增加导出Excel逻辑

ai/EstimateMarketTrendV20.py

@@ -6,21 +6,28 @@ from py_jftech import autowired, parse_date, prev_workday, format_date
 from ai.dao.robo_datas import get_base_info, get_index_list, get_fund_list
 from ai.data_access import DataAccess
 from ai.model_trainer import ModelTrainer
+from ai.noticer import send
 from ai.training_data_builder import TrainingDataBuilder
 from api import DataSync
 
 # 截止日期
 # max_date = None
-max_date = '2024-02-23'
+max_date = '2024-03-01'
 # max_date = '2024-01-11'
 
+toForecast = True  # 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
+
 # 待预测指数
 # PREDICT_LIST = [67, 121, 122, 123]
-PREDICT_LIST = [67, 121, 122, 123, 155, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 174,
-                175, 177, 178]
+PREDICT_LIST = [67, 121, 122, 123, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171,
+                174, 175, 177, 178]
 eco = [65, 66, 74, 134]
 index = [67, 68, 69, 70, 71, 72, 73, 75, 76, 77, 105, 106, 116, 117, 138, 139, 142, 143, 140, 141, 144, 145, 146]
-fund = [121, 122, 123, 155, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 174, 175, 177,
+fund = [121, 122, 123, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 174, 175,
+        177,
         178]
 
 
@@ -39,10 +46,15 @@ def predictionFromMoel(the_model, scaledX_forecast, predict_item, indexDict: dic
     content = f"""\n On day {forecastDay.strftime("%m/%d/%Y")}, the model predicts {predict_item} to be {predictionStr} in {str(numForecastDays)} business days. \n"""
     print(content)
     # 上传预测结果
-    # key = [k for k, v in indexDict.items() if v == predict_item]
-    # index_info = get_base_info(key)[0]
-    # upload_predict(index_info['ticker'], forecastDay, predictionStr)
-    # send(content)
+    key = [k for k, v in indexDict.items() if v == predict_item]
+    index_info = get_base_info(key)[0]
+    if uploadData:
+        from ai.noticer import upload_predict
+        upload_predict(index_info['ticker'], forecastDay, predictionStr)
+    if doReport:
+        from ai.reporter import do_reporter
+        do_reporter()
+    send(content)
     return prediction
 
 
@@ -78,8 +90,8 @@ def judgement(id, type, predict):
 
 ########################################
 if __name__ == '__main__':
-    sync()
-    toForecast = True  # False means test, True means forecast
+    if syncData:
+        sync()
     # define some parameters
     win1W = 5  # 1 week
     win1M = 21  # 1 Month

ai/dao/robo_datas.py

@@ -44,6 +44,6 @@ def get_fund_list(fund_ids=None, min_date=None, max_date=None, limit=None):
 def get_base_info(ids=None):
     sqls = []
     return f"""
-    SELECT rbd_id id,v_rbd_bloomberg_ticker ticker,v_rbd_type type FROM `robo_base_datum`
+    SELECT rbd_id id,v_rbd_bloomberg_ticker ticker,v_rbd_type type, rbd_datas datas FROM `robo_base_datum`
     {where(*sqls,rbd_id=to_tuple(ids))}
     """
\ No newline at end of file

ai/reporter.py

+import datetime
+import json
+
+import pandas as pd
+import requests
+
+symbols = ['ACWI', 'EWJ', 'MCHI', 'EEM', 'BKF', 'INDA', 'AAXJ', 'VGK', 'QQQ', 'SPY', 'SPX', 'IWN',
+           'IUSG', 'IWD', 'DON', 'GDX', 'TOLZ', 'XLU', 'XBI', 'ESGD', 'IGE', 'EMLC', 'IGAA',
+           'LQD', 'HYG', 'SHY', 'IEI', 'IEF', 'GLD', 'IYR', 'UUP', 'CEW', 'TLT']
+
+
+def do_reporter(start='2023-10-01', end=datetime.date.today()):
+    url = f"https://jrp.jfquant.com/api/v1.0/ai/predict?startTime={start}&endTime={end.strftime('%Y-%m-%d')}"
+    resp = requests.get(url)
+    datas = []
+    symbol_index_dict = {symbol: index for index, symbol in enumerate(symbols)}
+    for value in resp.json()['body'].values():
+        for item in value:
+            data = {
+                'Forcast On Date': item['aiPredict']['predictDate'],
+                'Ticker': item['bloombergTicker'].replace(' Index', '').replace(' Equity', ''),
+                'In 21 business days': 'UP' if item['aiPredict']['predict'] == 1 else 'DOWN',
+                'Ticker Name': item['indexName'],
+            }
+            datas.append(data)
+    sorted_data = sorted(datas, key=lambda x: symbol_index_dict[x['Ticker'].split(' ')[0]])
+    print(json.dumps(sorted_data, ensure_ascii=False))
+    pf = pd.DataFrame(sorted_data)
+    pf.to_excel("Forcast_Report.xlsx", index=False)
+
+
+if __name__ == '__main__':
+    do_reporter()