Best Backtesting Software Forex Factory

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]

Trading economic news

The majority of this sub is focused on technical analysis. I regularly ridicule such "tea leaf readers" and advocate for trading based on fundamentals and economic news instead, so I figured I should take the time to write up something on how exactly you can trade economic news releases.
This post is long as balls so I won't be upset if you get bored and go back to your drooping dick patterns or whatever.

How economic news is released

First, it helps to know how economic news is compiled and released. Let's take Initial Jobless Claims, the number of initial claims for unemployment benefits around the United States from Sunday through Saturday. Initial in this context means the first claim for benefits made by an individual during a particular stretch of unemployment. The Initial Jobless Claims figure appears in the Department of Labor's Unemployment Insurance Weekly Claims Report, which compiles information from all of the per-state departments that report to the DOL during the week. A typical number is between 100k and 250k and it can vary quite significantly week-to-week.
The Unemployment Insurance Weekly Claims Report contains data that lags 5 days behind. For example, the Report issued on Thursday March 26th 2020 contained data about the week ending on Saturday March 21st 2020.
In the days leading up to the Report, financial companies will survey economists and run complicated mathematical models to forecast the upcoming Initial Jobless Claims figure. The results of surveyed experts is called the "consensus"; specific companies, experts, and websites will also provide their own forecasts. Different companies will release different consensuses. Usually they are pretty close (within 2-3k), but for last week's record-high Initial Jobless Claims the reported consensuses varied by up to 1M! In other words, there was essentially no consensus.
The Unemployment Insurance Weekly Claims Report is released each Thursday morning at exactly 8:30 AM ET. (On Thanksgiving the Report is released on Wednesday instead.) Media representatives gather at the Frances Perkins Building in Washington DC and are admitted to the "lockup" at 8:00 AM ET. In order to be admitted to the lockup you have to be a credentialed member of a media organization that has signed the DOL lockup agreement. The lockup room is small so there is a limited number of spots.
No phones are allowed. Reporters bring their laptops and connect to a local network; there is a master switch on the wall that prevents/enables Internet connectivity on this network. Once the doors are closed the Unemployment Insurance Weekly Claims Report is distributed, with a heading that announces it is "embargoed" (not to be released) prior to 8:30 AM. Reporters type up their analyses of the report, including extracting key figures like Initial Jobless Claims. They load their write-ups into their companies' software, which prepares to send it out as soon as Internet is enabled. At 8:30 AM the DOL representative in the room flips the wall switch and all of the laptops are connected to the Internet, releasing their write-ups to their companies and on to their companies' partners.
Many of those media companies have externally accessible APIs for distributing news. Media aggregators and squawk services (like RanSquawk and TradeTheNews) subscribe to all of these different APIs and then redistribute the key economic figures from the Report to their own subscribers within one second after Internet is enabled in the DOL lockup.
Some squawk services are text-based while others are audio-based. FinancialJuice.com provides a free audio squawk service; internally they have a paid subscription to a professional squawk service and they simply read out the latest headlines to their own listeners, subsidized by ads on the site. I've been using it for 4 months now and have been pretty happy. It usually lags behind the official release times by 1-2 seconds and occasionally they verbally flub the numbers or stutter and have to repeat, but you can't beat the price!
Important - I’m not affiliated with FinancialJuice and I’m not advocating that you use them over any other squawk. If you use them and they misspeak a number and you lose all your money don’t blame me. If anybody has any other free alternatives please share them!

How the news affects forex markets

Institutional forex traders subscribe to these squawk services and use custom software to consume the emerging data programmatically and then automatically initiate trades based on the perceived change to the fundamentals that the figures represent.
It's important to note that every institution will have "priced in" their own forecasted figures well in advance of an actual news release. Forecasts and consensuses all come out at different times in the days leading up to a news release, so by the time the news drops everybody is really only looking for an unexpected result. You can't really know what any given institution expects the value to be, but unless someone has inside information you can pretty much assume that the market has collectively priced in the experts' consensus. When the news comes out, institutions will trade based on the difference between the actual and their forecast.
Sometimes the news reflects a real change to the fundamentals with an economic effect that will change the demand for a currency, like an interest rate decision. However, in the case of the Initial Jobless Claims figure, which is a backwards-looking metric, trading is really just self-fulfilling speculation that market participants will buy dollars when unemployment is low and sell dollars when unemployment is high. Generally speaking, news that reflects a real economic shift has a bigger effect than news that only matters to speculators.
Massive and extremely fast news-based trades happen within tenths of a second on the ECNs on which institutional traders are participants. Over the next few seconds the resulting price changes trickle down to retail traders. Some economic news, like Non Farm Payroll Employment, has an effect that can last minutes to hours as "slow money" follows behind on the trend created by the "fast money". Other news, like Initial Jobless Claims, has a short impact that trails off within a couple minutes and is subsequently dwarfed by the usual pseudorandom movements in the market.
The bigger the difference between actual and consensus, the bigger the effect on any given currency pair. Since economic news releases generally relate to a single currency, the biggest and most easily predicted effects are seen on pairs where one currency is directly effected and the other is not affected at all. Personally I trade USD/JPY because the time difference between the US and Japan ensures that no news will be coming out of Japan at the same time that economic news is being released in the US.
Before deciding to trade any particular news release you should measure the historical correlation between the release (specifically, the difference between actual and consensus) and the resulting short-term change in the currency pair. Historical data for various news releases (along with historical consensus data) is readily available. You can pay to get it exported into Excel or whatever, or you can scroll through it for free on websites like TradingEconomics.com.
Let's look at two examples: Initial Jobless Claims and Non Farm Payroll Employment (NFP). I collected historical consensuses and actuals for these releases from January 2018 through the present, measured the "surprise" difference for each, and then correlated that to short-term changes in USD/JPY at the time of release using 5 second candles.
I omitted any releases that occurred simultaneously as another major release. For example, occasionally the monthly Initial Jobless Claims comes out at the exact same time as the monthly Balance of Trade figure, which is a more significant economic indicator and can be expected to dwarf the effect of the Unemployment Insurance Weekly Claims Report.
USD/JPY correlation with Initial Jobless Claims (2018 - present)
USD/JPY correlation with Non Farm Payrolls (2018 - present)
The horizontal axes on these charts is the duration (in seconds) after the news release over which correlation was calculated. The vertical axis is the Pearson correlation coefficient: +1 means that the change in USD/JPY over that duration was perfectly linearly correlated to the "surprise" in the releases; -1 means that the change in USD/JPY was perfectly linearly correlated but in the opposite direction, and 0 means that there is no correlation at all.
For Initial Jobless Claims you can see that for the first 30 seconds USD/JPY is strongly negatively correlated with the difference between consensus and actual jobless claims. That is, fewer-than-forecast jobless claims (fewer newly unemployed people than expected) strengthens the dollar and greater-than-forecast jobless claims (more newly unemployed people than expected) weakens the dollar. Correlation then trails off and changes to a moderate/weak positive correlation. I interpret this as algorithms "buying the dip" and vice versa, but I don't know for sure. From this chart it appears that you could profit by opening a trade for 15 seconds (duration with strongest correlation) that is long USD/JPY when Initial Jobless Claims is lower than the consensus and short USD/JPY when Initial Jobless Claims is higher than expected.
The chart for Non Farm Payroll looks very different. Correlation is positive (higher-than-expected payrolls strengthen the dollar and lower-than-expected payrolls weaken the dollar) and peaks at around 45 seconds, then slowly decreases as time goes on. This implies that price changes due to NFP are quite significant relative to background noise and "stick" even as normal fluctuations pick back up.
I wanted to show an example of what the USD/JPY S5 chart looks like when an "uncontested" (no other major simultaneously news release) Initial Jobless Claims and NFP drops, but unfortunately my broker's charts only go back a week. (I can pull historical data going back years through the API but to make it into a pretty chart would be a bit of work.) If anybody can get a 5-second chart of USD/JPY at March 19, 2020, UTC 12:30 and/or at February 7, 2020, UTC 13:30 let me know and I'll add it here.

Backtesting

So without too much effort we determined that (1) USD/JPY is strongly negatively correlated with the Initial Jobless Claims figure for the first 15 seconds after the release of the Unemployment Insurance Weekly Claims Report (when no other major news is being released) and also that (2) USD/JPY is strongly positively correlated with the Non Farms Payroll figure for the first 45 seconds after the release of the Employment Situation report.
Before you can assume you can profit off the news you have to backtest and consider three important parameters.
Entry speed: How quickly can you realistically enter the trade? The correlation performed above was measured from the exact moment the news was released, but realistically if you've got your finger on the trigger and your ear to the squawk it will take a few seconds to hit "Buy" or "Sell" and confirm. If 90% of the price move happens in the first second you're SOL. For back-testing purposes I assume a 5 second delay. In practice I use custom software that opens a trade with one click, and I can reliably enter a trade within 2-3 seconds after the news drops, using the FinancialJuice free squawk.
Minimum surprise: Should you trade every release or can you do better by only trading those with a big enough "surprise" factor? Backtesting will tell you whether being more selective is better long-term or not.
Hold time: The optimal time to hold the trade is not necessarily the same as the time of maximum correlation. That's a good starting point but it's not necessarily the best number. Backtesting each possible hold time will let you find the best one.
The spread: When you're only holding a position open for 30 seconds, the spread will kill you. The correlations performed above used the midpoint price, but in reality you have to buy at the ask and sell at the bid. Brokers aren't stupid and the moment volume on the ECN jumps they will widen the spread for their retail customers. The only way to determine if the news-driven price movements reliably overcome the spread is to backtest.
Stops: Personally I don't use stops, neither take-profit nor stop-loss, since I'm automatically closing the trade after a fixed (and very short) amount of time. Additionally, brokers have a minimum stop distance; the profits from scalping the news are so slim that even the nearest stops they allow will generally not get triggered.
I backtested trading these two news releases (since 2018), using a 5 second entry delay, real historical spreads, and no stops, cycling through different "surprise" thresholds and hold times to find the combination that returns the highest net profit. It's important to maximize net profit, not expected value per trade, so you don't over-optimize and reduce the total number of trades taken to one single profitable trade. If you want to get fancy you can set up a custom metric that combines number of trades, expected value, and drawdown into a single score to be maximized.
For the Initial Jobless Claims figure I found that the best combination is to hold trades open for 25 seconds (that is, open at 5 seconds elapsed and hold until 30 seconds elapsed) and only trade when the difference between consensus and actual is 7k or higher. That leads to 30 trades taken since 2018 and an expected return of... drumroll please... -0.0093 yen per unit per trade.
Yep, that's a loss of approx. $8.63 per lot.
Disappointing right? That's the spread and that's why you have to backtest. Even though the release of the Unemployment Insurance Weekly Claims Report has a strong correlation with movement in USD/JPY, it's simply not something that a retail trader can profit from.
Let's turn to the NFP. There I found that the best combination is to hold trades open for 75 seconds (that is, open at 5 seconds elapsed and hold until 80 seconds elapsed) and trade every single NFP (no minimum "surprise" threshold). That leads to 20 trades taken since 2018 and an expected return of... drumroll please... +0.1306 yen per unit per trade.
That's a profit of approx. $121.25 per lot. Not bad for 75 seconds of work! That's a +6% ROI at 50x leverage.

Make it real

If you want to do this for realsies, you need to run these numbers for all of the major economic news releases. Markit Manufacturing PMI, Factory Orders MoM, Trade Balance, PPI MoM, Export and Import Prices, Michigan Consumer Sentiment, Retail Sales MoM, Industrial Production MoM, you get the idea. You keep a list of all of the releases you want to trade, when they are released, and the ideal hold time and "surprise" threshold. A few minutes before the prescribed release time you open up your broker's software, turn on your squawk, maybe jot a few notes about consensuses and model forecasts, and get your finger on the button. At the moment you hear the release you open the trade in the correct direction, hold it (without looking at the chart!) for the required amount of time, then close it and go on with your day.
Some benefits of trading this way: * Most major economic releases come out at either 8:30 AM ET or 10:00 AM ET, and then you're done for the day. * It's easily backtestable. You can look back at the numbers and see exactly what to expect your return to be. * It's fun! Packing your trading into 30 seconds and knowing that institutions are moving billions of dollars around as fast as they can based on the exact same news you just read is thrilling. * You can wow your friends by saying things like "The St. Louis Fed had some interesting remarks on consumer spending in the latest Beige Book." * No crayons involved.
Some downsides: * It's tricky to be fast enough without writing custom software. Some broker software is very slow and requires multiple dialog boxes before a position is opened, which won't cut it. * The profits are very slim, you're not going to impress your instagram followers to join your expensive trade copying service with your 30-second twice-weekly trades. * Any friends you might wow with your boring-ass economic talking points are themselves the most boring people in the world.
I hope you enjoyed this long as fuck post and you give trading economic news a try!
submitted by thicc_dads_club to Forex [link] [comments]

WolfpackBOT - The world's fastest and most secure trading bot

WolfpackBOT - The world's fastest and most secure trading bot

https://preview.redd.it/b2ffej55zfd21.png?width=768&format=png&auto=webp&s=196c912c5d4250be54d647648613545f74faec7d

INTRODUCTION

According to wikipedia, Blockchain is originally known as bloc chain, It is a growing list of records known as blocks which is linked using cryptography, each of these blocks contain a cryptographic hash of the initial block, a transaction data and a time stamp.
Since its emergence in the year 2008, when Nakamoto satoshi discovered and introduced bitcoin, there has been serious efforts to integrate the blockchain technology into several aspects of various process of global business , The blockchain technology has been described as having the potential to disrupt many industries with immutability, low-cost transaction, and enhanced maximum security. So many other blockchain implementations have been deployed and developed with unique features designed to specific use-cases.
The blockchain technology has made possible to issue assets through a distributed ledger framework. With cryptocurrency tokens, Assets can be given economic value in order to validate and initiate transactional processes.

ADVANTAGES OF BLOCKCHAIN:

  1. Decentralised payment processing,
  2. Creating an immutable system of recording,
  3. Reducing Cost of Transaction and
  4. Enhanced Security.
  5. Now that we have reminded ourselves of what blockchain technology is, let’s look into the subject matter.

ABOUT WolfpackBOT

WolfpackBOT is a highly advanced cryptocurrency trading software that allows for the execution of trades at lightning speed using proprietary trading algorithms, proprietary “Werewolf” Trading Analysis configurations, or user customized settings based on personal trading style. WolfpackBOT also allows for simultaneous trading access to all compatible cryptocurrency exchanges that are available to the bot, and all trading pairs with the WerewolfBOT subscription package.
WolfpackBOT is introducing an industry first, a beautiful automated cryptocurrency trading console: The WolfBOX. This efficient and sleek piece of hardware will conveniently allow for the full utilization of a bot subscription without the need for a VPS or dedicated computer. The WolfBOX will also include a built-in secure Hardware Wallet and RFID card reader to optimize ease-of-use and functionality.
WolfpackBOT trading software is enabled with limit, market, and “Wolf Trade” orders on all trading candles, including one-minute candles, with the widest array of technical trading indicators available on the market. WolfpackBOT's proprietary “Wolf Trade” orders provide superior market sell orders with a bite! WolfpackBOT is the only trading bot to feature live price scanning on your positions and also handles partial fills with ease, meaning you don’t miss out on orders. WolfpackBOT is incredibly fast and can fulfill up to 10,000 trades per day depending on market conditions and subscription package.
WolfpackBOT allows simultaneous trading access to all cryptocurrency exchanges that are available to the bot, and all trading pairs through the WerewolfBOT subscription plan. Not only do inferior bots allow limited access to one exchange and one trading pair per bot, they also store your API keys remotely on servers which are potentially susceptible to hacks and pump and dump attacks. User security and API key protection holds a high priority within the WolfpackBOT framework which is why it is the only trading bot that gives users full control with local management of their API keys.
Masternode and Proof of Work X11 Blockchain
Wolfcoin Blockchain with X11 Proof of Work Mining and Masternode Reward Systems The Wolfcoin blockchain and network are both designed and engineered to ensure store of value, transactional speed and security, and fungibility. The main goal of the Wolfcoin blockchain is to facilitate fast and secure transactions with a governance that helps sustain the network for the benefit of all users. The Wolfcoin blockchain is a two-tier network comprised of a Proof of Work (PoW) consensus mechanism powered by miners and a Proof of Service (PoSe) system powered by masternodes.
The Wolfcoin blockchain is secured through Proof of Work (PoW) in which miners attempt to solve difficult problems with specialized computers. When a problem is solved, the miner receives the right to add a new block to the blockchain. If the problem was solved correctly, the miner is rewarded once the block is added.
The second tier, which is powered by masternodes, enables Wolfcoin to facilitate private and instant transactions with Private Send and Instant Send. Masternodes are also rewarded when miners discover new blocks.
The block reward is distributed with 80% going to the masternodes and 20% going to miners. The masternode system is referred to as Proof of Service (PoSe), since the masternodes provide crucial services that support the features of the network.
Masternodes also oversee the network and have the power to reject improperly formed blocks from miners. If a miner tried to take the entire block reward for themselves, the masternode network would orphan the block ensuring that it would not be added to the blockchain.
In short, miners power the first tier, which is the basic sending and receiving of funds and prevention of double spending. Masternodes power the second tier, which provide the added features that make Wolfcoin different from other cryptocurrencies. Masternodes do not mine, and mining computers cannot serve as masternodes.
Additionally, each masternode is “secured” by 10,000 WOLF. Those WOLF remain under the sole control of their owner at all times. The funds are not locked in any way; however, if enough of the funds are moved or spent to cause the user’s holdings to drop below 10,000 Wolfcoin, the associated masternode will go offline and stop receiving rewards.
By pre-ordering your WolfpackBOT subscription, you will also receive Wolfcoin as a reward that can be utilized in the following ways:
  • Redeemable for WolfpackBOT subscriptions
  • Redeemable for the WolfBOX Console
  • Redeemable for WolfpackBOT and Wolfcoin apparel and merchandise
  • Fungible utility that can be exchanged for like value on exchanges
When you hold at least 10,000 Wolfcoin in your Wolfcoin wallet connected to a static IP address, you will become a masternode, meaning you will have a chance to receive 80 percent of the block reward every sixty seconds.

THE FEATURES

WolfpackBOT Automated Trading Software:

After the crowdsale, Wolfcoin will be the exclusive method of payment for WolfpackBOT Automated Trading Software subscriptions.

Multiple Technical Analysis Indicators:

WolfpackBOT offers the widest array of multiple Technical Analysis indicators, oscillators, configurations and settings available in the world of Automated Cryptocurrency Trading Bots. WolfpackBOT provides Bollinger Bands, Double EMA, Elliot Wave, EMA, EMA Cross, Fibonacci Sequence, KAMA, MA Cross, MACD, RSI, SMA, Stochastic, Stochastic RSI, Triple EMA, and many more!

Shorting Features:

WolfpackBOT includes Cryptocurrency Shorting Features that allow users to short their positions and buy them back at the lower price to maximize their returns.

Copyrighted Crash Protection:

Crash Protection, one of WolfpackBOT's most advanced features, enables users the option to automatically scan and convert all positions to a stable coin at the sign of our proprietary Hidden Bear Divergence Indicator, and then buy back into base currency to resume trading at the sign of our proprietary Hidden Bull Divergence Indicator.

Language Translator:

WolfpackBOT has a built in Language Translator that instantly translates the entire BOT into Dutch, English, French, German, or Spanish.

All Trading Pairs on all available Exchanges:

WolfpackBOT allows our customers to simultaneously trade on multiple cryptocurrency exchanges, and with all the exchange’s trading pairs available for trading. The best part is that it’s all possible on one bot with one subscription to the WerewolfBOT package!

Coin Selector:

While other automated trading platforms only allow for a limited amount of coins per subscription, WolfpackBOT allows all trading pairs and all coins to be traded on all the available major exchanges with the WerewolfBOT subscription. WolfpackBOT's proprietary Coin Selector allows for users to choose whether to trade all cryptocurrencies or blacklist some, thus not trading them at all, as well as search for the highest volume, greatest performing, or a specific volatility range of coins for a given timeframe.

Werewolf Configurations and Settings:

Werewolf Configurations and Settings are copyrighted trading algorithms that use proprietary optimum settings for trading: the perfect configuration for experienced and inexperienced traders alike. These settings can be adjusted to the current market trend, with preset configurations for bear, sideways, and bull markets.

Werewolf Ultimate:

Werewolf Ultimate is the ultimate choice when trading. It doesn't trade a particular trading pair or particular coins, it trades them all. It goes in for the kill to increase the potential returns. Crash Protection is a built-in feature in Werewolf Ultimate.

Werewolf Bull Market:

Werewolf Bull Market are preset settings and configurations that are usable when your Base Trading Pair is in a Bull Run. Werewolf Bull Market settings are optimized for such conditions and should only be used in a Bull Run Market.

Werewolf Sideways Market:

Werewolf Sideways Market are preset settings and configurations that are usable when your Base Trading Pair is trading sideways. Werewolf Sideways Market settings are optimized for such conditions and should only be used in a Sideways Trading Market.

Werewolf Bear Market:

Werewolf Bear Market are preset settings and configurations that are usable when your Base Trading Pair is in a Bear Run. Werewolf Bear Market settings are optimized for such conditions and should only be used in a Bear Run Market.

The WolfBOX Hardware Console:

WolfpackBOT also offers an industry first: a beautiful hardware console, The WolfBOX. Our console comes preloaded with WolfpackBOT Automated Trading Software and also includes a built-in secure hardware wallet. Some of the key features of the WolfBOX include our high-speed CPU, solid-state hard drive, built-in RFID card reader, and integrated Bitpay and Coinbase wallets.

Wolfpack Consulting

Our company offers its services and expertise as Cryptocurrency and Blockchain Specialists to individuals and companies. We offer consulting services in the fields of blockchain and cryptocurrency development and management.

Wolfpack Philanthropy

We are dedicated to the proposition that we have a responsibility to use a portion of our company’s revenue to help create a better world and a brighter future. As we move forward, our philanthropic efforts include environmental stewardship, renewable energy, human rights, economic development, as well as animal and wildlife rescue and conservation with an emphasis on dogs and wolves.

Wolfcoin Information

THE WOLFCOIN Wolfcoin is the coin that fuels all WolfpackBOT's projects.
This utility, coupled with the reward systems with mining and Masternoding capabilities, makes the use of Wolfcoin potentially appealing to all WolfpackBOT users whom are interested in receiving additional Wolfcoin for subscriptions, merchandise and other rewards such as passive cryptocurrency portfolio growth.
THE WOLFCOIN WALLET WolfpackBOT uses our proprietary Wolfcoin Core QT wallet.
February 2018 Conceptual development of WolfpackBOT Software
May 2018 Company Roadmap development Alpha models of WolfpackBOT Software
June 2018 Ongoing research, development, and testing
October 2018 Advertising and Marketing Campaign Starts Wallets available for payment; BTC, BTG, DASH, DOGE, ETC, ETH, LTC October 15 - Pre-registration begins
November 2018 November 1 - Crowdsale Stage I begins
December 2018 Official presentation of WolfpackBOT beta Software Preview Creation of Wolfcoin (WOLF: 300,000,000 coins pre-mined on Genesis Block) WolfpackBOT beta Software release to selected customers
December 21 - Launch network and mine Genesis block
December 22 - PoW / Mainnet
December 23 - Blockchain and network testing
December 28 - Iquidis Wolfcoin Block Explorer released on our website
January 2019 January 1 - Wolfcoin Core wallets available for download on the website January 1 - Wallet and Masternode Tutorial available January 1 - Masternode and PoW instructional videos available January 1 - Subscription Pre-order Coin Rewards disbursed Announcement listing WOLF on top-10 Exchange
February 2019 February 1 - Crowdsale Stage I Ends February 1 - Crowdsale Stage II Begins
March 2019 March 15 - Crowdsale Stage II Ends March 15 - Crowdsale Stage III Begins WolfpackBOT Software roll-out to contributors WolfBOX Console available for Pre-order
April 2019 WolfpackBOT Subscriptions available for customers First Major version released: automated, manual, and paper trading WolfpackBOT Live support center April 30 - Crowdsale Stage III Ends
May 2019 WolfBOX Consoles Pre-orders first shipment
June 2019 New trading features such as new exchanges, strategy options and indicators
July 2019 New trading features such as new exchanges, strategy options or indicators
August 2019 WolfpackBOT Software Trading Platform V2.0 Second major release: Strategy Marketplace and Back-testing
September 2019 New trading features such as new exchanges, strategy options or indicators
October 2019 WolfpackBOT Software Trading Platform V3.0 Third major release: Signals Marketplace (Supporting 3rd Party App Signals) Mobile Application for WolfpackBOT Software and Trading Platform
November 2019 New trading features such as new exchanges, strategy options or indicator
December 2019 WolfpackBOT Software Trading Platform V4.0
January 2020 WolfpackBOT Software Trading Platform V5.0 Fourth major release: Machine Learning Strategy Optimization

THE AMAZING TEAM

Philip Longhurst Chief Executive Officer The leader of our pack and the man behind the WolfpackBOT trading bot, Philip Longhurst is a mathematical genius, engineer, day trader, and animal rescuer. As an account manager for J.P. Morgan and MBNA Bank, Phil managed the accounts of several high-profile clients and businesses. He has been successfully trading stocks for over twenty-five years and has successfully applied his trading expertise and mathematical acumen to the cryptocurrency market since 2013.
Philip holds bachelor's degrees in mechanical engineering and business administration and is a loving husband, father, and family man who has been rescuing dogs since 1995. His driving desire is to use the success of Wolfpack Group to create a brighter future for humanity. He currently resides in the United States of America with his wife, daughter, and dogs.
Rogier Pointl Chief Financial Officer Rogier Pointl is a successful entrepreneur with nearly twenty-five years of experience in business management, marketing, financial administration, economics, and fintech. Rogier holds bachelor's degrees in Business Communications and Financial Administration. He is a pioneer in the field of virtual reality, having served as CEO and owner of Simworld, the first virtual reality racing center in Europe, where he oversaw the development of advanced simulator and virtual reality hardware and software.
Rogier is an experienced trader and has been trading stocks since 2007. He began applying his expertise to the cryptocurrency market in 2010, gaining experience as a Bitcoin miner along the way. Rogier is a loving husband and father and currently resides in the Netherlands with his wife and two daughters.
Jason Cormier Chief Technical Officer Jason Cormier is a humble -but extraordinary- individual who is blessed with a Mensa IQ of 151, he is continually driven by a desire for knowledge and self-growth. He is self-taught in Visual Basics, C#, C++, HTML, and CSS and began developing programs and applications at the age of 14, including the TCB Wallet, which was the first ever wallet program that held its users' log in names and passwords. Jason is a cryptocurrency guru whose expertise includes cryptocurrency mining farms, proof-of-stake, masternodes, and cryptocurrency trading.
Jason holds Associate degrees in Computer Science and Psychology, and currently resides in the United States of America with his wife and son.
Jay McKinney Chief Web Development and Design Officer Jay is a veteran of the Iraq War who put his life on the line in combat to protect our freedoms. To center himself while stationed in the Iraqi warzone, he taught himself C# as he knew honing his Web Development skills would help him provide a better future for himself and his family. Upon returning home safely, he worked his way through college and holds bachelor's degrees in Computer Programming and Web Development & Design.
Jay has worked for the Kentucky Housing Corporation, serving as a software engineer and web developer. He is a loving family man who currently resides in the United States of America with his wife and two children.
David Johnson Chief Software Development Officer David holds a Master of Science degree in Information Systems and a Bachelor's degree in Business Administration with a specialization in Information Systems, graduating with Magna Cum Laude status. He has worked for the Kentucky Housing Corporation, serving as a network analyst and software engineer. As an entrepreneur, he has owned his own web and software development company since 2009, creating and maintaining several websites in C# and PHP, and has been operating the crypto-oriented YouTube channel BigBits since 2017, where he discusses automated Cryptocurrency trading strategies.
David is a proud father of two and resides in the United States of America with his wife and children. Like any good Kentuckian, he is a huge fan of the University of Kentucky's college sports teams.
Gabriel Condrea Software and Web Development Officer Gabriel Condrea holds a bachelor's degree in electrical and computer engineering and has worked as a software developer and senior systems engineer in both the United States and the United Kingdom, working with a variety of programming languages and IDEs. He has used his expertise to create Manufacturing and SCADA systems in industrial applications.
Gabriel also applies his engineering skills to cryptocurrency day trading, seeking to automate the process. He loves to travel and currently resides in the United States with his girlfriend.
Igor Otorepec Chief Hardware Development Officer Igor is an engineer with twenty years of experience specializing in advanced PLC programming and industrial robotics. He is also an IT security expert and a CEC Certified Ethical Cracker who uses his skills to expose and patch security vulnerabilities in blockchain codes.
Igor is an advanced cryptocurrency trader and Kung Fu master who uses bio-hacking as a way of life to keep his 'chi' constantly centered. He currently resides in Austria with his loving wife.
Manik Ehhsan Director of Marketing and Public Relations Manik holds a Bachelor's degree in Computer Science and has over five years of experience in Web Development, Digital Marketing and Graphics Design. He has also managed the marketing for more than 30 successful Cryptocurrency start-ups and projects, and specializes in SEO and ASO. Manik is also a Cryptocurrency project promotion expert with an emphasis on Masternodes and building Social Media Communities.
Manik has focused his life on Cryptocurrency and currently resides in Bangladesh with his loving family.
Rance Garrison Chief Marketing Officer Rance Garrison holds a bachelor's degree in Business Administration and specialized in Seminary Studies for his Master's degree. He served as an AmeriCorps VISTA at WMMT-FM, the radio station owned by Appalshop, an arts and education center in Kentucky, and has also specialized in local cable television advertising. Rance is also a musician who has released several albums independently over the last decade.
Rance is very dedicated to his local community and is most excited by the potential implications of cryptocurrencies and blockchain technology for rural and remote economies. He currently resides in the United States of America with his wife, dog, and cats.
Paul Gabens Chief Public Relations Officer A master negotiator with a penchant for strategy, Paul Gabens brings more than twenty years of marketing and promotional experience in the automotive, hospitality, and entertainment industries to the Wolfpack. He is also an avid stock and cryptocurrency trader, having first entered into the cryptocurrency market two years ago, embracing his passion for crypto with the same vigor as his love for travel, classic cars, extreme roller coasters, and surfing.
Paul holds degrees in business management, marketing, and automotive aftermarket. He currently resides in the United States with his fiancé and two cats.
Blake Stanley Marketing and Social Media Officer Blake Stanley is a cryptocurrency enthusiast who also has over six years of experience managing both government and private sector client and customer relations. A strategic thinker and expert in the field of social media-based advertising, Blake also owns and manages his own online marketing company where he has been successfully curating and implementing online marketing and advertising strategies for his clients for the past three years.
Blake is a proud father and family man and currently lives in the United States with his daughter and fiancé.
Martin Kilgore Market and Trading Analyst Martin Kilgore holds bachelor’s degrees in both accounting and mathematics, having researched Knot Theory and the Jones Polynomial during his undergraduate studies, giving him a firm edge when analyzing market conditions. He has worked as a staff accountant for several governmental organizations.
Martin lives in the United States with his fiancé.
Jonathan McDonald Chief Trading Strategy Officer Jonathan has honed his trading skills over the past five years by studying and implementing economics, financial strategy, Forex trading analysis and trading bots. Through his constant learning, he discovered Cryptocurrency after seeing the difference in market volatility and high yield trading. His fine-tuned trading strategies complement Crypto markets perfectly, and he has been implementing trading strategies to the Cryptocurrency market for over a year with phenomenal results. Jonathan is constantly improving his trading skills with an emphasis on scalping techniques. He has applied his trading skillset to the WolfpackBOT and enjoys working alongside the Wolfpack in creating the fastest trading bot on the market.
Jonathan currently resides in Canada with his supportive girlfriend and family.
Web site: https://www.wolfpackbot.com/
Technical document: https://www.wolfpackbot.com/Pdf/whitepaper_en.pdf
Bounty0x username: idrixoxo
submitted by idrixoxo2015 to u/idrixoxo2015 [link] [comments]

WolfpackBOT - The world's fastest and most secure trading bot

WolfpackBOT - The world's fastest and most secure trading bot

https://preview.redd.it/n7wutgsuzfd21.png?width=800&format=png&auto=webp&s=d0dac7147b8e70584305f997732a248d6b088ff9

INTRODUCTION

Cryptocurrency is essentially digital money traded from one person to another through the use of pseudonyms. There are no intermediaries like banks, no governmental oversight or authority, and no fees. The “crypto” in cryptocurrency refers to the use of cryptography to ensure the security and privacy of every transaction.
New coins are created through a technique called mining. The process requires powerful computers that solve complex math problems. Each problem should take about 10 minutes to solve, and results in the creation of a predetermined number of coins. The total number of coins that can be created is fixed — there’s a limit of 21 million bitcoins that can be created. The number of coins rewarded for solving each problem dwindles as time goes on.
Bitcoin is believed to have been created in 2009 by Satoshi Nakamoto, an enigmatic figure who has so far proven all but impossible to definitively identify. By using cryptography to control the creation and tracking of a digital currency, Nakamoto took that power away from central authorities like governments.
Bitcoin was the first and most famous digital currency, but you can choose from more than 1,500, including ether, litecoin and even cryptokitties. For awhile, you saw these currencies only in the darkest corners of the internet, where people used them for all sorts of questionable, even illegal, activities. Drug dealers liked them because they made transactions all but invisible, and trolls at the Kremlin-backed Internet Research Agency used bitcoin to finance their campaign to influence the 2016 election.
That started to change in 2014, when Overstock became the first major US retailer to accept bitcoin. Companies like Expedia and Microsoft followed suit.
One of the biggest misconceptions about cryptocurrencies is that you need thousands of dollars to invest. It’s an easy assumption to make, especially in the case of bitcoin, which stayed under $1,000 from about 2010 to 2017. But then it took off, surpassing thousand-dollar milestones at a pace that seemed quicker than you could refresh your phone.
The staggering value is off-putting to many. But unlike most stocks, you can buy a fraction of a bitcoin so you don’t need thousands to get into the crypto game.

OVERVIEW OF WolfpackBOT

WolfpackBOT is a highly advanced cryptocurrency trading software that allows for the execution of trades at lightning speed using proprietary trading algorithms, proprietary “Werewolf” Trading Analysis configurations, or user customized settings based on personal trading style. WolfpackBOT also allows for simultaneous trading access to all compatible cryptocurrency exchanges that are available to the bot, and all trading pairs with the WerewolfBOT subscription package.
WolfpackBOT is introducing an industry first, a beautiful automated cryptocurrency trading console: The WolfBOX. This efficient and sleek piece of hardware will conveniently allow for the full utilization of a bot subscription without the need for a VPS or dedicated computer. The WolfBOX will also include a built-in secure Hardware Wallet and RFID card reader to optimize ease-of-use and functionality.
WolfpackBOT trading software is enabled with limit, market, and “Wolf Trade” orders on all trading candles, including one-minute candles, with the widest array of technical trading indicators available on the market. WolfpackBOT's proprietary “Wolf Trade” orders provide superior market sell orders with a bite! WolfpackBOT is the only trading bot to feature live price scanning on your positions and also handles partial fills with ease, meaning you don’t miss out on orders. WolfpackBOT is incredibly fast and can fulfill up to 10,000 trades per day depending on market conditions and subscription package.
WolfpackBOT allows simultaneous trading access to all cryptocurrency exchanges that are available to the bot, and all trading pairs through the WerewolfBOT subscription plan. Not only do inferior bots allow limited access to one exchange and one trading pair per bot, they also store your API keys remotely on servers which are potentially susceptible to hacks and pump and dump attacks. User security and API key protection holds a high priority within the WolfpackBOT framework which is why it is the only trading bot that gives users full control with local management of their API keys.
Masternode and Proof of Work X11 Blockchain
Wolfcoin Blockchain with X11 Proof of Work Mining and Masternode Reward Systems The Wolfcoin blockchain and network are both designed and engineered to ensure store of value, transactional speed and security, and fungibility. The main goal of the Wolfcoin blockchain is to facilitate fast and secure transactions with a governance that helps sustain the network for the benefit of all users. The Wolfcoin blockchain is a two-tier network comprised of a Proof of Work (PoW) consensus mechanism powered by miners and a Proof of Service (PoSe) system powered by masternodes.
The Wolfcoin blockchain is secured through Proof of Work (PoW) in which miners attempt to solve difficult problems with specialized computers. When a problem is solved, the miner receives the right to add a new block to the blockchain. If the problem was solved correctly, the miner is rewarded once the block is added.
The second tier, which is powered by masternodes, enables Wolfcoin to facilitate private and instant transactions with Private Send and Instant Send. Masternodes are also rewarded when miners discover new blocks.
The block reward is distributed with 80% going to the masternodes and 20% going to miners. The masternode system is referred to as Proof of Service (PoSe), since the masternodes provide crucial services that support the features of the network.
Masternodes also oversee the network and have the power to reject improperly formed blocks from miners. If a miner tried to take the entire block reward for themselves, the masternode network would orphan the block ensuring that it would not be added to the blockchain.
In short, miners power the first tier, which is the basic sending and receiving of funds and prevention of double spending. Masternodes power the second tier, which provide the added features that make Wolfcoin different from other cryptocurrencies. Masternodes do not mine, and mining computers cannot serve as masternodes.
Additionally, each masternode is “secured” by 10,000 WOLF. Those WOLF remain under the sole control of their owner at all times. The funds are not locked in any way; however, if enough of the funds are moved or spent to cause the user’s holdings to drop below 10,000 Wolfcoin, the associated masternode will go offline and stop receiving rewards.
By pre-ordering your WolfpackBOT subscription, you will also receive Wolfcoin as a reward that can be utilized in the following ways:
  • Redeemable for WolfpackBOT subscriptions
  • Redeemable for the WolfBOX Console
  • Redeemable for WolfpackBOT and Wolfcoin apparel and merchandise
  • Fungible utility that can be exchanged for like value on exchanges
When you hold at least 10,000 Wolfcoin in your Wolfcoin wallet connected to a static IP address, you will become a masternode, meaning you will have a chance to receive 80 percent of the block reward every sixty seconds.

THE FEATURES

WolfpackBOT Automated Trading Software:

After the crowdsale, Wolfcoin will be the exclusive method of payment for WolfpackBOT Automated Trading Software subscriptions.

Multiple Technical Analysis Indicators:

WolfpackBOT offers the widest array of multiple Technical Analysis indicators, oscillators, configurations and settings available in the world of Automated Cryptocurrency Trading Bots. WolfpackBOT provides Bollinger Bands, Double EMA, Elliot Wave, EMA, EMA Cross, Fibonacci Sequence, KAMA, MA Cross, MACD, RSI, SMA, Stochastic, Stochastic RSI, Triple EMA, and many more!

Shorting Features:

WolfpackBOT includes Cryptocurrency Shorting Features that allow users to short their positions and buy them back at the lower price to maximize their returns.

Copyrighted Crash Protection:

Crash Protection, one of WolfpackBOT's most advanced features, enables users the option to automatically scan and convert all positions to a stable coin at the sign of our proprietary Hidden Bear Divergence Indicator, and then buy back into base currency to resume trading at the sign of our proprietary Hidden Bull Divergence Indicator.

Language Translator:

WolfpackBOT has a built in Language Translator that instantly translates the entire BOT into Dutch, English, French, German, or Spanish.

All Trading Pairs on all available Exchanges:

WolfpackBOT allows our customers to simultaneously trade on multiple cryptocurrency exchanges, and with all the exchange’s trading pairs available for trading. The best part is that it’s all possible on one bot with one subscription to the WerewolfBOT package!

Coin Selector:

While other automated trading platforms only allow for a limited amount of coins per subscription, WolfpackBOT allows all trading pairs and all coins to be traded on all the available major exchanges with the WerewolfBOT subscription. WolfpackBOT's proprietary Coin Selector allows for users to choose whether to trade all cryptocurrencies or blacklist some, thus not trading them at all, as well as search for the highest volume, greatest performing, or a specific volatility range of coins for a given timeframe.

Werewolf Configurations and Settings:

Werewolf Configurations and Settings are copyrighted trading algorithms that use proprietary optimum settings for trading: the perfect configuration for experienced and inexperienced traders alike. These settings can be adjusted to the current market trend, with preset configurations for bear, sideways, and bull markets.

Werewolf Ultimate:

Werewolf Ultimate is the ultimate choice when trading. It doesn't trade a particular trading pair or particular coins, it trades them all. It goes in for the kill to increase the potential returns. Crash Protection is a built-in feature in Werewolf Ultimate.

Werewolf Bull Market:

Werewolf Bull Market are preset settings and configurations that are usable when your Base Trading Pair is in a Bull Run. Werewolf Bull Market settings are optimized for such conditions and should only be used in a Bull Run Market.

Werewolf Sideways Market:

Werewolf Sideways Market are preset settings and configurations that are usable when your Base Trading Pair is trading sideways. Werewolf Sideways Market settings are optimized for such conditions and should only be used in a Sideways Trading Market.

Werewolf Bear Market:

Werewolf Bear Market are preset settings and configurations that are usable when your Base Trading Pair is in a Bear Run. Werewolf Bear Market settings are optimized for such conditions and should only be used in a Bear Run Market.

The WolfBOX Hardware Console:

WolfpackBOT also offers an industry first: a beautiful hardware console, The WolfBOX. Our console comes preloaded with WolfpackBOT Automated Trading Software and also includes a built-in secure hardware wallet. Some of the key features of the WolfBOX include our high-speed CPU, solid-state hard drive, built-in RFID card reader, and integrated Bitpay and Coinbase wallets.

Wolfpack Consulting

Our company offers its services and expertise as Cryptocurrency and Blockchain Specialists to individuals and companies. We offer consulting services in the fields of blockchain and cryptocurrency development and management.

Wolfpack Philanthropy

We are dedicated to the proposition that we have a responsibility to use a portion of our company’s revenue to help create a better world and a brighter future. As we move forward, our philanthropic efforts include environmental stewardship, renewable energy, human rights, economic development, as well as animal and wildlife rescue and conservation with an emphasis on dogs and wolves.

Wolfcoin Information

THE WOLFCOIN Wolfcoin is the coin that fuels all WolfpackBOT's projects.
This utility, coupled with the reward systems with mining and Masternoding capabilities, makes the use of Wolfcoin potentially appealing to all WolfpackBOT users whom are interested in receiving additional Wolfcoin for subscriptions, merchandise and other rewards such as passive cryptocurrency portfolio growth.
THE WOLFCOIN WALLET WolfpackBOT uses our proprietary Wolfcoin Core QT wallet.
February 2018 Conceptual development of WolfpackBOT Software
May 2018 Company Roadmap development Alpha models of WolfpackBOT Software
June 2018 Ongoing research, development, and testing
October 2018 Advertising and Marketing Campaign Starts Wallets available for payment; BTC, BTG, DASH, DOGE, ETC, ETH, LTC October 15 - Pre-registration begins
November 2018 November 1 - Crowdsale Stage I begins
December 2018 Official presentation of WolfpackBOT beta Software Preview Creation of Wolfcoin (WOLF: 300,000,000 coins pre-mined on Genesis Block) WolfpackBOT beta Software release to selected customers
December 21 - Launch network and mine Genesis block
December 22 - PoW / Mainnet
December 23 - Blockchain and network testing
December 28 - Iquidis Wolfcoin Block Explorer released on our website
January 2019 January 1 - Wolfcoin Core wallets available for download on the website January 1 - Wallet and Masternode Tutorial available January 1 - Masternode and PoW instructional videos available January 1 - Subscription Pre-order Coin Rewards disbursed Announcement listing WOLF on top-10 Exchange
February 2019 February 1 - Crowdsale Stage I Ends February 1 - Crowdsale Stage II Begins
March 2019 March 15 - Crowdsale Stage II Ends March 15 - Crowdsale Stage III Begins WolfpackBOT Software roll-out to contributors WolfBOX Console available for Pre-order
April 2019 WolfpackBOT Subscriptions available for customers First Major version released: automated, manual, and paper trading WolfpackBOT Live support center April 30 - Crowdsale Stage III Ends
May 2019 WolfBOX Consoles Pre-orders first shipment
June 2019 New trading features such as new exchanges, strategy options and indicators
July 2019 New trading features such as new exchanges, strategy options or indicators
August 2019 WolfpackBOT Software Trading Platform V2.0 Second major release: Strategy Marketplace and Back-testing
September 2019 New trading features such as new exchanges, strategy options or indicators
October 2019 WolfpackBOT Software Trading Platform V3.0 Third major release: Signals Marketplace (Supporting 3rd Party App Signals) Mobile Application for WolfpackBOT Software and Trading Platform
November 2019 New trading features such as new exchanges, strategy options or indicator
December 2019 WolfpackBOT Software Trading Platform V4.0
January 2020 WolfpackBOT Software Trading Platform V5.0 Fourth major release: Machine Learning Strategy Optimization

THE AMAZING TEAM

Philip Longhurst Chief Executive Officer The leader of our pack and the man behind the WolfpackBOT trading bot, Philip Longhurst is a mathematical genius, engineer, day trader, and animal rescuer. As an account manager for J.P. Morgan and MBNA Bank, Phil managed the accounts of several high-profile clients and businesses. He has been successfully trading stocks for over twenty-five years and has successfully applied his trading expertise and mathematical acumen to the cryptocurrency market since 2013.
Philip holds bachelor's degrees in mechanical engineering and business administration and is a loving husband, father, and family man who has been rescuing dogs since 1995. His driving desire is to use the success of Wolfpack Group to create a brighter future for humanity. He currently resides in the United States of America with his wife, daughter, and dogs.
Rogier Pointl Chief Financial Officer Rogier Pointl is a successful entrepreneur with nearly twenty-five years of experience in business management, marketing, financial administration, economics, and fintech. Rogier holds bachelor's degrees in Business Communications and Financial Administration. He is a pioneer in the field of virtual reality, having served as CEO and owner of Simworld, the first virtual reality racing center in Europe, where he oversaw the development of advanced simulator and virtual reality hardware and software.
Rogier is an experienced trader and has been trading stocks since 2007. He began applying his expertise to the cryptocurrency market in 2010, gaining experience as a Bitcoin miner along the way. Rogier is a loving husband and father and currently resides in the Netherlands with his wife and two daughters.
Jason Cormier Chief Technical Officer Jason Cormier is a humble -but extraordinary- individual who is blessed with a Mensa IQ of 151, he is continually driven by a desire for knowledge and self-growth. He is self-taught in Visual Basics, C#, C++, HTML, and CSS and began developing programs and applications at the age of 14, including the TCB Wallet, which was the first ever wallet program that held its users' log in names and passwords. Jason is a cryptocurrency guru whose expertise includes cryptocurrency mining farms, proof-of-stake, masternodes, and cryptocurrency trading.
Jason holds Associate degrees in Computer Science and Psychology, and currently resides in the United States of America with his wife and son.
Jay McKinney Chief Web Development and Design Officer Jay is a veteran of the Iraq War who put his life on the line in combat to protect our freedoms. To center himself while stationed in the Iraqi warzone, he taught himself C# as he knew honing his Web Development skills would help him provide a better future for himself and his family. Upon returning home safely, he worked his way through college and holds bachelor's degrees in Computer Programming and Web Development & Design.
Jay has worked for the Kentucky Housing Corporation, serving as a software engineer and web developer. He is a loving family man who currently resides in the United States of America with his wife and two children.
David Johnson Chief Software Development Officer David holds a Master of Science degree in Information Systems and a Bachelor's degree in Business Administration with a specialization in Information Systems, graduating with Magna Cum Laude status. He has worked for the Kentucky Housing Corporation, serving as a network analyst and software engineer. As an entrepreneur, he has owned his own web and software development company since 2009, creating and maintaining several websites in C# and PHP, and has been operating the crypto-oriented YouTube channel BigBits since 2017, where he discusses automated Cryptocurrency trading strategies.
David is a proud father of two and resides in the United States of America with his wife and children. Like any good Kentuckian, he is a huge fan of the University of Kentucky's college sports teams.
Gabriel Condrea Software and Web Development Officer Gabriel Condrea holds a bachelor's degree in electrical and computer engineering and has worked as a software developer and senior systems engineer in both the United States and the United Kingdom, working with a variety of programming languages and IDEs. He has used his expertise to create Manufacturing and SCADA systems in industrial applications.
Gabriel also applies his engineering skills to cryptocurrency day trading, seeking to automate the process. He loves to travel and currently resides in the United States with his girlfriend.
Igor Otorepec Chief Hardware Development Officer Igor is an engineer with twenty years of experience specializing in advanced PLC programming and industrial robotics. He is also an IT security expert and a CEC Certified Ethical Cracker who uses his skills to expose and patch security vulnerabilities in blockchain codes.
Igor is an advanced cryptocurrency trader and Kung Fu master who uses bio-hacking as a way of life to keep his 'chi' constantly centered. He currently resides in Austria with his loving wife.
Manik Ehhsan Director of Marketing and Public Relations Manik holds a Bachelor's degree in Computer Science and has over five years of experience in Web Development, Digital Marketing and Graphics Design. He has also managed the marketing for more than 30 successful Cryptocurrency start-ups and projects, and specializes in SEO and ASO. Manik is also a Cryptocurrency project promotion expert with an emphasis on Masternodes and building Social Media Communities.
Manik has focused his life on Cryptocurrency and currently resides in Bangladesh with his loving family.
Rance Garrison Chief Marketing Officer Rance Garrison holds a bachelor's degree in Business Administration and specialized in Seminary Studies for his Master's degree. He served as an AmeriCorps VISTA at WMMT-FM, the radio station owned by Appalshop, an arts and education center in Kentucky, and has also specialized in local cable television advertising. Rance is also a musician who has released several albums independently over the last decade.
Rance is very dedicated to his local community and is most excited by the potential implications of cryptocurrencies and blockchain technology for rural and remote economies. He currently resides in the United States of America with his wife, dog, and cats.
Paul Gabens Chief Public Relations Officer A master negotiator with a penchant for strategy, Paul Gabens brings more than twenty years of marketing and promotional experience in the automotive, hospitality, and entertainment industries to the Wolfpack. He is also an avid stock and cryptocurrency trader, having first entered into the cryptocurrency market two years ago, embracing his passion for crypto with the same vigor as his love for travel, classic cars, extreme roller coasters, and surfing.
Paul holds degrees in business management, marketing, and automotive aftermarket. He currently resides in the United States with his fiancé and two cats.
Blake Stanley Marketing and Social Media Officer Blake Stanley is a cryptocurrency enthusiast who also has over six years of experience managing both government and private sector client and customer relations. A strategic thinker and expert in the field of social media-based advertising, Blake also owns and manages his own online marketing company where he has been successfully curating and implementing online marketing and advertising strategies for his clients for the past three years.
Blake is a proud father and family man and currently lives in the United States with his daughter and fiancé.
Martin Kilgore Market and Trading Analyst Martin Kilgore holds bachelor’s degrees in both accounting and mathematics, having researched Knot Theory and the Jones Polynomial during his undergraduate studies, giving him a firm edge when analyzing market conditions. He has worked as a staff accountant for several governmental organizations.
Martin lives in the United States with his fiancé.
Jonathan McDonald Chief Trading Strategy Officer Jonathan has honed his trading skills over the past five years by studying and implementing economics, financial strategy, Forex trading analysis and trading bots. Through his constant learning, he discovered Cryptocurrency after seeing the difference in market volatility and high yield trading. His fine-tuned trading strategies complement Crypto markets perfectly, and he has been implementing trading strategies to the Cryptocurrency market for over a year with phenomenal results. Jonathan is constantly improving his trading skills with an emphasis on scalping techniques. He has applied his trading skillset to the WolfpackBOT and enjoys working alongside the Wolfpack in creating the fastest trading bot on the market.
Jonathan currently resides in Canada with his supportive girlfriend and family.
Web site: https://www.wolfpackbot.com/
Technical document: https://www.wolfpackbot.com/Pdf/whitepaper_en.pdf
Bounty0x username: idrixoxo
submitted by idrixoxo2015 to u/idrixoxo2015 [link] [comments]

Automated Forex Trading Software - 3 Steps to Using Automated Software

If possible, always look for real-time results rather than back tested results ​​​​​​​ Bitcoin Revolution 2 Review as they are more reliable. At a minimum, I will only conclude that results of a Forex trading software or system are reliable when there are at least 1.5 years of results available. Why You Should Not Trust Online Test "Results" Too EasilyAnother thing is that even if you find profitable past results of the system, you cannot conclude that it will necessarily be profitable. This is because some results may be only hypothetical and it is also very easy to make up profitable results by simply working backwards and curve fitting the entire system's rules. Most hypothetically profitable Forex systems and software have lost a lot of money for me before.
Having tried most Forex trading systems on the Internet, I have concluded that they are very unreliable and I would not recommend using them to generate an income. Most traders who claim that they have profitable trading systems are unable to produce real time results simply because they know that their systems are unreliable too.Trying to consistently make a profit from day trading is quite impossible and requires a lot of luck. One Forex software that is making me a lot of money is a fully automated robot that trades mostly the EUR / USD currency pair for me.
Would you like to download profitable Forex trading technical analysis software to help you make money? This type of software is primarily focused on helping the user do technical analysis on various currency pairs. Others that I have tried are even able to trade automatically immediately after they identify profitable setups. Unfortunately, more than 90% of all currency trading software that I have tested do not make money, with some losing huge amounts of my money too with no risk control methods.
This type of software is programmed with indicators and chart analysis codes. It is capable of analyzing charts within split second and shows the user where the likely possible price targets over the short and long term are. Once it has identified the possible trend movement direction, it then plots the optimal entry and exit points to assist the trader in making decisions. Some can also enter trade positions automatically for you once they have identified these trends and target take-profit & stop-loss targets.
https://freepdfreview.com/bitcoin-revolution-2-review/
submitted by steffandevin11 to u/steffandevin11 [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)
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Getting Started

Hey guys! I found a super cool list of everything a new forex trader would need to get started! Originally made by to nate1357. Link to original thread http://redd.it/328cjr
Free Resources
Education:
www.babypips.com/school
www.informedtrades.com/f7
www.forex4noobs.com/forex-education
www.en.tradimo.com/learn/forex-trading
www.youtube.com/useTheTradeitsimple
www.traderscalm.com
www.orderflowtrading.com/LearnOrderFlow.aspx
www.profitube.com
Calendars:
www.forexfactory.com/calendar.php
www.dailyfx.com/calendar
www.fxstreet.com/economic-calendar
www.forexlive.com/EconomicCalendar
www.myfxbook.com/forex-economic-calendar
www.investing.com/economic-calendar
Free News Websites:
www.forexlive.com - Daily live news, analysis and resources
www.financemagnates.com - FX industry news and updates
www.fxstreet.com - Daily news, analysis and resources
www.forextell.com
www.forexcup.com/news
www.bloomberg.com/markets
Forums:
www.reddit.com/forex
www.forums.babypips.com/
www.forexfactory.com/forum.php
www.elitetrader.com/et/index.php
www.forex-tsd.com/
www.fxgears.com/forum/index.php
www.trade2win.com/boards
Margin / pip / position size calculators
www.myfxbook.com/forex-calculators
Brokerages:
There are many factors to consider when choosing a brokerage. Regulations typically force US traders to only trade at US brokerages, while international traders have more choice. After considering location you need to consider how much capital you will start trading with as many have minimum deposit levels. Once you’ve narrowed that down you can compared spreads and execution. ECN brokers execute your orders straight through to their liquidity providers, while market maker brokers may pair up your trades with other clients. Market maker brokers typically will partially hedge your positions on the interbank market. Many consider this to be a conflict of interest and prefer to trade at an ECN broker who would have an active motive to see you succeed. Lastly, brokers run inherently risky business models so it is important to consider the risk of bankruptcy.
www.forexpeacearmy.com - Aggregates broker reviews. Be warned though that people only seem to make bad reviews.
www.myfxbook.com/forex-broker-spreads - Live comparison of executable spreads
United States & International-
-Interactive Brokers
International Only-
-LMAX (whitelabel DarwinEx)
*DMA broker based in the UK. Note that as a DMA broker LMAX eliminates the ability for LPs to last-look transactions. This may result in reduced liquidity during volatile times as liquidity providers would be likely not to risk posting liquidity to LMAX's pool. *Tight spreads *Minimum deposit $10,000 *Fairly well diversified
-Dukascopy
*ECN based in Switzerland, but available elsewhere depending on local regulations.
*Tight spreads *Minimum deposit $100 *Fairly well diversified
-IC Markets *ECN based in Australia *Fair spreads on standard account, tight spreads on professional accounts. *Minimum deposit $200 *Fairly well diversified
-Pepperstone
*ECN broker based in Australia. *Fair spreads on standard account, tight spreads on professional accounts. *Minimum deposit $200 *Not well diversified
Software / Apps:
Desktop/mobile
Terminology/Acronyms:
www.forexlive.com/ForexJargon - Common terms and acronyms
FAQ:
I need to exchange money, how do I do it?
This isn’t what this sub is for. Your best bet is using your bank or an online exchange service. Be prepared to pay a hefty fee.
I have money in one currency and need to exchange it into another sometime in the future, should I wait?
Don’t ask us this. We speculate intraday in FX and shouldn’t be relied on to tell you what’s best for you. Exchange the money when you need it.
I have an FX account, should I start trading demo or live?
This is highly debatable. You should definitely demo trade until you have mastered how to use the trading platform on desktop and mobile. After that it’s up to you. Many think that the psychology of trading live vs demo trading is massively different. So it may pay to learn to trade live. Just be warned that most FX traders lose almost their entire first account so start with a low affordable balance.
What’s money management?
Money management is a form of risk management and is arguably the most important aspect of your trading when it comes to long term survival. You should always enter trades with a stop loss - the distance of the stop allows you to calculate how large of a percent of your account balance will be lost if your trade stops out. You can run a monte carlo simulation to figure out the risk of having a number of trades go against you in a row to drain your account. The general rule is that you should only risk losing 1-4% of your account per trade entered.
More on this here: www.investopedia.com/articles/forex/06/fxmoneymgmt.asp[35]
www.swing-trade-stocks.com/money-management.html[36]
What about automated trading?
Retail FX traders have been known to program “Expert Advisors” (EAs) to automate trading. It’s generally advisable to stay away from that until you’re very experienced. Never buy an EA from a developer because the vast majority of them are scams.
What indicators are best?
That’s up to you to test and find out. Many in this forum dislike oscillating indicators since they fail to capture the essence of what moves price. With experience you will discover what works best for you. In my experience indicators that are most popular with professional traders are those that provide trading “levels” such as pivot points, fibonacci, moving averages, trendlines, etc.
What timeframe should I trade?
Price action can vary in different timeframes. In longer term timeframes the price action and fundamentals are much more clear. Unfortunately it would take a very long time to figure out whether or not what you’re doing is successful on longer timeframes. In shorter timeframes you can often tell very quickly if what you’re doing is profitable. Unfortunately there’s a lot more “noise” on these levels which can prove deceptive for those trying to learn. Therefore the best bet is to use a multi-timeframe analysis, working from top-down to come up with trades.
Should I trade using fundamental analysis (FA) of technical analysis (TA)?
This is a long standing argument in these forums and elsewhere. I’ll settle it here - you should have an understanding of both. Yes there are traders who blindly ignore one of the other but a truly well rounded trader should understand and implement both into the analysis. The market is driven in the longer term through FA. But TA is necessary to give traders a place to enter and exit trades from a psychological risk/reward standpoint.
I’ve heard trading Binary Options is an easy way to make money?
The general advice is to stay away from binaries. The structure of binary options is so that when you lose the broker wins. This incentive has created a very scammy industry where there are few legitimate binary options brokers. In addition in order to be profitable in binaries you have to win 55-65% of the time. That’s a much higher premium over spot FX.
Am I actually exchanging currencies?
Yes and no. Your broker handles spot FX is currency pairs. Although they make an exchange at the settlement date they treat your position in your account as a virtual currency pair. Think of it like a contract where you can only buy or sell it as a pair. In this sense you are always long one currency while short another. You are merely speculating that one currency will appreciate or depreciate vs another.
Why didn't my order fill?
Even if price appears to cross over a line on your chart it does not guarantee a fill. Different charting platforms chart different prices - some chart the bid price, some the ask price and some the midpoint price. To fill a limit order price needs to cross your limit's price plus the spread at the time that it is crossing. If it does not equal or exceed the spread then it will not fill. Be wary that in general spreads are not fixed. So what may fill at one time may not at another.
submitted by ClassicalAnt6 to TeamOceanSky [link] [comments]

Python Charting Stocks/Forex for Technical Analysis Part 1 ... Best Backtesting Software - Forex Best Forex Trading Indicator for MT4 System's Software Free Forex Technical Analysis Training Course in Telugu Day 14 - How to Use Forex Indicators in MT4 Soft4Fx: The Forex Best Backtesting Software Thus Far ... How to Forex backtest EA or indicator in Your Mt4 Forex platfrom or Tarminal (Tutorial ) How To Start Backtesting Forex Trading Strategies with Forex Tester 2 How to Get Started with Free Forex Backtesting Software ... Forex Indicators - By Far, The Best Way To Trade - YouTube How to back test an indicator MT4 ( TrendFinderBest Indicator)

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