Forex Backtester - CNET Download - Free Software Downloads ...

Is Forex Tester 4 worth the outlay?

I'm interested in understanding back testing strategies, and have heard a few podcasts mention Forex Tester. I'm lost most weekends as i'm using IG INdex demo account, and cannot really trade or backtest on the platform.
Are there other, better software out there? Or even open-source or free?
submitted by ParanoidPete to Forex [link] [comments]

Former investment bank FX trader: Risk management part II

Former investment bank FX trader: Risk management part II
Firstly, thanks for the overwhelming comments and feedback. Genuinely really appreciated. I am pleased 500+ of you find it useful.
If you didn't read the first post you can do so here: risk management part I. You'll need to do so in order to make sense of the topic.
As ever please comment/reply below with questions or feedback and I'll do my best to get back to you.
Part II
  • Letting stops breathe
  • When to change a stop
  • Entering and exiting winning positions
  • Risk:reward ratios
  • Risk-adjusted returns

Letting stops breathe

We talked earlier about giving a position enough room to breathe so it is not stopped out in day-to-day noise.
Let’s consider the chart below and imagine you had a trailing stop. It would be super painful to miss out on the wider move just because you left a stop that was too tight.

Imagine being long and stopped out on a meaningless retracement ... ouch!
One simple technique is simply to look at your chosen chart - let’s say daily bars. And then look at previous trends and use the measuring tool. Those generally look something like this and then you just click and drag to measure.
For example if we wanted to bet on a downtrend on the chart above we might look at the biggest retracement on the previous uptrend. That max drawdown was about 100 pips or just under 1%. So you’d want your stop to be able to withstand at least that.
If market conditions have changed - for example if CVIX has risen - and daily ranges are now higher you should incorporate that. If you know a big event is coming up you might think about that, too. The human brain is a remarkable tool and the power of the eye-ball method is not to be dismissed. This is how most discretionary traders do it.
There are also more analytical approaches.
Some look at the Average True Range (ATR). This attempts to capture the volatility of a pair, typically averaged over a number of sessions. It looks at three separate measures and takes the largest reading. Think of this as a moving average of how much a pair moves.
For example, below shows the daily move in EURUSD was around 60 pips before spiking to 140 pips in March. Conditions were clearly far more volatile in March. Accordingly, you would need to leave your stop further away in March and take a correspondingly smaller position size.

ATR is available on pretty much all charting systems
Professional traders tend to use standard deviation as a measure of volatility instead of ATR. There are advantages and disadvantages to both. Averages are useful but can be misleading when regimes switch (see above chart).
Once you have chosen a measure of volatility, stop distance can then be back-tested and optimised. For example does 2x ATR work best or 5x ATR for a given style and time horizon?
Discretionary traders may still eye-ball the ATR or standard deviation to get a feeling for how it has changed over time and what ‘normal’ feels like for a chosen study period - daily, weekly, monthly etc.

Reasons to change a stop

As a general rule you should be disciplined and not change your stops. Remember - losers average losers. This is really hard at first and we’re going to look at that in more detail later.
There are some good reasons to modify stops but they are rare.
One reason is if another risk management process demands you stop trading and close positions. We’ll look at this later. In that case just close out your positions at market and take the loss/gains as they are.
Another is event risk. If you have some big upcoming data like Non Farm Payrolls that you know can move the market +/- 150 pips and you have no edge going into the release then many traders will take off or scale down their positions. They’ll go back into the positions when the data is out and the market has quietened down after fifteen minutes or so. This is a matter of some debate - many traders consider it a coin toss and argue you win some and lose some and it all averages out.
Trailing stops can also be used to ‘lock in’ profits. We looked at those before. As the trade moves in your favour (say up if you are long) the stop loss ratchets with it. This means you may well end up ‘stopping out’ at a profit - as per the below example.

The mighty trailing stop loss order
It is perfectly reasonable to have your stop loss move in the direction of PNL. This is not exposing you to more risk than you originally were comfortable with. It is taking less and less risk as the trade moves in your favour. Trend-followers in particular love trailing stops.
One final question traders ask is what they should do if they get stopped out but still like the trade. Should they try the same trade again a day later for the same reasons? Nope. Look for a different trade rather than getting emotionally wed to the original idea.
Let’s say a particular stock looked cheap based on valuation metrics yesterday, you bought, it went down and you got stopped out. Well, it is going to look even better on those same metrics today. Maybe the market just doesn’t respect value at the moment and is driven by momentum. Wait it out.
Otherwise, why even have a stop in the first place?

Entering and exiting winning positions

Take profits are the opposite of stop losses. They are also resting orders, left with the broker, to automatically close your position if it reaches a certain price.
Imagine I’m long EURUSD at 1.1250. If it hits a previous high of 1.1400 (150 pips higher) I will leave a sell order to take profit and close the position.
The rookie mistake on take profits is to take profit too early. One should start from the assumption that you will win on no more than half of your trades. Therefore you will need to ensure that you win more on the ones that work than you lose on those that don’t.

Sad to say but incredibly common: retail traders often take profits way too early
This is going to be the exact opposite of what your emotions want you to do. We are going to look at that in the Psychology of Trading chapter.
Remember: let winners run. Just like stops you need to know in advance the level where you will close out at a profit. Then let the trade happen. Don’t override yourself and let emotions force you to take a small profit. A classic mistake to avoid.
The trader puts on a trade and it almost stops out before rebounding. As soon as it is slightly in the money they spook and cut out, instead of letting it run to their original take profit. Do not do this.

Entering positions with limit orders

That covers exiting a position but how about getting into one?
Take profits can also be left speculatively to enter a position. Sometimes referred to as “bids” (buy orders) or “offers” (sell orders). Imagine the price is 1.1250 and the recent low is 1.1205.
You might wish to leave a bid around 1.2010 to enter a long position, if the market reaches that price. This way you don’t need to sit at the computer and wait.
Again, typically traders will use tech analysis to identify attractive levels. Again - other traders will cluster with your orders. Just like the stop loss we need to bake that in.
So this time if we know everyone is going to buy around the recent low of 1.1205 we might leave the take profit bit a little bit above there at 1.1210 to ensure it gets done. Sure it costs 5 more pips but how mad would you be if the low was 1.1207 and then it rallied a hundred points and you didn’t have the trade on?!
There are two more methods that traders often use for entering a position.
Scaling in is one such technique. Let’s imagine that you think we are in a long-term bulltrend for AUDUSD but experiencing a brief retracement. You want to take a total position of 500,000 AUD and don’t have a strong view on the current price action.
You might therefore leave a series of five bids of 100,000. As the price moves lower each one gets hit. The nice thing about scaling in is it reduces pressure on you to pick the perfect level. Of course the risk is that not all your orders get hit before the price moves higher and you have to trade at-market.
Pyramiding is the second technique. Pyramiding is for take profits what a trailing stop loss is to regular stops. It is especially common for momentum traders.

Pyramiding into a position means buying more as it goes in your favour
Again let’s imagine we’re bullish AUDUSD and want to take a position of 500,000 AUD.
Here we add 100,000 when our first signal is reached. Then we add subsequent clips of 100,000 when the trade moves in our favour. We are waiting for confirmation that the move is correct.
Obviously this is quite nice as we humans love trading when it goes in our direction. However, the drawback is obvious: we haven’t had the full amount of risk on from the start of the trend.
You can see the attractions and drawbacks of both approaches. It is best to experiment and choose techniques that work for your own personal psychology as these will be the easiest for you to stick with and build a disciplined process around.

Risk:reward and win ratios

Be extremely skeptical of people who claim to win on 80% of trades. Most traders will win on roughly 50% of trades and lose on 50% of trades. This is why risk management is so important!
Once you start keeping a trading journal you’ll be able to see how the win/loss ratio looks for you. Until then, assume you’re typical and that every other trade will lose money.
If that is the case then you need to be sure you make more on the wins than you lose on the losses. You can see the effect of this below.

A combination of win % and risk:reward ratio determine if you are profitable
A typical rule of thumb is that a ratio of 1:3 works well for most traders.
That is, if you are prepared to risk 100 pips on your stop you should be setting a take profit at a level that would return you 300 pips.
One needn’t be religious about these numbers - 11 pips and 28 pips would be perfectly fine - but they are a guideline.
Again - you should still use technical analysis to find meaningful chart levels for both the stop and take profit. Don’t just blindly take your stop distance and do 3x the pips on the other side as your take profit. Use the ratio to set approximate targets and then look for a relevant resistance or support level in that kind of region.

Risk-adjusted returns

Not all returns are equal. Suppose you are examining the track record of two traders. Now, both have produced a return of 14% over the year. Not bad!
The first trader, however, made hundreds of small bets throughout the year and his cumulative PNL looked like the left image below.
The second trader made just one bet — he sold CADJPY at the start of the year — and his PNL looked like the right image below with lots of large drawdowns and volatility.
Would you rather have the first trading record or the second?
If you were investing money and betting on who would do well next year which would you choose? Of course all sensible people would choose the first trader. Yet if you look only at returns one cannot distinguish between the two. Both are up 14% at that point in time. This is where the Sharpe ratio helps .
A high Sharpe ratio indicates that a portfolio has better risk-adjusted performance. One cannot sensibly compare returns without considering the risk taken to earn that return.
If I can earn 80% of the return of another investor at only 50% of the risk then a rational investor should simply leverage me at 2x and enjoy 160% of the return at the same level of risk.
This is very important in the context of Execution Advisor algorithms (EAs) that are popular in the retail community. You must evaluate historic performance by its risk-adjusted return — not just the nominal return. Incidentally look at the Sharpe ratio of ones that have been live for a year or more ...
Otherwise an EA developer could produce two EAs: the first simply buys at 1000:1 leverage on January 1st ; and the second sells in the same manner. At the end of the year, one of them will be discarded and the other will look incredible. Its risk-adjusted return, however, would be abysmal and the odds of repeated success are similarly poor.

Sharpe ratio

The Sharpe ratio works like this:
  • It takes the average returns of your strategy;
  • It deducts from these the risk-free rate of return i.e. the rate anyone could have got by investing in US government bonds with very little risk;
  • It then divides this total return by its own volatility - the more smooth the return the higher and better the Sharpe, the more volatile the lower and worse the Sharpe.
For example, say the return last year was 15% with a volatility of 10% and US bonds are trading at 2%. That gives (15-2)/10 or a Sharpe ratio of 1.3. As a rule of thumb a Sharpe ratio of above 0.5 would be considered decent for a discretionary retail trader. Above 1 is excellent.
You don’t really need to know how to calculate Sharpe ratios. Good trading software will do this for you. It will either be available in the system by default or you can add a plug-in.

VAR

VAR is another useful measure to help with drawdowns. It stands for Value at Risk. Normally people will use 99% VAR (conservative) or 95% VAR (aggressive). Let’s say you’re long EURUSD and using 95% VAR. The system will look at the historic movement of EURUSD. It might spit out a number of -1.2%.

A 5% VAR of -1.2% tells you you should expect to lose 1.2% on 5% of days, whilst 95% of days should be better than that
This means it is expected that on 5 days out of 100 (hence the 95%) the portfolio will lose 1.2% or more. This can help you manage your capital by taking appropriately sized positions. Typically you would look at VAR across your portfolio of trades rather than trade by trade.
Sharpe ratios and VAR don’t give you the whole picture, though. Legendary fund manager, Howard Marks of Oaktree, notes that, while tools like VAR and Sharpe ratios are helpful and absolutely necessary, the best investors will also overlay their own judgment.
Investors can calculate risk metrics like VaR and Sharpe ratios (we use them at Oaktree; they’re the best tools we have), but they shouldn’t put too much faith in them. The bottom line for me is that risk management should be the responsibility of every participant in the investment process, applying experience, judgment and knowledge of the underlying investments.Howard Marks of Oaktree Capital
What he’s saying is don’t misplace your common sense. Do use these tools as they are helpful. However, you cannot fully rely on them. Both assume a normal distribution of returns. Whereas in real life you get “black swans” - events that should supposedly happen only once every thousand years but which actually seem to happen fairly often.
These outlier events are often referred to as “tail risk”. Don’t make the mistake of saying “well, the model said…” - overlay what the model is telling you with your own common sense and good judgment.

Coming up in part III

Available here
Squeezes and other risks
Market positioning
Bet correlation
Crap trades, timeouts and monthly limits

***
Disclaimer:This content is not investment advice and you should not place any reliance on it. The views expressed are the author's own and should not be attributed to any other person, including their employer.
submitted by getmrmarket to Forex [link] [comments]

Looking for someone to collaborate with in exploring some of the fundamental questions in algo trading in relation to quantitative analysis and the Forex market specifically.

I got interested in both algo trading and Forex about the same time. I figured that if I was going to trade in the Forex market or any market there after, I was going to use algorithms to do the trading for me. I wanted to minimize the "human factor" from the trading equation. With the research I have done so far, it seems that human psychology and its volatile nature can skew ones ability to make efficient and logical trades consistently. I wanted to free myself from that burden and focus on other areas, specifically in creating a system that would allow me to generate algorithms that are profitable more often then not.
Consistently generating strategies that are more profitable then not is no easy task. There are a lot of questions one must first answer (to a satisfactory degree) before venturing forward in to the unknown abyss, lest you waste lots of time and money mucking about in the wrong direction. These following questions are what I have been trying to answer because I believe the answers to them are vital in pointing me in the right direction when it comes to generating profitable strategies.
Can quantitative analysis of the Forex market give an edge to a retail trader?
Can a retail trader utilize said edge to make consistent profits, within the market?
Are these profits enough to make a full time living on?
But before we answer these questions, there are even more fundamental questions that need to be answered.
To what degree if any is back-testing useful in generating successful algo strategies?
Are the various validation testing procedures such as monte carlo validation, multi market analysis, OOS testing, etc... useful when trying to validate a strategy and its ability to survive and thrive in future unseen markets?
What are the various parameters that are most successful? Example... 10% OOS, 20% OOS, 50%......?
What indicators if any are most successful in helping generate profitable strategies?
What data horizons are best suited to generate most successful strategies?
What acceptance criteria correlate with future performance of a strategy? Win/loss ratios, max draw-down, max consecutive losses, R2, Sharpe.....?
What constitutes a successful strategy? Low decay period? High stability? Shows success immediately once live? What is its half life? At what point do you cut it loose and say the strategy is dead? Etc....
And many many more fundamental questions....
As you can see answering these questions will be no easy or fast task, there is a lot of research and data mining that will have to be done. I like to approach things from a purely scientific method, make no assumptions about anything and use a rigorous approach when testing, validating any and all conclusions. I like to see real data and correlations that are actually there before I start making assumptions.
The reason I am searching for these answers is because, they are simply not available out on the internet. I have read many research papers on-line, and articles on this or that about various topics related to Forex and quantitative analysis, but whatever information there is, its very sparse or very vague (and there is no shortage of disinformation out there). So, I have no choice but to answer these questions myself.
I have and will be spending considerable time on the endeavour, but I am also not delusional, there is only so much 1 man can do and achieve with the resources at his disposal. And at the end of the whole thing, I can at least say I gave it a good try. And along the way learn some very interesting things (already had a few eureka moments).
Mo workflow so far has consisted of using a specific (free) software package that generate strategies. You can either use it to auto generate strategies or create very specific rules yourself and create the strategies from scratch. I am not a coder so I find this tool quite useful. I mainly use this tool to do lots of hypothesis testing as I am capable of checking for any possible correlations in the markets very fast, and then test for the significance if any of said correlations.
Anyways who I am looking for? Well if you are the type of person that has free time on their hands, is keen on the scientific method and rigorous testing and retesting of various hypothesis, hit me up. You don't need to be a coder or have a PHD in statistics. Just someone who is interested in answering the same questions I am.
Whats the end goal? I want to answer enough of these questions with enough certainty, whereby I can generate profitable algo strategies consistently. OR, maybe the answer is that It cant be done by small fry such as a retail trader. And that answer would be just as satisfactory, because It could save me a lot more time and money down the road, because I could close off this particular road and look elsewhere to make money.
submitted by no_witty_username to Forex [link] [comments]

So you wanna trade Forex? - tips and tricks inside

Let me just sum some stuff up for you newbies out there. Ive been trading for years, last couple of years more seriously and i turned my strategies into algorithms and i am currently up to 18 algorithms thats trading for me 24/7. Ive learned alot, listened to hundreds of podcasts and read tons of books + research papers and heres some tips and tricks for any newbie out there.

  1. Strategy - How to... When people say "you need a trading strategy!!" Its because trading is very hard and emotional. You need to stick to your rules at all times. Dont panic and move your stop loss or target unless your rules tell you to. Now how do you make these rules? Well this is the part that takes alot of time. If your rules are very simple (for example: "Buy if Last candles low was the lowest low of the past 10 candles." Lets make this a rule. You can backtest it manually by looking at a chart and going back in time and check every candle. or you can code it using super simple software like prorealtime, MT4 ++ Alot of software is basicly "click and drag" and press a button and it gives you backtest from 10-20-30 years ago in 5 seconds. This is the absolute easiest way to backtest rules and systems. If your trading "pure price action" with your drawn lines and shit, the only way to truly backtest that kind of trading is going in a random forex pair to a random point in time, could be 1 year ago, 1 month ago, 5 years ago.. and then you just trade! Move chart 1 candle at a time, draw your lines and do some "actual trading" and look at your results after moving forward in the chart. If you do not test your strategy your just going in blind, which could be disaster.. Maybe someone told u "this is the correct way to trade" or "this strategy is 90% sure to win every trade!!!" If you think you can do trading without a strategy, then your most likely going to look back at an empty account and wonder why you moved that stop loss or why you didnt take profit etc.. and then your gonna give up. People on youtube, forums, interwebz are not going to give you/sell you a working strategy thats gonna make you rich. If they had a working strategy, they would not give it away/sell it to you.
  2. Money management - How to.... Gonna keep this one short. Risk a small % of your capital on each trade. Dont risk 10%, dont risk 20%. You are going to see loosing trades, your probably gonna see 5-10 loss in a row!! If your trading a 1000$ account and your risking 100$ on each trade (10%) and you loose 5 in a row, your down -50% and probably you cant even trade cus of margin req. Game over.. Now how does one get super rich, super fast, from risking 1-3% of your account on each trade?? Well heres the shocking message: YOU CANT GET RICH FAST FROM TRADING UNLESS YOUR WILLING TO GO ALL IN! You can of course go all in on each trade and if you get em all right, you might get 1000%, then you go all in 1 more time and loose it all... The whole point of trading is NOT going bust. Not loosing everything, cus if you loose it all its game over and no more trading for you.
  3. Find your own trading style.... Everyone is different. You can have an average holding period of 1 month or you could be looking at a 1 min chart and average holding time = 10 minutes. For some, less volatility helps them sleep at night. For others, more volatility gives them a rush and some people crave this. There is no "correct" timeframes, or holding periods, or how much to profit or how much to loose. We are all individuals with different taste in risk. Some dont like risk, others wanna go all in to get rich over night. The smart approach is somewhere in the middle. If you dont risk anything, your not gonna get anything. If you risk everything, your most likely going to loose everything. When people are talking about trading style, this is kinda what that means.
  4. There are mainly 2 ways to trade: Divergence and Convergence. Or in other words: Mean reversion or trend following. Lets talk about them both: Trend following is trying to find a trend and stay with the trend until its over. Mean reversion is the belief that price is too far away from the average XX of price, and sooner or later, price will have to return to its average/mean (hence the name: MEAN reversion). Trend following systems usually see a lower winrate (30-40% winrate with no money management is not uncommon to see when backtesting trend following systems.. You can add good money management to get the winrate % higher. Why is the % winrate so low? Well a market, whatever that market is, tend to get real choppy and nasty right after a huge trend. So your gonna see alot of choppy fake signals that might kill 5-6 trades in a row, until the next huge trend starts which is going to cover all the losses from the small losses before the trend took off. Then you gotta hold that trade until trade is done. How do you define "when trend starts and stops"? Well thats back to point 1, find a strategy. Try defining rules for an entry and exit and see how it goes when you backtest it. For mean reversion the win % is usually high, like 70-90% winrate, but the average winning trade is alot smaller than the average loosing trade. this happens because you are basicly trying to catch a falling knife, or catch a booming rocket. Usually when trading mean reversion, waiting for price to actually reverse can very often leave you with being "too late", so you kinda have to find "the bottom" or "the top" before it actually has bottomed/ topped out and reversed. How can you do this you ask? Well your never going to hit every top or every bottom, but you can find ways to find "the bottom-ish" or "the top-ish", thens ell as soon as price reverts back to the mean. Sometimes your gonna wish you held on to the trade for longer, but again, back to point 1: Backtest your rules and figure that shit out.

Read these 4 points and try to follow them and you are at least 4 steps closer to being a profitable trader. Some might disagree with me on some points but i think for the majority, people are going to agree that these 4 points are pretty much universal. Most traders have done or are doing these things every day, in every trade.
Here is some GREAT material to read: Kevin Davey has won trading championship multiple times and he has written multiple great books, from beginner to advanced level. Recommend these books 100%, for example: Building winning algorithmic trading systems" will give you alot to work with when it comes to all 4 of the above points. Market wizards, Reminiscences of a stock operator are 2 books that are a great read but wont give you much "trading knowledge" that you can directly use for your trading. Books on "The turtles" are great reading. Then you have podcasts and youtube. I would stay away from youtube as much as possible when it comes to "Heres how to use the rsi!!!" or "this strategy will make you rich!!". Most youtube videoes are made by people who wanna sell you a course or a book. Most of this is just pure bullshit. Youtube can very harmfull and i would honestly advice about going there for "strategy adivce" and such. Podcasts tho are amazing, i highly recommend: Better systems trader, Chat with traders, Top traders unplugged, We study billionairs, to name a few :)
Also, on a less funny note.. Please realize that you are, and i am, real fucking stupid and lazy compared to the actual pro's out there. This is why you should not go "all in" on some blind stupid strategy youve heard about. This is why this is indeed VERY FUCKING HARD and most, if not everyone has busted an account or two before realizing just this. Your dumb.. your not going to be super rich within 1 year.. You can not start with 500$ account and make millions! (some might have been able to do this, but know that for every winner, theres 999 loosers behind him that failed... Might work fine first 5 trades, then 1 fuckup tho and ur gone..
And lastly: Try using a backtesting software. Its often FREE!!! (on a demo account) and often so simple a baby could use it. If your trading lines and such there exists web broweser "games" and softwares that lets you go "1 and 1 candle ahead" in random forex pairs and that lets you trade as if its "real" as it goes.
A big backtesting trap however is backtesting "losely" by just drawing lines and looking at chart going "oh i would have taken this trade FOR SURE!! I would have made so much money!!" however this is not actually backtesting, its cherry picking and its biased beyond the grave, and its going to hurt you. Try going 1 candle at a time doing "real and live" trades and see how it goes.

Bonus point!!
many people misunderstands what indicators like the RSI is telling you. Indeed something is "overbought" or "oversold" but only compared to the last average of xx amounts of bars/candles.
It doesn't tell you that RIGHT NOW is a great time to sell or buy. It only tells you that the math formula that is RSI, gives you a number between 1-100, and when its above 70 its telling you that momentum is up compared to the last average 14 candles. This is not a complete buy/sell signal. Its more like a filter if anything. This is true for MOST indicators. They INDICATE stuff. Dont use them as pure buy/sell signals.. At least backtest that shit first! Your probably gonna be shocked at the shitty results if you "buy wehn rsi is undeer 30 and sell when RSI is above 70".

Editedit: Huge post already, why not copy paste my comment with an example showing the difference in trend following vs mean reversion:
The thing about trend following is that we never know when a trade starts and when it ends. So what often happens is that you have to buy every breakout going up, but not every breakout is a new trend. Lets do an example. Check out the photo i included here: https://imageshost.eu/image/image.RcC

THE PHOTO IS JUST AN EXAMPLE THAT SHOWS WHY A TYPICAL TREND FOLLOWING STRATEGY HAVE A "LOW" WINRATE.
THE PHOTO IS NOT SHOWING AN EXAMPLE OF MY STRATEGIES OR TRADING.

  1. We identify the big orange trend up.
  2. We see the big break down (marked with the vertical red line) this is telling us we are not going higher just yet. Our upwards trend is broken. However we might continue going up in a new trend, but when will that trend come?
  3. We can draw the blue trend very earyly using highs and lows, lines up and down. Then we begin to look for breakouts of the upper blue line. So every time price breaks upper blue line we have to buy (cus how else are we going to "catch the next trend going up?)
As you can see we get 5 false breakouts before the real breakout happens!
Now if you could tell fake breakouts from real breakouts, your gonna be rich hehe. For everyone else: Take every signal you can get, put a "tight" stop loss so in case its a fake signal you only loose a little bit. Then when breakout happens as you can clearly see in chart, your going to make back all the small losses.
So in this example we fail 5 times, but get 1 HUGE new trend going further up. This 1 huge trade, unless we fuck it up and take profits too early or shit like that, is going to win back all those small losses + more.
This is why trend following has a low winrate. You get 5 small loss and 1 big win.

Now lets flip this! Imagine if your trading Mean reversion on all the same red arrows! So every time price hits the blue line, we go short back to the bottom (or middle) again! You would have won 5 trades with small profits, but on that last one you would get stopped out so hard. Meaning 5 small wins, 1 big loss (as some have pointed out in comments, if you where trading mean reverting you would wanna buy the lows as well as short the tops - photo was suppose to show why trend following strategies have a lower % winrate.)

Final edit: sorry this looks like a wall of text on ur phones.
submitted by RipRepRop to Forex [link] [comments]

Looking for someone to collaborate with in exploring some of the fundamental questions in algo trading in relation to quantitative analysis and the Forex market specifically.

I got interested in both algo trading and Forex about the same time. I figured that if I was going to trade in the Forex market or any market there after, I was going to use algorithms to do the trading for me. I wanted to minimize the "human factor" from the trading equation. With the research I have done so far, it seems that human psychology and its volatile nature can skew ones ability to make efficient and logical trades consistently. I wanted to free myself from that burden and focus on other areas, specifically in creating a system that would allow me to generate algorithms that are profitable more often then not.
Consistently generating strategies that are more profitable then not is no easy task. There are a lot of questions one must first answer (to a satisfactory degree) before venturing forward in to the unknown abyss, lest you waste lots of time and money mucking about in the wrong direction. These following questions are what I have been trying to answer because I believe the answers to them are vital in pointing me in the right direction when it comes to generating profitable strategies.
Can quantitative analysis of the Forex market give an edge to a retail trader?
Can a retail trader utilize said edge to make consistent profits, within the market?
Are these profits enough to make a full time living on?
But before we answer these questions, there are even more fundamental questions that need to be answered.
To what degree if any is back-testing useful in generating successful algo strategies?
Are the various validation testing procedures such as monte carlo validation, multi market analysis, OOS testing, etc... useful when trying to validate a strategy and its ability to survive and thrive in future unseen markets?
What are the various parameters that are most successful? Example... 10% OOS, 20% OOS, 50%......?
What indicators if any are most successful in helping generate profitable strategies?
What data horizons are best suited to generate most successful strategies?
What acceptance criteria correlate with future performance of a strategy? Win/loss ratios, max draw-down, max consecutive losses, R2, Sharpe.....?
What constitutes a successful strategy? Low decay period? High stability? Shows success immediately once live? What is its half life? At what point do you cut it loose and say the strategy is dead? Etc....
And many many more fundamental questions....
As you can see answering these questions will be no easy or fast task, there is a lot of research and data mining that will have to be done. I like to approach things from a purely scientific method, make no assumptions about anything and use a rigorous approach when testing, validating any and all conclusions. I like to see real data and correlations that are actually there before I start making assumptions.
The reason I am searching for these answers is because, they are simply not available out on the internet. I have read many research papers on-line, and articles on this or that about various topics related to Forex and quantitative analysis, but whatever information there is, its very sparse or very vague (and there is no shortage of disinformation out there). So, I have no choice but to answer these questions myself.
I have and will be spending considerable time on the endeavour, but I am also not delusional, there is only so much 1 man can do and achieve with the resources at his disposal. And at the end of the whole thing, I can at least say I gave it a good try. And along the way learn some very interesting things (already had a few eureka moments).
Mo workflow so far has consisted of using a specific (free) software package that generate strategies. You can either use it to auto generate strategies or create very specific rules yourself and create the strategies from scratch. I am not a coder so I find this tool quite useful. I mainly use this tool to do lots of hypothesis testing as I am capable of checking for any possible correlations in the markets very fast, and then test for the significance if any of said correlations.
Anyways who I am looking for? Well if you are the type of person that has free time on their hands, is keen on the scientific method and rigorous testing and retesting of various hypothesis, hit me up. You don't need to be a coder or have a PHD in statistics. Just someone who is interested in answering the same questions I am.
Whats the end goal? I want to answer enough of these questions with enough certainty, whereby I can generate profitable algo strategies consistently. OR, maybe the answer is that It cant be done by small fry such as a retail trader. And that answer would be just as satisfactory, because It could save me a lot more time and money down the road, because I could close off this particular road and look elsewhere to make money.
submitted by no_witty_username to algotrading [link] [comments]

Strategy - Why you should backtest your systems

Hello world. Long time lurker here. Wanna give a huge tip to all newbies out there thats thinking about strategies. (like "Buy when price crosses above moving average 10" or whatever)

If you think you have a strategy (a set of rules) then theres no excuse not to backtest the rules!
Lets say you wanna "Buy when moving average[10] crosses over moving average[20]" or whatever numbers or indicators or rules youre thinking about.
All you gotta do is sign up with a broker (demo account!! free) that provides you with a software that allows you to backtest! The coding is 9/10 times super duper easy to learn and use. Just google for it and you should find multiple brokers that can provide different types of software to run backtests.

"Cant you just backtest manually looking at chart?" - Well, you can! But this takes a long long time and you will not see exact results. Using a software you can test your strategy on all the different timeframes, multiple years back in time, on all the currency pairs you want to test it on.
Heres an example, its my own algorithm (algorithm just means "set of rules") that i have created for USD/JPY, backtested from 2004 -> 2019 thats 15 years of backtesting! My system seemed profitable and robust so i decided to run this system live and its actually in a trade right now. So far its been profitable and good to me.

For all you traders that do not rely on fixed systems/set of rules: Backtesting is very hard because if you rely on your own drawings and support/resistance lines, then backtesting is biased before you even begin.. unless your testing in "real time" which of course is much more valid. Non the less you should just scroll "back in time" on chart and start "trading" with "paper money" and just move 1 and 1 candle forward until you either see that your system is working or not.

https://imgur.com/a/p8aVdIT

Edit: Guess i never answered "Why" you should backtest. I started out trading stocks, then i realized "patterns" was a thing in every chart on all timeframes and i started to look at forex cus markets are plentiful and open 24/5. After a few hit and missed and busted tiny accounts i realized i needed to test my theories and strategies. Thats when i discovered that coming up with a strategy that actually works, year after year on multiple pairs, is not only hard, its realy fucking hard! And thats also when i realized how flawed my "plans to get rich in trading" really was.. When your just charting and drawing lines and trading on them, you dont really have a plan, and without a firm strict plan (at least for me) the pitfalls are many, and devastating. So i started looking at how to actually make profitable strategies, reading books and listening to podcasts, and today im running multiple strategies in multiple markets on multiple timeframes. So far ive been profitable. definitly not gonna quit my dayjob tomorrow, just so thats clear.. trading is risky and having a dayjob and monthly income is definitly something im gonna continue with lol.
submitted by RipRepRop to Forex [link] [comments]

Which are your Top 5 favourite coins out of the Top 100? An analysis.

I am putting together my investment portfolio for 2018 and made a complete summary of the current Top 100. Interestingly, I noticed that all coins can be categorized into 12 markets. Which markets do you think will play the biggest role in the coming year?
Here is a complete overview of all coins in an excel sheet including name, market, TPS, risk profile, time since launch (negative numbers mean that they are launching that many months in the future) and market cap. You can also sort by all of these fields of course. Coins written in bold are the strongest contenders within their market either due to having the best technology or having a small market cap and still excellent technology and potential. https://docs.google.com/spreadsheets/d/1s8PHcNvvjuy848q18py_CGcu8elRGQAUIf86EYh4QZo/edit#gid=0
The 12 markets are
  1. Currency 13 coins
  2. Platform 25 coins
  3. Ecosystem 9 coins
  4. Privacy 10 coins
  5. Currency Exchange Tool 8 coins
  6. Gaming & Gambling 5 coins
  7. Misc 15 coins
  8. Social Network 4 coins
  9. Fee Token 3 coins
  10. Decentralized Data Storage 4 coins
  11. Cloud Computing 3 coins
  12. Stable Coin 2 coins
Before we look at the individual markets, we need to take a look of the overall market and its biggest issue scalability first:
Cryptocurrencies aim to be a decentralized currency that can be used worldwide. Its goal is to replace dollar, Euro, Yen, all FIAT currencies worldwide. The coin that will achieve that will be worth several trillion dollars.
Bitcoin can only process 7 transactions per second (TPS). In order to replace all FIAT, it would need to perform at at least VISA levels, which usually processes around 3,000 TPS, up to 25,000 TPS during peak times and a maximum of 64,000 TPS. That means that this cryptocurrency would need to be able to perform at least several thousand TPS. However, a ground breaking technology should not look at current technology to set a goal for its use, i.e. estimating the number of emails sent in 1990 based on the number of faxes sent wasn’t a good estimate.
For that reason, 10,000 TPS is the absolute baseline for a cryptocurrency that wants to replace FIAT. This brings me to IOTA, which wants to connect all 80 billion IoT devices that are expected to exist by 2025, which constantly communicate with each other, creating 80 billion or more transactions per second. This is the benchmark that cryptocurrencies should be aiming for. Currently, 8 billion devices are connected to the Internet.
With its Lightning network recently launched, Bitcoin is realistically looking at 50,000 possible soon. Other notable cryptocurrencies besides IOTA and Bitcoin are Nano with 7,000 TPS already tested, Dash with several billion TPS possible with Masternodes, Neo, LISK and RHOC with 100,000 TPS by 2020, Ripple with 50,000 TPS, Ethereum with 10,000 with Sharding.
However, it needs to be said that scalability usually goes at the cost of decentralization and security. So, it needs to be seen, which of these technologies can prove itself resilient and performant.
Without further ado, here are the coins of the first market

Market 1 - Currency:

  1. Bitcoin: 1st generation blockchain with currently bad scalability currently, though the implementation of the Lightning Network looks promising and could alleviate most scalability concerns, scalability and high energy use.
  2. Ripple: Centralized currency that might become very successful due to tight involvement with banks and cross-border payments for financial institutions; banks and companies like Western Union and Moneygram (who they are currently working with) as customers customers. However, it seems they are aiming for more decentralization now.https://ripple.com/dev-blog/decentralization-strategy-update/. Has high TPS due to Proof of Correctness algorithm.
  3. Bitcoin Cash: Bitcoin fork with the difference of having an 8 times bigger block size, making it 8 times more scalable than Bitcoin currently. Further block size increases are planned. Only significant difference is bigger block size while big blocks lead to further problems that don't seem to do well beyond a few thousand TPS. Opponents to a block size argue that increasing the block size limit is unimaginative, offers only temporary relief, and damages decentralization by increasing costs of participation. In order to preserve decentralization, system requirements to participate should be kept low. To understand this, consider an extreme example: very big blocks (1GB+) would require data center level resources to validate the blockchain. This would preclude all but the wealthiest individuals from participating.Community seems more open than Bitcoin's though.
  4. Litecoin : Little brother of Bitcoin. Bitcoin fork with different mining algorithm but not much else.Copies everything that Bitcoin does pretty much. Lack of real innovation.
  5. Dash: Dash (Digital Cash) is a fork of Bitcoin and focuses on user ease. It has very fast transactions within seconds, low fees and uses Proof of Service from Masternodes for consensus. They are currently building a system called Evolution which will allow users to send money using usernames and merchants will find it easy to integrate Dash using the API. You could say Dash is trying to be a PayPal of cryptocurrencies. Currently, cryptocurrencies must choose between decentralization, speed, scalability and can pick only 2. With Masternodes, Dash picked speed and scalability at some cost of decentralization, since with Masternodes the voting power is shifted towards Masternodes, which are run by Dash users who own the most Dash.
  6. IOTA: 3rd generation blockchain called Tangle, which has a high scalability, no fees and instant transactions. IOTA aims to be the connective layer between all 80 billion IOT devices that are expected to be connected to the Internet in 2025, possibly creating 80 billion transactions per second or 800 billion TPS, who knows. However, it needs to be seen if the Tangle can keep up with this scalability and iron out its security issues that have not yet been completely resolved.
  7. Nano: 3rd generation blockchain called Block Lattice with high scalability, no fees and instant transactions. Unlike IOTA, Nano only wants to be a payment processor and nothing else, for now at least. With Nano, every user has their own blockchain and has to perform a small amount of computing for each transaction, which makes Nano perform at 300 TPS with no problems and 7,000 TPS have also been tested successfully. Very promising 3rd gen technology and strong focus on only being the fastest currency without trying to be everything.
  8. Decred: As mining operations have grown, Bitcoin’s decision-making process has become more centralized, with the largest mining companies holding large amounts of power over the Bitcoin improvement process. Decred focuses heavily on decentralization with their PoW Pos hybrid governance system to become what Bitcoin was set out to be. They will soon implement the Lightning Network to scale up. While there do not seem to be more differences to Bitcoin besides the novel hybrid consensus algorithm, which Ethereum, Aeternity and Bitcoin Atom are also implementing, the welcoming and positive Decred community and professoinal team add another level of potential to the coin.
  9. Aeternity: We’ve seen recently, that it’s difficult to scale the execution of smart contracts on the blockchain. Crypto Kitties is a great example. Something as simple as creating and trading unique assets on Ethereum bogged the network down when transaction volume soared. Ethereum and Zilliqa address this problem with Sharding. Aeternity focuses on increasing the scalability of smart contracts and dapps by moving smart contracts off-chain. Instead of running on the blockchain, smart contracts on Aeternity run in private state channels between the parties involved in the contracts. State channels are lines of communication between parties in a smart contract. They don’t touch the blockchain unless they need to for adjudication or transfer of value. Because they’re off-chain, state channel contracts can operate much more efficiently. They don’t need to pay the network for every time they compute and can also operate with greater privacy. An important aspect of smart contract and dapp development is access to outside data sources. This could mean checking the weather in London, score of a football game, or price of gold. Oracles provide access to data hosted outside the blockchain. In many blockchain projects, oracles represent a security risk and potential point of failure, since they tend to be singular, centralized data streams. Aeternity proposes decentralizing oracles with their oracle machine. Doing so would make outside data immutable and unchangeable once it reaches Aeternity’s blockchain. Of course, the data source could still be hacked, so Aeternity implements a prediction market where users can bet on the accuracy and honesty of incoming data from various oracles.It also uses prediction markets for various voting and verification purposes within the platform. Aeternity’s network runs on on a hybrid of proof of work and proof of stake. Founded by a long-time crypto-enthusiast and early colleague of Vitalik Buterin, Yanislav Malahov. Promising concept though not product yet
  10. Bitcoin Atom: Atomic Swaps and hybrid consenus. This looks like the only Bitcoin clone that actually is looking to innovate next to Bitcoin Cash.
  11. Dogecoin: Litecoin fork, fantastic community, though lagging behind a bit in technology.
  12. Bitcoin Gold: A bit better security than bitcoin through ASIC resistant algorithm, but that's it. Not that interesting.
  13. Digibyte: Digibyte's PoS blockchain is spread over a 100,000+ servers, phones, computers, and nodes across the globe, aiming for the ultimate level of decentralization. DigiByte rebalances the load between the five mining algorithms by adjusting the difficulty of each so one algorithm doesn’t become dominant. The algorithm's asymmetric difficulty has gained notoriety and been deployed in many other blockchains.DigiByte’s adoption over the past four years has been slow. It’s still a relatively obscure currency compared its competitors. The DigiByte website offers a lot of great marketing copy and buzzwords. However, there’s not much technical information about what they have planned for the future. You could say Digibyte is like Bitcoin, but with shorter blocktimes and a multi-algorithm. However, that's not really a difference big enough to truly set themselves apart from Bitcoin, since these technologies could be implemented by any blockchain without much difficulty. Their decentralization is probably their strongest asset, however, this also change quickly if the currency takes off and big miners decide to go into Digibyte.
  14. Bitcoin Diamond Asic resistant Bitcoin and Copycat

Market 2 - Platform

Most of the cryptos here have smart contracts and allow dapps (Decentralized apps) to be build on their platform and to use their token as an exchange of value between dapp services.
  1. Ethereum: 2nd generation blockchain that allows the use of smart contracts. Bad scalability currently, though this concern could be alleviated by the soon to be implemented Lightning Network aka Plasma and its Sharding concept.
  2. EOS: Promising technology that wants to be able do everything, from smart contracts like Ethereum, scalability similar to Nano with 1000 tx/second + near instant transactions and zero fees, to also wanting to be a platform for dapps. However, EOS doesn't have a product yet and everything is just promises still. Highly overvalued right now. However, there are lots of red flags, have dumped $500 million Ether over the last 2 months and possibly bought back EOS to increase the size of their ICO, which has been going on for over a year and has raised several billion dollars. All in all, their market cap is way too high for that and not even having a product.
  3. Cardano: Similar to Ethereum/EOS, however, only promises made with no delivery yet, highly overrated right now. Interesting concept though. Market cap way too high for not even having a product. Somewhat promising technology.
  4. VeChain: Singapore-based project that’s building a business enterprise platform and inventory tracking system. Examples are verifying genuine luxury goods and food supply chains. Has one of the strongest communities in the crypto world. Most hyped token of all, with merit though.
  5. Neo: Neo is a platform, similar to Eth, but more extensive, allowing dapps and smart contracts, but with a different smart contract gas system, consensus mechanism (PoS vs. dBfT), governance model, fixed vs unfixed supply, expensive contracts vs nearly free contracts, different ideologies for real world adoption. There are currently only 9 nodes, each of which are being run by a company/entity hand selected by the NEO council (most of which are located in china) and are under contract. This means that although the locations of the nodes may differ, ultimately the neo council can bring them down due to their legal contracts. In fact this has been done in the past when the neo council was moving 50 million neo that had been locked up. Also dbft (or neo's implmentation of it) has failed underload causing network outages during major icos. The first step in decentralization is that the NEO Counsel will select trusted nodes (Universities, business partners, etc.) and slowly become less centralized that way. The final step in decentralization will be allowing NEO holders to vote for new nodes, similar to a DPoS system (ARK/EOS/LISK). NEO has a regulation/government friendly ideology. Finally they are trying to work undewith the Chinese government in regards to regulations. If for some reason they wanted it shut down, they could just shut it down.
  6. Stellar: PoS system, similar goals as Ripple, but more of a platform than only a currency. 80% of Stellar are owned by Stellar.org still, making the currency centralized.
  7. Ethereum classic: Original Ethereum that decided not to fork after a hack. The Ethereum that we know is its fork. Uninteresing, because it has a lot of less resources than Ethereum now and a lot less community support.
  8. Ziliqa: Zilliqa is building a new way of sharding. 2400 tpx already tested, 10,000 tps soon possible by being linearly scalable with the number of nodes. That means, the more nodes, the faster the network gets. They are looking at implementing privacy as well.
  9. QTUM: Enables Smart contracts on the Bitcoin blockchain. Useful.
  10. Icon: Korean ethereum. Decentralized application platform that's building communities in partnership with banks, insurance providers, hospitals, and universities. Focused on ID verification and payments. No big differentiators to the other 20 Ethereums, except that is has a product. That is a plus. Maybe cheap alternative to Ethereum.
  11. LISK: Lisk's difference to other BaaS is that side chains are independent to the main chain and have to have their own nodes. Similar to neo whole allows dapps to deploy their blockchain to. However, Lisk is currently somewhat centralized with a small group of members owning more than 50% of the delegated positions. Lisk plans to change the consensus algorithm for that reason in the near future.
  12. Rchain: Similar to Ethereum with smart contract, though much more scalable at an expected 40,000 TPS and possible 100,000 TPS. Not launched yet. No product launched yet, though promising technology. Not overvalued, probably at the right price right now.
  13. ARDR: Similar to Lisk. Ardor is a public blockchain platform that will allow people to utilize the blockchain technology of Nxt through the use of child chains. A child chain, which is a ‘light’ blockchain that can be customized to a certain extent, is designed to allow easy self-deploy for your own blockchain. Nxt claims that users will "not need to worry" about security, as that part is now handled by the main chain (Ardor). This is the chief innovation of Ardor. Ardor was evolved from NXT by the same company. NEM started as a NXT clone.
  14. Ontology: Similar to Neo. Interesting coin
  15. Bytom: Bytom is an interactive protocol of multiple byte assets. Heterogeneous byte-assets (indigenous digital currency, digital assets) that operate in different forms on the Bytom Blockchain and atomic assets (warrants, securities, dividends, bonds, intelligence information, forecasting information and other information that exist in the physical world) can be registered, exchanged, gambled and engaged in other more complicated and contract-based interoperations via Bytom.
  16. Nxt: Similar to Lisk
  17. Stratis: Different to LISK, Stratis will allow businesses and organizations to create their own blockchain according to their own needs, but secured on the parent Stratis chain. Stratis’s simple interface will allow organizations to quickly and easily deploy and/or test blockchain functionality of the Ethereum, BitShares, BitCoin, Lisk and Stratis environements.
  18. Status: Status provides access to all of Ethereum’s decentralized applications (dapps) through an app on your smartphone. It opens the door to mass adoption of Ethereum dapps by targeting the fastest growing computer segment in the world – smartphone users.16. Ark: Fork of Lisk that focuses on a smaller feature set. Ark wallets can only vote for one delegate at a time which forces delegates to compete against each other and makes cartel formations incredibly hard, if not impossible.
  19. Neblio: Similar to Neo, but 30x smaller market cap.
  20. NEM: Is similar to Neo No marketing team, very high market cap for little clarilty what they do.
  21. Bancor: Bancor is a Decentralized Liquidity Network that allows you to hold any Ethereum token and convert it to any other token in the network, with no counter party, at an automatically calculated price, using a simple web wallet.
  22. Dragonchain: The Purpose of DragonChain is to help companies quickly and easily incorporate blockchain into their business applications. Many companies might be interested in making this transition because of the benefits associated with serving clients over a blockchain – increased efficiency and security for transactions, a reduction of costs from eliminating potential fraud and scams, etc.
  23. Skycoin: Transactions with zero fees that take apparently two seconds, unlimited transaction rate, no need for miners and block rewards, low power usage, all of the usual cryptocurrency technical vulnerabilities fixed, a consensus mechanism superior to anything that exists, resistant to all conceivable threats (government censorship, community infighting, cybenucleaconventional warfare, etc). Skycoin has their own consensus algorithm known as Obelisk written and published academically by an early developer of Ethereum. Obelisk is a non-energy intensive consensus algorithm based on a concept called ‘web of trust dynamics’ which is completely different to PoW, PoS, and their derivatives. Skywire, the flagship application of Skycoin, has the ambitious goal of decentralizing the internet at the hardware level and is about to begin the testnet in April. However, this is just one of the many facets of the Skycoin ecosystem. Skywire will not only provide decentralized bandwidth but also storage and computation, completing the holy trinity of commodities essential for the new internet. Skycion a smear campaign launched against it, though they seem legit and reliable. Thus, they are probably undervalued.

Market 3 - Ecosystem

The 3rd market with 11 coins is comprised of ecosystem coins, which aim to strengthen the ease of use within the crypto space through decentralized exchanges, open standards for apps and more
  1. Nebulas: Similar to how Google indexes webpages Nebulas will index blockchain projects, smart contracts & data using the Nebulas rank algorithm that sifts & sorts the data. Developers rewarded NAS to develop & deploy on NAS chain. Nebulas calls this developer incentive protocol – basically rewards are issued based on how often dapp/contract etc. is used, the more the better the rewards and Proof of devotion. Works like DPoS except the best, most economically incentivised developers (Bookkeeppers) get the forging spots. Ensuring brains stay with the project (Cross between PoI & PoS). 2,400 TPS+, DAG used to solve the inter-transaction dependencies in the PEE (Parallel Execution Environment) feature, first crypto Wallet that supports the Lightening Network.
  2. Waves: Decentralized exchange and crowdfunding platform. Let’s companies and projects to issue and manage their own digital coin tokens to raise money.
  3. Salt: Leveraging blockchain assets to secure cash loands. Plans to offer cash loans in traditional currencies, backed by your cryptocurrency assets. Allows lenders worldwide to skip credit checks for easier access to affordable loans.
  4. CHAINLINK: ChainLink is a decentralized oracle service, the first of its kind. Oracles are defined as an ‘agent’ that finds and verifies real-world occurrences and submits this information to a blockchain to be used in smart contracts.With ChainLink, smart contract users can use the network’s oracles to retrieve data from off-chain application program interfaces (APIs), data pools, and other resources and integrate them into the blockchain and smart contracts. Basically, ChainLink takes information that is external to blockchain applications and puts it on-chain. The difference to Aeternity is that Chainlink deploys the smart contracts on the Ethereum blockchain while Aeternity has its own chain.
  5. WTC: Combines blockchain with IoT to create a management system for supply chains Interesting
  6. Ethos unifyies all cryptos. Ethos is building a multi-cryptocurrency phone wallet. The team is also building an investment diversification tool and a social network
  7. Aion: Aion is the token that pays for services on the Aeternity platform.
  8. USDT: is no cryptocurrency really, but a replacement for dollar for trading After months of asking for proof of dollar backing, still no response from Tether.

Market 4 - Privacy

The 4th market are privacy coins. As you might know, Bitcoin is not anonymous. If the IRS or any other party asks an exchange who is the identity behind a specific Bitcoin address, they know who you are and can track back almost all of the Bitcoin transactions you have ever made and all your account balances. Privacy coins aim to prevent exactly that through address fungability, which changes addresses constantly, IP obfuscation and more. There are 2 types of privacy coins, one with completely privacy and one with optional privacy. Optional Privacy coins like Dash and Nav have the advantage of more user friendliness over completely privacy coins such as Monero and Enigma.
  1. Monero: Currently most popular privacy coin, though with a very high market cap. Since their privacy is all on chain, all prior transactions would be deanonymized if their protocol is ever cracked. This requires a quantum computing attack though. PIVX is better in that regard.
  2. Zcash: A decentralized and open-source cryptocurrency that hide the sender, recipient, and value of transactions. Offers users the option to make transactions public later for auditing. Decent privacy coin, though no default privacy
  3. Verge: Calls itself privacy coin without providing private transactions, multiple problems over the last weeks has a toxic community, and way too much hype for what they have.
  4. Bytecoin: First privacy-focused cryptocurrency with anonymous transactions. Bytecoin’s code was later adapted to create Monero, the more well-known anonymous cryptocurrency. Has several scam accusations, 80% pre-mine, bad devs, bad tech
  5. Bitcoin Private: A merge fork of Bitcoin and Zclassic with Zclassic being a fork of Zcash with the difference of a lack of a founders fee required to mine a valid block. This promotes a fair distribution, preventing centralized coin ownership and control. Bitcoin private offers the optional ability to keep the sender, receiver, and amount private in a given transaction. However, this is already offered by several good privacy coins (Monero, PIVX) and Bitcoin private doesn't offer much more beyond this.
  6. Komodo: The Komodo blockchain platform uses Komodo’s open-source cryptocurrency for doing transparent, anonymous, private, and fungible transactions. They are then made ultra-secure using Bitcoin’s blockchain via a Delayed Proof of Work (dPoW) protocol and decentralized crowdfunding (ICO) platform to remove middlemen from project funding. Offers services for startups to create and manage their own Blockchains.
  7. PIVX: As a fork of Dash, PIVX uses an advanced implementation of the Zerocoin protocol to provide it’s privacy. This is a form of zeroknowledge proofs, which allow users to spend ‘Zerocoins’ that have no link back to them. Unlike Zcash u have denominations in PIVX, so they can’t track users by their payment amount being equal to the amount of ‘minted’ coins, because everyone uses the same denominations. PIVX is also implementing Bulletproofs, just like Monero, and this will take care of arguably the biggest weakness of zeroknowledge protocols: the trusted setup.
  8. Zcoin: PoW cryptocurrency. Private financial transactions, enabled by the Zerocoin Protocol. Zcoin is the first full implementation of the Zerocoin Protocol, which allows users to have complete privacy via Zero-Knowledge cryptographic proofs.
  9. Enigma: Monero is to Bitcoin what enigma is to Ethereum. Enigma is for making the data used in smart contracts private. More of a platform for dapps than a currency like Monero. Very promising.
  10. Navcoin: Like bitcoin but with added privacy and pos and 1,170 tps, but only because of very short 30 second block times. Though, privacy is optional, but aims to be more user friendly than Monero. However, doesn't really decide if it wants to be a privacy coin or not. Same as Zcash.Strong technology, non-shady team.
  11. Tenx: Raised 80 million, offers cryptocurrency-linked credit cards that let you spend virtual money in real life. Developing a series of payment platforms to make spending cryptocurrency easier. However, the question is if full privacy coins will be hindered in growth through government regulations and optional privacy coins will become more successful through ease of use and no regulatory hindrance.

Market 5 - Currency Exchange Tool

Due to the sheer number of different cryptocurrencies, exchanging one currency for the other it still cumbersome. Further, merchants don’t want to deal with overcluttered options of accepting cryptocurrencies. This is where exchange tool like Req come in, which allow easy and simple exchange of currencies.
  1. Cryptonex: Fiat and currency exchange between various blockchain services, similar to REQ.
  2. QASH: Qash is used to fuel its liquid platform which will be an exchange that will distribute their liquidity pool. Its product, the Worldbook is a multi-exchange order book that matches crypto to crypto, and crypto to fiat and the reverse across all currencies. E.g., someone is selling Bitcoin is USD on exchange1 not owned by Quoine and someone is buying Bitcoin in EURO on exchange 2 not owned by Quoine. If the forex conversions and crypto conversions match then the trade will go through and the Worldbook will match it, it'll make the sale and the purchase on either exchange and each user will get what they wanted, which means exchanges with lower liquidity if they join the Worldbook will be able to fill orders and take trade fees they otherwise would miss out on.They turned it on to test it a few months ago for an hour or so and their exchange was the top exchange in the world by 4x volume for the day because all Worldbook trades ran through it. Binance wants BNB to be used on their one exchange. Qash wants their QASH token embedded in all of their partners. More info here https://www.reddit.com/CryptoCurrency/comments/8a8lnwhich_are_your_top_5_favourite_coins_out_of_the/dwyjcbb/?context=3
  3. Kyber: network Exchange between cryptocurrencies, similar to REQ. Features automatic coin conversions for payments. Also offers payment tools for developers and a cryptocurrency wallet.
  4. Achain: Building a boundless blockchain world like Req .
  5. Req: Exchange between cryptocurrencies.
  6. Bitshares: Exchange between cryptocurrencies. Noteworthy are the 1.5 second average block times and throughput potential of 100,000 transactions per second with currently 2,400 TPS having been proven. However, bitshares had several Scam accusations in the past.
  7. Loopring: A protocol that will enable higher liquidity between exchanges and personal wallets.
  8. ZRX: Open standard for dapps. Open, permissionless protocol allowing for ERC20 tokens to be traded on the Ethereum blockchain. In 0x protocol, orders are transported off-chain, massively reducing gas costs and eliminating blockchain bloat. Relayers help broadcast orders and collect a fee each time they facilitate a trade. Anyone can build a relayer.

Market 6 - Gaming

With an industry size of $108B worldwide, Gaming is one of the largest markets in the world. For sure, cryptocurrencies will want to have a share of that pie.
  1. Storm: Mobile game currency on a platform with 9 million players.
  2. Fun: A platform for casino operators to host trustless, provably-fair gambling through the use of smart contracts, as well as creating their own implementation of state channels for scalability.
  3. Electroneum: Mobile game currency They have lots of technical problems, such as several 51% attacks
  4. Wax: Marketplace to trade in-game items

Market 7 - Misc

There are various markets being tapped right now. They are all summed up under misc.
  1. OMG: Omise is designed to enable financial services for people without bank accounts. It works worldwide and with both traditional money and cryptocurrencies.
  2. Power ledger: Australian blockchain-based cryptocurrency and energy trading platform that allows for decentralized selling and buying of renewable energy. Unique market and rather untapped market in the crypto space.
  3. Populous: A platform that connects business owners and invoice buyers without middlemen. Invoice sellers get cash flow to fund their business and invoice buyers earn interest. Similar to OMG, small market.
  4. Monacoin: The first Japanese cryptocurrency. Focused on micro-transactions and based on a popular internet meme of a type-written cat. This makes it similar to Dogecoin. Very niche, tiny market.
  5. Revain: Legitimizing reviews via the blockchain. Interesting concept, though market not as big.
  6. Augur: Platform to forecast and make wagers on the outcome of real-world events (AKA decentralized predictions). Uses predictions for a “wisdom of the crowd” search engine. Not launched yet.
  7. Substratum: Revolutionzing hosting industry via per request billing as a decentralized internet hosting system. Uses a global network of private computers to create the free and open internet of the future. Participants earn cryptocurrency. Interesting concept.
  8. Veritaseum: Is supposed to be a peer to peer gateway, though it looks like very much like a scam.
  9. TRON: Tronix is looking to capitalize on ownership of internet data to content creators. However, they plagiarized their white paper, which is a no go. They apologized, so it needs to be seen how they will conduct themselves in the future. Extremely high market cap for not having a product, nor proof of concept.
  10. Syscoin: A cryptocurrency with a decentralized marketplace that lets people buy and sell products directly without third parties. Trying to remove middlemen like eBay and Amazon.
  11. Hshare: Most likely scam because of no code changes, most likely pump and dump scheme, dead community.
  12. BAT: An Ethereum-based token that can be exchanged between content creators, users, and advertisers. Decentralized ad-network that pays based on engagement and attention.
  13. Dent: Decentralizeed exchange of mobile data, enabling mobile data to be marketed, purchased or distributed, so that users can quickly buy or sell data from any user to another one.
  14. Ncash: End to end encrypted Identification system for retailers to better serve their customers .
  15. Factom Secure record-keeping system that allows companies to store their data directly on the Blockchain. The goal is to make records more transparent and trustworthy .

Market 8 - Social network

Web 2.0 is still going strong and Web 3.0 is not going to ignore it. There are several gaming tokens already out there and a few with decent traction already, such as Steem, which is Reddit with voting through money is a very interesting one.
  1. Mithril: As users create content via social media, they will be rewarded for their contribution, the better the contribution, the more they will earn
  2. Steem: Like Reddit, but voting with money. Already launched product and Alexa rank 1,000 Thumbs up.
  3. Rdd: Reddcoin makes the process of sending and receiving money fun and rewarding for everyone. Reddcoin is dedicated to one thing – tipping on social networks as a way to bring cryptocurrency awareness and experience to the general public.
  4. Kin: Token for the platform Kik. Kik has a massive user base of 400 million people. Replacing paying with FIAT with paying with KIN might get this token to mass adoption very quickly.

Market 9 - Fee token

Popular exchanges realized that they can make a few billion dollars more by launching their own token. Owning these tokens gives you a reduction of trading fees. Very handy and BNB (Binance Coin) has been one of the most resilient tokens, which have withstood most market drops over the last weeks and was among the very few coins that could show growth.
  1. BNB: Fee token for Binance
  2. Gas: Not a Fee token for an exchange, but it is a dividend paid out on Neo and a currency that can be used to purchase services for dapps.
  3. Kucoin: Fee token for Kucoin

Market 10 - Decentralized Data Storage

Currently, data storage happens with large companies or data centers that are prone to failure or losing data. Decentralized data storage makes loss of data almost impossible by distributing your files to numerous clients that hold tiny pieces of your data. Remember Torrents? Torrents use a peer-to-peer network. It is similar to that. Many users maintain copies of the same file, when someone wants a copy of that file, they send a request to the peer-to-peer network., users who have the file, known as seeds, send fragments of the file to the requester., he requester receives many fragments from many different seeds, and the torrent software recompiles these fragments to form the original file.
  1. Gbyte: Byteball data is stored and ordered using directed acyclic graph (DAG) rather than blockchain. This allows all users to secure each other's data by referencing earlier data units created by other users, and also removes scalability limits common for blockchains, such as blocksize issue.
  2. Siacoin: Siacoin is decentralized storage platform. Distributes encrypted files to thousands of private users who get paid for renting out their disk space. Anybody with siacoins can rent storage from hosts on Sia. This is accomplish via "smart" storage contracts stored on the Sia blockchain. The smart contract provides a payment to the host only after the host has kept the file for a given amount of time. If the host loses the file, the host does not get paid.
  3. Maidsafecoin: MaidSafe stands for Massive Array of Internet Disks, Secure Access for Everyone.Instead of working with data centers and servers that are common today and are vulnerable to data theft and monitoring, SAFE’s network uses advanced P2P technology to bring together the spare computing capacity of all SAFE users and create a global network. You can think of SAFE as a crowd-sourced internet. All data and applications reside in this network. It’s an autonomous network that automatically sets prices and distributes data and rents out hard drive disk space with a Blockchain-based storage solutions.When you upload a file to the network, such as a photo, it will be broken into pieces, hashed, and encrypted. The data is then randomly distributed across the network. Redundant copies of the data are created as well so that if someone storing your file turns off their computer, you will still have access to your data. And don’t worry, even with pieces of your data on other people’s computers, they won’t be able to read them. You can earn MadeSafeCoins by participating in storing data pieces from the network on your computer and thus earning a Proof of Resource.
  4. Storj: Storj aims to become a cloud storage platform that can’t be censored or monitored, or have downtime. Your files are encrypted, shredded into little pieces called 'shards', and stored in a decentralized network of computers around the globe. No one but you has a complete copy of your file, not even in an encrypted form.

Market 11 - Cloud computing

Obviously, renting computing power, one of the biggest emerging markets as of recent years, e.g. AWS and Digital Ocean, is also a service, which can be bought and managed via the blockchain.
  1. Golem: Allows easy use of Supercomputer in exchange for tokens. People worldwide can rent out their computers to the network and get paid for that service with Golem tokens.
  2. Elf: Allows easy use of Cloud computing in exchange for tokens.

Market 12 - Stablecoin

Last but not least, there are 2 stablecoins that have established themselves within the market. A stable coin is a coin that wants to be independent of the volatility of the crypto markets. This has worked out pretty well for Maker and DGD, accomplished through a carefully diversified currency fund and backing each token by 1g or real gold respectively. DO NOT CONFUSE DGD AND MAKER with their STABLE COINS DGX and DAI. DGD and MAKER are volatile, because they are the companies of DGX and DAI. DGX and DAI are the stable coins.
  1. DGD: Platform of the Stablecoin DGX. Every DGX coin is backed by 1g of gold and make use proof of asset consensus.
  2. Maker: Platform of the Stablecoin DAI that doesn't vary much in price through widespread and smart diversification of assets.
EDIT: Added a risk factor from 0 to 10. The baseline is 2 for any crypto. Significant scandals, mishaps, shady practices, questionable technology, increase the risk factor. Not having a product yet automatically means a risk factor of 6. Strong adoption and thus strong scrutiny or positive community lower the risk factor.
EDIT2: Added a subjective potential factor from 0 to 10, where its overall potential and a small or big market cap is factored in. Bitcoin with lots of potential only gets a 9, because of its massive market cap, because if Bitcoin goes 10x, smaller coins go 100x, PIVX gets a 10 for being as good as Monero while carrying a 10x smaller market cap, which would make PIVX go 100x if Monero goes 10x.
submitted by galan77 to CryptoCurrency [link] [comments]

Subreddit Stats: cs7646_fall2017 top posts from 2017-08-23 to 2017-12-10 22:43 PDT

Period: 108.98 days
Submissions Comments
Total 999 10425
Rate (per day) 9.17 95.73
Unique Redditors 361 695
Combined Score 4162 17424

Top Submitters' Top Submissions

  1. 296 points, 24 submissions: tuckerbalch
    1. Project 2 Megathread (optimize_something) (33 points, 475 comments)
    2. project 3 megathread (assess_learners) (27 points, 1130 comments)
    3. For online students: Participation check #2 (23 points, 47 comments)
    4. ML / Data Scientist internship and full time job opportunities (20 points, 36 comments)
    5. Advance information on Project 3 (19 points, 22 comments)
    6. participation check #3 (19 points, 29 comments)
    7. manual_strategy project megathread (17 points, 825 comments)
    8. project 4 megathread (defeat_learners) (15 points, 209 comments)
    9. project 5 megathread (marketsim) (15 points, 484 comments)
    10. QLearning Robot project megathread (12 points, 691 comments)
  2. 278 points, 17 submissions: davebyrd
    1. A little more on Pandas indexing/slicing ([] vs ix vs iloc vs loc) and numpy shapes (37 points, 10 comments)
    2. Project 1 Megathread (assess_portfolio) (34 points, 466 comments)
    3. marketsim grades are up (25 points, 28 comments)
    4. Midterm stats (24 points, 32 comments)
    5. Welcome to CS 7646 MLT! (23 points, 132 comments)
    6. How to interact with TAs, discuss grades, performance, request exceptions... (18 points, 31 comments)
    7. assess_portfolio grades have been released (18 points, 34 comments)
    8. Midterm grades posted to T-Square (15 points, 30 comments)
    9. Removed posts (15 points, 2 comments)
    10. assess_portfolio IMPORTANT README: about sample frequency (13 points, 26 comments)
  3. 118 points, 17 submissions: yokh_cs7646
    1. Exam 2 Information (39 points, 40 comments)
    2. Reformat Assignment Pages? (14 points, 2 comments)
    3. What did the real-life Michael Burry have to say? (13 points, 2 comments)
    4. PSA: Read the Rubric carefully and ahead-of-time (8 points, 15 comments)
    5. How do I know that I'm correct and not just lucky? (7 points, 31 comments)
    6. ML Papers and News (7 points, 5 comments)
    7. What are "question pools"? (6 points, 4 comments)
    8. Explanation of "Regression" (5 points, 5 comments)
    9. GT Github taking FOREVER to push to..? (4 points, 14 comments)
    10. Dead links on the course wiki (3 points, 2 comments)
  4. 67 points, 13 submissions: harshsikka123
    1. To all those struggling, some words of courage! (20 points, 18 comments)
    2. Just got locked out of my apartment, am submitting from a stairwell (19 points, 12 comments)
    3. Thoroughly enjoying the lectures, some of the best I've seen! (13 points, 13 comments)
    4. Just for reference, how long did Assignment 1 take you all to implement? (3 points, 31 comments)
    5. Grade_Learners Taking about 7 seconds on Buffet vs 5 on Local, is this acceptable if all tests are passing? (2 points, 2 comments)
    6. Is anyone running into the Runtime Error, Invalid DISPLAY variable when trying to save the figures as pdfs to the Buffet servers? (2 points, 9 comments)
    7. Still not seeing an ML4T onboarding test on ProctorTrack (2 points, 10 comments)
    8. Any news on when Optimize_Something grades will be released? (1 point, 1 comment)
    9. Baglearner RMSE and leaf size? (1 point, 2 comments)
    10. My results are oh so slightly off, any thoughts? (1 point, 11 comments)
  5. 63 points, 10 submissions: htrajan
    1. Sample test case: missing data (22 points, 36 comments)
    2. Optimize_something test cases (13 points, 22 comments)
    3. Met Burt Malkiel today (6 points, 1 comment)
    4. Heads up: Dataframe.std != np.std (5 points, 5 comments)
    5. optimize_something: graph (5 points, 29 comments)
    6. Schedule still reflecting shortened summer timeframe? (4 points, 3 comments)
    7. Quick clarification about InsaneLearner (3 points, 8 comments)
    8. Test cases using rfr? (3 points, 5 comments)
    9. Input format of rfr (2 points, 1 comment)
    10. [Shameless recruiting post] Wealthfront is hiring! (0 points, 9 comments)
  6. 62 points, 7 submissions: swamijay
    1. defeat_learner test case (34 points, 38 comments)
    2. Project 3 test cases (15 points, 27 comments)
    3. Defeat_Learner - related questions (6 points, 9 comments)
    4. Options risk/reward (2 points, 0 comments)
    5. manual strategy - you must remain in the position for 21 trading days. (2 points, 9 comments)
    6. standardizing values (2 points, 0 comments)
    7. technical indicators - period for moving averages, or anything that looks past n days (1 point, 3 comments)
  7. 61 points, 9 submissions: gatech-raleighite
    1. Protip: Better reddit search (22 points, 9 comments)
    2. Helpful numpy array cheat sheet (16 points, 10 comments)
    3. In your experience Professor, Mr. Byrd, which strategy is "best" for trading ? (12 points, 10 comments)
    4. Industrial strength or mature versions of the assignments ? (4 points, 2 comments)
    5. What is the correct (faster) way of doing this bit of pandas code (updating multiple slice values) (2 points, 10 comments)
    6. What is the correct (pythonesque?) way to select 60% of rows ? (2 points, 11 comments)
    7. How to get adjusted close price for funds not publicly traded (TSP) ? (1 point, 2 comments)
    8. Is there a way to only test one or 2 of the learners using grade_learners.py ? (1 point, 10 comments)
    9. OMS CS Digital Career Seminar Series - Scott Leitstein recording available online? (1 point, 4 comments)
  8. 60 points, 2 submissions: reyallan
    1. [Project Questions] Unit Tests for assess_portfolio assignment (58 points, 52 comments)
    2. Financial data, technical indicators and live trading (2 points, 8 comments)
  9. 59 points, 12 submissions: dyllll
    1. Please upvote helpful posts and other advice. (26 points, 1 comment)
    2. Books to further study in trading with machine learning? (14 points, 9 comments)
    3. Is Q-Learning the best reinforcement learning method for stock trading? (4 points, 4 comments)
    4. Any way to download the lessons? (3 points, 4 comments)
    5. Can a TA please contact me? (2 points, 7 comments)
    6. Is the vectorization code from the youtube video available to us? (2 points, 2 comments)
    7. Position of webcam (2 points, 15 comments)
    8. Question about assignment one (2 points, 5 comments)
    9. Are udacity quizzes recorded? (1 point, 2 comments)
    10. Does normalization of indicators matter in a Q-Learner? (1 point, 7 comments)
  10. 56 points, 2 submissions: jan-laszlo
    1. Proper git workflow (43 points, 19 comments)
    2. Adding you SSH key for password-less access to remote hosts (13 points, 7 comments)
  11. 53 points, 1 submission: agifft3_omscs
    1. [Project Questions] Unit Tests for optimize_something assignment (53 points, 94 comments)
  12. 50 points, 16 submissions: BNielson
    1. Regression Trees (7 points, 9 comments)
    2. Two Interpretations of RFR are leading to two different possible Sharpe Ratios -- Need Instructor clarification ASAP (5 points, 3 comments)
    3. PYTHONPATH=../:. python grade_analysis.py (4 points, 7 comments)
    4. Running on Windows and PyCharm (4 points, 4 comments)
    5. Studying for the midterm: python questions (4 points, 0 comments)
    6. Assess Learners Grader (3 points, 2 comments)
    7. Manual Strategy Grade (3 points, 2 comments)
    8. Rewards in Q Learning (3 points, 3 comments)
    9. SSH/Putty on Windows (3 points, 4 comments)
    10. Slight contradiction on ProctorTrack Exam (3 points, 4 comments)
  13. 49 points, 7 submissions: j0shj0nes
    1. QLearning Robot - Finalized and Released Soon? (18 points, 4 comments)
    2. Flash Boys, HFT, frontrunning... (10 points, 3 comments)
    3. Deprecations / errata (7 points, 5 comments)
    4. Udacity lectures via GT account, versus personal account (6 points, 2 comments)
    5. Python: console-driven development (5 points, 5 comments)
    6. Buffet pandas / numpy versions (2 points, 2 comments)
    7. Quant research on earnings calls (1 point, 0 comments)
  14. 45 points, 11 submissions: Zapurza
    1. Suggestion for Strategy learner mega thread. (14 points, 1 comment)
    2. Which lectures to watch for upcoming project q learning robot? (7 points, 5 comments)
    3. In schedule file, there is no link against 'voting ensemble strategy'? Scheduled for Nov 13-20 week (6 points, 3 comments)
    4. How to add questions to the question bank? I can see there is 2% credit for that. (4 points, 5 comments)
    5. Scratch paper use (3 points, 6 comments)
    6. The big short movie link on you tube says the video is not available in your country. (3 points, 9 comments)
    7. Distance between training data date and future forecast date (2 points, 2 comments)
    8. News affecting stock market and machine learning algorithms (2 points, 4 comments)
    9. pandas import in pydev (2 points, 0 comments)
    10. Assess learner server error (1 point, 2 comments)
  15. 43 points, 23 submissions: chvbs2000
    1. Is the Strategy Learner finalized? (10 points, 3 comments)
    2. Test extra 15 test cases for marketsim (3 points, 12 comments)
    3. Confusion between the term computing "back-in time" and "going forward" (2 points, 1 comment)
    4. How to define "each transaction"? (2 points, 4 comments)
    5. How to filling the assignment into Jupyter Notebook? (2 points, 4 comments)
    6. IOError: File ../data/SPY.csv does not exist (2 points, 4 comments)
    7. Issue in Access to machines at Georgia Tech via MacOS terminal (2 points, 5 comments)
    8. Reading data from Jupyter Notebook (2 points, 3 comments)
    9. benchmark vs manual strategy vs best possible strategy (2 points, 2 comments)
    10. global name 'pd' is not defined (2 points, 4 comments)
  16. 43 points, 15 submissions: shuang379
    1. How to test my code on buffet machine? (10 points, 15 comments)
    2. Can we get the ppt for "Decision Trees"? (8 points, 2 comments)
    3. python question pool question (5 points, 6 comments)
    4. set up problems (3 points, 4 comments)
    5. Do I need another camera for scanning? (2 points, 9 comments)
    6. Is chapter 9 covered by the midterm? (2 points, 2 comments)
    7. Why grade_analysis.py could run even if I rm analysis.py? (2 points, 5 comments)
    8. python question pool No.48 (2 points, 6 comments)
    9. where could we find old versions of the rest projects? (2 points, 2 comments)
    10. where to put ml4t-libraries to install those libraries? (2 points, 1 comment)
  17. 42 points, 14 submissions: larrva
    1. is there a mistake in How-to-learn-a-decision-tree.pdf (7 points, 7 comments)
    2. maximum recursion depth problem (6 points, 10 comments)
    3. [Urgent]Unable to use proctortrack in China (4 points, 21 comments)
    4. manual_strategynumber of indicators to use (3 points, 10 comments)
    5. Assignment 2: Got 63 points. (3 points, 3 comments)
    6. Software installation workshop (3 points, 7 comments)
    7. question regarding functools32 version (3 points, 3 comments)
    8. workshop on Aug 31 (3 points, 8 comments)
    9. Mount remote server to local machine (2 points, 2 comments)
    10. any suggestion on objective function (2 points, 3 comments)
  18. 41 points, 8 submissions: Ran__Ran
    1. Any resource will be available for final exam? (19 points, 6 comments)
    2. Need clarification on size of X, Y in defeat_learners (7 points, 10 comments)
    3. Get the same date format as in example chart (4 points, 3 comments)
    4. Cannot log in GitHub Desktop using GT account? (3 points, 3 comments)
    5. Do we have notes or ppt for Time Series Data? (3 points, 5 comments)
    6. Can we know the commission & market impact for short example? (2 points, 7 comments)
    7. Course schedule export issue (2 points, 15 comments)
    8. Buying/seeking beta v.s. buying/seeking alpha (1 point, 6 comments)
  19. 38 points, 4 submissions: ProudRamblinWreck
    1. Exam 2 Study topics (21 points, 5 comments)
    2. Reddit participation as part of grade? (13 points, 32 comments)
    3. Will birds chirping in the background flag me on Proctortrack? (3 points, 5 comments)
    4. Midterm Study Guide question pools (1 point, 2 comments)
  20. 37 points, 6 submissions: gatechben
    1. Submission page for strategy learner? (14 points, 10 comments)
    2. PSA: The grading script for strategy_learner changed on the 26th (10 points, 9 comments)
    3. Where is util.py supposed to be located? (8 points, 8 comments)
    4. PSA:. The default dates in the assignment 1 template are not the same as the examples on the assignment page. (2 points, 1 comment)
    5. Schedule: Discussion of upcoming trading projects? (2 points, 3 comments)
    6. [defeat_learners] More than one column for X? (1 point, 1 comment)
  21. 37 points, 3 submissions: jgeiger
    1. Please send/announce when changes are made to the project code (23 points, 7 comments)
    2. The Big Short on Netflix for OMSCS students (week of 10/16) (11 points, 6 comments)
    3. Typo(?) for Assess_portfolio wiki page (3 points, 2 comments)
  22. 35 points, 10 submissions: ltian35
    1. selecting row using .ix (8 points, 9 comments)
    2. Will the following 2 topics be included in the final exam(online student)? (7 points, 4 comments)
    3. udacity quiz (7 points, 4 comments)
    4. pdf of lecture (3 points, 4 comments)
    5. print friendly version of the course schedule (3 points, 9 comments)
    6. about learner regression vs classificaiton (2 points, 2 comments)
    7. is there a simple way to verify the correctness of our decision tree (2 points, 4 comments)
    8. about Building an ML-based forex strategy (1 point, 2 comments)
    9. about technical analysis (1 point, 6 comments)
    10. final exam online time period (1 point, 2 comments)
  23. 33 points, 2 submissions: bhrolenok
    1. Assess learners template and grading script is now available in the public repository (24 points, 0 comments)
    2. Tutorial for software setup on Windows (9 points, 35 comments)
  24. 31 points, 4 submissions: johannes_92
    1. Deadline extension? (26 points, 40 comments)
    2. Pandas date indexing issues (2 points, 5 comments)
    3. Why do we subtract 1 from SMA calculation? (2 points, 3 comments)
    4. Unexpected number of calls to query, sum=20 (should be 20), max=20 (should be 1), min=20 (should be 1) -bash: syntax error near unexpected token `(' (1 point, 3 comments)
  25. 30 points, 5 submissions: log_base_pi
    1. The Massive Hedge Fund Betting on AI [Article] (9 points, 1 comment)
    2. Useful Python tips and tricks (8 points, 10 comments)
    3. Video of overview of remaining projects with Tucker Balch (7 points, 1 comment)
    4. Will any material from the lecture by Goldman Sachs be covered on the exam? (5 points, 1 comment)
    5. What will the 2nd half of the course be like? (1 point, 8 comments)
  26. 30 points, 4 submissions: acschwabe
    1. Assignment and Exam Calendar (ICS File) (17 points, 6 comments)
    2. Please OMG give us any options for extra credit (8 points, 12 comments)
    3. Strategy learner question (3 points, 1 comment)
    4. Proctortrack: Do we need to schedule our test time? (2 points, 10 comments)
  27. 29 points, 9 submissions: _ant0n_
    1. Next assignment? (9 points, 6 comments)
    2. Proctortrack Onboarding test? (6 points, 11 comments)
    3. Manual strategy: Allowable positions (3 points, 7 comments)
    4. Anyone watched Black Scholes documentary? (2 points, 16 comments)
    5. Buffet machines hardware (2 points, 6 comments)
    6. Defeat learners: clarification (2 points, 4 comments)
    7. Is 'optimize_something' on the way to class GitHub repo? (2 points, 6 comments)
    8. assess_portfolio(... gen_plot=True) (2 points, 8 comments)
    9. remote job != remote + international? (1 point, 15 comments)
  28. 26 points, 10 submissions: umersaalis
    1. comments.txt (7 points, 6 comments)
    2. Assignment 2: report.pdf (6 points, 30 comments)
    3. Assignment 2: report.pdf sharing & plagiarism (3 points, 12 comments)
    4. Max Recursion Limit (3 points, 10 comments)
    5. Parametric vs Non-Parametric Model (3 points, 13 comments)
    6. Bag Learner Training (1 point, 2 comments)
    7. Decision Tree Issue: (1 point, 2 comments)
    8. Error in Running DTLearner and RTLearner (1 point, 12 comments)
    9. My Results for the four learners. Please check if you guys are getting values somewhat near to these. Exact match may not be there due to randomization. (1 point, 4 comments)
    10. Can we add the assignments and solutions to our public github profile? (0 points, 7 comments)
  29. 26 points, 6 submissions: abiele
    1. Recommended Reading? (13 points, 1 comment)
    2. Number of Indicators Used by Actual Trading Systems (7 points, 6 comments)
    3. Software Install Instructions From TA's Video Not Working (2 points, 2 comments)
    4. Suggest that TA/Instructor Contact Info Should be Added to the Syllabus (2 points, 2 comments)
    5. ML4T Software Setup (1 point, 3 comments)
    6. Where can I find the grading folder? (1 point, 4 comments)
  30. 26 points, 6 submissions: tomatonight
    1. Do we have all the information needed to finish the last project Strategy learner? (15 points, 3 comments)
    2. Does anyone interested in cryptocurrency trading/investing/others? (3 points, 6 comments)
    3. length of portfolio daily return (3 points, 2 comments)
    4. Did Michael Burry, Jamie&Charlie enter the short position too early? (2 points, 4 comments)
    5. where to check participation score (2 points, 1 comment)
    6. Where to collect the midterm exam? (forgot to take it last week) (1 point, 3 comments)
  31. 26 points, 3 submissions: hilo260
    1. Is there a template for optimize_something on GitHub? (14 points, 3 comments)
    2. Marketism project? (8 points, 6 comments)
    3. "Do not change the API" (4 points, 7 comments)
  32. 26 points, 3 submissions: niufen
    1. Windows Server Setup Guide (23 points, 16 comments)
    2. Strategy Learner Adding UserID as Comment (2 points, 2 comments)
    3. Connect to server via Python Error (1 point, 6 comments)
  33. 26 points, 3 submissions: whoyoung99
    1. How much time you spend on Assess Learner? (13 points, 47 comments)
    2. Git clone repository without fork (8 points, 2 comments)
    3. Just for fun (5 points, 1 comment)
  34. 25 points, 8 submissions: SharjeelHanif
    1. When can we discuss defeat learners methods? (10 points, 1 comment)
    2. Are the buffet servers really down? (3 points, 2 comments)
    3. Are the midterm results in proctortrack gone? (3 points, 3 comments)
    4. Will these finance topics be covered on the final? (3 points, 9 comments)
    5. Anyone get set up with Proctortrack? (2 points, 10 comments)
    6. Incentives Quiz Discussion (2-01, Lesson 11.8) (2 points, 3 comments)
    7. Anyone from Houston, TX (1 point, 1 comment)
    8. How can I trace my error back to a line of code? (assess learners) (1 point, 3 comments)
  35. 25 points, 5 submissions: jlamberts3
    1. Conda vs VirtualEnv (7 points, 8 comments)
    2. Cool Portfolio Backtesting Tool (6 points, 6 comments)
    3. Warren Buffett wins $1M bet made a decade ago that the S&P 500 stock index would outperform hedge funds (6 points, 12 comments)
    4. Windows Ubuntu Subsystem Putty Alternative (4 points, 0 comments)
    5. Algorithmic Trading Of Digital Assets (2 points, 0 comments)
  36. 25 points, 4 submissions: suman_paul
    1. Grade statistics (9 points, 3 comments)
    2. Machine Learning book by Mitchell (6 points, 11 comments)
    3. Thank You (6 points, 6 comments)
    4. Assignment1 ready to be cloned? (4 points, 4 comments)
  37. 25 points, 3 submissions: Spareo
    1. Submit Assignments Function (OS X/Linux) (15 points, 6 comments)
    2. Quantsoftware Site down? (8 points, 38 comments)
    3. ML4T_2017Spring folder on Buffet server?? (2 points, 5 comments)
  38. 24 points, 14 submissions: nelsongcg
    1. Is it realistic for us to try to build our own trading bot and profit? (6 points, 21 comments)
    2. Is the risk free rate zero for any country? (3 points, 7 comments)
    3. Models and black swans - discussion (3 points, 0 comments)
    4. Normal distribution assumption for options pricing (2 points, 3 comments)
    5. Technical analysis for cryptocurrency market? (2 points, 4 comments)
    6. A counter argument to models by Nassim Taleb (1 point, 0 comments)
    7. Are we demandas to use the sample for part 1? (1 point, 1 comment)
    8. Benchmark for "trusting" your trading algorithm (1 point, 5 comments)
    9. Don't these two statements on the project description contradict each other? (1 point, 2 comments)
    10. Forgot my TA (1 point, 6 comments)
  39. 24 points, 11 submissions: nurobezede
    1. Best way to obtain survivor bias free stock data (8 points, 1 comment)
    2. Please confirm Midterm is from October 13-16 online with proctortrack. (5 points, 2 comments)
    3. Are these DTlearner Corr values good? (2 points, 6 comments)
    4. Testing gen_data.py (2 points, 3 comments)
    5. BagLearner of Baglearners says 'Object is not callable' (1 point, 8 comments)
    6. DTlearner training RMSE none zero but almost there (1 point, 2 comments)
    7. How to submit analysis using git and confirm it? (1 point, 2 comments)
    8. Passing kwargs to learners in a BagLearner (1 point, 5 comments)
    9. Sampling for bagging tree (1 point, 8 comments)
    10. code failing the 18th test with grade_learners.py (1 point, 6 comments)
  40. 24 points, 4 submissions: AeroZach
    1. questions about how to build a machine learning system that's going to work well in a real market (12 points, 6 comments)
    2. Survivor Bias Free Data (7 points, 5 comments)
    3. Genetic Algorithms for Feature selection (3 points, 5 comments)
    4. How far back can you train? (2 points, 2 comments)
  41. 23 points, 9 submissions: vsrinath6
    1. Participation check #3 - Haven't seen it yet (5 points, 5 comments)
    2. What are the tasks for this week? (5 points, 12 comments)
    3. No projects until after the mid-term? (4 points, 5 comments)
    4. Format / Syllabus for the exams (2 points, 3 comments)
    5. Has there been a Participation check #4? (2 points, 8 comments)
    6. Project 3 not visible on T-Square (2 points, 3 comments)
    7. Assess learners - do we need to check is method implemented for BagLearner? (1 point, 4 comments)
    8. Correct number of days reported in the dataframe (should be the number of trading days between the start date and end date, inclusive). (1 point, 0 comments)
    9. RuntimeError: Invalid DISPLAY variable (1 point, 2 comments)
  42. 23 points, 8 submissions: nick_algorithm
    1. Help with getting Average Daily Return Right (6 points, 7 comments)
    2. Hint for args argument in scipy minimize (5 points, 2 comments)
    3. How do you make money off of highly volatile (high SDDR) stocks? (4 points, 5 comments)
    4. Can We Use Code Obtained from Class To Make Money without Fear of Being Sued (3 points, 6 comments)
    5. Is the Std for Bollinger Bands calculated over the same timespan of the Moving Average? (2 points, 2 comments)
    6. Can't run grade_learners.py but I'm not doing anything different from the last assignment (?) (1 point, 5 comments)
    7. How to determine value at terminal node of tree? (1 point, 1 comment)
    8. Is there a way to get Reddit announcements piped to email (or have a subsequent T-Square announcement published simultaneously) (1 point, 2 comments)
  43. 23 points, 1 submission: gong6
    1. Is manual strategy ready? (23 points, 6 comments)
  44. 21 points, 6 submissions: amchang87
    1. Reason for public reddit? (6 points, 4 comments)
    2. Manual Strategy - 21 day holding Period (4 points, 12 comments)
    3. Sharpe Ratio (4 points, 6 comments)
    4. Manual Strategy - No Position? (3 points, 3 comments)
    5. ML / Manual Trader Performance (2 points, 0 comments)
    6. T-Square Submission Missing? (2 points, 3 comments)
  45. 21 points, 6 submissions: fall2017_ml4t_cs_god
    1. PSA: When typing in code, please use 'formatting help' to see how to make the code read cleaner. (8 points, 2 comments)
    2. Why do Bollinger Bands use 2 standard deviations? (5 points, 20 comments)
    3. How do I log into the [email protected]? (3 points, 1 comment)
    4. Is midterm 2 cumulative? (2 points, 3 comments)
    5. Where can we learn about options? (2 points, 2 comments)
    6. How do you calculate the analysis statistics for bps and manual strategy? (1 point, 1 comment)
  46. 21 points, 5 submissions: Jmitchell83
    1. Manual Strategy Grades (12 points, 9 comments)
    2. two-factor (3 points, 6 comments)
    3. Free to use volume? (2 points, 1 comment)
    4. Is MC1-Project-1 different than assess_portfolio? (2 points, 2 comments)
    5. Online Participation Checks (2 points, 4 comments)
  47. 21 points, 5 submissions: Sergei_B
    1. Do we need to worry about missing data for Asset Portfolio? (14 points, 13 comments)
    2. How do you get data from yahoo in panda? the sample old code is below: (2 points, 3 comments)
    3. How to fix import pandas as pd ImportError: No module named pandas? (2 points, 4 comments)
    4. Python Practice exam Question 48 (2 points, 2 comments)
    5. Mac: "virtualenv : command not found" (1 point, 2 comments)
  48. 21 points, 3 submissions: mharrow3
    1. First time reddit user .. (17 points, 37 comments)
    2. Course errors/types (2 points, 2 comments)
    3. Install course software on macOS using Vagrant .. (2 points, 0 comments)
  49. 20 points, 9 submissions: iceguyvn
    1. Manual strategy implementation for future projects (4 points, 15 comments)
    2. Help with correlation calculation (3 points, 15 comments)
    3. Help! maximum recursion depth exceeded (3 points, 10 comments)
    4. Help: how to index by date? (2 points, 4 comments)
    5. How to attach a 1D array to a 2D array? (2 points, 2 comments)
    6. How to set a single cell in a 2D DataFrame? (2 points, 4 comments)
    7. Next assignment after marketsim? (2 points, 4 comments)
    8. Pythonic way to detect the first row? (1 point, 6 comments)
    9. Questions regarding seed (1 point, 1 comment)
  50. 20 points, 3 submissions: JetsonDavis
    1. Push back assignment 3? (10 points, 14 comments)
    2. Final project (9 points, 3 comments)
    3. Numpy versions (1 point, 2 comments)
  51. 20 points, 2 submissions: pharmerino
    1. assess_portfolio test cases (16 points, 88 comments)
    2. ML4T Assignments (4 points, 6 comments)

Top Commenters

  1. tuckerbalch (2296 points, 1185 comments)
  2. davebyrd (1033 points, 466 comments)
  3. yokh_cs7646 (320 points, 177 comments)
  4. rgraziano3 (266 points, 147 comments)
  5. j0shj0nes (264 points, 148 comments)
  6. i__want__piazza (236 points, 127 comments)
  7. swamijay (227 points, 116 comments)
  8. _ant0n_ (205 points, 149 comments)
  9. ml4tstudent (204 points, 117 comments)
  10. gatechben (179 points, 107 comments)
  11. BNielson (176 points, 108 comments)
  12. jameschanx (176 points, 94 comments)
  13. Artmageddon (167 points, 83 comments)
  14. htrajan (162 points, 81 comments)
  15. boyko11 (154 points, 99 comments)
  16. alyssa_p_hacker (146 points, 80 comments)
  17. log_base_pi (141 points, 80 comments)
  18. Ran__Ran (139 points, 99 comments)
  19. johnsmarion (136 points, 86 comments)
  20. jgorman30_gatech (135 points, 102 comments)
  21. dyllll (125 points, 91 comments)
  22. MikeLachmayr (123 points, 95 comments)
  23. awhoof (113 points, 72 comments)
  24. SharjeelHanif (106 points, 59 comments)
  25. larrva (101 points, 69 comments)
  26. augustinius (100 points, 52 comments)
  27. oimesbcs (99 points, 67 comments)
  28. vansh21k (98 points, 62 comments)
  29. W1redgh0st (97 points, 70 comments)
  30. ybai67 (96 points, 41 comments)
  31. JuanCarlosKuriPinto (95 points, 54 comments)
  32. acschwabe (93 points, 58 comments)
  33. pharmerino (92 points, 47 comments)
  34. jgeiger (91 points, 28 comments)
  35. Zapurza (88 points, 70 comments)
  36. jyoms (87 points, 55 comments)
  37. omscs_zenan (87 points, 44 comments)
  38. nurobezede (85 points, 64 comments)
  39. BelaZhu (83 points, 50 comments)
  40. jason_gt (82 points, 36 comments)
  41. shuang379 (81 points, 64 comments)
  42. ggatech (81 points, 51 comments)
  43. nitinkodial_gatech (78 points, 59 comments)
  44. harshsikka123 (77 points, 55 comments)
  45. bkeenan7 (76 points, 49 comments)
  46. moxyll (76 points, 32 comments)
  47. nelsongcg (75 points, 53 comments)
  48. nickzelei (75 points, 41 comments)
  49. hunter2omscs (74 points, 29 comments)
  50. pointblank41 (73 points, 36 comments)
  51. zheweisun (66 points, 48 comments)
  52. bs_123 (66 points, 36 comments)
  53. storytimeuva (66 points, 36 comments)
  54. sva6 (66 points, 31 comments)
  55. bhrolenok (66 points, 27 comments)
  56. lingkaizuo (63 points, 46 comments)
  57. Marvel_this (62 points, 36 comments)
  58. agifft3_omscs (62 points, 35 comments)
  59. ssung40 (61 points, 47 comments)
  60. amchang87 (61 points, 32 comments)
  61. joshuak_gatech (61 points, 30 comments)
  62. fall2017_ml4t_cs_god (60 points, 50 comments)
  63. ccrouch8 (60 points, 45 comments)
  64. nick_algorithm (60 points, 29 comments)
  65. JetsonDavis (59 points, 35 comments)
  66. yjacket103 (58 points, 36 comments)
  67. hilo260 (58 points, 29 comments)
  68. coolwhip1234 (58 points, 15 comments)
  69. chvbs2000 (57 points, 49 comments)
  70. suman_paul (57 points, 29 comments)
  71. masterm (57 points, 23 comments)
  72. RolfKwakkelaar (55 points, 32 comments)
  73. rpb3 (55 points, 23 comments)
  74. venkatesh8 (54 points, 30 comments)
  75. omscs_avik (53 points, 37 comments)
  76. bman8810 (52 points, 31 comments)
  77. snladak (51 points, 31 comments)
  78. dfihn3 (50 points, 43 comments)
  79. mlcrypto (50 points, 32 comments)
  80. omscs-student (49 points, 26 comments)
  81. NellVega (48 points, 32 comments)
  82. booglespace (48 points, 23 comments)
  83. ccortner3 (48 points, 23 comments)
  84. caa5042 (47 points, 34 comments)
  85. gcalma3 (47 points, 25 comments)
  86. krushnatmore (44 points, 32 comments)
  87. sn_48 (43 points, 22 comments)
  88. thenewprofessional (43 points, 16 comments)
  89. urider (42 points, 33 comments)
  90. gatech-raleighite (42 points, 30 comments)
  91. chrisong2017 (41 points, 26 comments)
  92. ProudRamblinWreck (41 points, 24 comments)
  93. kramey8 (41 points, 24 comments)
  94. coderafk (40 points, 28 comments)
  95. niufen (40 points, 23 comments)
  96. tholladay3 (40 points, 23 comments)
  97. SaberCrunch (40 points, 22 comments)
  98. gnr11 (40 points, 21 comments)
  99. nadav3 (40 points, 18 comments)
  100. gt7431a (40 points, 16 comments)

Top Submissions

  1. [Project Questions] Unit Tests for assess_portfolio assignment by reyallan (58 points, 52 comments)
  2. [Project Questions] Unit Tests for optimize_something assignment by agifft3_omscs (53 points, 94 comments)
  3. Proper git workflow by jan-laszlo (43 points, 19 comments)
  4. Exam 2 Information by yokh_cs7646 (39 points, 40 comments)
  5. A little more on Pandas indexing/slicing ([] vs ix vs iloc vs loc) and numpy shapes by davebyrd (37 points, 10 comments)
  6. Project 1 Megathread (assess_portfolio) by davebyrd (34 points, 466 comments)
  7. defeat_learner test case by swamijay (34 points, 38 comments)
  8. Project 2 Megathread (optimize_something) by tuckerbalch (33 points, 475 comments)
  9. project 3 megathread (assess_learners) by tuckerbalch (27 points, 1130 comments)
  10. Deadline extension? by johannes_92 (26 points, 40 comments)

Top Comments

  1. 34 points: jgeiger's comment in QLearning Robot project megathread
  2. 31 points: coolwhip1234's comment in QLearning Robot project megathread
  3. 30 points: tuckerbalch's comment in Why Professor is usually late for class?
  4. 23 points: davebyrd's comment in Deadline extension?
  5. 20 points: jason_gt's comment in What would be a good quiz question regarding The Big Short?
  6. 19 points: yokh_cs7646's comment in For online students: Participation check #2
  7. 17 points: i__want__piazza's comment in project 3 megathread (assess_learners)
  8. 17 points: nathakhanh2's comment in Project 2 Megathread (optimize_something)
  9. 17 points: pharmerino's comment in Midterm study Megathread
  10. 17 points: tuckerbalch's comment in Midterm grades posted to T-Square
Generated with BBoe's Subreddit Stats (Donate)
submitted by subreddit_stats to subreddit_stats [link] [comments]

Stockus: Fantasy Trading Blockchain Platform

Pre-ICO: Stockus: Fantasy Trading Blockchain Platform
Stockus Crypto Summary
Hi everybody! I’m happy to introduce the Stockus Project to you. It is a new and exciting project on which our team is working on now. The main ideas and its realization are explained further. It will be nice if they are interesting for you.
Stockus. Fantasy trading platform based on the blockchain technology.
Our goal is to create a leading financial simulator based on open ledger technology in order to provide participants with a reliable, transparent trading platform and opportunities to earn large cash prizes. Stockus – is a fantasy trading platform based on smart contracts. Participants place trades individually or in teams. The application allows users to enrol in various tournaments and earn cash rewards without an initial investment of capital.
Gaming Capital Globally
The online gaming industry is rapidly growing, with figures indicating total earnings of 99.6billion USD in 2016 alone. This is an impressive amount; however it pales in comparison to the size of the financial markets. The daily turnover of the Forex market amounted to 5.1trillion USD in 2016. Approximately 10-15 million individual market participants actively trade on Forex worldwide with the total volume generated by retail traders being equal to 293billion USD daily. Statistics show that the average starting capital of a retail trader is somewhere in the region of 700 USD. Within 4 months of trading 97% of all retail traders lose their initial investment and leave the market. The amounts that such traders lose on the currency market amount to tens of millions of dollars annually.
$10 against $700
Our approach differs substantially from the business model of the classic broker. There are two fundamental pillars on which Stockus was built. The first one is that exchange trading for the retail participant is comparable to a game, where players place bets on the direction of the market. And the second one is the players prefer to pay small-one off buy-ins for the chance to win large cash prizes in tournaments as apposed to putting large deposits at risk on leveraged trading accounts. There is clearly a drastic difference between a trader who suffers the loss of their entire deposit of $700 whilst trading on Forex, and a player who buys into a trading tournament for $10 with the chance of winning a massive prize. That same $10 deposit would get the trader nowhere on the Forex market, whereas on Stockus he stands to win thousands of dollars without the requirement of a large investment upfront. Our approach is light years apart from the business model of a traditional broker in the sense that it aims to protect the trader without limiting their gains. Traders are now faced with the choice of trading on the market with a high degree of risk or playing Stockus with limited risk whilst maintaining their earning potential. This is a new opportunity to trader and we believe that they will chose in our favour.
Equal odds of winning
The probability of winning in a fully subscribed Stockus tournament is approximately 3-3.5% which is roughly equal to the chances of turning a profit whilst trading on the Forex market. However $10 gets you nowhere on a forex brokerage account, whereas in Stockus you can enter a trading competition and stand to win tens or even hundreds of thousands of dollars with the same amount. The benefit of Stockus is that each player has a limited loss, but gets an equal chance to win large prizes. Fantasy trading – the Stockus platform is designed to be a direct competitor to traditional brokers by attracting a large number of participants. There is no sense in funding a leverage forex account and risking the entire deposit when the trader can enter a tournament and win fantastic amounts of prize money in a variety of competitions. The development of trading skills and ability to collect large gains solely with the merit of experience and knowledge is the main advantage of Stockus. Millions of players with the ambition, aptitude and skill will be able to compete for the large cash rewards with limited downside. In the past such individuals were faced with a choice between financial markets or betting games. Now, such players have an innovative alternative in the form of Stockus.
How to become a millionaire
Stockus is a financial simulator based on a social media platform which allows any player to participate in a tournament of their choice. If a certain trader prefers a short-term, high frequency game, they can join a daily tournament with large prizes. If, on the other hand, the player is more partial to a long term, trend-based approach, the weekly or monthly tournament is more suited to this style and the prizes can reach astronomical levels. It is crucial to note that the size of the prize is not restricted, which means that the more players buy into the competition, the higher the winning pot. The payouts for larger tournaments can potentially reach six figures or more. The game consists of the following: Professional tournaments which will constantly increase in number. A small buy-in amount is paid to enter the tournament and compete against other traders. The winners immediately receive a payout to their account balance. Friendly tournaments which allow anyone to participate free of charge. The main purpose of these trading challenges is to educate new players and allow existing users to refine their strategies in preparation for the professional games. Decentralised challenges which users can host independently by selecting their competitors and forming a private league. Team tournaments allow players to team up with other traders and compete against each other in groups throughout several rounds.
Players or teams who lose their initial capital have the option to buy back in and continue trading. As opposed to leveraged trading, where each loss is a direct hit to the capital and savings of the trader, Stockus allows players to continue trading for as long as they wish. Players have the ability to improve their chances by purchasing leverage, analytical tools and other extras for additional payments. Members of the Stockus community can exchange feedback, tips and trade ideas with each other. A referral program encourages players to invite their friends. The main attraction for most traders will be the professional tournaments. During the development of our tournament system, the team drew a lot of inspiration from the structure of the competitions held by the fantasy trading platform FanDuel. The capitalisation of FanDuel as an organisation is in the billions, and the platform’s phenomenal success along with hundreds of thousands of members testifies to the scalability and potential of such a model when applied in a different area.
The Principles of Platform Monetisation
Stockus aims to monetise fantasy trading by applying a small commission on each buy-in as well as charging additional fees for bonus features such as refunding, leverage, analytics, etc. Each player can purchase extras in order to improve their chances of winning and gain an edge over their competition. Additional initiatives such as referral programmes and promotions allow players to help others and earn additional tokens for their efforts.
Testing the game
Stockus utilises a unique trading platform which our team modelled around the popular MT4 trading software. This proprietary platform allows players to trade stocks, futures, currency pairs and options in real time on a broad selection of global venues. The Stockus model was throughout several beta rounds hosted on the Facebook developer platform in order to enhance the software and improve functionality. This testing base also allowed us to confirm the viability of the concept and saleability of the offering. This period allowed us to gather valuable data on user preferences, as well as collect feedback and verify the validity of the game concept. Users actively participated in the trading tournaments and purchased additional features in order to boost their chances of earnings a prize. We saw a healthy amount of competition for the prize spots, with many players repurchasing funds or unlocking leverage to get the upper hand on their rival traders. Our developers also expanded the capabilities of the platform during this time, adding several different tournament types as well as options trading during the testing phase. We have now developed a completed version of the game based on the results of these extensive tests, which we are excited to bring to your attention.
Blockchain as a foundation for trust
Stockus is innovating by allowing all types of traders to compete in tournaments with limited risk and on equal terms. Ethereum allows us to create smart contracts which automatically determine and verify the outcome of each trading tournament, as well as paying out the rewards to the winners. The principles of crypto can be used to process and distribute the gains from the various tournaments in an efficient and transparent manner. This solution is optimal due to its security and scalability as the number of players and competitions grows. Unlike a typical brokerageplatform, the entire infrastructure of Stockus is built on blockchain, making the setup robust and secure. One of the toughest challenges we faced during the beta testing phase was gaining the trust of the players. Some users raised concerns regarding the authenticity of the tournament results and likelihood of an actual payout. The blockchain addresses such concerns and puts any doubts to rest due to the transparent and objective manner in which the smart contracts will determine winners as well as the final payout of the prizes. This transparency creates an element of trust amongst users and enhances the eligibility of the tournament series. A second challenge addressed by the blockchain infrastructure is raising the required funds and launching the game within a period of 3months. An ICO offers a priceless opportunity to meet our targets and achieve the ultimate objective of building a trading simulator which will offer an innovative and groundbreaking alternative to the traditional forex trading approach. A third argument in favour of an ICO and the blockchain solution is the ability to issue our own tokens, which will essentially act as a cryptocurrency derivative within our game. These tokens will have a value versus Ethereum and other cryptocurrencies which is directly dependent on the popularity and success of the game. Should the demand for ingame services and tournaments continue to increase as we expect, so will the value of the tokens in relation to other currencies.
Stockus Tokens
Stockus tokens are an integral component of the Stockus economy and ecosystem. Owners of these tockens will have access to the following services: - Participation in trading tournaments - Act as witnesses and judges in the trading tournaments - Receive rewards and prizes in the competitions, promotions and tournaments - Purchase additional services and bonus features - Hosting tournaments - Receive referral rewards for inviting friends The tokens play a key role in the economic processes at play in the Stockus environment. These tokens can be purchased in the application, received from other players, won in a tournament, or as a reward for acting as witness or judge in determining the results of a competition. Additional tokens can also be received as a reward for inviting friends to play. Tokens can also be acquired through the preliminary offering of Stockus tokens via Ether (ENT). The Stockus interface will also integrate third party trading solutions such as Shapershift and Coinbase for those users who do not already hold ENT. The initial offering of Stockus tokens will take place in the form of a preliminary ICO. Anyone can subscribe to the offering in exchange for ENT or other cryptocurrencies such as BTC or STEEM. We plan to offer 5,000,000 of our tokens at a rate of 300 tokens for 1 ENT.
Tournament Result Verification
The decentralised tournament verification system is an elegant and robust solution for all users as it prevents any manipulation or abuse of the competition results. All token holders will be able to act as witnesses or judges when determining the winners of each tournament, allowing the public to verify the results via open ledger technology. Should a single participant disagree with the results, an independent confirmation of the tournament results is established by the witnesses. If the conclusion regarding the winners of a tournament is unanimous and there are no disagreements between participants, no added verification via witness is required and the system automatically processes a payout.
Stockus ICO and Development plans
The bulk of raised capital will be directed at the following: - Development of 2 professional tournaments: the WFT (Weekly Fantasy Tournament) and DFT (Daily Fantasy Tournament). These will be completed in 3 months. - A promotional campaign which will ensure that the userbase reaches critical mass and the project becomes sustainable by increasing the prize amounts in the WFT to the order of tens of thousands. - The development of a social network within Stockus, which would allow players to exchange opinions, experiences and advice, as well as form trading societies and teams. - The development of a mobile version of the trading application. - Development and production of at least one new trading competition every 2 months. The game should have at least 6 different tournament types by the end of the first year. The Stockus development team is pleased to present our project for your review and assessment. We hope the summary has made a positive impression and look forward to your support and feedback.
Thank you in advance for your time and attention.
Stockus Developers
tl;dr New blockchain platform allowing fantasy trading, limited capital at risk for the chance to make substantial amounts of money. Project currently under development, ICO later in the year, feel free to ask any questions!
Facebook: https://www.facebook.com/StockusProject/ Website: www.stockus.io Twitter: https://twitter.com/stockusproject
submitted by Stockus_Project to icocrypto [link] [comments]

Looking for Beta Users for Groundbreaking P2P Swap Trading System, Maximum Leverage, Minimum Risk, All Markets

I'm looking for traders to take positions (either in agreement with, or contrary to) my research/opinion or using your own trade setups and fundamental/macro outlook - via our P2P swap platform. You can use actual capital or I can give you test capital, basically, play money, to trade with me and my team and all I ask for is feedback on the system and the ability to quote you (which is not mandatory, but it would be nice). You can trade stocks, bonds, commodities, forex and forex pairs long or short, or swap the exposures directly for another asset, ex. S&P 500 for the LSE 100, Apple for Google, etc. Fees start at 5 bp, the best available from anywhere. Digital leverage is available, up to 10,000x worth (double digit profits/losses can be had from 11 basis points in movement, or less - so be careful), with no possibility of a Margin Call since the trades are pre-funded. If you think the opportunity is worthwhile, feel free to contact us or see info sheet below for more...
Macro Trading Has a New Power Tool: Peer to Peer, Counterparty Risk-Free Swaps for Value Transfer & Trading
Trade the value of over 45,000 tickers of instruments in every asset class from every major geography and exchange with up to 10,000x pricing leverage without concerns of counterparty/credit/default risk or margin calls. Very big claims… substantiated by a very big discovery in value transfer and security.
Veritaseum is the worldwide leader in the financial implementation of “smart contracts” – unbreakable, self-executing bilateral agreements between two or more parties. We use these smart contracts to create peer to peer swaps for the transfer of value through the “blockchain,” a worldwide, open ledger of pseudonymous transactions that can be seen and audited by anyone, any time in the cloud. The blockchain is considered unhackable and one of the most secure implementations of cryptography to date.
With the use of financial “wallets”, client side applications that use a simple interface to guide users in the quick (as in minutes – enter ticker(s), amount to risk, collateral, expiry and leverage required) creation of a smart contract (in this case a P2P swap), users trade OTC directly with other parties – totally bypassing and intermediating exchanges, with even less risk. Monetary value is committed up front, a leverage factor is digitally dialed in (anywhere from 2x to 10,000x) and the smart contract is created and sent to the blockchain to await a match. Once matched, the funds are locked into the transaction until expiry, at which point profits and losses are distributed along with principal and unused collateral (the capital chosen to be put at risk). A novel, risk averse, extremely powerful, and quite frankly - ingenious way to trade macro strategies.
Not only can one go long or short any ticker in any asset class from any region for any currency, one can go long one ticker relative to another. For instance, those with a bearish outlook on the S&P 500 normally short it for USD. You can now short (pay) the S&P 500 index directly while going long (receiving) Eurozone equities (or 10 year treasury yields, or Swiss francs or the CNYJPY pair or bitocin), in a single transaction – with or out without leverage.
Since the exchange is peer to peer, we never hold or control any of your assets, hence you are not exposed to our balance sheet, credit, default or counterparty risks (the blockchain is your effective counterparty). Veritaseum is a software concern, not a financial concern or intermediary! You can always track your assets and trade through the blockchain at any time. The capital is loaded in the wallet in the form of bitcoin, and for those who choose to minimize exposure to bitcoin market price volatility, leverage can be used to nearly eliminate the noise. You can also conduct trades using a demo mode and test coins, so as to use the system without risking actual capital.

Smart Contracts as Transaction Vehicles: The Safest Possible Way To Exchange Value

Veritaseum's UltraCoin smart contracts are: 1. highly flexible - you design your own derivatives yourself using your own parameters via our simple graphical user interface 2. self-executing 3. autonomous 4. unbreachable: we call them, the unbreakable promise! They are backed, fortified and stored by/on the blockchain itself 5. uber-transparent: simple click the "trace transaction" button to find the location and historical travel path of your assets anytime, from anywhere you have an internet connection

Trading Through a Balance Sheet-Based Financial Institution vs. Distributed, Decentralized, P2P Software Concern

What I do want to accomplish is the education through the fact that the Bitcoin protocol has given rise to the genesis of a new type of company, with a new business model that can offer a totally new type of product. As you were able to see from above, Veritaseum's UltraCoin offers a very uniquer product with many if not all of the attributes that potential competitors offer, with a slew of attributes that others can't touch. This is done at 1/5th of the price and at much less risk! When dealing with Veritaseum's UltraCoin, you can never get Gox'd because we never have (nor do we want) possession of your coins or fiat - every, at any time. Because we don't user our balance sheet (we are a software company, not a centralized exchange or brokedealer) you:
This is just the beginning of what is capable with our Internet 2.0 business models.
I implore you to download our:
There's also a lot of BTC industry research available for download as well as our blog which has some of the best fundamental and macro research available on the web. Hardcore traders, investors and speculators should check out my latest piece: It's All Out War, Pt 3: Is the Danish Krone Peg to Euro More Fragile Than Glass Beads? The Danish National Bank Infers So!
Any individuals or entities looking to provide liquidity to the system, individuals/companies who wish to partner, accredited investors looking for a piece of the action (you have to be willing to sign and NDA, we are quite open to working with anybody), or those who simply want to shoot the breeze should feel free to contact us.
An example of an UltraCoin smart contract summary
Here's some info about me, my team and what we're doing at Veritaseum:
Cordially, Reggie Middleton CEO, VeritaseumTM Inc.
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How to Backtest Trading Strategies with FXCM Forex ... Best Backtesting Software - Forex Soft4Fx: The Forex Best Backtesting Software Thus Far ... How to PROPERLY TEST a FOREX STRATEGY?! *BONUS: Download ... How to Get Started with Free Forex Backtesting Software ... How to BACKTEST a Forex Trading Strategy - YouTube Free Forex Trading Simulator - back tester - YouTube

There is a free version that you can use to follow this guide. However, if you ... The third and last step is to download a forex backtesting software called Soft4FX. Soft4FX is not a standalone software, but an expert advisor for MetaTrader 4. It has its own interface, but it relies on MetaTrader for key functionalities such as charting tools, sound effects, and other design elements. The ... Another popular forex strategy backtesting option on MT4 is 'Forex Tester'. Unlike Strategy Tester, Forex Tester is not free, and can be used both for manual and automated trading activities. This automated backtesting software provides traders with pre-formed strategies. It has 10 manual programs and 5 expert advisors, along with 16 years of historical price data, and a risk calculation and ... Software that will allow you to find the working methods and dismiss the losing ones while you backtest your strategies. Get Forex Tester, the best trading simulator for backtesting, a training platform and a prediction app all in one, and make every trade work for your total success on the currency market Forex Kalender Market Heat Map Market Sentiment Indikation DAX30 Analyse EUR/USD Analyse Dow Jones Analyse ... Wenn es also um das Prüfen von Trading Strategien geht, gibt es keine Backtesting Software, die den Menschen komplett ersetzen kann – insbesondere, wenn es sich um einen Menschen handelt, der mit den richtigen Werkzeugen ausgestattet ist. Allgemeiner Kontext zum Backtesting. Wenn ... Five free back tests a day and a free trial period. Of course there are also plenty of paid backtesting software options out there. You can make backtesting as simple or as complex as you want but all that matters is whether you can follow your system in real time and whether it makes money in the long term. These are examples of four great ... Free Backtesting Tools for the Non-Programmer . There really is no “one size fits all” backtesting tool out there that can backtest virtually any strategy under the sun without the user knowing some programming. If you’re serious about trading, then I urge you to learn enough programming to be able to backtest. But if you’d like to ... Strategy backtesting is a crucial element of a good trading system. Since it is a relatively good indicator of whether you have an edge in the market, it gives you confidence in your strategy. Before we have a closer look at how to backtest a trading strategy, let’s start by answering a crucial question. Table of Contents. 1 What is Backtesting in Forex? 1.1 What are the benefits of ... Forex Simulator. Develop profitable trading strategies. Whether you want to learn forex trading or to improve a trading strategy. You need the right tools to succeed. We see demo-trading provided by most brokerage firms as a trap. Our team is determined to reveal and resolve the problems with demo accounts. Forex Backtester free download - BackTester, Robot Forex 2014 Profesional, Forex All-In-One-Widget, and many more programs The next option for backtesting a forex strategy manually is FXBlue simulator. Fxblue is a website that provides various services to forex traders including some free tools. One of them is an add-on that is installed on the MT4 and simulates charts. For downloading the FXBlue simulator, go to FX Blue Trading Simulator v3 for MT4 or for MT5 click on free download. I’ll explain the process for ...

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How to Backtest Trading Strategies with FXCM Forex ...

This video will show you How to Backtest a Forex Trading Strategy, as well as 3 TIPS on BACKTESTING... Trading Platform I Use: https://www.tradingview.com/... Free Download P/L Simulator: http://bit.ly/monte-carlo-simulator-excel-yt (please note, the download link does not work if you're using a Safari web browser.... The Forex Best Backtesting Software Thus Far! Heikin Ashi Backtest Part 1 Soft4Fx Forex Simulator: https://d2t.link/soft4fx In this video, I share what I c... In this video I talk about what I think is the best Forex backtesting software on the market right now. The reason I wanted to do this video is because one, by farm seems to be outranking the rest. In this video, I talk about how to backtest trading strategies with FXCM forex simulator. Follow me on Instagram @datkidgreatness Follow me on Snapchat @datk... Learn how to get free Forex backtesting software. ★ SUBSCRIBE: http://tradr.cc/mu8d Some traders don't get started with backtesting because they don't want t... Looking for a free manual forex MT4 back tester? http://trk.pepperstonepartners.com/SHR Pepperstone offers a free full functional trading simulator. For MT4 ...

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