How to Use the Weekly Time Frame in Forex Trading

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The Federal Reserve and the United States government are pumping extreme amounts of money into the economy, already totaling over $484 billion. They are doing so because it already had a goal to inflate the United States Dollar (USD) so that the market can continue to all-time highs. It has always had this goal. They do not care how much inflation goes up by now as we are going into a depression with the potential to totally crash the US economy forever. They believe the only way to save the market from going to zero or negative values is to inflate it so much that it cannot possibly crash that low. Even if the market does not dip that low, inflation serves the interest of powerful people.
The impending crash of the stock market has ramifications for Bitcoin, as, though there is no direct ongoing-correlation between the two, major movements in traditional markets will necessarily affect Bitcoin. According to the Blockchain Center’s Cryptocurrency Correlation Tool, Bitcoin is not correlated with the stock market. However, when major market movements occur, they send ripples throughout the financial ecosystem which necessary affect even ordinarily uncorrelated assets.
Therefore, Bitcoin will reach X price on X date after crashing to a price of X by X date.

Stock Market Crash

The Federal Reserve has caused some serious consternation with their release of ridiculous amounts of money in an attempt to buoy the economy. At face value, it does not seem to have any rationale or logic behind it other than keeping the economy afloat long enough for individuals to profit financially and politically. However, there is an underlying basis to what is going on which is important to understand in order to profit financially.
All markets are functionally price probing systems. They constantly undergo a price-discovery process. In a fiat system, money is an illusory and a fundamentally synthetic instrument with no intrinsic value – similar to Bitcoin. The primary difference between Bitcoin is the underlying technology which provides a slew of benefits that fiat does not. Fiat, however, has an advantage in being able to have the support of powerful nation-states which can use their might to insure the currency’s prosperity.
Traditional stock markets are composed of indices (pl. of index). Indices are non-trading market instruments which are essentially summaries of business values which comprise them. They are continuously recalculated throughout a trading day, and sometimes reflected through tradable instruments such as Exchange Traded Funds or Futures. Indices are weighted by market capitalizations of various businesses.
Price theory essentially states that when a market fails to take out a new low in a given range, it will have an objective to take out the high. When a market fails to take out a new high, it has an objective to make a new low. This is why price-time charts go up and down, as it does this on a second-by-second, minute-by-minute, day-by-day, and even century-by-century basis. Therefore, market indices will always return to some type of bull market as, once a true low is formed, the market will have a price objective to take out a new high outside of its’ given range – which is an all-time high. Instruments can only functionally fall to zero, whereas they can grow infinitely.
So, why inflate the economy so much?
Deflation is disastrous for central banks and markets as it raises the possibility of producing an overall price objective of zero or negative values. Therefore, under a fractional reserve system with a fiat currency managed by a central bank – the goal of the central bank is to depreciate the currency. The dollar is manipulated constantly with the intention of depreciating its’ value.
Central banks have a goal of continued inflated fiat values. They tend to ordinarily contain it at less than ten percent (10%) per annum in order for the psyche of the general populace to slowly adjust price increases. As such, the markets are divorced from any other logic. Economic policy is the maintenance of human egos, not catering to fundamental analysis. Gross Domestic Product (GDP) growth is well-known not to be a measure of actual growth or output. It is a measure of increase in dollars processed. Banks seek to produce raising numbers which make society feel like it is growing economically, making people optimistic. To do so, the currency is inflated, though inflation itself does not actually increase growth. When society is optimistic, it spends and engages in business – resulting in actual growth. It also encourages people to take on credit and debts, creating more fictional fiat.
Inflation is necessary for markets to continue to reach new heights, generating positive emotional responses from the populace, encouraging spending, encouraging debt intake, further inflating the currency, and increasing the sale of government bonds. The fiat system only survives by generating more imaginary money on a regular basis.
Bitcoin investors may profit from this by realizing that stock investors as a whole always stand to profit from the market so long as it is managed by a central bank and does not collapse entirely. If those elements are filled, it has an unending price objective to raise to new heights. It also allows us to realize that this response indicates that the higher-ups believe that the economy could crash in entirety, and it may be wise for investors to have multiple well-thought-out exit strategies.

Economic Analysis of Bitcoin

The reason why the Fed is so aggressively inflating the economy is due to fears that it will collapse forever or never rebound. As such, coupled with a global depression, a huge demand will appear for a reserve currency which is fundamentally different than the previous system. Bitcoin, though a currency or asset, is also a market. It also undergoes a constant price-probing process. Unlike traditional markets, Bitcoin has the exact opposite goal. Bitcoin seeks to appreciate in value and not depreciate. This has a quite different affect in that Bitcoin could potentially become worthless and have a price objective of zero.
Bitcoin was created in 2008 by a now famous mysterious figure known as Satoshi Nakamoto and its’ open source code was released in 2009. It was the first decentralized cryptocurrency to utilize a novel protocol known as the blockchain. Up to one megabyte of data may be sent with each transaction. It is decentralized, anonymous, transparent, easy to set-up, and provides myriad other benefits. Bitcoin is not backed up by anything other than its’ own technology.
Bitcoin is can never be expected to collapse as a framework, even were it to become worthless. The stock market has the potential to collapse in entirety, whereas, as long as the internet exists, Bitcoin will be a functional system with a self-authenticating framework. That capacity to persist regardless of the actual price of Bitcoin and the deflationary nature of Bitcoin means that it has something which fiat does not – inherent value.
Bitcoin is based on a distributed database known as the “blockchain.” Blockchains are essentially decentralized virtual ledger books, replete with pages known as “blocks.” Each page in a ledger is composed of paragraph entries, which are the actual transactions in the block.
Blockchains store information in the form of numerical transactions, which are just numbers. We can consider these numbers digital assets, such as Bitcoin. The data in a blockchain is immutable and recorded only by consensus-based algorithms. Bitcoin is cryptographic and all transactions are direct, without intermediary, peer-to-peer.
Bitcoin does not require trust in a central bank. It requires trust on the technology behind it, which is open-source and may be evaluated by anyone at any time. Furthermore, it is impossible to manipulate as doing so would require all of the nodes in the network to be hacked at once – unlike the stock market which is manipulated by the government and “Market Makers”. Bitcoin is also private in that, though the ledge is openly distributed, it is encrypted. Bitcoin’s blockchain has one of the greatest redundancy and information disaster recovery systems ever developed.
Bitcoin has a distributed governance model in that it is controlled by its’ users. There is no need to trust a payment processor or bank, or even to pay fees to such entities. There are also no third-party fees for transaction processing. As the ledge is immutable and transparent it is never possible to change it – the data on the blockchain is permanent. The system is not easily susceptible to attacks as it is widely distributed. Furthermore, as users of Bitcoin have their private keys assigned to their transactions, they are virtually impossible to fake. No lengthy verification, reconciliation, nor clearing process exists with Bitcoin.
Bitcoin is based on a proof-of-work algorithm. Every transaction on the network has an associated mathetical “puzzle”. Computers known as miners compete to solve the complex cryptographic hash algorithm that comprises that puzzle. The solution is proof that the miner engaged in sufficient work. The puzzle is known as a nonce, a number used only once. There is only one major nonce at a time and it issues 12.5 Bitcoin. Once it is solved, the fact that the nonce has been solved is made public.
A block is mined on average of once every ten minutes. However, the blockchain checks every 2,016,000 minutes (approximately four years) if 201,600 blocks were mined. If it was faster, it increases difficulty by half, thereby deflating Bitcoin. If it was slower, it decreases, thereby inflating Bitcoin. It will continue to do this until zero Bitcoin are issued, projected at the year 2140. On the twelfth of May, 2020, the blockchain will halve the amount of Bitcoin issued when each nonce is guessed. When Bitcoin was first created, fifty were issued per block as a reward to miners. 6.25 BTC will be issued from that point on once each nonce is solved.
Unlike fiat, Bitcoin is a deflationary currency. As BTC becomes scarcer, demand for it will increase, also raising the price. In this, BTC is similar to gold. It is predictable in its’ output, unlike the USD, as it is based on a programmed supply. We can predict BTC’s deflation and inflation almost exactly, if not exactly. Only 21 million BTC will ever be produced, unless the entire network concedes to change the protocol – which is highly unlikely.
Some of the drawbacks to BTC include congestion. At peak congestion, it may take an entire day to process a Bitcoin transaction as only three to five transactions may be processed per second. Receiving priority on a payment may cost up to the equivalent of twenty dollars ($20). Bitcoin mining consumes enough energy in one day to power a single-family home for an entire week.

Trading or Investing?

The fundamental divide in trading revolves around the question of market structure. Many feel that the market operates totally randomly and its’ behavior cannot be predicted. For the purposes of this article, we will assume that the market has a structure, but that that structure is not perfect. That market structure naturally generates chart patterns as the market records prices in time. In order to determine when the stock market will crash, causing a major decline in BTC price, we will analyze an instrument, an exchange traded fund, which represents an index, as opposed to a particular stock. The price patterns of the various stocks in an index are effectively smoothed out. In doing so, a more technical picture arises. Perhaps the most popular of these is the SPDR S&P Standard and Poor 500 Exchange Traded Fund ($SPY).
In trading, little to no concern is given about value of underlying asset. We are concerned primarily about liquidity and trading ranges, which are the amount of value fluctuating on a short-term basis, as measured by volatility-implied trading ranges. Fundamental analysis plays a role, however markets often do not react to real-world factors in a logical fashion. Therefore, fundamental analysis is more appropriate for long-term investing.
The fundamental derivatives of a chart are time (x-axis) and price (y-axis). The primary technical indicator is price, as everything else is lagging in the past. Price represents current asking price and incorrectly implementing positions based on price is one of the biggest trading errors.
Markets and currencies ordinarily have noise, their tendency to back-and-fill, which must be filtered out for true pattern recognition. That noise does have a utility, however, in allowing traders second chances to enter favorable positions at slightly less favorable entry points. When you have any market with enough liquidity for historical data to record a pattern, then a structure can be divined. The market probes prices as part of an ongoing price-discovery process. Market technicians must sometimes look outside of the technical realm and use visual inspection to ascertain the relevance of certain patterns, using a qualitative eye that recognizes the underlying quantitative nature
Markets and instruments rise slower than they correct, however they rise much more than they fall. In the same vein, instruments can only fall to having no worth, whereas they could theoretically grow infinitely and have continued to grow over time. Money in a fiat system is illusory. It is a fundamentally synthetic instrument which has no intrinsic value. Hence, the recent seemingly illogical fluctuations in the market.
According to trade theory, the unending purpose of a market or instrument is to create and break price ranges according to the laws of supply and demand. We must determine when to trade based on each market inflection point as defined in price and in time as opposed to abandoning the trend (as the contrarian trading in this sub often does). Time and Price symmetry must be used to be in accordance with the trend. When coupled with a favorable risk to reward ratio, the ability to stay in the market for most of the defined time period, and adherence to risk management rules; the trader has a solid methodology for achieving considerable gains.
We will engage in a longer term market-oriented analysis to avoid any time-focused pressure. The Bitcoin market is open twenty-four-hours a day, so trading may be done when the individual is ready, without any pressing need to be constantly alert. Let alone, we can safely project months in advance with relatively high accuracy. Bitcoin is an asset which an individual can both trade and invest, however this article will be focused on trading due to the wide volatility in BTC prices over the short-term.

Technical Indicator Analysis of Bitcoin

Technical indicators are often considered self-fulfilling prophecies due to mass-market psychology gravitating towards certain common numbers yielded from them. They are also often discounted when it comes to BTC. That means a trader must be especially aware of these numbers as they can prognosticate market movements. Often, they are meaningless in the larger picture of things.

Volume – derived from the market itself, it is mostly irrelevant. The major problem with volume for stocks is that the US market open causes tremendous volume surges eradicating any intrinsic volume analysis. This does not occur with BTC, as it is open twenty-four-seven. At major highs and lows, the market is typically anemic. Most traders are not active at terminal discretes (peaks and troughs) because of levels of fear. Volume allows us confidence in time and price symmetry market inflection points, if we observe low volume at a foretold range of values. We can rationalize that an absolute discrete is usually only discovered and anticipated by very few traders. As the general market realizes it, a herd mentality will push the market in the direction favorable to defending it. Volume is also useful for swing trading, as chances for swing’s validity increases if an increase in volume is seen on and after the swing’s activation. Volume is steadily decreasing. Lows and highs are reached when volume is lower.

Therefore, due to the relatively high volume on the 12th of March, we can safely determine that a low for BTC was not reached.

VIX – Volatility Index, this technical indicator indicates level of fear by the amount of options-based “insurance” in portfolios. A low VIX environment, less than 20 for the S&P index, indicates a stable market with a possible uptrend. A high VIX, over 20, indicates a possible downtrend. VIX is essentially useless for BTC as BTC-based options do not exist. It allows us to predict the market low for $SPY, which will have an indirect impact on BTC in the short term, likely leading to the yearly low. However, it is equally important to see how VIX is changing over time, if it is decreasing or increasing, as that indicates increasing or decreasing fear. Low volatility allows high leverage without risk or rest. Occasionally, markets do rise with high VIX.

As VIX is unusually high, in the forties, we can be confident that a downtrend for the S&P 500 is imminent.

RSI (Relative Strength Index): The most important technical indicator, useful for determining highs and lows when time symmetry is not availing itself. Sometimes analysis of RSI can conflict in different time frames, easiest way to use it is when it is at extremes – either under 30 or over 70. Extremes can be used for filtering highs or lows based on time-and-price window calculations. Highly instructive as to major corrective clues and indicative of continued directional movement. Must determine if longer-term RSI values find support at same values as before. It is currently at 73.56.
Secondly, RSI may be used as a high or low filter, to observe the level that short-term RSI reaches in counter-trend corrections. Repetitions based on market movements based on RSI determine how long a trade should be held onto. Once a short term RSI reaches an extreme and stay there, the other RSI’s should gradually reach the same extremes. Once all RSI’s are at extreme highs, a trend confirmation should occur and RSI’s should drop to their midpoint.

Trend Definition Analysis of Bitcoin

Trend definition is highly powerful, cannot be understated. Knowledge of trend logic is enough to be a profitable trader, yet defining a trend is an arduous process. Multiple trends coexist across multiple time frames and across multiple market sectors. Like time structure, it makes the underlying price of the instrument irrelevant. Trend definitions cannot determine the validity of newly formed discretes. Trend becomes apparent when trades based in counter-trend inflection points continue to fail.
Downtrends are defined as an instrument making lower lows and lower highs that are recurrent, additive, qualified swing setups. Downtrends for all instruments are similar, except forex. They are fast and complete much quicker than uptrends. An average downtrend is 18 months, something which we will return to. An uptrend inception occurs when an instrument reaches a point where it fails to make a new low, then that low will be tested. After that, the instrument will either have a deep range retracement or it may take out the low slightly, resulting in a double-bottom. A swing must eventually form.
A simple way to roughly determine trend is to attempt to draw a line from three tops going upwards (uptrend) or a line from three bottoms going downwards (downtrend). It is not possible to correctly draw a downtrend line on the BTC chart, but it is possible to correctly draw an uptrend – indicating that the overall trend is downwards. The only mitigating factor is the impending stock market crash.

Time Symmetry Analysis of Bitcoin

Time is the movement from the past through the present into the future. It is a measurement in quantified intervals. In many ways, our perception of it is a human construct. It is more powerful than price as time may be utilized for a trade regardless of the market inflection point’s price. Were it possible to perfectly understand time, price would be totally irrelevant due to the predictive certainty time affords. Time structure is easier to learn than price, but much more difficult to apply with any accuracy. It is the hardest aspect of trading to learn, but also the most rewarding.
Humans do not have the ability to recognize every time window, however the ability to define market inflection points in terms of time is the single most powerful trading edge. Regardless, price should not be abandoned for time alone. Time structure analysis It is inherently flawed, as such the markets have a fail-safe, which is Price Structure. Even though Time is much more powerful, Price Structure should never be completely ignored. Time is the qualifier for Price and vice versa. Time can fail by tricking traders into counter-trend trading.
Time is a predestined trade quantifier, a filter to slow trades down, as it allows a trader to specifically focus on specific time windows and rest at others. It allows for quantitative measurements to reach deterministic values and is the primary qualifier for trends. Time structure should be utilized before price structure, and it is the primary trade criterion which requires support from price. We can see price structure on a chart, as areas of mathematical support or resistance, but we cannot see time structure.
Time may be used to tell us an exact point in the future where the market will inflect, after Price Theory has been fulfilled. In the present, price objectives based on price theory added to possible future times for market inflection points give us the exact time of market inflection points and price.
Time Structure is repetitions of time or inherent cycles of time, occurring in a methodical way to provide time windows which may be utilized for inflection points. They are not easily recognized and not easily defined by a price chart as measuring and observing time is very exact. Time structure is not a science, yet it does require precise measurements. Nothing is certain or definite. The critical question must be if a particular approach to time structure is currently lucrative or not.
We will measure it in intervals of 180 bars. Our goal is to determine time windows, when the market will react and when we should pay the most attention. By using time repetitions, the fact that market inflection points occurred at some point in the past and should, therefore, reoccur at some point in the future, we should obtain confidence as to when SPY will reach a market inflection point. Time repetitions are essentially the market’s memory. However, simply measuring the time between two points then trying to extrapolate into the future does not work. Measuring time is not the same as defining time repetitions. We will evaluate past sessions for market inflection points, whether discretes, qualified swings, or intra-range. Then records the times that the market has made highs or lows in a comparable time period to the future one seeks to trade in.
What follows is a time Histogram – A grouping of times which appear close together, then segregated based on that closeness. Time is aligned into combined histogram of repetitions and cycles, however cycles are irrelevant on a daily basis. If trading on an hourly basis, do not use hours.

Yearly Lows (last seven years): 1/1/13, 4/10/14, 1/15/15, 1/17/16, 1/1/17, 12/15/18, 2/6/19
Monthly Mode: 1, 1, 1, 1, 2, 4, 12
Daily Mode: 1, 1, 6, 10, 15, 15, 17
Monthly Lows (for the last year): 3/12/20 (10:00pm), 2/28/20 (7:09am), 1/2/20 (8:09pm), 12/18/19 (8:00am), 11/25/19 (1:00am), 10/24/19 (2:59am), 9/30/19 (2:59am), 8/29,19 (4:00am), 7/17/19 (7:59am), 6/4/19 (5:59pm), 5/1/19 (12:00am), 4/1/19 (12:00am)
Daily Lows Mode for those Months: 1, 1, 2, 4, 12, 17, 18, 24, 25, 28, 29, 30
Hourly Lows Mode for those Months (Military time): 0100, 0200, 0200, 0400, 0700, 0700, 0800, 1200, 1200, 1700, 2000, 2200
Minute Lows Mode for those Months: 00, 00, 00, 00, 00, 00, 09, 09, 59, 59, 59, 59
Day of the Week Lows (last twenty-six weeks):

Weighted Times are repetitions which appears multiple times within the same list, observed and accentuated once divided into relevant sections of the histogram. They are important in the presently defined trading time period and are similar to a mathematical mode with respect to a series. Phased times are essentially periodical patterns in histograms, though they do not guarantee inflection points
Evaluating the yearly lows, we see that BTC tends to have its lows primarily at the beginning of every year, with a possibility of it being at the end of the year. Following the same methodology, we get the middle of the month as the likeliest day. However, evaluating the monthly lows for the past year, the beginning and end of the month are more likely for lows.
Therefore, we have two primary dates from our histogram.
1/1/21, 1/15/21, and 1/29/21
2:00am, 8:00am, 12:00pm, or 10:00pm
In fact, the high for this year was February the 14th, only thirty days off from our histogram calculations.
The 8.6-Year Armstrong-Princeton Global Economic Confidence model states that 2.15 year intervals occur between corrections, relevant highs and lows. 2.15 years from the all-time peak discrete is February 9, 2020 – a reasonably accurate depiction of the low for this year (which was on 3/12/20). (Taking only the Armstrong model into account, the next high should be Saturday, April 23, 2022). Therefore, the Armstrong model indicates that we have actually bottomed out for the year!
Bear markets cannot exist in perpetuity whereas bull markets can. Bear markets will eventually have price objectives of zero, whereas bull markets can increase to infinity. It can occur for individual market instruments, but not markets as a whole. Since bull markets are defined by low volatility, they also last longer. Once a bull market is indicated, the trader can remain in a long position until a new high is reached, then switch to shorts. The average bear market is eighteen months long, giving us a date of August 19th, 2021 for the end of this bear market – roughly speaking. They cannot be shorter than fifteen months for a central-bank controlled market, which does not apply to Bitcoin. (Otherwise, it would continue until Sunday, September 12, 2021.) However, we should expect Bitcoin to experience its’ exponential growth after the stock market re-enters a bull market.
Terry Laundy’s T-Theory implemented by measuring the time of an indicator from peak to trough, then using that to define a future time window. It is similar to an head-and-shoulders pattern in that it is the process of forming the right side from a synthetic technical indicator. If the indicator is making continued lows, then time is recalculated for defining the right side of the T. The date of the market inflection point may be a price or indicator inflection date, so it is not always exactly useful. It is better to make us aware of possible market inflection points, clustered with other data. It gives us an RSI low of May, 9th 2020.
The Bradley Cycle is coupled with volatility allows start dates for campaigns or put options as insurance in portfolios for stocks. However, it is also useful for predicting market moves instead of terminal dates for discretes. Using dates which correspond to discretes, we can see how those dates correspond with changes in VIX.
Therefore, our timeline looks like:

2/14/20 – yearly high ($10372 USD)
3/12/20 – yearly low thus far ($3858 USD)
5/9/20 – T-Theory true yearly low (BTC between 4863 and 3569)
5/26/20 – hashrate difficulty halvening
11/14/20 – stock market low
1/15/21 – yearly low for BTC, around $8528
8/19/21 – end of stock bear market
11/26/21 – eighteen months from halvening, average peak from halvenings (BTC begins rising from $3000 area to above $23,312)
4/23/22 – all-time high

Taken from my blog: http://aliamin.info/2020/

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Financial websites started in the late 1990s with many more coming online in the 2000s. Some, such as Bankrate, started out in print decades ago before transforming into one of the best financial websites. Financial planning websites can help you to learn how to manage your money and to build wealth in a more effective way.
Learn about the best financial websites and financial blogs from M1 Finance Users of the best financial websites today
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Read about the best financial websites and financial blogs from M1 Finance The Wall Street Journal
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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

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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
53 points, 1 submission: agifft3_omscs
1. [Project Questions] Unit Tests for optimize_something assignment (53 points, 94 comments)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
23 points, 1 submission: gong6
1. Is manual strategy ready? (23 points, 6 comments)
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)
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)
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)
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)
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)
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)
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)
20 points, 2 submissions: pharmerino
1. assess_portfolio test cases (16 points, 88 comments)
2. ML4T Assignments (4 points, 6 comments)

Top Commenters

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

Top Submissions

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

Top Comments

34 points: jgeiger's comment in QLearning Robot project megathread
31 points: coolwhip1234's comment in QLearning Robot project megathread
30 points: tuckerbalch's comment in Why Professor is usually late for class?
23 points: davebyrd's comment in Deadline extension?
20 points: jason_gt's comment in What would be a good quiz question regarding The Big Short?
19 points: yokh_cs7646's comment in For online students: Participation check #2
17 points: i__want__piazza's comment in project 3 megathread (assess_learners)
17 points: nathakhanh2's comment in Project 2 Megathread (optimize_something)
17 points: pharmerino's comment in Midterm study Megathread
17 points: tuckerbalch's comment in Midterm grades posted to T-Square

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What attracts the majority of investors to binary options is definitely the prospect of high payments (170-180% of initial investment amount) in short time periods of between 1 minute up to 1 week. This really is essentially the most widely used investing opportunities since it produces big money fast. For example; In order to make money you place a investment of $25 on USD/AUD call, predicting the option will move upward. Then you choose a expiration period of 5 minutes and the price goes up after 5 minutes. You just banked $15 inside of 5 minutes. ==================================================================================== ==> CLICK TO HEAD OVER TO Xtreme Profit Cloud DOWNLOAD LINK ==================================================================================== What's Xtreme Profit Cloud! The Xtreme Profit Cloud is a FREE signal generating software which scans the present trend and based upon certain factors, forecasts what the outcome of a trade could be and supplies you with all the results; It's going to only take a few seconds to locate a lucrative trade and you'll find out if you should buy and sell up or down, the time frame, and everything you should turn a profit. Rather than spending hours watching the current market you could have the software do the "hard" work for you. The Xtreme Profit Cloud is actually designed so it will be super easy for any trader, and in this way you can start buying and selling inside the application and make quick trades that helps you save time on each trade. The littlest alteration in the strike price (price you purchased the option pair) can be the difference of your win or loss. The the Xtreme Profit Cloud focuses on foreign currencies and offers time periods of 15 - 30 minutes Additionally, it includes audio announcements so even when you’re looking at the page you’ll notice whenever a signal has been going off. Some software on the market simply don't work, however this one is claiming an incredible successful trades rate of 80+% or more, and that is across over 10 trades trades. In all honesty, I wasn't using a huge investment per trade but when I feel ready i'll start putting in more money per trade. Another cool advantage of the application is the automation mode that let's you set it and forget it so that you can make the revenue even when you're not viewing your computer screen. To get started using the Xtreme Profit Cloud software you need to shell out the bare minimum which varies depending on the options broker recommended in the a Xtreme Profit Cloud members area. This is exactly what you need to do to get going trading with Xtreme Profit Cloud right now!
FIRST- You need to open a forex account with the recommended broker to use the Xtreme Profit Cloud software. Inside the video you are advised to deposit the absolute minimum but in this step you are told to deposit just as much as you really can afford because you are given a 100% matching deposit bonus. NOW- This step requires you install the software so that you can start trading immediately. With an above average internet connection it should not be too difficult to get started within a few minutes from this point. That’s all there is to the training area for Xtreme Profit Cloud. Once you follow those Two main steps the next thing you ought to do is simply open up the Xtreme Profit Cloud software and wait for signals. There isn't any place in the world in which the software isn't able to do the job. Making use of this software puts you a step in front of other investors plus you don’t need to be an expert or specialist to use this software or even be aware of the charts/trends to generate an income.
The rate of success the web page is boasting is higher than what I'm achieving by a number of percents. That level of successful trades is still respectable so I will stick using it.
Here's my final thoughts: For anybody that has a dream about generating income online, now is the time to get going. There are people out there that like to make it as hard as possible to make money by purchasing millions of products, never focusing and those people are the ones that will probably NOT want to continue on from this point. The truth is that we recommend the Xtreme Profit Cloud I'm not really into marketing many products because there are a few exceptions on the net, but this is a suggestion I didn't want to pass up. With this particular software you'll get a special strategy of online wealth creation and you could make the most of a strategy used by many to produce $350 up to $1k profit daily. As soon as you download the program you can fire it up and start utilizing the methods you pick up in the members area to earn over $250 per day online. So,Why are you still waiting for? Go ahead and Download and install it right now using the link below 100 % free.. ==> CLICK TO HEAD OVER TO Xtreme Profit Cloud DOWNLOAD LINK

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Most Effective Strategies to Trade with Stochastic ...

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