Update utils and strategies

2023-09-16 17:28:21 +08:00
parent 7936634114
commit 35ae37867f
27 changed files with 2036 additions and 48 deletions
--- a/.github/workflows/greetings.yml
+++ b/.github/workflows/greetings.yml
@@ -25,3 +25,18 @@ jobs:
        with:
          repo-token: ${{ secrets.GITHUB_TOKEN }}
          favorite-image: 'https://your.favorite/image1.png,https://your.favorite/image2.png'
+  
+  post-lgtm-image:
+    runs-on: ubuntu-latest
+    if: (!contains(github.actor, '[bot]')) # Exclude bot comment
+    steps:
+      - uses: ddradar/choose-random-action@v2
+        id: act
+        with:
+          contents: |
+            https://example.com/your-lgtm-image-1.jpg
+            https://example.com/your-lgtm-image-2.jpg
+            https://example.com/your-lgtm-image-3.jpg
+      - uses: ddradar/lgtm-action@v2.0.2
+        with:
+          image-url: ${{ steps.act.outputs.selected }}
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@@ -1,22 +1,38 @@
 {
-    // Use IntelliSense to learn about possible attributes.
-    // Hover to view descriptions of existing attributes.
-    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
-    "version": "0.2.0",
-    "configurations": [
-      {
-        "type": "node",
-        "request": "launch",
-        "name": "Launch fund",
-        "args": [
-            "${workspaceFolder}/pyfunds/fund-strategy/src/utils/fund-stragegy/index.ts"
-        ],
-        "runtimeArgs": [
-            "-r",
-            "ts-node/register",
-            "-r", "tsconfig-paths/register"
-        ],
-        "env": { "TS_NODE_PROJECT": "${workspaceFolder}/tsconfig.json" }
-      },
-    ]
-  }
+  // Use IntelliSense to learn about possible attributes.
+  // Hover to view descriptions of existing attributes.
+  // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+  "version": "0.2.0",
+  "configurations": [
+    {
+      "name": "Python: Qbot",
+      "type": "python",
+      "request": "launch",
+      "program": "${file}",
+      "console": "integratedTerminal",
+      "justMyCode": true
+    },
+    {
+      "name": "Python: Qbot",
+      "type": "python",
+      "request": "launch",
+      "cwd": "${workspaceRoot}",
+    },
+    {
+      "type": "node",
+      "request": "launch",
+      "name": "Launch fund",
+      "args": [
+        "${workspaceFolder}/pyfunds/fund-strategy/src/utils/fund-stragegy/index.ts"
+      ],
+      "runtimeArgs": [
+        "-r",
+        "ts-node/register",
+        "-r", "tsconfig-paths/register"
+      ],
+      "env": {
+        "TS_NODE_PROJECT": "${workspaceFolder}/tsconfig.json"
+      }
+    },
+  ]
+}
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@@ -1 +1,70 @@
-
+// Place your settings in this file to overwrite default and user settings.
+{
+    "files.exclude": {
+        "out": true, // set this to true to hide the "out" folder with the compiled JS files
+        "dist": true,
+        "**/*.pyc": true,
+        ".nyc_output": true,
+        "obj": true,
+        "bin": true,
+        "**/__pycache__": true,
+        "**/node_modules": true,
+        ".vscode-test": false,
+        ".vscode test": false,
+        "**/.mypy_cache/**": true
+    },
+    "search.exclude": {
+        "out": true, // set this to false to include "out" folder in search results
+        "dist": true,
+        "**/node_modules": true,
+        "coverage": true,
+        "languageServer*/**": true,
+        ".vscode-test": true,
+        ".vscode test": true
+    },
+    "[python]": {
+        "editor.formatOnSave": true,
+        "editor.codeActionsOnSave": {
+            "source.organizeImports": true
+        }
+    },
+    "[typescript]": {
+        "editor.defaultFormatter": "esbenp.prettier-vscode",
+        "editor.formatOnSave": true
+    },
+    "[javascript]": {
+        "editor.defaultFormatter": "esbenp.prettier-vscode",
+        "editor.formatOnSave": true
+    },
+    "[JSON]": {
+        "editor.defaultFormatter": "esbenp.prettier-vscode",
+        "editor.formatOnSave": true
+    },
+    "[YAML]": {
+        "editor.defaultFormatter": "esbenp.prettier-vscode",
+        "editor.formatOnSave": true
+    },
+    "typescript.tsdk": "./node_modules/typescript/lib", // we want to use the TS server from our node_modules folder to control its version
+    "python.linting.enabled": false,
+    "python.pythonPath": "/usr/local/anaconda3/bin/python",
+    "python.formatting.provider": "black",
+    "python.sortImports.args": ["--profile", "black"],
+    "typescript.preferences.quoteStyle": "single",
+    "javascript.preferences.quoteStyle": "single",
+    "typescriptHero.imports.stringQuoteStyle": "'",
+    "prettier.printWidth": 120,
+    "prettier.singleQuote": true,
+    "editor.codeActionsOnSave": {
+        "source.fixAll.eslint": true
+    },
+    "python.languageServer": "Default",
+    "python.linting.pylintEnabled": false,
+    "python.linting.flake8Enabled": true,
+    "cucumberautocomplete.skipDocStringsFormat": true,
+    "python.linting.flake8Args": [
+        // Match what black does.
+        "--max-line-length=88"
+    ],
+    "typescript.preferences.importModuleSpecifier": "relative",
+    "debug.javascript.usePreview": false
+}
--- a/README.md
+++ b/README.md
@@ -64,9 +64,9 @@

 ## Quick Start

-Qbot是一个免费的投研平台，提供从数据获取、交易策略开发、策略回测、模拟交易到最终实盘交易的全闭环流程。在实盘接入前，有股票、基金评测和策略回测，在模拟环境下做交易验证，近乎实盘的时延、滑点仿真。故，本平台提供GUI前端/客户端（部分功能也支持网页），后端做数据处理、交易调度，实现事件驱动的交易流程。对于策略研究部分，尤其强调机器学习、强化学习的AI策略，结合多因子模型提高收益比。
+Qbot是一个免费的量化投研平台，提供从数据获取、交易策略开发、策略回测、模拟交易到最终实盘交易的全闭环流程。在实盘接入前，有股票、基金评测和策略回测，在模拟环境下做交易验证，近乎实盘的时延、滑点仿真。故，本平台提供GUI前端/客户端（部分功能也支持网页），后端做数据处理、交易调度，实现事件驱动的交易流程。对于策略研究部分，尤其强调机器学习、强化学习的AI策略，结合多因子模型提高收益比。

-但本项目可能需要一一点python基础知识，有一点点交易经验，会更容易体会作者的初衷，解决当下产品空缺和广大散户朋友的交易痛点，现在直接免费开源出来！
+但本项目可能需要一点点python基础知识，有一点点交易经验，会更容易体会作者的初衷，解决当下产品空缺和广大散户朋友的交易痛点，现在直接免费开源出来！

 ```bash
 cd ~ # $HOME as workspace
@@ -194,15 +194,13 @@ pip install -r requirements.txt
 python main.py
 ```

-主要包含四个窗口，如果启动界面有问题可以参考这里的启动方式。
+主要包含四个窗口，如果启动界面未显示或有问题可以参考下图中对应的启动方式。👉 点击[这里](gui/mainframe.py)查看源码，下文也有文字介绍。

 ![image](https://github.com/UFund-Me/Qbot/assets/29084184/9f1dcc07-ca76-4600-a02c-76104fb28c51)

-👉 点击[这里](gui/mainframe.py)查看源码
-
 #### 后端/服务端

-1. 选基、选股助手（对应客户端第二个菜单：AI选股/选基）
+1. 选基、选股助手（对应前端/客户端第二个菜单：AI选股/选基）

 运行命令

@@ -212,11 +210,12 @@ go build
 ./investool webserver
 ```

-2. 基金策略在线分析（对应客户端第四个菜单：基金投资策略分析）
+2. 基金策略在线分析（对应于前端/客户端第四个菜单：基金投资策略分析）

 需要 node 开发环境: `npm`、`node`，点击[查看](pyfunds/fund-strategies/README.md)详细操作文档

 <details><summary>版本信息（作为参考）</summary>
+
 ```
 ▶ go version
 go version go1.20.4 darwin/amd64
@@ -227,6 +226,7 @@ v19.7.0
 ▶ npm --version
 9.5.0
 ```
+
 </details>

 使用docker运行项目，在项目路径下运行以下命令构建项目的docker镜像
--- a/27
+++ b/27
@@ -0,0 +1,27 @@
+load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
+
+http_archive(
+    name = "rules_python",
+    sha256 = "c03246c11efd49266e8e41e12931090b613e12a59e6f55ba2efd29a7cb8b4258",
+    strip_prefix = "rules_python-0.11.0",
+    url = "https://github.com/bazelbuild/rules_python/archive/refs/tags/0.11.0.tar.gz",
+)
+
+load("@rules_python//python:pip.bzl", "pip_install")
+load("@rules_python//python:repositories.bzl", "python_register_toolchains")
+
+# Use a hermetic Python interpreter so that builds are reproducible
+# irrespective of the Python version available on the host machine.
+python_register_toolchains(
+    name = "python3_10",
+    python_version = "3.10",
+)
+
+load("@python3_10//:defs.bzl", "interpreter")
+
+# Translate requirements.txt into a @third_party external repository.
+pip_install(
+    name = "third_party",
+    python_interpreter_target = interpreter,
+    requirements = "//third_party:requirements.txt",
+)
--- a/docs/README.md
+++ b/docs/README.md
@@ -68,9 +68,9 @@

 ## Quick Start

-Qbot是一个免费的投研平台，提供从数据获取、交易策略开发、策略回测、模拟交易到最终实盘交易的全闭环流程。在实盘接入前，有股票、基金评测和策略回测，在模拟环境下做交易验证，近乎实盘的时延、滑点仿真。故，本平台提供GUI前端/客户端（部分功能也支持网页），后端做数据处理、交易调度，实现事件驱动的交易流程。对于策略研究部分，尤其强调机器学习、强化学习的AI策略，结合多因子模型提高收益比。
+Qbot是一个免费的量化投研平台，提供从数据获取、交易策略开发、策略回测、模拟交易到最终实盘交易的全闭环流程。在实盘接入前，有股票、基金评测和策略回测，在模拟环境下做交易验证，近乎实盘的时延、滑点仿真。故，本平台提供GUI前端/客户端（部分功能也支持网页），后端做数据处理、交易调度，实现事件驱动的交易流程。对于策略研究部分，尤其强调机器学习、强化学习的AI策略，结合多因子模型提高收益比。

-但本项目可能需要一一点python基础知识，有一点点交易经验，会更容易体会作者的初衷，解决当下产品空缺和广大散户朋友的交易痛点，现在直接免费开源出来！
+但本项目可能需要一点点python基础知识，有一点点交易经验，会更容易体会作者的初衷，解决当下产品空缺和广大散户朋友的交易痛点，现在直接免费开源出来！

 ```bash
 cd ~ # $HOME as workspace
@@ -197,16 +197,13 @@ pip install -r requirements.txt
 # if run on Mac, please use 'pythonw main.py'
 python main.py
 ```
-
-主要包含四个窗口，如果启动界面有问题可以参考这里的启动方式。
+主要包含四个窗口，如果启动界面未显示或有问题可以参考下图中对应的启动方式。👉 点击[这里](https://github.com/UFund-Me/Qbot/blob/main/gui/mainframe.py#L122-L141)查看源码，下文也有文字介绍。

 ![image](https://github.com/UFund-Me/Qbot/assets/29084184/9f1dcc07-ca76-4600-a02c-76104fb28c51)

-👉 点击[这里](https://github.com/UFund-Me/Qbot/blob/main/gui/mainframe.py#L122-L141)查看源码
-
 #### 后端/服务端

-1. 选基、选股助手（对应客户端第二个菜单：AI选股/选基）
+1. 选基、选股助手（对应前端/客户端第二个菜单：AI选股/选基）

 运行命令

@@ -216,11 +213,12 @@ go build
 ./investool webserver
 ```

-2. 基金策略在线分析（对应客户端第四个菜单：基金投资策略分析）
+2. 基金策略在线分析（对应于前端/客户端第四个菜单：基金投资策略分析）

 需要 node 开发环境: `npm`、`node`，点击[查看](https://github.com/UFund-Me/Qbot/blob/main/pyfunds/fund-strategies/README.md)详细操作文档。

 <details><summary>版本信息（作为参考）</summary>
+
 ```
 ▶ go version
 go version go1.20.4 darwin/amd64
@@ -231,6 +229,7 @@ v19.7.0
 ▶ npm --version
 9.5.0
 ```
+
 </details>

 使用docker运行项目，在项目路径下运行以下命令构建项目的docker镜像
--- a/gui/imgs/UFund.png
+++ b/gui/imgs/UFund.png
--- a/gui/panels/panel_backtest.py
+++ b/gui/panels/panel_backtest.py
@@ -13,9 +13,9 @@ class PanelBacktest(wx.Panel):
    def __init__(self, parent):
        super(PanelBacktest, self).__init__(parent)

-        # # 回测按钮
-        # self.btn_bkt = wx.Button(self, label="回测")
-        # self.Bind(wx.EVT_BUTTON, OnBkt, self.btn_bkt)
+        # 回测按钮
+        self.btn_bkt = wx.Button(self, label="回测")
+        self.Bind(wx.EVT_BUTTON, OnBkt, self.btn_bkt)

        # 进度条

--- a/pytrader/data/data_utils.py
+++ b/pytrader/data/data_utils.py
@@ -1,3 +1,17 @@
+'''
+Author: Charmve yidazhang1@gmail.com
+Date: 2022-12-04 12:03:50
+LastEditors: Charmve yidazhang1@gmail.com
+LastEditTime: 2023-07-02 20:11:13
+FilePath: /Qbot/pytrader/data/data_utils.py
+Version: 1.0.1
+Blogs: charmve.blog.csdn.net
+GitHub: https://github.com/Charmve
+Description: 
+
+Copyright (c) 2023 by Charmve, All Rights Reserved. 
+Licensed under the MIT License.
+'''
 import os

 import pandas as pd
@@ -18,21 +32,32 @@ def load_data(codes, start_time="20100101", end_time="20211231"):
 def load_from_file(code):
    path = os.path.dirname(__file__)
    filename = "{}/{}.csv".format(os.path.dirname(path) + "/data/indexes", code)
-    # print("stack data:" + filename)
+    print("stack data:" + filename)
    if os.path.exists(filename):
        df = pd.read_csv(filename, index_col=[0])
-        # print(df.head())
+        print(df.head())
        df.rename(
            columns={"trade_date": "date", "ts_code": "code", "vol": "volume"},
            inplace=True,
        )
        df["date"] = df["date"].apply(lambda x: str(x))
-        df = df[["code", "open", "high", "low", "close", "date", "volume"]]
-        # df = df[['open', 'high', 'low', 'close', 'date', 'volume']]
-        df.index = df["date"]
+        # df = df[["code", "open", "high", "low", "close", "date", "volume"]]
+        df = df[['open', 'high', 'low', 'close', 'date', 'volume']]
+        df.index = df["code"]
        df.sort_index(ascending=True, inplace=True)
        df["rate"] = df["close"].pct_change()
    else:
        print("load_from_file error")
        return None
    return df
+
+class DataUtil:
+    def __init__(self, df_results, benchmarks=['000300.SH']):
+        self.df_results = df_results
+        self.benchmarks = benchmarks
+
+    def to_backtrader_dataframe(self.df):
+        df.index = pd.to_datetime(df.index)
+        df['openinterest'] = 0
+        df = df[['open', 'high', 'low', 'close', 'volume', 'openinterest']]
+        return df
--- a/pytrader/easyquant/strategy/strategyTemplate.py
+++ b/pytrader/easyquant/strategy/strategyTemplate.py
@@ -7,7 +7,7 @@ from pandas import DataFrame

 from ..context import Context
 from ..event_engine import Event
-from easytrader.webtrader import WebTrader
+from ..easytrader.webtrader import WebTrader


 class StrategyTemplate:
--- a/pytrader/strategies/lgb_strategy.py
+++ b/pytrader/strategies/lgb_strategy.py
@@ -9,9 +9,168 @@ import matplotlib.pyplot as plt
 import pandas as pd
 import talib as ta
 from base import Strategy
-from data.data_utils import load_data, load_from_file
+# from data.data_utils import load_data, load_from_file
 from IPython.display import display
-from model.lgb import LGBModel
+# from model.lgb import LGBModel
+
+import os
+
+import pandas as pd
+
+
+def load_data(codes, start_time="20100101", end_time="20211231"):
+    dfs = []
+    for code in codes:
+        df = load_from_file(code)
+        # df.dropna(inplace=True)
+        dfs.append(df)
+    df_all = pd.concat(dfs, axis=0)
+    df_all.sort_index(inplace=True)
+    df_all = df_all.loc[start_time:end_time]
+    return df_all
+
+
+def load_from_file(code):
+    path = os.path.dirname(__file__)
+    filename = "{}/{}.csv".format(os.path.dirname(path) + "/data/indexes", code)
+    # print("stack data:" + filename)
+    if os.path.exists(filename):
+        df = pd.read_csv(filename, index_col=[0])
+        # print(df.head())
+        df.rename(
+            columns={"trade_date": "date", "ts_code": "code", "vol": "volume"},
+            inplace=True,
+        )
+        df["date"] = df["date"].apply(lambda x: str(x))
+        ## code,date,close,open,high,low,volume,amount
+        # df = df[["code", "open", "high", "low", "close", "date", "volume"]]
+        df = df[['open', 'high', 'low', 'close', 'date', 'volume']]
+        df.index = df["date"]
+        df.sort_index(ascending=True, inplace=True)
+        df["rate"] = df["close"].pct_change()
+    else:
+        print("load_from_file error")
+        return None
+    return df
+
+
+import numpy as np
+import pandas as pd
+import lightgbm as lgb
+from sklearn.metrics import r2_score, accuracy_score
+
+class LGBModel:
+    def __init__(self, regression = True):
+        self.regression = regression
+    def fit(self, dataset):
+        X_train, X_valid, y_train, y_valid = dataset.split()
+
+        dtrain = lgb.Dataset(X_train, label=y_train)
+        dvalid = lgb.Dataset(X_valid, label=y_valid)
+
+        #params = {"objective": 'mse', "verbosity": -1}
+        # 参数
+        params_regression = {
+            'learning_rate': 0.1,
+            'metrics':{'auc','mse'},
+            'lambda_l1': 0.1,
+            'lambda_l2': 0.2,
+            'max_depth': 4,
+            'objective': 'mse'#'mse',  # 目标函数
+        }
+
+        params = {'num_leaves': 90,
+                  'min_data_in_leaf': 30,
+                  'objective': 'multiclass',
+                  'num_class': 10,
+                  'max_depth': -1,
+                  'learning_rate': 0.03,
+                  "min_sum_hessian_in_leaf": 6,
+                  "boosting": "gbdt",
+                  "feature_fraction": 0.9,
+                  "bagging_freq": 1,
+                  "bagging_fraction": 0.8,
+                  "bagging_seed": 11,
+                  "lambda_l1": 0.1,
+                  "verbosity": -1,
+                  "nthread": 15,
+                  'metric': {'multi_logloss'},
+                  "random_state": 2022,
+                  #'device': 'gpu'
+                  }
+
+        if self.regression:
+            params = params_regression
+        self.model = lgb.train(
+            params,
+            dtrain,
+            num_boost_round=1000,
+            valid_sets=[dtrain, dvalid],
+            valid_names=["train", "valid"],
+            early_stopping_rounds=50,
+            verbose_eval=True,
+            # evals_result=evals_result,
+            #**kwargs
+        )
+        y_pred = self.model.predict(X_valid)
+        if not self.regression:
+            y_pred = np.argmax(y_pred, axis=1)
+            print('accuracy:',accuracy_score(y_pred, y_valid))
+
+            y_pred_train = np.argmax(self.model.predict(X_train), axis=1)
+            print('accuracy_train:',accuracy_score(y_pred_train, y_train))
+        else:
+            print('R2系数：', r2_score(y_valid, y_pred))
+            print('训练集——R2系数：', r2_score(y_train, self.model.predict(X_train)))
+
+    def predict(self, dataset):
+        if self.model is None:
+            raise ValueError("model is not fitted yet!")
+        x_test,_ = dataset.get_data(date_range=['20160101', '20211231'])
+        pred = self.model.predict(x_test)
+        print(pred)
+        if not self.regression:
+            return pd.Series(np.argmax(pred, axis=1), index=x_test.index)
+        else:
+            return pd.Series(pred, index=x_test.index)
+
+
+# if __name__ == '__main__':
+#     from bak.data.dataset import Dataset
+#     from engine.data.datahandler import DataHandler
+
+#     fields = ['Return($close,5)', 'Return($close,20)', 'Ref($close,126)/$close -1','$close','$open','$high','$low','$volume','$amount']
+#     names = ['return_5', 'return_20', 'return_126','close','open','high','low','volume','amount']
+
+#     #fields += ['Ref($close,-5)/$close -1']
+#     #names += ['return_-5']
+
+#     #ds = Dataset(codes=, fields=fields, feature_names=names,
+#     #             label_expr='QCut(Ref($close,-20)/$close -1,10)')
+#     #print(ds.df)
+#     codes = ['512690.SH', '512170.SH', '512660.SH','159928.SZ','512010.SH']
+#     codes = ['159915.SZ','510300.SH','512690.SH', '512170.SH', '512660.SH','159928.SZ','512010.SH']
+#     codes = ['159928.SZ','510050.SH','512010.SH','513100.SH','518880.SH','511220.SH','511010.SH','161716.SZ']
+#     codes = [
+#         '000300.SH',
+#         '000905.SH',
+#         '399006.SZ', #创业板
+#         '000852.SH', #中证1000
+#         '399324.SZ', #深证红利
+#         #'000922.SH', #中证红利
+#         '399997.SZ', #中证白酒
+#         '399396.SZ', #食品饮料
+
+#         '000013.SH',#上证企债
+#         '000016.SH' #上证50
+#     ]
+#     ds = Dataset(codes=codes, handler=DataHandler())
+#     print(ds.df)
+
+#     m = LGBModel()
+#     m.fit(ds)
+#     pred = m.predict(ds)
+#     print(pred)


 # Step 1: load dataset and generate features
--- a/qbot/setup.py
+++ b/qbot/setup.py
--- a/qbot/strategies/adx_strategy.py
+++ b/qbot/strategies/adx_strategy.py
@@ -0,0 +1,64 @@
+'''
+Author: Charmve yidazhang1@gmail.com
+Date: 2023-03-18 18:06:06
+LastEditors: Charmve yidazhang1@gmail.com
+LastEditTime: 2023-03-18 18:14:39
+FilePath: /Qbot/qbot/strategies/adx_strategy.py
+Version: 1.0.1
+Blogs: charmve.blog.csdn.net
+GitHub: https://github.com/Charmve
+Description: 获取股票数据并进行量化回测——基于ADX和MACD趋势策略
+
+https://blog.csdn.net/ndhtou222/article/details/121219649
+
+Copyright (c) 2023 by Charmve, All Rights Reserved. 
+Licensed under the MIT License.
+'''
+import pandas as pd  
+import numpy as np
+import matplotlib.pyplot as plt
+#正常显示画图时出现的中文和负号
+from pylab import mpl
+mpl.rcParams['font.sans-serif']=['SimHei']
+mpl.rcParams['axes.unicode_minus']=False
+#不显示警告信息
+import warnings
+warnings.filterwarnings('ignore')
+
+from get_stack_data import get_from_tushare
+import pyfolio as pf
+import talib as ta
+
+def adx_strategy(df,ma1=13,ma2=55,ma3=89,adx=25):
+    #计算MACD和ADX指标
+    df['EMA1'] = ta.EMA(df.close,ma1)
+    df['EMA2'] = ta.EMA(df.close,ma2)
+    df['EMA3'] = ta.EMA(df.close,ma3)
+    df['MACD'],df['MACDSignal'],df['MACDHist'] = ta.MACD(df.close,12,26,9)
+    df['ADX'] = ta.ADX(df.high,df.low,df.close,14)
+    #设计买卖信号:21日均线大于42日均线且42日均线大于63日均线;ADX大于前值小于25；MACD大于前值
+    df['Buy_Sig'] =(df['EMA1']>df['EMA2'])&(df['EMA2']>df['EMA3'])&(df['ADX']<=adx)\
+                    &(df['ADX']>df['ADX'].shift(1))&(df['MACDHist']>df['MACDHist'].shift(1))
+    df.loc[df.Buy_Sig,'Buy_Trade'] = 1
+    df.loc[df.Buy_Trade.shift(1)==1,'Buy_Trade'] = " "
+    #避免最后三天内出现交易
+    df.Buy_Trade.iloc[-3:]  = " " 
+    df.loc[df.Buy_Trade==1,'Buy_Price'] = df.close
+    df.Buy_Price = df.Buy_Price.ffill()
+    df['Buy_Daily_Return']= (df.close - df.Buy_Price)/df.Buy_Price
+    df.loc[df.Buy_Trade.shift(3)==1,'Sell_Trade'] = -1
+    df.loc[df.Sell_Trade==-1,'Buy_Total_Return'] = df.Buy_Daily_Return
+    df.loc[(df.Sell_Trade==-1)&(df.Buy_Daily_Return==0),'Buy_Total_Return'] = \
+                                (df.Buy_Price - df.Buy_Price.shift(1))/df.Buy_Price.shift(1)
+    df.loc[(df.Sell_Trade==-1)&(df.Buy_Trade.shift(1)==1),'Buy_Total_Return'] = \
+                                (df.close-df.Buy_Price.shift(2))/df.Buy_Price.shift(2)
+    #返回策略的日收益率
+    return df.Buy_Total_Return.fillna(0)
+
+df=get_from_tushare('300002')
+df.close.plot(figsize=(12,6))
+plt.title('神州泰岳股价走势\n2010-2021',size=15)
+
+pf.create_simple_tear_sheet((df.close.pct_change()).fillna(0).tz_localize('UTC'))
+
+pf.create_simple_tear_sheet(adx_strategy(df).tz_localize('UTC'))
--- a/qbot/strategies/get_stack_data.py
+++ b/qbot/strategies/get_stack_data.py
@@ -0,0 +1,84 @@
+'''
+Author: Charmve yidazhang1@gmail.com
+Date: 2023-03-18 18:09:25
+LastEditors: Charmve yidazhang1@gmail.com
+LastEditTime: 2023-03-18 18:09:26
+FilePath: /Qbot/qbot/strategies/get_stack_data.py
+Version: 1.0.1
+Blogs: charmve.blog.csdn.net
+GitHub: https://github.com/Charmve
+Description: 
+
+Copyright (c) 2023 by Charmve, All Rights Reserved. 
+Licensed under the MIT License.
+'''
+
+#使用tushare旧版获取数据
+import tushare as ts
+def get_from_tushare(code,adj='hfq',start='2010-01-01',end='2021-11-05'):
+    df=ts.get_k_data(code,autype=adj,start=start,end=end)
+    df.index=pd.to_datetime(df.date)
+    #原数据已默认按日期进行了排序
+    return df
+ 
+#使用tushare pro获取数据
+import tushare as ts
+token='输入你自己的token'
+pro=ts.pro_api(token)
+ts.set_token(token)
+def get_from_tushare_pro(code,adj='hfq',start='2010-01-01',end='2021-11-05'):
+    #code:输入数字字符串，如‘300002’
+    #start和end输入'年-月-日'需转为'年月日'格式
+    if code.startswith('6'):
+        code=code+'.SH'
+    else:
+        code=code+'.SZ'
+    start=''.join(start.split('-'))
+    end=''.join(end.split('-'))
+    df=ts.pro_bar(ts_code=code,adj=adj,start_date=start,end_date=end)
+    #原数据是倒序的，所以将时间设置为索引，根据索引重新排序
+    df.index=pd.to_datetime(df.trade_date)
+    df=df.sort_index()
+    return df
+ 
+#使用akshare获取数据,其数据源来自新浪，与tushare旧版本相似
+import akshare as ak
+def get_from_akshare(code,adj='hfq',start='2010-01-01',end='2021-11-05'):
+    if code.startswith('6'):
+        code='sh'+code
+    else:
+        code='sz'+code
+    start=''.join(start.split('-'))
+    end=''.join(end.split('-'))
+    df = ak.stock_zh_a_daily(symbol=code, start_date=start, end_date=end, adjust=adj)
+    return df
+ 
+#使用baostock获取数据
+import baostock as bs
+def get_from_baostock(code,adj='hfq',start='2010-01-01',end='2021-11-05'):
+    if code.startswith('6'):
+        code='sh.'+code
+    else:
+        code='sz.'+code
+    #转换复权为数字
+    if adj=='hfq':
+        adj='1'
+    elif adj=='qfq':
+        adj='2'
+    else:
+        adj='3'
+    #必须登陆和登出系统
+    bs.login() #登陆系统
+    rs = bs.query_history_k_data_plus(code,
+       fields="date,code,open,high,low,close,volume",
+       start_date=start, end_date=end,
+       frequency="d", adjustflag=adj)
+    #adjustflag：复权类型，默认不复权：3；1：后复权；2：前复权
+    data_list = []
+    while (rs.error_code == '0') & rs.next():
+        data_list.append(rs.get_row_data())
+    #将数据转为dataframe格式
+    df = pd.DataFrame(data_list, columns=rs.fields)
+    df.index=pd.to_datetime(df.date)
+    bs.logout() #登出系统 
+    return df
--- a/qbot/strategies/klines_bt.py
+++ b/qbot/strategies/klines_bt.py
@@ -0,0 +1,106 @@
+'''
+Author: Charmve yidazhang1@gmail.com
+Date: 2023-03-12 18:44:54
+LastEditors: Charmve yidazhang1@gmail.com
+LastEditTime: 2023-05-17 09:35:01
+FilePath: /Qbot/qbot/strategies/klines_bt.py
+Version: 1.0.1
+Blogs: charmve.blog.csdn.net
+GitHub: https://github.com/Charmve
+Description: 
+
+Copyright (c) 2023 by Charmve, All Rights Reserved. 
+Licensed under the MIT License.
+'''
+from datetime import datetime
+import backtrader
+from loguru import logger
+import matplotlib.pyplot as plt
+import pandas as pd
+import efinance
+
+
+def get_k_data(stock_code, begin: datetime, end: datetime) -> pd.DataFrame:
+    """
+    根据efinance工具包获取股票数据
+    :param stock_code:股票代码
+    :param begin: 开始日期
+    :param end: 结束日期
+    :return:
+    """
+    # stock_code = '600519'  # 股票代码，茅台
+    k_dataframe: pd.DataFrame = efinance.stock.get_quote_history(
+        stock_code, beg=begin.strftime("%Y%m%d"), end=end.strftime("%Y%m%d"))
+    k_dataframe = k_dataframe.iloc[:, :9]
+    k_dataframe.columns = ['name', 'code', 'date', 'open', 'close', 'high', 'low', 'volume', 'turnover']
+    k_dataframe.index = pd.to_datetime(k_dataframe.date)
+    k_dataframe.drop(['name', 'code', 'date'], axis=1, inplace=True)
+    return k_dataframe
+
+
+class KlinesStrategy(backtrader.Strategy):  # 策略
+    def __init__(self):
+        # 初始化交易指令、买卖价格和手续费
+        self.close_price = self.datas[0].close  # 这里加一个数据引用，方便后续操作
+        self.sma = backtrader.indicators.SimpleMovingAverage(self.datas[0], period=5)  # 借用这个策略，计算5日的均线
+
+    def notify_order(self, order):  # 固定写法，查看订单情况
+        # 查看订单情况
+        if order.status in [order.Submitted, order.Accepted]:  # 接受订单交易，正常情况
+            return
+        if order.status in [order.Completed]:
+            if order.isbuy():
+                logger.debug('已买入, 购入金额 %.2f' % order.executed.price)
+            elif order.issell():
+                logger.debug('已卖出, 卖出金额 %.2f' % order.executed.price)
+        elif order.status in [order.Canceled, order.Margin, order.Rejected]:
+            logger.debug('订单取消、保证金不足、金额不足拒绝交易')
+
+    def next(self):  # 固定的函数，框架执行过程中会不断循环next()，过一个K线，执行一次next()
+        # 此时调用 self.datas[0]即可查看当天的数据
+        # 执行买入条件判断：当天收盘价格突破5日均线
+        if self.close_price[0] > self.sma[0]:
+            # 执行买入
+            logger.debug("buy 500 in {}, 预期购入金额 {}, 剩余可用资金 {}", self.datetime.date(), self.data.close[0],
+                         self.broker.getcash())
+            self.buy(size=500, price=self.data.close[0])
+        # 执行卖出条件已有持仓，且收盘价格跌破5日均线
+        if self.position:
+            if self.close_price[0] < self.sma[0]:
+                # 执行卖出
+                logger.debug("sell in {}, 预期卖出金额 {}, 剩余可用资金 {}", self.datetime.date(), self.data.close[0],
+                             self.broker.getcash())
+                self.sell(size=500, price=self.data.close[0])
+
+
+if __name__ == '__main__':
+    # 获取数据
+    start_time = datetime(2015, 1, 1)
+    end_time = datetime(2021, 1, 1)
+    dataframe = get_k_data('600519', begin=start_time, end=end_time)
+    # =============== 为系统注入数据 =================
+    # 加载数据
+    data = backtrader.feeds.PandasData(dataname=dataframe, fromdate=start_time, todate=end_time)
+    # 初始化cerebro回测系统
+    cerebral_system = backtrader.Cerebro()  # Cerebro引擎在后台创建了broker(经纪人)实例，系统默认每个broker的初始资金量为10000
+    # 将数据传入回测系统
+    cerebral_system.adddata(data)  # 导入数据，在策略中使用 self.datas 来获取数据源
+    # 将交易策略加载到回测系统中
+    cerebral_system.addstrategy(KlinesStrategy)
+    # =============== 系统设置 ==================
+    # 设置启动资金为 100000
+    start_cash = 1000000
+    cerebral_system.broker.setcash(start_cash)
+    # 设置手续费 万2.5
+    cerebral_system.broker.setcommission(commission=0.00025)
+    logger.debug('初始资金: {} 回测期间：from {} to {}'.format(start_cash, start_time, end_time))
+    # 运行回测系统
+    cerebral_system.run()
+    # 获取回测结束后的总资金
+    portvalue = cerebral_system.broker.getvalue()
+    pnl = portvalue - start_cash
+    # 打印结果
+    logger.debug('净收益: {}', pnl)
+    logger.debug("总资金: {}", portvalue)
+    cerebral_system.plot(style='candlestick')
+    plt.show()
--- a/qbot/strategies/util.py
+++ b/qbot/strategies/util.py
@@ -0,0 +1,96 @@
+'''
+Author: Charmve yidazhang1@gmail.com
+Date: 2023-03-14 01:49:02
+LastEditors: Charmve yidazhang1@gmail.com
+LastEditTime: 2023-03-14 01:50:11
+FilePath: /Qbot/qbot/strategies/util.py
+Version: 1.0.1
+Blogs: charmve.blog.csdn.net
+GitHub: https://github.com/Charmve
+Description: 
+
+Copyright (c) 2023 by Charmve, All Rights Reserved. 
+Licensed under the MIT License.
+'''
+
+import baostock as bs
+import pandas as pd
+import talib as ta
+import matplotlib.pyplot as plt
+
+
+def computeMACD(code, startdate, enddate):
+    login_result = bs.login(user_id='anonymous', password='123456')
+    print(login_result)
+    # 获取股票日 K 线数据
+    rs = bs.query_history_k_data(code,
+                                 "date,code,close,tradeStatus",
+                                 start_date=startdate,
+                                 end_date=enddate,
+                                 frequency="d", adjustflag="3")
+    # 打印结果集
+    result_list = []
+    while (rs.error_code == '0') & rs.next():
+        # 获取一条记录，将记录合并在一起
+        result_list.append(rs.get_row_data())
+        df = pd.DataFrame(result_list, columns=rs.fields)
+        # 剔除停盘数据
+        df2 = df[df['tradeStatus'] == '1']
+        # 获取 dif,dea,hist，它们的数据类似是 tuple，且跟 df2 的 date 日期一一对应
+        # 记住了 dif,dea,hist 前 33 个为 Nan，所以推荐用于计算的数据量一般为你所求日期之间数据量的 3 倍
+    # 这里计算的 hist 就是 dif-dea,而很多证券商计算的 MACD=hist*2=(difdea)*2
+    dif, dea, hist = ta.MACD(df2['close'].astype(float).values, fastperiod=12, slowperiod=26, signalperiod=9)
+    df3 = pd.DataFrame({'dif': dif[33:], 'dea': dea[33:], 'hist':hist[33:]},index=df2['date'][33:], columns=['dif', 'dea','hist'])
+    df3.plot(title='MACD')
+    plt.show()
+    # 寻找 MACD 金叉和死叉
+    datenumber = int(df3.shape[0])
+    for i in range(datenumber - 1):
+        if ((df3.iloc[i, 0] <= df3.iloc[i, 1]) & (df3.iloc[i + 1, 0] >= df3.iloc[i + 1, 1])):
+            print("MACD 金叉的日期：" + df3.index[i + 1])
+    if ((df3.iloc[i, 0] >= df3.iloc[i, 1]) & (df3.iloc[i + 1, 0] <=df3.iloc[i + 1, 1])):
+        print("MACD 死叉的日期：" + df3.index[i + 1])
+    bs.logout()
+    return (dif, dea, hist)
+
+def calculateEMA(period, closeArray, emaArray=[]):
+    length = len(closeArray)
+    nanCounter = np.count_nonzero(np.isnan(closeArray))
+    if not emaArray:
+        emaArray.extend(np.tile([np.nan], (nanCounter + period - 1)))
+        firstema = np.mean(closeArray[nanCounter:nanCounter + period - 1])
+        emaArray.append(firstema)
+        for i in range(nanCounter + period, length):
+            ema = (2 * closeArray[i] + (period - 1) * emaArray[-1]) / (period + 1)
+            emaArray.append(ema)
+    return np.array(emaArray)
+
+
+def calculateMACD(closeArray, shortPeriod=12, longPeriod=26, signalPeriod=9):
+    ema12 = calculateEMA(shortPeriod, closeArray, [])
+    ema26 = calculateEMA(longPeriod, closeArray, [])
+    diff = ema12 - ema26
+
+    dea = calculateEMA(signalPeriod, diff, [])
+    macd = (diff - dea)*2
+
+    fast_values = diff   # 快线
+    slow_values = dea    # 慢线
+    diff_values = macd   # macd
+    # return fast_values, slow_values, diff_values  # 返回所有的快慢线和macd值
+    return fast_values[-1], slow_values[-1], diff_values[-1]    # 返回最新的快慢线和macd值
+    # return round(fast_values[-1],5), round(slow_values[-1],5), round(diff_values[-1],5)
+
+def getMACD():
+    data = RequestUtil.sendRequest_GET(UrlConstant.Get_K_Line)
+    closeArray = [float(i[4]) for i in data]
+    closeArray.reverse()
+    return calculateMACD(closeArray)
+
+
+if __name__ == '__main__':
+    code = 'sh.600000'
+    startdate = '2022-03-01'
+    enddate = '2023-03-18'
+    (dif, dea, hist) = computeMACD(code, startdate, enddate)
+    print((dif, dea, hist))
--- a/utils/common/AShareDailyData.py
+++ b/utils/common/AShareDailyData.py
@@ -0,0 +1,246 @@
+'''
+Author: Charmve yidazhang1@gmail.com
+Date: 2023-03-12 20:11:54
+LastEditors: Charmve yidazhang1@gmail.com
+LastEditTime: 2023-03-12 20:12:24
+FilePath: /Qbot/utils/common/AShareDailyData.py
+Version: 1.0.1
+Blogs: charmve.blog.csdn.net
+GitHub: https://github.com/Charmve
+Description: 
+
+Copyright (c) 2023 by Charmve, All Rights Reserved. 
+Licensed under the MIT License.
+'''
+
+import multiprocessing
+import os
+import sys
+import traceback
+from datetime import datetime, timedelta, time
+from time import sleep
+from typing import List
+
+from tqdm import tqdm
+from vnpy.trader.constant import Interval, Exchange
+from vnpy.trader.database import database_manager, BarOverview
+from vnpy.trader.object import HistoryRequest, BarData
+
+from utils import log
+
+sys.path.append(os.getcwd())
+
+from TuShare import tushare_client, to_split_ts_codes, TS_DATE_FORMATE
+
+
+class AShareDailyDataManager:
+
+    def __init__(self):
+        """"""
+        self.tushare_client = tushare_client
+        self.symbols = None
+        self.trade_cal = None
+        self.bar_overviews: List[BarOverview] = None
+        self.init()
+
+    def init(self):
+        """"""
+        self.tushare_client.init()
+        self.symbols = self.tushare_client.symbols
+        self.trade_cal = self.tushare_client.trade_cal
+        self.bar_overviews = database_manager.get_bar_overview()
+
+    def download_all(self):
+        """
+        使用tushare下载A股股票全市场日线数据
+        :return:
+        """
+        log.info("开始下载A股股票全市场日线数据")
+        if self.symbols is not None:
+            with tqdm(total=len(self.symbols)) as pbar:
+                for tscode, list_date in zip(self.symbols['ts_code'], self.symbols['list_date']):
+                    symbol, exchange = to_split_ts_codes(tscode)
+
+                    pbar.set_description_str("下载A股日线数据股票代码:" + tscode)
+                    start_date = datetime.strptime(list_date, TS_DATE_FORMATE)
+                    req = HistoryRequest(symbol=symbol,
+                                         exchange=exchange,
+                                         start=start_date,
+                                         end=datetime.now(),
+                                         interval=Interval.DAILY)
+                    bardata = self.tushare_client.query_history(req=req)
+
+                    if bardata:
+                        try:
+                            database_manager.save_bar_data(bardata)
+                        except Exception as ex:
+                            log.error(tscode + "数据存入数据库异常")
+                            log.error(ex)
+                            traceback.print_exc()
+
+                    pbar.update(1)
+                    log.info(pbar.desc)
+
+        log.info("A股股票全市场日线数据下载完毕")
+
+    def get_newest_bar_data(self, symbol: str, exchange: Exchange, interval: Interval) -> BarData or None:
+        """"""
+        for overview in self.bar_overviews:
+            if exchange == overview.exchange and interval == overview.interval and symbol == overview.symbol:
+                bars = database_manager.load_bar_data(symbol=symbol, exchange=exchange, interval=interval,
+                                                      start=overview.end, end=overview.end)
+                return bars[0] if bars is not None else None
+        return None
+
+    def update_newest(self):
+        """
+        使用tushare更新本地数据库中的最新数据，默认本地数据库中原最新的数据之前的数据都是完备的
+        :return:
+        """
+        log.info("开始更新最新的A股股票全市场日线数据")
+        if self.symbols is not None:
+            with tqdm(total=len(self.symbols)) as pbar:
+                for tscode, list_date in zip(self.symbols['ts_code'], self.symbols['list_date']):
+                    symbol, exchange = to_split_ts_codes(tscode)
+
+                    newest_local_bar = self.get_newest_bar_data(symbol=symbol,
+                                                                exchange=exchange,
+                                                                interval=Interval.DAILY)
+                    if newest_local_bar is not None:
+                        pbar.set_description_str("正在处理股票代码：" + tscode + "本地最新数据：" +
+                                                 newest_local_bar.datetime.strftime(TS_DATE_FORMATE))
+                        start_date = newest_local_bar.datetime + timedelta(days=1)
+                    else:
+                        pbar.set_description_str("正在处理股票代码：" + tscode + "无本地数据")
+                        start_date = datetime.strptime(list_date, TS_DATE_FORMATE)
+                    req = HistoryRequest(symbol=symbol,
+                                         exchange=exchange,
+                                         start=start_date,
+                                         end=datetime.now(),
+                                         interval=Interval.DAILY)
+                    bardata = self.tushare_client.query_history(req=req)
+                    if bardata:
+                        try:
+                            database_manager.save_bar_data(bardata)
+                        except Exception as ex:
+                            log.error(tscode + "数据存入数据库异常")
+                            log.error(ex)
+                            traceback.print_exc()
+
+                    pbar.update(1)
+                    log.info(pbar.desc)
+
+        log.info("A股股票全市场日线数据更新完毕")
+
+    def check_update_all(self):
+        """
+        这个方法太慢了，不建议调用。
+        这个方法用于本地数据库已经建立，但可能有部分数据缺失时使用
+        使用tushare检查更新所有的A股股票全市场日线数据
+        检查哪一个交易日的数据是缺失的，补全它
+        检查上市后是否每个交易日都有数据，若存在某一交易日无数据，尝试从tushare查询该日数据，若仍无，则说明当天停盘
+        :return:
+        """
+        log.info("开始检查更新所有的A股股票全市场日线数据")
+
+        if self.symbols is not None:
+            with tqdm(total=len(self.symbols)) as pbar:
+                for tscode, list_date in zip(self.symbols['ts_code'], self.symbols['list_date']):
+                    pbar.set_description_str("正在检查A股日线数据，股票代码:" + tscode)
+
+                    symbol, exchange = to_split_ts_codes(tscode)
+
+                    local_bar = database_manager.load_bar_data(symbol=symbol,
+                                                               exchange=exchange,
+                                                               interval=Interval.DAILY,
+                                                               start=datetime.strptime(list_date, TS_DATE_FORMATE),
+                                                               end=datetime.now())
+                    local_bar_dates = [bar.datetime.strftime(TS_DATE_FORMATE) for bar in local_bar]
+
+                    index = (self.trade_cal[exchange.value][(self.trade_cal[exchange.value].cal_date == list_date)])
+                    trade_cal = self.trade_cal[exchange.value].iloc[index.index[0]:]
+                    for trade_date in trade_cal['cal_date']:
+                        if trade_date not in local_bar_dates:
+                            req = HistoryRequest(symbol=symbol,
+                                                 exchange=exchange,
+                                                 start=datetime.strptime(trade_date, TS_DATE_FORMATE),
+                                                 end=datetime.strptime(trade_date, TS_DATE_FORMATE),
+                                                 interval=Interval.DAILY)
+                            bardata = self.tushare_client.query_history(req=req)
+                            if bardata:
+                                log.info(tscode + "本地数据库缺失：" + trade_date)
+                                try:
+                                    database_manager.save_bar_data(bardata)
+                                except Exception as ex:
+                                    log.error(tscode + "数据存入数据库异常")
+                                    log.error(ex)
+                                    traceback.print_exc()
+                    pbar.update(1)
+                    log.info(pbar.desc)
+
+        log.info("A股股票全市场日线数据检查更新完毕")
+
+
+a_share_daily_data_manager = AShareDailyDataManager()
+
+
+def auto_update(start_time: time = time(18, 0)):
+    """
+    每日盘后自动更新最新日线数据到本地数据库
+    """
+    log.info("启动A股股票全市场日线数据定时更新")
+    run_parent(start_time=start_time)
+
+
+def run_parent(start_time: time = time(18, 0)):
+    """
+    运行父进程，定时启动子进程下载任务
+    :return:
+    """
+    log.info("启动A股股票全市场日线数据定时更新父进程")
+
+    # 每天晚上18：30从tushare更新当时K线数据
+    UPDATE_TIME = start_time
+
+    child_process = None
+
+    while True:
+        current_time = datetime.now().time()
+
+        if current_time.hour == UPDATE_TIME.hour and current_time.minute == UPDATE_TIME.minute and child_process is None:
+            log.info("启动日线数据更新子进程")
+            child_process = multiprocessing.Process(target=run_child)
+            child_process.start()
+            log.info("日线数据更新子进程启动成功")
+
+        if (not (current_time.hour == UPDATE_TIME.hour and current_time.minute == UPDATE_TIME.minute)) \
+                and child_process is not None:
+            child_process.join()
+            child_process = None
+            log.info("数据更新子进程关闭成功")
+            log.info("进入A股股票全市场日线数据定时更新父进程")
+
+        sleep(10)
+
+
+def run_child():
+    """
+    子线程下载数据
+    :return:
+    """
+    log.info("启动A股股票全市场日线数据定时更新子进程")
+
+    try:
+        a_share_daily_data_manager.update_newest()
+    except Exception:
+        log.info("子进程异常")
+        traceback.print_exc()
+
+
+if __name__ == '__main__':
+    log.info("自动更新A股股票全市场日线数据")
+
+    # a_share_daily_data_manager.download_all()
+    # a_share_daily_data_manager.update_newest()
+    # a_share_daily_data_manager.check_update_all()
+    auto_update(start_time=time(21, 47))
--- a/utils/common/BaseService.py
+++ b/utils/common/BaseService.py
@@ -0,0 +1,271 @@
+'''
+Author: Charmve yidazhang1@gmail.com
+Date: 2023-03-10 00:45:44
+LastEditors: Charmve yidazhang1@gmail.com
+LastEditTime: 2023-03-12 20:10:22
+FilePath: /Qbot/utils/common/BaseService.py
+Version: 1.0.1
+Blogs: charmve.blog.csdn.net
+GitHub: https://github.com/Charmve
+Description: 
+
+Copyright (c) 2023 by Charmve, All Rights Reserved. 
+Licensed under the MIT License.
+'''
+# -*-coding=utf-8-*-
+
+import datetime
+import json
+import os
+import re
+import time
+
+import parsel
+import requests
+from configure.util import send_message_via_wechat
+from loguru import logger
+
+
+class BaseService(object):
+    def __init__(self, logfile="default.log"):
+        self.logger = logger
+        self.logger.add(logfile)
+        self.init_const_data()
+        self.params = None
+        self.cookies = None
+
+    def init_const_data(self):
+        """
+        常见的数据初始化
+        """
+        self.today = datetime.datetime.now().strftime("%Y-%m-%d")
+
+    def check_path(self, path):
+        if not os.path.exists(path):
+            try:
+                os.makedirs(path)
+            except Exception as e:
+                self.logger.error(e)
+
+    def get_url_filename(self, url):
+        return url.split("/")[-1]
+
+    def save_iamge(self, content, path):
+        with open(path, "wb") as fp:
+            fp.write(content)
+
+    def get(self, url, _json=False, binary=False, retry=5):
+
+        start = 0
+        while start < retry:
+
+            try:
+                r = requests.get(
+                    url=url,
+                    params=self.params,
+                    headers=self.headers,
+                    cookies=self.cookies,
+                )
+
+            except Exception as e:
+                self.logger.error("base class error ".format(e))
+                start += 1
+                continue
+
+            else:
+                if _json:
+                    result = r.json()
+                elif binary:
+                    result = r.content
+                else:
+                    r.encoding = "utf8"
+                    result = r.text
+                return result
+
+        return None
+
+    def post(self, url, post_data, _json=False, binary=False, retry=5):
+
+        start = 0
+        while start < retry:
+
+            try:
+                r = requests.post(url=url, headers=self.headers, data=post_data)
+
+            except Exception as e:
+                print(e)
+                start += 1
+                continue
+
+            else:
+                if _json:
+                    result = r.json()
+                elif binary:
+                    result = r.content
+                else:
+                    result = r.text
+                return result
+
+        return None
+
+    @property
+    def headers(self):
+        raise NotImplementedError
+
+    def parse(self, content):
+        """
+        页面解析
+        """
+        response = parsel.Selector(text=content)
+
+        return response
+
+    def process(self, data, history=False):
+        """
+        数据存储
+        """
+        pass
+
+    def time_str(self, x):
+        return x.strftime("%Y-%m-%d")
+
+    def trading_time(self):
+        """
+        判定时候交易时间 0 为交易时间， 1和-1为非交易时间
+        :return:
+        """
+        TRADING = 0
+        MORNING_STOP = -1
+        AFTERNOON_STOP = 1
+        NOON_STOP = -1
+        current = datetime.datetime.now()
+        year, month, day = current.year, current.month, current.day
+        start = datetime.datetime(year, month, day, 9, 23, 0)
+        noon_start = datetime.datetime(year, month, day, 12, 58, 0)
+
+        morning_end = datetime.datetime(year, month, day, 11, 31, 0)
+        end = datetime.datetime(year, month, day, 15, 2, 5)
+
+        if current > start and current < morning_end:
+            return TRADING
+
+        elif current > noon_start and current < end:
+            return TRADING
+
+        elif current > end:
+            return AFTERNOON_STOP
+
+        elif current < start:
+            return MORNING_STOP
+
+        else:
+            return NOON_STOP
+
+    def notify(self, title):
+        send_message_via_wechat(title)
+
+    def weekday(self, day=datetime.datetime.now().strftime("%Y-%m-%d")):
+        """判断星期几"""
+
+        if re.search(f"\d{4}-\d{2}-\d{2}", day):
+            fmt = "%Y-%m-%d"
+        elif re.search("\d{8}", day):
+            fmt = "%Y%m%d"
+        else:
+            raise ValueError("请输入正确的日期格式")
+
+        current_date = datetime.datetime.strptime(day, fmt)
+        year_2000th = datetime.datetime(year=2000, month=1, day=2)
+        day_diff = current_date - year_2000th
+        return day_diff.days % 7
+
+    def is_weekday(self, day=datetime.datetime.now().strftime("%Y-%m-%d")):
+        if self.weekday(day) in [0, 6]:
+            return False
+        else:
+            return True
+
+    def execute(self, cmd, data, conn, logger=None):
+
+        cursor = conn.cursor()
+
+        if not isinstance(data, tuple):
+            data = (data,)
+        try:
+            cursor.execute(cmd, data)
+        except Exception as e:
+            conn.rollback()
+            logger.error("执行数据库错误 {},{}".format(e, cmd))
+            ret = None
+        else:
+            ret = cursor.fetchall()
+            conn.commit()
+
+        return ret
+
+    def jsonp2json(self, str_):
+        return json.loads(str_[str_.find("{") : str_.rfind("}") + 1])
+
+    def set_proxy_param(self, proxy):
+        self.proxy_ip = proxy
+
+    def get_proxy(self, retry=10):
+
+        if not hasattr(self, "proxy_ip"):
+            raise AttributeError("Please set proxy ip before use it")
+
+        proxyurl = f"http://{self.proxy_ip}/dynamicIp/common/getDynamicIp.do"
+        count = 0
+        for i in range(retry):
+            try:
+                r = requests.get(proxyurl, timeout=10)
+            except Exception as e:
+                print(e)
+                count += 1
+                print("代理获取失败,重试" + str(count))
+                time.sleep(1)
+
+            else:
+                js = r.json()
+                proxyServer = "://{0}:{1}".format(js.get("ip"), js.get("port"))
+
+                proxies_random = {
+                    "http": "http" + proxyServer,
+                    "https": "https" + proxyServer,
+                }
+                return proxies_random
+
+        return None
+
+    def convert_timestamp(self, t):
+        return datetime.datetime.fromtimestamp(int(t / 1000)).strftime("%Y-%m-%d")
+
+
+class HistorySet(object):
+    def __init__(self, expire=1800):
+        self.data = {}
+        self.expire = expire
+
+    def add(self, value):
+        now = datetime.datetime.now()
+        expire = now + datetime.timedelta(seconds=self.expire)
+        try:
+            hash(value)
+        except:  # noqa E722
+            raise ValueError("value not hashble")
+        else:
+            self.data.update({value: expire})
+
+    def is_expire(self, value):
+        # 没有过期 返回 False
+        if value not in self.data or self.data[value] < datetime.datetime.now():
+            return True
+        else:
+            return False
+
+
+if __name__ == "__main__":
+    base = BaseService()
+    base.is_weekday()
+    # base.set_proxy_param()
+    print(base.get_proxy())
--- a/utils/common/TuShare.py
+++ b/utils/common/TuShare.py
@@ -0,0 +1,235 @@
+import requests
+import tushare as ts
+from tushare.pro import client
+from pytz import timezone
+from typing import List, Optional, Dict
+import pandas as pd
+from datetime import datetime, timedelta
+import time
+import traceback
+
+from vnpy.trader.object import HistoryRequest, BarData
+from vnpy.trader.constant import Exchange, Interval
+
+from utils import log
+
+CHINA_TZ = timezone("Asia/Shanghai")
+
+tushare_token: str = ""
+
+MAX_QUERY_SIZE: int = 5000
+TS_DATE_FORMATE: str = '%Y%m%d'
+MAX_QUERY_TIMES: int = 500
+
+EXCHANGE_TS2VT: Dict[str, Exchange] = {
+    'SH': Exchange.SSE,
+    'SZ': Exchange.SZSE
+}
+
+EXCHANGE_VT2TS: Dict[Exchange, str] = {v: k for k, v in EXCHANGE_TS2VT.items()}
+
+
+def to_ts_symbol(symbol: str, exchange: Exchange):
+    """
+    转换合约代码为tushare查询代码
+    """
+    if exchange == Exchange.SSE:
+        tcode = f'{symbol}' + '.' + f'{EXCHANGE_VT2TS[exchange]}'
+    elif exchange == Exchange.SZSE:
+        tcode = f'{symbol}' + '.' + f'{EXCHANGE_VT2TS[exchange]}'
+    else:
+        print("目前只研究深圳证券交易所和上海证券交易所A股股票！")
+        raise TypeError("目前只研究深圳证券交易所和上海证券交易所A股股票！")
+    return tcode
+
+
+def to_split_ts_codes(tscode: str):
+    symbol, exchange_ts = tscode.split('.')
+    exchange = EXCHANGE_TS2VT[exchange_ts]
+    return symbol, exchange
+
+
+class TuShareClient:
+    """
+    从TuShare中查询历史数据的Client
+    tushare日线数据说明：交易日每天15点~16点之间更新数据，daily接口是未复权行情，停牌期间不提供数据。
+    tushare调取说明：基础积分每分钟内最多调取500次，每次5000条数据
+    """
+
+    def __init__(self):
+        """"""
+
+        self.pro: client.DataApi = None
+
+        self.inited: bool = False
+
+        # 获得所有股票代码
+        self.symbols: pd.DataFrame = None
+
+        # 获得交易日历
+        self.trade_cal: Dict[str, pd.DataFrame] = None
+
+    def init(self, token: str = "") -> bool:
+        """"""
+        if self.inited:
+            return True
+
+        if token:
+            ts.set_token(tushare_token)
+        else:
+            ts.set_token(tushare_token)
+
+        try:
+            self.pro = ts.pro_api()
+            self.stock_list()
+            self.trade_day_list()
+        except (BaseException, "tushare连接失败"):
+            return False
+
+        self.inited = True
+        return True
+
+    def query_history(self, req: HistoryRequest) -> Optional[List[BarData]]:
+        """
+        从tushare里查询历史数据
+        :param req:查询请求
+        :return: Optional[List[BarData]]
+        """
+        if self.symbols is None:
+            return None
+
+        symbol = req.symbol
+        exchange = req.exchange
+        interval = req.interval
+        start = req.start.strftime(TS_DATE_FORMATE)
+        end = req.end.strftime(TS_DATE_FORMATE)
+
+        if interval is not Interval.DAILY:
+            return None
+        if exchange not in [Exchange.SSE, Exchange.SZSE]:
+            return None
+
+        tscode = to_ts_symbol(symbol, exchange)
+
+        # 修改查询数据逻辑，在每次5000条数据的限制下，很可能一次无法读取完
+        cnt = 0
+        df: pd.DataFrame = None
+        while datetime.strptime(start, TS_DATE_FORMATE) <= datetime.strptime(end, TS_DATE_FORMATE):
+            # 保证每次查询最多5000天数据
+            start_date = datetime.strptime(start, TS_DATE_FORMATE)
+            simulate_end_date = min(datetime.strptime(end, TS_DATE_FORMATE),
+                                    start_date + timedelta(days=MAX_QUERY_SIZE))
+            simulate_end = simulate_end_date.strftime(TS_DATE_FORMATE)
+
+            # 保证每次调用时间在60/500=0.12秒内，以保证每分钟调用次数少于500次
+            # begin_time = time.time()
+            tushare_df = None
+            while True:
+                try:
+                    tushare_df = self.pro.query('daily', ts_code=tscode, start_date=start, end_date=simulate_end)
+                except (requests.exceptions.SSLError, requests.exceptions.ConnectionError) as e:
+                    log.error(e)
+                    # traceback.print_exc()
+                    # ('Connection aborted.', ConnectionResetError(10054, '远程主机强迫关闭了一个现有的连接。', None, 10054, None))
+                    if '10054' in str(e):
+                        sleep_time = 60.0
+                        log.info("请求过于频繁，sleep：" + str(sleep_time) + "s")
+                        time.sleep(sleep_time)
+                        log.info("继续发送请求：" + tscode)
+                        continue  # 继续发请求
+                    else:
+                        raise Exception(e)  # 其他异常，抛出来
+                break
+            if tushare_df is not None:
+                if df is None:
+                    df = tushare_df
+                else:
+                    df = pd.concat([df, tushare_df], ignore_index=True)
+            # end_time = time.time()
+            # delta = round(end_time - begin_time, 3)
+            # if delta < 60 / MAX_QUERY_TIMES:
+            sleep_time = 0.50
+            log.info("sleep：" + str(sleep_time) + "s")
+            time.sleep(sleep_time)
+
+            cnt += 1
+            start = (simulate_end_date + timedelta(days=1)).strftime(TS_DATE_FORMATE)
+
+        data: List[BarData] = []
+
+        if df is not None:
+            for ix, row in df.iterrows():
+                date = datetime.strptime(row.trade_date, '%Y%m%d')
+                date = CHINA_TZ.localize(date)
+
+                if pd.isnull(row['open']):
+                    log.info(symbol + '.' + EXCHANGE_VT2TS[exchange] + row['trade_date'] + "open_price为None")
+                elif pd.isnull(row['high']):
+                    log.info(symbol + '.' + EXCHANGE_VT2TS[exchange] + row['trade_date'] + "high_price为None")
+                elif pd.isnull(row['low']):
+                    log.info(symbol + '.' + EXCHANGE_VT2TS[exchange] + row['trade_date'] + "low_price为None")
+                elif pd.isnull(row['close']):
+                    log.info(symbol + '.' + EXCHANGE_VT2TS[exchange] + row['trade_date'] + "close_price为None")
+                elif pd.isnull(row['amount']):
+                    log.info(symbol + '.' + EXCHANGE_VT2TS[exchange] + row['trade_date'] + "volume为None")
+
+                row.fillna(0)
+                bar = BarData(
+                    symbol=symbol,
+                    exchange=exchange,
+                    interval=interval,
+                    datetime=date,
+                    open_price=row['open'],
+                    high_price=row['high'],
+                    low_price=row['low'],
+                    close_price=row['close'],
+                    volume=row['amount'],
+                    gateway_name='tushare'
+                )
+
+                data.append(bar)
+        return data
+
+    def stock_list(self):
+        """
+        调用tushare stock_basic 接口
+        获得上海证券交易所和深圳证券交易所所有股票代码
+        获取基础信息数据，包括股票代码、名称、上市日期、退市日期等
+        :return:
+        """
+        if self.symbols is None:
+            symbols_sse = self.pro.query('stock_basic', exchange=Exchange.SSE.value, fields='ts_code,symbol,name,'
+                                                                                            'fullname,enname,market,'
+                                                                                            'list_status,list_date,'
+                                                                                            'delist_date,is_hs')
+            symbols_szse = self.pro.query('stock_basic', exchange=Exchange.SZSE.value, fields='ts_code,symbol,name,'
+                                                                                              'fullname,enname,market,'
+                                                                                              'list_status,list_date,'
+                                                                                              'delist_date,is_hs')
+            self.symbols = pd.concat([symbols_sse, symbols_szse], axis=0, ignore_index=True)
+
+    def trade_day_list(self):
+        """
+        查询交易日历
+        :return:
+        """
+        if self.trade_cal is None:
+            self.trade_cal = dict()
+            self.trade_cal[Exchange.SSE.value] = self.pro.query('trade_cal', exchange=Exchange.SSE.value, is_open='1')
+            self.trade_cal[Exchange.SZSE.value] = self.pro.query('trade_cal', exchange=Exchange.SZSE.value, is_open='1')
+
+
+tushare_client = TuShareClient()
+
+if __name__ == "__main__":
+    print("测试TuShare数据接口")
+    # tushare_client = TuShareClient()
+    tushare_client.init()
+    # print(tushare_client.symbols)
+    # print(tushare_client.trade_cal)
+
+    req = HistoryRequest(symbol='600600', exchange=Exchange.SSE,
+                         start=datetime(year=1999, month=11, day=10), end=datetime.now(), interval=Interval.DAILY)
+
+    ts_data = tushare_client.query_history(req)
+    print(len(ts_data))
--- a/utils/common/init.py
+++ b/utils/common/init.py
--- a/utils/common/utils.py
+++ b/utils/common/utils.py
@@ -0,0 +1,36 @@
+import logging
+
+
+class logger:
+    def __init__(self, path, clevel=logging.INFO, Flevel=logging.INFO):
+        self.logger = logging.getLogger(path)
+        self.logger.setLevel(logging.DEBUG)
+        fmt = logging.Formatter('[%(asctime)s] [%(levelname)s] %(message)s', '%Y-%m-%d %H:%M:%S')
+        # 设置CMD日志
+        sh = logging.StreamHandler()
+        sh.setFormatter(fmt)
+        sh.setLevel(clevel)
+        # 设置文件日志
+        fh = logging.FileHandler(path, encoding='utf-8')
+        fh.setFormatter(fmt)
+        fh.setLevel(Flevel)
+        self.logger.addHandler(sh)
+        self.logger.addHandler(fh)
+
+    def debug(self, message):
+        self.logger.debug(message)
+
+    def info(self, message):
+        self.logger.info(message)
+
+    def war(self, message):
+        self.logger.warn(message)
+
+    def error(self, message):
+        self.logger.error(message)
+
+    def cri(self, message):
+        self.logger.critical(message)
+
+
+log = logger("log.txt")
--- a/utils/configure/
+++ b/utils/configure/
@@ -0,0 +1,237 @@
+# -*- coding: utf-8 -*-
+
+import datetime
+import random
+import smtplib
+import time
+import warnings
+from email.mime.text import MIMEText
+from email.header import Header
+from email.utils import parseaddr, formataddr
+import json
+import pandas as pd
+import re
+import requests
+from .settings import config, get_config_data,DBSelector
+
+
+def notify(title='', desp=''):
+    warnings.warn("该接口需要收费了，请使用企业微信")
+    url = f"https://sc.ftqq.com/{config['WECHAT_ID']}.send?text={title}&desp={desp}"
+    try:
+        res = requests.get(url, timeout=5)
+    except Exception as e:
+        print(e)
+        return False
+
+    else:
+        try:
+            js = res.json()
+            result = True if js['data']['errno'] == 0 else False
+            if result:
+                print('发送成功')
+                return True
+            else:
+                print('发送失败')
+                return False
+
+        except Exception as e:
+            print(e)
+            print(res.text)
+
+
+def read_web_headers_cookies(website, headers=False, cookies=False):
+    config = get_config_data('web_headers.json')
+    return_headers = None
+    return_cookies = None
+
+    if headers:
+        return_headers = config[website]['headers']
+
+    if cookies:
+        return_headers = config[website]['cookies']
+
+    return return_headers, return_cookies
+
+
+def send_message_via_wechat(_message):  # 默认发送给自己
+    _config = config['enterprise_wechat']
+    userid = _config['userid']
+    agentid = _config['agentid']
+    corpid = _config['corpid']
+    corpsecret = _config['corpsecret']
+
+    response = requests.get(f"https://qyapi.weixin.qq.com/cgi-bin/gettoken?corpid={corpid}&corpsecret={corpsecret}")
+    data = json.loads(response.text)
+    access_token = data['access_token']
+
+    json_dict = {
+        "touser": userid,
+        "msgtype": "text",
+        "agentid": agentid,
+        "text": {
+            "content": _message
+        },
+        "safe": 0,
+        "enable_id_trans": 0,
+        "enable_duplicate_check": 0,
+        "duplicate_check_interval": 1800
+    }
+    json_str = json.dumps(json_dict)
+    response_send = requests.post(f"https://qyapi.weixin.qq.com/cgi-bin/message/send?access_token={access_token}",
+                                  data=json_str)
+    return json.loads(response_send.text)['errmsg'] == 'ok'
+
+
+def rsa_encrypt():
+    import rsa
+    (pubkey, privkey) = rsa.newkeys(1024)
+    print('pubkey >>>> {}'.format(pubkey))
+    print('privkey >>>> {}'.format(privkey))
+
+    with open('pub.pem', 'w') as f:
+        f.write(pubkey.save_pkcs1().decode())
+
+    with open('datasender.pem', 'w') as f:
+        f.write(privkey.save_pkcs1().decode())
+
+    message = ''
+    print("message encode {}".format(message.encode()))
+    crypto = rsa.encrypt(message.encode(), pubkey)  # 加密数据为bytes
+
+    print('密文:\n{}'.format(crypto))
+
+    with open('encrypt.bin', 'wb') as f:
+        f.write(crypto)
+    # 解密
+    e_message = rsa.decrypt(crypto, privkey)  # 解密数据也是为bytes
+    print("解密后\n{}".format(e_message.decode()))
+
+
+def rsa_decrypt():
+    import rsa
+    with open('encrypt.bin', 'rb') as f:
+        content = f.read()
+
+    file = 'priva.pem'
+    with open(file, 'r') as f:
+        privkey = rsa.PrivateKey.load_pkcs1(f.read().encode())
+
+    e_message = rsa.decrypt(content, privkey)  # 解密数据也是为bytes
+    print("解密后\n{}".format(e_message.decode()))
+
+
+def market_status():
+    '''
+    收盘
+    '''
+    now = datetime.datetime.now()
+    end = datetime.datetime(now.year, now.month, now.day, 15, 2, 5)
+    return True if now < end else False
+
+
+def _format_addr(s):
+    name, addr = parseaddr(s)
+    return formataddr((Header(name, 'utf-8').encode(), addr))
+
+
+def send_from_aliyun(title, content, TO_MAIL_=config['mail']['qq']['user'], types='plain'):
+    username = config['aliyun']['EMAIL_USER_ALI']  # 阿里云
+    password = config['aliyun']['LOGIN_EMAIL_ALYI_PASSWORD']  # 阿里云
+    stmp = smtplib.SMTP()
+
+    msg = MIMEText(content, types, 'utf-8')
+    subject = title
+    msg['Subject'] = Header(subject, 'utf-8')
+    msg['From'] = _format_addr('{} <{}>'.format('数据推送', username))
+    msg['To'] = TO_MAIL_
+
+    try:
+        stmp.connect('smtp.qiye.aliyun.com', 25)
+        stmp.login(username, password)
+        stmp.sendmail(username, TO_MAIL_, msg.as_string())
+    except Exception as e:
+        time.sleep(10 + random.randint(1, 5))
+        stmp = smtplib.SMTP()
+        stmp.connect('smtp.qiye.aliyun.com', 25)
+        stmp.login(username, password)
+        stmp.sendmail(username, TO_MAIL_, msg.as_string())
+    else:
+        print('发送完毕')
+
+
+def send_sms(content):
+    '''
+    一个海外的短信接口
+    '''
+    from twilio.rest import Client
+
+    client = Client(config.twilio_account_sid, config.twilio_auth_token)
+    try:
+        message = client.messages.create(
+            body=content,
+            from_=config.FROM_MOBILE,
+            to=config.TO_MOBILE
+        )
+    except Exception as e:
+        print(e)
+
+
+
+def jsonp2json(str_):
+    return json.loads(str_[str_.find('{'):str_.rfind('}') + 1])
+
+def js2json(str_):
+    import demjson
+    return demjson.decode(str_[str_.find('{'):str_.rfind('}') + 1])
+
+def bond_filter(code):
+    m = re.search('^(11|12)', code)
+    return True if m else False
+
+
+def get_holding_list(filename=None):
+    '''
+    获取持仓列表
+    '''
+    df = pd.read_csv(filename, encoding='gbk')
+    df['证券代码'] = df['证券代码'].astype(str)
+    df['kzz'] = df['证券代码'].map(bond_filter)
+    df = df[df['kzz'] == True]
+    return df['证券代码'].tolist()
+
+def mongo_convert_df(doc,condition=None,project=None):
+    import pandas as pd
+    result =[]
+    for item in doc.find(condition,project):
+        result.append(item)
+    return pd.DataFrame(result)
+
+def get_jsl_code(table):
+    # from settings import DBSelector
+    engine = DBSelector().get_engine('db_stock','kh')
+    df = pd.read_sql(table,engine)
+    return df
+
+def fmt_date(x,src='%Y%m%d',trgt='%Y-%m-%d'):
+    return datetime.datetime.strptime(x, src).strftime(trgt)
+
+
+def calendar(start_date,end_date):
+    from .settings import get_tushare_pro
+
+    src='%Y-%m-%d'
+    trgt='%Y%m%d'
+    start_date = fmt_date(start_date,src,trgt)
+    end_date = fmt_date(end_date,src,trgt)
+
+    pro = get_tushare_pro()
+    df = pro.trade_cal(exchange='SSE', start_date=start_date, end_date=end_date, is_open='1')
+
+    cal = df['trade_date'].tolist()
+    cal = list(map(fmt_date, cal))
+
+    return cal
+
+if __name__ == '__main__':
+    print(get_jsl_code('tb_bond_jisilu'))
--- a/utils/configure/init.py
+++ b/utils/configure/init.py
--- a/utils/configure/config.json
+++ b/utils/configure/config.json
@@ -0,0 +1,99 @@
+{
+    "mysql": {
+      "local": {
+        "host": "",
+        "port": 3306,
+        "user": "",
+        "password": ""
+      },
+      "qq": {
+        "host": "",
+        "port": 2222,
+        "user": "",
+        "password": ""
+      },
+      "ubuntu": {
+        "host": "1",
+        "port": 3306,
+        "user": "",
+        "password": ""
+      },
+      "ptrade": {
+        "host": "",
+        "port": 3306,
+        "user": "",
+        "password": ""
+      },
+      "tencent-1c": {
+        "host": "",
+        "port": 3306,
+        "user": "",
+        "password": ""
+      }
+    },
+    "redis": {
+      "qq": {
+        "host": "127.0.0.1",
+        "port": 6379,
+        "password": ""
+      }
+    },
+    "mail": {
+      "qq": {
+        "user": ""
+      }
+    },
+    "data_path": "",
+    "mongo": {
+      "qq": {
+        "host": "",
+        "port": 11111,
+        "user": "",
+        "password": ""
+      },
+      "local": {
+        "host": "127.0.0.1",
+        "port": 17017,
+        "user": null,
+        "password": null
+      }
+    },
+    "WECHAT_ID": "",
+    "jsl_cookies": {
+      "auto_reload": "",
+      "kbzw_r_uname": "",
+      "kbz_newcookie": "1",
+      "kbzw__Session": "",
+      "Hm_lvt_164fe01b1433a19b507595a43bf58262": "",
+      "Hm_lpvt_164fe01b1433a19b507595a43bf58262": ""
+    },
+    "twillio": {
+      "twilio_account_sid": "",
+      "twilio_auth_token": ""
+    },
+    "jsl_monitor": {
+      "EXPIRE_TIME": 1800,
+      "MONITOR_PERCENT": 8,
+      "ACCESS_INTERVAL": 20,
+      "JSL_USER": "",
+      "JSL_PASSWORD": ""
+    },
+    
+    "aliyun": {
+      "EMAIL_USER_XT": "",
+      "EMAIL_USER_ALI": "",
+      "LOGIN_EMAIL_ALYI_PASSWORD": ""
+    },
+    "ts_token": "",
+    "holding_file": "v",
+    "xc_server": "",
+    "xc_token_pro": "",
+    
+    "enterprise_wechat": {
+      "userid": "",
+      "agentid": "",
+      "corpid": "",
+      "corpsecret": ""
+    }
+  }
+  
--- a/utils/configure/sample_config.json
+++ b/utils/configure/sample_config.json
@@ -0,0 +1,99 @@
+{
+    "mysql": {
+      "local": {
+        "host": "",
+        "port": 3306,
+        "user": "",
+        "password": ""
+      },
+      "qq": {
+        "host": "",
+        "port": 2222,
+        "user": "",
+        "password": ""
+      },
+      "ubuntu": {
+        "host": "1",
+        "port": 3306,
+        "user": "",
+        "password": ""
+      },
+      "ptrade": {
+        "host": "",
+        "port": 3306,
+        "user": "",
+        "password": ""
+      },
+      "tencent-1c": {
+        "host": "",
+        "port": 3306,
+        "user": "",
+        "password": ""
+      }
+    },
+    "redis": {
+      "qq": {
+        "host": "127.0.0.1",
+        "port": 6379,
+        "password": ""
+      }
+    },
+    "mail": {
+      "qq": {
+        "user": ""
+      }
+    },
+    "data_path": "",
+    "mongo": {
+      "qq": {
+        "host": "",
+        "port": 11111,
+        "user": "",
+        "password": ""
+      },
+      "local": {
+        "host": "127.0.0.1",
+        "port": 17017,
+        "user": null,
+        "password": null
+      }
+    },
+    "WECHAT_ID": "",
+    "jsl_cookies": {
+      "auto_reload": "",
+      "kbzw_r_uname": "",
+      "kbz_newcookie": "1",
+      "kbzw__Session": "",
+      "Hm_lvt_164fe01b1433a19b507595a43bf58262": "",
+      "Hm_lpvt_164fe01b1433a19b507595a43bf58262": ""
+    },
+    "twillio": {
+      "twilio_account_sid": "",
+      "twilio_auth_token": ""
+    },
+    "jsl_monitor": {
+      "EXPIRE_TIME": 1800,
+      "MONITOR_PERCENT": 8,
+      "ACCESS_INTERVAL": 20,
+      "JSL_USER": "",
+      "JSL_PASSWORD": ""
+    },
+    
+    "aliyun": {
+      "EMAIL_USER_XT": "",
+      "EMAIL_USER_ALI": "",
+      "LOGIN_EMAIL_ALYI_PASSWORD": ""
+    },
+    "ts_token": "",
+    "holding_file": "v",
+    "xc_server": "",
+    "xc_token_pro": "",
+    
+    "enterprise_wechat": {
+      "userid": "",
+      "agentid": "",
+      "corpid": "",
+      "corpsecret": ""
+    }
+  }
+  
--- a/utils/configure/settings.py
+++ b/utils/configure/settings.py
@@ -0,0 +1,104 @@
+'''
+Author: Charmve yidazhang1@gmail.com
+Date: 2023-03-02 00:12:18
+LastEditors: Charmve yidazhang1@gmail.com
+LastEditTime: 2023-03-09 23:29:18
+FilePath: /Qbot/utils/configure/settings.py
+Version: 1.0.1
+Blogs: charmve.blog.csdn.net
+Description: 
+
+Copyright (c) 2023 by Charmve, All Rights Reserved. 
+'''
+# -*-coding=utf-8-*-
+
+import os
+import json
+
+
+def get_config_data(config_file='config.json'):
+    json_file = os.path.join(os.path.dirname(__file__), config_file)
+    with open(json_file, 'r', encoding='utf8') as f:
+        _config = json.load(f)
+        return _config
+
+
+config = get_config_data()
+
+
+def config_dict(*args):
+    result = config
+    for arg in args:
+        try:
+            result = result[arg]
+        except:
+            print('找不到对应的key')
+            return None
+
+    return result
+
+
+class DBSelector(object):
+    '''
+    数据库选择类
+    '''
+
+    def __init__(self):
+        self.json_data = config
+
+    def config(self, db_type='mysql', local='qq'):
+        db_dict = self.json_data[db_type][local]
+        user = db_dict['user']
+        password = db_dict['password']
+        host = db_dict['host']
+        port = db_dict['port']
+        return (user, password, host, port)
+
+    def get_engine(self, db, type_='qq'):
+        from sqlalchemy import create_engine
+        user, password, host, port = self.config(db_type='mysql', local=type_)
+        try:
+            engine = create_engine(
+                'mysql+pymysql://{}:{}@{}:{}/{}?charset=utf8'.format(user, password, host, port, db))
+        except Exception as e:
+            print(e)
+            return None
+        return engine
+
+    def get_mysql_conn(self, db, type_='qq',use_dict=False):
+        import pymysql
+        user, password, host, port = self.config(db_type='mysql', local=type_)
+        try:
+            if use_dict:
+                conn = pymysql.connect(host=host, port=port, user=user, password=password, db=db, charset='utf8mb4',read_timeout=10,cursorclass= pymysql.cursors.DictCursor)
+            else:
+                conn = pymysql.connect(host=host, port=port, user=user, password=password, db=db, charset='utf8mb4',read_timeout=10)
+        except Exception as e:
+            print(e)
+            return None
+        else:
+            return conn
+
+    def mongo(self, location_type='qq', async_type=False):
+        user, password, host, port = self.config('mongo', location_type)
+        connect_uri = f'mongodb://{user}:{password}@{host}:{port}'
+        if async_type:
+            from motor.motor_asyncio import AsyncIOMotorClient
+            client = AsyncIOMotorClient(connect_uri)
+        else:
+            import pymongo
+            client = pymongo.MongoClient(connect_uri)
+        return client
+
+
+def get_tushare_pro():
+    import xcsc_tushare as xc
+    xc_token_pro = config.get('xc_token_pro')
+    xc_server = config.get('xc_server')
+    xc.set_token(xc_token_pro)
+    pro = xc.pro_api(env='prd', server=xc_server)
+    return pro
+
+
+if __name__ == '__main__':
+    pass
--- a/utils/thsauto
+++ b/utils/thsauto