python 收益與風險圖表集成

呈現
1.盈虧狀態的買賣區間及標註上買賣信號點
2.資金曲線及資金最大回撤點
3.基準收益曲線及使用策略後的收益曲線

例程代碼

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  # 分割子圖

plt.rcParams['font.sans-serif']=['SimHei'] #用來正常顯示中文標籤
plt.rcParams['axes.unicode_minus']=False #用來正常顯示負號

#股票數據獲取及處理接口
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()#計算最近N1個交易日最高價
    # expanding 從最開始到當前的最大值
    expan_max = stockdata.Close.expanding().max()
    stockdata['N1_High'].fillna(value=expan_max,inplace=True)#目前出現過的最大值填充前N1個nan

    stockdata['N2_Low'] = stockdata.Low.rolling(window=N2).min()#計算最近N2個交易日最低價
    expan_min = stockdata.Close.expanding().min()
    stockdata['N2_Low'].fillna(value=expan_min,inplace=True)#目前出現過的最小值填充前N2個nan

    #收盤價超過N1最高價 買入股票持有
    buy_index = stockdata[stockdata.Close > stockdata.N1_High.shift(1)].index
    stockdata.loc[buy_index,'signal'] = 1
    #收盤價超過N2最低價 賣出股票持有
    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

#初始化變量
skip_days = 0
cash_hold = 100000 #初始資金
posit_num = 0 #持股數目
market_total = 0 #持股市值

#創建圖表
fig = plt.figure(figsize=(10, 8), dpi=100, facecolor="white")#創建fig對象
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,:])

#獲取股票交易數據
df_stockload = GetStockDatApi("600410.SS",datetime.datetime(2018, 10, 1), datetime.datetime(2019, 4, 1))

for kl_index, today in df_stockload.iterrows():
    # 買入/賣出執行代碼
    if today.signal == 1 and skip_days == 0:  # 買入
        start = df_stockload.index.get_loc(kl_index)
        skip_days = -1
        posit_num = int(cash_hold / today.Close) #資金轉化爲股票
        cash_hold = 0
        graph_trade.annotate('買入',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:  # 賣出 避免未買先賣
        end = df_stockload.index.get_loc(kl_index)
        skip_days = 0
        cash_hold = int(posit_num * today.Close) #股票轉化爲資金
        market_total = 0

        if df_stockload.Close[end] < df_stockload.Close[start]:  # 賠錢顯示綠色
            graph_trade.fill_between(df_stockload.index[start:end], 0, df_stockload.Close[start:end], color='green', alpha=0.38)
        else:  # 賺錢顯示紅色
            graph_trade.fill_between(df_stockload.index[start:end], 0, df_stockload.Close[start:end], color='red', alpha=0.38)
        graph_trade.annotate('賣出',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: #空倉
        df_stockload.loc[kl_index,'total'] = cash_hold

#計算基準收益/趨勢突破策略收益
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)

#計算收盤價曲線當前的滾動最高值
df_stockload['max_close'] = df_stockload['Close'].expanding().max()
df_stockload[['max_close','Close']].plot(grid=True,ax=graph_trade)

#計算資金曲線當前的滾動最高值
df_stockload['max_total'] = df_stockload['total'].expanding().max()
df_stockload[['max_total','total']].plot(grid=True,ax=graph_total)

#計算資金曲線在滾動最高值之後所回撤的百分比
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')]

#標註滾動最大點及最大回撤點
graph_total.annotate('滾動最大點',
                     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('最大回撤點',
                     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')

#圖表顯示參數配置
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)  # 設置標籤字體
graph_trade.set_xlabel("")
graph_trade.set_title(u'華勝天成 收益與風險度量')
graph_total.set_xlabel("")

plt.show()

輸出結果