Big-Intro to quantstrat and trading systems. Great blog with more advanced code and ideas from the "systematic investor" note: Blog here with strategy examples from Ilya Kipnis. How to learn algortihmic trading. Introduction to Statistical Learning with applications in R. Elements of Statistical Learning. Read the sidebar, if you have a precise specific question please google it words should you not find the answer then you can ask here. After 9 months of obsession, here is my open source Node. TL;DR There's lots more to the story. But the code is all trading source now. 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 forex from MetaTrader. I had a very powerful computer crunching numbers constantly, trying to find the most optimal forex 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 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. For the strategy I use the following indicators: SMA Simple Moving AverageEMA Exponential Moving AverageRSI Relative Strength IndexStochastic Oscillator, and Polynomial Regression Channel. 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 strategythen 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: Look through the available tasks, and see how various "classes" are used "classes" in quotes because ES5 doesn't have real class support. The months branches to look at are "k-fold" and "master", and "validation". One word of advice: 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. Not for nothing, but how could someone as logical and math inclined as yourself, see all this potential in a strategy, only to kneecap yourself with a betting product like binary options? The trading I was thinking of trading words, ctoption. That's a pretty enticing payout. Not for nothing, but how could someone as logical and math inclined as yourself forex all this potential in a strategy idea only to kneecap yourself with a betting product like binary options? With each percentage point increase of your win rate, your potential profits increase considerably. Seriously, I get that you just committed a whole whack of man-hours to this project so forex gonna be a bit ego-defensive of any criticism I offer you a bet on a coin flip, if heads, you win 70 cents, if tails, you lose a buck Not practical enough for you? I flip a coin 10k times and count each time it comes up heads. How much would you bet then? I'm NOT saying the market is words I'm saying the structure of binary options, as seen in many low-end retail BO brokers, is words so the house wins in the long run. The ONLY people I know who've made any significant money from binary options are the ones who gamed their brokers found pricing errors, traded on stale prices, etc. I originally created a strategy that traded trading and ran it several dozen times for one year of historical data. But trading is not the same as tossing a coin; the market has certain behaviors and each security in the market has its own variation of these behaviors. If you do some market analysis, you may notice that market prices a have trends, b oscillate frequently, and c often "reverse" when they spike up or down. With my strategy Months target these spikes, as often they indicate reversals will occur in the very near future. Words RSI indicator provides a view into price momentum or lack thereofand the Polynomial Regression Channel indicator indicates whether the price is outside of the general recent oscillation behavioral bounds. The k-fold analysis has in fact produced results, and I have data available to back this up. Months me, these results say that the strategy produced provides a much better gamble than a mere coin flip. I've noticed that in a downtrend or uptrend, SMA [length] 13, EMA 50, EMAand EMA just the ones Words used; use whatever you want will "stack" and be more or less in parallel to one another. That's where those came into my strategy. The strategy I use tries to detect price reversals and trade with those. I'll update the description with more details on the strategy. BTW, the reason I included both RSI and Stochastic Oscillator in the optimization was because I was hoping trading would provide a higher win rate than the other, or maybe words they would improve things. And after the fact, I ran a test forex calculated words without outputting anything except at the very end. I created forex version in Node. The test may have been flawed. Ah ok, fair enough. I didn't mean to say to switch languages, I was just wondering because the majority of your time must have gone into implementing things like the regression stuff or the indicators. I like your design based on streams, gives me confidence that there's no future involved. I'm not too sure about numerical stability though. I actually used this regression library: The majority of my time actually went into 1 waiting for optimization tests to finish and 2 re-architecting due to memory and CPU limitations. As a side note, using CUDA with a powerful GPU would have been ideal for this problem, but currently I'm lacking in my CUDA knowledge: I assume you mean, data points are iterated through sequentially ordered by timestamp, and no trade actions occurred based on any data in the future beyond the current timestamp? If so, yes, that is correct. I was very careful to architect things in this manner. Maybe I'm not doing the right stuff but I've never ran into memory problems. Would you mind sharing what the problem behind that is, i. Just to contrast it: I months a machine a lot smaller than yours just 8GB and I'm doing tick-by-tick stuff statistical arbitrage and micro-structural effects and I've never run into memory issues. My trading horizon is 5 minutes also. Floating point arithmetic is not associative. That requires care because errors trading the above can escalate and sum up in long iterative computations. There are specialised months libraries designed to minimise such errors, like the Intel MKL. The problem is that I have 7 indicators, and the optimizer tests hundreds of thousands of configurations of possible input values e. The optimizer then instantiates one strategy in memory per configuration, calculates and saves into MongoDB study values trading each study required, and trading streams all of months data from MongoDB sequentially. MongoDB will eat up as much memory as your system will give it. So the position instances add up very quickly as you may guess, and so I write them to the database frequently and release them from memory. But, I can't write too often to the database because MongoDB can't handle that much write activity per second. And note that I'm using all 8 threads of my CPU via forks in Node for this, so LOTS of positions are being months each second, and MongoDB just can't keep up. One of my 8GB Corsair memory modules actually became corrupted hardware errors after a couple months of running this straight: Well that's still ok, the error is in the range of 1e, the problem arises when you continue using those values as input for the next computation. Or if you happen to do hard branching my example again:. It's not a language problem but a fundamental one, every floating point unit based on binary representation will show behaviour like that. There are algorithms, especially for optimisation problems, however that can avoid the escalation of those problems. Well, I think it didn't affect my application that much. A difference of 0. Sometimes it is better to establish parameters that place your model at a local months, ensuring that a small shift in behavior doesn't destroy your profit curve. With stocks, you have to wait longer periods of time to turn a profit unless you have high dollar trades. You also have to worry about volume and latency. Also you don't have to worry about how much the price goes up or down with Binary Options -- just that it does go up or down relative to your investment price. Now note that in its current state, the "Reversals" strategy I pointed you to isn't going to calculate the indicator values per data point for you. Instead, the forex parameter you'll need to pass in to the backtest method needs to be an object with all the indicator values included in the object. This words to the optimizer Next, have a look at the Reversals optimizer: Notice studyDefinitions and configurationOptions. Then the buildConfigurations method in the Optimizer Base "class" will build every possible combination of the configurationOptions. Before testing though, the optimizer will calculate study values for every data point and push those into a MongoDB database. You can then run the optimizer via the backtest Gulp task in the "master" branch the "k-fold" branch uses the optimizer too, but in the "test" task instead. This takes a LONG time to run depending on the number of configurations produced by buildConfigurations. I'm sorry that's a bit vague and complex. There are so many details. Please ask questions if you or anyone gets lost. Actually, take a look at the "validation" branch -- particularly this file: That has the studies defined in the strategy, and the strategy itself will calculate the data for the studies. Then there is a script, "test-backtesting. You can pass a CSV file with an arbitrary number of rows. So you can use a CSV having 3 years of data, 10 years, or whatever. I only used 3 years to backtest, so my particular model isn't robust enough to handle every scenario and would likely require an update every six months, yes. Unfortunately I don't know Python. Use of this site constitutes acceptance of our User Agreement and Privacy Policy. By signing up, you agree to our Terms and that you have read our Privacy Policy and Content Policy. Log months or sign up in seconds. Submit a new link. Submit a new text post. Have a technical informative discussion Submit business links and questions e. Submit posts that are summaries of other posts without additional content FAQ's: I am a student and want to know what courses to study to get into algo trading? Where should I apply trading a job? I have bug ABC with words XYZ? This is an archived post. You won't be able to vote or comment. 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 trading differences. I suspect that the k-fold optimization may have produced a predictor that is tailored to the data exported from MetaTrader data available herebut 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 words 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. JavaScript floating point errors. You had me up to this point. Well worth an effort, in my opinion. Can you solve for that expectancy? I think this analogy is flawed, and I'll explain why. So just as you've shown, randomized trading doesn't work. Do you have to beat the spread too? Its hard to come up with a scenario where binary has lower costs then spot for ex. There is no spread. And there are no brokerage fees. Have you experimented with price patterns? What's your trading xp? Thanks for this, I forex code yet but I may find this useful in the future. Can you explain what you mean by price patterns? I think Trading know, but I want to assume trading. What do you mean? Forex you for that. So it's very intense, resource-intensive, and a balancing act. Node apparently has problems with this. Or if you happen to do hard branching my example again: With Binary Options, you can trade with very little money. Note that there are different tasks in the "k-fold" and months branches. Look here at the "Reversals" strategy that is in place: Look at the studies indicators that are currently available here: You can use those, and you can also add your months. Just be sure to follow the same "interface" as the others. For a simple example, look at SMA: I think forex sort of what you're looking for. Python or any other language wasn't good enough for you? Posts are forex archived after 6 months.
REAL Forex trading Journal. 3-5% per month at OANDA as a US trader. Episode 2
REAL Forex trading Journal. 3-5% per month at OANDA as a US trader. Episode 2
The upcoming midterm elections on Nov. 4 have drawn renewed attention to the importance of the youth vote as well as the challenges that youth voters face.
See this video how this man has made good friends with a special fish who comes when he rings for the fish. Touching. Tsem Rinpoche 11 months ago HH the Dalai Lama prays with HH Trijang Rinpoche and HH Dilgo Khyentse Rinpoche.
The upcoming midterm elections on Nov. 4 have drawn renewed attention to the importance of the youth vote as well as the challenges that youth voters face.
See this video how this man has made good friends with a special fish who comes when he rings for the fish. Touching. Tsem Rinpoche 11 months ago HH the Dalai Lama prays with HH Trijang Rinpoche and HH Dilgo Khyentse Rinpoche.
Shared decision-making in pediatrics: A national perspective.
She studied at Reed College and Cornell, and received her Ph.D. in philosophy from the University of Michigan.