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Releasing a Decade of Forex Tick Data I Crawled and Converted

Releasing a Decade of Forex Tick Data I Crawled and Converted

Intro:

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

Download Links:

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

Stats Overview:

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

About the Data:

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

User Resources:

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

Windows

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

Linux

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

download-day.py:

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

download-year.py

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

dukascopy.py

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

utc-timestamp-convert.py

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

Downloading historical Forex tick data and importing them in to Python using Pandas

Downloading historical Forex tick data and importing them in to Python using Pandas submitted by jonromero to Python [link] [comments]

My third post: Downloading historical Forex tick data and importing them in to Python using Pandas

submitted by jonromero to algotrading [link] [comments]

Unexpected result when doing simple subtraction

So I'm working on a Forex strategy back testing script using pandas. For part of the strategy, I need to get the absolute value of num1 - num2.
for example
ans = abs(1.31968-1.31977)
expected result is 0.0009
however I am getting 9.000000000014552e-05 which is throwing my results off.
Any ideas, suggestions?
submitted by EggChen_vs_Lopan to learnpython [link] [comments]

[P] Forex machine learning strategy with Python: features processing

Hi, i’m preparing data (in Pandas) for a machine learning Forex strategy. Data comes from FRED, FXCM, Alpha vantage ecc. How could different features be aggregated in a pandas dataframe? For example fundamental data with price time series (GBPUSD + technical indicators + GDP + interest rates ecc). There is a problem with date adaptation, a feature is daily while others generally monthly. I know scaling and features selection/reduction with PCA but i’m interested in preprocessing and joining of features with different scale/values/timeframe. Please tell me a detailed process in pandas or Scikit Learn to obtain fundamental and price features perfectly merged and ready for a machine learning training/test. From cleaning to scaling. Then many ML models like Random trees or Svm will be compared choosing the best performer. Thank you very much.
submitted by fedejuvara86 to MachineLearning [link] [comments]

Downloading historical Forex tick data and importing them into Python using Pandas

submitted by jonromero to programming [link] [comments]

Forex and stock trading automation what do you think?

Hello, the reason I'm opening this discussion is that for a while I've been thinking about building forex/stock trading automation system(something like a bot but in a more sophisticated way). I have a decent python experience with over 1 year of hands-on deep learning and general purpose projects so it is valid to say I'm quite experienced with tensorflow 2.x, keras, numpy, pandas, matplotlib and most of standard python libraries. I'm currently learning c++ for performance needs that cannot be met with python alone. I've been intending to use my background to create a trading system I have not thought of specifics but my general overview of a problem solution might be to create a combination of deep learning models and maybe some reinforcement learning techniques as well(which something I'm currently learning). The question is do you think of a criteria to get best outcomes in terms of prediction accuracy and be able to deliver something that might turn profitable at some point? I have not started working on the project yet but when I read about the topic in kaggle and general machine learning forums, the idea of forex bots/ auto-trading systems is sometimes met with skepticism: some argue it's nearly impossible to create something useful and some others claim that they were successful to create deep learning models (my best guess is RNN/LSTM architectures) with variable accuracy(60-90%). I don't think experimentation will hurt, I mean I can start working something out and figure out the best results for myself but I thought it's a good idea to ask for guidance from those who have similabetter background/experience as/than myself as well as others who might have tried already and hear the feedback first. What do you think?
submitted by emadboctor to algotrading [link] [comments]

MaiestasHaven | A Tale of Two Towns

MaiestasHaven | A Tale of Two Towns
Hello fellow Redditors!
We're (as of the moment of writing) in the middle of our first whitelist-only curated map, dubbed "A Tale of Two Towns", where we have two distinct towns with a vastly different set of government ideology and economic systems in place! Thanks to districts and titles, we have the ability to simulate locality to laws, in effect paving the way for groups of people to join us who want to give us a try, while having full town autonomy! Of course, if you're by yourself and you want to give a well-organized world a go, both our existing towns welcome you with open arms :)
As one of the few servers to do so successfully, we have two sets of a dual currency setup going. As of this map, that includes foreign exchange ('forex') where people can get by solely by trading in the various currencies of the world if they so choose. Be sure to talk to SatsukiShizuka if you have questions about that - she's been the bank for several rounds and the designer of one of our economical systems.
On the other hand, if you're a builder, we have several custom mods, including more block types! We run a 60 day meteor cycle (mostly coinciding with SLG update cycles) so plenty of time to get things ready! Additionally, we have our own blog: https://www.praefator.nl/ where you can read up on my latest musings, as well as reading about server history, our roadmap, and our mods as well as the proposed government structure when multiple concurrent governments are a thing (come Beta 10.0).
With all that said, here is the nitty-gritty info to get you started:
Server Name: MaiestasHaven | A Tale of Two Towns Server Location: The Netherlands Brief Description: Greetings! We're well underway with our first whitelist-only map, with plenty of room to expand!
We have two fully functional economies ready for you to sink your teeth in! We feature two separate economic systems, each with dual currencies, a well-organized world, foreign exchange, plans for a fully Renaissance-themed capital, an Asian secondary town as well as Megaproject 3.0, of which you can see a sample in this month's screenshot contest winning entry!
Server Admins: Praefator (Owner) & Inistaras Server Staff: Ferchibald, SatsukiShizuka, Sans Tark, squishy-panda, NNMike, Acularius
Password protected?: Yes, whitelist-only IP & Port: eco.praefator.nl:2053 Discord: https://discord.gg/qgf6yt9 World Size: 4 km2 World Difficulty: Beyond High World Objective: Let's connect Durmgrist and Mingyang!
The latest megabuild, currently being planned in SP prior to its enactment on the production server
submitted by dragonsupremacy to EcoGlobalSurvival [link] [comments]

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submitted by ViralMedia007 to FREECoursesEveryday [link] [comments]

Automated Parallax BB strategy - v1 (Update)

Automated Parallax BB strategy - v1 (Update)
This is a quick update to my previous post on automating this strategy. I ran some more tests and wanted to share the results.
Test
I did make some minor modifications since the previous tests. Some parameters in the algorithm were tweaked, otherwise the algorithm itself is mostly unchanged. Therefore I'm still considering this v1 of the algorithm.
For this round, I ran the test on all major pairs for 2017 - 2019 on H4 and D1 timeframes. Again, each test was run on one pair+timeframe at a time.
Risk amount for each trade was 1%.
Results
https://preview.redd.it/iuax5qp6frc51.png?width=1686&format=png&auto=webp&s=07cd3df1791ced925f9971777d35c46f62566399
https://preview.redd.it/hmsuyyz9frc51.png?width=1648&format=png&auto=webp&s=b08c317e9caeed6bf20c584fa40888f1559147c7
Similar to what paulv1333 observed in their implementation, the performance got worse as we stepped down into lower timeframes. However, it looks promising at the D1 timeframe.
Next Steps
  • I need to decide if it's worth investing time to get this system profitable on lower time frames or just focus on D1
  • Still need to work on improving the signal quality + win rate
  • Experiment with other parameters and trade management strategies
  • Run algo tests with universe of all 28 pairs
Tools
For those interested, here's some details on my workflow/tools.
The algorithm is implemented in C# on LEAN. One downside of this is that it doesn't come with any charting capabilities. For now, I'm generating TradingView indicators from my test data to plot trades in TradingView. From here, I'll look through the trades and come up with ideas for optimizations.
I use Jupyter Notebooks+Python+pandas to analyze the data generated by the algo and do some experimentation with ideas here.
submitted by lucrumlabs to Forex [link] [comments]

Forex and stock trading automation what do you think?

Hello, the reason I'm opening this discussion is that for a while I've been thinking about building forex/stock trading automation system(something like a bot but in a more sophisticated way). I have a decent python experience with over 1 year of hands-on deep learning and general purpose projects so it is valid to say I'm quite experienced with tensorflow 2.x, keras, numpy, pandas, matplotlib and most of standard python libraries. I'm currently learning c++ for performance needs that cannot be met with python alone. I've been intending to use my background to create a trading system I have not thought of specifics but my general overview of a problem solution might be to create a combination of deep learning models and maybe some reinforcement learning techniques as well(which something I'm currently learning). The question is do you think of a criteria to get best outcomes in terms of prediction accuracy and be able to deliver something that might turn profitable at some point? I have not started working on the project yet but when I read about the topic in kaggle and general machine learning forums, the idea of forex bots/ auto-trading systems is sometimes met with skepticism: some argue it's nearly impossible to create something useful and some others claim that they were successful to create deep learning models (my best guess is RNN/LSTM architectures) with variable accuracy(60-90%). I don't think experimentation will hurt, I mean I can start working something out and figure out the best results for myself but I thought it's a good idea to ask for guidance from those who have similabetter background/experience as/than myself as well as others who might have tried already and hear the feedback first. What do you think?
submitted by emadboctor to Python [link] [comments]

Forex and stock trading automation what do you think?

Hello, the reason I'm opening this discussion is that for a while I've been thinking about building forex/stock trading automation system(something like a bot but in a more sophisticated way). I have a decent python experience with over 1 year of hands-on deep learning and general purpose projects so it is valid to say I'm quite experienced with tensorflow 2.x, keras, numpy, pandas, matplotlib and most of standard python libraries. I'm currently learning c++ for performance needs that cannot be met with python alone. I've been intending to use my background to create a trading system I have not thought of specifics but my general overview of a problem solution might be to create a combination of deep learning models and maybe some reinforcement learning techniques as well(which something I'm currently learning). The question is do you think of a criteria to get best outcomes in terms of prediction accuracy and be able to deliver something that might turn profitable at some point? I have not started working on the project yet but when I read about the topic in kaggle and general machine learning forums, the idea of forex bots/ auto-trading systems is sometimes met with skepticism: some argue it's nearly impossible to create something useful and some others claim that they were successful to create deep learning models (my best guess is RNN/LSTM architectures) with variable accuracy(60-90%). I don't think experimentation will hurt, I mean I can start working something out and figure out the best results for myself but I thought it's a good idea to ask for guidance from those who have similabetter background/experience as/than myself as well as others who might have tried already and hear the feedback first. What do you think?
submitted by emadboctor to deeplearning [link] [comments]

Forex and stock trading automation what do you think?

Hello, the reason I'm opening this discussion is that for a while I've been thinking about building forex/stock trading automation system(something like a bot but in a more sophisticated way). I have a decent python experience with over 1 year of hands-on deep learning and general purpose projects so it is valid to say I'm quite experienced with tensorflow 2.x, keras, numpy, pandas, matplotlib and most of standard python libraries. I'm currently learning c++ for performance needs that cannot be met with python alone. I've been intending to use my background to create a trading system I have not thought of specifics but my general overview of a problem solution might be to create a combination of deep learning models and maybe some reinforcement learning techniques as well(which something I'm currently learning). The question is do you think of a criteria to get best outcomes in terms of prediction accuracy and be able to deliver something that might turn profitable at some point? I have not started working on the project yet but when I read about the topic in kaggle and general machine learning forums, the idea of forex bots/ auto-trading systems is sometimes met with skepticism: some argue it's nearly impossible to create something useful and some others claim that they were successful to create deep learning models (my best guess is RNN/LSTM architectures) with variable accuracy(60-90%). I don't think experimentation will hurt, I mean I can start working something out and figure out the best results for myself but I thought it's a good idea to ask for guidance from those who have similabetter background/experience as/than myself as well as others who might have tried already and hear the feedback first. What do you think?
submitted by emadboctor to MLQuestions [link] [comments]

Book Review : Basic Python for Finance

tl;dr - way better books out there
so the pros are
  1. it's cheap
  2. it's only 200 pages
cons
  1. it's poorly edited - some sections literally repeat themselves. in his defense it's self published, so proofreading, etc, may have been skipped for costs
  2. uncoded references - sharpe ratio, backtesting, connectivity - all mentioned, no examples given
  3. a number of trading strategies covered, but they're all two liners ( apart from an SMA crossover ), no code, no real discussion on them
in summary - it's a small book about graphing timeseries from pandas really. in parts it comes across as a rehash of other books or web pages, some of the data is lifted straight from other people's projects ( Jason Brownlee, etc ). says it covers stocks and bonds, but there's 5 lines covering bonds. apparently forex traders use timeseries to determines entry and exit points for stocks so, in conclusion, get one of Yves books.
best of luck
Link as requested
https://www.amazon.co.uk/gp/product/1699920257/ref=ppx_yo_dt_b_asin_image_o00_s00?ie=UTF8&psc=1
submitted by EdenHouse81 to algotrading [link] [comments]

Forex and stock trading automation what do you think?

Hello, the reason I'm opening this discussion is that for a while I've been thinking about building forex/stock trading automation system(something like a bot but in a more sophisticated way). I have a decent python experience with over 1 year of hands-on deep learning and general purpose projects so it is valid to say I'm quite experienced with tensorflow 2.x, keras, numpy, pandas, matplotlib and most of standard python libraries. I'm currently learning c++ for performance needs that cannot be met with python alone. I've been intending to use my background to create a trading system I have not thought of specifics but my general overview of a problem solution might be to create a combination of deep learning models and maybe some reinforcement learning techniques as well(which something I'm currently learning). The question is do you think of a criteria to get best outcomes in terms of prediction accuracy and be able to deliver something that might turn profitable at some point? I have not started working on the project yet but when I read about the topic in kaggle and general machine learning forums, the idea of forex bots/ auto-trading systems is sometimes met with skepticism: some argue it's nearly impossible to create something useful and some others claim that they were successful to create deep learning models (my best guess is RNN/LSTM architectures) with variable accuracy(60-90%). I don't think experimentation will hurt, I mean I can start working something out and figure out the best results for myself but I thought it's a good idea to ask for guidance from those who have similabetter background/experience as/than myself as well as others who might have tried already and hear the feedback first. What do you think?
submitted by emadboctor to dataanalysis [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]

MAME 0.214

MAME 0.214

With the end of September almost here, it’s time to see what goodies MAME 0.214 delivers. This month, we’ve got support for five more Nintendo Game & Watch titles (Fire, Flagman, Helmet, Judge and Vermin), four Chinese computers from the 1980s, and three Motorola CPU evaluation kits. Cassette support has been added or fixed for a number of systems, the Dragon Speech Synthesis module has been emulated, and the Dragon Sound Extension module has been fixed. Acorn Archimedes video, sound and joystick support has been greatly improved.
On the arcade side, remaining issues in Capcom CPS-3 video emulation have been resolved and CD images have been upgraded to CHD version 5, Sega versus cabinet billboard support has been added to relevant games, and long-standing issues with music tempo in Data East games have been worked around.
Of course, you can get the source and Windows binary packages from the download page.

MAMETesters Bugs Fixed

New working machines

New working clones

Machines promoted to working

Clones promoted to working

New machines marked as NOT_WORKING

New clones marked as NOT_WORKING

New working software list additions

Software list items promoted to working

New NOT_WORKING software list additions

Source Changes

submitted by cuavas to emulation [link] [comments]

How can I pass this dict into a pandas dataframe?

Hello all,
I'm sure this is something incredibly simple which I'm just unable to see.
http://exchangeratesapi.io/ is a forex website with a great, free API on historical forex data. I'm using it to learn working with an API.
I have the following (you can just copy-paste and run this, no API key required)...
import requests import pandas as pd import json start = "?start_at=2018-01-01" end = "&end_at=2018-01-10" url = "https://api.exchangeratesapi.io/history" req = requests.get(url+start+end).json() df = pd.DataFrame(data=req) df.head(3) 
This returns four columns;
and this returns as expected, however the rate column is literally returned as a dict instead of a column.
How can I fix this to behave as you would expect, so the rate column is split into two: currency, rate.
Thanks!
submitted by pw0803 to learnpython [link] [comments]

MAME 0.214

MAME 0.214

With the end of September almost here, it’s time to see what goodies MAME 0.214 delivers. This month, we’ve got support for five more Nintendo Game & Watch titles (Fire, Flagman, Helmet, Judge and Vermin), four Chinese computers from the 1980s, and three Motorola CPU evaluation kits. Cassette support has been added or fixed for a number of systems, the Dragon Speech Synthesis module has been emulated, and the Dragon Sound Extension module has been fixed. Acorn Archimedes video, sound and joystick support has been greatly improved.
On the arcade side, remaining issues in Capcom CPS-3 video emulation have been resolved and CD images have been upgraded to CHD version 5, Sega versus cabinet billboard support has been added to relevant games, and long-standing issues with music tempo in Data East games have been worked around.
Of course, you can get the source and Windows binary packages from the download page.

MAMETesters Bugs Fixed

New working machines

New working clones

Machines promoted to working

Clones promoted to working

New machines marked as NOT_WORKING

New clones marked as NOT_WORKING

New working software list additions

Software list items promoted to working

New NOT_WORKING software list additions

Source Changes

submitted by cuavas to MAME [link] [comments]

MAME 0.214

MAME 0.214

With the end of September almost here, it’s time to see what goodies MAME 0.214 delivers. This month, we’ve got support for five more Nintendo Game & Watch titles (Fire, Flagman, Helmet, Judge and Vermin), four Chinese computers from the 1980s, and three Motorola CPU evaluation kits. Cassette support has been added or fixed for a number of systems, the Dragon Speech Synthesis module has been emulated, and the Dragon Sound Extension module has been fixed. Acorn Archimedes video, sound and joystick support has been greatly improved.
On the arcade side, remaining issues in Capcom CPS-3 video emulation have been resolved and CD images have been upgraded to CHD version 5, Sega versus cabinet billboard support has been added to relevant games, and long-standing issues with music tempo in Data East games have been worked around.
Of course, you can get the source and Windows binary packages from the download page.

MAMETesters Bugs Fixed

New working machines

New working clones

Machines promoted to working

Clones promoted to working

New machines marked as NOT_WORKING

New clones marked as NOT_WORKING

New working software list additions

Software list items promoted to working

New NOT_WORKING software list additions

Source Changes

submitted by cuavas to cade [link] [comments]

20 year old in search for financial advice to reaching stability.

Hi Reddit.
As a young individual starting out in the industry I'm interested in, I'm looking to utilise some of my income towards savings, investments, etc, in order to become more financially stable..
Short summary of what I do:
In the past 10 months, I've taken couple steps to save some of my income however I feel like I'm not doing enough and need some guidance.
Steps I've taken:
-Saving 30-45% of my salary monthly through simple budget control as well as money saving applications.
-Contributed 10% of my annual income to buying shares for my company.
-Investing 2% of personal income to retirement fund (+7% from employer).
I've not been fanatically saving for the past 10 months as I'm an avid traveller and have other hobbies (body art, fitness, etc) but ensured I have at least 3 months of my monthly income saved at all times.
Can you guys advise me on what steps should I be taking? Should I start investing? I'm at a loss...
I would like to accumulate more income if possible, however increasing my savings would be just fine...
I've been thinking about crypto currencies however I'm not too keen due to the market being volatile... I'm additionally thinking about Forex but with lack of education and time (Full degree and full time job) it's extremely difficult..
Any materials to read perhaps?
Thank you in advance and apologies for the text wall! - Panda.
TL:DR - Steps a 20 year old should take to increase income/savings.
submitted by TheSenPanda to UKPersonalFinance [link] [comments]

20 year old requiring advice on additional steps to stability.

Hi Reddit.
As a young individual starting out in the industry I'm interested in, I'm looking to utilise some of my income towards savings, investments, etc, in order to become more financially stable..
Short summary of what I do:
In the past 10 months, I've taken couple steps to save some of my income however I feel like I'm not doing enough and need some guidance.
Steps I've taken:
-Saving 30-45% of my salary monthly through simple budget control as well as money saving applications.
-Contributed 10% of my annual income to buying shares for my company.
-Investing 2% of personal income to retirement fund (+7% from employer).
I've not been fanatically saving for the past 10 months as I'm an avid traveller and have other hobbies (body art, fitness, etc) but ensured I have at least 3 months of my monthly income saved at all times.
Can you guys advise me on what steps should I be taking? Should I start investing? I'm at a loss...
I would like to accumulate more income if possible, however increasing my savings would be just fine...
I've been thinking about crypto currencies however I'm not too keen due to the market being volatile... I'm additionally thinking about Forex but with lack of education and time (Full degree and full time job) it's extremely difficult..
Any materials to read perhaps?
Thank you in advance and apologies for the text wall! - Panda.
TL:DR - Steps a 20 year old should take to increase income/savings.
submitted by TheSenPanda to personalfinance [link] [comments]

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