问Y轴上的matplotlib数据堆栈
EN

Stack Overflow用户

提问于 2021-02-21 22:24:29

回答 1查看 76关注 0票数 0

我得到了这个代码：

fig = plt.figure(figsize=(20,8))
ax1 = fig.add_subplot(111,  ylabel='Price in $')
df['Close'].plot(ax=ax1, color='r', lw=2.)
signals[['short_mavg', 'long_mavg']].plot(ax=ax1, lw=2.)

ax1.plot(signals.loc[signals.positions == 1.0].index, 
         signals.short_mavg[signals.positions == 1.0],
         '^', markersize=10, color='m')
         
ax1.plot(signals.loc[signals.positions == -1.0].index, 
         signals.short_mavg[signals.positions == -1.0],
         'v', markersize=10, color='k')
         
plt.show()

问题是:来自df的所有'^‘、'v’和‘Date’值都放在Y轴上=(

我添加了我的jupyter笔记本和.csv示例csv数据的所有代码部分：

2013.12.17,00:00,0.89469,0.89571,0.88817,0.88973,4
2013.12.18,00:00,0.88974,0.89430,0.88200,0.88595,4

代码：

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
df = pd.read_csv("AUDUSD.csv",header = None)
df.columns = ['Date', 'Time', 'Open', 'High', 'Low', 'Close', 'Volume'] 
df=df.set_index('Date')

第二个df：

short_window = 20
long_window = 90
signals = pd.DataFrame(index=df.index) 
signals['signal'] = 0.0 
#calculating MAs
signals['short_mavg'] = df['Close'].rolling(short_window).mean()
signals['long_mavg'] = df['Close'].rolling(long_window).mean() 

signals['signal'][short_window:] = np.where(signals['short_mavg'][short_window:] 
                                            > signals['long_mavg'][short_window:], 1.0, 0.0) 
signals['positions'] = signals['signal'].diff()

python

pandas

matplotlib

游戏社交场景解决方案

整合腾讯在社交娱乐及游戏体验方面的技术能力，满足玩家对游戏内即时通信和语音互动的社交刚性需求，提升游戏黏性

回答 1

Stack Overflow用户

回答已采纳

发布于 2021-02-24 03:12:18

我用你的代码用雅虎财经的货币数据创建了一个图表。可能是时间序列数据没有索引。请检查您的数据和我的数据内容。

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline

import yfinance as yf

ticker = yf.Ticker("AUDUSD=X")
df = ticker.history(start='2013-01-01', end='2021-01-01')
short_window = 20
long_window = 90
signals = pd.DataFrame(index=df.index) 
signals['signal'] = 0.0 
#calculating MAs
signals['short_mavg'] = df['Close'].rolling(short_window).mean()
signals['long_mavg'] = df['Close'].rolling(long_window).mean() 

signals['signal'][short_window:] = np.where(signals['short_mavg'][short_window:] 
                                            > signals['long_mavg'][short_window:], 1.0, 0.0) 
signals['positions'] = signals['signal'].diff()
fig = plt.figure(figsize=(20,8))
ax1 = fig.add_subplot(111,  ylabel='Price in $')
df['Close'].plot(ax=ax1, color='r', lw=2.)
signals[['short_mavg', 'long_mavg']].plot(ax=ax1, lw=2.)

ax1.plot(signals.loc[signals.positions == 1.0].index, 
         signals.short_mavg[signals.positions == 1.0],
         '^', markersize=10, color='m')
         
ax1.plot(signals.loc[signals.positions == -1.0].index, 
         signals.short_mavg[signals.positions == -1.0],
         'v', markersize=10, color='k')
plt.show()

signals

    signal  short_mavg  long_mavg   positions
Date                
2012-12-31  0.0     NaN     NaN     NaN
2013-01-01  0.0     NaN     NaN     0.0
2013-01-02  0.0     NaN     NaN     0.0
2013-01-03  0.0     NaN     NaN     0.0
2013-01-04  0.0     NaN     NaN     0.0
...     ...     ...     ...     ...
2020-12-25  1.0     0.749732    0.727486    0.0
2020-12-28  1.0     0.750791    0.727987    0.0
2020-12-29  1.0     0.751951    0.728454    0.0
2020-12-30  1.0     0.753096    0.728910    0.0
2020-12-31  1.0     0.754453    0.729403    0.0