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find_max_rsrs.py
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find_max_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
quotation = use_quotation('tushare')
date = pd.to_datetime(datetime.date.today())
if date.dayofweek + 1 in [6, 7]: # 剔除周末的日期，避免混淆
date = date - datetime.timedelta(date.dayofweek - 4)
max_score = 0
def get_ols(x, y):
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
def get_zscore(slope_series):
mean = np.mean(slope_series)
std = np.std(slope_series)
return (slope_series[-1] - mean) / std
def get_rsrs_score(stock_code, days=60, slop_days=18, M=400, MA=10, buy_score=0.7, sail_score=-1.4, use_ma=1):
# m + n + days
data = quotation.get_bars(stock_code, M + slop_days + days,
fields=['close', 'high', 'low'],
end_dt=date)
data['ma'] = data.close.rolling(MA).mean()
# M+days
ols_data = [get_ols(data.low[i:i + slop_days], data.high[i:i + slop_days]) for i in range(M + days)]
slope_series = [ols_data[i][1] for i in range(M + days)]
r2 = [ols_data[i][2] for i in range(M + days)]
result = []
signal = []
position = 0 # 是否持仓，持仓：1，不持仓：0
last_rsrs_score = 0
last_ma = data.ma[M - 1]
# > -1.2 持仓
# 最近的N天
for i in range(days):
current_ma = data.ma[M + i]
close = data.close[M + i]
rsrs_score = get_zscore(slope_series[i:M + i]) * r2[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_score:
if use_ma == 0 or (use_ma == 1 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_max_args(stock_code, days, slop_days=18, M=400, MA=10, buy_score=0.7, sail_score=-1.4, use_ma=0):
(rsrs_score, slopes, signal, close) = get_rsrs_score(stock_code, days, slop_days, M,
MA, buy_score, sail_score, use_ma)
df = pd.DataFrame(rsrs_score, columns=['rsrs_score'])
df['slopes'] = slopes
df['signal'] = signal
df['close'] = close
df["策略"] = (1 + df.close.pct_change(1).fillna(0) * signal).cumprod()
df["基准"] = df['close'] / df['close'][0]
print("slop_days=%s, M=%s, MA=%s,buy_score = %s, sail_score = %s use_ma:%s 基准收益率: %s 收益率: %s" % (slop_days, M,
MA,
buy_score,
sail_score,
use_ma,
df["基准"].values[-1],
df["策略"].values[
-1]))
if df["策略"].values[-1] > df["基准"].values[-1]:
fig, axes = plt.subplots(2, 1, sharex=True, figsize=(18, 12))
df[['策略', '基准', 'signal']].plot(ax=axes[0], grid=True, title='收益', figsize=(20, 10))
df.rsrs_score.plot(ax=axes[1], title='rsrs_score', grid=True)
plt.savefig('slop_days%s_M%s_MA%s_buy_score%s_sail_score%s_usema%s.png' % (slop_days, M, MA, buy_score,
sail_score, use_ma))
return df["策略"].values[-1]
M = 600
slop_days = 18
days = 600
data = quotation.get_bars('002230', M + slop_days + days,
fields=['close', 'high', 'low'],
end_dt=date)
# data['ma'] = data.close.rolling(20).mean()
# for i in range(M):
# HS300.loc[i, 'beta_norm'] = (HS300.loc[i, 'beta'] - HS300.loc[:i - 1, 'beta'].mean()) / HS300.loc[:i - 1,
# 'beta'].std()
# M+days
ols_data = [get_ols(data.low[i:i + slop_days], data.high[i:i + slop_days]) for i in range(len(data))]
slope_series = [ols_data[i][1] for i in range(M + days)]
r2 = [ols_data[i][2] for i in range(M + days)]
# 斜率直方图
plt.figure(figsize=(15,5))
plt.hist(slope_series, bins= 100, range= None, weights= None, cumulative= False,
bottom= None, histtype= 'bar', align= 'mid', orientation= 'vertical', rwidth= None, log= False, color= 'r',
label='直方图', stacked= False)
plt.show()
# slop_days=17, M=200, MA=0,buy_score = 0.9, sail_score = -1.3 use_ma:0 收益率: 2.2461242244347384
# slop_days=10, M=200, MA=10,buy_score = 1.0, sail_score = -1.2 基准收益率: 1.6168427594779367 收益率: 2.257659818289085
# slop_days=10, M=200, MA=10,buy_score = 0.9, sail_score = -1.2
# slop_days=10, M=250, MA=1,buy_score = 0.6, sail_score = -1.5 use_ma:0 基准收益率: 1.3719224724986905 收益率: 1.3719224724986916
get_max_args('002230', 600, 10, 250, 1, 0.9, -1.5, 0)
# slop_days=17, M=200, MA=1,buy_score = 1.0, sail_score = -1.4 use_ma:0 基准收益率: 1.652887346165983 收益率: 2.3299197792940176
# slop_days=17, M=200, MA=1,buy_score = 1.0, sail_score = -1.3 use_ma:0 基准收益率: 1.652887346165983 收益率: 2.4466786338581996
# get_max_args('002230', 600, 10, 200, 1, 1.0, -1.3, 0)
# slop_days=17, M=200, MA=1,buy_score = 0.9, sail_score = -1.3 use_ma:0 基准收益率: 1.652887346165983 收益率: 2.2461242244347384
# get_max_args('002230', 600, 10, 200, 10, 1.0, -1.2, 1)
# # get_max_args('002230', 600, 10, 200, 10, 0.9, -1.2, 1)
# get_max_args('002230', 240, 17, 200, 1, 0.9, -1.0, 0)
# get_max_args('002230', 240, 17, 200, 1, 1.0, -1.3, 0)
# get_max_args('002230', 240, 17, 200, 1, 0.9, -1.4, 0)
# get_max_args('002230', 240, 17, 200, 5, 1.0, -1.3, 1)
# get_max_args('002230', 240, 17, 200, 10, 1.0, -1.3, 1)
# get_max_args('002230', 240, 17, 200, 12, 1.0, -1.3, 1)
# get_max_args('002230', 240, 17, 200, 14, 1.0, -1.3, 1)
# get_max_args('002230', 240, 17, 200, 20, 1.0, -1.3, 1)
# get_max_args('002230', 240, 17, 200, 17, 1.0, -1.3, 1)
#
# 4*4*3*7*8 = 2688
# for slop_days in [10, 15, 17]:
# for M in [200, 250, 300, 400]:
# for buy_score in [0.6, 0.7, 0.9, 1.0]:
# for sail_score in [-1.0, -1.2, -1.2, -1.3, -1.4, -1.5]:
# for use_ma in [0, 1]:
# if use_ma:
# for MA in [10, 15, 20]:
# score = get_max_args('002230', 600, slop_days, M, MA, buy_score, sail_score, use_ma)
# if score > max_score:
# max_score = score
# print("当前最大值slop_days=%s, M=%s, MA=%s,buy_score = %s, sail_score = %s,use_ma:%s 收益率: "
# "%s" % (
# slop_days, M, MA, buy_score, sail_score, use_ma, max_score))
# else:
# score = get_max_args('002230', 600, slop_days, M, 1, buy_score, sail_score, use_ma)
# if score > max_score:
# max_score = score
# print("当前最大值slop_days=%s, M=%s, MA=%s,buy_score = %s, sail_score = %s use_ma:0 收益率: %s" % (
# slop_days, M, 0, buy_score, sail_score, max_score))
#
# print(max_score)