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@@ -1,7 +1,7 @@
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# -*- encoding:utf-8 -*-
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import numpy as np
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from keras.models import load_model
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-import joblib
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+import random
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holder_stock_list = [
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@@ -17,40 +17,28 @@ holder_stock_list = [
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'300341.SZ', '300670.SZ', '300018.SZ', '600268.SH', '002879.SZ',
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# 基础建设
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'300041.SZ', '603568.SH', '000967.SZ', '603018.SH',
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- # B
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21
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- '002555.SZ', '002174.SZ',
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- # ROE
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- '002976.SZ', '002847.SZ', '002597.SZ', '300686.SZ', '000708.SZ', '603948.SH', '600507.SH', '300401.SZ', '002714.SZ', '600732.SH', '300033.SZ', '300822.SZ', '300821.SZ',
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- '002458.SZ', '000708.SZ', '600732.SH', '603719.SH', '300821.SZ', '300800.SZ', '300816.SZ', '300812.SZ', '603195.SH', '300815.SZ', '603053.SH', '603551.SH', '002975.SZ',
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25
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- '603949.SH', '002970.SZ', '300809.SZ', '002968.SZ', '300559.SZ', '002512.SZ', '300783.SZ', '300003.SZ', '603489.SH', '300564.SZ', '600802.SH', '002600.SZ',
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- '000933.SZ', '601918.SH', '000651.SZ', '002916.SZ', '000568.SZ', '000717.SZ', '600452.SH', '603589.SH', '600690.SH', '603886.SH', '300117.SZ', '000858.SZ', '002102.SZ',
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27
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- '300136.SZ', '600801.SH', '600436.SH', '300401.SZ', '002190.SZ', '300122.SZ', '002299.SZ', '603610.SH', '002963.SZ', '600486.SH', '300601.SZ', '300682.SZ', '300771.SZ',
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28
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- '000868.SZ', '002607.SZ', '603068.SH', '603508.SH', '603658.SH', '300571.SZ', '603868.SH', '600768.SH', '300760.SZ', '002901.SZ', '603638.SH', '601100.SH', '002032.SZ',
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- '600083.SH', '600507.SH', '603288.SH', '002304.SZ', '000963.SZ', '300572.SZ', '000885.SZ', '600995.SH', '300080.SZ', '601888.SH', '000048.SZ', '000333.SZ', '300529.SZ',
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- '000537.SZ', '002869.SZ', '600217.SH', '000526.SZ', '600887.SH', '002161.SZ', '600267.SH', '600668.SH', '600052.SH', '002379.SZ', '603369.SH', '601360.SH', '002833.SZ',
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- '002035.SZ', '600031.SH', '600678.SH', '600398.SH', '600587.SH', '600763.SH', '002016.SZ', '603816.SH', '000031.SZ', '002555.SZ', '603983.SH', '002746.SZ', '603899.SH',
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- '300595.SZ', '300632.SZ', '600809.SH', '002507.SZ', '300198.SZ', '600779.SH', '603568.SH', '300638.SZ', '002011.SZ', '603517.SH', '000661.SZ', '300630.SZ', '000895.SZ',
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- '002841.SZ', '300602.SZ', '300418.SZ', '603737.SH', '002755.SZ', '002803.SZ', '002182.SZ', '600132.SH', '300725.SZ', '600346.SH', '300015.SZ', '300014.SZ', '300628.SZ',
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- '000789.SZ', '600368.SH', '300776.SZ', '600570.SH', '000509.SZ', '600338.SH', '300770.SZ', '600309.SH', '000596.SZ', '300702.SZ', '002271.SZ', '300782.SZ', '300577.SZ',
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- '603505.SH', '603160.SH', '300761.SZ', '603327.SH', '002458.SZ', '300146.SZ', '002463.SZ', '300417.SZ', '600566.SH', '002372.SZ', '600585.SH', '000848.SZ', '600519.SH',
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- '000672.SZ', '300357.SZ', '002234.SZ', '603444.SH', '300236.SZ', '603360.SH', '002677.SZ', '300487.SZ', '600319.SH', '002415.SZ', '000403.SZ', '600340.SH', '601318.SH',
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-
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-
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+ # 华为
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+ '300687.SZ','002316.SZ','300339.SZ','300378.SZ','300020.SZ','300634.SZ','002570.SZ',
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+ '600801.SH', '300113.SZ','002555.SZ', '002174.SZ',
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]
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+ROE_stock_list = [ # ROE
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+'002976.SZ', '002847.SZ', '002597.SZ', '300686.SZ', '000708.SZ', '603948.SH', '600507.SH', '300401.SZ', '002714.SZ', '600732.SH', '300033.SZ', '300822.SZ', '300821.SZ',
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+'002458.SZ', '000708.SZ', '600732.SH', '603719.SH', '300821.SZ', '300800.SZ', '300816.SZ', '300812.SZ', '603195.SH', '300815.SZ', '603053.SH', '603551.SH', '002975.SZ',
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+'603949.SH', '002970.SZ', '300809.SZ', '002968.SZ', '300559.SZ', '002512.SZ', '300783.SZ', '300003.SZ', '603489.SH', '300564.SZ', '600802.SH', '002600.SZ',
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+'000933.SZ', '601918.SH', '000651.SZ', '002916.SZ', '000568.SZ', '000717.SZ', '600452.SH', '603589.SH', '600690.SH', '603886.SH', '300117.SZ', '000858.SZ', '002102.SZ',
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+'300136.SZ', '600801.SH', '600436.SH', '300401.SZ', '002190.SZ', '300122.SZ', '002299.SZ', '603610.SH', '002963.SZ', '600486.SH', '300601.SZ', '300682.SZ', '300771.SZ',
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+'000868.SZ', '002607.SZ', '603068.SH', '603508.SH', '603658.SH', '300571.SZ', '603868.SH', '600768.SH', '300760.SZ', '002901.SZ', '603638.SH', '601100.SH', '002032.SZ',
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+'600083.SH', '600507.SH', '603288.SH', '002304.SZ', '000963.SZ', '300572.SZ', '000885.SZ', '600995.SH', '300080.SZ', '601888.SH', '000048.SZ', '000333.SZ', '300529.SZ',
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+'000537.SZ', '002869.SZ', '600217.SH', '000526.SZ', '600887.SH', '002161.SZ', '600267.SH', '600668.SH', '600052.SH', '002379.SZ', '603369.SH', '601360.SH', '002833.SZ',
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+'002035.SZ', '600031.SH', '600678.SH', '600398.SH', '600587.SH', '600763.SH', '002016.SZ', '603816.SH', '000031.SZ', '002555.SZ', '603983.SH', '002746.SZ', '603899.SH',
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+'300595.SZ', '300632.SZ', '600809.SH', '002507.SZ', '300198.SZ', '600779.SH', '603568.SH', '300638.SZ', '002011.SZ', '603517.SH', '000661.SZ', '300630.SZ', '000895.SZ',
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+'002841.SZ', '300602.SZ', '300418.SZ', '603737.SH', '002755.SZ', '002803.SZ', '002182.SZ', '600132.SH', '300725.SZ', '600346.SH', '300015.SZ', '300014.SZ', '300628.SZ',
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+'000789.SZ', '600368.SH', '300776.SZ', '600570.SH', '000509.SZ', '600338.SH', '300770.SZ', '600309.SH', '000596.SZ', '300702.SZ', '002271.SZ', '300782.SZ', '300577.SZ',
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+'603505.SH', '603160.SH', '300761.SZ', '603327.SH', '002458.SZ', '300146.SZ', '002463.SZ', '300417.SZ', '600566.SH', '002372.SZ', '600585.SH', '000848.SZ', '600519.SH',
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+'000672.SZ', '300357.SZ', '002234.SZ', '603444.SH', '300236.SZ', '603360.SH', '002677.SZ', '300487.SZ', '600319.SH', '002415.SZ', '000403.SZ', '600340.SH', '601318.SH',
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-def read_data(path):
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- lines = []
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- with open(path) as f:
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- for line in f.readlines()[:]:
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- line = eval(line.strip())
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- if line[-2][0].startswith('0') or line[-2][0].startswith('3'):
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- lines.append(line)
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-
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- size = len(lines[0])
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- train_x=[s[:size - 2] for s in lines]
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- train_y=[s[size-1] for s in lines]
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- return np.array(train_x),np.array(train_y),lines
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+]
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import pymongo
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@@ -94,12 +82,15 @@ A_concept_code_list = [ 'TS2', # 5G
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gainian_map = {}
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hangye_map = {}
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+Z_list = [] # 自选
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+R_list = [] # ROE
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+O_list = [] # 其他
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+
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def predict_today(file, day, model='10_18d', log=True):
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lines = []
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with open(file) as f:
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for line in f.readlines()[:]:
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line = eval(line.strip())
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- # if line[-1][0].startswith('0') or line[-1][0].startswith('3'):
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lines.append(line)
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size = len(lines[0])
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@@ -124,10 +115,6 @@ def predict_today(file, day, model='10_18d', log=True):
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if stock['name'].startswith('ST') or stock['name'].startswith('N') or stock['name'].startswith('*'):
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continue
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- if stock['ts_code'] in holder_stock_list:
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- print(stock['ts_code'], stock['name'], '维持买入评级')
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-
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- # 跌的
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k_table_list = list(k_table.find({'code':line[-1][0], 'tradeDate':{'$lte':day}}).sort("tradeDate", pymongo.DESCENDING).limit(5))
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# if k_table_list[0]['close'] > k_table_list[-1]['close']*1.20:
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# continue
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@@ -160,21 +147,29 @@ def predict_today(file, day, model='10_18d', log=True):
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else:
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gainian_map[c['name']] = 1
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- print(line[-1], stock['name'], stock['sw_industry'], str(concept_detail_list), 'buy', k_table_list[0]['pct_chg'])
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+ if stock['ts_code'] in holder_stock_list:
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+ print(line[-1], stock['name'], stock['sw_industry'], str(concept_detail_list), 'buy', k_table_list[0]['pct_chg'])
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+ print(stock['ts_code'], stock['name'], '买入评级')
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+ Z_list.append([stock['name'], stock['sw_industry'], k_table_list[0]['pct_chg']])
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+ elif stock['ts_code'] in ROE_stock_list:
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+ print(line[-1], stock['name'], stock['sw_industry'], str(concept_detail_list), 'buy', k_table_list[0]['pct_chg'])
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+ print(stock['ts_code'], stock['name'], '买入评级')
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+ R_list.append([stock['name'], stock['sw_industry'], k_table_list[0]['pct_chg']])
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+ else:
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+ O_list.append([stock['name'], stock['sw_industry'], k_table_list[0]['pct_chg']])
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if log is True:
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with open('D:\\data\\quantization\\predict\\' + str(day) + '_mix.txt', mode='a', encoding="utf-8") as f:
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f.write(str(line[-1]) + ' ' + stock['name'] + ' ' + stock['sw_industry'] + ' ' + str(concept_detail_list) + ' buy' + '\n')
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- elif result[0][1] > 0.4:
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- if stock['ts_code'] in holder_stock_list:
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+ elif result[0][1] > 0.5:
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+ if stock['ts_code'] in holder_stock_list or stock['ts_code'] in ROE_stock_list:
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print(stock['ts_code'], stock['name'], '震荡评级')
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-
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- elif result[0][2] > 0.5:
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- if stock['ts_code'] in holder_stock_list:
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+ elif result[0][2] > 0.4:
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+ if stock['ts_code'] in holder_stock_list or stock['ts_code'] in ROE_stock_list:
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print(stock['ts_code'], stock['name'], '赶紧卖出')
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else:
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- if stock['ts_code'] in holder_stock_list:
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+ if stock['ts_code'] in holder_stock_list or stock['ts_code'] in ROE_stock_list:
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print(stock['ts_code'], stock['name'], result[0],)
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# print(gainian_map)
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@@ -189,6 +184,25 @@ def predict_today(file, day, model='10_18d', log=True):
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print(gainian_list)
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print(hangye_list)
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+ print('-----买入列表---------')
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+ print(Z_list)
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+ print(R_list)
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+ print(O_list)
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+
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+ print('------随机结果--------')
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+ random.shuffle(Z_list)
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+ print('自选')
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+ print(Z_list[:3])
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+
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+ random.shuffle(R_list)
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+ print('ROE')
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+ print(R_list[:3])
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+
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+ random.shuffle(O_list)
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+ print('其他')
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+ print(O_list[:3])
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+
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+
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def _read_pfile_map(path):
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s_list = []
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with open(path, encoding='utf-8') as f:
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@@ -227,6 +241,6 @@ if __name__ == '__main__':
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# predict(file_path='D:\\data\\quantization\\stock6_5_test.log', model_path='5d_dnn_seq.h5')
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# predict(file_path='D:\\data\\quantization\\stock6_test.log', model_path='15m_dnn_seq.h5')
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# multi_predict()
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- predict_today("D:\\data\\quantization\\stock215_18d_20200323.log", 20200327, model='215_18d_mix_6D_ma5_s_seq.h5', log=True)
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+ predict_today("D:\\data\\quantization\\stock216_18d_20200327.log", 20200327, model='216_18d_mix_6D_ma5_s_seq.h5', log=True)
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# join_two_day(20200305, 20200305)
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# check_everyday(20200311, 20200312)
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