# -*- encoding:utf-8 -*- import numpy as np from keras.models import load_model import joblib holder_stock_list = [ '000063.SZ', '002093.SZ' '300253.SZ', '300807.SZ', # b账户 ] 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(file, day, model='10_18d', log=True): lines = [] with open(file) 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]) model=load_model(model) for line in lines: train_x = np.array([line[:size - 1]]) train_x_a = train_x[:,:18*24] train_x_a = train_x_a.reshape(train_x.shape[0], 18, 24) # train_x_b = train_x[:, 18*18:18*18+2*18] # train_x_b = train_x_b.reshape(train_x.shape[0], 18, 2, 1) train_x_c = train_x[:,18*24:] 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] + result[0][1] > 0.5: 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['sw_industry'] in hangye_map: i_c = hangye_map[stock['sw_industry']] hangye_map[stock['sw_industry']] = i_c + 1 else: hangye_map[stock['sw_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['sw_industry'], str(concept_detail_list), 'buy', k_table_list[0]['pct_chg']) if log is True: with open('D:\\data\\quantization\\predict\\' + str(day) + '_lstm.txt', mode='a', encoding="utf-8") as f: f.write(str(line[-1]) + ' ' + stock['name'] + ' ' + stock['sw_industry'] + ' ' + str(concept_detail_list) + ' buy' + '\n') 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] + 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],) # 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) 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\\stock160_18d_20200312.log", 20200313, model='160_18d_lstm_5D_ma5_s_seq.h5', log=True) # join_two_day(20200305, 20200305) # check_everyday(20200311, 20200312)