_util_log_x is under [QAStandard#0.0.2@602-x] Protocol
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:
if isinstance(logs, str):
ui_log.emit(logs)
if isinstance(logs, list):
for iStr in logs:
ui_log.emit(iStr)
if ui_progress is not None and ui_progress_int_value is not None:
ui_progress.emit(ui_progress_int_value) |
save file
Arguments:
file_dir {str:direction} -- 文件的地址
Keyword Arguments:
client {Mongodb:Connection} -- Mongo Connection (default: {DATABASE})
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 a, v, files in os.walk(file_dir):
for file in files:
if (str(file)[0:2] == 'sh' and int(str(file)[2]) == 6) or \
(str(file)[0:2] == 'sz' and int(str(file)[2]) == 0) or \
(str(file)[0:2] == 'sz' and int(str(file)[2]) == 3):
QA_util_log_info('Now_saving ' + str(file)
[2:8] + '\'s 5 min tick')
fname = file_dir + os.sep + file
df = reader.get_df(fname)
df['code'] = str(file)[2:8]
df['market'] = str(file)[0:2]
df['datetime'] = [str(x) for x in list(df.index)]
df['date'] = [str(x)[0:10] for x in list(df.index)]
df['time_stamp'] = df['datetime'].apply(
lambda x: QA_util_time_stamp(x))
df['date_stamp'] = df['date'].apply(
lambda x: QA_util_date_stamp(x))
data_json = json.loads(df.to_json(orient='records'))
__coll.insert_many(data_json) |
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
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
for exc in exclude_ip_list:
if exc in future_ip_list:
future_ip_list.remove(exc) |
[summary]
Keyword Arguments:
section {str} -- [description] (default: {'MONGODB'})
option {str} -- [description] (default: {'uri'})
default_value {[type]} -- [description] (default: {DEFAULT_DB_URI})
Returns:
[type] -- [description]
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'})
default_value {[type]} -- [description] (default: {DEFAULT_DB_URI})
Returns:
[type] -- [description]
"""
res = self.client.quantaxis.usersetting.find_one({'section': section})
if res:
return res.get(option, default_value)
else:
self.set_config(section, option, default_value)
return default_value |
[summary]
Keyword Arguments:
section {str} -- [description] (default: {'MONGODB'})
option {str} -- [description] (default: {'uri'})
default_value {[type]} -- [description] (default: {DEFAULT_DB_URI})
Returns:
[type] -- [description]
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'})
default_value {[type]} -- [description] (default: {DEFAULT_DB_URI})
Returns:
[type] -- [description]
"""
t = {'section': section, option: default_value}
self.client.quantaxis.usersetting.update(
{'section': section}, {'$set':t}, upsert=True) |
[summary]
Arguments:
config {[type]} -- [description]
section {[type]} -- [description]
option {[type]} -- [description]
DEFAULT_VALUE {[type]} -- [description]
Keyword Arguments:
method {str} -- [description] (default: {'get'})
Returns:
[type] -- [description]
def get_or_set_section(
self,
config,
section,
option,
DEFAULT_VALUE,
method='get'
):
"""[summary]
Arguments:
config {[type]} -- [description]
section {[type]} -- [description]
option {[type]} -- [description]
DEFAULT_VALUE {[type]} -- [description]
Keyword Arguments:
method {str} -- [description] (default: {'get'})
Returns:
[type] -- [description]
"""
try:
if isinstance(DEFAULT_VALUE, str):
val = DEFAULT_VALUE
else:
val = json.dumps(DEFAULT_VALUE)
if method == 'get':
return self.get_config(section, option)
else:
self.set_config(section, option, val)
return val
except:
self.set_config(section, option, val)
return val |
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
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('-')))
elif isinstance(date, int):
return date |
类型datetime.datatime
:param date: int 8位整数
:return: 类型str
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 |
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
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_util_log_info('WRONG DATETIME FORMAT {}'.format(time))
return datetime.datetime.strptime(_time, '%Y-%m-%d %H:%M:%S') |
:param dt: pythone datetime.datetime
:return: 1999-02-01 string type
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 |
:param dt: pythone datetime.datetime
:return: 1999-02-01 09:30:91 string type
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 strdatetime |
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
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 |
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
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 len(str(time_)) == 16:
# yyyy-mm-dd hh:mm格式
return time.mktime(time.strptime(time_, '%Y-%m-%d %H:%M'))
else:
timestr = str(time_)[0:19]
return time.mktime(time.strptime(timestr, '%Y-%m-%d %H:%M:%S')) |
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
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:
return datetime.datetime.fromtimestamp(timestamp)
except Exception as e:
# it won't work ??
try:
return datetime.datetime.fromtimestamp(timestamp / 1000)
except:
try:
return datetime.datetime.fromtimestamp(timestamp / 1000000)
except:
return datetime.datetime.fromtimestamp(timestamp / 1000000000) |
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_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}
"""
time_stamp = QA_util_date_stamp(strtime)
coll = client.quantaxis.trade_date
temp_str = coll.find_one({'date_stamp': {"$gte": time_stamp}})
time_real = temp_str['date']
time_id = temp_str['num']
return {'time_real': time_real, 'id': time_id} |
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
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_one({'num': idx})
return temp_str['date'] |
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 -- 是否是交易时间
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 -- 是否是交易时间
"""
coll = client.quantaxis.stock_day
date = str(date)[0:10]
is_trade = coll.find_one({'code': code, 'date': date})
try:
len(is_trade)
return True
except:
return False |
quantaxis的时间选择函数,约定时间的范围,比如早上9点到11点
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_list.append('<')
if gte != None:
fun_list.append('>=')
if lte != None:
fun_list.append('<=')
assert len(fun_list) > 0
true_list = []
try:
for item in fun_list:
if item == '>':
if __realtime.strftime('%H') > gt:
true_list.append(0)
else:
true_list.append(1)
elif item == '<':
if __realtime.strftime('%H') < lt:
true_list.append(0)
else:
true_list.append(1)
elif item == '>=':
if __realtime.strftime('%H') >= gte:
true_list.append(0)
else:
true_list.append(1)
elif item == '<=':
if __realtime.strftime('%H') <= lte:
true_list.append(0)
else:
true_list.append(1)
except:
return Exception
if sum(true_list) > 0:
return False
else:
return True |
'耗时长度的装饰器'
:param func:
:param args:
:param kwargs:
:return:
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) |
涨停价
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() |
明日跌停价
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() |
return medium
Keyword Arguments:
lower {[type]} -- [description] (default: {200000})
higher {[type]} -- [description] (default: {1000000})
Returns:
[type] -- [description]
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]
"""
return self.data.query('amount>={}'.format(lower)).query('amount<={}'.format(higher)) |
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
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, data.close)))
down_shadow = abs(data.low - (min(data.open, data.close)))
entity = abs(data.open - data.close)
towards = True if data.open < data.close else False
print('=' * 15)
print('up_shadow : {}'.format(up_shadow))
print('down_shadow : {}'.format(down_shadow))
print('entity: {}'.format(entity))
print('towards : {}'.format(towards))
return up_shadow, down_shadow, entity, data.date, data.code |
标准格式是numpy
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 |
掘金实现方式
save current day's stock_min data
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("9c5601171e97994686b47b5cbfe7b2fc8bb25b09")
except:
raise ModuleNotFoundError
# 股票代码格式化
code_list = list(
map(
lambda x: "SHSE." + x if x[0] == "6" else "SZSE." + x,
QA_fetch_get_stock_list().code.unique().tolist(),
))
coll = client.stock_min
coll.create_index([
("code", pymongo.ASCENDING),
("time_stamp", pymongo.ASCENDING),
("date_stamp", pymongo.ASCENDING),
])
err = []
def __transform_gm_to_qa(df, type_):
"""
将掘金数据转换为 qa 格式
"""
if df is None or len(df) == 0:
raise ValueError("没有掘金数据")
df = df.rename(columns={
"eob": "datetime",
"volume": "vol",
"symbol": "code"
}).drop(["bob", "frequency", "position", "pre_close"], axis=1)
df["code"] = df["code"].map(str).str.slice(5, )
df["datetime"] = pd.to_datetime(df["datetime"].map(str).str.slice(
0, 19))
df["date"] = df.datetime.map(str).str.slice(0, 10)
df = df.set_index("datetime", drop=False)
df["date_stamp"] = df["date"].apply(lambda x: QA_util_date_stamp(x))
df["time_stamp"] = (
df["datetime"].map(str).apply(lambda x: QA_util_time_stamp(x)))
df["type"] = type_
return df[[
"open",
"close",
"high",
"low",
"vol",
"amount",
"datetime",
"code",
"date",
"date_stamp",
"time_stamp",
"type",
]]
def __saving_work(code, coll):
QA_util_log_info(
"##JOB03 Now Saving STOCK_MIN ==== {}".format(code), ui_log=ui_log)
try:
for type_ in ["1min", "5min", "15min", "30min", "60min"]:
col_filter = {"code": str(code)[5:], "type": type_}
ref_ = coll.find(col_filter)
end_time = str(now_time())[0:19]
if coll.count_documents(col_filter) > 0:
start_time = ref_[coll.count_documents(
col_filter) - 1]["datetime"]
print(start_time)
QA_util_log_info(
"##JOB03.{} Now Saving {} from {} to {} == {}".format(
["1min",
"5min",
"15min",
"30min",
"60min"
].index(type_),
str(code)[5:],
start_time,
end_time,
type_,
),
ui_log=ui_log,
)
if start_time != end_time:
df = history(
symbol=code,
start_time=start_time,
end_time=end_time,
frequency=MIN_SEC[type_],
df=True
)
__data = __transform_gm_to_qa(df, type_)
if len(__data) > 1:
# print(QA_util_to_json_from_pandas(__data)[1::])
# print(__data)
coll.insert_many(
QA_util_to_json_from_pandas(__data)[1::])
else:
start_time = "2015-01-01 09:30:00"
QA_util_log_info(
"##JOB03.{} Now Saving {} from {} to {} == {}".format(
["1min",
"5min",
"15min",
"30min",
"60min"
].index(type_),
str(code)[5:],
start_time,
end_time,
type_,
),
ui_log=ui_log,
)
if start_time != end_time:
df = history(
symbol=code,
start_time=start_time,
end_time=end_time,
frequency=MIN_SEC[type_],
df=True
)
__data = __transform_gm_to_qa(df, type_)
if len(__data) > 1:
# print(__data)
coll.insert_many(
QA_util_to_json_from_pandas(__data)[1::])
# print(QA_util_to_json_from_pandas(__data)[1::])
except Exception as e:
QA_util_log_info(e, ui_log=ui_log)
err.append(code)
QA_util_log_info(err, ui_log=ui_log)
executor = ThreadPoolExecutor(max_workers=2)
res = {
executor.submit(__saving_work, code_list[i_], coll)
for i_ in range(len(code_list))
}
count = 0
for i_ in concurrent.futures.as_completed(res):
QA_util_log_info(
'The {} of Total {}'.format(count,
len(code_list)),
ui_log=ui_log
)
strProgress = "DOWNLOAD PROGRESS {} ".format(
str(float(count / len(code_list) * 100))[0:4] + "%")
intProgress = int(count / len(code_list) * 10000.0)
QA_util_log_info(
strProgress,
ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intProgress
)
count = count + 1
if len(err) < 1:
QA_util_log_info("SUCCESS", ui_log=ui_log)
else:
QA_util_log_info(" ERROR CODE \n ", ui_log=ui_log)
QA_util_log_info(err, ui_log=ui_log) |
分钟线结构返回datetime 日线结构返回date
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]) |
返回DataStruct.price的一阶差分
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 |
返回DataStruct.price的方差 variance
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 |
返回bar的涨跌幅
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 |
返回bar振幅
def bar_amplitude(self):
"返回bar振幅"
res = (self.high - self.low) / self.low
res.name = 'bar_amplitude'
return res |
返回DataStruct.price的调和平均数
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 |
返回DataStruct.price的百分比变化
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 |
返回DataStruct.close的百分比变化
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 |
归一化
def normalized(self):
'归一化'
res = self.groupby('code').apply(lambda x: x / x.iloc[0])
return res |
返回一个基于代码的迭代器
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
) |
'give the time,code tuple and turn the dict'
:param time:
:param code:
:return: 字典dict 类型
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 |
plot the market_data
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 = Kline(
'CodePackage_' + self.if_fq + '_' + self.type,
width=1360,
height=700,
page_title='QUANTAXIS'
)
bar = Bar()
data_splits = self.splits()
for ds in data_splits:
data = []
axis = []
if ds.type[-3:] == 'day':
datetime = np.array(ds.date.map(str))
else:
datetime = np.array(ds.datetime.map(str))
ohlc = np.array(
ds.data.loc[:,
['open',
'close',
'low',
'high']]
)
kline.add(
ds.code[0],
datetime,
ohlc,
mark_point=["max",
"min"],
is_datazoom_show=True,
datazoom_orient='horizontal'
)
return kline
else:
data = []
axis = []
ds = self.select_code(code)
data = []
#axis = []
if self.type[-3:] == 'day':
datetime = np.array(ds.date.map(str))
else:
datetime = np.array(ds.datetime.map(str))
ohlc = np.array(ds.data.loc[:, ['open', 'close', 'low', 'high']])
vol = np.array(ds.volume)
kline = Kline(
'{}__{}__{}'.format(code,
self.if_fq,
self.type),
width=1360,
height=700,
page_title='QUANTAXIS'
)
bar = Bar()
kline.add(self.code, datetime, ohlc,
mark_point=["max", "min"],
# is_label_show=True,
is_datazoom_show=True,
is_xaxis_show=False,
# is_toolbox_show=True,
tooltip_formatter='{b}:{c}', # kline_formater,
# is_more_utils=True,
datazoom_orient='horizontal')
bar.add(
self.code,
datetime,
vol,
is_datazoom_show=True,
datazoom_xaxis_index=[0,
1]
)
grid = Grid(width=1360, height=700, page_title='QUANTAXIS')
grid.add(bar, grid_top="80%")
grid.add(kline, grid_bottom="30%")
return grid |
查询data
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 |
仿dataframe的groupby写法,但控制了by的code和datetime
Keyword Arguments:
by {[type]} -- [description] (default: {None})
axis {int} -- [description] (default: {0})
level {[type]} -- [description] (default: {None})
as_index {bool} -- [description] (default: {True})
sort {bool} -- [description] (default: {True})
group_keys {bool} -- [description] (default: {True})
squeeze {bool} -- [description] (default: {False})
observed {bool} -- [description] (default: {False})
Returns:
[type] -- [description]
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:
by {[type]} -- [description] (default: {None})
axis {int} -- [description] (default: {0})
level {[type]} -- [description] (default: {None})
as_index {bool} -- [description] (default: {True})
sort {bool} -- [description] (default: {True})
group_keys {bool} -- [description] (default: {True})
squeeze {bool} -- [description] (default: {False})
observed {bool} -- [description] (default: {False})
Returns:
[type] -- [description]
"""
if by == self.index.names[1]:
by = None
level = 1
elif by == self.index.names[0]:
by = None
level = 0
return self.data.groupby(
by=by,
axis=axis,
level=level,
as_index=as_index,
sort=sort,
group_keys=group_keys,
squeeze=squeeze
) |
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