def sys_risk_analyse(is_realtime_update=False):
"""
when less then -ultimate_coefficient*sys_risk_range, will be zero position;
when greater then ultimate_coefficient*sys_risk_range, will be max position;
others, will be linearly increased by sys_score
"""
"""
:param max_position: float type,
:param ultimate_coefficient: float type,
:return: position,sys_score,is_sys_risk
"""
shzh_weight=0.65
#is_sys_risk=False
shz_code_str='999999'
print(shz_code_str,'----------------------------------')
shz_stock=tds.Stockhistory(shz_code_str,'D')
#print(shz_stock.h_df.tail(10))
if is_realtime_update:
k_data=get_real_stock_k(shz_code_str='999999')
k_data=['2016/04/21',2954.38,2990.69,2935.05,3062.58,189000000,2.1115e+11]
shz_stock.update_realtime_hist_df(k_data)
#print(shz_stock.h_df.tail(10))
#shangzheng_ma_score,shangzheng_score,k_position=shz_stock.get_market_score()
shz_stock.form_temp_df(shz_code_str)
#print(shangzheng_ma_score,shangzheng_score,k_position)
scz_code_str='399001'
zxb_code_str='399005'
chy_code_str='399006'
chy_code_str='300044'
print(chy_code_str,'----------------------------------')
chy_stock=tds.Stockhistory(chy_code_str,'D')
if is_realtime_update:
k_data=get_real_stock_k(shz_code_str='399006')
#k_data=['2016/04/21',2954.38,2990.69,2935.05,3062.58,189000000,2.1115e+11]
shz_stock.update_realtime_hist_df(k_data)
#chuangye_ma_score,chuangye_score,k_position=chy_stock.get_market_score()
chy_stock.form_temp_df(chy_code_str)
#print(chuangye_ma_score,chuangye_score,k_position)
#sys_score=round(0.65*shangzheng_score+0.35*chuangye_score,2) #-5 ~5
chy_first_date=chy_stock.temp_hist_df.head(1).iloc[0].date
shz_temp_df=shz_stock.temp_hist_df.set_index('date')
chy_temp_df=chy_stock.temp_hist_df.set_index('date')
#shz_temp_df=shz_stock.temp_hist_df.tail(1000).set_index('date')
#chy_temp_df=chy_stock.temp_hist_df.tail(1000).set_index('date')
shz_temp_df=shz_temp_df.fillna(0)
chy_temp_df=chy_temp_df.fillna(0)
shz_temp_df['sys_score']=shzh_weight*shz_temp_df['k_score']+(1-shzh_weight)*chy_temp_df['k_score']
shz_temp_df['position']=shzh_weight*shz_temp_df['position']+(1-shzh_weight)*chy_temp_df['position']
shz_temp_df['operation']=shz_temp_df['position']-shz_temp_df['position'].shift(1)
shz_temp_df.to_csv('shz_temp_df.csv')
sys_df=shz_temp_df[['sys_score','position','operation']].round(3)
sys_df.to_csv('sys.csv')
return sys_df
def back_test_one_stock(stock_symbol,
rate_to_confirm=0.0001,
temp_dir=fc.ALL_TEMP_DIR,
bs_temp_dir=fc.ALL_BACKTEST_DIR):
if stock_symbol == '000029' and source == 'easyhistory':
return
s_stock = tds.Stockhistory(stock_symbol,
'D',
test_num=0,
source='yh',
rate_to_confirm=0.01)
if s_stock.h_df.empty:
print('New stock %s and no history data' % stock_symbol)
return
result_df = s_stock.form_temp_df(stock_symbol)
s_stock.form_regression_result(save_dir=bs_temp_dir,
rate_to_confirm=0.0001)
#recent_trend = s_stock.get_recent_trend(num=20,column='close')
s_stock.diff_ma_score(ma=[10, 30, 60, 120, 250],
target_column='close',
win_num=5)
temp_hist_df = s_stock.temp_hist_df.set_index('date')
try:
temp_hist_df.to_csv(temp_dir + '%s.csv' % stock_symbol)
except:
pass
"""
temp_hist_df_tail = temp_hist_df.tail(1)
temp_hist_df_tail['code'] = stock_symbol
"""
return
def filter(self, filter_type, symbols=None):
target_symbols = []
if symbols and isinstance(symbols, str):
target_symboles = [symbols]
elif symbols and isinstance(symbols, str):
target_symboles = symbols
else:
target_symboles = self.get_all_symbols()
stock_hist_anlyse = tds.Stockhistory(code_str='000001', ktype='D')
for symbol in target_symboles:
pass
return
def back_test_dapan(test_codes,
k_num=0,
source='yh',
rate_to_confirm=0.01,
processor_id=0):
i = 0
for stock_symbol in test_codes:
if stock_symbol == '000029' and source == 'easyhistory':
continue
print(i, stock_symbol)
s_stock = tds.Stockhistory(stock_symbol,
'D',
test_num=k_num,
source=source,
rate_to_confirm=rate_to_confirm)
if s_stock.h_df.empty:
print('New stock %s and no history data' % stock_symbol)
continue
if True:
if dapan_stocks and (stock_symbol in dapan_stocks):
dapan_criteria = (
(s_stock.temp_hist_df['o_change'] > 0.30) &
(s_stock.temp_hist_df['pos20'].shift(1) <= 1.0))
dapan_regress_column_type = 'open'
dapan_high_o_df, dapan_high_open_columns = s_stock.regress_common(
dapan_criteria,
post_days=[0, -1, -2, -3, -4, -5, -10, -20, -60],
regress_column=dapan_regress_column_type,
base_column='open',
fix_columns=[
'date', 'close', 'p_change', 'o_change', 'position',
'pos20', 'oo_chg', 'oh_chg', 'ol_chg', 'oc_chg'
])
dapan_high_o_df['code'] = stock_symbol
dapan_high_o_df['ho_index'] = np.where(
dapan_high_o_df['pos20'] <= 0, 0,
(dapan_high_o_df['o_change'] /
dapan_high_o_df['pos20']).round(2))
dapan_ho_df = dapan_ho_df.append(dapan_high_o_df)
else:
pass
def get_all_regress_summary(given_stocks=[],
confirm=0.01,
dest_file=fc.ALL_SUMMARY_FILE):
all_result_df = tds.pd.DataFrame({})
"""
latest_temp_df = tds.pd.read_csv( fc.ALL_TEMP_FILE)
latest_temp_df['code'] = latest_temp_df['code'].apply(lambda x: pds.format_code(x))
stock_codes = latest_temp_df['code'].values.tolist()
latest_temp_df = latest_temp_df.set_index('code')
#print(latest_temp_df.ix['000014'].date)
"""
#given_stocks = ['000001','000002']
for stock_symbol in given_stocks:
s_stock = tds.Stockhistory(stock_symbol,
'D',
test_num=0,
source='yh',
rate_to_confirm=confirm)
result_series = s_stock.get_regression_result(
rate_to_confirm=confirm,
refresh_regression=False,
from_csv=True,
bs_csv_dir=fc.ALL_BACKTEST_DIR,
temp_csv_dir=fc.ALL_TEMP_DIR)
if not result_series.empty:
test_result_df = tds.pd.DataFrame({stock_symbol: result_series}).T
all_result_df = all_result_df.append(test_result_df,
ignore_index=False)
if dest_file:
try:
all_result_df['code'] = all_result_df.index
all_result_df['name'] = all_result_df['code'].apply(
lambda x: pds.format_name_by_code(x, CHINESE_DICT))
del all_result_df['code']
#dest_file = 'D:/work/result/all_summary1.csv'
all_result_df.to_csv(dest_file, encoding='utf-8')
except:
pass
return all_result_df
def get_stock_position(stock_synbol='399006',is_realtime_update=False,index_weight=0.65):
"""
when less then -ultimate_coefficient*sys_risk_range, will be zero position;
when greater then ultimate_coefficient*sys_risk_range, will be max position;
others, will be linearly increased by sys_score
"""
"""
:param stock_synbol: str type, 个股指数
:param is_realtime_update: bool type, 是否实时更新仓位
:param index_weight: float type, 指数仓位占的比重
:return: position,sys_score,is_sys_risk
"""
refer_index='999999' #参考指数
if stock_synbol<'000999':#深证指数
refer_index = '399001'
elif stock_synbol>'002000' and stock_synbol<'002999':#中小板指数
refer_index ='399005'
elif stock_synbol>'300000' and stock_synbol<'309999':#创业板指数
refer_index = '399006'
elif stock_synbol > '600000' and stock_synbol<'609999':#上证指数
refer_index = '999999'
else:#返回系统仓位
stock_synbol='399006'
pass
#index_weight=0.65#指数占的比重
#is_sys_risk=False
print('refer_index=%s' % refer_index,'----------------------------------')
index_stock=tds.Stockhistory(refer_index,'D')
#print(shz_stock.h_df.tail(10))
if is_realtime_update:
k_data=get_real_stock_k(code_str=refer_index)
k_data=['2016/04/21',2954.38,2990.69,2935.05,3062.58,189000000,2.1115e+11]
index_stock.update_realtime_hist_df()
#print(shz_stock.h_df.tail(10))
#shangzheng_ma_score,shangzheng_score,k_position=index_stock.get_market_score()
index_stock.form_temp_df(refer_index)
max_series,max_value,min_series,min_value=index_stock.get_max(column_name='close',latest_num=10)
#print(max_series,max_value,min_series,min_value)
max_series,max_value,min_series,min_value=index_stock.get_max(column_name='close',latest_num=20)
#print(max_series,max_value,min_series,min_value)
max_series,max_value,min_series,min_value=index_stock.get_max(column_name='close',latest_num=30)
#index_stock.boduan_analyze()
#print(max_series,max_value,min_series,min_value)
#print(shangzheng_ma_score,shangzheng_score,k_position)
print(stock_synbol,'----------------------------------')
s_stock=tds.Stockhistory(stock_synbol,'D')
if is_realtime_update:
k_data=get_real_stock_k(code_str=stock_synbol)
#k_data=['2016/04/21',2954.38,2990.69,2935.05,3062.58,189000000,2.1115e+11]
s_stock.update_realtime_hist_df()
#chuangye_ma_score,chuangye_score,k_position=s_stock.get_market_score()
s_stock.is_island_reverse_up()
s_stock.form_temp_df(stock_synbol)
index_temp_df=index_stock.temp_hist_df.set_index('date')
if s_stock.temp_hist_df.empty:
index_temp_df['sys_score']=index_temp_df['k_score']
index_temp_df['position']=index_temp_df['position']
index_temp_df['operation']=index_temp_df['position']-index_temp_df['position'].shift(1)
return index_temp_df[['sys_score','position','operation']].round(3)
#print(chuangye_ma_score,chuangye_score,k_position)
#sys_score=round(0.65*shangzheng_score+0.35*chuangye_score,2) #-5 ~5
#stock_first_date=stock.temp_hist_df.head(1).iloc[0].date
stock_temp_df=s_stock.temp_hist_df.set_index('date')
#shz_temp_df=shz_stock.temp_hist_df.tail(1000).set_index('date')
#chy_temp_df=chy_stock.temp_hist_df.tail(1000).set_index('date')
i_temp_df=index_temp_df.fillna(0)
s_temp_df=stock_temp_df.fillna(0)
s_temp_df['sys_score']=index_weight*i_temp_df['k_score']+(1-index_weight)*s_temp_df['k_score']
s_temp_df['pos']=index_weight*i_temp_df['position']+(1-index_weight)*s_temp_df['position']
s_temp_df['oper']=i_temp_df['position']-i_temp_df['position'].shift(1)
s_temp_df.to_csv('%s.csv'%stock_synbol)
select_columns=['close','p_change','gap','star','star_chg','ma5_chg','ma10_chg','k_rate','p_rate','island','atr_in','reverse','cross1','cross2','cross3','sys_score','pos','oper','std','tangle_p']
stock_df=s_temp_df[select_columns].round(3)
stock_df.to_csv('stock_%s.csv' % stock_synbol)
print(stock_df.tail(80))
return stock_df
def back_test_stocks(test_codes,
k_num=0,
source='yh',
rate_to_confirm=0.01,
processor_id=0,
save_type='',
all_result_columns=[],
trend_columns=[],
all_temp_columns=[],
deep_star_columns=[]):
i = 0
ma_num = 20
regress_column_type = 'close'
all_result_df = tds.pd.DataFrame({}, columns=all_result_columns)
all_trend_result_df = tds.pd.DataFrame({}, columns=trend_columns)
all_temp_hist_df = tds.pd.DataFrame({}, columns=all_temp_columns)
#deep_star_columns = ['date','close','p_change','o_change','position','low_high_open','high_o_day0','high_o_day1','high_o_day3',
# 'high_o_day5','high_o_day10','high_o_day20','high_o_day50']
#deep_star_columns = []
deep_star_df = tds.pd.DataFrame({}, columns=deep_star_columns)
print('processor_id=%s : %s' % (processor_id, test_codes))
for stock_symbol in test_codes:
if stock_symbol == '000029' and source == 'easyhistory':
continue
print('processor_id=%s :%s,%s' % (processor_id, i, stock_symbol))
s_stock = tds.Stockhistory(stock_symbol,
'D',
test_num=k_num,
source=source,
rate_to_confirm=rate_to_confirm)
if s_stock.h_df.empty:
print('New stock %s and no history data' % stock_symbol)
continue
if True:
#try:
result_df = s_stock.form_temp_df(stock_symbol)
test_result = s_stock.regression_test(rate_to_confirm)
recent_trend = s_stock.get_recent_trend(num=ma_num, column='close')
s_stock.diff_ma(ma=[10, 30], target_column='close', win_num=5)
temp_hist_df = s_stock.temp_hist_df.set_index('date')
#temp_hist_df.to_csv('C:/hist/day/temp/%s.csv' % stock_symbol)
temp_hist_df_tail = temp_hist_df.tail(1)
temp_hist_df_tail['code'] = stock_symbol
all_temp_hist_df = all_temp_hist_df.append(temp_hist_df_tail)
#columns = ['close','p_change','o_change','position','low_high_open','high_o_day0','high_o_day1','high_o_day3','high_o_day5','high_o_day10','high_o_day20']
#high_o_df,high_open_columns = s_stock.regress_high_open(regress_column = regress_column_type,base_column='open')
#criteria = s_stock.temp_hist_df['low_high_open']!= 0
criteria = ((s_stock.temp_hist_df['star_l'] > 0.50) &
(s_stock.temp_hist_df['l_change'] < -3.0) &
(s_stock.temp_hist_df['pos20'].shift(1) < 0.2))
high_o_df, high_open_columns = s_stock.regress_common(
criteria,
post_days=[0, -1, -2, -3, -4, -5, -10, -20, -60],
regress_column=regress_column_type,
base_column='close',
fix_columns=[
'date', 'close', 'p_change', 'o_change', 'position',
'pos20', 'MAX20high', 'star_l'
])
high_o_df['code'] = stock_symbol
high_o_df['star_index'] = np.where(
high_o_df['pos20'] <= 0, 0,
(high_o_df['star_l'] / high_o_df['pos20'] *
((high_o_df['MAX20high'] - high_o_df['close']) /
high_o_df['MAX20high'])).round(2))
deep_star_df = deep_star_df.append(high_o_df)
i = i + 1
if test_result.empty:
pass
else:
test_result_df = tds.pd.DataFrame(test_result.to_dict(),
columns=all_result_columns,
index=[stock_symbol])
all_result_df = all_result_df.append(test_result_df,
ignore_index=False)
if recent_trend.empty:
pass
else:
trend_result_df = tds.pd.DataFrame(recent_trend.to_dict(),
columns=trend_columns,
index=[stock_symbol])
all_trend_result_df = all_trend_result_df.append(
trend_result_df, ignore_index=False)
#except:
# print('Regression test exception for stock: %s' % stock_symbol)
if save_type == 'csv': #write to csv
all_temp_hist_df_file_name = 'C:/work/temp1/all_temp_hist_%s' % processor_id + '.csv'
all_result_df_file_name = 'C:/work/temp1/all_result_%s' % processor_id + '.csv'
deep_star_df_file_name = 'C:/work/temp1/deep_star_%s' % processor_id + '.csv'
all_trend_result_df_file_name = 'C:/work/temp1/all_trend_result_%s' % processor_id + '.csv'
all_temp_hist_df.to_csv(all_temp_hist_df_file_name)
all_result_df.to_csv(all_result_df_file_name)
deep_star_df.to_csv(deep_star_df_file_name)
all_trend_result_df.to_csv(all_trend_result_df_file_name)
return all_temp_hist_df, all_result_df, deep_star_df, all_trend_result_df
def back_test(k_num=0,given_codes=[],except_stocks=['000029'], type='stock', source='easyhistory',rate_to_confirm = 0.01,dapan_stocks=['000001','000002']):
"""
高于三天收盘最大值时买入，低于三天最低价的最小值时卖出： 33策略
"""
"""
:param k_num: string type or int type: mean counts of history if int type; mean start date of history if date str
:param given_codes: str type,
:param except_stocks: list type,
:param type: str type, force update K data from YH
:return: source: history data from web if 'easyhistory', history data from YH if 'YH'
"""
#addition_name = ''
#if type == 'index':
addition_name = type
all_codes = []
all_stop_codes = []
all_stocks = []
all_trade_codes = []
#print('source=',source)
if source =='yh' or source=='YH':
hist_dir='C:/中国银河证券海王星/T0002/export/'
#print(given_codes,except_stocks)
all_stop_codes,all_stocks1 = get_stopped_stocks(given_codes,except_stocks,hist_dir)
#print('all_stocks1=',('150251' in all_stocks1))
all_trade_codes = list(set(all_stocks1).difference(set(all_stop_codes)))
else:
hist_dir='C:/hist/day/data/'
all_stop_codes,all_stocks = get_stopped_stocks(given_codes,except_stocks,hist_dir)
#print('all_stocks2=',('150251' in all_stocks))
all_trade_codes = list(set(all_stocks).difference(set(all_stop_codes)))
#print('all_trade_codes=',('150251' in all_trade_codes))
#all_codes = ['300128', '002288', '002156', '300126','300162','002717','002799','300515','300516','600519',
# '000418','002673','600060','600887','000810','600115','600567','600199','000596','000538','002274','600036','600030','601398']
column_list = ['count', 'mean', 'std', 'max', 'min', '25%','50%','75%','cum_prf',
'fuli_prf','yearly_prf','last_trade_date','last_trade_price','min_hold_count',
'max_hold_count','avrg_hold_count','this_hold_count','exit','enter',
'position','max_amount_rate','max_amount_distance','break_in',
'break_in_count','break_in_date', 'break_in_distance','success_rate','days']
all_result_df = tds.pd.DataFrame({}, columns=column_list)
i=0
trend_column_list = ['count', 'mean','chg_fuli', 'std', 'min', '25%', '50%', '75%', 'max', 'c_state',
'c_mean', 'pos_mean', 'ft_rate', 'presure', 'holding', 'close','cont_num','amount_rate','ma_amount_rate']
all_trend_result_df = tds.pd.DataFrame({}, columns=trend_column_list)
all_temp_hist_df = tds.pd.DataFrame({}, columns=[])
ma_num = 20
stock_basic_df=ts.get_stock_basics()
basic_code = stock_basic_df['name'].to_dict()
basic_code_keys = basic_code.keys()
#print('all_trade_codes=',all_trade_codes)
#high_open_columns = ['date','close','p_change','o_change','position','low_high_open','high_o_day0','high_o_day1','high_o_day3',
# 'high_o_day5','high_o_day10','high_o_day20','high_o_day50']
high_open_columns = []
deep_star_df = tds.pd.DataFrame({}, columns=high_open_columns)
dapan_ho_df = tds.pd.DataFrame({}, columns=high_open_columns)
regress_column_type = 'close'
for stock_symbol in all_trade_codes:
if stock_symbol=='000029' and source=='easyhistory':
continue
print(i,stock_symbol)
s_stock=tds.Stockhistory(stock_symbol,'D',test_num=k_num,source=source,rate_to_confirm=rate_to_confirm)
if True:
#try:
result_df = s_stock.form_temp_df(stock_symbol)
test_result = s_stock.regression_test(rate_to_confirm)
recent_trend = s_stock.get_recent_trend(num=ma_num,column='close')
s_stock.diff_ma(ma=[10,30],target_column='close',win_num=5)
temp_hist_df = s_stock.temp_hist_df.set_index('date')
#temp_hist_df.to_csv('C:/hist/day/temp/%s.csv' % stock_symbol)
temp_hist_df_tail = temp_hist_df.tail(1)
temp_hist_df_tail['code'] = stock_symbol
all_temp_hist_df= all_temp_hist_df.append(temp_hist_df_tail)
#columns = ['close','p_change','o_change','position','low_high_open','high_o_day0','high_o_day1','high_o_day3','high_o_day5','high_o_day10','high_o_day20']
#high_o_df,high_open_columns = s_stock.regress_high_open(regress_column = regress_column_type,base_column='open')
#criteria = s_stock.temp_hist_df['low_high_open']!= 0
criteria = ((s_stock.temp_hist_df['star_l']> 0.50) & (s_stock.temp_hist_df['l_change']<-3.0) & (s_stock.temp_hist_df['pos20'].shift(1)<0.2))
high_o_df,high_open_columns = s_stock.regress_common(criteria,post_days=[0,-1,-2,-3,-4,-5,-10,-20,-60],regress_column = regress_column_type,
base_column='close',fix_columns=['date','close','p_change','o_change','position','pos20','MAX20high','star_l'])
high_o_df['code'] = stock_symbol
high_o_df['star_index'] = np.where(high_o_df['pos20']<=0,0,(high_o_df['star_l']/high_o_df['pos20']*((high_o_df['MAX20high']-high_o_df['close'])/high_o_df['MAX20high'])).round(2))
deep_star_df= deep_star_df.append(high_o_df)
if dapan_stocks and (stock_symbol in dapan_stocks):
dapan_criteria = ((s_stock.temp_hist_df['o_change']> 0.30) & (s_stock.temp_hist_df['pos20'].shift(1)<=1.0))
dapan_regress_column_type = 'open'
dapan_high_o_df,dapan_high_open_columns = s_stock.regress_common(dapan_criteria,post_days=[0,-1,-2,-3,-4,-5,-10,-20,-60],regress_column = dapan_regress_column_type,
base_column='open',fix_columns=['date','close','p_change','o_change','position','pos20','oo_chg','oh_chg','ol_chg','oc_chg'])
dapan_high_o_df['code'] = stock_symbol
dapan_high_o_df['ho_index'] = np.where(dapan_high_o_df['pos20']<=0,0,(dapan_high_o_df['o_change']/dapan_high_o_df['pos20']).round(2))
dapan_ho_df= dapan_ho_df.append(dapan_high_o_df)
else:
pass
#print(test_result)
#print(recent_trend)
i = i+1
if test_result.empty:
pass
else:
test_result_df = tds.pd.DataFrame(test_result.to_dict(), columns=column_list, index=[stock_symbol])
all_result_df = all_result_df.append(test_result_df,ignore_index=False)
if recent_trend.empty:
pass
else:
trend_result_df = tds.pd.DataFrame(recent_trend.to_dict(), columns=trend_column_list, index=[stock_symbol])
all_trend_result_df = all_trend_result_df.append(trend_result_df,ignore_index=False)
#except:
# print('Regression test exception for stock: %s' % stock_symbol)
#print(result_df.tail(20))
#all_result_df = all_result_df.sort_index(axis=0, by='sum', ascending=False)
all_result_df = all_result_df.sort_values(axis=0, by='cum_prf', ascending=False)
all_trend_result_df = all_trend_result_df.sort_values(axis=0, by='chg_fuli', ascending=False)
result_summary = all_result_df.describe()
result_codes = all_result_df.index.values.tolist()
result_codes_dict = {}
on_trade_dict = {}
valid_dict = {}
for code in result_codes:
if code in basic_code_keys:
result_codes_dict[code] = basic_code[code]
else:
result_codes_dict[code] = 'NA'
if code in all_stop_codes:
on_trade_dict[code] = 1
else:
on_trade_dict[code] = 0
if code in except_stocks:
valid_dict[code] = 1
else:
valid_dict[code] = 0
"""
all_temp_dict = {}
all_temp_codes = all_temp_hist_df.index.values.tolist()
for code in result_codes:
if code in all_temp_codes:
all_temp_dict[code]= basic_code[code]
else:
result_codes_dict[code] = 'NA'
all_temp_hist_df['name'] = tds.Series(result_codes_dict,index=all_result_df.index)
"""
#print(result_codes_dict)
#print(tds.pd.DataFrame(result_codes_dict, columns=['name'], index=list(result_codes_dict.keys())))
#all_result_df['name'] = result_codes_dict
all_result_df['name'] = tds.Series(result_codes_dict,index=all_result_df.index)
deep_star_df['name'] = tds.Series(result_codes_dict,index=deep_star_df.index)
deep_star_df = deep_star_df[['code','name','star_index']+high_open_columns]
dapan_codes_dict = {}
if dapan_ho_df.empty:
pass
else:
for code in dapan_stocks:
if code in basic_code_keys:
dapan_codes_dict[code] = basic_code[code]
else:
dapan_codes_dict[code] = 'NA'
dapan_ho_df['name'] = tds.Series(dapan_codes_dict,index=dapan_ho_df.index)
dapan_ho_df = dapan_ho_df[['code','name','ho_index']+dapan_high_open_columns]
all_trend_result_df['name'] = tds.Series(result_codes_dict,index=all_trend_result_df.index)
all_result_df['stopped'] = tds.Series(on_trade_dict,index=all_result_df.index)
all_trend_result_df['stopped'] = tds.Series(on_trade_dict,index=all_trend_result_df.index)
all_result_df['invalid'] = tds.Series(valid_dict, index=all_result_df.index)
all_trend_result_df['invalid'] = tds.Series(valid_dict, index=all_trend_result_df.index)
all_result_df['max_r'] = all_result_df['max']/all_result_df['cum_prf']
ma_c_name = '%s日趋势数' % ma_num
trend_column_chiness = {'count':ma_c_name, 'mean': '平均涨幅','chg_fuli': '复利涨幅', 'std': '标准差', 'min': '最小涨幅', '25%': '25%', '50%': '50%', '75%': '75%', 'max': '最大涨幅', 'c_state': '收盘价状态',
'c_mean': '平均收盘价', 'pos_mean': '平均仓位', 'ft_rate': '低点反弹率', 'presure': '压力', 'holding': '支撑', 'close': '收盘价','cont_num': '连涨天数', 'name': '名字', 'stopped': '停牌','invalid': '除外',
'amount_rate':'量比','ma_amount_rate':'短长量比'}
print(all_trend_result_df)
all_trend_result_df_chinese = all_trend_result_df.rename(index=str, columns=trend_column_chiness)
print(all_result_df)
print(all_result_df.describe())
if isinstance(k_num, str):
k_num = k_num.replace('/','').replace('-','')
latest_date_str = pds.tt.get_latest_trade_date(date_format='%Y/%m/%d')
latest_date_str = latest_date_str.replace('/','').replace('-','')
rate_to_confirm_str = '%s' % rate_to_confirm
rate_to_confirm_str = 'rate' + rate_to_confirm_str.replace('.', '_')
#print('latest_date_str=',latest_date_str)
tail_name = '%s_from_%s_%s.csv' % (latest_date_str,k_num,rate_to_confirm_str)
#all_result_df['yearly_prf'] = all_result_df['fuli_prf']**(1.0/(all_result_df['days']/365.0))
result_column_list = ['count','name', 'mean', 'std', 'max', 'min', 'cum_prf',
'fuli_prf','yearly_prf','success_rate','last_trade_date','last_trade_price','min_hold_count',
'max_hold_count','avrg_hold_count','this_hold_count','exit','enter',
'position','max_amount_rate','max_amount_distance','break_in',
'break_in_count','break_in_date', 'break_in_distance',
'stopped','invalid','max_r','25%','50%','75%',]
all_result_df = all_result_df[result_column_list]
all_result_df.to_csv('./temp/regression_test_' + addition_name +tail_name)
deep_star_df.to_csv('./temp/pos20_star_%s'% regress_column_type + addition_name +tail_name)
dapan_ho_df.to_csv('./temp/dapan_high_open_%s'% regress_column_type + addition_name +tail_name)
if all_result_df.empty:
pass
else:
consider_df = all_result_df[(all_result_df['max_amount_rate']>2.0) & (all_result_df['position']>0.35) & (all_result_df['stopped']==0) & (all_result_df['invalid']==0)]# & (all_result_df['last_trade_price'] ==0)]
consider_df.to_csv('./temp/consider_' + addition_name +tail_name)
active_df = all_result_df[(all_result_df['max_r']<0.4) & (all_result_df['name']!='NA') & # (all_result_df['min']>-0.08) & (all_result_df['position']>0.35) &
(all_result_df['max']>(3.9 *all_result_df['min'].abs())) & (all_result_df['invalid']==0) &(all_result_df['stopped']==0)]
active_df['active_score'] = active_df['fuli_prf']/active_df['max_r']/active_df['std']*active_df['fuli_prf']/active_df['cum_prf']
active_df = active_df.sort_values(axis=0, by='active_score', ascending=False)
active_df.to_csv('./temp/active_' + addition_name +tail_name)
tupo_df = all_result_df[(all_result_df['break_in_distance']!=0) &(all_result_df['break_in_distance']<=20) &
(all_result_df['position']>0.35) & (all_result_df['stopped']==0) &
(all_result_df['invalid']==0) & (all_result_df['name']!='NA') & (all_result_df['last_trade_price']!=0)]# & (all_result_df['last_trade_price'] ==0)]
tupo_df.to_csv('./temp/tupo_' + addition_name +tail_name)
result_summary.to_csv('./temp/result_summary_' + addition_name +tail_name)
all_trend_result_df_chinese.to_csv('./temp/trend_result_%s' % ma_num + addition_name +'%s_to_%s_%s.csv' % (k_num,latest_date_str,rate_to_confirm_str))
if not all_temp_hist_df.empty:
#all_temp_hist_df = all_temp_hist_df[column_list]
all_temp_hist_df = all_temp_hist_df.set_index('code')
all_temp_hist_df.to_csv('./temp/all_temp_' + addition_name +tail_name)
reverse_df = all_temp_hist_df[(all_temp_hist_df['reverse']>0) &
(all_temp_hist_df['LINEARREG_ANGLE8']<-2.0) &
(all_temp_hist_df['position']>0.35)]#
#reverse_df['r_sort'] = reverse_df['star_chg']/reverse_df['pos20']
reverse_df.to_csv('./temp/reverse_df_' + addition_name +tail_name)
long_turn_min_angle = -0.5
short_turn_min_angle = 0.2
ma30_df = all_temp_hist_df[(all_temp_hist_df['LINEARREG_ANGLE14MA120']>long_turn_min_angle) &
(all_temp_hist_df['LINEARREG_ANGLE14MA250']>long_turn_min_angle) &
(all_temp_hist_df['LINEARREG_ANGLE14MA60']>long_turn_min_angle) &
(all_temp_hist_df['LINEARREG_ANGLE14MA30']<1.0) &
(all_temp_hist_df['LINEARREG_ANGLE5MA5']>short_turn_min_angle) &
(all_temp_hist_df['LINEARREG_ANGLE6MA10']>short_turn_min_angle) &
(all_temp_hist_df['LINEARREG_ANGLE5MA5']>=all_temp_hist_df['LINEARREG_ANGLE6MA10']) &
(all_temp_hist_df['LINEARREG_ANGLE8ROC1']>0.0) &
(all_temp_hist_df['close']>all_temp_hist_df['ma30']) &
(all_temp_hist_df['position']>0.35)]#
ma30_df.to_csv('./temp/ma30_df_' + addition_name +tail_name)
return all_result_df
#back_test(k_num='2015/08/30',given_codes=['000004','000005'],except_stocks=['000029'], type='stock', source='YH')
def get_latest_temp_df(today_df_high_open=[]):
all_trade_code = [
'600460', '002075', '600367', '002797', '002224', '600393', '002523',
'300201', '600262', '600671', '002796', '603029', '603101', '300509',
'300511', '603822', '603528', '603799', '603399', '300340', '300510',
'600891', '600639', '002165', '600540', '600392', '600012', '300341',
'300493', '000518', '300014', '601000', '300395', '002670', '600438',
'002771', '002155', '000005', '603188', '000890', '000570', '002070',
'002297', '300481', '600975', '300307', '603901', '000910', '000799',
'002038', '002010', '300267', '300483', '300394', '300100', '002509',
'002364', '002173', '600715', '002279', '601958', '300507', '002340',
'600507', '300449', '002108', '002631', '600335', '002512', '002567',
'000421', '300373', '002722', '002237', '300258', '000037', '300389',
'300278', '002709', '300409', '000066', '600330', '300004', '600160',
'002696', '002082', '002204', '600733', '600156', '600547', '600382',
'300438', '600021', '002076', '000425', '002676', '300508', '600623',
'300327', '300131', '300040', '000611', '002006', '600767', '600490',
'601199', '600352', '002100', '300134', '600057', '300068', '002750',
'000526', '600869', '300318', '002335', '600883', '002713', '600359',
'002379', '002057', '300219', '300351', '300088', '300429', '002309',
'002049', '000606', '002715', '000099', '300037', '300342', '000055',
'000988', '002273', '002466', '603368', '603678', '000586', '600522',
'002132', '002341', '002245', '600370', '600836', '300376', '300143',
'603117', '000839', '600745', '000813', '600425', '002041', '002147',
'600575', '002136', '002440', '600258', '002716', '002664', '002646',
'002422', '002514', '600489', '000504', '002234', '002196', '002215',
'002601', '600260', '600250', '000838', '002222', '600885', '002249',
'603338', '002674', '600590', '300123', '600117', '603726', '603398',
'601700', '603011', '300073', '002299', '600054', '600201', '000683',
'002752', '000810', '000936', '000533', '002589', '002217', '002229',
'600137', '300273', '603026', '600693', '002153', '002015', '000796',
'600619', '600366', '002124', '002074', '002596', '600562', '600641',
'002580', '601113', '600053', '600785', '600493', '300118', '300266',
'603198', '600992', '300474', '603589', '002312', '000597', '002545',
'300360', '000782', '002350', '000008', '000516', '600505', '601069',
'000545', '000880', '300012', '002135', '002023', '300207', '600305',
'603729', '002756', '600615', '002460', '000046', '002296', '603969',
'600083', '300250', '000567', '603006', '300283', '002103', '002056',
'603778', '600313', '002532', '002307', '002730', '002571', '002557',
'002508', '002473', '002672', '002418', '600882', '300420', '300393',
'300017', '300243', '600006', '000078', '600529', '600467', '600183',
'600232', '002758', '300427', '002271', '600782', '600620', '002590',
'002708', '002706', '601003', '600829', '600198', '002489', '300132',
'002628', '600987', '002548', '300485', '600311', '002091', '002793',
'300008', '002534', '002684', '000679', '600399', '002463', '002395',
'002324', '300280', '000856', '600961', '002541', '002648', '600309',
'000831', '002111', '600990', '002387', '600896', '002125', '002652',
'002115', '000150', '600821', '300137', '603568', '603828', '002597',
'000061', '601689', '600386', '603919', '603899', '300116', '600797',
'002456', '000733', '002043', '002725', '300330', '002189', '000715',
'300048', '002367', '600423', '600582', '603309', '600241', '300034',
'300223', '600618', '300198', '000748', '600712', '600568', '600068',
'600834', '002605', '000937', '601311', '300178', '002419', '000888',
'600594', '300402', '300237', '000686', '300398', '000848', '603128',
'000852', '000677', '603333', '600080', '600593', '600862', '600702',
'000420', '600967', '601880', '002639', '600703', '002625', '600543',
'600076', '000678', '600444', '600559', '000797', '600605', '600638',
'300505', '000780', '600272', '300056', '600395', '300156', '600796',
'002772', '600112', '600617', '600343', '300107', '600275', '002088',
'600790', '000158', '000665', '002470', '603798', '300080', '600293',
'000688', '002381', '000595', '300259', '600694', '601222', '600249',
'600461', '002094', '300498', '600988', '600722', '600480', '002318',
'601398', '603308', '002538', '002231', '600207', '600552', '300133',
'600680', '300148', '300326', '002685', '002726', '600558', '300261',
'300170', '000998', '600267', '002468', '002479', '002518', '600180',
'603600', '002721', '300444', '300375', '600317', '300421', '300268',
'601008', '300407', '603355', '002477', '600270', '000922', '002531',
'002099', '002579', '600086', '002020', '002635', '603588', '002643',
'000815', '002186', '002086', '600401', '000705', '600436', '600872',
'601006', '300368', '603868', '002360', '601388', '002163', '002391',
'600685', '600854', '000096', '000708', '600219', '600648', '600973',
'300482', '603023', '000430', '000557', '002087', '300235', '600976',
'002667', '600486', '600778', '002614', '300195', '600809', '600055',
'603718', '600758', '600545', '300274', '000619', '600662', '002282',
'002496', '300035', '300128', '002200', '002408', '300334', '002502',
'002443', '002167', '002436', '300227', '300269', '000426', '000969',
'002411', '601226', '600165', '600825', '600815', '300305', '000912',
'300425', '000762', '002346', '000534', '603366', '600845', '000973',
'600791', '600036', '002656', '000862', '002310', '000820', '002465',
'600336', '002275', '002775', '603223', '601872', '002256', '603567',
'000029', '300321', '600580', '600833', '002382', '002574', '600478',
'600610', '300328', '603099', '000673', '002003', '600521', '002723',
'002438', '002521', '002277', '601886', '002384', '002116', '300026',
'000019', '600331', '600810', '600735', '600064', '600284', '002434',
'600766', '002671', '000050', '000546', '603315', '600398', '000596',
'000886', '600476', '600770', '601798', '300058', '600560', '000584',
'603028', '300359', '000498', '000697', '002174', '300440', '603997',
'300119', '002079', '002678', '600128', '002254', '600622', '603456',
'000010', '600146', '002738', '002537', '600841', '600589', '000661',
'000833', '000738', '000423', '002742', '002618', '300193', '600200',
'002458', '002166', '000712', '300271', '601233', '600897', '600687',
'300403', '600288', '300371', '603701', '600028', '300066', '002516',
'300101', '300159', '002033', '600210', '600133', '600387', '002292',
'300091', '603077', '600354', '000065', '000058', '600356', '601566',
'002641', '600186', '600650', '300329', '600634', '002334', '600706',
'300234', '600082', '300325', '000062', '002113', '300196', '002097',
'600500', '600235', '603300', '000011', '002561', '600678', '000895',
'600088', '601899', '600819', '000927', '002252', '300072', '603518',
'603116', '000825', '000981', '300108', '002790', '000735', '000903',
'002677', '002464', '000967', '002146', '002118', '600221', '300350',
'601789', '002024', '002311', '002454', '002592', '300031', '600851',
'600114', '600531', '600261', '000532', '601336', '002689', '600895',
'600776', '300365', '002484', '600039', '600418', '600369', '300408',
'300054', '002779', '300145', '600138', '600130', '600333', '300463',
'002327', '000157', '300106', '600327', '600017', '002578', '300182',
'002151', '000809', '000659', '600993', '002185', '601890', '002258',
'601877', '002724', '600372', '600020', '000819', '603018', '002753',
'002747', '600121', '600572', '002121', '002504', '002130', '000822',
'300344', '600748', '600246', '002494', '002714', '600105', '000561',
'601166', '601668', '600225', '600677', '601800', '000926', '002317',
'603979', '600865', '002322', '002718', '300475', '000626', '603088',
'603806', '600812', '002276', '300491', '300129', '300357', '000039',
'600135', '300296', '000999', '002270', '300506', '002760', '600071',
'600587', '603508', '000042', '600816', '002455', '603066', '000976',
'002208', '000021', '300304', '600742', '603686', '002080', '002453',
'603616', '600195', '600131', '600391', '000023', '600717', '600316',
'300249', '002300', '300199', '601155', '002178', '600095', '600389',
'601777', '000881', '002042', '300114', '600890', '002637', '600101',
'002026', '000777', '002045', '600893', '000798', '600242', '600238',
'000710', '601012', '002662', '300281', '600405', '002161', '300070',
'002361', '603369', '300297', '002182', '600613', '002081', '300415',
'300239', '603939', '600523', '000669', '300293', '002485', '600421',
'601021', '300165', '000049', '002140', '603703', '600859', '000869',
'300374', '000566', '300049', '600107', '600211', '600506', '002588',
'300397', '300358', '002370', '601318', '300489', '600005', '600008',
'600031', '600035', '600048', '600097', '600113', '600120', '600122',
'600123', '600129', '600139', '600149', '600153', '600168', '600170',
'600173', '600178', '600182', '600189', '600209', '600215', '600218',
'600226', '600228', '600231', '600298', '600315', '600318', '600321',
'600326', '600355', '600358', '600363', '600365', '600381', '600385',
'600397', '600408', '600409', '600455', '600468', '600469', '600470',
'600485', '600488', '600509', '600511', '600533', '600539', '600556',
'600565', '600571', '600576', '600577', '600592', '600609', '600614',
'600630', '600636', '600640', '600644', '600645', '600653', '600663',
'600676', '600679', '600683', '600688', '600696', '600714', '600728',
'600734', '600738', '600749', '600751', '600753', '600768', '600777',
'600792', '600808', '600822', '600843', '600848', '600856', '600857',
'600868', '600870', '600873', '600877', '600898', '600966', '600999',
'601001', '601005', '601018', '601058', '601088', '601101', '601168',
'601179', '601288', '601333', '601599', '601601', '601666', '601939',
'601988', '603008', '603268', '603318', '603519', '603611', '603808',
'603838', '603869', '603918', '603993', '000002', '000004', '000007',
'000038', '000070', '000089', '000159', '000166', '000401', '000409',
'000415', '000416', '000505', '000509', '000511', '000524', '000525',
'000537', '000540', '000547', '000553', '000555', '000565', '000568',
'000571', '000581', '000582', '000598', '000608', '000610', '000612',
'000617', '000628', '000629', '000633', '000635', '000636', '000638',
'000651', '000667', '000670', '000693', '000717', '000718', '000720',
'000729', '000732', '000736', '000752', '000755', '000757', '000766',
'000790', '000806', '000818', '000835', '000836', '000883', '000887',
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'300493', '601000', '600438', '000005', '002070', '300483', '300100',
'002509', '600715', '601958', '300507', '002631', '002709', '300004',
'600733', '600021', '000425', '600623', '002006', '600767', '000606',
'002273', '002132', '600836', '002136', '000504', '002601', '603011',
'000533', '002015', '600562', '600493', '002545', '000516', '000545',
'002023', '000046', '002672', '300420', '300393', '600198', '002489',
'002534', '600990', '600896', '603828', '002189', '603309', '300198',
'002605', '000420', '002639', '600543', '600076', '300505', '300056',
'600343', '600694', '603308', '300261', '000815', '300368', '002391',
'600758', '000619', '300227', '000973', '002465', '002003', '002384',
'600735', '002671', '000697', '600146', '002537', '600841', '000738',
'600133', '600356', '300234', '603300', '000011', '000981', '002311',
'002454', '002779', '300145', '600372', '002504', '600246', '000561',
'600677', '300475', '600071', '002453', '600316', '002300', '600095',
'300070', '600107', '600189', '000628', '300276', '600184', '002137',
'300486', '600960', '300007', '600661', '300233', '002150', '600066',
'000560', '600118', '603861', '002170', '300433', '002260', '002247',
'600765', '600361', '000906', '002681', '000963', '000529', '002598',
'300126', '000601', '000563', '601908', '000523', '600724', '300147',
'300160', '300002', '600984', '000020', '600420', '002338', '300333',
'603866', '300155', '600667', '603299', '002575', '000980', '000786',
'002107', '002268', '300162', '002305', '002475', '600094', '601028',
'300188', '002640', '300085', '000978', '300257', '002624', '300306',
'002359', '300275', '002565', '300390', '002445', '300313', '002560',
'601238', '000802', '000662', '002757', '300381', '002323'
]
all_stop_code = [
'002507', '300019', '300163', '600468', '000887', '600645', '002766',
'600732', '000555', '000693', '300472', '000568', '600091', '300423',
'002207', '002499', '000537', '002175', '002554', '600614', '600153',
'002675', '600355', '600873', '000911', '300405', '300220', '600806',
'600234', '000968', '000524', '300488', '002352', '600701', '002129',
'603779', '600265', '002209', '600571', '399106', '600321', '002439',
'300366', '600113', '601599', '002291', '300282', '000033', '300057',
'300176', '002264', '000962', '002491', '600209', '600817', '002131',
'600636', '600546', '000004', '000755', '300044', '600182', '600866',
'002349', '002599', '600485', '300265', '000582', '000818', '000553',
'000732', '600656', '002203', '002219', '002319', '300432', '600898',
'002739', '000836', '000932', '000155', '000617', '399006', '600592',
'600577', '300071', '300025', '002686', '002798', '002112', '002354',
'601018', '600129', '399001', '000007', '002145', '002630', '600035',
'002110', '600230', '600539', '300434', '002212', '300285', '300457',
'000790', '002663', '000635', '002735', '000571', '600469', '300298',
'300104', '000505', '300339', '603918', '600375', '603318', '300038',
'002608', '300338', '000415', '000923', '600228', '002555', '300437',
'600346', '600738', '002591', '600734', '300391', '002272', '600358',
'002373', '600768', '600509', '603519', '000511', '000401', '000509',
'600751', '600247', '300495', '002347', '600339', '002762', '002246',
'600556', '300441', '000636', '300184', '300512', '002405', '300299',
'002190', '300465', '002386', '300125', '300362', '002306', '600653',
'300462', '300142', '600533', '002046', '600120', '300061', '600149',
'000002', '600728', '002012', '002044', '002505', '399008', '300332',
'002691', '300067', '000608', '002617', '600603', '300450', '600363',
'000720', '002759', '000038', '002164', '600870', '600215', '600488',
'000670', '300046', '603808', '002089', '300308', '600714', '300045',
'002612', '600749', '603611', '002230', '000159', '601001', '002611',
'603993', '002469', '000717', '000547', '002524', '002134', '002647',
'000752', '002371', '600777', '300300', '002065', '600640', '300251',
'300225', '000950', '300062', '600301', '300242', '000651', '600520',
'000565', '000917', '002288', '300185', '000920', '600576', '600581',
'002199', '002694', '600097', '600675', '300292', '300428', '300323',
'399005', '002072', '002587', '300053', '002331', '601168', '002547',
'002492', '601918', '600408', '300426', '300226', '002482', '002654',
'000953', '600318', '600753', '603339', '000610', '300290', '300113',
'000409', '300043', '000975', '600877', '300331', '000757', '600857',
'600721', '600760', '002101', '603838', '300411', '600843', '000893',
'002283', '002703', '600696', '600822', '300464', '000612', '002389',
'600306', '000300', '600145', '600319', '600511', '000806', '000736',
'000997', '600139', '600122', '002569', '600212', '999999', '399007',
'000955', '002069', '000718', '603268', '000766', '600226', '300005',
'002211', '002515', '300335', '002289', '600793', '002429', '002712',
'002355', '600455', '600608', '002447', '600725', '600390', '002542',
'600663', '600173', '300410', '300173', '600856', '600710', '603959',
'002201', '600432', '399004', '603869', '002278', '600381', '000835',
'000638', '002052', '002655', '300221', '300110', '300180', '000633',
'600848', '600385', '600179', '600365', '002314', '300295', '002573',
'002280', '000925', '002682', '002619'
]
today_df_high_open = all_trade_code
today_df_high_open = list(
set(today_df_high_open).difference(set(all_stop_code)))
if today_df_high_open:
first_code = today_df_high_open[0]
stock_hist_anlyse = tds.Stockhistory(code_str=first_code, ktype='D')
result_df = stock_hist_anlyse.form_temp_df(first_code)
result_df = result_df.tail(1)
#temp_df['code'] = pd.Series({'code':[first_code]})
result_df.insert(2, 'code',
pd.Series([first_code], index=result_df.index.values))
#temp_df['code'] = pd.Series([first_code],index=temp_df.index.values)
result_df = result_df.set_index('code')
if len(today_df_high_open) == 1:
return result_df
today_df_high_open.pop(0)
column_list = result_df.columns.values.tolist()
atr_in_data = {}
atr_in_df = pd.DataFrame(data=atr_in_data, columns=column_list)
for high_open_code in today_df_high_open:
temp_df = stock_hist_anlyse.form_temp_df(high_open_code)
temp_df = temp_df.tail(1)
temp_df.insert(
2, 'code',
pd.Series([high_open_code], index=temp_df.index.values))
temp_df = temp_df.set_index('code')
if temp_df.loc[high_open_code, 'atr_in'] > 0:
atr_in_df = atr_in_df.append(temp_df, ignore_index=False)
#print(temp_df)
result_df = result_df.append(temp_df, ignore_index=False)
print(result_df)
print('atr in df :')
print(atr_in_df)
island_df = result_df[result_df.island > 0]
reverse_df = result_df[result_df.reverse > 0]
atr_in_df = result_df[(result_df.atr_in > 0)
& (result_df.position > 0.5)]
tangle_p_df = result_df[result_df.tangle_p > 0]
gt2_rmb_df = result_df[result_df.gt2_rmb > 0]
gt3_rmb_df = result_df[result_df.gt3_rmb > 0]
island_df.to_csv('island.csv')
reverse_df.to_csv('reverse.csv')
atr_in_df.to_csv('atr_in.csv')
tangle_p_df.to_csv('tangle_p.csv')
gt2_rmb_df.to_csv('gt2_rmb.csv')
gt2_rmb_df.to_csv('gt3_rmb.csv')
print(island_df)
print(reverse_df)
print(atr_in_df)
result_df.to_csv('result.csv')
return result_df
else:
column_list = [
'close', 'p_change', 'rmb_rate', 'gap', 'star', 'star_h',
'star_chg', 'ma5_chg', 'ma10_chg', 'k_rate', 'p_rate', 'island',
'atr_in', 'reverse', 'cross1', 'cross2', 'cross3', 'k_score',
'position', 'operation', 'std', 'tangle_p', 'tangle_p1', 'gt2_rmb',
'gt3_rmb'
]
empty_data = {}
empty_df = pd.DataFrame(data=empty_data, columns=column_list)
return empty_df
import tradeStrategy as tds
import pandas as pd
import file_config as fc
stock_symbol = '600742'
"""
import pandas as pd
file_name = 'C:/work/temp/bs_%s.csv' % stock_symbol
temp_hist_df = pd.read_csv(file_name,index_col=0)
print(temp_hist_df)
"""
s_stock = tds.Stockhistory(stock_symbol,
'D',
test_num=0,
source='yh',
rate_to_confirm=0.01)
result_df = s_stock.form_temp_df(stock_symbol)
test_result = s_stock.regression_test0(0.01)
s_stock.form_regression_temp_df(rate_to_confirm=0.0001)
s_stock.form_regression_result(save_dir='C:/work/temp/')
test_result = s_stock.get_regression_result(refresh_regression=False,
rate_to_confirm=0.0001,
from_csv=True,
temp_csv_dir='C:/work/temp/')
test_result = s_stock.get_and_save_regression_result()
test_result = s_stock.get_regression_result_from_csv()
ALL_BACKTEST_DIR = 'D:/work/backtest/'