def test_cache_mm():
df = xa.get_daily("SH501018", prev=100)
l1 = len(df)
xa.set_backend(backend="memory")
xa.get_daily("SH501018", prev=50)
df = xa.get_daily("SH501018", prev=100)
l2 = len(df)
assert l1 == l2
def test_iw():
xa.set_backend(backend="csv", path="./")
df = xa.get_daily("iw-399006.XSHE", end="20200226")
assert (
df[(df["date"] == "2019-04-01") & (df["code"] == "300271.XSHE")].iloc[0].weight
== 0.9835
)
df = xa.universal.get_index_weight_range(
"399006.XSHE", start="2018-01-01", end="2020-02-01"
)
assert (
df[(df["date"] == "2019-04-01") & (df["code"] == "300271.XSHE")].iloc[0].weight
== 0.9835
)
def main():
xa.set_backend(backend="memory", precached="20200301")
render_github(
"SH501018",
"SZ160216",
"SZ162411",
"SZ160723",
"SZ161129",
"SZ162719",
"SH513030",
"SH513500",
"SZ161130",
"SZ163208",
"SZ160416",
"SZ161125",
"SH513100",
"SZ165513",
"SZ161815",
"SZ161128",
)
render_github("SZ164906", "SH513050", cols="3c")
render_github("SH513880", "SH513520", "SH513000", "SH501021", cols="3crt")
"""
一个简单展示 qdii 实时净值预测的例子，最大限度的利用缓存而减少网络请求
"""
import pandas as pd
import xalpha as xa
import logging
xa.set_backend(backend="csv", path="../../../lof/data", precached="20200103")
# xa.set_proxy("socks5://127.0.0.1:1080")
logger = logging.getLogger("xalpha")
logger.setLevel(logging.DEBUG)
ch = logging.StreamHandler()
ch.setLevel(logging.DEBUG)
logger.addHandler(ch)
@xa.universal.lru_cache_time(ttl=60)
def cached_get_rt(code, **kws):
return xa.get_rt(code, handler=False)
@xa.universal.lru_cache_time(ttl=1800)
def cached_get_bar(code, *args, **kws):
if code.startswith("commodities/"):
kws["handler"] = False
return xa.get_bar(code, *args, **kws)
return None
import sys
import pytest
sys.path.insert(0, "../")
import xalpha as xa
xa.set_backend(backend="memory", prefix="pytest-")
def test_compare():
c = xa.Compare(("37450", "USD"), "SH501018", start="20200101")
c.corr()
c.v()
def test_get_currency():
assert (xa.toolbox.get_currency_code("indices/india-50-futures") ==
"currencies/inr-cny")
assert xa.toolbox._get_currency_code("JPY") == "100JPY/CNY"
def test_qdii_predict():
hb = xa.QDIIPredict(
"SZ162411",
t1dict={".SPSIOP": 91},
t0dict={
"commodities/brent-oil": 40 * 0.9,
"commodities/crude-oil": 60 * 0.9,
},
positions=True,
)
def test_peb_range():
xa.set_backend(backend="csv", path="./")
df = xa.get_daily("peb-000807.XSHG", prev=100, end="20200202")
assert round(df[df["date"] == "2020-01-03"].iloc[0]["pe"], 2) == 30.09
def test_peb_history():
xa.set_backend(backend="csv", path="./")
h = xa.universal.PEBHistory("SH000807", end="20200302")
h.summary()
h.v() # matplotlib is required for this
assert round(h.df.iloc[0]["pe"], 2) == 19.67
def csv_cache():
xa.set_backend(backend="csv", path="./")
yield
xa.set_backend(backend="memory", path="pytest-")
