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- # -*- encoding:utf-8 -*-
- import numpy as np
- from keras.models import load_model
- import random
- from mix.stock_source import *
- import pymongo
- from util.mongodb import get_mongo_table_instance
- code_table = get_mongo_table_instance('tushare_code')
- k_table = get_mongo_table_instance('stock_day_k')
- stock_concept_table = get_mongo_table_instance('tushare_concept_detail')
- all_concept_code_list = list(get_mongo_table_instance('tushare_concept').find({}))
- gainian_map = {}
- hangye_map = {}
- Z_list = [] # 自选
- R_list = [] # ROE
- O_list = [] # 其他
- def predict_today(file, day, model='10_18d', log=True):
- industry_list = get_hot_industry(day)
- lines = []
- with open(file) as f:
- for line in f.readlines()[:]:
- line = eval(line.strip())
- lines.append(line)
- size = len(lines[0])
- model=load_model(model)
- for line in lines:
- train_x = np.array([line[:size - 1]])
- train_x_tmp = train_x[:,:30*19]
- train_x_a = train_x_tmp.reshape(train_x.shape[0], 30, 19, 1)
- # train_x_b = train_x_tmp.reshape(train_x.shape[0], 18, 24)
- train_x_c = train_x[:,30*19:]
- result = model.predict([train_x_c, train_x_a, ])
- # print(result, line[-1])
- stock = code_table.find_one({'ts_code':line[-1][0]})
- if result[0][0] > 0.5 and stock['sw_industry'] in industry_list:
- if line[-1][0].startswith('688'):
- continue
- # 去掉ST
- if stock['name'].startswith('ST') or stock['name'].startswith('N') or stock['name'].startswith('*'):
- continue
- k_table_list = list(k_table.find({'code':line[-1][0], 'tradeDate':{'$lte':day}}).sort("tradeDate", pymongo.DESCENDING).limit(5))
- # 指定某几个行业
- # if stock['industry'] in industry:
- concept_code_list = list(stock_concept_table.find({'ts_code':stock['ts_code']}))
- concept_detail_list = []
- if len(concept_code_list) > 0:
- for concept in concept_code_list:
- for c in all_concept_code_list:
- if c['code'] == concept['concept_code']:
- concept_detail_list.append(c['name'])
- if stock['ts_code'] in zixuan_stock_list:
- # print(line[-1], stock['name'], stock['sw_industry'], str(concept_detail_list), 'buy', k_table_list[0]['pct_chg'])
- print(stock['ts_code'], stock['name'], '买入评级', k_table_list[0]['pct_chg'])
- Z_list.append([stock['name'], stock['sw_industry'], k_table_list[0]['pct_chg']])
- elif stock['ts_code'] in ROE_stock_list:
- print(stock['ts_code'], stock['name'], '买入评级', k_table_list[0]['pct_chg'])
- R_list.append([stock['name'], stock['sw_industry'], k_table_list[0]['pct_chg']])
- else:
- O_list.append([stock['name'], stock['sw_industry'], k_table_list[0]['pct_chg']])
- if log is True:
- with open('D:\\data\\quantization\\predict\\' + str(day) + '_mix.txt', mode='a', encoding="utf-8") as f:
- f.write(str(line[-1]) + ' ' + stock['name'] + ' ' + stock['sw_industry'] + ' ' + str(concept_detail_list) + ' ' + str(result[0][0]) + '\n')
- # elif result[0][1] > 0.5:
- # if stock['ts_code'] in holder_stock_list:
- # print(stock['ts_code'], stock['name'], '震荡评级')
- # elif result[0][2] > 0.4:
- # if stock['ts_code'] in holder_stock_list:
- # print(stock['ts_code'], stock['name'], '赶紧卖出')
- # else:
- # if stock['ts_code'] in holder_stock_list or stock['ts_code'] in ROE_stock_list:
- # print(stock['ts_code'], stock['name'], result[0],)
- # print(gainian_map)
- # print(hangye_map)
- # gainian_list = [(key, gainian_map[key])for key in gainian_map]
- # gainian_list = sorted(gainian_list, key=lambda x:x[1], reverse=True)
- #
- # hangye_list = [(key, hangye_map[key])for key in hangye_map]
- # hangye_list = sorted(hangye_list, key=lambda x:x[1], reverse=True)
- # print(gainian_list)
- # print(hangye_list)
- print('-----买入列表---------')
- print(Z_list)
- print(R_list)
- print(O_list)
- print('------随机结果--------')
- # random.shuffle(Z_list)
- # print('自选')
- # print(Z_list[:3])
- random.shuffle(R_list)
- print('ROE')
- print(R_list[:3])
- O_list.extend(Z_list)
- O_list.extend(Z_list)
- random.shuffle(O_list)
- print('其他')
- print(O_list[:3])
- def _read_pfile_map(path):
- s_list = []
- with open(path, encoding='utf-8') as f:
- for line in f.readlines()[:]:
- s_list.append(line)
- return s_list
- def join_two_day(a, b):
- a_list = _read_pfile_map('D:\\data\\quantization\\predict\\' + str(a) + '.txt')
- b_list = _read_pfile_map('D:\\data\\quantization\\predict\\dmi_' + str(b) + '.txt')
- for a in a_list:
- for b in b_list:
- if a[2:11] == b[2:11]:
- print(a)
- def check_everyday(day, today):
- a_list = _read_pfile_map('D:\\data\\quantization\\predict\\' + str(day) + '.txt')
- x = 0
- for a in a_list:
- print(a[:-1])
- k_day_list = list(k_table.find({'code':a[2:11], 'tradeDate':{'$lte':int(today)}}).sort('tradeDate', pymongo.DESCENDING).limit(5))
- if k_day_list is not None and len(k_day_list) > 0:
- k_day = k_day_list[0]
- k_day_0 = k_day_list[-1]
- k_day_last = k_day_list[1]
- if ((k_day_last['close'] - k_day_0['pre_close'])/k_day_0['pre_close']) < 0.2:
- print(k_day['open'], k_day['close'], 100*(k_day['close'] - k_day_last['close'])/k_day_last['close'])
- x = x + 100*(k_day['close'] - k_day_last['close'])/k_day_last['close']
- print(x/len(a_list))
- if __name__ == '__main__':
- # predict(file_path='D:\\data\\quantization\\stock6_5_test.log', model_path='5d_dnn_seq.h5')
- # predict(file_path='D:\\data\\quantization\\stock6_test.log', model_path='15m_dnn_seq.h5')
- # multi_predict()
- # predict_today("D:\\data\\quantization\\stock405_30d_20200413.log", 20200413, model='405_30d_mix_5D_ma5_s_seq.h5', log=True)
- # 模型A
- predict_today("D:\\data\\quantization\\stock603_30d_20200415.log", 20200415, model='603_30d_mix_5D_ma5_s_seq.h5', log=True)
- # join_two_day(20200305, 20200305)
- # check_everyday(20200311, 20200312)
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