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- # -*- encoding:utf-8 -*-
- import numpy as np
- from keras.models import load_model
- import joblib
- holder_stock_list = [
- '000063.SZ',
- '002796.SZ',
- '002373.SZ',
- '300253.SZ',
- '300059.SZ',
- # b账户
- '002373.SZ',
- '300422.SZ',
- ]
- def read_data(path):
- lines = []
- with open(path) as f:
- for line in f.readlines()[:]:
- line = eval(line.strip())
- if line[-2][0].startswith('0') or line[-2][0].startswith('3'):
- lines.append(line)
- size = len(lines[0])
- train_x=[s[:size - 2] for s in lines]
- train_y=[s[size-1] for s in lines]
- return np.array(train_x),np.array(train_y),lines
- 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({}))
- industry = ['家用电器', '元器件', 'IT设备', '汽车服务',
- '汽车配件', '软件服务',
- '互联网', '纺织',
- '塑料', '半导体',]
- A_concept_code_list = [ 'TS2', # 5G
- 'TS24', # OLED
- 'TS26', #健康中国
- 'TS43', #新能源整车
- 'TS59', # 特斯拉
- 'TS65', #汽车整车
- 'TS142', # 物联网
- 'TS153', # 无人驾驶
- 'TS163', # 雄安板块-智慧城市
- 'TS175', # 工业自动化
- 'TS232', # 新能源汽车
- 'TS254', # 人工智能
- 'TS258', # 互联网医疗
- 'TS264', # 工业互联网
- 'TS266', # 半导体
- 'TS269', # 智慧城市
- 'TS271', # 3D玻璃
- 'TS295', # 国产芯片
- 'TS303', # 医疗信息化
- 'TS323', # 充电桩
- 'TS328', # 虹膜识别
- 'TS361', # 病毒
- ]
- gainian_map = {}
- hangye_map = {}
- def predict_today(day, model='10_18d', log=True):
- lines = []
- with open('D:\\data\\quantization\\stock' + model + '_' + str(day) +'.log') as f:
- for line in f.readlines()[:]:
- line = eval(line.strip())
- # if line[-1][0].startswith('0') or line[-1][0].startswith('3'):
- lines.append(line)
- size = len(lines[0])
- train_x=[s[:size - 1] for s in lines]
- np.array(train_x)
- estimator = joblib.load('km_dmi_18.pkl')
- models = []
- for x in range(0, 12):
- models.append(load_model(model + '_dnn_seq_' + str(x) + '.h5'))
- x = 24 # 每条数据项数
- k = 18 # 周期
- for line in lines:
- # print(line)
- v = line[1:x*k + 1]
- v = np.array(v)
- v = v.reshape(k, x)
- v = v[:,4:8]
- v = v.reshape(1, 4*k)
- # print(v)
- r = estimator.predict(v)
- train_x = np.array([line[:size - 1]])
- result = models[r[0]].predict(train_x)
- # print(result, line[-1])
- stock = code_table.find_one({'ts_code':line[-1][0]})
- if result[0][0] > 0.6 or result[0][1] > 0.6:
- 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
- if stock['ts_code'] in holder_stock_list:
- print(stock['ts_code'], stock['name'], '维持买入评级')
- # 跌的
- k_table_list = list(k_table.find({'code':line[-1][0], 'tradeDate':{'$lte':day}}).sort("tradeDate", pymongo.DESCENDING).limit(5))
- # if k_table_list[0]['close'] > k_table_list[-1]['close']*1.20:
- # continue
- # if k_table_list[0]['close'] < k_table_list[-1]['close']*0.90:
- # continue
- # if k_table_list[-1]['close'] > 80:
- # continue
- # 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 = []
- # 处理行业
- if stock['industry'] in hangye_map:
- i_c = hangye_map[stock['industry']]
- hangye_map[stock['industry']] = i_c + 1
- else:
- hangye_map[stock['industry']] = 1
- 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 c['name'] in gainian_map:
- g_c = gainian_map[c['name']]
- gainian_map[c['name']] = g_c + 1
- else:
- gainian_map[c['name']] = 1
- print(line[-1], stock['name'], stock['industry'], str(concept_detail_list), 'buy', k_table_list[0]['pct_chg'])
- if log is True:
- with open('D:\\data\\quantization\\predict\\' + str(day) + '.txt', mode='a', encoding="utf-8") as f:
- f.write(str(line[-1]) + ' ' + stock['name'] + ' ' + stock['industry'] + ' ' + str(concept_detail_list) + ' buy' + '\n')
- # concept_list = list(stock_concept_table.find({'ts_code':stock['ts_code']}))
- # concept_list = [c['concept_code'] for c in concept_list]
- elif result[0][2] > 0.5:
- if stock['ts_code'] in holder_stock_list:
- print(stock['ts_code'], stock['name'], '震荡评级')
- elif result[0][3] > 0.5 or result[0][4] > 0.5:
- 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:
- print(stock['ts_code'], stock['name'], result[0], r[0])
- print(gainian_map)
- print(hangye_map)
- 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)
- 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(20200303, model='11_18d', log=True)
- join_two_day(20200303, 20200303)
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