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

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()

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)

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)

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')

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')

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')

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')

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

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')

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