Top 10 BEST Forex Trading Strategies PDF

Giving away all Trading courses materials or Ecommerce courses materials at low cost

Yup, as stated. Tried to post in relevant subreddit but kept getting deleted by mob bot.
PM me u/fistno or email [email protected] if you like the following:
Trading courses material:

Stocks, forex ebooks and forum pdf, indicators
Jtrader small cap trading
YTC trading
Trading in the Zone
spartan fx renko box
Crypto Trading
Alexander Elder - Lessons From a Traders' Camp 1999
Footprint Deep Dive
Learn, Plan, Profit - Your A-Z Blueprint To Mastering The Stock
MambaFX Day Trading Scalping Bundle 02 19
Forex Trading Pro Indicators
Investors Live trade on the fly
Investors Live Textbook Trading
Investors Live Tandem Trader
James Dalton
SMB Reading the Tape
Tim Syke Collection
Steven Dux Trading Tech
Steven Dux Duxinator & Freedom Challenge
Akil Stokes & Jason Graystone - TierOneTrading
Andrew Keene - Ichimoku Cloud Course
Auction Market Foundations Course-Tom Alexander
Avdo - ForexGrid Mentoring Program
Axia Futures - The Footprint Course
Cameron Fous – Epic Sequal! FOUS4x2! New Day Trading
Elliott Wave Ultimate
Forex4noobs
Gary Dayton – Trade Tops & Bottoms
Hanzo Shadowcode
ICT
Investopedia Academy - Advanced Options Trading
market profile training
OFA Volume Profile Course
Peter Fader VSA Course
Ryan Teo Price Action Trading Institute
Sang Lucci
Simpler Trading - INTRODUCTION TO THINKSCRIPT
Simpler Trading - The Bullseye System
SMB Foundation Forex
Super Trade
Tim Grittani - Trading Tickers
Timothy Morge - Market Geometry
tradeguider-vsa-chart-reading-masterclass
Trader Dale Forex
Trader Dante Bund Method
Understanding Global Fundamentals Course by Chris Lori
Wolf Trading – A Day Trading Guide (Roland Wolf
Wyckoff Trading Making Profits With Demand And Supply

or Ecommerce tools/ courses material:

wordpress themes/ woocomerce/ yoast plugins/ etc
SEO all food that you need
SEO MEGA 2013
1000+ PLR articles
Adrian Morrison - EcomSuiteX
Benjamin Joseph - Amazon FBA Secrets 3.0
Benjamin Joseph - Amazon FBA Secrets 3.0 UP1
Matt Clark Amazon Sellers Summit Barcelona
Stefan.James.Life.Business.Mastery Accelerator
Amazon Yuping.Power.Sourcing.Course
Tai Lopez Courses
19 Copywriting Secrets to Convert Leads to Customers.zip
CrazyTalk Pipeline 8.rar
Niche Authority Domination Cou… [2018].zip
Dan Kennedy Mind Hijacking
Dan.Kennedy.Source.Code.to.Building Attraction
Dan.Lok.Perfect.Closing.Script
Facebook.Ads.Mastery.Strategy
Grant Cardone courses
Grant.Cardone.and.Frank.Kern.Branding.Webinar
Helium.10.Elite.Amazon.FBA.Masterminds
Kevin.David.Amazon.FBA.Ninja
Kevin.David.Digital.Course.Secrets.2019
submitted by fistno to u/fistno [link] [comments]

Adobe XD Portrait UI UX Design list

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submitted by AdProfessional9918 to u/AdProfessional9918 [link] [comments]

Using Python and Pandas to explore trader sentiment data

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

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