HistData.com – Free Forex Historical Data

Forex Factory CSV Data

Scrapped Forex Factory Calendar, because got tired of keeping my algorithms purely technical.
There is data from 2012-01-01. Feel free to use it. The event and its meta data can be matched on the ID field.
https://www.dropbox.com/sh/sh4mw4igpbhggdg/AADwpdCW-o8EmDbLiXlPxDCCa?dl=0
submitted by barumal to algotrading [link] [comments]

Can powershell be used to divide a csv file of an entire month of forex tick data into smaller files of 30 minutes interval?

The format for the tick data is like this:
Date/Time Price
2018/08/01 00:00:00.070 111.832
2018/08/01 00:00:00.078 111.831
Is there any way to split a large csv file containing an entire month of price data into smaller files containing 30 minutes of price data?
submitted by Ifffrt to PowerShell [link] [comments]

Closing Prices to CSV file script for MetaTrader 4 – Free Forex EA Robots

submitted by forexearobots to u/forexearobots [link] [comments]

Forex History Dump CSV script for MetaTrader 4

Forex History Dump CSV script for MetaTrader 4 submitted by forexearobots to u/forexearobots [link] [comments]

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]

I have created a monster.

I have been trading for 3 months (6 months demo before that). Up until 3 days ago I have always traded with discipline, set SL, understood risk management and make reports out of downloadable CSV data from the broker. I even journal each trade at the end of the day. Each trade I make risks from 0.5% - 2% depending on how confident I am on the particular trade. The first 2 months of grind made 5% and 7% respectively.
Several days ago, I lost 3 trades in a row and felt like George Costanza. It was especially demoralizing because I followed the technical, fundamental, trend, and confirmed with indicator, etc... yet, each went straight for my SL. I took the day off and reflected on what I did wrong. I lost 6% of my capital that day, a whole month's work.
The very next day, during the Fed chair Powell speech, I focused on EUUSD, and as the chart started to run higher and higher, I am not sure what came over me, I entered long at 1.18401 and risked 20% of my capital. I was going to enter my usual 2% risk, but the greed (subconsciously?) in me added an extra 0. The very second the trade was entered, I felt a hot flash and my heart started pumping, I entered into loss territory, my heart sunk as I watch it go down 10 pips, 15 pips, if only for 15 seconds. Then it started going up, and it was exhilarating watching the profits. I had the good sense to enter TP at 1.189, and it got there 15 minutes later. I had just made a little over 10% of my capital in 15 minutes. Recovered yesterday's 6% loss and then some.
I told my self that this was a one time thing, stupid and impulsive thing to do... until the next day...
I saw a good opportunity with USD/JPY. I didn't even bother to check anything, technical, fundamental, indicators, NOTHING! Just that vertical cliff short candle... , my god, that full short candle, and the speed! This time, very much a conscious decision, I entered short with 30% of my capital at 106.5. 4 hours later, I hit my TP at 105.5. I had made 30% of my capital in 4 hours.
In the last 2 trading days, up 40% of my capital, including my previous 2 months of measly 12% in comparison, I am roughly up 50% of my original capital in 3 months.
This has been a good week to say the least. But I am afraid I have created an insatiable monster. The greed has overtaken good sense, and this is quite possibly the origin story of a blown account.
submitted by DodoGizmo to Forex [link] [comments]

The best way to export account history to CSV?

Hi,
I was wondering how to export the daily historical Net Liquidating Value of the whole account to CSV? What I get from the Account Statement, is historical Cash and Forex separate from the actual Orders and Trades and the current Net Liquidating Value. Should I go the API route? Thank you!
submitted by astrae_research to thinkorswim [link] [comments]

PSA: I forked a Tampermonkey script to extract your order/product data from AliExpress into a csv list.

Aloha!
Export aliexpress orders to clipboard as csv is a Tampermonkey skript that will:
Like all those scripts this one is still depending on scraping the web page. You have to advance through the order pages manually, grabbing every page and pasting it to editoExcel/Sheets....
It grabs the historic exchange rates from api.exchangeratesapi.io. If you're not a EURo guy/gal, you can just ignore the columns, as the script keeps the original amounts, e.g. in USD.
This is basically a fork of two existing scripts that didn't fulfill my needs (credited in the Readme). I even retained their names.

Hope this can be helpful to you. Let me know what you think and have fun using it!
submitted by ohuf to Aliexpress [link] [comments]

Need good forex data

Hello forex community! I am looking for a place where I can download good high quality forex data by the tick in a .csv format for all the majors from Jan 01 2000 or earlier to Dec 31 2019. I am ok with paying some money but not too much money for such data.
Does any one have any recommendations? Thank you all kindly in advance!
submitted by BogdanovCoding to Forex [link] [comments]

How to optimise the speed of my Pandas code?

Hi learnpython,
My first attempt at writing my own project. Prior to this I had never used classes or Pandas so it's been a difficult learning curve. I was hoping to get some feedback on the overall structure - does everything look sensible? Are there better ways of writing some bits?
I also wanted to specifically check how I can increase the execution speed. I currently iterate rows which Pandas did say will be slow, but I couldn't see a workaround. The fact it is quite slow makes me think there is a better solution that I'm missing.
To run the code yourself download a .csv of Forex data and store in same folder as script - I used Yahoo finance GBP USD.
"""This program simulates a Double SMA (single moving average) trading strategy. The user provides a .csv file containing trade history and two different window sizes for simple moving averages (smallest number first). The .csv must contain date and close columns - trialled on Yahoo FX data). The program will generate a 'buy' signal when the short SMA is greater than the long SMA, and vice versa. The results of each trade are stored and can be output to a .csv file.""" import pandas as pd class DoubleSMA(): """Generates a Double SMA trading system.""" def __init__(self, name, sma_a, sma_b): """Don't know what goes here.""" self.name = name self.sma_a = sma_a self.sma_b = sma_b self.index = 0 self.order = 'Start' self.signal = '' def gen_sma(self, dataset, sma): """Calculates SMA and adds as column to dataset.""" col_title = 'sma' + str(sma) dataset[col_title] = dataset['Close'].rolling(sma).mean() return dataset def gen_signal(self, row, dataset): """Generates trade signal based on comparison of SMAs.""" if row[0] == (dataset.shape[0] - 1): #Reached final line of dataset; close current trade. self.order = 'Finish' elif row[3] > row[4]: self.signal = 'Buy' elif row[3] < row[4]: self.signal = 'Sell' def append_result(row, result, order): """Adds 'entry' details to results dataframe (i.e. opens trade).""" result = result.append({"Entry date": row[1], "Pair": "GBPUSD", "Order": order, "Entry price": row[2]}, ignore_index=True) return result def trade(row, order, signal, index, result): """Executes a buy or sell routine depending on signal. Flips between 'buy' and 'sell' on each trade.""" if order == 'Start': order = signal result = append_result(row, result, order) elif order == 'Finish': result.iloc[index, 1] = row[1] result.iloc[index, 5] = row[2] elif order != signal: #Close current trade result.iloc[index, 1] = row[1] result.iloc[index, 5] = row[2] index += 1 order = signal result = append_result(row, result, order) return order, index, result def result_df(): """Creates a dataframe to store the results of each trade.""" result = pd.DataFrame({"Entry date": [], "Exit date": [], "Pair": [], "Order": [], "Entry price": [], "Exit price": [], "P/L": []}) return result def dataset_df(): """Opens and cleans up the data to be analysed.""" dataset = pd.read_csv('GBPUSD 2003-2020 Yahoo.csv', usecols=['Date', 'Close']) dataset.dropna(inplace=True) dataset['Close'] = dataset['Close'].round(4) return dataset def store_result(result): """Outputs results table to .csv.""" result.to_csv('example.csv') def calc_pl(result): """Calculates the profil/loss of each row of result dataframe.""" pass #Complete later dataset = dataset_df() result = result_df() sma_2_3 = DoubleSMA('sma_2_3', 2, 3) dataset = sma_2_3.gen_sma(dataset, sma_2_3.sma_a) dataset = sma_2_3.gen_sma(dataset, sma_2_3.sma_b) dataset.dropna(inplace=True) dataset.reset_index(inplace=True, drop=True) for row in dataset.itertuples(): sma_2_3.gen_signal(row, dataset) sma_2_3.order, sma_2_3. index, result = trade(row, sma_2_3.order, sma_2_3.signal, sma_2_3.index, result) calc_pl(result) print(result) store_result(result) 
submitted by tbYuQfzB to learnpython [link] [comments]

For those of you interested in the algo trading bot I posted a couple weeks ago, I will be releasing a market watcher only version of it (beta) for free

Hi guys, I know a lot of people expressed interest in the algo trading bot I posted about a few weeks ago: https://www.reddit.com/passive_income/comments/c8ocr5/after_months_of_tweaking_ive_finally_got_my_algo/

I thought about it, and I finally figured out how to release it publicly without it impacting my trading.
For the public version, I'm going to strip out the live trading functionality, and have it be a market-watcher only system. Essentially, you'll be able to configure specific strategies, and have it notify you when that strategy has a buy/sell signal emerge (so you'd have to do the trade manually)

You'll be able to run a backtest on any particular planned trading pair (eg USD-TSLA, BTC-ZEC, CAD-GBP), which will output a CSV sheet with backtested data against every possible strategy ordered by profitability.

You'll then be able to create market watchers (what I dub 'Tipsters', essentially live paper-traders) with the following settings:

These tipsters will then watch the market live, and simulate trading/keep track of simulated profitability.
App will have the following features:

There's a couple other 'surprises' I plan on having in the app too, but I'll keep those a secret for now (mostly hilarious stuff, but may wind up being useful).


So the question is, would you make use of something like this? Would you find it useful?
submitted by MrGruntsworthy to passive_income [link] [comments]

How to assemble lots of csv files into a master workbook for Power Query?

Hello excel
I have a web scrapper collecting data into a csv. The csv file is named with the day’s date that the data came from and so is the sheet inside it that holds the data. Here is what that looks like -> I have been collecting data for 14 days, hence I have 14 csv files.
What’s in the csv file? Size in column A, price in column B, and a timestamp (h:mm:ss.000) in column C. I am collecting trade data from the USD-AUD Forex market.

A (Size) B (Price) C (Time)
1 500 $1.48 18:00:37.564
2 1200 $1.47 18:01:45.123
This is oversimplified, as there are tens of thousands of rows sometimes. I would like to run analysis on this data, I am curious as to the best practice for organizing this data for analyzation via power query and power pivot?
Current method is creating a master xlsx file, then I open up each csv file one by one and use the ‘move/copy’ to create a copy in this master workbook. Now that I have all the data sorted in sheets that are labeled with the dates they came from, I am wondering if I am doing this the best way or if I should be loading them in through power query or something. I am fairly new to power query and power pivot but I learn very quick.
Would a database be a better idea for this data? Each market generates around 30-80k trades on an average day, however that can approach 200k to 400k rows when there is volatility.
I welcome any analytical advice you might have, especially if you're a trader. I am attempting to look at the coupling/decoupling of several markets, this being one, as a potential tool for trading targets and am open to any suggestions on how analyze this.
submitted by LavishManatee to excel [link] [comments]

Moving python webscrapper to the cloud....how?

Hello aws,
I have a python script, several actually, pulling from several pages on the same main site.
I am pulling forex data, 24/7, for a stats capstone project and the values are being logged to a .csv file. The script keeps the .csv it is logging too locked until 23:59:59, where it closes out that day's .csv and then opens a new one for the new day logging. Had a power outage here and since I am capturing this data from my house, that was the end for my data capture.
How would I go about putting this application on the cloud to avoid this problem? Also, this program runs on windows now, I would like to move it over to a raspberry pi. How would I go about doing that? Would I be able to use the Windows 10 IOT or should I use linux? What ways would I need to adapt this python script to work on linux?
Any recommendations are much appreciated on both fronts, thank you!
submitted by LavishManatee to aws [link] [comments]

Help setting up semi automated system

I am after a trading platform that can do the following
At market open: buy Stop loss = buyx(constant) Sell = buyy(constant) OR close order at: market open
submitted by peachesxxxx to Forex [link] [comments]

Dukascopy/Tickstory forex volume data is not trading volume

Getting Dukascopy / Tickstory forex data (I think the most famous free source of forex tick data), I noticed that the tick data csv has "bid volume" and "ask volume" columns.
Getting bar data for them, the "volume" column is just the sum of the "bid volume" of all ticks in the bar.

The way I understand the tick data, "bid volume" and "ask volume" are not real trading volumes, but rather the quantities in the top level of the order book (highest bid and lowest ask). If this is true, the "volume" data column in the bar data is very misleading, and this very famous and widely used data source does not contain 1) actual trading prices 2) trading volume.
Am I missing something? Dukascopy data can be obtained here: https://www.dukascopy.com/plugins/fxMarketWatch/?historical_data
submitted by cruvadom to algotrading [link] [comments]

Big Data Set for Crypto Backtesting

I have a huge data set from the past couple of months with the price of cryptocurrencies off of CoinMarketCap as well as Forex prices off of XRates. The CoinMarketCap data is every five minutes and the XRates data is every minute. I collected data between August and November. The data is in csv format. I want to share all this data. Is there a good place I can share it? Thanks for the help!
submitted by Dotherightthing253 to algotrading [link] [comments]

Eyes to today U.S. Retail Sales data

Eyes to today U.S. Retail Sales data

https://preview.redd.it/qkj8eltoy1531.png?width=1366&format=png&auto=webp&s=83da8b2c97f5d408f4357f71cf5c6931f6e56c02
Weekly Trend: Overbought
1st Resistance: 1. 1268
2nd Resistance: 1.1370
1st Support: 1.1100
2nd Support: 1.098
Analysis by SGT Markets
Full review of SGT: https://www.topasiafx.com/best-forex-brokesgt-markets
#Forex #Analysis #ForexBroker #EURUSD #GBPUSD #AUDUSD
submitted by ronykhanfx to TopAsiaFX [link] [comments]

code check: First time with classes

Ive done a few small projects but this is the first project that I've made that uses classes. So far the class should take in the location of the csv file, then open is and split it up into each element going on a table. This is all supposed to be handling simple forex data.
if you see any issues please let me know. the compiler I'm using throws no errors rn.

 import csv class fxData: """ takes in the location of the raw data as a string creates an object with attribute rawData is the instance of a csv file on run time """ def __init__(self,rawData): raw=open(rawData,"r") listOfStrings = raw.read() splitlist=listOfStrings.split("\n") self.fxList=[] for row in splitlist: d = row.split(",") #makes it seperate elements if d == [""]:#checks for the end of list break date = d[0] #date t = d[1] #time o = float(d[2]) #open h = float(d[3]) #high l = float(d[4]) #low c = float(d[5]) #close row_data = [date,t,o,h,l,c] self.fxList.append(row_data) """ creates a list of a table for the time period data """ def rawTimeData(self,fxList,timeperiod,row): returnData = self.fxList[row:row+timeperiod] return returnData; #creates simple moving aerage for that one element def sma(self,returnData): counter=0 total = 0 for x in returnData: counter +=1 total=returnData[5] + total average = total/counter return average 

submitted by nomoreerrorsplox to learnpython [link] [comments]

Daily Trading Thread - Friday 2.9.18

Hi everyone! Thanks for joining. This sub is for active traders of crypto and stocks, those looking to make a fat YUGE profit. While all are welcome, we are more geared for traders with a serious mindset. Post your ideas for today here.
Follow us on StockTwits and chat live on our Discord: trader chat.
Wiki: resources
FINVIZ HEATMAP - FINVIZ FUTURES - FOREX - NEWS FEED
FEB 9th FRI Fear & Greed Index
Economic Calendar: Results & More
Time Release For Actual Expected Prior
10:00:00 AM Wholesale Inventories Dec - 0.2% 0.2%
Ex-Dividend: Calendar
Ex- Div Company Amt Yield
AAPL Apple Rg 0.63 0.02
ANCX Access Natl Rg 0.15 0.02
BGSF BG Staffing Rg 0.25 0.06
CDR Cedar Real Trt R Rg 0.05 0.08
COF Capital One Finl Rg 0.40 0.02
COL Rockwell Collins Rg 0.33 0.01
COP ConocoPhillips Rg 0.29 0.02
CSV Carriage Service Rg 0.08 0.01
CWT Cal Water Serv G Rg 0.19 0.02
FIBK 1st Intst Banc Rg-A 0.28 0.02
GORO Gold Resource Rg 0.00 0.01
GWW WW Grainger Rg 1.28 0.02
HP Helmerich&Payne Rg 0.70 0.04
IBTX Independent Bnk Rg 0.12 0.01
LAZ Lazard Rg-A 1.71 0.03
LOGM LogMeIn Rg 0.30 0.01
MRLN Marlin Business Rg 0.14 0.02
NATI Natl Instruments Rg 0.23 0.02
NBL Noble Energy Rg 0.10 0.01
OA Orbital ATK Rg 0.32 0.01
OPY Oppenheim NVtg Rg-A 0.11 0.00
OSK Oshkosh Rg 0.24 0.01
PAG Penske Auto Grou Rg 0.34 0.03
PZZA Papa Johns Intl Rg 0.23 0.01
ROSE Rosetta Resources 0.10 0.00
SC Santander USA Rg 0.05 0.00
SCHN SCHNITZER STEEL IND 0.19 0.02
SJW SJW Group 0.28 0.02
SONA Southern Ntl Bancor - Registered 0.08 0.02
WMK Weis Markets Rg 0.30 0.03
XOM Exxon Mobil Rg 0.77 0.04
Earnings Reports: Morningstar Earnings Calendar & Results
Company Release Est. EPS
Applied Genetic Technologies (AGTC) Afternoon -0.09
Brookfield Infrastructure Partners (BIP) Morning 0.56
Buckeye Partners (BPL) Morning 0.88
CAE (CAE) Morning 0.22
Cameco (CCJ) Morning 0.29
Cboe Global Markets (CBOE) Morning 0.88
Essent Group (ESNT) Morning 0.78
Gorman-Rupp (GRC) Morning 0.31
ImmunoGen (IMGN) Morning -0.10
Malibu Boats (MBUU) Morning 0.47
Moody's (MCO) Morning 1.45
Motorcar Parts of America (MPAA) Morning 0.49
Newmark Group (NMRK) Morning 0.30
Newpark Resources (NR) Afternoon 0.03
NGL Energy Partners (NGL) Morning 0.19
Oaktree Strategic Income (OCSI) Morning 0.20
PG&E (PCG) Morning 0.73
Semiconductor Manufacturing Int'l (SMI) Morning N/A
Tenneco (TEN) Morning 1.64
Ventas (VTR) Morning 1.03
Xinyuan Real Estate (XIN) Morning N/A
PRE-MARKET MOVERS: $PIRS $FEYE $NVDA $SOXL $DGAZ $NWL $XIV $VALE $YANG $LABU $STM $SQ $AIG $RIG $QLD $FAS $TECL $CLF
ROCKET BOT - FINVIZ TOP GAINERS - FINVIZ TOP LOSERS
Crypto Watch List: BTC XRP ETH LTC XVG XRB GAS NEO WTC PPT SALT FUN OMG ICX ETC STEEM POE EOS SC ZCL XLM LEND VEN
COIN MARKET CAP - COINDESK NEWS - RISING/FALLING
Disclaimer: The opinions in this thread and forum are solely the opinions of the individual account holders and contributors. The info should not be regarded as investment advice or as a recommendation of any particular security. All investments entail risks. As with most things in life, caveat emptor.
submitted by theprofitgod to The_Profit [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]

Who are the best data providers for forex sentiment data? Paid or free

I'm looking for data sources that provide sentiment data for forex mainly in a csv,xml or API format. I need the raw data, not just pre-made interactive charts.
Here are some of the current sources I've found - FCMX - $1000/month for unlimited access. $1000 for 6 months of historic data. https://www.fxsentimentmarket.com/ - Data looks pretty cheap which is nice but makes me weary of the quality.
Thanks!
submitted by bbennett36 to Forex [link] [comments]

What's the fastest and easiest way to turn my data into timeseries data?

Hi, complete beginner here. I have a question on how to prepare my data for ML.
I'm trying to turn forex tick data (from 10000 plus separate CSV files) formated like below:
Datetime Price
20180823 13:35:44.617 110.979
20180823 13:35:45.818 110.9
20180823 13:35:45.833 110.98
20180823 13:35:45.908 110.989
...into timeseries data.
My question is, what is the easiest way to do this by a complete beginner?
EDIT: I forgot to mention that I need the time series data to have uniform time intervals, with each file being a unique record in the dataset.
submitted by Ifffrt to learnmachinelearning [link] [comments]

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MetaTrader 5 Data Downloader - YouTube

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