

In [1]:

    
import ts_charting as charting
import pandas.io.data as pdd
import pandas as pd
charting.figsize(13, 10)



In [2]:

    
df = pdd.get_data_yahoo('AAPL')
spy = pdd.get_data_yahoo('SPY')
df = df.tail(100)
spy = spy.tail(100)
returns = df.Close.pct_change()
spy_returns = spy.Close.pct_change()

Simple ohlc plot

importing ts_charting will install the ohlc_plot for pd.DataFrame. Also we plot markers for every day that ends up higher than yesterdays close.



In [4]:

    
fig = charting.figure(1)
df.ohlc_plot()
(df.Close.pct_change() > 0).fplot_markers('close up', yvalues=(df.Open + df.Close) / 2, color='green')
fig.line(460)



In [5]:

    
overnight = np.log(df.Open / df.Close.shift(1))
intraday = np.log(df.Close / df.Open)

overnight_roll = pd.rolling_sum(overnight, 10)
intraday_roll = pd.rolling_sum(intraday, 10)

Multiple Y-axis

fplot supports a yax parameters which creates a named axis. This allows you to have an infinite number of y-axes. It also supports secondary_y which is the same as yax="right"

A right side spline will be added for each y-axis.



In [7]:

    
fig = charting.figure(1)
df.ohlc_plot()
(df.Close.pct_change() > 0).fplot_markers('close up', yvalues=(df.Open + df.Close) / 2, color='green')

(returns + 1).cumprod().fplot('equity', secondary_y=True)

overnight_roll.fplot('overnight returns', yax='10day')
intraday_roll.fplot('intraday returns', yax='10day')

Multiple Subplots

Note that due to misunderstanding matplotlib, subplots are selected via a set_ax. Probably need to change that, and the datetime formatter looks wonky for plots other than the first.



In [9]:

    
fig = charting.figure(2)
fig.set_ax(1)

df.ohlc_plot()
(df.Close.pct_change() > 0).fplot_markers('close up', yvalues=(df.Open + df.Close) / 2, color='green')

overnight_roll.fplot('overnight returns', yax='10day')
intraday_roll.fplot('intraday returns', yax='10day')

fig.set_ax(2)
(returns + 1).cumprod().fplot('equity')
(spy_returns + 1).cumprod().fplot('spy')

fig.set_xticks('MS')

sharex

Note the difference bewteen subplot 2 and 3. Both only plot the last 10 days.



In [11]:

    
fig = charting.figure(3)
fig.set_ax(1)

df.ohlc_plot()
(df.Close.pct_change() > 0).fplot_markers('close up', yvalues=(df.Open + df.Close) / 2, color='green')

overnight_roll.fplot('overnight returns', yax='10day')
intraday_roll.fplot('intraday returns', yax='10day')

fig.set_ax(2)
(returns + 1).cumprod().tail(10).fplot('equity')
(spy_returns + 1).cumprod().tail(10).fplot('spy')

fig.set_ax(3, sharex=1)
(returns + 1).cumprod().tail(10).fplot('equity')
(spy_returns + 1).cumprod().tail(10).fplot('spy')

Highlight horizontal span

fig.hl_span(start, end)



In [13]:

    
fig = charting.figure(2)
fig.set_ax(1)

df.ohlc_plot()
(df.Close.pct_change() > 0).fplot_markers('close up', yvalues=(df.Open + df.Close) / 2, color='green')

overnight_roll.fplot('overnight returns', yax='10day')
intraday_roll.fplot('intraday returns', yax='10day')
fig.hl_span("4/29/2013", "5/20/2013", color='red')

fig.set_ax(2, sharex=1)
(returns + 1).cumprod().fplot('equity')
(spy_returns + 1).cumprod().fplot('spy')

fig.hl_span("3/18/2013", "4/08/2013", color='red')

reindex

plot takes a method parameters which gets passed along to an internal reindex call. This can be used to find values for missing or mis-aligned datasets



In [15]:

    
week_max = df.High.resample('W', 'max', label='left').tshift(-1, '1h')
week_min = df.Low.resample('W', 'min', label='left').tshift(-3, '1h')
# note these are both sparse (anchored on saturday) and misaligned (happens at 11 or 9 PM)
week_max









    Out[15]:





Date
2013-02-09 23:00:00    484.94
2013-02-16 23:00:00    462.73
2013-02-23 23:00:00    455.12
2013-03-02 23:00:00    435.43
2013-03-09 23:00:00    444.23
2013-03-16 23:00:00    462.10
2013-03-23 23:00:00    469.95
2013-03-30 23:00:00    443.70
2013-04-06 23:00:00    437.99
2013-04-13 23:00:00    427.89
2013-04-20 23:00:00    418.77
2013-04-27 23:00:00    453.23
2013-05-04 23:00:00    465.75
2013-05-11 23:00:00    457.90
2013-05-18 23:00:00    448.35
2013-05-25 23:00:00    457.10
2013-06-01 23:00:00    454.43
2013-06-08 23:00:00    449.08
2013-06-15 23:00:00    435.70
2013-06-22 23:00:00    408.66
2013-06-29 23:00:00    422.98
dtype: float64



In [16]:

    
fig = charting.figure(1)
fig.set_ax(1)

df.ohlc_plot()
week_max.fplot('week_max', method='ffill', color='green')
week_min.fplot('week_min', method='ffill', color='red')

TickLocator

By default the TimestampLocator will make a best guess how where to place ticks. This is done by grabbing the lowest freq that can support a min_tick of 5.

However, you can explicitly set the tick frequency and also pass in custom dates



In [20]:

    
fig = charting.figure(3)
fig.set_ax(1)
(returns + 1).cumprod().fplot('equity')
fig.set_xticks('MS') # pandas freq

fig.set_ax(2, sharex=1)
(returns + 1).cumprod().fplot('equity')
fig.set_xticks([pd.Timestamp("2013-03-01"), pd.Timestamp("2013-05-14")]) # explicit dates

fig.set_ax(3, sharex=1)
(returns + 1).cumprod().fplot('equity')
fig.set_xticks(returns > 0.02) # set tick whenver we are above .02