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rsrs.py
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import numpy as np
import matplotlib.pyplot as plt
from easyquant.quotation import use_quotation
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
import pandas as pd
import datetime
import jqdatasdk as jq
from easytrader.utils.misc import file2dict
config = file2dict('jqdata.json')
jq.auth(config["user"], config["password"])
quotation = use_quotation('jqdata')
class ETFQuant:
# 初始化函数
def __init__(self, stkList):
self.edate = datetime.date.today()
self.date = pd.to_datetime(self.edate)
if self.date.dayofweek + 1 in [6, 7]: # 剔除周末的日期，避免混淆
self.date = self.date - datetime.timedelta(self.date.dayofweek - 4)
# print('today:',self.edate)
self.stock_pool = stkList
self.mom = 20
self.ref = '000300.XSHG'
self.N, self.M = 17, 400
self.RSRS_threshold = 0.7
# 20日收益率动量拟合取斜率最大的
def get_rank(self):
self.slope_series = self.initial_slope_series()[:-1]
rank = []
for stock in self.stock_pool:
data = jq.get_price(stock, end_date=self.date, count=self.mom)
score = np.polyfit(np.arange(len(data)), data.close / data.close[0], 1)[0]
rank.append([stock, 100 * score])
rank.sort(key=lambda x: x[-1], reverse=True)
return rank[0]
def get_ols(self, x, y):
try:
slope, intercept = np.polyfit(x, y, 1)
r2 = 1 - (sum((y - (slope * x + intercept)) ** 2) / ((len(y) - 1) * np.var(y, ddof=1)))
return (intercept, slope, r2)
except Exception as e:
print(e)
def initial_slope_series(self):
data = jq.get_price(self.ref, end_date=self.date, count=self.M + self.N, fields=['high', 'low'])
return [self.get_ols(data.low[i:i + self.N], data.high[i:i + self.N])[1] for i in range(self.M)]
# 因子标准化
def get_zscore(self, slope_series):
mean = np.mean(slope_series)
std = np.std(slope_series)
return (slope_series[-1] - mean) / std
def get_rsrs_score(self, stock_code, days=60, slop_days=18, M=400, MA=10,buy_sail = 0.7, sail_score = -1.4):
self.M = M
# m + n + days
data = quotation.get_bars(stock_code, self.M + slop_days + days,
fields=['close', 'high', 'low'],
end_dt=self.date)
data['ma'] = data.close.rolling(MA).mean()
# M+days
ols_data = [self.get_ols(data.low[i:i + slop_days], data.high[i:i + slop_days]) for i in range(self.M + days)]
slope_series = [ols_data[i][1] for i in range(self.M + days)]
r2 = [ols_data[i][2] for i in range(self.M + days)]
result = []
signal = []
position = 1 # 是否持仓，持仓：1，不持仓：0
last_rsrs_score = 0
last_ma = data.ma[self.M -1]
# > -1.2 持仓
# 最近的N天
for i in range(days):
current_ma = data.ma[self.M + i]
close = data.close[self.M + i]
rsrs_score = self.get_zscore(slope_series[i:self.M + i]) * r2[self.M + i]
# M 天序列
result.append(rsrs_score)
signal.append(position)
if rsrs_score < sail_score:
position = 0
# 10日线拐头
elif (rsrs_score > buy_sail and current_ma > last_ma):
position = 1
last_rsrs_score = rsrs_score
last_ma = current_ma
return result, slope_series[-days:], signal, data.close[-days:].values
def get_timing_signal(self, stock_code):
high_low_data = jq.get_price(stock_code, end_date=self.date, count=self.N, fields=['high', 'low'])
intercept, slope, r2 = self.get_ols(high_low_data.low, high_low_data.high)
self.slope_series.append(slope)
rsrs_score = self.get_zscore(self.slope_series[-self.M:]) * r2
global RSRS
RSRS.append(rsrs_score)
# print("rsrs factor:\t\t{:.3f}".format(rsrs_score))
if rsrs_score > self.RSRS_threshold:
return "BUY"
elif rsrs_score < -self.RSRS_threshold:
return "SELL"
else:
return "KEEP"
def run(self, date=datetime.date.today()):
self.date = pd.to_datetime(date)
if self.date.dayofweek + 1 in [6, 7]: # 剔除周末的日期，避免混淆
self.date = date - datetime.timedelta(self.date.dayofweek - 4)
check_out_list = self.get_rank()
timing_signal = self.get_timing_signal()
return [self.date.strftime('%Y-%m-%d'), check_out_list, timing_signal]
fig, axes = plt.subplots(3, 1, sharex=True, figsize=(18, 12))
# , '000300.XSHG'['159949.XSHE'
stkList = ['002230']
eq = ETFQuant(stkList)
days = 240
# trading_dates = get_trade_da0ys(end_date=eq.date, count=eq.N)
trading_dates = jq.get_trade_days(end_date=eq.date, count=days)
RSRS = pd.DataFrame([0 for i in range(days)], index=trading_dates, columns=['0'])
stock_price = pd.DataFrame([1 for i in range(days)], index=trading_dates, columns=['1'])
slope = pd.DataFrame([1 for i in range(days)], index=trading_dates, columns=['1'])
def get_payments(stock_code, days, slop_days=18, M=400, MA=10,buy_sail = 0.7, sail_score = -1.4):
data = eq.get_rsrs_score(stock_code, days, slop_days, M, MA, buy_sail, sail_score)
RSRS[stock_code + "ma18"] = data[0]
slope[stock_code + "ma18"] = data[1]
# data = eq.get_rsrs_score(stock_code, days, 5)
# RSRS[stock_code+"ma5"] = data[0]
# slope[stock_code+"ma5"] = data[1]
# data = eq.get_rsrs_score(stock_code, days, 10)
# RSRS[stock_code + "ma10"] = data[0]
# slope[stock_code + "ma10"] = data[1]
stock_price[stock_code] = data[3]
stock_price[stock_code + "策略"] = (1 + stock_price[stock_code].pct_change(1).fillna(0) * data[2]).cumprod()
# stock_price[stock_code] = stock_price[stock_code].pct_change(1)
stock_price[stock_code] = stock_price[stock_code] / stock_price[stock_code][0]
stock_price[stock_code+"signal"] = data[2]
print("days=%s, slop_days=%s, M=%s, MA=%s,buy_sail = %s, sail_score = %s 基准收益率: %s 收益率: %s" % (days, slop_days, M,
MA,
buy_sail,sail_score,
stock_price[stock_code][-1],
stock_price[stock_code + "策略"][
-1]))
# get_payments('002230', days, slop_days=10, M=200, MA=1, buy_sail = 0.8, sail_score = -0.9 )
get_payments('002230', days, slop_days=18, M=600, MA=10,buy_sail = 0.8, sail_score = -1.4 )
# get_max_args('002230', days, slop_days=18, M=600, MA=10,buy_sail = 0.6, sail_score = -1.4 )
# get_max_args('002230', days, slop_days=18, M=600, MA=10,buy_sail = 0.5, sail_score = -1.4 )
# get_max_args('002230', days, slop_days=18, M=600, MA=10,buy_sail = 0.4, sail_score = -1.4 )
# get_max_args('002230', days, slop_days=18, M=600, MA=10,buy_sail = 0.8, sail_score = -1.4 )
# get_max_args('002230', days, slop_days=18, M=400, MA=10,buy_sail = 0.7, sail_score = -1.4 )
# get_max_args('002230', days, slop_days=18, M=400, MA=10,buy_sail = 0.6, sail_score = -1.4 )
# get_max_args('002230', days, slop_days=18, M=400, MA=10,buy_sail = 0.5, sail_score = -1.4 )
# get_max_args('002230', days, slop_days=18, M=400, MA=10,buy_sail = 0.4, sail_score = -1.4 )
# get_max_args('002230', days, slop_days=18, M=400, MA=10,buy_sail = 0.8, sail_score = -1.5 )
# get_max_args('002230', days, slop_days=18, M=400, MA=10,buy_sail = 0.7, sail_score = -1.5 )
# get_max_args('002230', days, slop_days=18, M=400, MA=10,buy_sail = 0.6, sail_score = -1.5 )
# get_max_args('002230', days, slop_days=18, M=400, MA=10,buy_sail = 0.5, sail_score = -1.5 )
# get_max_args('002230', days, slop_days=18, M=400, MA=10,buy_sail = 0.4, sail_score = -1.5 )
# get_max_args('002230', days, slop_days=18, M=400, MA=10,buy_sail = 0.8, sail_score = -1.3 )
# get_max_args('002230', days, slop_days=18, M=400, MA=10,buy_sail = 0.7, sail_score = -1.3 )
# get_max_args('002230', days, slop_days=18, M=400, MA=10,buy_sail = 0.6, sail_score = -1.3 )
# get_max_args('002230', days, slop_days=18, M=400, MA=10,buy_sail = 0.5, sail_score = -1.3 )
# get_max_args('002230', days, slop_days=18, M=400, MA=10,buy_sail = 0.4, sail_score = -1.3 )
# # print("now:", datetime.datetime.now())
# for date in trading_dates: print('target and signal:\t', eq.run(date))
RSRS['买'] = 0.8
RSRS['卖'] = -0.9
RSRS.plot(ax=axes[1], title='score', grid=True)
slope.plot(ax=axes[2], title='斜率', grid=True)
stock_price.plot(ax=axes[0], title='价格', grid=True)
#
plt.show()
# get_price('159949.XSHE', end_date=datetime.date.today(), count=3)