everyday

stock/dnn_predict_dmi_everyday.py

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+# -*- encoding:utf-8 -*-
+import numpy as np
+from keras.models import load_model
+import joblib
+
+
+holder_stock_list = [
+                     '000063.SZ'
+                     '300538.SZ',
+                     '002261.SZ',
+                     '002475.SZ',
+                     '300037.SZ'
+                     '300059.SZ',
+                     '300244.SZ',
+                     '300803.SZ',
+                     '300102.SZ',
+                     '002815.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')
+
+            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 and 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\\' + 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(20200227, model='11_18d', log=True)
+    join_two_day(20200226, 20200225)