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import json
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import os
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from django.http import HttpResponse
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from django.shortcuts import render, get_object_or_404
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from LSTMPredictStock import run
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from stock_predict import models
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from datetime import datetime as dt
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from apscheduler.scheduler import Scheduler
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from .models import Company
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import pandas as pd
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LOCAL = False
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def get_hist_predict_data(stock_code):
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recent_data,predict_data = None,None
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# company = models.Company.objects.get(stock_code=stock_code)
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company = get_object_or_404(Company, stock_code=stock_code)
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if company.historydata_set.count() <= 0:
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history_data = models.HistoryData()
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history_data.company = company
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history_data.set_data(run.get_hist_data(stock_code=stock_code,recent_day=20))
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history_data.save()
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recent_data = history_data.get_data()
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else:
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all_data = company.historydata_set.all()
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for single in all_data:
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now = dt.now()
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end_date = single.get_data()[-1][0]
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end_date = dt.strptime(end_date,"%Y-%m-%d")
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if LOCAL & (now.date() > end_date.date()): # 更新预测数据
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single.set_data(run.get_hist_data(stock_code=stock_code,recent_day=20))
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single.save()
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recent_data = single.get_data()
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break
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if company.predictdata_set.count() <= 0:
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predict_data = models.PredictData()
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predict_data.company = company
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predict_data.set_data(run.prediction(stock_code,pre_len=10))
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predict_data.save()
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predict_data = predict_data.get_data()
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else:
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all_data = company.predictdata_set.all()
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for single in all_data:
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now = dt.now()
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start_date = dt.strptime(single.start_date,"%Y-%m-%d")
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if LOCAL & (now.date() > start_date.date()): # 更新预测数据
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single.set_data(run.prediction(stock_code, pre_len=10))
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single.save()
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predict_data = single.get_data()
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break
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return recent_data,predict_data
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def get_crawl_save_data():
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"""
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将10个公司的指标数据爬取并保存到数据库
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"""
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# 此处应是从网上爬取数据，并保存为csv文件
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parent_dir = os.path.dirname(__file__) # "stock_predict/views.py"
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file_dir = os.path.join(parent_dir, "stock_index/")
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for file_name in os.listdir(file_dir):
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file_path = os.path.join(file_dir, file_name)
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data_frame = pd.read_csv(file_path)
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stock_code = file_name.split('.')[0]
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company = get_object_or_404(Company, stock_code=stock_code)
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for index,row in data_frame.iterrows():
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company.stockindex_set.create(ri_qi=row['ri_qi'],zi_jin=row['zi_jin'],qiang_du=row['qiang_du'],feng_xian=row['feng_xian'],
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zhuan_qiang=row['zhuan_qiang'],chang_yu=row['chang_yu'],jin_zi=row['jin_zi'],zong_he=row['zong_he'])
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def get_stock_index(stock_code):
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"""
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获取股票的各项指标数据
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"""
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company = get_object_or_404(Company, stock_code=stock_code)
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if company.stockindex_set.count() <= 0:
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# 将爬取的数据存入数据库
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get_crawl_save_data()
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# 从数据库获取近三天的数据
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indexs = company.stockindex_set.all().order_by('-ri_qi')[:3].values()
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return list(indexs)
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def home(request):
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recent_data,predict_data = get_hist_predict_data("600718")
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data = {"recent_data":recent_data,"stock_code":"600718","predict_data":predict_data}
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data['indexs'] = get_stock_index("600718")
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return render(request,"stock_predict/home.html",{"data":json.dumps(data)}) # json.dumps(list)
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def predict_stock_action(request):
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stock_code = request.POST.get('stock_code',None)
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# print("stock_code:\n",stock_code)
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recent_data, predict_data = get_hist_predict_data(stock_code)
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data = {"recent_data": recent_data, "stock_code": stock_code, "predict_data": predict_data}
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data['indexs'] = get_stock_index(stock_code)
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return render(request, "stock_predict/home.html", {"data": json.dumps(data)}) # json.dumps(list)
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sched = Scheduler()
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# 定时任务
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# @sched.interval_schedule(seconds=2) # 每2s执行一次
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@sched.cron_schedule(hour=0,minute=0) # 每日凌晨调度一次
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def train_models():
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run.train_all_stock()
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sched.start()
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