basic_tickers = ['SHY','SPY','XLB','XLE','XLF','XLI','XLK','XLP','XLU','XLV','XLY']
etf_tickers = basic_tickers
mc_budget = [0.8, 0.2]
if len(etf_tickers)>2:
for e in range(2,len(etf_tickers)):
mc_budget.append(0.0)
pl=read_csv(etf_tickers)
#周线
wp = pl.resample('W', loffset=pd.offsets.timedelta(days=0)).last().dropna()
# 选择时间
end_year = 2019
start_year = 2004
hist_p = wp[wp.index.year<=end_year]
hist_p = hist_p[hist_p.index.year>=start_year]
evaluated_data = hist_p.copy()
hist_p = wp[wp.index.year<start_year]
supporting_data = hist_p.copy()
# back testing
# Performance for Strategy
start_time = process_time()
pfm, s, w, accumulated_return, total_return, \
annual_return, annual_std, sharpe_ratio, \
max_loss, indicators = get_performance(\
supporting_data, evaluated_data, mc_budget, get_shares)
print('Running Time: ', process_time()-start_time)
s_d = s.diff()
s_d.iloc[0] = s.iloc[0]
s_d = s_d.drop(['Cash'], axis=1)
#s_d = s_d.drop(s_d.index[-1])
share_file = get_filename(start_year, end_year, 'xlsx', 'Reporting\\results')
s_d.to_excel(share_file, encoding='utf-8')
0.00] #
pl = read_csv(etf_tickers, end_date)
#周线
wp = pl.resample('W', loffset=pd.offsets.timedelta(days=-6)).mean().dropna()
# 选择时间
end_year = 2018
start_year = 2004
hist_p = wp[wp.index.year <= end_year]
hist_p = hist_p[hist_p.index.year >= start_year]
evaluated_data = hist_p.copy()
hist_p = wp[wp.index.year < start_year]
supporting_data = hist_p.copy()
# back testing
# Performance for Strategy
start_time = process_time()
pfm, s, w, accumulated_return, total_return, annual_return, annual_std, sharpe_ratio, max_loss, indicators = get_performance(
supporting_data, evaluated_data, mc_budget, get_shares)
print('Running Time: ', process_time() - start_time)
result = display_performance(w,
accumulated_return,
total_return,
annual_return,
annual_std,
sharpe_ratio,
max_loss,
annual=0)
figname = get_figname(start_year, end_year)
show_indicators = indicators[indicators.columns[:2]]
plot_performance(w, accumulated_return, show_indicators, figname)
csv_file = get_figname(start_year, end_year, 'csv')
csv_performance(csv_file,
'w',
portfolio = 'Portfolio'
#周线
wp = pl.resample('W', loffset=pd.offsets.timedelta(days=-6)).mean().dropna()
# 准备测试数据
end_year = 2018
start_year = 2013
hist_p = wp[wp.index.year<=end_year]
hist_p = hist_p[hist_p.index.year>=start_year]
evaluated_data = hist_p.copy()
hist_p = wp[wp.index.year<start_year]
supporting_data = hist_p.copy()
# Performance for Strategy
start_time = process_time()
pfm, s, w, accumulated_return, total_return, annual_return, annual_std, sharp_ratio, max_loss, risk_bias = get_performance(supporting_data, evaluated_data, get_shares)
print('Running Time: ', process_time()-start_time, 's')
print('Total Risk Bias: ', risk_bias)
title = display_performance(pfm, s, w, accumulated_return, total_return, annual_return, annual_std, sharp_ratio, max_loss, annual=0)
figname = get_figname(start_year, end_year)
plot_performance(w, accumulated_return, figname)
#pfm.to_csv('checking1.csv')
#s.to_csv('checking2.csv')
#w.to_csv('checking3.csv')
#accumulated_return.to_csv('checking4.csv')
'''
sy = start_year
ey = end_year
for i in range(sy,ey+1):
end_year = i
start_year = i