def bar(code,
conn=None,
start_date=None,
end_date=None,
freq='D',
asset='E',
market='',
adj=None,
ma=[],
factors=[],
retry_count=3):
"""
BAR数据
Parameters:
------------
code:证券代码，支持股票,ETF/LOF,期货/期权,港股
con:服务器连接 ，通过ts.api()或者ts.xpi()获得
start_date:开始日期 YYYY-MM-DD/YYYYMMDD
end_date:结束日期 YYYY-MM-DD/YYYYMMDD
freq:支持1/5/15/30/60分钟,周/月/季/年
asset:证券类型 E:股票和交易所基金，INDEX:沪深指数,X:期货/期权/港股/中概美国/中证指数/国际指数
market:市场代码，通过ts.get_markets()获取
adj:复权类型,None不复权,qfq:前复权,hfq:后复权
ma:均线,支持自定义均线频度，如：ma5/ma10/ma20/ma60/maN
factors因子数据，目前支持以下两种：
vr:量比,默认不返回，返回需指定：factor=['vr']
tor:换手率，默认不返回，返回需指定：factor=['tor']
以上两种都需要：factor=['vr', 'tor']
retry_count:网络重试次数
Return
----------
DataFrame
code:代码
open：开盘close/high/low/vol成交量/amount成交额/maN均价/vr量比/tor换手率
期货(asset='X')
code/open/close/high/low/avg_price：均价 position：持仓量 vol：成交总量
"""
code = code.strip().upper()
for _ in range(retry_count):
try:
if conn is None:
print(ct.MSG_NOT_CONNECTED)
return None
api, xapi = conn
ktype = freq.strip().upper()
asset = asset.strip().upper()
mkcode = _get_mkcode(code, asset=asset,
xapi=xapi) if market == '' else market
if asset in ['E', 'INDEX']:
func = getattr(api, ct.ASSET[asset])
else:
ktype = 'XD' if ktype == 'D' else ktype
func = getattr(xapi, ct.ASSET['X'])
if ktype in ct.KTYPE_LOW_COLS:
data = pd.DataFrame()
for i in range(100):
ds = func(ct.KTYPE[ktype], mkcode, code, i * 800, 800)
df = api.to_df(ds)
data = data.append(df) if i == 0 else df.append(
data, ignore_index=True)
if len(ds) < 800:
break
data['datetime'] = data['datetime'].apply(
lambda x: str(x[0:10]))
if ktype in ct.KTYPE_ARR:
data = pd.DataFrame()
for i in range(100):
ds = func(ct.KTYPE[ktype], mkcode, code, i * 800, 800)
df = api.to_df(ds)
data = data.append(df) if i == 0 else df.append(
data, ignore_index=True)
if len(ds) < 800:
break
data['datetime'] = pd.to_datetime(data['datetime'])
data = data.assign(code=str(code)) \
.set_index('datetime', drop=True, inplace=False) \
.drop(ct.T_DROP_COLS, axis=1)[ None if start_date == '' else start_date :
None if end_date == '' else end_date]
data = data.sort_index(ascending=False)
if asset in ['E', 'INDEX']:
data = data[ct.BAR_E_COLS]
if ktype in ct.KTYPE_ARR:
data['vol'] = data['vol'] / 100
else:
data = data[ct.BAR_X_COLS]
if mkcode in [28, 29, 30, 47, 60]:
data.columns = ct.BAR_X_FUTURE_COLS
data = data[ct.BAR_X_FUTURE_RL_COLS]
else:
data = data.drop(['price', 'position'], axis=1)
data.columns = ct.BAR_X_OTHER_COLS
if asset == 'E':
if adj is not None:
df = factor_adj(code)
if ktype in ct.KTYPE_LOW_COLS:
data = data.merge(df,
left_index=True,
right_index=True)
data['adj_factor'] = data['adj_factor'].fillna(
method='bfill')
else:
def get_val(day):
return df.ix[day]['adj_factor']
data['adj_factor'] = data.index.map(
lambda x: get_val(str(x)[0:10]))
for col in ct.BAR_E_COLS[1:5]:
if adj == 'hfq':
data[col] = data[col] * data['adj_factor']
else:
data[col] = data[col] * data['adj_factor'] / float(
df['adj_factor'][0])
data[col] = data[col].map(ct.FORMAT)
data = data.drop('adj_factor', axis=1)
if factors is not None and len(factors) > 0:
if 'tor' in factors:
df = factor_shares(code)
if ktype in ct.KTYPE_LOW_COLS:
data = data.merge(df,
left_index=True,
right_index=True)
data['floats'] = data['floats'].fillna(
method='bfill')
else:
def get_val(day):
return df.ix[day]['floats']
data['floats'] = data.index.map(
lambda x: get_val(str(x)[0:10]))
data['tor'] = data['vol'] / data['floats']
data['tor'] = data['tor'].map(ct.FORMAT)
data['tor'] = data['tor'].astype(float)
data = data.drop('floats', axis=1)
if 'vr' in factors:
data['vol5'] = MA(data['vol'], 5)
data['mean'] = data['vol5'].shift(-5)
data['vr'] = (data['vol'] / data['mean']).map(
ct.FORMAT)
data['vr'] = data['vr'].astype(float)
data = data.drop(['vol5', 'mean'], axis=1)
if ma is not None and len(ma) > 0:
for a in ma:
if isinstance(a, int):
data['ma%s' % a] = MA(data['close'],
a).map(ct.FORMAT).shift(-(a - 1))
data['ma%s' % a] = data['ma%s' % a].astype(float)
for col in ['open', 'high', 'low', 'close']:
data[col] = data[col].astype(float)
data['p_change'] = data['close'].pct_change(-1) * 100
data['p_change'] = data['p_change'].map(ct.FORMAT).astype(float)
return data
except:
return None
else:
data['p_change'] = data['close'].pct_change(-1) * 100
data['p_change'] = data['p_change'].map(ct.FORMAT).astype(float)
return data
raise IOError(ct.NETWORK_URL_ERROR_MSG)
def pro_bar(ts_code='',
api=None,
start_date='',
end_date='',
freq='D',
asset='E',
exchange='',
adj=None,
ma=[],
factors=None,
adjfactor=False,
contract_type='',
retry_count=3):
"""
BAR数据
Parameters:
------------
ts_code:证券代码，支持股票,ETF/LOF,期货/期权,港股,数字货币
start_date:开始日期 YYYYMMDD
end_date:结束日期 YYYYMMDD
freq:支持1/5/15/30/60分钟,周/月/季/年
asset:证券类型 E:股票和交易所基金，I:沪深指数,C:数字货币,FT:期货 FD:基金/O期权/H港股/中概美国/中证指数/国际指数
exchange:市场代码，用户数字货币行情
adj:复权类型,None不复权,qfq:前复权,hfq:后复权
ma:均线,支持自定义均线频度，如：ma5/ma10/ma20/ma60/maN
factors因子数据，目前支持以下两种：
vr:量比,默认不返回，返回需指定：factor=['vr']
tor:换手率，默认不返回，返回需指定：factor=['tor']
以上两种都需要：factor=['vr', 'tor']
retry_count:网络重试次数
Return
----------
DataFrame
code:代码
open：开盘close/high/low/vol成交量/amount成交额/maN均价/vr量比/tor换手率
期货(asset='X')
code/open/close/high/low/avg_price：均价 position：持仓量 vol：成交总量
"""
today = datetime.datetime.today().date()
today = str(today)[0:10]
start_date = '' if start_date is None else start_date
end_date = today if end_date == '' or end_date is None else end_date
ts_code = ts_code.strip().upper() if asset != 'C' else ts_code.strip(
).lower()
start_date = start_date.replace('-', '')
end_date = end_date.replace('-', '')
if len(freq.strip()) >= 3:
freq = freq.strip().lower()
else:
freq = freq.strip().upper() if asset != 'C' else freq.strip().lower()
asset = asset.strip().upper()
api = api if api is not None else pro_api()
for _ in range(retry_count):
try:
if asset == 'E':
if freq == 'D':
data = api.daily(ts_code=ts_code,
start_date=start_date,
end_date=end_date)
if factors is not None and len(factors) > 0:
ds = api.daily_basic(ts_code=ts_code,
start_date=start_date,
end_date=end_date)[[
'trade_date', 'turnover_rate',
'volume_ratio'
]]
ds = ds.set_index('trade_date')
data = data.set_index('trade_date')
data = data.merge(ds,
left_index=True,
right_index=True)
data = data.reset_index()
if ('tor' in factors) and ('vr' not in factors):
data = data.drop('volume_ratio', axis=1)
if ('vr' in factors) and ('tor' not in factors):
data = data.drop('turnover_rate', axis=1)
if freq == 'W':
data = api.weekly(ts_code=ts_code,
start_date=start_date,
end_date=end_date)
if freq == 'M':
data = api.monthly(ts_code=ts_code,
start_date=start_date,
end_date=end_date)
if 'min' in freq:
data = api.mins(ts_code=ts_code,
start_time=start_date,
end_time=end_date,
freq=freq)
data['trade_date'] = data['trade_time'].map(
lambda x: x.replace('-', '')[0:8])
data['pre_close'] = data['close'].shift(-1)
if adj is not None:
fcts = api.adj_factor(
ts_code=ts_code,
start_date=start_date,
end_date=end_date)[['trade_date', 'adj_factor']]
data = data.set_index('trade_date', drop=False).merge(
fcts.set_index('trade_date'),
left_index=True,
right_index=True,
how='left')
if 'min' in freq:
data = data.sort_values('trade_time', ascending=False)
data['adj_factor'] = data['adj_factor'].fillna(
method='bfill')
for col in PRICE_COLS:
if adj == 'hfq':
data[col] = data[col] * data['adj_factor']
if adj == 'qfq':
data[col] = data[col] * data['adj_factor'] / float(
fcts['adj_factor'][0])
data[col] = data[col].map(FORMAT)
for col in PRICE_COLS:
data[col] = data[col].astype(float)
if adjfactor is False:
data = data.drop('adj_factor', axis=1)
if 'min' not in freq:
data['change'] = data['close'] - data['pre_close']
data['pct_chg'] = data['change'] / data[
'pre_close'] * 100
data['pct_chg'] = data['pct_chg'].map(
lambda x: FORMAT(x)).astype(float)
else:
data = data.drop(['trade_date', 'pre_close'], axis=1)
elif asset == 'I':
if freq == 'D':
data = api.index_daily(ts_code=ts_code,
start_date=start_date,
end_date=end_date)
if freq == 'W':
data = api.index_weekly(ts_code=ts_code,
start_date=start_date,
end_date=end_date)
if freq == 'M':
data = api.index_monthly(ts_code=ts_code,
start_date=start_date,
end_date=end_date)
if 'min' in freq:
data = api.mins(ts_code=ts_code,
start_time=start_date,
end_time=end_date,
freq=freq)
elif asset == 'FT':
if freq == 'D':
data = api.fut_daily(ts_code=ts_code,
start_date=start_date,
end_date=end_date,
exchange=exchange)
if 'min' in freq:
data = api.mins(ts_code=ts_code,
start_time=start_date,
end_time=end_date,
freq=freq)
elif asset == 'O':
if freq == 'D':
data = api.opt_daily(ts_code=ts_code,
start_date=start_date,
end_date=end_date,
exchange=exchange)
if 'min' in freq:
data = api.mins(ts_code=ts_code,
start_time=start_date,
end_time=end_date,
freq=freq)
elif asset == 'FD':
if freq == 'D':
data = api.fund_daily(ts_code=ts_code,
start_date=start_date,
end_date=end_date)
if 'min' in freq:
data = api.mins(ts_code=ts_code,
start_time=start_date,
end_time=end_date,
freq=freq)
if asset == 'C':
if freq == 'd':
freq = 'daily'
elif freq == 'w':
freq = 'week'
data = api.coinbar(exchange=exchange,
symbol=ts_code,
freq=freq,
start_dae=start_date,
end_date=end_date,
contract_type=contract_type)
if ma is not None and len(ma) > 0:
for a in ma:
if isinstance(a, int):
data['ma%s' % a] = MA(data['close'],
a).map(FORMAT).shift(-(a - 1))
data['ma%s' % a] = data['ma%s' % a].astype(float)
data['ma_v_%s' % a] = MA(data['vol'],
a).map(FORMAT).shift(-(a - 1))
data['ma_v_%s' % a] = data['ma_v_%s' % a].astype(float)
data = data.reset_index(drop=True)
except Exception as e:
return None
else:
return data
raise IOError('ERROR.')
def pro_bar(ts_code='',
pro_api=None,
start_date=None,
end_date=None,
freq='D',
asset='E',
market='',
adj=None,
ma=[],
retry_count=3):
"""
BAR数据
Parameters:
------------
ts_code:证券代码，支持股票,ETF/LOF,期货/期权,港股,数字货币
start_date:开始日期 YYYYMMDD
end_date:结束日期 YYYYMMDD
freq:支持1/5/15/30/60分钟,周/月/季/年
asset:证券类型 E:股票和交易所基金，I:沪深指数,C:数字货币,FT:期货 FD:基金/O期权/H港股/中概美国/中证指数/国际指数
market:市场代码，通过ts.get_markets()获取
adj:复权类型,None不复权,qfq:前复权,hfq:后复权
ma:均线,支持自定义均线频度，如：ma5/ma10/ma20/ma60/maN
factors因子数据，目前支持以下两种：
vr:量比,默认不返回，返回需指定：factor=['vr']
tor:换手率，默认不返回，返回需指定：factor=['tor']
以上两种都需要：factor=['vr', 'tor']
retry_count:网络重试次数
Return
----------
DataFrame
code:代码
open：开盘close/high/low/vol成交量/amount成交额/maN均价/vr量比/tor换手率
期货(asset='X')
code/open/close/high/low/avg_price：均价 position：持仓量 vol：成交总量
"""
ts_code = ts_code.strip().upper()
api = pro_api if pro_api is not None else pro_api()
for _ in range(retry_count):
try:
freq = freq.strip().upper()
asset = asset.strip().upper()
if asset == 'E':
if freq == 'D':
df = api.daily(ts_code=ts_code,
start_date=start_date,
end_date=end_date)
if adj is not None:
fcts = api.adj_factor(
ts_code=ts_code,
start_date=start_date,
end_date=end_date)[['trade_date', 'adj_factor']]
data = df.set_index('trade_date', drop=False).merge(
fcts.set_index('trade_date'),
left_index=True,
right_index=True,
how='left')
data['adj_factor'] = data['adj_factor'].fillna(
method='bfill')
for col in PRICE_COLS:
if adj == 'hfq':
data[col] = data[col] * data['adj_factor']
else:
data[col] = data[col] * data[
'adj_factor'] / float(
fcts['adj_factor'][0])
data[col] = data[col].map(FORMAT)
for col in PRICE_COLS:
data[col] = data[col].astype(float)
data = data.drop('adj_factor', axis=1)
else:
data = df
elif asset == 'I':
if freq == 'D':
data = api.index_daily(ts_code=ts_code,
start_date=start_date,
end_date=end_date)
elif asset == 'FT':
if freq == 'D':
data = api.fut_daily(ts_code=ts_code,
start_dae=start_date,
end_date=end_date,
exchange=market)
elif asset == 'FD':
if freq == 'D':
data = api.fund_daily(ts_code=ts_code,
start_dae=start_date,
end_date=end_date)
if asset == 'C':
#//////////////////////// well soon
pass
if ma is not None and len(ma) > 0:
for a in ma:
if isinstance(a, int):
data['ma%s' % a] = MA(data['close'],
a).map(FORMAT).shift(-(a - 1))
data['ma%s' % a] = data['ma%s' % a].astype(float)
data['ma_v_%s' % a] = MA(data['vol'],
a).map(FORMAT).shift(-(a - 1))
data['ma_v_%s' % a] = data['ma_v_%s' % a].astype(float)
return data
except Exception as e:
print(e)
return None
else:
return
raise IOError('ERROR.')