量化交易——python数据分析及可视化

该项目分为两个部分：一是数据计算，二是可视化，三是MACD策略

一、计算MACD

1、数据部分

数据来源：tushare
数据字段包含：日期，开盘价，收盘价，最低价，最高价，涨跌
需要计算的数据：macd，diff，dea

2、MACD的计算

（1）计算指数移动平均值（EMA）

12日EMA的算式为
EMA（12）=前一日EMA（12）×11/13+今日收盘价×2/13
26日EMA的算式为
EMA（26）=前一日EMA（26）×25/27+今日收盘价×2/27

（2）计算离差值（DIF）

DIF=今日EMA（12）－今日EMA（26）

（3）计算DIF的9日EMA

根据离差值计算其9日的EMA，即离差平均值，是所求的MACD值。为了不与指标原名相混淆，此值又名
DEA或DEM。
今日DEA（MACD）=前一日DEA×8/10+今日DIF×2/10。
计算出的DIF和DEA的数值均为正值或负值。
用（DIF-DEA）×2即为MACD柱状图。

二、可视化

1、可视化工具：pyecharts

pyecharts官网

三、MACD策略

代码实现如下：

from typing import List, Sequence, Union
from pyecharts import options as opts
from pyecharts.commons.utils import JsCode
from pyecharts.charts import Kline, Line, Bar, Grid# 数据
#时间，
#ema重构版
import pandas as pd
import numpy as np
import tushare as ts
def macd(code,start_date,end_date):'''获取数据预处理计算股票的ema、dif、dea、macd主要有9日，12日，26日'''#获取数据pro=ts.pro_api()#获取日k线数据df=pro.daily(ts_code=code,start_date=start_date,end_date=end_date)#删除不需要的数据df=df.drop('ts_code',axis=1)df=df.drop('pre_close',axis=1)df=df.drop('change',axis=1)df=df.drop('pct_chg',axis=1)df=df.drop('amount',axis=1)df=df.sort_values(by='trade_date')#将成交量缩小一万倍df.loc[:,['vol']]=df.loc[:,['vol']].apply(lambda x:x/10000)df.loc[:,['vol']]=df.loc[:,['vol']].round(2)'''需要输出哪些数据？return:    日期，开盘价，收盘价，最低价，最高价，涨跌，macd，dif，deadate,open,close,low,high,vol,涨跌，macd，diff，dea'''#ema计算函数def ema(num,data=df):ema = [data['close'][0]] * len(data)for i in range(1, len(data)):ema[i] = ema[i-1] * (num-1)/(num+1) + data['close'][i] * 2/(num+1)return ema#计算deadef dea(num,data):#传入数组numpy.arraydea = [data[0]] * len(data)for i in range(1, len(data)):dea[i] = dea[i-1] * (num-1)/(num+1) + data[i] * 2/(num+1)return np.array(dea)ema_12=np.array(ema(12))ema_26=np.array(ema(26))dif=ema_12-ema_26dea=dea(9,dif)macd=(dif-dea)*2#转化为pddf1={'macd':macd,'dif':dif,'dea':dea}df1=pd.DataFrame(df1)#连接数据df=df.join(df1)df=df.round(2)print(df)#判断涨跌，1为涨，0为跌def f(x):if x>=0:return 1else:return 0x=(df['close']-df['open']).apply(f)#调整列顺序df=df.loc[:,['trade_date','open','close','low','high','vol','macd','dif','dea']]#插入涨跌数据df.insert(loc=6,column='x',value=x)return df.values#转化为列表macd=macd(code='000001.SZ',start_date='20230101',end_date='20230320')
echarts_data =macd.tolist()
print(type(echarts_data))
print(echarts_data)
'''
echarts_data = [["2015-10-16", 18.4, 18.58, 18.33, 18.79, 67.00, 1, 0.04, 0.11, 0.09],["2015-10-19", 18.56, 18.25, 18.19, 18.56, 55.00, 0, -0.00, 0.08, 0.09],["2016-12-30", 17.53, 17.6, 17.47, 17.61, 22.00, 0, -0.05, -0.03, -0.01],["2017-01-03", 17.6, 17.92, 17.57, 17.98, 28.00, 1, 0.00, 0.00, 0.00],
]
'''
def split_data(origin_data) -> dict:datas = []times = []vols = []macds = []difs = []deas = []for i in range(len(origin_data)):datas.append(origin_data[i][1:])times.append(origin_data[i][0:1][0])vols.append(origin_data[i][5])macds.append(origin_data[i][7])difs.append(origin_data[i][8])deas.append(origin_data[i][9])vols = [int(v) for v in vols]return {"datas": datas,"times": times,"vols": vols,"macds": macds,"difs": difs,"deas": deas,}def split_data_part() -> Sequence:mark_line_data = []idx = 0tag = 0vols = 0for i in range(len(data["times"])):if data["datas"][i][5] != 0 and tag == 0:idx = ivols = data["datas"][i][4]tag = 1if tag == 1:vols += data["datas"][i][4]if data["datas"][i][5] != 0 or tag == 1:mark_line_data.append([{"xAxis": idx,"yAxis": float("%.2f" % data["datas"][idx][3])if data["datas"][idx][1] > data["datas"][idx][0]else float("%.2f" % data["datas"][idx][2]),"value": vols,},{"xAxis": i,"yAxis": float("%.2f" % data["datas"][i][3])if data["datas"][i][1] > data["datas"][i][0]else float("%.2f" % data["datas"][i][2]),},])idx = ivols = data["datas"][i][4]tag = 2if tag == 2:vols += data["datas"][i][4]if data["datas"][i][5] != 0 and tag == 2:mark_line_data.append([{"xAxis": idx,"yAxis": float("%.2f" % data["datas"][idx][3])if data["datas"][i][1] > data["datas"][i][0]else float("%.2f" % data["datas"][i][2]),"value": str(float("%.2f" % (vols / (i - idx + 1)))) + " M",},{"xAxis": i,"yAxis": float("%.2f" % data["datas"][i][3])if data["datas"][i][1] > data["datas"][i][0]else float("%.2f" % data["datas"][i][2]),},])idx = ivols = data["datas"][i][4]return mark_line_datadef calculate_ma(day_count: int):result: List[Union[float, str]] = []for i in range(len(data["times"])):if i < day_count:result.append("-")continuesum_total = 0.0for j in range(day_count):sum_total += float(data["datas"][i - j][1])result.append(abs(float("%.2f" % (sum_total / day_count))))return resultdef draw_chart():kline = (Kline().add_xaxis(xaxis_data=data["times"]).add_yaxis(series_name="",y_axis=data["datas"],itemstyle_opts=opts.ItemStyleOpts(color="#ef232a",color0="#14b143",border_color="#ef232a",border_color0="#14b143",),markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max", name="最大值"),opts.MarkPointItem(type_="min", name="最小值"),]),markline_opts=opts.MarkLineOpts(label_opts=opts.LabelOpts(position="middle", color="blue", font_size=15),data=split_data_part(),symbol=["circle", "none"],),).set_series_opts(markarea_opts=opts.MarkAreaOpts(is_silent=True, data=split_data_part())).set_global_opts(title_opts=opts.TitleOpts(title="K线周期图表", pos_left="0"),xaxis_opts=opts.AxisOpts(type_="category",is_scale=True,boundary_gap=False,axisline_opts=opts.AxisLineOpts(is_on_zero=False),splitline_opts=opts.SplitLineOpts(is_show=False),split_number=20,min_="dataMin",max_="dataMax",),yaxis_opts=opts.AxisOpts(is_scale=True, splitline_opts=opts.SplitLineOpts(is_show=True)),tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="line"),datazoom_opts=[opts.DataZoomOpts(is_show=False, type_="inside", xaxis_index=[0, 0], range_end=100),opts.DataZoomOpts(is_show=True, xaxis_index=[0, 1], pos_top="97%", range_end=100),opts.DataZoomOpts(is_show=False, xaxis_index=[0, 2], range_end=100),],# 三个图的 axis 连在一块# axispointer_opts=opts.AxisPointerOpts(#     is_show=True,#     link=[{"xAxisIndex": "all"}],#     label=opts.LabelOpts(background_color="#777"),# ),))kline_line = (Line().add_xaxis(xaxis_data=data["times"]).add_yaxis(series_name="MA5",y_axis=calculate_ma(day_count=5),is_smooth=True,linestyle_opts=opts.LineStyleOpts(opacity=0.5),label_opts=opts.LabelOpts(is_show=False),).set_global_opts(xaxis_opts=opts.AxisOpts(type_="category",grid_index=1,axislabel_opts=opts.LabelOpts(is_show=False),),yaxis_opts=opts.AxisOpts(grid_index=1,split_number=3,axisline_opts=opts.AxisLineOpts(is_on_zero=False),axistick_opts=opts.AxisTickOpts(is_show=False),splitline_opts=opts.SplitLineOpts(is_show=False),axislabel_opts=opts.LabelOpts(is_show=True),),))# Overlap Kline + Lineoverlap_kline_line = kline.overlap(kline_line)# Bar-1bar_1 = (Bar().add_xaxis(xaxis_data=data["times"]).add_yaxis(series_name="Volumn",y_axis=data["vols"],xaxis_index=1,yaxis_index=1,label_opts=opts.LabelOpts(is_show=False),# 根据 echarts demo 的原版是这么写的# itemstyle_opts=opts.ItemStyleOpts(#     color=JsCode("""#     function(params) {#         var colorList;#         if (data.datas[params.dataIndex][1]>data.datas[params.dataIndex][0]) {#           colorList = '#ef232a';#         } else {#           colorList = '#14b143';#         }#         return colorList;#     }#     """)# )# 改进后在 grid 中 add_js_funcs 后变成如下itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""function(params) {var colorList;if (barData[params.dataIndex][1] > barData[params.dataIndex][0]) {colorList = '#ef232a';} else {colorList = '#14b143';}return colorList;}""")),).set_global_opts(xaxis_opts=opts.AxisOpts(type_="category",grid_index=1,axislabel_opts=opts.LabelOpts(is_show=False),),legend_opts=opts.LegendOpts(is_show=False),))# Bar-2 (Overlap Bar + Line)bar_2 = (Bar().add_xaxis(xaxis_data=data["times"]).add_yaxis(series_name="MACD",y_axis=data["macds"],xaxis_index=2,yaxis_index=2,label_opts=opts.LabelOpts(is_show=False),itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""function(params) {var colorList;if (params.data >= 0) {colorList = '#ef232a';} else {colorList = '#14b143';}return colorList;}""")),).set_global_opts(xaxis_opts=opts.AxisOpts(type_="category",grid_index=2,axislabel_opts=opts.LabelOpts(is_show=False),),yaxis_opts=opts.AxisOpts(grid_index=2,split_number=4,axisline_opts=opts.AxisLineOpts(is_on_zero=False),axistick_opts=opts.AxisTickOpts(is_show=False),splitline_opts=opts.SplitLineOpts(is_show=False),axislabel_opts=opts.LabelOpts(is_show=True),),legend_opts=opts.LegendOpts(is_show=False),))line_2 = (Line().add_xaxis(xaxis_data=data["times"]).add_yaxis(series_name="DIF",y_axis=data["difs"],xaxis_index=2,yaxis_index=2,label_opts=opts.LabelOpts(is_show=False),).add_yaxis(series_name="DIF",y_axis=data["deas"],xaxis_index=2,yaxis_index=2,label_opts=opts.LabelOpts(is_show=False),).set_global_opts(legend_opts=opts.LegendOpts(is_show=False)))# 最下面的柱状图和折线图overlap_bar_line = bar_2.overlap(line_2)# 最后的 Gridgrid_chart = Grid()# 这个是为了把 data.datas 这个数据写入到 html 中,还没想到怎么跨 series 传值# demo 中的代码也是用全局变量传的grid_chart.add_js_funcs("var barData = {}".format(data["datas"]))# K线图和 MA5 的折线图grid_chart.add(overlap_kline_line,grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", height="60%"),)# Volumn 柱状图grid_chart.add(bar_1,grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", pos_top="71%", height="10%"),)# MACD DIFS DEASgrid_chart.add(overlap_bar_line,grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", pos_top="82%", height="14%"),)grid_chart.render("professional_kline_chart.html")if __name__ == "__main__":data = split_data(origin_data=echarts_data)draw_chart()

文章持续更新中。。。