Forex Data

Forex Data

Posted By : Webtunix Solution

Forex data is data that is collected from the Forex market. It provides insight on what is changing in the market, where money can be made, as well as at risk of being lost. The changing of currencies and stocks also determines the exchange rate between currencies.

Time Series Analysis using ARIMA Model

In [84]:

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
df = pd.read_csv('/home/webtunix/Downloads/EURUSD=X.csv')
df

Out[84]:

  Date Open High Low Close Adj Close Volume
0 2020-07-17 1.138650 1.144165 1.137889 1.139212 1.139212 0
1 2020-07-20 1.143955 1.146789 1.140524 1.144296 1.144296 0
2 2020-07-21 1.145764 1.149801 1.142622 1.145869 1.145869 0
3 2020-07-22 1.153509 1.160093 1.150854 1.153403 1.153403 0
4 2020-07-23 1.156671 1.162426 1.154215 1.156872 1.156872 0
... ... ... ... ... ... ... ...
256 2021-07-12 1.187366 1.188213 1.183670 1.187296 1.187296 0
257 2021-07-13 1.186493 1.187790 1.179343 1.186521 1.186521 0
258 2021-07-14 1.177440 1.182801 1.177260 1.177537 1.177537 0
259 2021-07-15 1.183334 1.185115 1.180596 1.183334 1.183334 0
260 2021-07-16 1.181307 1.182300 1.179400 1.181181 1.181181 0

I261 rows × 7 columns

n [85]:

from pandas import datetime
df['Date'] = pd.to_datetime(df.Date,format='%Y-%m-%d')
df.index = df['Date']
plt.figure(figsize=(16,8))
plt.plot(df['Close'], label='Close Price history')

Out[85]:

[<matplotlib.lines.Line2D at 0x7f886b129828>]

ARIMA Model

In [86]:

from pandas import datetime
from pandas.plotting import autocorrelation_plot
def parser(x):
return pd.to_datetime(df.Date,format='%Y-%m-%d')
df = pd.read_csv('/home/webtunix/Downloads/EURUSD=X.csv', header=0, parse_dates=[0], index_col=0, date_parser=parser)
autocorrelation_plot(df)
plt.plot()

Out[86]:

[]

In [4]:

print(df.head())
df.plot()
plt.show()
                Open      High       Low     Close  Adj Close  Volume
Date                                                                 
2020-07-17  1.138650  1.144165  1.137889  1.139212   1.139212       0
2020-07-20  1.143955  1.146789  1.140524  1.144296   1.144296       0
2020-07-21  1.145764  1.149801  1.142622  1.145869   1.145869       0
2020-07-22  1.153509  1.160093  1.150854  1.153403   1.153403       0
2020-07-23  1.156671  1.162426  1.154215  1.156872   1.156872       0

In [64]:

 

import matplotlib.dates as mdates
%matplotlib inline
import seaborn as sns
my_year_month_fmt = mdates.DateFormatter('%m/%y')
data = pd.read_csv('/home/webtunix/Downloads/EURUSD=X.csv')
data.head(10)

 

Out[64]:

  Date Open High Low Close Adj Close Volume
0 2020-07-17 1.138650 1.144165 1.137889 1.139212 1.139212 0
1 2020-07-20 1.143955 1.146789 1.140524 1.144296 1.144296 0
2 2020-07-21 1.145764 1.149801 1.142622 1.145869 1.145869 0
3 2020-07-22 1.153509 1.160093 1.150854 1.153403 1.153403 0
4 2020-07-23 1.156671 1.162426 1.154215 1.156872 1.156872 0
5 2020-07-24 1.159501 1.164009 1.158171 1.159608 1.159608 0
6 2020-07-27 1.165257 1.177953 1.165257 1.165257 1.165257 0
7 2020-07-28 1.176651 1.177579 1.170100 1.176928 1.176928 0
8 2020-07-29 1.172058 1.177899 1.171495 1.171880 1.171880 0
9 2020-07-30 1.178689 1.180735 1.173268 1.178287 1.178287 0

In [65]: Moving Average

short_rolling = data.rolling(window=20).mean()
short_rolling.head(20)

 

Out[65]:

  Open High Low Close Adj Close Volume
0 NaN NaN NaN NaN NaN NaN
1 NaN NaN NaN NaN NaN NaN
2 NaN NaN NaN NaN NaN NaN
3 NaN NaN NaN NaN NaN NaN
4 NaN NaN NaN NaN NaN NaN
5 NaN NaN NaN NaN NaN NaN
6 NaN NaN NaN NaN NaN NaN
7 NaN NaN NaN NaN NaN NaN
8 NaN NaN NaN NaN NaN NaN
9 NaN NaN NaN NaN NaN NaN
10 NaN NaN NaN NaN NaN NaN
11 NaN NaN NaN NaN NaN NaN
12 NaN NaN NaN NaN NaN NaN
13 NaN NaN NaN NaN NaN NaN
14 NaN NaN NaN NaN NaN NaN
15 NaN NaN NaN NaN NaN NaN
16 NaN NaN NaN NaN NaN NaN
17 NaN NaN NaN NaN NaN NaN
18 NaN NaN NaN NaN NaN NaN
19 1.169727 1.174497 1.166116 1.169748 1.169748 0.0

 

In [8]:

# Calculating the short-window simple moving average
long_rolling = data.rolling(window=100).mean()
long_rolling.tail()

Out[8]:

  Open High Low Close Adj Close Volume
256 1.200753 1.203508 1.197616 1.200819 1.200819 0.0
257 1.200451 1.203207 1.197272 1.200517 1.200517 0.0
258 1.200073 1.202861 1.196934 1.200141 1.200141 0.0
259 1.199737 1.202470 1.196583 1.199803 1.199803 0.0
260 1.199390 1.202111 1.196285 1.199454 1.199454 0.0

In [66]:

data['Close'].plot(grid=True,figsize=(8,5))

Out[66]: <AxesSubplot:>

In [67]:

data['42d'] = np.round(data['Close'].rolling(window=42).mean(),2)
data['252d'] = np.round(data['Close'].rolling(window=252).mean(),2)
data.tail

Out[67]:

<bound method NDFrame.tail of            Date      Open  
    High       Low     
0    2020-07-17  1.138650  1.144165  1.137889  1.139212   
1.139212       0   
1    2020-07-20  1.143955  1.146789  1.140524  1.144296   
1.144296       0   
2    2020-07-21  1.145764  1.149801  1.142622  1.145869  
 1.145869       0   
3    2020-07-22  1.153509  1.160093  1.150854  1.153403   
1.153403       0   
4    2020-07-23  1.156671  1.162426  1.154215  1.156872   
1.156872       0   
..          ...       ...       ...       ...       ...   
     ...     ...   
256  2021-07-12  1.187366  1.188213  1.183670  1.187296   
1.187296       0   
257  2021-07-13  1.186493  1.187790  1.179343  1.186521  
 1.186521       0   
258  2021-07-14  1.177440  1.182801  1.177260  1.177537  
 1.177537       0   
259  2021-07-15  1.183334  1.185115  1.180596  1.183334   
1.183334       0   
260  2021-07-16  1.181307  1.182300  1.179400  1.181181   
1.181181       0   

Close Adj Close Volume \
      42d  252d  
0     NaN   NaN  
1     NaN   NaN  
2     NaN   NaN  
3     NaN   NaN  
4     NaN   NaN  
..    ...   ...  
256  1.21   1.2  
257  1.20   1.2  
258  1.20   1.2  
259  1.20   1.2  
260  1.20   1.2  

[261 rows x 9 columns]>

In [68]:

data[['Close','42d','252d']].plot(grid=True,figsize=(8,5))

Out[68]:

<AxesSubplot:>

In [70]:

data['42-252'] = data['42d'] - data['252d']
X = 50
data['Stance'] = np.where(data['42-252'] > X, 1, 0)
data['Stance'] = np.where(data['42-252'] < -X, -1, data['Stance'])
data['Stance'].value_counts()

Out[70]:

0    261
Name: Stance, dtype: int64

In [78]:

data['Stance'].plot()

Out[78]:

<AxesSubplot:>

In [88]:

data['Market Returns'] = np.log(data['Close'] / data['Close'].shift(1))
data['Strategy'] = data['Market Returns'] * data['Stance'].shift(1)
data[['Market Returns','Strategy']].cumsum().plot(grid=True,figsize=(8,5))

Out[88]:

<AxesSubplot:>

In [80]:

data["diff"] = data["42d"] - data["252d"]
data[["Close", "diff"]].plot(subplots=True, figsize=(8, 5))

Out[80]:

array([<AxesSubplot:>, <AxesSubplot:>]
, dtype=object)

In [81]:

sigdiff = 100.0
data["Signal"] = np.where(data["diff"]>sigdiff,1,0)
data["Signal"] = np.where(data["diff"]<-sigdiff,-1,0)
data[["Close","diff","Signal"]].plot(subplots=True, figsize=(8,8))

Out[81]:

array([<AxesSubplot:>, <AxesSubplot:>, 
<AxesSubplot:>], dtype=object)

Log Returns

In [82]:

data["Returns"] = np.log(data["Close"] / data["Close"].shift(1))
data["Strategy"] = (data["Signal"] * data["Returns"])
data["Earnings"] = data["Strategy"].cumsum()
data[["Close", "Signal", "Earnings"]].plot(subplots=True, figsize=(10, 8))

Out[82]:

array([<AxesSubplot:>, <AxesSubplot:>, 
<AxesSubplot:>], dtype=object)

I used ARIMA Model to analyze and forecast the data. I calculated moving

average and logarithimc returns,autocorrealtion function and signal generation.

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