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Releasing a Decade of Forex Tick Data I Crawled and Converted

Releasing a Decade of Forex Tick Data I Crawled and Converted

Intro:

In my exploration of the world of big data and I became curious about tick data. Unfortunately, market data is almost always behind a paywall or de-sampled to the point of uselessness. After discovering the Dukascopy API, I knew I wanted to make this data available for all in a more accessible format. Over the course of a few months, I downloaded, cleaned, parsed, and compressed over a decade of Forex tick data on 37 currency pairs and commodities. Today I am happy to finally release the final result of my work to the DataHoarder community!

Download Links:

Warning: I have rented a seedbox for the next 3 months from seedbox.io but I have been having some issues. If you have any issues with the torrent please leave a comment. Also, PLEASE SEED when you are done. This is quite a large data set and I can only push so much data on my own.
Torrent File: https://drive.google.com/file/d/18ymZWeFLJK7FggK_iiWZ-TxgWIVdJVvv/view?usp=sharingCompanion Blog Post: https://www.driftinginrecursion.com/post/dukascopy_opensource_data/

Stats Overview:

Totals Quantities
Total Files 463
Total Line Count 8,495,770,706
Total Data Points 33,983,082,824
Total Decompressed Size 501 GB
Total Compressed Size 61 GB

About the Data:

The data was collected from https://www.dukascopy.com/ via a public API that allows for the download of tick data on the hour level. These files come in the form of a .bi5 file. The data starts as early as 2004 all the way to 2019.
These files were decompressed, then merged into yearly CSV’s named in the following convention. “AUDCHF_tick_UTC+0_00_2011.csv” or ‘Pair_Resolution_Timezone_Year.csv’
These CSV’s are split into 3 categories “Majors”, “Crosses”, “Commodities”.
Majors, Crosses, and Commodities have had their timestamps modified so that they are in the official UTC ISO standard. This was originally done for a Postgresql database that quickly became obsolesced. Any files that have been modified are appended with a “-Parse”. These timestamps have been modified in the following format.
Millisecond timestamps to UTC +00:00 time [2017.01.01 22:37:08.014] -- [2017-01-01T22:37:08.014+00:00]
https://preview.redd.it/x6g277skfiu51.png?width=1399&format=png&auto=webp&s=35cd6735c1826424580919ac3377612377a3107c

User Resources:

For those looking to use this data in a live context or update it frequently, I have included a number of tools for both Windows and Linux that will be useful.

Windows

The ~/dukascopy/resources/windows folder contains a third party tool written in java that can download and convert Dukascopy’s .bi5 files. I have also included the latest zstd binaries from Zstandard Github page.

Linux

Linux is my daily driver in 99% of cases, so I have developed all my scraping tools using Linux only tools. In the ~/dukascopy/resources/linux folder you will find a number of shell script and pyhton3 files that I used to collect this data. There are quite a few files in this directory but I will cover the core ones below.

download-day.py:

This file is used to download a single symbol for a single day and then convert and merge all 24 .bi5 files into a single CSV.

download-year.py

This file is used to download a single symbol for a full year and then convert and merge all .bi5 files into a single CSV.

dukascopy.py

This file contains all the core logic for downloading and converting data from dukascopy.

utc-timestamp-convert.py

This tad slow but works well enough. It requires the pandas project and parses timestamps into the UTC ISO standard. This is useful for those looking to maintain the format of new files with the those in this repo, or those looking to use this in a SQL database.
submitted by jtimperio to DataHoarder [link] [comments]

How-to & FAQ for holding Bitcoin and Ethereum in an RRSP/TFSA

A few months ago I came across a way to hold crypto in my RRSP/TFSA and have been answering questions about how to do that in comments, DM's, and Skype consults. I figured it would be helpful to put together one big comprehensive FAQ. Cryptocurrency is treated as a commodity by the CRA and you must pay capital gains taxes on any profits if held outside a TFSA. If bitcoin goes to $1m as some are predicting, the Canadian government is going to be taxing a huge windfall in capital gains taxes.
BACKGROUND
Bitcoin & other crypto cannot be held directly in a RRSP/TFSA, and there are no eligible ETF's in North America yet. However, the ETN COINXBT which trades on the Stockholm Stock Exchange in Sweden (Nasdaq Stockholm) is eligible.
ABOUT COINXBT
COINXBT holds bitcoin directly and its price per share is based on a 0.005 multiple of the current bitcoin price.
For example, if the current price of bitcoin is $10000USD, a share of COINXBT will be worth $50USD (ie: $493 Swedish Kroner)
Company's website and full prospectus at: https://xbtprovider.com/
Price quote / chart: https://www.bloomberg.com/quote/COINXBT:SS
HOW-TO TRADE
Only some Canadian brokerages allow you to trade on eligible international exchanges in your TFSA. Some do not.
Typically placing trades on international exchanges online is not an option and must be made over the phone broker-assisted at a much higher cost than typical North American securities.
CANADIAN BROKERS
I've called pretty much every brokerage to inquire if international securities can be held in a TFSA and what the fee is to transact. You may want to call yourself to see if policies have changed, but here's a summary:
Not available, or not available in RRSP/TFSA:
FAQ's
Are you sure it's legal? I'm quite sure it's illegal.
Who is your broker? CAD account?
How do I make a trade once I'm ready?
How do I calculate the number of shares to trade to max out my TFSA?
Market or Limit order?
When can I trade? Is it only possible to make the trade while the Swedish market is open and the TSX are open at the same time? Or can you place the order at any time of day?
Which number did you use to contact the brokerages?
What happens when there is a fork?
What about other cryptocurrencies?
How is the price of COINXBT determined?
Why not just buy GBTC?
Why can't I place a trade online myself?
Feedback
If you've managed to get crypto into your RRSP/TFSA in any other ways than listed above please do leave a comment and I'll update the post. Thanks!
submitted by Bastiat to BitcoinCA [link] [comments]

Using Python and Pandas to explore trader sentiment data

FXCM’s Speculative Sentiment Index (SSI) focuses on buyers and sellers, comparing how many are active in the market and producing a ratio to indicate how traders are behaving in relation to a particular currency pair. A positive SSI ratio indicates more buyers are in the market than sellers, while a negative SSI ratio indicates that more sellers are in the market. FXCM’s sentiment data was designed around this index, providing 12 sentiment measurements per minute (click here for an overview of each measurement.)
The sample data is stored in a GNU compressed zip file on FXCM’s GitHub as https://sampledata.fxcorporate.com/sentiment/{instrument}.csv.gz. To download the file, we’ll use this URL, but change {instrument} to the instrument of our choice. For this example we’ll use EUUSD price.
import datetime import pandas as pd url = 'https://sampledata.fxcorporate.com/sentiment/EURUSD.csv.gz' data = pd.read_csv(url, compression='gzip', index_col='DateTime', parse_dates=True) """Convert data into GMT to match the price data we will download later""" import pytz data = data.tz_localize(pytz.timezone('US/Eastern')) data = data.tz_convert(pytz.timezone('GMT')) """Use pivot method to pivot Name rows into columns""" sentiment_pvt = data.tz_localize(None).pivot(columns='Name', values='Value') 
Now that we have downloaded sentiment data, it would be helpful to have the price data for the same instrument over the same period for analysis. Note the sentiment data is in 1-minute increments, so I will need to pull 1-minute EURUSD candles. We could pull this data into a DataFrame quickly and easily using fxcmpy, however the limit of the number of candles we can pull using fxcmpy is 10,000, which is fewer than the number of 1-minute candles in January 2018. Instead, we can download the candles in 1-week packages from FXCM’s GitHub and create a loop to compile them into a DataFrame. This sounds like a lot of work, but really it’s only a few lines of code. Similarly to the sentiment data, historical candle data is stored in GNU zip files which can be called by their URL.
url = 'https://candledata.fxcorporate.com/' periodicity='m1' ##periodicity, can be m1, H1, D1 url_suffix = '.csv.gz' symbol = 'EURUSD' start_dt = datetime.date(2018,1,2)##select start date end_dt = datetime.date(2018,2,1)##select end date start_wk = start_dt.isocalendar()[1] end_wk = end_dt.isocalendar()[1] year = str(start_dt.isocalendar()[0]) data=pd.DataFrame() for i in range(start_wk, end_wk+1): url_data = url + periodicity + '/' + symbol + '/' + year + '/' + str(i) + url_suffix print(url_data) tempdata = pd.read_csv(url_data, compression='gzip', index_col='DateTime', parse_dates=True) data=pd.concat([data, tempdata]) """Combine price and sentiment data""" frames = data['AskClose'], sentiment_pvt.tz_localize(None) combineddf = pd.concat(frames, axis=1, join_axes=[sentiment_pvt.tz_localize(None).index], ignore_index=False).dropna() combineddf 
At this point you can begin your exploratory data analysis. We started by viewing the descriptive statistics of the data, creating a heatmap of the correlation matrix, and plotting a histogram of the data to view its distribution. View this articleto see our sample code and the results.
submitted by JasonRogers to AlgoTradingFXCM [link] [comments]

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