data.forexsb.com - Download Historical Forex Data

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]

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]

How to download free tick data

submitted by grebfar to algotrading [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]

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]

Where can i download a CSV/Excel file of a currency pair?

I work with data a lot for my current position, looking to download the CSV files for the pair of USD/CAD on an excel file/workbook
anyone know where i can find this?
im looking for data that goes as far back as possible with the option of dates ranging from every entry, M1, M15, M30, H1, H4, etc
thanks forex!
submitted by rawrtherapy to Forex [link] [comments]

machine learning problem

Hi guys, I have a problem. We are trying machine learning on forex. We are trying to predict direction of "tommorows" candle. Due using machine learning with python we have decided using Daily charts, because daily is more accurate and it's not taking too much time.
The reason why Im typing here is asking for help, because Im not sure how to handle this problem and where to take data from.
Todays mechanical process is -. Open Metatrader -> History center -> export data (for each individual currency pair) -> save as .csv and then parse data and work with this. [Its exhausting] This is how we obtain data for machine learning. Plain for using was like: Everyday when I come home from work (about 7 PM), I download data, let my model to predict "future", then I make some market orders and go sleep. But there is a problem. When I come home I can have data only for yesterday's candle, information about todays candle are missing, because day has not ended yet. I cant manualy obrtain data for today but it won't be todays EOD data, but it will be today's "till 7 PM data). So in this case, my model can not work properly, because it has been learned on EOD prices.
I'm desperate. I don't know how to solve this problem. I'm trying to figure this out about 2 weeks. I'm still nowhere. Can anyone suggest me something? Does anyone has any idea please?
Thank you so much and have a wonderful day.
submitted by ferryboy to algotrading [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]

Do you think someone will buy my C++ source code of a backtest/trade system?

Hi all,
This is not an AD, but I'm asking your opinion whether someone may be interested in my system, or whehter it's worth that I do some work to find a buyer. This is a backtesting and trade system, not strategy.
Main functions and features: * Written in C++, using Qt framework. Can run on Windows, Linux, and Mac. * Console program, very fast. * Currently only support Forex, but it's easy to extend to support other markets, such as Stock. * Backtesting. Candle bar based backtesting. It's not difficult to support tick based backtesting. * Optimization. Currently only brute force is supported, but it's quite easy to plug in other methods. * Walkforward testing. * Multiple threading. OpenMp is used for multithreading, each backtesting task is allocated on a CPU core. * One robot can use multiple instruments and multiple time frames. * Live trading system. Currently Oanda REST API is implemented. One robot can place buy/sell order on different account, to avoid no-hedging limitation. I have used it on my live account, and it works well. * Any more trade platforms should be able to be integrated to the system, such as FIX, but I didnt do any investigation yet. * Some indicators are included. MA, RSI, MACD, PSAR, etc, using ta_lib. New indicators can be developed easily. * Dukascopy data download. Incremental download tick data from Dukascopy, very fast. * Oanda data download. Using Oanda REST API, incremental, very fast. * Simple charting. So the report contains a balance chart, very intuitive. * Some other features, such as convert Dukascopy tick data to internal candle binary data, convert candle data to MT4 CSV, etc. * Unit testing. Some key components are unit tested. * High quality code. I'm an experienced developer. You can check my code quality and style from my open source C++ library, cpgf, on github, though it's not finance related. * Very good performance. The bar based backtesting system is highly optimized. * Since the architecture is good, the system can be used as either a program or a library. * About 50K lines of C++ code. * I spent about one year partial time on it.
My question is, do you think anyone will be interested to buy my source code? And how much do you think I should charge for? Or beside selling the source code, what do you think I can do to earn some money via the system? My original purpose developing such a system is to use by myself, but I'm not sure when I can find a good strategy to earn some money for me.
Thanks
submitted by wqking to algotrading [link] [comments]

Subreddit Stats: cs7646_fall2017 top posts from 2017-08-23 to 2017-12-10 22:43 PDT

Period: 108.98 days
Submissions Comments
Total 999 10425
Rate (per day) 9.17 95.73
Unique Redditors 361 695
Combined Score 4162 17424

Top Submitters' Top Submissions

  1. 296 points, 24 submissions: tuckerbalch
    1. Project 2 Megathread (optimize_something) (33 points, 475 comments)
    2. project 3 megathread (assess_learners) (27 points, 1130 comments)
    3. For online students: Participation check #2 (23 points, 47 comments)
    4. ML / Data Scientist internship and full time job opportunities (20 points, 36 comments)
    5. Advance information on Project 3 (19 points, 22 comments)
    6. participation check #3 (19 points, 29 comments)
    7. manual_strategy project megathread (17 points, 825 comments)
    8. project 4 megathread (defeat_learners) (15 points, 209 comments)
    9. project 5 megathread (marketsim) (15 points, 484 comments)
    10. QLearning Robot project megathread (12 points, 691 comments)
  2. 278 points, 17 submissions: davebyrd
    1. A little more on Pandas indexing/slicing ([] vs ix vs iloc vs loc) and numpy shapes (37 points, 10 comments)
    2. Project 1 Megathread (assess_portfolio) (34 points, 466 comments)
    3. marketsim grades are up (25 points, 28 comments)
    4. Midterm stats (24 points, 32 comments)
    5. Welcome to CS 7646 MLT! (23 points, 132 comments)
    6. How to interact with TAs, discuss grades, performance, request exceptions... (18 points, 31 comments)
    7. assess_portfolio grades have been released (18 points, 34 comments)
    8. Midterm grades posted to T-Square (15 points, 30 comments)
    9. Removed posts (15 points, 2 comments)
    10. assess_portfolio IMPORTANT README: about sample frequency (13 points, 26 comments)
  3. 118 points, 17 submissions: yokh_cs7646
    1. Exam 2 Information (39 points, 40 comments)
    2. Reformat Assignment Pages? (14 points, 2 comments)
    3. What did the real-life Michael Burry have to say? (13 points, 2 comments)
    4. PSA: Read the Rubric carefully and ahead-of-time (8 points, 15 comments)
    5. How do I know that I'm correct and not just lucky? (7 points, 31 comments)
    6. ML Papers and News (7 points, 5 comments)
    7. What are "question pools"? (6 points, 4 comments)
    8. Explanation of "Regression" (5 points, 5 comments)
    9. GT Github taking FOREVER to push to..? (4 points, 14 comments)
    10. Dead links on the course wiki (3 points, 2 comments)
  4. 67 points, 13 submissions: harshsikka123
    1. To all those struggling, some words of courage! (20 points, 18 comments)
    2. Just got locked out of my apartment, am submitting from a stairwell (19 points, 12 comments)
    3. Thoroughly enjoying the lectures, some of the best I've seen! (13 points, 13 comments)
    4. Just for reference, how long did Assignment 1 take you all to implement? (3 points, 31 comments)
    5. Grade_Learners Taking about 7 seconds on Buffet vs 5 on Local, is this acceptable if all tests are passing? (2 points, 2 comments)
    6. Is anyone running into the Runtime Error, Invalid DISPLAY variable when trying to save the figures as pdfs to the Buffet servers? (2 points, 9 comments)
    7. Still not seeing an ML4T onboarding test on ProctorTrack (2 points, 10 comments)
    8. Any news on when Optimize_Something grades will be released? (1 point, 1 comment)
    9. Baglearner RMSE and leaf size? (1 point, 2 comments)
    10. My results are oh so slightly off, any thoughts? (1 point, 11 comments)
  5. 63 points, 10 submissions: htrajan
    1. Sample test case: missing data (22 points, 36 comments)
    2. Optimize_something test cases (13 points, 22 comments)
    3. Met Burt Malkiel today (6 points, 1 comment)
    4. Heads up: Dataframe.std != np.std (5 points, 5 comments)
    5. optimize_something: graph (5 points, 29 comments)
    6. Schedule still reflecting shortened summer timeframe? (4 points, 3 comments)
    7. Quick clarification about InsaneLearner (3 points, 8 comments)
    8. Test cases using rfr? (3 points, 5 comments)
    9. Input format of rfr (2 points, 1 comment)
    10. [Shameless recruiting post] Wealthfront is hiring! (0 points, 9 comments)
  6. 62 points, 7 submissions: swamijay
    1. defeat_learner test case (34 points, 38 comments)
    2. Project 3 test cases (15 points, 27 comments)
    3. Defeat_Learner - related questions (6 points, 9 comments)
    4. Options risk/reward (2 points, 0 comments)
    5. manual strategy - you must remain in the position for 21 trading days. (2 points, 9 comments)
    6. standardizing values (2 points, 0 comments)
    7. technical indicators - period for moving averages, or anything that looks past n days (1 point, 3 comments)
  7. 61 points, 9 submissions: gatech-raleighite
    1. Protip: Better reddit search (22 points, 9 comments)
    2. Helpful numpy array cheat sheet (16 points, 10 comments)
    3. In your experience Professor, Mr. Byrd, which strategy is "best" for trading ? (12 points, 10 comments)
    4. Industrial strength or mature versions of the assignments ? (4 points, 2 comments)
    5. What is the correct (faster) way of doing this bit of pandas code (updating multiple slice values) (2 points, 10 comments)
    6. What is the correct (pythonesque?) way to select 60% of rows ? (2 points, 11 comments)
    7. How to get adjusted close price for funds not publicly traded (TSP) ? (1 point, 2 comments)
    8. Is there a way to only test one or 2 of the learners using grade_learners.py ? (1 point, 10 comments)
    9. OMS CS Digital Career Seminar Series - Scott Leitstein recording available online? (1 point, 4 comments)
  8. 60 points, 2 submissions: reyallan
    1. [Project Questions] Unit Tests for assess_portfolio assignment (58 points, 52 comments)
    2. Financial data, technical indicators and live trading (2 points, 8 comments)
  9. 59 points, 12 submissions: dyllll
    1. Please upvote helpful posts and other advice. (26 points, 1 comment)
    2. Books to further study in trading with machine learning? (14 points, 9 comments)
    3. Is Q-Learning the best reinforcement learning method for stock trading? (4 points, 4 comments)
    4. Any way to download the lessons? (3 points, 4 comments)
    5. Can a TA please contact me? (2 points, 7 comments)
    6. Is the vectorization code from the youtube video available to us? (2 points, 2 comments)
    7. Position of webcam (2 points, 15 comments)
    8. Question about assignment one (2 points, 5 comments)
    9. Are udacity quizzes recorded? (1 point, 2 comments)
    10. Does normalization of indicators matter in a Q-Learner? (1 point, 7 comments)
  10. 56 points, 2 submissions: jan-laszlo
    1. Proper git workflow (43 points, 19 comments)
    2. Adding you SSH key for password-less access to remote hosts (13 points, 7 comments)
  11. 53 points, 1 submission: agifft3_omscs
    1. [Project Questions] Unit Tests for optimize_something assignment (53 points, 94 comments)
  12. 50 points, 16 submissions: BNielson
    1. Regression Trees (7 points, 9 comments)
    2. Two Interpretations of RFR are leading to two different possible Sharpe Ratios -- Need Instructor clarification ASAP (5 points, 3 comments)
    3. PYTHONPATH=../:. python grade_analysis.py (4 points, 7 comments)
    4. Running on Windows and PyCharm (4 points, 4 comments)
    5. Studying for the midterm: python questions (4 points, 0 comments)
    6. Assess Learners Grader (3 points, 2 comments)
    7. Manual Strategy Grade (3 points, 2 comments)
    8. Rewards in Q Learning (3 points, 3 comments)
    9. SSH/Putty on Windows (3 points, 4 comments)
    10. Slight contradiction on ProctorTrack Exam (3 points, 4 comments)
  13. 49 points, 7 submissions: j0shj0nes
    1. QLearning Robot - Finalized and Released Soon? (18 points, 4 comments)
    2. Flash Boys, HFT, frontrunning... (10 points, 3 comments)
    3. Deprecations / errata (7 points, 5 comments)
    4. Udacity lectures via GT account, versus personal account (6 points, 2 comments)
    5. Python: console-driven development (5 points, 5 comments)
    6. Buffet pandas / numpy versions (2 points, 2 comments)
    7. Quant research on earnings calls (1 point, 0 comments)
  14. 45 points, 11 submissions: Zapurza
    1. Suggestion for Strategy learner mega thread. (14 points, 1 comment)
    2. Which lectures to watch for upcoming project q learning robot? (7 points, 5 comments)
    3. In schedule file, there is no link against 'voting ensemble strategy'? Scheduled for Nov 13-20 week (6 points, 3 comments)
    4. How to add questions to the question bank? I can see there is 2% credit for that. (4 points, 5 comments)
    5. Scratch paper use (3 points, 6 comments)
    6. The big short movie link on you tube says the video is not available in your country. (3 points, 9 comments)
    7. Distance between training data date and future forecast date (2 points, 2 comments)
    8. News affecting stock market and machine learning algorithms (2 points, 4 comments)
    9. pandas import in pydev (2 points, 0 comments)
    10. Assess learner server error (1 point, 2 comments)
  15. 43 points, 23 submissions: chvbs2000
    1. Is the Strategy Learner finalized? (10 points, 3 comments)
    2. Test extra 15 test cases for marketsim (3 points, 12 comments)
    3. Confusion between the term computing "back-in time" and "going forward" (2 points, 1 comment)
    4. How to define "each transaction"? (2 points, 4 comments)
    5. How to filling the assignment into Jupyter Notebook? (2 points, 4 comments)
    6. IOError: File ../data/SPY.csv does not exist (2 points, 4 comments)
    7. Issue in Access to machines at Georgia Tech via MacOS terminal (2 points, 5 comments)
    8. Reading data from Jupyter Notebook (2 points, 3 comments)
    9. benchmark vs manual strategy vs best possible strategy (2 points, 2 comments)
    10. global name 'pd' is not defined (2 points, 4 comments)
  16. 43 points, 15 submissions: shuang379
    1. How to test my code on buffet machine? (10 points, 15 comments)
    2. Can we get the ppt for "Decision Trees"? (8 points, 2 comments)
    3. python question pool question (5 points, 6 comments)
    4. set up problems (3 points, 4 comments)
    5. Do I need another camera for scanning? (2 points, 9 comments)
    6. Is chapter 9 covered by the midterm? (2 points, 2 comments)
    7. Why grade_analysis.py could run even if I rm analysis.py? (2 points, 5 comments)
    8. python question pool No.48 (2 points, 6 comments)
    9. where could we find old versions of the rest projects? (2 points, 2 comments)
    10. where to put ml4t-libraries to install those libraries? (2 points, 1 comment)
  17. 42 points, 14 submissions: larrva
    1. is there a mistake in How-to-learn-a-decision-tree.pdf (7 points, 7 comments)
    2. maximum recursion depth problem (6 points, 10 comments)
    3. [Urgent]Unable to use proctortrack in China (4 points, 21 comments)
    4. manual_strategynumber of indicators to use (3 points, 10 comments)
    5. Assignment 2: Got 63 points. (3 points, 3 comments)
    6. Software installation workshop (3 points, 7 comments)
    7. question regarding functools32 version (3 points, 3 comments)
    8. workshop on Aug 31 (3 points, 8 comments)
    9. Mount remote server to local machine (2 points, 2 comments)
    10. any suggestion on objective function (2 points, 3 comments)
  18. 41 points, 8 submissions: Ran__Ran
    1. Any resource will be available for final exam? (19 points, 6 comments)
    2. Need clarification on size of X, Y in defeat_learners (7 points, 10 comments)
    3. Get the same date format as in example chart (4 points, 3 comments)
    4. Cannot log in GitHub Desktop using GT account? (3 points, 3 comments)
    5. Do we have notes or ppt for Time Series Data? (3 points, 5 comments)
    6. Can we know the commission & market impact for short example? (2 points, 7 comments)
    7. Course schedule export issue (2 points, 15 comments)
    8. Buying/seeking beta v.s. buying/seeking alpha (1 point, 6 comments)
  19. 38 points, 4 submissions: ProudRamblinWreck
    1. Exam 2 Study topics (21 points, 5 comments)
    2. Reddit participation as part of grade? (13 points, 32 comments)
    3. Will birds chirping in the background flag me on Proctortrack? (3 points, 5 comments)
    4. Midterm Study Guide question pools (1 point, 2 comments)
  20. 37 points, 6 submissions: gatechben
    1. Submission page for strategy learner? (14 points, 10 comments)
    2. PSA: The grading script for strategy_learner changed on the 26th (10 points, 9 comments)
    3. Where is util.py supposed to be located? (8 points, 8 comments)
    4. PSA:. The default dates in the assignment 1 template are not the same as the examples on the assignment page. (2 points, 1 comment)
    5. Schedule: Discussion of upcoming trading projects? (2 points, 3 comments)
    6. [defeat_learners] More than one column for X? (1 point, 1 comment)
  21. 37 points, 3 submissions: jgeiger
    1. Please send/announce when changes are made to the project code (23 points, 7 comments)
    2. The Big Short on Netflix for OMSCS students (week of 10/16) (11 points, 6 comments)
    3. Typo(?) for Assess_portfolio wiki page (3 points, 2 comments)
  22. 35 points, 10 submissions: ltian35
    1. selecting row using .ix (8 points, 9 comments)
    2. Will the following 2 topics be included in the final exam(online student)? (7 points, 4 comments)
    3. udacity quiz (7 points, 4 comments)
    4. pdf of lecture (3 points, 4 comments)
    5. print friendly version of the course schedule (3 points, 9 comments)
    6. about learner regression vs classificaiton (2 points, 2 comments)
    7. is there a simple way to verify the correctness of our decision tree (2 points, 4 comments)
    8. about Building an ML-based forex strategy (1 point, 2 comments)
    9. about technical analysis (1 point, 6 comments)
    10. final exam online time period (1 point, 2 comments)
  23. 33 points, 2 submissions: bhrolenok
    1. Assess learners template and grading script is now available in the public repository (24 points, 0 comments)
    2. Tutorial for software setup on Windows (9 points, 35 comments)
  24. 31 points, 4 submissions: johannes_92
    1. Deadline extension? (26 points, 40 comments)
    2. Pandas date indexing issues (2 points, 5 comments)
    3. Why do we subtract 1 from SMA calculation? (2 points, 3 comments)
    4. Unexpected number of calls to query, sum=20 (should be 20), max=20 (should be 1), min=20 (should be 1) -bash: syntax error near unexpected token `(' (1 point, 3 comments)
  25. 30 points, 5 submissions: log_base_pi
    1. The Massive Hedge Fund Betting on AI [Article] (9 points, 1 comment)
    2. Useful Python tips and tricks (8 points, 10 comments)
    3. Video of overview of remaining projects with Tucker Balch (7 points, 1 comment)
    4. Will any material from the lecture by Goldman Sachs be covered on the exam? (5 points, 1 comment)
    5. What will the 2nd half of the course be like? (1 point, 8 comments)
  26. 30 points, 4 submissions: acschwabe
    1. Assignment and Exam Calendar (ICS File) (17 points, 6 comments)
    2. Please OMG give us any options for extra credit (8 points, 12 comments)
    3. Strategy learner question (3 points, 1 comment)
    4. Proctortrack: Do we need to schedule our test time? (2 points, 10 comments)
  27. 29 points, 9 submissions: _ant0n_
    1. Next assignment? (9 points, 6 comments)
    2. Proctortrack Onboarding test? (6 points, 11 comments)
    3. Manual strategy: Allowable positions (3 points, 7 comments)
    4. Anyone watched Black Scholes documentary? (2 points, 16 comments)
    5. Buffet machines hardware (2 points, 6 comments)
    6. Defeat learners: clarification (2 points, 4 comments)
    7. Is 'optimize_something' on the way to class GitHub repo? (2 points, 6 comments)
    8. assess_portfolio(... gen_plot=True) (2 points, 8 comments)
    9. remote job != remote + international? (1 point, 15 comments)
  28. 26 points, 10 submissions: umersaalis
    1. comments.txt (7 points, 6 comments)
    2. Assignment 2: report.pdf (6 points, 30 comments)
    3. Assignment 2: report.pdf sharing & plagiarism (3 points, 12 comments)
    4. Max Recursion Limit (3 points, 10 comments)
    5. Parametric vs Non-Parametric Model (3 points, 13 comments)
    6. Bag Learner Training (1 point, 2 comments)
    7. Decision Tree Issue: (1 point, 2 comments)
    8. Error in Running DTLearner and RTLearner (1 point, 12 comments)
    9. My Results for the four learners. Please check if you guys are getting values somewhat near to these. Exact match may not be there due to randomization. (1 point, 4 comments)
    10. Can we add the assignments and solutions to our public github profile? (0 points, 7 comments)
  29. 26 points, 6 submissions: abiele
    1. Recommended Reading? (13 points, 1 comment)
    2. Number of Indicators Used by Actual Trading Systems (7 points, 6 comments)
    3. Software Install Instructions From TA's Video Not Working (2 points, 2 comments)
    4. Suggest that TA/Instructor Contact Info Should be Added to the Syllabus (2 points, 2 comments)
    5. ML4T Software Setup (1 point, 3 comments)
    6. Where can I find the grading folder? (1 point, 4 comments)
  30. 26 points, 6 submissions: tomatonight
    1. Do we have all the information needed to finish the last project Strategy learner? (15 points, 3 comments)
    2. Does anyone interested in cryptocurrency trading/investing/others? (3 points, 6 comments)
    3. length of portfolio daily return (3 points, 2 comments)
    4. Did Michael Burry, Jamie&Charlie enter the short position too early? (2 points, 4 comments)
    5. where to check participation score (2 points, 1 comment)
    6. Where to collect the midterm exam? (forgot to take it last week) (1 point, 3 comments)
  31. 26 points, 3 submissions: hilo260
    1. Is there a template for optimize_something on GitHub? (14 points, 3 comments)
    2. Marketism project? (8 points, 6 comments)
    3. "Do not change the API" (4 points, 7 comments)
  32. 26 points, 3 submissions: niufen
    1. Windows Server Setup Guide (23 points, 16 comments)
    2. Strategy Learner Adding UserID as Comment (2 points, 2 comments)
    3. Connect to server via Python Error (1 point, 6 comments)
  33. 26 points, 3 submissions: whoyoung99
    1. How much time you spend on Assess Learner? (13 points, 47 comments)
    2. Git clone repository without fork (8 points, 2 comments)
    3. Just for fun (5 points, 1 comment)
  34. 25 points, 8 submissions: SharjeelHanif
    1. When can we discuss defeat learners methods? (10 points, 1 comment)
    2. Are the buffet servers really down? (3 points, 2 comments)
    3. Are the midterm results in proctortrack gone? (3 points, 3 comments)
    4. Will these finance topics be covered on the final? (3 points, 9 comments)
    5. Anyone get set up with Proctortrack? (2 points, 10 comments)
    6. Incentives Quiz Discussion (2-01, Lesson 11.8) (2 points, 3 comments)
    7. Anyone from Houston, TX (1 point, 1 comment)
    8. How can I trace my error back to a line of code? (assess learners) (1 point, 3 comments)
  35. 25 points, 5 submissions: jlamberts3
    1. Conda vs VirtualEnv (7 points, 8 comments)
    2. Cool Portfolio Backtesting Tool (6 points, 6 comments)
    3. Warren Buffett wins $1M bet made a decade ago that the S&P 500 stock index would outperform hedge funds (6 points, 12 comments)
    4. Windows Ubuntu Subsystem Putty Alternative (4 points, 0 comments)
    5. Algorithmic Trading Of Digital Assets (2 points, 0 comments)
  36. 25 points, 4 submissions: suman_paul
    1. Grade statistics (9 points, 3 comments)
    2. Machine Learning book by Mitchell (6 points, 11 comments)
    3. Thank You (6 points, 6 comments)
    4. Assignment1 ready to be cloned? (4 points, 4 comments)
  37. 25 points, 3 submissions: Spareo
    1. Submit Assignments Function (OS X/Linux) (15 points, 6 comments)
    2. Quantsoftware Site down? (8 points, 38 comments)
    3. ML4T_2017Spring folder on Buffet server?? (2 points, 5 comments)
  38. 24 points, 14 submissions: nelsongcg
    1. Is it realistic for us to try to build our own trading bot and profit? (6 points, 21 comments)
    2. Is the risk free rate zero for any country? (3 points, 7 comments)
    3. Models and black swans - discussion (3 points, 0 comments)
    4. Normal distribution assumption for options pricing (2 points, 3 comments)
    5. Technical analysis for cryptocurrency market? (2 points, 4 comments)
    6. A counter argument to models by Nassim Taleb (1 point, 0 comments)
    7. Are we demandas to use the sample for part 1? (1 point, 1 comment)
    8. Benchmark for "trusting" your trading algorithm (1 point, 5 comments)
    9. Don't these two statements on the project description contradict each other? (1 point, 2 comments)
    10. Forgot my TA (1 point, 6 comments)
  39. 24 points, 11 submissions: nurobezede
    1. Best way to obtain survivor bias free stock data (8 points, 1 comment)
    2. Please confirm Midterm is from October 13-16 online with proctortrack. (5 points, 2 comments)
    3. Are these DTlearner Corr values good? (2 points, 6 comments)
    4. Testing gen_data.py (2 points, 3 comments)
    5. BagLearner of Baglearners says 'Object is not callable' (1 point, 8 comments)
    6. DTlearner training RMSE none zero but almost there (1 point, 2 comments)
    7. How to submit analysis using git and confirm it? (1 point, 2 comments)
    8. Passing kwargs to learners in a BagLearner (1 point, 5 comments)
    9. Sampling for bagging tree (1 point, 8 comments)
    10. code failing the 18th test with grade_learners.py (1 point, 6 comments)
  40. 24 points, 4 submissions: AeroZach
    1. questions about how to build a machine learning system that's going to work well in a real market (12 points, 6 comments)
    2. Survivor Bias Free Data (7 points, 5 comments)
    3. Genetic Algorithms for Feature selection (3 points, 5 comments)
    4. How far back can you train? (2 points, 2 comments)
  41. 23 points, 9 submissions: vsrinath6
    1. Participation check #3 - Haven't seen it yet (5 points, 5 comments)
    2. What are the tasks for this week? (5 points, 12 comments)
    3. No projects until after the mid-term? (4 points, 5 comments)
    4. Format / Syllabus for the exams (2 points, 3 comments)
    5. Has there been a Participation check #4? (2 points, 8 comments)
    6. Project 3 not visible on T-Square (2 points, 3 comments)
    7. Assess learners - do we need to check is method implemented for BagLearner? (1 point, 4 comments)
    8. Correct number of days reported in the dataframe (should be the number of trading days between the start date and end date, inclusive). (1 point, 0 comments)
    9. RuntimeError: Invalid DISPLAY variable (1 point, 2 comments)
  42. 23 points, 8 submissions: nick_algorithm
    1. Help with getting Average Daily Return Right (6 points, 7 comments)
    2. Hint for args argument in scipy minimize (5 points, 2 comments)
    3. How do you make money off of highly volatile (high SDDR) stocks? (4 points, 5 comments)
    4. Can We Use Code Obtained from Class To Make Money without Fear of Being Sued (3 points, 6 comments)
    5. Is the Std for Bollinger Bands calculated over the same timespan of the Moving Average? (2 points, 2 comments)
    6. Can't run grade_learners.py but I'm not doing anything different from the last assignment (?) (1 point, 5 comments)
    7. How to determine value at terminal node of tree? (1 point, 1 comment)
    8. Is there a way to get Reddit announcements piped to email (or have a subsequent T-Square announcement published simultaneously) (1 point, 2 comments)
  43. 23 points, 1 submission: gong6
    1. Is manual strategy ready? (23 points, 6 comments)
  44. 21 points, 6 submissions: amchang87
    1. Reason for public reddit? (6 points, 4 comments)
    2. Manual Strategy - 21 day holding Period (4 points, 12 comments)
    3. Sharpe Ratio (4 points, 6 comments)
    4. Manual Strategy - No Position? (3 points, 3 comments)
    5. ML / Manual Trader Performance (2 points, 0 comments)
    6. T-Square Submission Missing? (2 points, 3 comments)
  45. 21 points, 6 submissions: fall2017_ml4t_cs_god
    1. PSA: When typing in code, please use 'formatting help' to see how to make the code read cleaner. (8 points, 2 comments)
    2. Why do Bollinger Bands use 2 standard deviations? (5 points, 20 comments)
    3. How do I log into the [email protected]? (3 points, 1 comment)
    4. Is midterm 2 cumulative? (2 points, 3 comments)
    5. Where can we learn about options? (2 points, 2 comments)
    6. How do you calculate the analysis statistics for bps and manual strategy? (1 point, 1 comment)
  46. 21 points, 5 submissions: Jmitchell83
    1. Manual Strategy Grades (12 points, 9 comments)
    2. two-factor (3 points, 6 comments)
    3. Free to use volume? (2 points, 1 comment)
    4. Is MC1-Project-1 different than assess_portfolio? (2 points, 2 comments)
    5. Online Participation Checks (2 points, 4 comments)
  47. 21 points, 5 submissions: Sergei_B
    1. Do we need to worry about missing data for Asset Portfolio? (14 points, 13 comments)
    2. How do you get data from yahoo in panda? the sample old code is below: (2 points, 3 comments)
    3. How to fix import pandas as pd ImportError: No module named pandas? (2 points, 4 comments)
    4. Python Practice exam Question 48 (2 points, 2 comments)
    5. Mac: "virtualenv : command not found" (1 point, 2 comments)
  48. 21 points, 3 submissions: mharrow3
    1. First time reddit user .. (17 points, 37 comments)
    2. Course errors/types (2 points, 2 comments)
    3. Install course software on macOS using Vagrant .. (2 points, 0 comments)
  49. 20 points, 9 submissions: iceguyvn
    1. Manual strategy implementation for future projects (4 points, 15 comments)
    2. Help with correlation calculation (3 points, 15 comments)
    3. Help! maximum recursion depth exceeded (3 points, 10 comments)
    4. Help: how to index by date? (2 points, 4 comments)
    5. How to attach a 1D array to a 2D array? (2 points, 2 comments)
    6. How to set a single cell in a 2D DataFrame? (2 points, 4 comments)
    7. Next assignment after marketsim? (2 points, 4 comments)
    8. Pythonic way to detect the first row? (1 point, 6 comments)
    9. Questions regarding seed (1 point, 1 comment)
  50. 20 points, 3 submissions: JetsonDavis
    1. Push back assignment 3? (10 points, 14 comments)
    2. Final project (9 points, 3 comments)
    3. Numpy versions (1 point, 2 comments)
  51. 20 points, 2 submissions: pharmerino
    1. assess_portfolio test cases (16 points, 88 comments)
    2. ML4T Assignments (4 points, 6 comments)

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MetaTrader 5 Data Downloader - YouTube MetaTrader 4 Data Downloader Tick Data Downloader tutorial How to import MT4 history data from csv files, forex guidance Download historical Forex data for FREE in 3 Simple Steps How to Download Historial Forex Data - Metatrader 4 ... How to import CSV files into MetaTrader4 and perform ...

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MetaTrader 5 Data Downloader - YouTube

How to Backtest and download forex history data to you computer - Duration: 30:02. Fit Money 62,454 views. 30:02 . Get FREE historical data for Amibroker in 3 Simple Steps - Duration: 5:05 ... In this video I show you how to delete previous MetaTrader 4 currency pair data and upload your own data to perform consistent back tests. Steps 1. Open MT4 ... The Data Downloader is a MetaTrader 4 Tool that allow the user to export the forex data in CSV format. Available on MQL5 Market: https://mql5.com/3eq6p. You may not be seeing all of the Forex historical data that is available and that can be a bad thing. ★ Get clean, Daylight Savings Time adjusted MT4 data he... How to import CSV files into MetaTrader4 and perform strategy backtests. - Duration: 5:34. ... How to Download Historial Forex Data - Metatrader 4 Tutorial - Duration: 3:49. Trading Heroes 37,501 ... The Data Downloader is a MetaTrader 5 Tool that allow the user to export the forex data in CSV format. Available on MQL5 Market: https://mql5.com/3ioyy How to import MT4 history data from csv files, data source : www.histdata.com Do you need good robot ? Please contact : https://t.me/DNX_system 100% FREE, NO...

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