def market_data_panel(self, freq="D"):
start, end = self.date_range
df = DataAPI.candle(self.universe, start=start, end=end,
freq=freq).transpose(2, 1, 0)
if freq == "D":
df.major_axis = df.major_axis.normalize()
return df
def read_benchmark(start, end, index_code="000300.XSHG", freq="D"):
"""
获取指数行情数据
:param start:起始时间(datetime)
:param end:结束时间(datetime)
:param index_code:指数代码(str)
:param freq:数据频率(str)
:return:指数行情。返回一个benchmark对象
包含五个属性。"index", "close", "open", "high", "low"。
"index":一个MultiIndex Series的序列。索引为date (level 0) 和 asset (level 1),包含一列factor值。其中asset的值固定是index_code,
factor值固定是1。用于fxdayu_alphaman.selector.selector_analysis.get_stocklist_mean_return中
作为stock_list参数来计算指数收益。
"close":收盘价。(pandas.Dateframe ),index为datetime,column.name 为index_code,值为对应指数的收盘价。
"open":开盘价。(pandas.Dateframe ),index为datetime,column.name 为index_code,值为对应指数的开盘价。
"high":最高价。(pandas.Dateframe ),index为datetime,column.name 为index_code,值为对应指数的最高价。
"low":最低价。(pandas.Dateframe ),index为datetime,column.name 为index_code,值为对应指数的最低价。
"""
benchmark = Benchmark()
benchmark_value = DataAPI.candle((index_code,), freq=freq, start=start, end=end)
benchmark.open = benchmark_value.minor_xs("open")
if len(benchmark.open)==0:
raise ValueError
benchmark.high = benchmark_value.minor_xs("high")
benchmark.low = benchmark_value.minor_xs("low")
benchmark.close = benchmark_value.minor_xs("close")
benchmark.open.index.name = "date"
benchmark.high.index.name = "date"
benchmark.low.index.name = "date"
benchmark.close.index.name = "date"
benchmark.index = pd.DataFrame(data=benchmark.open.index)
benchmark.index["asset"] = index_code
benchmark.index["factor"] = 1
benchmark.index = benchmark.index.set_index(["date", "asset"])
return benchmark
def get_price_data(pool, start, end, max_window=10):
data = DataAPI.candle(
tuple(pool),
"D",
start=start - datetime.timedelta(days=max_window),
end=end + datetime.timedelta(days=max_window))
data = data.replace(to_replace=0, value=np.NaN)
return data
def market_data(self):
start, end = self.date_range
df = DataAPI.candle(self.universe,
fields=["close"],
start=start,
end=end,
freq=self._freq).loc[:, :, "close"].T
df.index.name = "order_book_id"
series = df.stack()
series.name = "close"
return series
def benchmark_rets(self, code="000300.XSHG", freq="D", isIndex=True):
start, end = self.date_range
adjust = None
if not isIndex:
adjust = "before"
benchmark = DataAPI.candle(code,
freq=freq,
fields="close",
start=start,
end=end,
adjust=adjust)
benchmark_rets = benchmark["close"].pct_change()
benchmark_rets.name = "benchmark_rets"
if freq == "D":
benchmark_rets.index = benchmark_rets.index.normalize()
return benchmark_rets
def read_benchmark(start, end, index_code="000300.XSHG", freq="D"):
"""
获取指数行情数据
:param start:起始时间(datetime)
:param end:结束时间(datetime)
:param index_code:指数代码(str)
:param freq:数据频率(str)
:return:指数行情。返回一个benchmark对象
包含五个属性。"index", "close", "open", "high", "low"。
"index":一个MultiIndex Series的序列。索引为date (level 0) 和 asset (level 1),包含一列factor值。其中asset的值固定是index_code,
factor值固定是1。用于fxdayu_alphaman.selector.selector_analysis.get_stocklist_mean_return中
作为stock_list参数来计算指数收益。
"close":收盘价。(pandas.Dateframe ),index为datetime,column.name 为index_code,值为对应指数的收盘价。
"open":开盘价。(pandas.Dateframe ),index为datetime,column.name 为index_code,值为对应指数的开盘价。
"high":最高价。(pandas.Dateframe ),index为datetime,column.name 为index_code,值为对应指数的最高价。
"low":最低价。(pandas.Dateframe ),index为datetime,column.name 为index_code,值为对应指数的最低价。
"""
benchmark = Benchmark()
try:
benchmark_value = DataAPI.candle((index_code, ),
freq=freq,
start=start,
end=end)
benchmark.open = benchmark_value.minor_xs("open")
if len(benchmark.open) == 0:
raise ValueError
benchmark.high = benchmark_value.minor_xs("high")
benchmark.low = benchmark_value.minor_xs("low")
benchmark.close = benchmark_value.minor_xs("close")
benchmark.open.index.name = "date"
benchmark.high.index.name = "date"
benchmark.low.index.name = "date"
benchmark.close.index.name = "date"
except:
index_value = ts.get_k_data(code=index_code[0:6],
start=start.strftime("%Y-%m-%d"),
end=end.strftime("%Y-%m-%d"),
ktype=freq,
index=True)
date = index_value.pop('date')
index_value["date"] = pd.to_datetime(date + " 15:00:00",
format='%Y-%m-%d %H:%M:%S')
benchmark.close = index_value[["date", "close"]]
benchmark.open = index_value[["date", "open"]]
benchmark.high = index_value[["date", "high"]]
benchmark.low = index_value[["date", "low"]]
benchmark.close.columns = ["date", index_code]
benchmark.open.columns = ["date", index_code]
benchmark.high.columns = ["date", index_code]
benchmark.low.columns = ["date", index_code]
benchmark.close = benchmark.close.set_index("date")
benchmark.open = benchmark.open.set_index("date")
benchmark.high = benchmark.high.set_index("date")
benchmark.low = benchmark.low.set_index("date")
benchmark.index = pd.DataFrame(data=benchmark.open.index)
benchmark.index["asset"] = index_code
benchmark.index["factor"] = 1
benchmark.index = benchmark.index.set_index(["date", "asset"])
return benchmark
from fxdayu_data import DataAPI
print(DataAPI.candle("000001.XSHE"))
