A place for redditors to discuss quantitative trading, statistical methods, econometrics, programming, implementation, automated strategies, and bounce ideas off each other for constructive criticism. Feel free to submit papers/links of things you find interesting.
Hello and welcome to the MLFX (Machine Learning Forex) sub! MLFX is the culmination of 10 years of academic research into using machine learning to create predictive algorithms for the Forex markets. Recently we've put more focus on creating a consumer product and we're looking for people to help us with Beta testing the system. There's still a decent amount of work for us to do before we'd consider MLFX v1.0 to be "feature complete" but we're already getting some pretty amazing results. Now comes the test of consistency! This is what our signals look like (27/08/2020) https://preview.redd.it/jvwqvw0oahj51.jpg?width=1128&format=pjpg&auto=webp&s=1c80e3390d7e63bddbda2987ba613cb9adb12d62 We're currently working on a way to check every trade after the fact and determine if it was a winner or loser, we will also plot each individual trade on a chart (like below) as we already have that functionality. Red = SL | Blue = TP | Solid = Result Our algorithm uses multi-agent systems and evolutionary algorithms to create agents (traders) that have been specifically optimized to perform in that current market. I'm not going to go too in-depth in this post if you want to find out more you can here: https://www.mlforex.com/algorithm/agents If you're interested in assisting with beta testing please join our Telegram group and express your interest. https://t.me/joinchat/IFyZTEw_uhEHe0Oo5uSAVw
what is the learning pathway to make these machine learning forex strategies ?
My knowledge of MQL4 didn't help me with the patterns that you use your eye to recognize it like ( head and shoulders, divergence, fibonacci retracement ... etc) What is the learning path for me to use machine learning to feed the robot with historical data and point out the range of the pattern example " from Hour 14 at day 14/5/2019 to hour 21 at day 17/5/2019" that is a head and shoulder (I pick these ranges through manual backtest) and so on through the whole data set so the algorithm can learn to recognize the pattern through these examples and perform trading on future data? I already started to learn python but I don't want to get distracted with learning stuff that I won't need to make these machine learning robots so I better ask the experts first.
[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.
e-Forex Magazine | Machine learning stirs up competition in FX Algo Trading
fintech #algotrading #hedgefunds #quants #hft
e-Forex Magazine | Machine learning stirs up competition in FX Algo TradingIvy Schmerken Though capital markets firms have been adopting artificial intelligence and machine learning to train algorithms for equity trading, recently this trend has expanded to foreign exchange. Ivy Schmerken, Editorial Director at Flextrade Systems, has written widely about this topic and we asked her to revisit it for e-Forex. By crunching vast quantities of data by computer, machine learning algorithms can identify hidden patterns in past data and learn to forecast stock market returns or FX currency pairs. Large banks have been investing millions into advanced technologies such as AI and machine learning to capture a bigger share of the algo trading market. JP Morgan developed a new algorithm dubbed DNA – or Deep Neural Network for Algo Execution to merge what a multitude of algos do into a single strategy, allowing the framework to decide how a client’s order should be executed, reported Reuters ..... Continue reading at: https://www.e-forex.net/articles/aug-2020-machine-learning-stirs-up-competition-in-fx-algo-trading.html
Anyone experienced with Statistical/Machine/Deep Learning in Forex?
After fiddling around with EAs, I got interested in ML. Maybe it was just because of the hype, or because way back in college I did a project on neural networks, I was interested to see if ML/Deep Learning has any application in trading. According to the guy who wrote the book on Deep Learning in Python, there was a disclaimer early on that DL is not applicable to markets, because of their inherently unpredictable nature. I took his advice and stopped knocking on that door. But now, I am finding books on Statistical Learning, and these authors don't shy away from claiming that Stock Markets and Finance in general benefit from Machine Learning concepts or thereabouts. They are hinting at it, at least (I just got started). So, my question to anyone in the community who has experience with this: is there any scope for a retail trader to gain some insight through ML/DL/SL? Even if it is about getting better at identifying patterns, or finding probabilities of possible price direction, or better optimisation of regression analysis. Anything that makes it worth it?
Anyone experienced with Statistical/Machine/Deep Learning in Forex?
After fiddling around with EAs, I got interested in ML. Maybe it was just because of the hype, or because way back in college I did a project on neural networks, I was interested to see if ML/DL has any application in trading. According to the guy who wrote the book on Deep Learning in Python (along with creating the Keras librarby in Python), there was a disclaimer early on that DL is not applicable to markets, because of their inherently unpredictable nature. I took his advice and stopped knocking on that door. But now, I am finding books on Statistical Learning (also another advanced one by these authors), and these authors don't shy away from claiming that Stock Markets and Finance in general benefit from Machine Learning concepts or thereabouts. They are hinting at it, at least (I just got started). So, my question to anyone in the community who has experience with this: is there any scope for a retail trader to gain some insight through ML/DL/SL? Even if it is about getting better at identifying patterns, or finding probabilities of possible price direction, or better optimisation of regression analysis. Anything that makes it worth it?
Help with Machine learning applied to Forex Trading
Hello boys, i'm a python coder with 4 years of experience behind my back and a Forex trader. I know almost nothing about AI so i've come here to ask some guidance about an idea that came up in my mind.I've trained a lot in the Forex market until i got some positive edge in my entries( i don't use indicators or whatever) and i wanted to create a bot that can replicate my trading style. I am unable right now to put down specific rules that would allow me to automate the strategy so i was wondering if there is a way to teach a bot my trading style with AI. What i mean is: assuming I am able to feed the bot with a set of entry points (data)(like in the screenshot), how can I make the bot try to learn and replicate my trading style ? Where do i start? Thanks for the replies. https://preview.redd.it/tl0xlqfwm7d21.png?width=2238&format=png&auto=webp&s=72e3a7e999c1571989281baca696c9cd69f5079c
Hello, I‘m on this algotrading journey starting with crypto for exactly one year now. My strategies have not been profitable yet. I have programmed my bot, teached it all the technical indicators I got to know myself first, have left it live trading and losing half of its assigned budget. I‘ve read alot about backtesting on this forum and started learning this wierd pine script language on Tradingview. I got good looking backtest results based on some simple RSI/MACD scripts. The bot got some fresh budget assigned to lose some of it again. Leaving it running for three months the blue backtest profit hill is slowly turning red as well in the meantime. Overfitted to the max obviously. I tried implementing my own backtester to add some machine learning to even more overfit the overfitted values. I somehow left it in the dark for now and have never used it for live trading yet reading so much about overfitted backtesting within this sub. Since two months I have now completely stopped using backtesting due to this disappointing experience and completely went towards paper trading (using virtual budget on my bot). I have also tried to minimize usage of technical indicators because of the lagging. I consider all the coins instead of only BTC now. The price action is clearly linked to BTC tho (very visible!). Managing my (virtual) budget and allocation towards trades is a big learning for me as well. I slightly look into backtesting again to validate my strategies. Still not profitable but won‘t give up there. I feel heavy doubts sometimes using it but it gives back hope as well. How do you balance your efforts from backtesting compared to live/paper trading? With my full time job I have somehow developed a ritual when coming home the first thing in wifi range is checking the paper trades that my bot has done over the day. Most often re-writing the whole strategy due to bad performance. I need to get more patient on that. Next I want to read more about Forex. Ordered my first book about it yesterday. Just wanted to share my story. Hope it can motivate or helps in some way.
I want to learn to predict stocks using machine learning
I am in college right now and I have to write a research paper on any topic related to artificial intelligence. I have a huge interest in stock markets, so I think this research paper will be a good excuse to learn predicting stocks. I am a computer science major, but I have absolutely no idea about Machine Learning. So, What are the good resources, places to start learning machine learning and predicting stocks. Here's my topic: Predicting stock market using machine learning for day-trading and swing trading. it could also be forex or crypto trading if that could be predicted more accurately.
Note: The mention of O levels and A levels throughout the post refers to the British patterned system of education. O levels is equivalent to the 9th and 10th grade and Alevels is equivalent to the 11th and 12th grade in the American High School System Hi, I am a Pakistani guy currently on a gap year after doing A levels from one of the most prestigious schools in the country on a 100% scholarship. I did my O levels from a very less renowned school because of my financial condition but my grades allowed me admission into my A levels school. That is why most of my ECAS were done in the last 2 years. I am looking to apply to the following international universities with full scholarship/financial aid: Majors Intended: Computer Science / Data Science / Artificial Intelligence Universities: NYUAD (ED), Duke, Rice, Vanderbilt, Northwestern, Case Western Reserve University, Washington and Lee, Colby, Skidmore, CMU Qatar, Georgetown Qatar, NTU, HKUST, HKU, CityU Academics
Valedictorian in O levels with the grades 8A*s,2As and a GPA of 4.33
Valedictorian in A levels with the grades 5A*s and a GPA of 4.33 from a Top 5 school in the country
Took the subjects Math, Additional Math, Biology, Chemistry, Physics, English and Accounting along with other compulsory subjects in O levels
Took the subjects Math, Further Math, Physics, Economics and Accounting in A levels. The subject Further Math comprises mostly of university-level courses relating to Linear Algebra, Calculus, Mechanics and Statistics. My course load is counted as one of the heaviest one in the British education system
SAT:1570 800 Math, 770 EBRW
SAT II Math Level II: 790
SAT II Physics: 800
Extra-Curriculars / Awards
Head of the most reputed STEM club in the country, CORDS
Won 4 highly prestigious National Stem Olympiads winning 10+ category awards. Led the team in one of them
Performed my duties as the Computer Science and Cryptography category head at the National STEM Olympiad Scinnova organized by our club
Was selected as the captain of the team that represented our school in the National Science Bowl
Received Bronze honour in the International Youth Math Challenge
Taught Math and Further Math to students after being selected as the Teacher's Assistant in Senior Year
Did a 2-month Data Analyst Internship for a financial advisory firm. Analyzed the performance of a stock listed company over the last 5 years and prepared a report explaining the trends in the performances with regards to the micro and macro economic factors. My report was recognized as the top one amongst the reports of other interns
Taught 50+ hours of Math, English and Urdu to the support staff of our school
Currently doing a Front End Developer internship under the mentorship of a company
Creating and Reviewing content for 3 months as the Algebra and Statistics Math Expert for the app of a San Fransisco based company named Photomath. The app has over 150 million downloads over all platforms
An academic councilor at an app based startup named Nemphis
Moderator in the largest online O/A Levels online community. Have written several articles helping students with Alevels, SATs, MOOCs and more. Councelled over 200 students privately
Currently authoring a python library for analyzing WhatsApp text messages using statistical visualizations and word clouds
Top 2% in the world on the competitive programming website codewars.com
Programmed several automated forex trading expert advisors using the MQL4 language for a client
Developed several python based web apps including Scrabble Solver, Cryptography Tool and more
Did a 23 course + 6 project Data Scientist with Python track from Datacamp
Did a 5 course SQL Fundamentals track from Datacamp
Have done several other MOOC's relating to Python, Machine Learning, Data Science etc
An active user on the data science platform Kaggle
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Machine Beats Human: Using Machine Learning in Forex.This is the another post of the series: How to build your own algotrading platform. Machine learning and trading is a very interesting subject. It is also a subject where you can spend tons of time writing code and reading papers and then a kid can beat you while playing Mario Kart. Therefore, Forex trading is tremendously tricky for machine learning systems, due to its time-dependent and non-deterministic nature. You don’t have time to sit and calculate, and you have to intrinsically understand the context of the market. This also causes problems with training and validation exercises, as applying algorithms that are produced from this process, while actually in live ... MACHINE LEARNING FOREX TOOLS signal forecasting. Stop wasting time trying to find the perfect strategy. Start earning with machine learning today! Try our beta, it's FREE. TIME LEFT. 00. Days. 00. Hrs. 00. Min. 00. Sec. Use the power of machine learning to achieve your financial goals. Beginner-friendly. If you can follow simple instructions and set up a trading account, you can start making ... Looking for consultation on a highly profitable machine-learning strategy applicable to forex pairs and/or gold. The main objective is an outstanding win-rate, which should be higher than 80%. Whether it's gold or forex. A huge plus would be if the strategy is applicable to multiple forex pairs and/or gold. Requirements: - documentation of strategy research, preferably in a jupyter notebook or ... Deep Learning for Forex Trading. Fabrice Daniel. Follow. Aug 4, 2019 · 10 min read. Many research papers cover the prediction of financial time series but only a small number of them speak about ... The installation of machine learning algorithms in the FoRex trading online market can automatically make the transactions of buying/selling. All the transactions in the experiment are performed ... To use machine learning for trading, we start with historical data (stock price/forex data) and add indicators to build a model in R/Python/Java. We then select the right Machine learning algorithm to make the predictions. Before understanding how to use Machine Learning in Forex markets, let’s look at some of the terms related to ML.
How to Build a Winning Machine Learning FOREX Strategy in ...
This is the first video in the series where we will start to tackle the creation of financial feature functions that we will use as indicators for a machine ... In this video we are going learn how about the various sources for historical FOREX data. Primarily, we will be using data from Dukascopy bank. There are man... Published on May 22, 2017. Fair warning! THIS IS A LONG VIDEO. I made this video long because I feel that many people may not have had a science background, or a math background to the level where... Forex Algorithmic Market Making System evolved on 4 Computers, 72 CPU cores total in about 20 minutes. Machine Learning and Pattern Recognition for Algorithmic Forex and Stock Trading: Intro by sentdex. 10:24. Quick look at our Data: Machine learning for Stocks and Forex Technical Analysis by sentdex. 11:00. Machine Learning and Pattern Recognition for Stocks and Forex Part 3 by sentdex. 6:26. Percent Change: Machine Learning for Automated Trading in Forex and Stocks Part 4 by sentdex. 12:40 ... This video is ALL about the Williams Accumulation Distribution financial analysis indicator. It "supposedly" represents the amount of buying and selling in t...