Influenza_fund_linkage_system/app_test/other_pages/page5.py

import os

import django
from django.conf import settings

os.chdir('D:/python/djangoProject/test_Bootstrap')
# 设置 Django 环境变量
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'test_Bootstrap.settings')
# print('开始初始化')
# # 强制初始化 Django
django.setup()
# print("Django configured.")

print("Starting Streamlit...")
import streamlit as st
from  streamlit_vertical_slider import vertical_slider
from st_pages import Page, Section, show_pages, add_page_title, add_indentation
add_page_title()
add_indentation()

import streamlit as st
import streamlit as st

# 定义点击回调函数
def reset_weights():
    st.session_state.slider_values = [32, 12, 43, 12, 12]
    st.session_state.reset_trigger += 1

# 初始化 session state 中的键
if 'slider_values' not in st.session_state:
    st.session_state.slider_values = [32, 12, 43, 12, 12]
if 'reset_trigger' not in st.session_state:
    st.session_state.reset_trigger = 0
if 'fund_code' not in st.session_state:
    st.session_state['fund_code'] = ''
col1, col2 = st.columns([0.8, 0.2])

with col1:
    # 使用 HTML 和内联CSS来增加字体大小
    st.markdown("""
    <div style='font-size: 34px; font-weight: bold;'>
        基金推荐
    </div>
    """, unsafe_allow_html=True)

# with col2:
#     st.button("使用默认权重", key="hidden_button", on_click=reset_weights)
col1, col2 = st.columns([0.8, 0.2])
with col1:
    st.markdown('######')
# 创建滑块
columns = st.columns(5)
labels = [
    "国家流感中心周报数据",
    "北京疾控中心数据",
    "百度流感指数数据",
    "药品相关股票数据",
    "流感相关基金数据"
]
descriptions = [
    "详细数据来自国家流感中心的周报。",
    "来自北京市疾控中心的相关数据。",
    "基于百度搜索指数的流感数据。",
    "涉及流感药品的股票数据。",
    "投资于流感相关领域的基金数据。"
]


# 定义点击回调函数
def reset_model_weights():
    st.session_state.model_slider_values = [2, 12, 43, 12, 12]
    st.session_state.reset_trigger += 1

# 初始化 session state 中的键
if 'model_slider_values' not in st.session_state:
    st.session_state.model_slider_values = [2, 12, 43, 12, 12]
if 'reset_trigger' not in st.session_state:
    st.session_state.reset_trigger = 0

# #从滑块获取模型权重
model_values_list = [2, 12, 43, 12, 12]
# st.write('滑块数值：',model_values_list)
# print(model_values_list)

#基金预测函数
# 添加项目根目录到sys.path
import os
import sys
project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), r'D:\python\djangoProject\\test_Bootstrap'))
sys.path.append(project_root)
from app_test.add_fund_data import add_fund_data
from app_test.VAR import VAR_run
from app_test.RF import RF_run
from app_test.ARIMA import ARIMA_run
import pandas as pd
import json
from pyecharts import options as opts
import streamlit.components.v1 as components
import numpy as np
from app_test.models import RecommendedFund


def fund_predect(fund_code,idx):
    # fund_code = st.session_state.fund_code
    print(f'开始预测')
    data = add_fund_data(fund_code)
    VAR_result = VAR_run(data, 'fund_data', '')
    power_var = model_values_list[2] / (model_values_list[1] + model_values_list[2])
    power_rf = model_values_list[1] / (model_values_list[1] + model_values_list[2])
    VAR_result = VAR_result.to_frame(name='fund_data')
    # print(VAR_result, type(VAR_result))
    RF_result = RF_run(data, 'fund_data',
                       ['liugan_index', 'infection_number_x', 'infection_number_y', 'jijin_data', 'shoupan'])
    # print(ARIMA_run(data,'fund_data',['liugan_index','infection_number_x', 'infection_number_y', 'jijin_data', 'shoupan']))
    # print(RF_result, type(RF_result))
    VAR = [item[0] for item in VAR_result.values.tolist()]
    RF = [item[0] for item in RF_result.values.tolist()]
    pre = [VAR[i] * power_var + RF[i] * power_rf for i in range(len(VAR))]
    # 找到列表中的最小值和最大值
    min_val = min(pre)
    max_val = max(pre)

    # 计算每个值相对于最小值的差异比例
    pre = [(x - min_val) / (max_val - min_val) for x in pre]

    # 将差异比例放大
    pre= [x * 100 for x in pre]
    print("放大差异后的列表：", pre)
    # print(pre,type(pre))
    date_column = VAR_result.iloc[:, 0]
    date = date_column.index.tolist()
    date = [str(i)[:10] for i in date]
    print('这是预测结果')
    result = pd.DataFrame({
        'date': date,
        'prediction': pre
    })
    print(result, type(result))
    # 可视化预测结果
    # date_js = json.dumps(date)
    # data_js = json.dumps(pre)
    fund_recommand(date,pre,idx)
    return date,pre

def result_visualization(date_js, data_js,pic_name,fund_code):
    col = columns = st.columns(1)[0]
    with col:
        st.markdown(f"##### {pic_name}")
        st.markdown("基金代码：" + fund_code)  # 使用 Markdown 来提供一致的文本框高度
        # st.markdown()

    date_js = json.dumps(date_js)
    data_js = json.dumps(data_js)
    html_content = f"""
        <style>
        #echarts-container {{
            position: fixed;
            bottom: 0;
            left: 50%;
            transform: translateX(-50%);
            width: 700px;
            height: 300px;
            background: #f5f5f5;
            z-index: 100;
        }}
        </style>
        <div id="echarts-container"></div>
        <script src="https://cdn.bootcdn.net/ajax/libs/echarts/5.5.0/echarts.min.js"></script>
        <script>
        var main = echarts.init(document.getElementById("echarts-container"));

        option = {{
            backgroundColor: '#f5f5f5',  // 设置背景颜色为浅灰色
            legend: {{
                data: [\'{pic_name}\']
            }},
            xAxis: {{
                type: 'category',
                data: {date_js}
            }},
            yAxis: {{
                type: 'value'
            }},
            series: [{{
                name: '{pic_name}',
                data: {data_js},
                type: 'line'
            }}]
        }};
        main.setOption(option);
        </script>
        """

    # 使用 Streamlit 的 HTML 函数将 HTML 内容嵌入页面中
    components.html(html_content, height=350)
def fund_recommand(date,pre,idx):
    date_js = date
    data_js = pre
    print("===数据===")
    print("date_js:",date_js)
    print("data_js:",data_js)
    is_up = False
    print("基金推荐结果：",fund_name[idx])
    print(data_js)
    print(data_js[0],data_js[-1])
    if(data_js[0]<data_js[-1]):
        is_up = True
    print("is_up:",is_up)
    print("fund_name:",fund_name[idx])
    if is_up:
        #存入数据库
        fund_of_up.append([data_js,date_js,idx])
        #可视化
        # result_visualization(date_js, data_js,fund_name[idx]+"预测走势",idx)

fund_codes = ["000684","017313","006218","011308","015619"]
fund_name = ["长盛养老健康混合A","农银医疗精选股票C","富国生物医药科技混合A","富国生物医药科技混合C","宏利红利先锋混合C"]

def run_recommend():
    #基金筛选并存入列表然后放入数据库
    global fund_of_up
    fund_of_up = []
    for idx,fc in enumerate(fund_codes):
        fund_predect(fc,idx)

    #存入数据库RecommendedFund表
    RecommendedFund.objects.all().delete()#清空数据库原有数据
    for data in fund_of_up:
        # 使用get_or_create来避免重复数据
        obj, created = RecommendedFund.objects.get_or_create(fund_code=fund_codes[data[2]], defaults={'fund_name': fund_name[data[2]],'data_js':data[0],'date_js':data[1]})
        if created:
            print(f"Added new record for date {fund_codes[data[2]]}")
        else:
            print(f"Record for date {fund_codes[data[2]]} already exists.")
    print('成功存入数据库')


#读取数据库中的被推荐基金数据并可视化
def show_recommended_fund():
    recommended_fund = RecommendedFund.objects.all()
    for fund in recommended_fund:
        pic_name = fund.fund_name
        fund_code = fund.fund_code
        date_js = fund.date_js
        data_js = fund.data_js
        result_visualization(date_js, data_js, pic_name, fund_code)

show_recommended_fund()
# run_recommend()