How to find Forex historical data that will help you ...

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]

Dukascopy forex data

I've been trying to get data from the Dukascopy forex historicals for quite some time now, and I'd like to summarize what I've done so far, and what I still need, in order to help anyone else that also wants to use it.
First, just downloading the data is a pain. The URL that you have to get it from is
 https://datafeed.dukascopy.com/datafeed/{PAIR}/{YEAR}/{MONTH}/{DAY}/{HOUR}h_ticks.bi5 {PAIR} is the currency pair, for example "AUDUSD", "EURUSD", or "USDJPY" {YEAR} is the year, for example "2010", "2014", or "2017" {MONTH} is the month, a two digit number. For some reason, months are zero-indexed. For example, "00" corresponds to January, "05" is June, "11" is December. {DAY} is the day of the month, and as far as I can tell, it is NOT zero-indexed. Again, it is two digits wide. {HOUR} is the hour of the day. For some reason, Dukascopy stores each hour of the day separately. It is zero-indexed, so "00" to "23" 
Now that you have a *.bi5 file, you have to extract it. *.bi5 files are lzma compressed files, so find a way to extract them. I used 7z command line.
Now once you've extracted it, you'll notice it's still a binary file. The data is stored in 20 byte wide rows, with each 4 byte segment corresponding to a piece of data. Example:
[ TIME ] [ ASKP ] [ BIDP ] [ ASKV ] [ BIDV ] 0000 0800 0002 2f51 0002 2f47 4096 6666 4013 3333 TIME is a 32-bit big-endian integer representing the number of milliseconds that have passed since the beginning of this hour. ASKP is a 32-bit big-endian integer representing the asking price of the pair, multiplied by 100,000. BIDP is a 32-bit big-endian integer representing the bidding price of the pair, multiplied by 100,000. ASKV is a 32-bit big-endian floating point number representing the asking volume, divided by 1,000,000. BIDV is a 32-bit big-endian floating point number representing the bidding volume, divided by 1,000,000. 
This is how far I've gotten so far before I noticed that something is wrong. The contents of the *.bi5 file do not match the contents of the file that you can download from the official front-end, here: https://www.dukascopy.com/swiss/english/marketwatch/historical/ .
For example, the January 8, 2010 *.csv file does not match in any way with the *.bi5 file of the corresponding day. Does anyone know what I am doing wrong?
EDIT: Another question is about the hours: what time zone are these files relative to? It seems that the data starts showing up from the last two hours of Sunday, going through the week, and then stopping some time before Friday ends, all relative to whatever timezone this is in.
submitted by Allurisk to algotrading [link] [comments]

Forex Sentiment Data Overview, it's Application in Algo trading, and Free Sample Data

From Commitment of Traders (COT) to the Daily Sentiment Index (DSI), to the Put/Call ratio and more, sentiment data has long been highly sought after by both professional and retail traders in the mission to get an edge in the market. Equity and futures traders can access this market data relatively easily due to the centralization of the market they are trading.

But what about Forex traders? There is no single centralized exchange for the Foreign Exchange market therefore sentiment data is difficult to obtain and can be extremely pricey for Forex traders. Furthermore, if a trader had access to such data, the sample set may be limited and not closely reflect the actual market.

In order for Forex sentiment data to be valuable, the data must be derived from a large, far reaching sample of Forex traders. FXCM boasts important Forex trading volumes and a significant trader sample and the broker’s large sample size is one of the most representative samples of the entire retail Forex market. Therefore, the data can be used to help predict movement of the rate of an instrument in the overall market.

This sentiment data shows the retail trader positioning and is derived from the buyer-to-seller ratio among retail FXCM traders. At a glance, you can see historical and current trader positioning in the market. A positive ratio indicates there are more traders that are long for every trader that is short. A negative ratio is indicative of a higher number of traders that are short for every long trader. For example, a ratio of 2.5 would mean that there are 2.5 traders that are long for every short trader and -2.5 would mean just the opposite.

When it comes to algo trading, sentiment can be used as a contrarian indicator to help predict potential moves and locate trading opportunities. When there is an extreme ratio or net volume reading, the majority of traders are either long or short a specific instrument. It is expected that the traders who are currently in these positions will eventually close out therefore bring the ratio back to neutral. Consequently, there tends to be a sharp price movement or a reversal.

When extremes like this are present in the market, a mean reversion automated strategy can be implemented to take advantage of the moves in the market that are expected to ensue. If sentiment is skewed very high or very low, price is moving away from the mean. However, over time it is expected to regress back to the mean resulting in a more neutral reading. Neutral would be considered a number close to 1.0 or -1.0. It is recommended that a confirmation indicator or two be coded into the mean reversion strategy as well.

Free one-month sample of the historical Sentiment Data can be accessed by pasting this link in your browser https://sampledata.fxcorporate.com/sentiment/{instrument}.csv.gz and changing the {instrument}: to the pair or CFD you would like to download data for. For example, for USD/JPY data download you would use this link: https://sampledata.fxcorporate.com/sentiment/USDJPY.csv.gz.
When the file downloads, it will be a GNU zip compressed file so you will need to use a decompression utility to open it. To open the file with 7zip, open the downloads folder, click on your file, and click ‘copy path’. Then open 7Zip and paste your clipboard into the address bar and click enter. Then click the ‘extract’ button. This will open a window where you can designate a destination to copy your new csv file. Click OK, and navigate back to your file explorer to see your csv file.
You can find more details about the sentiment data by checking out FXCM’s Github page: https://github.com/fxcm/MarketData/tree/masteSentiment
submitted by JasonRogers to AlgoTradingFXCM [link] [comments]

Obtaining accurate historic past data for Forex trading algorithm

I want to make an excel file macro file that downloads historic forex data as well as stock(nasdaq,asx). The goal of this is to have a complete list of historic data that updates daily with price open and close stats for me to paper test my model against.
Surely other Algorithmic traders have had to do a similar thing to paper test their strategies. Any ideas? Could there already be software that has this and can export to CSV?
submitted by peachesxxxx to Forex [link] [comments]

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How To Download Historical Data in Metatrader 4 - YouTube

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