Add get forex data

.gitignore

@@ -135,3 +135,13 @@ fastquant_0.0.0.9000.tar.gz
 
 # VSCode Workspace files
 *.code-workspace
+
+# PyCharm Workspace files
+*.idea/
+
+# tmp pickle test files
+*.pickle
+
+# tmp forex data
+/python/fastquant/data/*.txt
+/python/fastquant/data/*.zip

README.md

@@ -8,11 +8,13 @@
 **fastquant** allows you to easily backtest investment strategies with as few as 3 lines of python code. Its goal is to promote data driven investments by making quantitative analysis in finance accessible to everyone.
 
 ## Features
-1. Easily access historical stock data
+1. Easily access historical stock and forex data
 2. Backtest and optimize trading strategies with only 3 lines of code
 
 <sup>`*` - Both Yahoo Finance and Philippine stock data data are accessible straight from fastquant<sup>
 
+<sup>`*` - Support one minute foreign exchange data from Forextester.com and conversion to other time period<sup>
+
 Check out our blog posts in the fastquant [website](https://enzoampil.github.io/fastquant-blog/) and this intro [article](https://towardsdatascience.com/backtest-your-trading-strategy-with-only-3-lines-of-python-3859b4a4ab44?source=friends_link&sk=ec647b6bb43fe322013248fd1d473015) on Medium!
 
 ## Installation
@@ -110,6 +112,39 @@ get_crypto_data("BTCUSDT", "2019-01-01", "2019-03-01")
 
 *R does NOT have support for backtesting yet*
 
+## Get forex data
+
+All symbols from [forextester.com](https://forextester.com/) are accessible via `get_forex_data`.This method fetch one minute data from source and convert it to other time period, M1, M15, H1, D1, W1 are supported time period now.
+
+### Python
+First, call `get_forex_data` with the following parameters:
+```
+from fastquant import get_forex_data
+df = get_forex_data('EURUSD', period='D1', read_from_local=False)
+print(df)
+                      dt    open    high     low   close
+    2001-01-02 23:01:00  0.9507  0.9509  0.9505  0.9507
+    2001-01-03 00:00:00  0.9506  0.9569  0.9262  0.9283
+    2001-01-04 00:00:00  0.9283  0.9536  0.9283  0.9532
+    2001-01-05 00:00:00  0.9532  0.9591  0.9464  0.9583
+    2001-01-07 23:02:00  0.9583  0.9585  0.9576  0.9584
+                  ...     ...     ...     ...     ...
+    2020-08-25 00:00:00  1.1792  1.1841  1.1784  1.1833
+    2020-08-26 00:00:00  1.1833  1.1839  1.1772  1.1839
+    2020-08-27 00:00:00  1.1839  1.1895  1.1767  1.1819
+    2020-08-28 00:00:00  1.1819  1.1918  1.1810  1.1906
+    2020-08-30 22:00:00  1.1906  1.1918  1.1901  1.1916
+[6127 rows x 5 columns]
+```
+this process download online data, transfer it, save cache files in disk, first call will need 20 minutes or more and 2.3GB free RAM to generate cache files.
+
+After then, set `read_from_local=Ture` and you will get a second response if don't need update local data to newest.
+
+### R
+
+```
+pass
+```
 ## Backtest trading strategies
 
 *Note: Support for backtesting in R is pending*

python/fastquant/data/__init__.py

@@ -25,3 +25,6 @@
 # Twitter
 from fastquant.data.web.twitter import tweepy_api
 from fastquant.data.web.twitter import get_twitter_sentiment
+
+# Forex
+from fastquant.data.forex.forex import get_forex_data

python/fastquant/data/forex/forex.py

@@ -0,0 +1,49 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import numpy as np
+
+# Import from config
+from fastquant.config import DATA_FORMAT_COLS
+
+# Import package
+from fastquant.data.forex.forextester import get_forextester_data, get_local_data
+
+
+def get_forex_data(symbol, start_date=None, end_date=None, source="forextester", period='D1', read_from_local=False):
+    """Returns pricing data for a specified forex pair.
+
+    Parameters
+    ----------
+    symbol : str
+        Symbol of the forex in the forextester.
+        https://forextester.com/data/datasources
+    start_date : str
+        Starting date (YYYY-MM-DD) of the period that you want to get data on
+        in most cases we need more wide time period to test strategy, so keep this to None
+    end_date : str
+        Ending date (YYYY-MM-DD) of the period you want to get data on
+        in most cases we need more wide time period to test strategy, so keep this to None
+    source : str
+         Source of forex history data
+    period : str
+         time period you want, support 1 minute,15 minutes,1 hour,1 day,1 week
+         this parameter must one of them:["M1", "M15", "H1", "D1", "W1"]
+    read_from_local : bool
+        if this parameter set False, method get data from online
+        if set it to True, method get data from local pickle file, faster than set it to False
+
+    Returns
+    -------
+    pandas.DataFrame
+    """
+
+    if source == "forextester":
+        if read_from_local is False:
+            df = get_forextester_data(symbol, start_date, end_date, period)
+        else:
+            df = get_local_data(symbol, start_date, end_date, period)
+    else:
+        raise Exception("Source must be forextester")
+
+    return df