ai 通用性改动

ai/EstimateMarketTrendV20.py

@@ -16,7 +16,7 @@ from api import DataSync
 # 截止日期
 max_date = None
 
-toForecast = True  # False means test, True means forecast
+toForecast = False  # False means test, True means forecast
 syncData = False  # 开启会同步数据库指数及基金数据
 uploadData = False  # 开启会上传预测结果
 doReport = True  # 开启会生成Excel报告

ai/reporter.py

@@ -4,7 +4,6 @@ import random
 
 import pandas as pd
 import requests
-from py_jftech import format_date
 
 from ai.config import LABEL_TAG, LABEL_RANGE
 from ai.dao import robo_predict
@@ -57,9 +56,13 @@ def do_reporter2(records=None, excel_name=None):
             'result': result
         }
         datas.append(data)
+    RMSE = get_RMSE(datas)
+    RefRMSE = get_RefRMSE(datas)
     symbol_index_dict = {symbol: index for index, symbol in enumerate(symbols)}
     sorted_data = sorted(datas, key=lambda x: symbol_index_dict[x['Ticker'].split(' ')[0]])
     pf = pd.DataFrame(sorted_data)
+    pf['RMSE'] = RMSE
+    pf['RefRMSE'] = RefRMSE
     pf.to_excel(excel_name if excel_name else "Forcast_Report_chu.xlsx", index=False)
 
 
@@ -69,6 +72,32 @@ def map_to_label(ret):
             return label
 
 
+def get_RMSE(datas):
+    """
+     Root Mean Square Error (RMSE)
+    @param data: 预测值
+    @return:
+    """
+    datas = [data for data in datas if data.get('real outcome label') is not None]
+    tags = {v: k for k, v in LABEL_TAG.items()}
+    return (sum(
+        [(tags.get(data['real outcome label']) - tags.get(data['In 21 business days'])) ** 2 for data in datas]) / len(
+        datas)) ** 0.5
+
+
+def get_RefRMSE(datas):
+    """
+     Root Mean Square Error (Reference RMSE)
+    @param data: 预测值
+    @return:
+    """
+    datas = [data for data in datas if data.get('real outcome label') is not None]
+    tags = {v: k for k, v in LABEL_TAG.items()}
+    return (sum(
+        [(tags.get(data['real outcome label']) - tags.get(data['random variable label'])) ** 2 for data in
+         datas]) / len(datas)) ** 0.5
+
+
 def is_right(id, type, start, predict):
     predict_term = 21
     navs = []