PythonでTA-Lib・matplotlib・pandasを使用して株価テクニカル分析チャートを超簡単に作成（移動平均・ボリンジャーバンド・出来高・MACD・RSI）

＊株価ローソク足チャート作成についてはこちらへ

1. TA-Libインストール（linux）

macやwindowsの場合はドキュメント参照

$ curl -L http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz -O
$ tar -xzf ta-lib-0.4.0-src.tar.gz
$ cd ta-lib/
$ ./configure --prefix=/usr
$ make
$ sudo make install

2. ツールインストール

$ pip install TA-Lib mpl_finance matplotlib pandas-datareader

3. 移動平均チャート作成

ma5.py

import pandas_datareader as pdr
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import talib as ta
from mpl_finance import candlestick_ohlc

df = pdr.get_data_yahoo('AAPL', '2019-08-09', '2019-09-19')
df['ma5'] = ta.SMA(df['Close'], timeperiod=5)
df = df[5:]
ax = plt.subplot()
ax.xaxis.set_major_formatter(mdates.DateFormatter('%m/%d'))
candlestick_ohlc(ax, zip(mdates.date2num(df.index), df['Open'], df['High'], df['Low'], df['Close']), width=0.4)
ax.plot(mdates.date2num(df.index), df['ma5'])
plt.title('apple')
plt.savefig('ma5.png')

チャート作成

＄ python ma5.py

4. ボリンジャーバンドチャート作成

bbands.py

import pandas_datareader as pdr
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import talib as ta
from mpl_finance import candlestick_ohlc

df = pdr.get_data_yahoo('AAPL', '2019-08-09', '2019-09-19')
df['upper'], df['middle'], df['lower'] = ta.BBANDS(df['Close'], timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)
df = df[5:]
ax = plt.subplot()
ax.xaxis.set_major_formatter(mdates.DateFormatter('%m/%d'))
candlestick_ohlc(ax, zip(mdates.date2num(df.index), df['Open'], df['High'], df['Low'], df['Close']), width=0.4)
ax.plot(mdates.date2num(df.index), df['upper'])
ax.plot(mdates.date2num(df.index), df['middle'])
ax.plot(mdates.date2num(df.index), df['lower'])
plt.title('apple')
plt.savefig('bbands.png')

チャート作成

＄ python bbands.py

5. 出来高チャート作成

volume.py

import pandas_datareader as pdr
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
from mpl_finance import candlestick_ohlc

df = pdr.get_data_yahoo('AAPL', '2019-08-16', '2019-09-19')
fig = plt.figure()
ax1 = fig.add_axes((0.1, 0.4, 0.8, 0.5))
ax1.tick_params(labelbottom = False, bottom = False)
ax1.set_title('apple')
ax2 = fig.add_axes((0.1, 0.1, 0.8, 0.2), sharex=ax1)
ax2.xaxis.set_major_formatter(mdates.DateFormatter('%m/%d'))
candlestick_ohlc(ax1, zip(mdates.date2num(df.index), df['Open'], df['High'], df['Low'], df['Close']), width=0.4)
ax2.bar(mdates.date2num(df.index),df['Volume'])
plt.savefig('volume.png')

チャート作成

$ python volume.py

6. MACDチャート作成

macd.py

import pandas_datareader as pdr
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import talib as ta
from mpl_finance import candlestick_ohlc

df = pdr.get_data_yahoo('AAPL', '2019-06-29', '2019-09-19')
df['macd'], df['macdsignal'], df['macdhist'] = ta.MACD(df['Close'], fastperiod=12, slowperiod=26, signalperiod=9)
df = df[33:]
fig = plt.figure()
ax1 = fig.add_axes((0.1, 0.4, 0.8, 0.5))
ax1.tick_params(labelbottom = False, bottom = False)
ax1.set_title('apple')
ax2 = fig.add_axes((0.1, 0.1, 0.8, 0.2), sharex=ax1)
ax2.xaxis.set_major_formatter(mdates.DateFormatter('%m/%d'))
candlestick_ohlc(ax1, zip(mdates.date2num(df.index), df['Open'], df['High'], df['Low'], df['Close']), width=0.4)
ax2.plot(mdates.date2num(df.index),df['macd'])
ax2.plot(mdates.date2num(df.index),df['macdsignal'])
plt.savefig('macd.png')

チャート作成

$ python macd.py

7. RSIチャート作成

rsi.py

import pandas_datareader as pdr
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import talib as ta
from mpl_finance import candlestick_ohlc

df = pdr.get_data_yahoo('AAPL', '2019-06-29', '2019-09-19')
df['rsi'] = ta.RSI(df['Close'], timeperiod=14)
df = df[33:]
fig = plt.figure()
ax1 = fig.add_axes((0.1, 0.4, 0.8, 0.5))
ax1.tick_params(labelbottom = False, bottom = False)
ax1.set_title('apple')
ax2 = fig.add_axes((0.1, 0.1, 0.8, 0.2), sharex=ax1)
ax2.xaxis.set_major_formatter(mdates.DateFormatter('%m/%d'))
candlestick_ohlc(ax1, zip(mdates.date2num(df.index), df['Open'], df['High'], df['Low'], df['Close']), width=0.4)
ax2.plot(mdates.date2num(df.index),df['rsi'])
ax2.plot(mdates.date2num(df.index),[70]*25)
ax2.plot(mdates.date2num(df.index),[30]*25)
plt.savefig('rsi.png')

チャート作成

$ python rsi.py

以上、超簡単！

8. 参考

9. 関連記事