我使用pandas.DataFrame.rolling
计算了移动平均线。所以我的Dataframe看起来像这样,
CurrencyPair TimeStamp Open High Low Close MA50
40 EURUSD 2017-07-10 16:00:00 1.1397 1.1401 1.1396 1.1397 NaN
41 EURUSD 2017-07-10 15:00:00 1.1389 1.1396 1.1386 1.1396 NaN
42 EURUSD 2017-07-10 14:00:00 1.1393 1.1396 1.1389 1.1390 NaN
43 EURUSD 2017-07-10 13:00:00 1.1393 1.1394 1.1387 1.1391 NaN
44 EURUSD 2017-07-10 12:00:00 1.1390 1.1395 1.1386 1.1392 NaN
45 EURUSD 2017-07-10 11:00:00 1.1392 1.1393 1.1384 1.1390 NaN
46 EURUSD 2017-07-10 10:00:00 1.1387 1.1395 1.1385 1.1395 NaN
47 EURUSD 2017-07-10 09:00:00 1.1397 1.1398 1.1387 1.1389 NaN
48 EURUSD 2017-07-10 08:00:00 1.1417 1.1418 1.1399 1.1403 NaN
49 EURUSD 2017-07-10 07:00:00 1.1400 1.1416 1.1400 1.1416 1.142272
50 EURUSD 2017-07-10 06:00:00 1.1410 1.1411 1.1399 1.1399 1.142154
51 EURUSD 2017-07-10 05:00:00 1.1405 1.1409 1.1404 1.1409 1.142068
52 EURUSD 2017-07-10 04:00:00 1.1406 1.1407 1.1402 1.1404 1.141952
53 EURUSD 2017-07-10 03:00:00 1.1406 1.1407 1.1403 1.1406 1.141804
我设法使用来自TimeStamp
和OHLC
的数据来绘制烛台图表,但我不确定如何在烛台图表上使用相同的轴绘制我的移动平均线。我尝试使用eurusd['MA50'].plot(ax = ax)
,但收到错误ValueError: ordinal must be >= 1
。
这是我的代码,
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import datetime as dt
from mpl_finance import candlestick_ohlc
eurusd = pd.read_csv('fxhistoricaldata_EURUSD_hour.csv')
eurusd['TimeStamp'] = pd.to_datetime(eurusd['TimeStamp'], format = '%d/%m/%Y %H:%M')
# Set date & time range
# start_date (Y-m-d H-M) & end_date (Y-m-d H-M)
mask = (eurusd['TimeStamp'] >= '2017-07-10 3:00') & (eurusd['TimeStamp'] <= '2017-07-12 8:00')
eurusd = eurusd.loc[mask]
# Calculate Moving Averages MA50
eurusd = eurusd.reindex(columns = np.append(eurusd.columns, ['MA50']))
eurusd['MA50'] = eurusd[['Close']].rolling(50).mean()
print(eurusd)
eurusd['TimeStamp'] = eurusd['TimeStamp'].map(lambda d: mdates.datestr2num(dt.datetime.strftime(d, '%Y-%m-%d %H:%M')))
fig, ax = plt.subplots()
ax.xaxis_date()
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y/%m/%d %H:%M'))
ax.grid(True, color = 'k', alpha = 0.5)
plt.xticks(rotation = 45)
plt.xlabel("Date")
plt.ylabel("Price")
plt.title("EUR/USD")
candlestick_ohlc(ax, eurusd[['TimeStamp','Open','High','Low','Close']].values, width = 0.01, colorup = 'g')
eurusd['MA50'].plot(ax = ax)
plt.show()
这是完整的错误日志,
ticks = self.get_major_ticks()
File "C:\Users\Meng Da\AppData\Local\Programs\Python\Python36-32\lib\site-
packages\matplotlib\axis.py", line 1320, in get_major_ticks
numticks = len(self.get_major_locator()())
File "C:\Users\Meng Da\AppData\Local\Programs\Python\Python36-32\lib\site-
packages\matplotlib\dates.py", line 986, in __call__
self.refresh()
File "C:\Users\Meng Da\AppData\Local\Programs\Python\Python36-32\lib\site-
packages\matplotlib\dates.py", line 1006, in refresh
dmin, dmax = self.viewlim_to_dt()
File "C:\Users\Meng Da\AppData\Local\Programs\Python\Python36-32\lib\site-
packages\matplotlib\dates.py", line 763, in viewlim_to_dt
return num2date(vmin, self.tz), num2date(vmax, self.tz)
File "C:\Users\Meng Da\AppData\Local\Programs\Python\Python36-32\lib\site-
packages\matplotlib\dates.py", line 401, in num2date
return _from_ordinalf(x, tz)
File "C:\Users\Meng Da\AppData\Local\Programs\Python\Python36-32\lib\site-
packages\matplotlib\dates.py", line 254, in _from_ordinalf
dt = datetime.datetime.fromordinal(ix).replace(tzinfo=UTC)
ValueError: ordinal must be >= 1
非常感谢任何帮助。谢谢!
答案 0 :(得分:1)
好像你有斧头的问题。你究竟想通过subplot2grid
实现什么目标?如果你删除它然后它开箱即用。请注意,我使用了candlestick2_ohlc
。您还需要添加日期,我相信您已经知道了这些日期。
from pandas_datareader import data
import matplotlib.pyplot as plt
from matplotlib.finance import candlestick2_ohlc
aapl = data.DataReader('AAPL', 'google', '2017-06-01')
aapl['MA50'] = aapl["High"].rolling(10).mean()
aapl.reset_index(inplace=True)
fig, ax = plt.subplots()
plt.xticks(rotation = 45)
plt.xlabel("Date")
plt.ylabel("Price")
plt.title("EUR/USD")
candlestick2_ohlc(ax, aapl.Open, aapl.High, aapl.Low, aapl.Close, width=1, colorup='g')
aapl.MA50.plot(ax=ax)
plt.show()
答案 1 :(得分:1)
我设法使用ax.plot(eurusd['TimeStamp'], eurusd['MA50'])
来解决问题。以防任何人遇到类似的问题。