在 Python 股票交易策略画图中标记买卖点 以下是这种标记方法两个例子，其他策略可以类似处理。 相关关系分析_MACD 交易策略.py import numpy as np import pandas as pd import matplotlib as mpl import matplotlib.pyplot as plt import seaborn as sns import warnings; warnings.filterwarnings(action='once') import tushare as ts from matplotlib.figure import Figure import matplotlib.dates as mdates import warnings; warnings.filterwarnings(action='once') df = ts.get_hist_data('600006',start='2019-06-01') df = df.sort_index(0) #直接保存 df.to_csv('600006.csv') # Import Data df = pd.read_csv('600006.csv') data = df['close'].values 

doublediff = np.diff(np.sign(np.diff(data))) print('doublediff',doublediff) peak_locations = np.where(doublediff == -2)[0] + 1 doublediff2 = np.diff(np.sign(np.diff(-1*data))) trough_locations = np.where(doublediff2 == -2)[0] + 1 #print('doublediff2',doublediff2) #print('trough_locations',trough_locations) def get_EMA(df,N): for i in range(len(df)): if i==0: if i>0: df.loc[i,'ema']=df.loc[i,'close'] ema=list(df['ema']) return ema def get_MACD(df,short=12,long=26,M=9): a=get_EMA(df,short) b=get_EMA(df,long) df['diff']=pd.Series(a)-pd.Series(b) #print(df['diff']) for i in range(len(df)): if i==0: if i>0: df.loc[i,'dea']=df.loc[i,'diff'] df['macd']=2*(df['diff']-df['dea']) return df macd1=get_MACD(df,12,26,9) #print(get_MACD(df,12,26,9).info()) df.loc[i,'ema']=(2*df.loc[i,'close']+(N-1)*df.loc[i-1,'ema'])/(N+1) df.loc[i,'dea']=(2*df.loc[i,'diff']+(M-1)*df.loc[i-1,'dea'])/(M+1) single = np.diff(np.sign(np.diff(df['diff']))) ',single) print('single peak_locationsA = np.where(single== 2)[0] + 1 trough_locationsA = np.where(single== -2)[0] + 1 print('trough_locationsA ',trough_locationsA) print('peak_locationsA ',peak_locationsA) df['close_mean5']=np.round(df['close'].rolling(window=5,center=False).mean(),2) df['close_mean20']=np.round(df['close'].rolling(window=20,center=False).mean(),2) df['close_m5-20']=df['close_mean5']-df['close_mean20'] df['diff']=np.sign(df['close_mean5']-df['close_mean20']) df['signal'] = np.sign(df['diff'] - df['diff'].shift(1)) #print('df[signal]',df['signal']) cross_up=np.where(df['signal'] == 1)[0] + 1 cross_down=np.where(df['signal'] == -1)[0] + 1 print('df[cross_down]',cross_down) print('df[cross_up]',cross_up) # Draw Plot 

fig = plt.figure(figsize=(12, 8), dpi=100) ax = fig.add_subplot(111) # 添加子图:1 行 1 列第 1 个 ax.plot('date', 'close', data=df, label='close') ax.plot('date', 'close_mean5', data=df, label='close_mean5') ax.plot('date', 'close_mean20', data=df, label='close_mean20') ax.scatter(df.date[trough_locationsA], df.close[trough_locationsA], marker='v', color='tab:green', s=200, label='清仓')#marker=mpl.markers.CARETUPBASE ax.scatter(df.date[peak_locationsA], df.close[peak_locationsA], marker='^', color='tab:red', s=200, label='开仓买入') for t, p in zip(trough_locationsA[1::3], peak_locationsA[::2]): ax.text(df.date[p], df.close[p], df.date[p], horizontalalignment='center', color='darkred')#df.close1[p]*(1+0.05) ax.text(df.date[t], df.close[t], df.date[t], horizontalalignment='center', color='darkgreen')#df.close1[t]*(1.0-0.05) #ax.text(df.date[p], df.close_mean5[p], df.close[p], horizontalalignment='center', color='darkred')#df.close1[p]*(1+0.05) print('df.close[p]: ',df.close[p]) print('df.close[t]: ',df.close[t]) # Annotate xtick_location = df.index.tolist()[::6] xtick_labels = df.date.tolist()[::6] for tick in ax.get_xticklabels(): tick.set_rotation(90) plt.xlabel('date', fontsize=20) # 配置横坐标 plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m/%d/%Y')) plt.gca().xaxis.set_major_locator(mdates.DayLocator()) fig.autofmt_xdate() plt.legend() fig.show() 20 日均线策略的效果.py 

# -*- coding: utf-8 -*- import numpy as np import pandas as pd import matplotlib as mpl import matplotlib.pyplot as plt import seaborn as sns import warnings; warnings.filterwarnings(action='once') import tushare as ts from matplotlib.figure import Figure import matplotlib.dates as mdates import warnings; warnings.filterwarnings(action='once') df = ts.get_hist_data('600006',start='2019-06-01') df = df.sort_index(0) #直接保存 df.to_csv('600006.csv') # Import Data df = pd.read_csv('600006.csv') data = df['close'].values df['close_mean5']=np.round(df['close'].rolling(window=5,center=False).mean(),2) df['close_mean20']=np.round(df['close'].rolling(window=20,center=False).mean(),2) df['close_m5-20']=df['close_mean5']-df['close_mean20'] df['diff']=np.sign(df['close_mean5']-df['close_mean20']) df['signal'] = np.sign(df['diff'] - df['diff'].shift(1)) #print('df[signal]',df['signal']) cross_up=np.where(df['signal'] == 1)[0] + 1 

cross_down=np.where(df['signal'] == -1)[0] + 1 print('df[cross_down]',cross_down) print('df[cross_up]',cross_up) # Draw Plot fig = plt.figure(figsize=(12, 8), dpi=100) ax = fig.add_subplot(111) # 添加子图:1 行 1 列第 1 个 #plt.figure(figsize=(16,10), dpi= 80) ax.plot('date', 'close', data=df, label='close') ax.plot('date', 'close_mean5', data=df, label='close_mean5') ax.plot('date', 'close_mean20', data=df, label='close_mean20') ax.scatter(df.date[cross_down], df.close_mean5[cross_down], marker='v', color='tab:green', s=200, label='cross_down')#marker=mpl.markers.CARETUPBASE ax.scatter(df.date[cross_up], df.close_mean20[cross_up], marker='^', color='tab:red', s=200, label='cross_up') for t, p in zip(cross_down[1::5], cross_up[::3]): ax.text(df.date[p], df.close_mean5[p], df.date[p], horizontalalignment='center', color='darkred')#df.close1[p]*(1+0.05) ax.text(df.date[t], df.close_mean5[t], df.date[t], horizontalalignment='center', color='darkgreen')#df.close1[t]*(1.0-0.05) #ax.text(df.date[p], df.close_mean5[p], df.close[p], horizontalalignment='center', color='darkred')#df.close1[p]*(1+0.05) print('df.close_mean5[p]: ',df.close_mean5[p]) print('df.close[t]: ',df.close[t]) # Annotate #plt.ylim(50,750) xtick_location = df.index.tolist()[::6] xtick_labels = df.date.tolist()[::6] for tick in ax.get_xticklabels(): tick.set_rotation(90) #fig.xticks(ticks=xtick_location, labels=xtick_labels, rotation=45, fontsize=12, alpha=.7) #plt.title("Peak and Troughs of 600006", fontsize=22) #fig.yticks(fontsize=12, alpha=.7) plt.xlabel('', fontsize=20) # 配置横坐标 plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m/%d/%Y')) plt.gca().xaxis.set_major_locator(mdates.DayLocator()) fig.autofmt_xdate() plt.legend() fig.show() 欢迎转载，不需要注明来处，python 是自由软件,由它派生 出来的东西也没有必要私藏起来 xie380205 2019-10-13