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Trading Toolbox - MATLAB
What toolbox required for algorithmic trading (forex) with Matlab?
Hi, I know there are several programming language available for algorithmic trading, but if anyone is familiar with this, what are the toolboox required for algorithmic trading (forex) with matlab? I only have limited budget for the license (home usage license), do I only need financial toolbox and trading toolbox or the whole computational financial toolbox? thanks
I am not a person who complains. So none of my friends knows what I am going through. I hope to get some relaxation by letting it all out here. I give on all my projects. Every single one of them. I keep trying and trying. It just does not work. I have been stuck on my masters thesis since august 2019 because I can't find a way to solve LMIs with Matlab or scilab. It's a masters in physics. A two years program. Yet, I'm in my third year. May even go to a fourth. Just because of that coding problem. Since 2017 I have been trying FOREX trading. I have learned strategies. Attended seminars on psychology. Man... Here we are in 2020. Still win consistently. Let alone make a consistent income. The woman I thought was to be my future wife dumped me in 2016. Over the phone. 3 years later, I finally fell in love again. This second lady was everything I ever wanted to get from a partner. For the first month. Then during the following three months I was dumped using some shameful methods. I never even knew you could dump someone like that and have the courage to say "let's stay friends". This year I met another lady. Her psychological makeup was one of the things I dreamed of. Still dream about it to this day. She said she was ready to get into a serious relationship. She even promised it. Then she finally went back to ex. A "toxic" ex. Her words, not mine. I kept getting into these endeavours again and again. Believing each time will be the good one. Since I had learned from the past. I can't remember how much money I lost from Forex. How stupid I have looked when trying to get serious with a lady. I even went to my supervisor and explained my research problem. He said I should look for a way! I just give up. The one good thing is that I stopped battling with chronic diseases. After 3 years, I can finally spend more than a month at full health. Or at least 75%. I believe the one thing I can do now is to keep playing video games. I tried. I really did. I stopped no earlier than today. I'm in tears writing this. To anyone reading this, thanks for your time.
When going for an automated trading platform it is very important to look for some important features before you decide on the automated trading platform you want to trade on. Different automated trading platforms offer different services which have their own pros and cons and might suit certain strategies and better than the others. We have discussed important features that you should consider while choosing an algorithmic trading platform.
A backtest is a historical simulation of an algorithmic trading strategy to see how it would’ve performed on the data in the past. Backtest results usually show the strategy’s performance in terms of profits and losses and some popular performance statistics like Sharpe Ratio or Information ratio which help to quantify the strategy’s return on risk. Hence a good backtesting software can be a great plus for an automated trading platform. Backtests can be divided into two categories ‘Research Backtesters’ and ‘Event-Driven Backtesting’.
Choice of programming language is very important while deciding which platform to use for automating your trading strategy. Different languages have different pros and cons. Most commonly used programming languages used for algorithmic trading are C++, C#, Java, R, Python, and MATLAB. You can refer to one of our recent posts on top backtesting platforms where we’ve discussed popular programming languages.
Different automated trading platforms provide access to/support trading/backtesting of certain securities only; some provide specific access to data feeds like Bloomberg and Thomson/Reuters. For instance, there are platforms dedicated to Forex trading or Equities trading only that too in specific markets. You need to make sure what the automated trading platform offers and then decide based on your needs. The frequency of data that you would need should also be taken into account. Some strategies would require daily EOD data while some other strategies might require intraday trading data.
Different automated stock trading platforms vary in ease of use. Some platforms may require actual programming expertise while others may not. Most platforms provide a demo version which can help you decide what fits your comfort level. The complexity of platforms can be different for different assets traded, and one should check the different tools & features available to analyze the specific asset class.
Number of Strategies Allowed
Sometimes there might be restrictions on the number of long or short strategies loaded on a particular account and you might need extra accounts for more strategies. You should also check if you have enough memory on your computer for multiple accounts if required as it can be memory intensive. Some platforms also offer their own trading strategies as add-ons which can be subscribed by paying a periodic or one-time fee.
Trading commissions can impact your profits to a great extent. Carefully choose the plan which suits your trading requirements. Also, check if there are initial and/or monthly fees and what is offered against it to make sure you are only paying for services which you actually want.
Technical Support & Customer Service
Automated Trading platforms are expected to have an extremely high “up-time” and rarely go out of service. Before choosing the platform you should check the history of outages and if there have been any other issues in the past, how soon were those resolved, and how knowledgeable and helpful was the support team.
TLDR; Learn from scratch or pay to get a code to build off I have a strategy I traded manually last semester on forex that netted me some gains. (Around 3 percent over 40 trades). Unfortunately I no longer have the tabulated results so take it as you wish. The strategy only profited around 40 percent of the time at what was ideally a 2 to 1 risk. Problem is I can't really evaluate the efficacy for it because it took almost a couple of months to make those 40 trades on 1 min timeframes on forex pairs. Not to mention all the liberties I took exiting early. I intend to try to make an EA out of this on MT4 so i can really test it in a truly mechanical environment and learn from the shortcomings/advantages of this strategy. But I am not proficient at coding, I only know abit of C++ and MATLAB. So I am considering either going at it myself or hiring someone to code it for me (Python -> ZMQ -> MT4). Heres the strat: It might be stupid but I think there will be alot to learn watching it fail. https://drive.google.com/file/d/1gOuUCGjfqcvEmSfbI1REEUBN-UWlUf0J/view?usp=sharing Basically, it uses ATR for the exit sizes, Bollinger band and MA crosses for entry and direction. If it closes out of the money it will wait until the MAs cross before entering a trade again. Only one trade will be opened at one time. This is a pretty vanilla idea that can be found all over the internet tbh. But I want to see how and why it wouldn't work. Will this be too difficult of a maiden project on MT4? Should I learn and build off of a sample code instead?
My name is Marek Hawk and I am a professional trader of Horizon Trading team. For today’s article we selected a controversial topic often discussed in trading community, it is the Martingale trading system. Would you be interested in a trading strategy that is virtually 100% profitable? Many finds this system as gambler’s tool with high risk exposure and they do not think about starting with the Martingale at all. But the Martingale strategy is based on probability theory, and if your pockets are deep enough, it has a near 100% success rate. Does it really work? Is it possible to win always in the end? Let’s find out!
How Martingale Works
The Martingale system is a simple process that involves doubling your bets after a loss. The idea is that if you can make a trade that offers probabilities, you eventually win and make enough money on the win to cover all your previous losses, and have a profit left over equal to your first bet. The Martingale is a mechanism of placing double bet in case of loss. A martingale strategy relies on the theory of mean reversion. In the end you should win at some time, the theory says, and you should make a profit. Let’s look at mechanics of the martingale system.
Examples of the Martingale Strategy
Let’s assume trading strategy which works with 1:1 risk-reward ratio and initial risk for first trade is 100 $. Your initial account balance is 10 000 USD. https://preview.redd.it/43szidw2w9b31.png?width=635&format=png&auto=webp&s=443f0d0f241e73e094ae197636ca5ad83a184cb5 First trade was speculation on growth and your long trade worked out. In another situation you found an interesting entry level to open the new long trade, but the speculation did not work out and you are back to the initial bankroll value. Next trade you needed to double your risk and you needed to open a larger position to cover the previous loss and to gain profit. Unfortunately, you suffered another loss and you had to double your risk again to 400 $. Next trade you won, and your equity raised to 10 200 $. The problem with the Martingale is the situation when you get a long streak of losing trades. You must always double your risk and in the end your last trade in the row could be huge portion of the capital, and you can be still wrong…
One of the reasons the Martingale strategy is so popular in the currency market is because, unlike stocks, currencies tend to go back to their mean. Although companies in term of probabilities can bankrupt more often, countries cannot. For example, even if currency is devalued or depreciated, the chances that currency's value reaches zero are very low. Principle of averaging the price of the Martingale trading strategy. The forex market also offers the ability to earn interest which allows traders to offset a portion of their losses with interest income. This means that a martingale trader may want to only trade the strategy on currency pairs in the direction of positive carry. In other words, they would buy a currency with a high interest rate and earn that interest while, at the same time, selling a currency with a low interest rate. With many lots, interest income can be very substantial and could work to reduce your average entry price.
In this article I described principles of the Martingale trading strategy. The system may look like a perfect winning system; however, it carries huge risk exposure of your capital. A trader needs to be prepared to work on professional money management techniques in case of building working Martingale system. I gave you some hints which tools can help managing this mathematical trading system. I described the examples of trading the Martingale trading techniques on forex markets.
After 9 months of obsession, here is my open source Node.js framework for backtesting forex trading strategies
TL;DR There's lots more to the story. But the code is all open source now. Have at it. I'm too exhausted to continue with this. If you'd like more details, feel free to message me. If you happen to carry on with this project or use any ideas from it, I would greatly appreciate it if you could keep in touch on your findings. If anyone has any insights, please feel free to comment or message me. I've spent the last nine months working furiously on this. I started a project for backtesting strategies against data I exported from MetaTrader. I had a very powerful computer crunching numbers constantly, trying to find the most optimal configuration of strategy indicator inputs that would results in the highest win rate and profit possible. Eventually, after talking with a data scientist, I realized my backtesting optimizer was suffering from something called overfitting. He then recommend using the k-fold cross-validation technique. So, I modified things (in the "k-fold" forex-backtesting branch), and in fact it provided very optimistic results when backtested against MetaTrader data (60 - 70% win rate for 3 years). However, I had collected 3 months of data from a trading site (by intercepting their Web Socket data), and when I performed validation tests against that data using the k-fold results created from the MetaTrader data, I only got a ~57% win rate or so. In order to break even with Binary Options trading, you need at least a 58% win rate. So in short, the k-fold optimization results produce a good result when validation tested against data exported from MetaTrader, but they do not produce a good result when validation tested against the trading site's data. I have two theories on why this ended up not working with the trading site's data:
The trading site I collected data from uses Reuters data. The prices in the MetaTrader data I used are different from the prices in the the trading site's data. Basically the the trading site's data is offset and is slightly higher than the MetaTrader data (and there may be other differences). I suspect that the k-fold optimization may have produced a predictor that is tailored to the data exported from MetaTrader (data available here), but it does not work as well on the the trading site's data.
The script I used to collect data from the trading site disconnects from the trading site periodically for maybe 10 minutes every, and so when it does, the strategy indicator calculations used when validating against the collected data have to start all over due to gaps, and so potential trades are lost.
For the strategy I use the following indicators: SMA (Simple Moving Average), EMA (Exponential Moving Average), RSI (Relative Strength Index), Stochastic Oscillator, and Polynomial Regression Channel. forex-backtesting has an optimizer which tries hundreds of thousands of combinations of values for each of these indicators, combined, and saves the results to a MongoDB database. It can take days to run depending on how many configurations there are. Basically the strategy tries to detect price reversals and trade with those. So if it "thinks" the price is going to go down within the next five minutes, it places a 5 minutes PUT trade. The Polynomial Regression Channel indicator is the most important indicator; if the price deviates outside the upper or lower value for this indicator (and other indicators meet their criteria for the strategy), then a trade is initiated. The optimizer tries to find the best values for the upper and lower values (standard deviations from the middle regression line). Additionally, I think it might be best to enter trades at the 59th or 00th second of each minute. So I have used minute tick data for backtesting. Also, I apologize that some of the code is messy. I tried to keep it clean but ended up hacking some of it in desperation toward the end :) gulpfile.js is a good place to start as far as figuring out how to use the tools available. Look through the available tasks, and see how various "classes" are used ("classes" in quotes because ES5 doesn't have real class support). The best branches to look at are "k-fold" and "master", and "validation". One word of advice: never, ever create an account with Tradorax. They will call you every other day, provide very bad customer support, hang up the phone on you, and they will make it almost impossible to withdraw your money.
Hey guys! This is first time for me to be on Reddit, so please kindly let me know if I post in wrong place. I am quite new to stock market and do not have that much fund as I could probably invest about $1000. But, I guess it is worth to start trading as it seems that I finally find something that I am interested in. I have been doing a paper trading, but I realize there are moments I miss a place to buy. Although I have tried to trade with my CIBC InvestorEdge but, the commission really hurts me as my capital is too small. Moreover, I am interested in either day trading or swing trading, so commission really matters, which is the reason why I would like to use Questrade as their benefit for ETFs is just too good to be true. Back to what I was saying, this is very frustrating and I was trying to find a way to solve this problem (enter at the price I want. I used limit order but.... it sometimes requires even faster calculation which is not really ideal), and I heard there are ways to do with computer programming. I am currently majoring in mathematics, and know how to program in python, Java and matlab. I was looking into MQL4 but it does not seem to be working for buying ETFs or Stocks. Please give me some tips what to do. FYI, I am currently in BC Canada, so I believe I wouldn't be able to do Forex.
Had a system developed for connecting Python, R, Matlab which downloads (free) forex tick data in realtime storing it, then converts to timeframes tables in Mssql database hosted on own aws servers (free for first year), for running quant algos in realtime.
there is no real volume in Forex, tick volume is worthless. that being said, Op didn't say if he needed his data to be Live, he can still export his data manually as cvs file including volume (as worthless as it maybe). i lose money for a living. Post # 6; Quote; Last Post: Mar 27, 2015 3:36pm Mar 27, 2015 3:36pm zerrrow Joined Nov 2014 Status: Member 8 Posts. Ok, The best way is to use ... Then we have plans to write posts about practical aspects of algorithmic trading in MATLAB. How to create modern automatic trading strategies such as: Statistical arbitrage pairs trading / mean reversion / market neutral trading strategies based on cointegration / bollinger bands / kalman filter etc for commodities, stocks and Forex. The toolbox lets you integrate streaming and event-based data into MATLAB ®, enabling you to develop financial trading strategies and algorithms that analyze and react to the market in real time. You can build algorithmic or automated trading strategies that work across multiple asset classes, instrument types, and trading markets while integrating with industry-standard or proprietary trade ... Develop trading systems with MATLAB. Algorithmic trading is a trading strategy that uses computational algorithms to drive trading decisions, usually in electronic financial markets. Applied in buy-side and sell-side institutions, algorithmic trading forms the basis of high-frequency trading, FOREX trading, and associated risk and execution analytics. Builders and users of algorithmic trading ... Matlab trading strategies, best stock broker nigeria.. Binary option 60 second matlab practice currency intraday trading strategy no deposit binary option affiliate. Best online broker for binary. Discusses performance characteristics of high frequency trading strategies and the requirements for implementation Join us for a partner webinar with MATLAB to learn how to analyze equity earnings ... Foren-Übersicht ‹ Forex Strategien, Analysen and Ressourcen ‹ Forex Markt; Internet Marketing & SEO by www.seoline.de; Ändere Schriftgröße; Druckansicht; FAQ; Model, Simulate and Control a Drone in MATLAB & SIMULINK. Allgemeines und Aktuelles zum Thema Devisen- und Währungsmarkt. 1 Beitrag • Seite 1 von 1. Model, Simulate and Control a Drone in MATLAB & SIMULINK. Beitrag von mitsumi ... I'll be willing to share my matlab code with you to confirm that we're on the same page. Right now, i have my Renko outputs as inputs into a system of feed-forward-back-propagation neural networks to forecast the next weekly close. Not to sound like someone who has bought into the proverbial snake oil, but the capture shows that the NN forecasted a higher close for this week 143.29 vs. 141.2 ...
You will learn how MATLAB® and add-on products can be used for data gathering, preparation and visualization, model development, backtesting, calibration, integration with existing systems and ... Free MATLAB Trial: https://goo.gl/yXuXnS Request a Quote: https://goo.gl/wNKDSg Contact Us: https://goo.gl/RjJAkE Learn more about MATLAB: https://goo.gl/8QV... Getting Started with Trading Toolbox, Part 2: Specify Contracts From the series (ON MATHWORKS SITE): Getting Started with Trading Toolbox: Anshuman Mishra, MathWorks Connect to Interactive Brokers ...