import tushare as ts import datetime # 设置 token，用于认证 ts.set_token('530fbc2b682d65696dbeec010a893f70d6953fbb6842151003c3e12f') # 初始化 tushare pro = ts.pro_api() df = pro.daily(fields = 'ts_code,trade_date,open,close',start_date='20180701', end_date='20180718') def get_stock_poll(df): stock_pool=[] for code in df['ts_code'].unique(): temp_df = df[df['ts_code'] == code ] for i in range(len(temp_df)-3): if (temp_df.iloc[i + 3]['close'] > temp_df.iloc[i + 2]['close']) and \ (temp_df.iloc[i + 2]['close'] > temp_df.iloc[i + 2]['open']) and \ (temp_df.iloc[i + 1]['close'] > temp_df.iloc[i + 1]['open']) and \ (temp_df.iloc[i]['close'] > temp_df.iloc[i]['open'])and \ (temp_df.iloc[i + 3]['close'] > temp_df.iloc[i + 2]['close'])and \ (temp_df.iloc[i + 2]['close'] > temp_df.iloc[i + 1]['close']) and\ (temp_df.iloc[i + 1]['close'] > temp_df.iloc[i]['close']): stock_pool.append(code) break return stock_pool def buy_stock(stock_pool,df): buy_list = [] for code in stock_pool: temp_df = df[df['ts_code']==code] for i in range(len(temp_df-2)): if(temp_df.iloc[i + 2]['close'] < temp_df.iloc[i + 2]['open']) and \ (temp_df.iloc[i + 1]['close'] < temp_df.iloc[i + 1]['open']) and \ (temp_df.iloc[i]['close'] < temp_df.iloc[i]['open'])and \ (temp_df.iloc[i + 2]['close'] < temp_df.iloc[i + 1]['close'])and \ (temp_df.iloc[i + 1]['close'] < temp_df.iloc[i]['close']): buy_list.append(code) break return buy_list def sell_stock(buy_stock,df): sell_list = [] for stock in buy_list: buy_date=dateime.datetime.strptime(stock[1],"%Y-%m-%d") current_date = datetime.datetime.today() days_held = (current_date - buy_date).days if days_held >=3: sell_list.append(stock[0]) print("sell list:",sell_list)要求上述代码在聚宽上运行

解释代码：import tushare as ts import datetime import time import random import pandas as pd import requests import json import pandas as pd

这段代码导入了一些Python库/模块，例如tushare，datetime，time，random，pandas，requests等。通过这些库，我们可以进行各种操作，比如获取金融数据、操作日期时间、生成随机数、请求网络数据等。具体地，这段代码...

优化以下ptyhon代码import datetime import time import matplotlib.pyplot as plt # 初始化图形 plt.ion() # 时间列表和高度列表 time_list = [] height_list = [] # 创建柱状图 fig, ax = plt.subplots() # 设置图形初始属性 ax.set_xlabel('Time') ax.set_ylabel('Height') ax.set_title('Real-time Bar Chart') # 开始循环 while True: # 获取当前时间和高度 current_time = datetime.datetime.now().strftime("%H:%M:%S") current_height = datetime.datetime.now().second # 添加时间和高度到列表 time_list.append(current_time) height_list.append(current_height) # 清空图形 ax.clear() # 绘制柱状图 ax.bar(time_list, height_list) # 设置x轴标签旋转角度 plt.xticks(rotation=45) # 刷新图形 plt.draw() plt.pause(0.1)

import matplotlib.pyplot as plt # 初始化图形 plt.ion() # 时间列表和高度列表 time_list = [] height_list = [] # 创建柱状图 fig, ax = plt.subplots() # 设置图形初始属性 ax.set_xlabel('Time') ax.set_...

import pandas as pd import matplotlib.pyplot as plt plt.rcParams['font.sans-serif'] = ['SimHei'] import tushare as ts import os import tushare as ts ts.set_token('bb00bf226ce5a2f24de40a18c62596d511508ecc4b83b7c62b17b86d') pro = ts.pro_api() def get_data(code,start,end): df=pro.daily(ts_code=code,autype='qfq',start_date=start,end_date=end) print(df) df.index = pd.to_datetime(df.trade_date) #设置把日期作为索引 #df['ma'] = 0.0 # Backtrader需要用到 #df['openinterest'] = 0.0 # Backtrader需要用到 #定义两个新的列ma和openinterest df = df[['open', 'high', 'low', 'close', 'vol']] #重新设置df取值，并返回df return df def acquire_code(): #只下载一只股票数据，且只用CSV保存未来可以有自己的数据库 inp_code =input('600893.SH') inp_start = input('2019-01-01') inp_end = input('2020-01-01') df = get_data(inp_code,inp_start,inp_end) print(df.info()) #输出统计各列的数据量 print("—"*30) #分割线 print(df.describe()) #输出常用统计参数 df.sort_index(inplace=True) #把股票数据按照时间正序排列 path = os.path.join(os.path.join(os.getcwd(), "D:\学习\AI\project\pythonProject-tushare\data"), inp_code + "yy.csv") #os.path地址拼接，''数据地址''为文件保存路径 # path = os.path.join(os.path.join(os.getcwd(),"数据地址"),inp_code+"_30M.csv") df.to_csv(path) acquire_code() 代码修正

ts.set_token('bb00bf226ce5a2f24de40a18c62596d511508ecc4b83b7c62b17b86d') pro = ts.pro_api() def get_data(code,start,end): df=pro.daily(ts_code=code,autype='qfq',start_date=start,end_date=end) ...

修改这串代码import jqdatasdk as jq from datetime import datetime # 设置聚宽账户信息 jq.auth('username', 'password') jq.set_token('API token') # 设置选股条件 price_limit = 20 # 股票价格上限 pe_limit = 50 # 市盈率上限 pb_limit = 5 # 市净率上限 # 获取当前时间 now = datetime.now().strftime('%H:%M:%S') # 判断是否处于集合竞价时段 if now >= '09:20:00' and now <= '09:25:00': # 获取股票池 stocks = jq.get_all_securities(types=['stock']).index.tolist() # 筛选符合条件的股票 q = jq.query(jq.valuation.code, jq.valuation.pe_ratio, jq.valuation.pb_ratio).filter( jq.valuation.pe_ratio < pe_limit, jq.valuation.pb_ratio < pb_limit) df = jq.get_fundamentals(q, date=None) stocks = df['code'].tolist() # 过滤股票价格 prices = jq.get_price(stocks, end_date=datetime.now(), frequency='minute', fields=['close']) stocks = prices[prices['close'] <= price_limit].index.tolist() # 获取持仓信息 positions = jq.get_position('stock') # 调整持仓 for stock in positions: if stock in stocks: # 如果当前持仓的股票仍符合条件，则继续持有 pass else: # 如果当前持仓的股票不符合条件，则卖出 jq.order(stock, amount=0, side='sell') # 集合竞价买入 for stock in stocks: jq.order(stock, amount=100, side='buy', style='limit', price=price_limit)

# 设置选股条件 price_limit = 20 # 股票价格上限 pe_limit = 50 # 市盈率上限 pb_limit = 5 # 市净率上限 # 集合竞价买入 for stock in stocks: jq.order(stock, amount=100, side='buy', style='limit', price=...

import pandas as pd # 读入数据 data = pd.read_csv("data.csv") # 转换时间字段为时间类型 data['timestamp'] = pd.to_datetime(data['timestamp']) # 设置时间字段为索引 data.set_index('timestamp', inplace=True) # 缺失值填充 data.fillna(method='ffill', inplace=True) # 按照时间顺序排序 data.sort_index(inplace=True) # 将数据保存到新文件中 data.to_csv("processed_data.csv")解释这段代码

"import pandas as pd" 是在 Python 程序中导入 Pandas 库的语句。Pandas 是一个强大的数据处理和分析工具，常用于数据清洗、数据转换、数据分析等操作。"pd" 是导入 Pandas 库后，为了方便调用其中的函数和类而设置...

# 统计性描述 print(df1.describe()) # 将日期转换为数字 df1['date'] = df1['date'].apply(lambda x: date2num(pd.to_datetime(x))) # 获取日期数据的最小值和最大值 date_min = mdates.date2num(df1['date'].min()) date_max = mdates.date2num(df1['date'].max()) # 绘制K线图 fig, ax = plt.subplots() ax.plot(df1['date'], df1['close'], label='Close') ax.plot(df1['date'], df1['open'], label='Open') ax.plot(df1['date'], df1['high'], label='High') ax.plot(df1['date'], df1['low'], label='Low') ax.legend() ax.set_xlabel('Date') ax.set_ylabel('Price') ax.set_title('坤彩科技') # 设置横轴的显示格式和间隔 #from matplotlib.dates import MonthLocator, DateFormatter #ax.xaxis.set_major_locator(MonthLocator()) # 设置横坐标主刻度为月份 #ax.xaxis.set_major_formatter(DateFormatter('%Y-%m')) # 设置刻度标签的格式为"年-月"，可以根据需要进行修改 ax.xaxis.set_major_locator(YearLocator(base=1)) # 设置横坐标主刻度为年份 ax.xaxis.set_major_formatter(DateFormatter('%Y')) # 设置刻度标签的格式为"年" ax.xaxis.set_minor_locator(MonthLocator(bymonth=(3, 6, 9, 12))) # 设置横坐标次刻度为季度 ax.tick_params(axis='x', which='minor', labelsize=8, labelrotation=45) # 设置次刻度标签的大小和旋转角度 font = fm.FontProperties(size=10, style='italic') # 设置斜体字体属性 plt.xticks(fontproperties=font) # 设置刻度标签为斜体 plt.savefig('a1.jpg') # 保存图表 plt.show() # 显示图表写一个循环，相同上述绘图，从1到14

import pandas as pd import matplotlib.pyplot as plt import matplotlib.dates as mdates from matplotlib.dates import YearLocator, DateFormatter, MonthLocator import matplotlib.font_manager as fm # 定义...

data = ts.pro_bar把结束时间设置为未来20天

如果你想把数据的结束时间设置为未来20天，你可以使用 Python 中的 datetime 模块来实现。具体来说，你可以按照以下步骤进行操作： 1. 导入 datetime 模块： from datetime import datetime, timedelta ...

import datetime import matplotlib.pyplot as plt import bilibili_api from bilibili_api import video这些是什么

这些是 Python 中的模块和库，用于实现不同的功能： - datetime 模块提供了日期和时间处理相关的函数和类，可以方便地进行时间格式转换、时间差计算等操作。 - matplotlib.pyplot 模块是 matplotlib 库的一...

优化这段代码# import modules 导入模块 from sklearn.model_selection import train_test_split from sklearn.decomposition import PCA import pandas as pd from sklearn import svm import numpy as np import math import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib import colors from sklearn.model_selection import train_test_split from sklearn import datasets from matplotlib.colors import ListedColormap from sklearn.svm import SVC from sklearn.preprocessing import StandardScaler from sklearn.model_selection import StratifiedShuffleSplit,StratifiedKFold from sklearn.model_selection import GridSearchCV from sklearn.model_selection import GridSearchCV, LeaveOneOut, cross_val_predict from sklearn.model_selection import KFold from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.neighbors import KNeighborsClassifier from sklearn import svm from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import roc_auc_score from sklearn.metrics import roc_auc_score import math import datetime import multiprocessing as mp from sklearn.ensemble import StackingClassifier from sklearn.pipeline import make_pipeline from sklearn.svm import LinearSVC import random

import pandas as pd import numpy as np import math import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib import colors from matplotlib.colors import ListedColormap from sklearn....

from datetime import datetime,timedelta s_date = datetime.datetime.strptime('20230501', '%Y%m%d').date()

第一行应该为 import datetime，而不是 from datetime import datetime,timedelta，因为后面用到的是 datetime.datetime 而不是 datetime。第二行的代码与你之前给出的代码是相同的，将字符串类型的日期 '...

解释代码from math import sqrt from datetime import datetime from sklearn.metrics import mean_absolute_error from sklearn.metrics import mean_squared_error

- datetime类：来自datetime模块，用于处理日期和时间相关的操作。 - mean_absolute_error函数：来自sklearn.metrics模块，用于计算平均绝对误差。 - mean_squared_error函数：来自sklearn.metrics模块...

#coding:utf8 import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import numpy as np sns.set_style('darkgrid') import matplotlib.font_manager as fm myfont=fm.FontProperties(fname=r'./data/simhei.ttf') #请完善下面的函数 def push_week(new_data): ############ Begin ############ new_data=new_data[new_data['type']==4].copy() #选取样本 new_data['weekdays'] = pd.to_datetime(new_data['time']).apply(lambda x: x.weekday()+1) #时间转化 week_days = new_data.groupby('weekdays')['user_id'].count() #统计购买次数 fig=plt.figure(figsize=(8,6)) #设置大小 bar_width = 0.33 # 设置宽度 plt.bar(week_days.index.values , week_days.values, bar_width, label='下单的次数') plt.xlabel('时间',fontproperties=myfont,fontsize=9) plt.ylabel('数量',fontproperties=myfont,fontsize=9) plt.title('一周内每天的下单情况',fontproperties=myfont,fontsize=12) plt.xticks(week_days.index.values, ('周一', '周二', '周三', '周四', '周五', '周六', '周日'),fontproperties=myfont,fontsize=9) plt.ylim(0,300) plt.legend(prop=myfont) ############ End ############ plt.savefig('./task2/task2_week.png') plt.close(fig) def push_date(new_data): new_data = new_data[(new_data['type'] == 4) & (pd.to_datetime(new_data['time']) < pd.to_datetime('2016-03-01'))].copy() #选出2016年数据 new_data['days'] = [x.day for x in pd.to_datetime(new_data['time'])] #选出天数 renew=new_data.groupby('days')['sku_id'].count() fig = plt.figure(figsize=(8, 6)) plt.plot(renew.index.values,renew.values,label='购买次数') plt.xlabel('天数',fontproperties=myfont,fontsize=9) plt.ylabel('次数',fontproperties=myfont,fontsize=9) plt.title('购买量和月内日期的关系',fontproperties=myfont,fontsize=12) plt.legend(prop=myfont) ############ End ############ plt.savefig('./task2/task2_date.png') plt.close(fig) 报错src/task2_test.py:22: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime instead. data['weekdays'] = pd.to_datetime(data['time']).apply(pd.datetime.weekday) + 1 购买意愿与星期之间的关系图完成！购买意愿与日期之间的关系图完成！

这个错误是因为pandas.datetime类已经被弃用，建议使用datetime库来代替。可以将代码中的pd.datetime.weekday()改为pd.datetime().weekday()，即可解决这个问题。修改后的代码如下： python #coding:utf8 ...

import tushare as ts import datetime def has_five_consecutive_rises(data): close = data['close'] if len(close) < 5 or any(close[-5:] != close.iloc[-1]): return False for i in range(1, 5): if close.iloc[-i-1] >= close.iloc[-i]: return False return True today = datetime.datetime.now().strftime('%Y-%m-%d') three_months_ago = (datetime.datetime.now() - datetime.timedelta(days=90)).strftime('%Y-%m-%d') data = ts.get_hist_data('hs300', start=three_months_ago, end=today) for code in data.index: stock_data = ts.get_hist_data(code, start=three_months_ago, end=today) if has_five_consecutive_rises(stock_data): print(code)用聚宽运行上述代码

上述代码使用了tushare包，需要在聚宽中先安装tushare包并且获取tushare的token。同时，聚宽提供的get_price函数可以获取股票历史价格数据，可以用来替代tushare包中的get_hist_data函数。下面是修改后的代码： ...

请优化这段代码，import pandas as pdimport matplotlib.pyplot as plt# 读取csv文件df = pd.read_csv("AAPL.csv")# 将日期列转化为pandas的日期类型df["Date"] = pd.to_datetime(df["Date"])# 设置日期列为数据框的索引df.set_index("Date", inplace=True)# 绘制收盘价折线图plt.plot(df["Close"])plt.title("AAPL Stock Price")plt.xlabel("Date")plt.ylabel("Price")plt.show()# 绘制成交量柱状图plt.bar(df.index, df["Volume"])plt.title("AAPL Stock Volume")plt.xlabel("Date")plt.ylabel("Volume")plt.show()# 绘制K线图from mpl_finance import candlestick_ochlimport matplotlib.dates as mdatesdf_ochl = df[["Open", "Close", "High", "Low"]]df_ochl.reset_index(inplace=True)df_ochl["Date"] = df_ochl["Date"].map(mdates.date2num)fig, ax = plt.subplots()candlestick_ochl(ax, df_ochl.values, width=0.6, colorup="green", colordown="red")ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))plt.title("AAPL Stock Price (Candlestick Chart)")plt.xlabel("Date")plt.ylabel("Price")plt.show()

import pandas as pd import matplotlib.pyplot as plt from mplfinance.original_flavor import candlestick_ochl import matplotlib.dates as mdates # 读取csv文件，并将日期列转化为pandas的日期类型，并将日期...

#导入包 import pandas as pd import pandas_datareader import fix_yahoo_finance as yf import matplotlib.pyplot as plt import datetime #爬取08-18十年数据 start = datetime.datetime(2008,1,1) end = datetime.datetime(2018,1,1) #必须要调用这个函数 yf.pdr_override() #股票数据 stock_data = pandas_datareader.data.get_data_yahoo("SPY",start,end) print(stock_data) #数据可视化 stock_data['Close'].plot() plt.title('股票每日收盘价') plt.show()解释每行代码

import datetime # 导入 datetime 包，用于生成日期时间 # 爬取 08-18 十年数据 start = datetime.datetime(2008,1,1) # 指定开始时间为 2008 年 1 月 1 日 end = datetime.datetime(2018,1,1) # 指定结束时间为 ...

import pyowm import datetime # 获取当前时间 now = datetime.datetime.now() # 获取上个月的时间 last_month = now.replace(month=now.month-1) # 初始化OpenWeatherMap对象 owm = pyowm.OWM('cff205d4bc569aaffdb80114250e52df') # 把'your-api-key'替换成你的API Key # 获取上个月的天气情况 mgr = owm.weather_manager() observation = mgr.weather_at_place('上海')我在mgr.weather_at_place('上海')被返回Invalid API Key provided

import datetime import pyowm # 获取当前时间 now = datetime.datetime.now() # 获取上个月的时间 last_month = now.replace(month=now.month-1) # 初始化OpenWeatherMap对象 owm = pyowm.OWM('your-api-key') #...

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