呈现
1.盈亏状态的买卖区间及标注上买卖信号点
2.资金曲线及资金最大回撤点
3.基准收益曲线及使用策略后的收益曲线

例程代码

import pandas_datareader.data as web
import pandas as pd
import numpy as np
import datetime
import matplotlib.pyplot as plt

import matplotlib.gridspec as gridspec  # 分割子图

plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签
plt.rcParams['axes.unicode_minus']=False #用来正常显示负号

#股票数据获取及处理接口
def GetStockDatApi(stockName=None,stockTimeS=None,stockTimeE=None, N1=15,N2=5):

    stockdata = web.DataReader(stockName, "yahoo", stockTimeS, stockTimeE)

    stockdata['N1_High'] = stockdata.High.rolling(window=N1).max()#计算最近N1个交易日最高价
    # expanding 从最开始到当前的最大值
    expan_max = stockdata.Close.expanding().max()
    stockdata['N1_High'].fillna(value=expan_max,inplace=True)#目前出现过的最大值填充前N1个nan

    stockdata['N2_Low'] = stockdata.Low.rolling(window=N2).min()#计算最近N2个交易日最低价
    expan_min = stockdata.Close.expanding().min()
    stockdata['N2_Low'].fillna(value=expan_min,inplace=True)#目前出现过的最小值填充前N2个nan

    #收盘价超过N1最高价 买入股票持有
    buy_index = stockdata[stockdata.Close > stockdata.N1_High.shift(1)].index
    stockdata.loc[buy_index,'signal'] = 1
    #收盘价超过N2最低价 卖出股票持有
    sell_index = stockdata[stockdata.Close < stockdata.N2_Low.shift(1)].index
    stockdata.loc[sell_index,'signal'] = 0
    stockdata['signal'].fillna(method = 'ffill',inplace = True)
    stockdata['signal'] = stockdata.signal.shift(1)
    stockdata['signal'].fillna(method = 'bfill',inplace = True)

    return stockdata

#初始化变量
skip_days = 0
cash_hold = 100000 #初始资金
posit_num = 0 #持股数目
market_total = 0 #持股市值

#创建图表
fig = plt.figure(figsize=(10, 8), dpi=100, facecolor="white")#创建fig对象
gs = gridspec.GridSpec(3, 1, left=0.05, bottom=0.1, right=0.96, top=0.96, wspace=None, hspace=0.05, height_ratios=[4,2,2])
graph_trade = fig.add_subplot(gs[0,:])
graph_total = fig.add_subplot(gs[1,:])
graph_profit = fig.add_subplot(gs[2,:])

#获取股票交易数据
df_stockload = GetStockDatApi("600410.SS",datetime.datetime(2018, 10, 1), datetime.datetime(2019, 4, 1))

for kl_index, today in df_stockload.iterrows():
    # 买入/卖出执行代码
    if today.signal == 1 and skip_days == 0:  # 买入
        start = df_stockload.index.get_loc(kl_index)
        skip_days = -1
        posit_num = int(cash_hold / today.Close) #资金转化为股票
        cash_hold = 0
        graph_trade.annotate('买入',xy=(kl_index,df_stockload.Close.asof(kl_index)),xytext=(kl_index, df_stockload.Close.asof(kl_index)+2),arrowprops=dict(facecolor='r',shrink=0.1),horizontalalignment='left',verticalalignment='top')

    elif today.signal == 0 and skip_days == -1:  # 卖出 避免未买先卖
        end = df_stockload.index.get_loc(kl_index)
        skip_days = 0
        cash_hold = int(posit_num * today.Close) #股票转化为资金
        market_total = 0

        if df_stockload.Close[end] < df_stockload.Close[start]:  # 赔钱显示绿色
            graph_trade.fill_between(df_stockload.index[start:end], 0, df_stockload.Close[start:end], color='green', alpha=0.38)
        else:  # 赚钱显示红色
            graph_trade.fill_between(df_stockload.index[start:end], 0, df_stockload.Close[start:end], color='red', alpha=0.38)
        graph_trade.annotate('卖出',xy=(kl_index,df_stockload.Close.asof(kl_index)),xytext=(kl_index+datetime.timedelta(days=5), df_stockload.Close.asof(kl_index)+2),arrowprops=dict(facecolor='g',shrink=0.1),horizontalalignment='left',verticalalignment='top')

    if skip_days == -1: #持股
        market_total = int(posit_num * today.Close)
        df_stockload.loc[kl_index,'total'] = market_total
    else: #空仓
        df_stockload.loc[kl_index,'total'] = cash_hold

#计算基准收益/趋势突破策略收益
df_stockload['benchmark_profit'] = np.log(df_stockload.Close/df_stockload.Close.shift(1))
df_stockload['trend_profit'] = df_stockload.signal*df_stockload.benchmark_profit
df_stockload[['benchmark_profit','trend_profit']].cumsum().plot(grid=True,ax=graph_profit)

#计算收盘价曲线当前的滚动最高值
df_stockload['max_close'] = df_stockload['Close'].expanding().max()
df_stockload[['max_close','Close']].plot(grid=True,ax=graph_trade)

#计算资金曲线当前的滚动最高值
df_stockload['max_total'] = df_stockload['total'].expanding().max()
df_stockload[['max_total','total']].plot(grid=True,ax=graph_total)

#计算资金曲线在滚动最高值之后所回撤的百分比
df_stockload['per_total'] = df_stockload['total'] / df_stockload['max_total']
min_point_total = df_stockload.sort_values(by=['per_total']).iloc[[0], df_stockload.columns.get_loc('per_total')]
max_point_total = df_stockload[df_stockload.index <= min_point_total.index[0]].sort_values \
    (by=['total'], ascending=False).iloc[[0], df_stockload.columns.get_loc('total')]

#标注滚动最大点及最大回撤点
graph_total.annotate('滚动最大点',
                     xy=(max_point_total.index[0], df_stockload.total.asof(max_point_total.index[0])),
                     xytext=(max_point_total.index[0], df_stockload.total.asof(max_point_total.index[0]) + 4),
                     arrowprops=dict(facecolor='yellow', shrink=0.1), horizontalalignment='left',
                     verticalalignment='top')
graph_total.annotate('最大回撤点',
                     xy=(min_point_total.index[0], df_stockload.total.asof(min_point_total.index[0])),
                     xytext=(min_point_total.index[0], df_stockload.total.asof(min_point_total.index[0]) + 4),
                     arrowprops=dict(facecolor='yellow', shrink=0.1), horizontalalignment='left',
                     verticalalignment='top')

#图表显示参数配置
for label in graph_trade.xaxis.get_ticklabels():
    label.set_visible(False)
for label in graph_total.xaxis.get_ticklabels():
    label.set_visible(False)
for label in graph_profit.xaxis.get_ticklabels():
    label.set_rotation(45)
    label.set_fontsize(10)  # 设置标签字体
graph_trade.set_xlabel("")
graph_trade.set_title(u'华胜天成 收益与风险度量')
graph_total.set_xlabel("")

plt.show()

输出结果