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From tick to candlestick

I have tick data for a Forex pair

Here is an example EURUSD/EURUSD-2012-06.csv

EUR/USD,20120601 00:00:00.207,1.23618,1.2363
EUR/USD,20120601 00:00:00.209,1.23618,1.23631
EUR/USD,20120601 00:00:00.210,1.23618,1.23631
EUR/USD,20120601 00:00:00.211,1.23623,1.23631
EUR/USD,20120601 00:00:00.240,1.23623,1.23627
EUR/USD,20120601 00:00:00.423,1.23622,1.23627
EUR/USD,20120601 00:00:00.457,1.2362,1.23626
EUR/USD,20120601 00:00:01.537,1.2362,1.23625
EUR/USD,20120601 00:00:03.010,1.2362,1.23624
EUR/USD,20120601 00:00:03.012,1.2362,1.23625

Full tick data can be downloaded here http://dl.free.fr/k4vVF7aOD

Columns:

Symbol,Datetime,Bid,Ask

I would like to convert this tick from tick data into candlestick data (also called OHLC Open High Low Close Close) I will say that I want to get the M15 timeline (15 minutes) as an example

I would like to use the Python and Pandas libraries to achieve this.

I did a small part of the work ... by reading the tick on the tick data file

Here is the code

#!/usr/bin/env python

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.finance import candlestick
from datetime import *

def conv_str_to_datetime(x):
    return(datetime.strptime(x, '%Y%m%d %H:%M:%S.%f'))

df = pd.read_csv('test_EURUSD/EURUSD-2012-07.csv', names=['Symbol', 'Date_Time', 'Bid', 'Ask'], converters={'Date_Time': conv_str_to_datetime})

PipPosition = 4
df['Spread'] = (df['Ask'] - df['Bid']) * 10**PipPosition

print(df)

print("="*10)

print(df.ix[0])

but now I don’t know how to start the rest of the work ...

I want to get data like

Symbol,Datetime_open_candle,open_price,high_price,low_price,close_price

The price of a candle will be based on the Bid column.

, Datetime_open_candle ( , , dt1) Datetime_open_candle (, dt2).

, , , dt1 dt2 ( dt1 dt2)

dt1 dt2 , , ...

"" , , open/high/low/close.

, , ( ), .

+5
1
In [59]: df
Out[59]:
                             Symbol      Bid      Ask
Datetime
2012-06-01 00:00:00.207000  EUR/USD  1.23618  1.23630
2012-06-01 00:00:00.209000  EUR/USD  1.23618  1.23631
2012-06-01 00:00:00.210000  EUR/USD  1.23618  1.23631
2012-06-01 00:00:00.211000  EUR/USD  1.23623  1.23631
2012-06-01 00:00:00.240000  EUR/USD  1.23623  1.23627
2012-06-01 00:00:00.423000  EUR/USD  1.23622  1.23627
2012-06-01 00:00:00.457000  EUR/USD  1.23620  1.23626
2012-06-01 00:00:01.537000  EUR/USD  1.23620  1.23625
2012-06-01 00:00:03.010000  EUR/USD  1.23620  1.23624
2012-06-01 00:00:03.012000  EUR/USD  1.23620  1.23625

In [60]: grouped = df.groupby('Symbol')

In [61]: ask =  grouped['Ask'].resample('15Min', how='ohlc')

In [62]: bid = grouped['Bid'].resample('15Min', how='ohlc')

In [63]: pandas.concat([ask, bid], axis=1, keys=['Ask', 'Bid'])
Out[63]:
                                Ask                                 Bid
                               open     high      low    close     open     high      low   close
Symbol  Datetime
EUR/USD 2012-06-01 00:15:00  1.2363  1.23631  1.23624  1.23625  1.23618  1.23623  1.23618  1.2362
+11

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