pythonでローソク足(candle chart)の描画 ref: https://qiita.com/u1and0/items/1d9afdb7216c3d2320ef

file0.txt

import numpy as np
import pandas as pd


def randomwalk(periods=None, start=None, end=None, freq='B', tz=None,
               normalize=False, name=None, closed=None, tick=1, **kwargs):
    """Returns random up/down pandas Series.

    Usage:
        ```
        import datetime
        randomwalk(100)  # Returns +-1up/down 100days from now.
        randomwalk(100, freq='H')  # Returns +-1up/down 100hours from now.
        randomwalk(100, ,tick=0.1 freq='S')  # Returns +-0.1up/down 100seconds from now.
        randomwalk(100, start=datetime.datetime.today())  # Returns +-1up/down 100days from now.
        randomwalk(100, end=datetime.datetime.today())
            # Returns +-1up/down back to 100 days from now.
        randomwalk(start=datetime.datetime(2000,1,1), end=datetime.datetime.today())
            # Returns +-1up/down from 2000-1-1 to now.
        randomwalk(100, freq='H').resample('D').ohlc()  # random OHLC data
        ```

    Args:
        periods: int
        start: start time (default datetime.now())
        end: end time
        freq: ('M','W','D','B','H','T','S') (default 'B')
        tz: time zone
        tick: up/down unit size (default 1)

    Returns:
        pandas Series with datetime index
    """
    if not start and not end:
        start = pd.datetime.today().date()  # default arg of `start`
    index = pd.DatetimeIndex(start=start, end=end, periods=periods, freq=freq, tz=tz,
                             normalize=normalize, name=name, closed=closed, **kwargs)
    bullbear = pd.Series(tick * np.random.randint(-1, 2, len(index)),
                         index=index, name=name, **kwargs)  # tick * (-1,0,1のどれか)
    price = bullbear.cumsum()  # 累積和
    return price

file1.txt

np.random.seed(1)  # ランダムステートのリセット。常に同じランダムウォークが出来上がる
rw = randomwalk(60*24*90, freq='T', tick=0.01)
rw.head(5)

file10.txt

import matplotlib.pyplot as plt
import matplotlib.finance as mpf

def sma(ohlc, period):
    sma = ohlc.close.rolling(period).mean()
    vstack = np.vstack((range(len(sma)), sma.values.T)).T  # x軸データを整数に
    return vstack

fig = plt.figure()
ax = plt.subplot()

# candle
ohlc = np.vstack((range(len(df)), df.values.T)).T  # x軸データを整数に
mpf.candlestick_ohlc(ax, ohlc, width=0.8, colorup='r', colordown='b')

# sma
sma5 = sma(df, 5)
sma25 = sma(df, 25)
ax.plot(sma5[:, 0], sma5[:, 1])
ax.plot(sma25[:, 0], sma25[:, 1])


# xticks
xtick0 = (5 - df.index[0].weekday()) % 5  # 最初の月曜日のインデックス
plt.xticks(range(xtick0, len(df), 5), [x.strftime('%Y-%m-%d') for x in df.index][xtick0::5])
ax.grid(True)  # グリッド表示
ax.set_xlim(-1, len(df))  # x軸の範囲
fig.autofmt_xdate()  # x軸のオートフォーマット
plt.show()

file11.txt

import plotly as py
py.offline.init_notebook_mode(connected=False) 

file12.txt

fo = [[2000,1190547,1.36],
    [2001,1170662,1.33],
    [2002,1153855,1.32],
    [2003,1123610,1.29],
    [2004,1110721,1.29],
    [2005,1062530,1.26],
    [2006,1092674,1.32],
    [2007,1089818,1.34],
    [2008,1091156,1.37],
    [2009,1070035,1.37],
    [2010,1071304,1.39],
    [2011,1050806,1.39],
    [2012,1037101,1.41],
    [2013,1029816,1.43],
    [2014,1003532,1.42],
    [2015,1005656,1.46]]
raw = pd.DataFrame(fo, columns=['year', 'births', 'birth rate'])
raw

file13.txt

data = [
    py.graph_objs.Scatter(y=raw["births"], name="births"),
]
layout = py.graph_objs.Layout(
    title="title",
    legend={"x":0.8, "y":0.1},
    xaxis={"title":""},
    yaxis={"title":""},
)
fig = py.graph_objs.Figure(data=data, layout=layout)
py.offline.iplot(fig, show_link=False)

file14.txt

data = [
    py.graph_objs.Bar(x=raw["year"], y=raw["births"], name="Births"),
    py.graph_objs.Scatter(x=raw["year"], y=raw["birth rate"], name="Birth Rate", yaxis="y2")
]
layout = py.graph_objs.Layout(
    title="Births and Birth Rate in Japan",
    legend={"x":0.8, "y":0.1},
    xaxis={"title":"Year"},
    yaxis={"title":"Births"},
    yaxis2={"title":"Birth Rate", "overlaying":"y", "side":"right"},
)
fig = py.graph_objs.Figure(data=data, layout=layout)
py.offline.iplot(fig)
#py.offline.plot(fig)

file15.txt

from plotly.offline import init_notebook_mode, iplot
from plotly.tools import FigureFactory as FF
init_notebook_mode(connected=True) # Jupyter notebook用設定

file16.txt

fig = FF.create_candlestick(df.open, df.high, df.low, df.close, dates=df.index)
py.offline.iplot(fig)

file17.txt

fig = FF.create_candlestick(df.open, df.high, df.low, df.close)

xtick0 = (5-df.index[0].weekday())%5 #最初の月曜日のインデックス
fig['layout'].update({
    'xaxis':{
        'showgrid': True,↔
        'ticktext': [x.strftime('%Y-%m-%d') for x in df.index][xtick0::5],
        'tickvals': np.arange(xtick0,len(df),5)
    }
})

py.offline.iplot(fig)

file18.txt

def sma(data, window, columns='close'):
    return data[columns].rolling(window).mean()

sma5 = sma(df, 5)

file19.txt

fig = FF.create_candlestick(df.open, df.high, df.low, df.close, dates=df.index)

add_line = Scatter( x=df.index,  y=df.close,  name= 'close values',
                   line=Line(color='black'))
fig['data'].extend([add_line])
↔
py.offline.iplot(fig, filename='candlestick_and_trace', validate=False)

file2.txt

2017-03-19 00:00:00    0.00
2017-03-19 00:01:00   -0.01
2017-03-19 00:02:00   -0.02
2017-03-19 00:03:00   -0.02
2017-03-19 00:04:00   -0.02
Freq: T, dtype: float64

file20.txt

from plotly.graph_objs import *
fig = FF.create_candlestick(df.open, df.high, df.low, df.close, dates=df.index)
add_line = [Scatter(x=df.index, y=df.close.rolling(5).mean(), name='SMA5', line=Line(color='r')),
            Scatter(x=df.index, y=df.close.rolling(15).mean(), name='SMA15', line=Line(color='b')),
            Scatter(x=df.index, y=df.close.rolling(25).mean(), name='SMA25', line=Line(color='g'))]
↔
fig['data'].extend(add_line)
fig['layout'].update({'xaxis':{'showgrid': True}})

py.offline.iplot(fig, filename='candlestick_and_trace', validate=False)

file21.txt

np.random.seed(10)
ra = randomwalk(60*24*360, freq='T', tick=0.01) + 115
df1 = ra.resample('B').ohlc()

file22.txt

import plotly.graph_objs as go
fig = FF.create_candlestick(df1.open, df1.high, df1.low, df1.close, dates=df1.index)
add_line = [go.Scatter(x=df1.index, y=df1.close.rolling(75).mean(), name='SMA75', line=Line(color='r')),
            go.Scatter(x=df1.index, y=df1.close.ewm(75).mean(), name='EMA75', line=Line(color='b'))]

fig['data'].extend(add_line)
fig['layout'].update({'xaxis':{'showgrid': True}})

py.offline.iplot(fig, filename='candlestick_and_trace', validate=False)

file23.txt

import plotly.graph_objs as pyg
from datetime import datetime

def to_unix_time(*dt):
    """datetimeをunix秒に変換
    引数: datetimeの入ったリスト
    戻り値: unix秒に直されたリスト"""
    epoch =  datetime.utcfromtimestamp(0)
    ep = [(i - epoch).total_seconds() * 1000 for i in list(*dt)]
    return ep

fig = FF.create_candlestick(df1.open, df1.high, df1.low, df1.close, dates=df1.index)
add_line = [pyg.Scatter(x=df1.index, y=df1.close.rolling(75).mean(), name='SMA75', line=Line(color='r')),
            pyg.Scatter(x=df1.index, y=df1.close.ewm(75).mean(), name='EMA75', line=Line(color='b')),
            pyg.Scatter(x=df1.index, y=df1.close.rolling(75).mean(), name='SMA75', mode='markers'),
            pyg.Scatter(x=df1.index, y=df1.close.ewm(75).mean(), name='EMA75', mode='markers')]

fig['data'].extend(add_line)  # プロットするデータの追加
fig['layout'].update(xaxis = {'showgrid': True,
                               'type': 'date',
                               'range':to_unix_time([datetime(2017,9,1), datetime(2018,1,1)])})  # レイアウトの変更

py.offline.iplot(fig, filename='candlestick_and_trace', validate=False)

file3.txt

rw.plot()

file4.txt

df = rw.resample('B').ohlc() + 115  # 初期値は115円
df.head()

file5.txt

df.plot()

file6.txt

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.finance as mpf
from matplotlib import ticker
import matplotlib.dates as mdates
import pandas as pd

def candlechart(ohlc, width=0.8):
    """入力されたデータフレームに対してローソク足チャートを返す
        引数:
            * ohlc:
                *データフレーム
                * 列名に'open'", 'close', 'low', 'high'を入れること
                * 順不同"
            * widrh: ローソクの線幅
        戻り値: ax: subplot"""
    fig, ax = plt.subplots()
    # ローソク足
    mpf.candlestick2_ohlc(ax, opens=ohlc.open.values, closes=ohlc.close.values,
                          lows=ohlc.low.values, highs=ohlc.high.values,
                          width=width, colorup='r', colordown='b')

    # x軸を時間にする
    xdate = ohlc.index
    ax.xaxis.set_major_locator(ticker.MaxNLocator(6))

    def mydate(x, pos):
        try:
            return xdate[int(x)]
        except IndexError:
            return ''

    ax.xaxis.set_major_formatter(ticker.FuncFormatter(mydate))
    ax.format_xdata = mdates.DateFormatter('%Y-%m-%d')

    fig.autofmt_xdate()
    fig.tight_layout()

    return fig, ax

candlechart(df)

file7.txt

(<matplotlib.figure.Figure at 0x207a86dd080>,
 <matplotlib.axes._subplots.AxesSubplot at 0x207a6a225c0>)

file8.txt

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.finance as mpf
from matplotlib import ticker
import matplotlib.dates as mdates
import pandas as pd

fig = plt.figure()
ax = plt.subplot()

ohlc = np.vstack((range(len(df)), df.values.T)).T #x軸データを整数に
mpf.candlestick_ohlc(ax, ohlc, width=0.8, colorup='r', colordown='b')

xtick0 = (5-df.index[0].weekday())%5 #最初の月曜日のインデックス

plt.xticks(range(xtick0,len(df),5), [x.strftime('%Y-%m-%d') for x in df.index][xtick0::5])
ax.grid(True) #グリッド表示
ax.set_xlim(-1, len(df)) #x軸の範囲
fig.autofmt_xdate() #x軸のオートフォーマット

file9.txt

import matplotlib.pyplot as plt
import matplotlib.finance as mpf
from randomwalk import *

fig = plt.figure()
ax = plt.subplot()

# candle
ohlc = np.vstack((range(len(df)), df.values.T)).T  # x軸データを整数に
mpf.candlestick_ohlc(ax, ohlc, width=0.8, colorup='r', colordown='b')

# sma
sma = df.close.rolling(5).mean()
vstack = np.vstack((range(len(sma)), sma.values.T)).T  # x軸データを整数に
ax.plot(vstack[:, 0], vstack[:, 1])

# xticks
xtick0 = (5 - df.index[0].weekday()) % 5  # 最初の月曜日のインデックス
plt.xticks(range(xtick0, len(df), 5), [x.strftime('%Y-%m-%d') for x in df.index][xtick0::5])
ax.grid(True)  # グリッド表示
ax.set_xlim(-1, len(df))  # x軸の範囲
fig.autofmt_xdate()  # x軸のオートフォーマット
plt.show()