main script
main script
# jungle.py
# import sys, io, os, inspect
import numpy as np, pandas as pd; _=np
pd.set_option('display.precision', 6)
import matplotlib.pyplot as plt
# from dqsbt.util2 import util as aid # 加载数据
# from util import ttr # 计算指标
# import candleplotter as cplt
# from qsutil import pkl
# from texttable import Texttable
# from packages import report as rp
# from qsutil import pkl; _cn_dict = pkl.pkl_load('d:/db/amipy/data/codename_dict.pkl')
import momentums_config as cfg
# import utility as ut
from qsutil import utility as ut
from qsutil import bbplot as bbp; _=bbp
import flower as fl; _=fl
p=dict(mfile='D:\\algolab\\study\\momentums\\momentums.py') # __file__
_pn = [n for n in dir(cfg) if not n.startswith('__')]
_pv = [getattr(cfg, n) for n in _pn]
p.update(dict(zip(_pn, _pv)))
from collections import namedtuple
# import pfbroker as pfb
_=namedtuple,
import jungle as jg
def pre_proc():
'''
>>> ohlc, ind = pre_proc()
'''
ohlc, _codes, _dnames=ut.load(subset=cfg.subset)
ind = ut.indicator(ohlc, n1=cfg.n1, n2=cfg.n2)
return ohlc, ind
class RocAlgo(jg.Jungle):
def __init__(self,
cfg, ind,
weight,
nComponent=2,
nAdjustPos=5,
dbg=True,
):
super().__init__(cfg, ind,
nComponent=nComponent, nAdjustPos=nAdjustPos, dbg=dbg)
self.weight = weight
def run(self, ):
mcdf = self.ind.loc[:, ('close roc'.split())]
n = len(self.codes)
for i, row in enumerate(mcdf.itertuples()):
dt,clos, rocs = row[0], row[1:1+n], row[1+n:]
dts = self.dt2dts(dt)
close_dict={ k:v for k,v in zip(self.codes, clos)}
_ = dts, close_dict
# 循环时最好依据数据出现的情况的顺序来处理,
# 比如首先遇到的是na值, 处理之. 很简单.
# 用continue来处理之(继续下一行的循环). 避免太多的elif语句
if pd.isna(rocs[0]): continue
# 开仓调仓日
if i%self.nAdjustPos==0:
# 开仓
if self.b.balance is None:
# print(i, dts, clos, rocs); _=input('pause')
buy_list, sorted_roc = self.sort_rocs(rocs)
self.price =[clos[i] for i in self.buy_iloc]
# 等权重配置方式
# 收盘前5分钟买入
code=buy_list
bbo = self.o.create_batch_buy_order(i, code, percent=self.weight)
# print('最新订单:\n', self.o); print('\n历史订单:'); self.o.porders() # 最新订单和历史订单
self.b._first_batch_buy(bbo)
# self.pause()
# 收盘后收集收盘价
self.check_totcap(i)
# _=input('建仓之后pause')
# 调仓
else:
buy_list, sorted_roc = self.sort_rocs(rocs)
self.price =[clos[i] for i in self.buy_iloc]
if sorted(buy_list)==sorted(self.get_ccc()):
# print('调仓日, 动量排序进榜个股与持仓个股名单相同, 无需操作.')
pass
# 动量选股之后, 有新的进榜个股
else:
print(f'调仓日: i={i}, buy_list={buy_list}, 头寸个股名单={self.get_ccc()}')
remove_gg, addon_gg = self.get_addon_remove(buy_list)
# print(f'卖出个股={remove_gg}, 买入个股={addon_gg}')
# Todo
# . 卖出头寸里的 退榜个股, 及时drop该席位: pos=pos.drop(psn, level=0)
# . 买入 新的进榜个股, 及时新建一个单行单列头寸: 设置psn=100+psn
# other_pos=pd.DataFrame(),
# 并联到头寸里: pos=pd.concat([pos, other_pos], axis=1)
# . 按照动量顺序重排头寸席位
# b.sort_position_seat(buylist)
if len(remove_gg)==1:
code=remove_gg[0]
so = self.o.create_sell_order(i, code, 0.)
self.b.sell_target_percent(so)
if len(addon_gg)==1:
code=addon_gg[0]
# print(f'准备买入进榜个股={code}')
bo = self.o.create_buy_order(i, code, 0.5)
self.b.buy_target_percent(bo, buy_list)
# 头寸合并,排序
# self.b.position = pd.concat([self.b.position, self.b.other_pos], axis=1)
self.b.sort_position_seat(buy_list)
# print(self.o)
# print(self.b.get_position())
# self.pause()
# 收盘后查资
self.check_totcap(i)
# _=input('建仓之后pause')
# 非调仓日
else:
# 尚未建仓时
if self.b.balance==None:
pass
# 有仓位时
elif self.get_v0()>0:#第一号仓位有的
self.check_totcap(i)
pass
# 空仓时, 只登记个时间戳而已
elif self.get_v0()==0:
self.log_dt() #
pass
print(f'nAdjustPos={self.nAdjustPos}, totcap={self.b.totcap:.2f}')
# self.post_proc()
# self.draw_c0()
# self.check_result()
#%%
def utest2(cfg, ind, nAdjustPos=5, nComponent=2, weight=(0.5, 0.5)):
'''
>>> ohlc, ind = pre_proc()
>>> jgl = utest2(cfg, ind, 8, 2, (0.5, 0.5)) # 14.4w
>>> jgl = utest2(cfg, ind, 4, 2, (0.9, 0.1)) # 15.5w
>>> jgl = utest2(cfg, ind, 4, 3, (0.6, 0.2, 0.2)) # 15.5w
'''
jgl = RocAlgo(cfg, ind,
weight=weight,
nComponent=nComponent,
nAdjustPos=nAdjustPos,
)
jgl.run()
return jgl
def save_etf(start_date='1900-01-01', end_date='2020-12-31'):
''' 下载主要etf数据, 并且保存到db.
>>> op=DbOperation()
>>> op.save_etf()
df_dict = save_etf()
'''
import util.tdxhq as hq, time
start_date='2020-01-01'
major_etf_codes = '512880 512660 512800 512170 159928'.split()
print('Downloading 主要etf的日线数据 from tdxhq......\n')
print(f"start date = {start_date:s}, end date={end_date:s}")
df_dict={}
for i, code in enumerate(major_etf_codes):
print(f'{i:2d}', 'downloading.......:' , code)
df=hq.test_get_k_data(code, sdate=start_date, edate=end_date)
df.index=pd.DatetimeIndex(df.index)
df = df.assign(code=code[:6])
df = df.rename(columns={'vol':'volume'})
# df = df.drop(columns='date')
# cols = list(df)
cols = 'open high low close volume amount code'.split()
df = df.loc[:, cols]
# df.to_sql('table_etf', conn, if_exists='append', dtype={'code':'CHAR'})
time.sleep(2)
df_dict.update({code:df})
return df_dict
def _draw(df_dict):
fig, ax = plt.subplots(len(df_dict), 1)
for i, (k,v) in enumerate(df_dict.items()):
(v.close/v.close[0]).plot(ax=ax[i], label=k, legend=True)
fig, ax = plt.subplots(1, 1)
for i, (k,v) in enumerate(df_dict.items()):
(v.close/v.close[0]).plot(ax=ax, label=k, legend=True)
duanqs
