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- # -*- encoding:utf-8 -*-
- import numpy as np
- from keras.models import load_model
- import joblib
- 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
- def _score(fact, line):
- with open('dnn_predict_dmi_18d.txt', 'a') as f:
- f.write(str([line[-2], line[-1]]) + "\n")
- up_right = 0
- up_error = 0
- if fact[0] == 1:
- up_right = up_right + 1.12
- elif fact[1] == 1:
- up_right = up_right + 1.06
- elif fact[2] == 1:
- up_right = up_right + 1
- up_error = up_error + 0.5
- elif fact[3] == 1:
- up_error = up_error + 1
- up_right = up_right + 0.94
- else:
- up_error = up_error + 1
- up_right = up_right + 0.88
- return up_right,up_error
- def predict(file_path='', model_path='15min_dnn_seq.h5', idx=-1):
- test_x,test_y,lines=read_data(file_path)
- model=load_model(model_path)
- score = model.evaluate(test_x, test_y)
- print('DNN', score)
- up_num = 0
- up_error = 0
- up_right = 0
- down_num = 0
- down_error = 0
- down_right = 0
- i = 0
- result=model.predict(test_x)
- win_dnn = []
- for r in result:
- fact = test_y[i]
- if idx in [-2]:
- if r[0] > 0.5 or r[1] > 0.5:
- pass
- # if fact[0] == 1:
- # up_right = up_right + 1.12
- # elif fact[1] == 1:
- # up_right = up_right + 1.06
- # elif fact[2] == 1:
- # up_right = up_right + 1
- # elif fact[3] == 1:
- # up_right = up_right + 0.94
- # else:
- # up_error = up_error + 1
- # up_right = up_right + 0.88
- # up_num = up_num + 1
- else:
- if r[0] > 0.6 or r[1] > 0.6:
- tmp_right,tmp_error = _score(fact, lines[i])
- up_right = tmp_right + up_right
- up_error = tmp_error + up_error
- up_num = up_num + 1
- elif r[3] > 0.5 or r[4] > 0.5:
- if fact[0] == 1:
- down_error = down_error + 1
- down_right = down_right + 1.12
- elif fact[1] == 1:
- down_error = down_error + 1
- down_right = down_right + 1.06
- elif fact[2] == 1:
- down_right = down_right + 1
- elif fact[3] == 1:
- down_right = down_right + 0.94
- else:
- down_right = down_right + 0.88
- down_num = down_num + 1
- i = i + 1
- if up_num == 0:
- up_num = 1
- if down_num == 0:
- down_num = 1
- print('DNN', up_right, up_num, up_right/up_num, up_error/up_num, down_right/down_num, down_error/down_num)
- return win_dnn,up_right/up_num,down_right/down_num
- def multi_predict(model='14_18d'):
- r = 0;
- p = 0
- for x in range(0, 12): # 0,2,3,4,6,8,9,10,11
- # for x in [5,9,11,0,3,4,8]: #10_18,0没数据需要重新计算 [0,2,3,4,5,9,10,11]
- # for x in [0,1,10]:
- # for x in [2,4,7,10]: # 2表现最好 优秀的 0,8正确的反向指标,(9错误的反向指标 样本量太少)
- print(x)
- # for x in [0,2,5,6,7]: # 5表现最好
- win_dnn, up_ratio,down_ratio = predict(file_path='D:\\data\\quantization\\kmeans\\stock' + model + '_test_' + str(x) + '.log',
- model_path=model + '_dnn_seq_' + str(x) + '.h5', idx=x)
- r = r + up_ratio
- p = p + down_ratio
- print(r, p)
- 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')
- 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',]
- def predict_today(day, model='10_18d'):
- 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:
- 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)
- # if r[0] in [1,6,10]:
- # train_x = np.array([line[:size - 1]])
- #
- # result = models[r[0]].predict(train_x)
- # if result[0][3] > 0.5 or result[0][4] > 0.5:
- # stock = code_table.find_one({'ts_code':line[-1][0]})
- # if stock['name'].startswith('ST') or stock['name'].startswith('N') or stock['name'].startswith('*'):
- # continue
- # if line[0] > 80:
- # continue
- # if stock['industry'] in industry:
- # pass
- # # print(line[-1], stock['name'], stock['industry'], 'sell')
- if r[0] in [2,5,9,10,11]:
- train_x = np.array([line[:size - 1]])
- result = models[r[0]].predict(train_x)
- # print(result, line[-1])
- if result[0][0] > 0.6 or result[0][1] > 0.6:
- if line[-1][0].startswith('688'):
- continue
- # 去掉ST
- stock = code_table.find_one({'ts_code':line[-1][0]})
- if stock['name'].startswith('ST') or stock['name'].startswith('N') or stock['name'].startswith('*'):
- continue
- # 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:
- print(line[-1], stock['name'], stock['industry'], 'buy')
- if __name__ == '__main__':
- # predict(file_path='D:\\data\\quantization\\stock16_18d_test.log', model_path='16_18d_cnn_seq.h5')
- # predict(file_path='D:\\data\\quantization\\stock6_test.log', model_path='15m_dnn_seq.h5')
- multi_predict(model='19_18d')
- # predict_today(20200229, model='11_18d')
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