课程设计基于python的股价查询、比较系统源码(可视化k线走势)+详细代码注释.zip

import traceback from datetime import datetime import plotly as py import plotly.graph_objs as go import tool pyplt = py.offline.plot pro = tool.get_pro() # 读取股票代码与名称字典 # ts.set_token('b94ecd1e37a3628890e89b90e0259b6db72b0ae0b17dadd2c28c5c4c') # pro = ts.pro_api() # df = pd.read_csv("stock_code.csv") # df = pro.daily(ts_code='000001.sz', start_date='20190701', end_date='20190930')#直接保存 class Chart_Plot: global pro, df def __init__(self, **params): # 处理参数格式，符合tushare调用规范 # import pdb # pdb.set_trace() self.start_date = params.get("start_date").replace("-", "") self.end_date = params.get("end_date").replace("-", "") # 获取指定股票代码的名称 self.stock1_code = tool.getStockCode(params.get("stock_id", "000001")) self.name1 = tool.getName(self.stock1_code) self.stock1 = None # 获取要对比的股票代码的名称 self.stock2_code = tool.getStockCode(params.get("stock_id2", "000002")) self.stock2 = pro.daily(ts_code=self.stock2_code, start_date=self.start_date, end_date=self.end_date) self.name2 = tool.getName(self.stock2_code) self.stock2 = None self.index_type = params.get("index_type") self.graph_type = params.get("graph_type") self.a_h = params.get("a_h") self.graph_title = "{},{}--{}".format(self.stock1_code, self.name1, self.graph_type) def candle_stick(self, period="day"): # 日K线图 # 默认获取日k,如果period不为日k，则获取对应值 try: if not self.stock1_code: return "该股票代码不存在", self.graph_title if period == "周k": self.stock1 = pro.weekly(ts_code=self.stock1_code, start_date=self.start_date, end_date=self.end_date) elif period == "月k": self.stock1 = pro.monthly(ts_code=self.stock1_code, start_date=self.start_date, end_date=self.end_date) else: self.stock1 = pro.daily(ts_code=self.stock1_code, start_date=self.start_date, end_date=self.end_date) except Exception as e: traceback.print_exc() return str(e),self.graph_title strdate = self.stock1['trade_date'].tolist() # 日期字符串转时间序列 date = [datetime.strptime(i, '%Y%m%d') for i in strdate] candle_trace = go.Candlestick(x=date, open=self.stock1.open, high=self.stock1.high, low=self.stock1.low, close=self.stock1.close, increasing=dict(line=dict(color='#ff0000')), decreasing=dict(line=dict(color='#00ff00')), name=self.name1) candle_data = [candle_trace] candle_layout = {'title': self.name1, 'yaxis': {'title': '价格'}} candle_fig = dict(data=candle_data, layout=candle_layout) div = pyplt(candle_fig, output_type='div', include_plotlyjs=False, auto_open=False, show_link=False) return div,self.graph_title def twoline_graph(self): # 半年线图 # self.stock1['close']取出来的值都是带有索引值的两列的矩阵，具体看excl表格 # tolist函数则将他们变为列表，除去索引值 if not self.stock1_code: return "该股票代码不存在", self.graph_title self.stock1 = pro.daily(ts_code=self.stock1_code, start_date=self.start_date, end_date=self.end_date) close = self.stock1['close'].tolist() strdate = self.stock1['trade_date'].tolist() date = [datetime.strptime(i, '%Y%m%d') for i in strdate] trace = [go.Scatter( x=date, y=close )] layout = dict( title=self.name1, xaxis=dict(title='日期'), yaxis=dict(title='价格') ) fig = dict(data=trace, layout=layout) div = pyplt(fig, output_type='div', include_plotlyjs=False, auto_open=False, show_link=False) return div, self.graph_title def high_low(self): if not self.stock1_code: return "该股票代码不存在", "" self.stock1 = pro.daily(ts_code=self.stock1_code, start_date=self.start_date, end_date=self.end_date) high = self.stock1['high'].tolist() low = self.stock1['low'].tolist() strdate = self.stock1['trade_date'].tolist() date = [datetime.strptime(i, '%Y%m%d') for i in strdate] trace = [go.Scatter( x=date, y=high ), go.Scatter( x=date, y=low ) ] layout = dict( title=self.stock1_code + ":" + self.name1, xaxis=dict(title='日期'), yaxis=dict(title='价格') ) # print (layout) # {'title': 'ETF50', 'xaxis': {'title': '日期'}, 'yaxis': {'title': '价格'}} fig = dict(data=trace, layout=layout) # print (fig) div = pyplt(fig, output_type='div', include_plotlyjs=False, auto_open=False, show_link=False) return div, self.graph_title def plot_pes(self): try: if not self.stock1_code: return "该股票代码不存在", self.graph_title if self.index_type == 'LYR': self.stock1 = pro.daily_basic(ts_code=self.stock1_code, start_date=self.start_date, end_date=self.end_date, fields='trade_date,pe') else: self.stock1 = pro.daily_basic(ts_code=self.stock1_code, start_date=self.start_date, end_date=self.end_date, fields='trade_date,pe_ttm') except Exception as e: traceback.print_exc() return str(e),self.graph_title df = self.stock1 # 处理数据 dates, pes = [], [] for idx, row in df.iterrows(): trade_date, pe = row[0], row[1] current_date = datetime.strptime(trade_date, '%Y%m%d') dates.append(current_date) pes.append(pe) # 画图语句：go.Scatter trace = [go.Scatter( x=dates, y=pes )] layout = dict( title=self.name1, xaxis=dict(title='日期'), yaxis=dict(title='价格') ) fig = dict(data=trace, layout=layout) div = pyplt(fig, output_type='div', include_plotlyjs=False, auto_open=False, show_link=False) return div, self.graph_title def plot_amplitude(self): graph_title = "{},{};{},{}--{}".format(self.stock1_code, self.name1, self.stock2_code, self.name2, self.graph_type) if not self.stock1_code: return "该股票代码不存在", graph_title if not self.stock2_code: return "该对比股票代码不存在", graph_title self.stock1 = pro.daily(ts_code=self.stock1_code, start_date=self.start_date, end_date=self.end_date) self.stock2 = pro.daily(ts_code=self.stock2_code, start_date=self.start_date, end_date=self.end_date) # 振幅计算公式：A=(high-low)/pre_close dates1, dates2, As1, As2 = [], [], [], [] for idx, row in self.stock1.iterrows(): date1, high1, low1, preclose1 = row[1], row[3], row[4], row[6] A1 = ((high1 - low1) / preclose1) * 100 current_date = datetime.strptime(date1, '%Y%m%d') dates1.append(current_date) As1.append(A1) for idx, row in self.stock2.iterrows(): date2, high2, low2, preclose2 =