/
qdiipred.py
146 lines (127 loc) · 3.6 KB
/
qdiipred.py
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"""
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")
logger = logging.getLogger("xalpha")
logger.setLevel(logging.DEBUG)
ch = logging.StreamHandler()
ch.setLevel(logging.DEBUG)
logger.addHandler(ch)
@xa.universal.lru_cache_time(ttl=180)
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
xa.set_handler(method="rt", f=cached_get_rt)
xa.set_handler(method="bar", f=cached_get_bar)
qdiis = [
"SH501018",
"SZ160416",
"SZ161129",
"SZ160723",
"SZ160216",
"SZ162411",
"SZ163208",
"SZ162719",
"SZ165513",
"SZ161815", # fr
"SZ161116", # lu
"SZ164701",
"SZ160719",
"SZ164824",
"SH513030",
"SZ160140",
"SZ161714",
"SZ165510",
"SZ164906",
"SH513050",
]
nonqdiis = [
"SH501021",
"SH513880",
"SH513520",
"SH513000",
"SH510510",
"SZ159922",
"SH510500",
"SH512500",
"SZ159920",
]
data = {
"code": [],
"name": [],
"t1": [],
"t0": [],
"now": [],
"t1rate": [],
"t0rate": [],
"position": [],
}
for c in qdiis:
p = xa.QDIIPredict(c, fetch=True, save=True, positions=True)
try:
data["t1"].append(round(p.get_t1(return_date=False), 4))
data["t1rate"].append(round(p.get_t1_rate(return_date=False), 2))
try:
data["t0"].append(round(p.get_t0(return_date=False), 4))
data["t0rate"].append(round(p.get_t0_rate(return_date=False), 2))
except ValueError:
data["t0"].append("-")
data["t0rate"].append("-")
data["position"].append(round(p.get_position(return_date=False), 3))
data["now"].append(xa.get_rt(c)["current"])
data["code"].append(c)
data["name"].append(xa.get_rt(c)["name"])
except xa.exceptions.NonAccurate as e:
print("%s cannot be predicted exactly now" % c)
print(e.reason)
for c in nonqdiis:
try:
p = xa.RTPredict(c)
data["t0"].append(round(p.get_t0(return_date=False), 4))
data["t0rate"].append(round(p.get_t0_rate(return_date=False), 2))
data["t1"].append(xa.get_rt("F" + c[2:])["current"])
data["t1rate"].append("-")
data["position"].append("-")
data["now"].append(xa.get_rt(c)["current"])
data["code"].append(c)
data["name"].append(xa.get_rt(c)["name"])
except xa.exceptions.NonAccurate as e:
print("%s cannot be predicted exactly now" % c)
print(e.reason)
df = pd.DataFrame(data)
htmlstr = (
"""<html>
<meta charset="UTF-8">
<link rel="stylesheet" type="text/css" href="https://cdn.datatables.net/1.10.20/css/jquery.dataTables.css">
<script src="https://ajax.aspnetcdn.com/ajax/jQuery/jquery-3.4.1.min.js"></script>
<script type="text/javascript" charset="utf8" src="https://cdn.datatables.net/1.10.20/js/jquery.dataTables.js"></script>
<script>
$(document).ready( function () {
$('#df').DataTable({"scrollY": "88%",
"scrollCollapse": true,
"paging": false,
"fixedHeader": true
});
} );
</script>
<style>
td, th {
text-align: center;
}
#df tbody tr:hover {
background-color: #ffff99;
}
</style>"""
+ df.to_html(table_id="df", index=False)
+ "</html>"
)
with open("demo.html", "w") as f:
f.writelines([htmlstr])