#!/usr/bin/env python
# coding: utf-8

# # Price Channel, Donchian Channel, High Low(HL) Bands

# In[1]:


import numpy as np
import pandas as pd
from highcharts import Highstock


# ## データ
# 
# [ヒストリカルデータ取得 :: デューカスコピー・ジャパン | 自動売買も裁量もJForex!](https://www.dukascopy.jp/marketwatch/historical/)
# 
# ```
# $ head USDJPY_1\ Min_Bid_2000.01.01_2018.01.01.csv
# Time (UTC),Open,High,Low,Close,Volume
# 2003.05.04 21:00:00,118.94,118.952,118.94,118.952,253
# 2003.05.04 21:01:00,118.961,118.967,118.958,118.967,154.6
# 2003.05.04 21:02:00,118.972,118.972,118.955,118.955,219.7
# 2003.05.04 21:03:00,118.953,118.961,118.949,118.949,309.9
# 2003.05.04 21:04:00,118.953,118.953,118.946,118.946,229.4
# 2003.05.04 21:05:00,118.952,118.954,118.944,118.944,112.2
# 2003.05.04 21:06:00,118.95,118.952,118.945,118.945,170.2
# 2003.05.04 21:07:00,118.947,118.956,118.947,118.947,124.5
# 2003.05.04 21:08:00,118.946,118.954,118.934,118.934,355
# $ wc -l USDJPY_1\ Min_Bid_2000.01.01_2018.01.01.csv
# 5509561 USDJPY_1 Min_Bid_2000.01.01_2018.01.01.csv
# ```

# ## データを読み込む

# In[2]:


filepath1 = 'USDJPY_1 Min_Bid_2000.01.01_2018.01.01.csv'
dtype1 = { 'time': str, 'open': float, 'high': float, 'low': float, 'close': float, 'volume': float }
names1 = ['time', 'open', 'high', 'low', 'close', 'volume']
get_ipython().run_line_magic('time', "df = pd.read_csv(filepath_or_buffer=filepath1, dtype=dtype1, header=0, index_col='time', names=names1, parse_dates=['time'])")
# CPU times: user 15.5 s, sys: 3.77 s, total: 19.2 s
# Wall time: 20.5 s

df.head()


# ## 日足のローソク足を 5 本にするため時間の計算
# 
# UTC → UTC+3

# In[3]:


#%time pd.DataFrame({ 'open': df['open'], 'high': df['high'], 'low': df['low'], 'close': df['close'], 'volume': df['volume'] }, index=df.index + pd.DateOffset(hours=3))
#%time pd.DataFrame({ 'open': df['open'].values, 'high': df['high'].values, 'low': df['low'].values, 'close': df['close'].values, 'volume': df['volume'].values }, index=df.index + pd.DateOffset(hours=3))
# index を変える場合、 Series だと DataFrame を生成するのに 6 秒程度、 ndarray だと DataFrame を生成するのに 1 秒程度

data2 = { 'open': df['open'].values, 'high': df['high'].values, 'low': df['low'].values, 'close': df['close'].values, 'volume': df['volume'].values }
columns2 = ['open', 'high', 'low', 'close', 'volume']
index2 = df.index + pd.DateOffset(hours=3)
get_ipython().run_line_magic('time', 'df2 = pd.DataFrame(data=data2, columns=columns2, index=index2)')
# CPU times: user 62.5 ms, sys: 453 ms, total: 516 ms
# Wall time: 524 ms

df2.head()


# ## リサンプリング

# In[4]:


# 日足の始値、高値、安値、終値を計算する
# Series ごとに始値、高値、安値、終値の計算結果が得られる
# 始値を参照する場合は df['open']['open'] のように記述する
get_ipython().run_line_magic('time', "df3 = df2.resample(rule='D').ohlc()")
# CPU times: user 250 ms, sys: 78.1 ms, total: 328 ms
# Wall time: 348 ms

# 日足の出来高を計算する
get_ipython().run_line_magic('time', "df4 = df2.resample(rule='D').sum()")
# CPU times: user 156 ms, sys: 93.8 ms, total: 250 ms
# Wall time: 241 ms

# 日足の DataFrame
# 土日も計算結果に含まれるため dropna で取り除く
data5 = { 'open': df3['open']['open'].values, 'high': df3['high']['high'].values, 'low': df3['low']['low'].values, 'close': df3['close']['close'].values, 'volume': df4['volume'].values }
columns5 = ['open', 'high', 'low', 'close', 'volume']
get_ipython().run_line_magic('time', 'df5 = pd.DataFrame(data=data5, columns=columns5, index=df3.index).dropna()')
# CPU times: user 0 ns, sys: 0 ns, total: 0 ns
# Wall time: 2.91 ms

df5.head()


# ## 高値と安値を計算する
# 
# * [pandas.DataFrame.rolling — pandas 0.23.0 documentation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.rolling.html)

# In[5]:


# high = pd.rolling_max(df2, 20)
# /path/to/dir/lib/python3.5/site-packages/ipykernel_launcher.py:1: FutureWarning: pd.rolling_max is deprecated for DataFrame and will be removed in a future version, replace with 
# 	DataFrame.rolling(window=20,center=False).max()
#   """Entry point for launching an IPython kernel.
# pd.rolling_max と pd.rolling_min は deprecated になったみたい

period = 20
# %time df5.rolling(window=period).max()
# CPU times: user 0 ns, sys: 0 ns, total: 0 ns
# Wall time: 48.3 ms
get_ipython().run_line_magic('time', "high = df5['high'].rolling(window=period).max()")
get_ipython().run_line_magic('time', "low = df5['low'].rolling(window=period).min()")
get_ipython().run_line_magic('time', "pd.DataFrame(data={ 'high': high, 'low': low }).tail()")


# ## Highstock 向けの DataFrame を作る

# In[6]:


time6 = (df5.index.values // pd.Timedelta('1ms')).astype(np.int64)
data6 = { 'time': time6, 'open': df5['open'].values, 'high': df5['high'].values, 'low': df5['low'].values, 'close': df5['close'].values }
columns6 = ['time', 'open', 'high', 'low', 'close']
df6 = pd.DataFrame(data=data6, columns=columns6)
data7 = { 'time': time6, 'hlhigh': high.values }
columns7 = ['time', 'hlhigh']
df7 = pd.DataFrame(data=data7, columns=columns7)
data8 = { 'time': time6, 'hllow': low.values }
columns8 = ['time', 'hllow']
df8 = pd.DataFrame(data=data8, columns=columns8)
df6.merge(df7).merge(df8).tail()


# ## Highstock のローソク足を表示する

# In[7]:


H = Highstock()

H.add_data_set(df6.values.tolist(), 'candlestick', '米ドル/円')
H.add_data_set(df7.values.tolist(), 'line', 'High')
H.add_data_set(df8.values.tolist(), 'line', 'Low')

options = {
    'plotOptions': {
        'line': {
            'lineWidth': 1
        }
    },
    'rangeSelector': {
        'selected': 1
    },
    'title': {
        'text': '米ドル/円 daily'
    }
}
H.set_dict_options(options)

H


# ## Highstock の SMA を表示する
# 
# chart types の sma に対応していない。
# [highstock_types.py](https://github.com/kyper-data/python-highcharts/blob/developer/highcharts/highstock/highstock_types.py) の `PLOT_OPTION_ALLOWED_ARGS` を見ると sma がない。

# In[8]:


H2 = Highstock()

H2.add_data_set(df6.values.tolist(), 'candlestick', '米ドル/円', id='USDJPY')
#H2.add_data_set(linkedTo='USDJPY', params={ 'index': 3, 'period': 20 }, type='sma')
#KeyError: 'sma'
#TypeError: add_data_set() missing 1 required positional argument: 'data'

options2 = {
#    'plotOptions': {
#        'sma': {
#            'linkedTo': 'USDJPY',
#            'lineWidth': 1
#        }
#    },
    'rangeSelector': {
        'selected': 1
    },
    'title': {
        'text': '米ドル/円 daily'
    }
}
#OptionTypeError: Not An Accepted Option Type: sma
H2.set_dict_options(options2)

H2