增加准确率统计

ai/reporter.py

@@ -3,10 +3,12 @@ import json
 
 import pandas as pd
 import requests
+from py_jftech import format_date
 
-from ai.config import LABEL_TAG
+from ai.EstimateMarketTrendV20 import sync
+from ai.config import LABEL_TAG, LABEL_RANGE
 from ai.dao import robo_predict
-from ai.dao.robo_datas import get_base_info
+from ai.dao.robo_datas import get_base_info, get_index_list, get_fund_list
 
 symbols = ['ACWI', 'EWJ', 'MCHI', 'EEM', 'BKF', 'INDA', 'AAXJ', 'VGK', 'QQQ', 'SPY', 'SPX', 'IWN',
            'IUSG', 'IWD', 'DON', 'GDX', 'TOLZ', 'XLU', 'XBI', 'ESGD', 'IGE', 'EMLC', 'IGAA',
@@ -52,5 +54,50 @@ def do_reporter2():
     pf.to_excel("Forcast_Report.xlsx", index=False)
 
 
+def map_to_label(ret):
+    for label, (lower, upper) in LABEL_RANGE.items():
+        if float(lower) <= ret < float(upper):
+            return label
+
+
+def is_right(id, type, start, predict):
+    from datetime import datetime
+    predict_term = 21
+    navs = []
+    if type == 'INDEX':
+        navs = get_index_list(index_ids=id, min_date=start, limit=predict_term)
+        navs = [nav['rid_close'] for nav in navs]
+    elif type == 'FUND':
+        navs = get_fund_list(fund_ids=id, min_date=start, limit=predict_term)
+        navs = [nav['rfn_nav_cal'] for nav in navs]
+    if len(navs) == predict_term:
+        rtn = navs[-1] / navs[0] - 1
+        real = map_to_label(rtn)
+        result = True if predict == real or (rtn < 0 and predict < 0) or (rtn > 0 and predict > 0) else False
+        return result
+    return None
+
+
+def statistics():
+    datas = []
+    index_info = get_base_info()
+    info = {x['id']: x for x in index_info}
+    records = robo_predict.get_list()
+    for item in records:
+        result = is_right(item['rbd_id'], info.get(item['rbd_id'])['type'], item['date'], item['predict'])
+        data = {
+            'Forcast On Date': format_date(item['date']),
+            'Ticker': info.get(item['rbd_id'])['ticker'].replace(' Index', '').replace(' Equity', ''),
+            'In 21 business days': LABEL_TAG.get(item['predict']),
+            'Ticker Name': json.loads(info.get(item['rbd_id'])['datas'])['chineseName'],
+            'result': result
+        }
+        datas.append(data)
+    pf = pd.DataFrame(datas)
+    pf.to_excel("result.xlsx", index=False)
+
+
 if __name__ == '__main__':
     do_reporter2()
+    # sync()
+    # statistics()