EUR/USD Forecast: Long-term Perspective - ForexTV

Former investment bank FX trader: News trading and second order thinking part 2/2

Former investment bank FX trader: News trading and second order thinking part 2/2
Thanks for all the upvotes and comments on the previous pieces:
From the first half of the news trading note we learned some ways to estimate what is priced in by the market. We learned that we are trading any gap in market expectations rather than the result itself. A good result when the market expected a fantastic result is disappointing! We also looked at second order thinking. After all that, I hope the reaction of prices to events is starting to make more sense to you.

Before you understand the core concepts of pricing in and second order thinking, price reactions to events can seem mystifying at times
We'll add one thought-provoking quote. Keynes (that rare economist who also managed institutional money) offered this analogy. He compared selecting investments to a beauty contest in which newspaper readers would write in with their votes and win a prize if their votes most closely matched the six most popularly selected women across all readers:
It is not a case of choosing those (faces) which, to the best of one’s judgment, are really the prettiest, nor even those which average opinions genuinely thinks the prettiest. We have reached the third degree where we devote our intelligences to anticipating what average opinion expects the average opinion to be.
Trading is no different. You are trying to anticipate how other traders will react to news and how that will move prices. Perhaps you disagree with their reaction. Still, if you can anticipate what it will be you would be sensible to act upon it. Don't forget: meanwhile they are also trying to anticipate what you and everyone else will do.

Part II
  • Preparing for quantitative and qualitative releases
  • Data surprise index
  • Using recent events to predict future reactions
  • Buy the rumour, sell the fact
  • The trimming position effect
  • Reversals
  • Some key FX releases

Preparing for quantitative and qualitative releases

The majority of releases are quantitative. All that means is there’s some number. Like unemployment figures or GDP.
Historic results provide interesting context. We are looking below the Australian unemployment rate which is released monthly. If you plot it out a few years back you can spot a clear trend, which got massively reversed. Knowing this trend gives you additional information when the figure is released. In the same way prices can trend so do economic data.

A great resource that's totally free to use
This makes sense: if for example things are getting steadily better in the economy you’d expect to see unemployment steadily going down.
Knowing the trend and how much noise there is in the data gives you an informational edge over lazy traders.
For example, when we see the spike above 6% on the above you’d instantly know it was crazy and a huge trading opportunity since a) the fluctuations month on month are normally tiny and b) it is a huge reversal of the long-term trend.
Would all the other AUDUSD traders know and react proportionately? If not and yet they still trade, their laziness may be an opportunity for more informed traders to make some money.
Tradingeconomics.com offers really high quality analysis. You can see all the major indicators for each country. Clicking them brings up their history as well as an explanation of what they show.
For example, here’s German Consumer Confidence.

Helpful context
There are also qualitative events. Normally these are speeches by Central Bankers.
There are whole blogs dedicated to closely reading such texts and looking for subtle changes in direction or opinion on the economy. Stuff like how often does the phrase "in a good place" come up when the Chair of the Fed speaks. It is pretty dry stuff. Yet these are leading indicators of how each member may vote to set interest rates. Ed Yardeni is the go-to guy on central banks.

Data surprise index

The other thing you might look at is something investment banks produce for their customers. A data surprise index. I am not sure if these are available in retail land - there's no reason they shouldn't be but the economic calendars online are very basic.
You’ll remember we talked about data not being good or bad of itself but good or bad relative to what was expected. These indices measure this difference.
If results are consistently better than analysts expect then you’ll see a positive number. If they are consistently worse than analysts expect a negative number. You can see they tend to swing from positive to negative.

Mean reversion at its best! Data surprise indices measure how much better or worse data came in vs forecast
There are many theories for this but in general people consider that analysts herd around the consensus. They are scared to be outliers and look ‘wrong’ or ‘stupid’ so they instead place estimates close to the pack of their peers.
When economic conditions change they may therefore be slow to update. When they are wrong consistently - say too bearish - they eventually flip the other way and become too bullish.
These charts can be interesting to give you an idea of how the recent data releases have been versus market expectations. You may try to spot the turning points in macroeconomic data that drive long term currency prices and trends.

Using recent events to predict future reactions

The market reaction function is the most important thing on an economic calendar in many ways. It means: what will happen to the price if the data is better or worse than the market expects?
That seems easy to answer but it is not.
Consider the example of consumer confidence we had earlier.
  • Many times the market will shrug and ignore it.
  • But when the economic recovery is predicated on a strong consumer it may move markets a lot.
Or consider the S&P index of US stocks (Wall Street).
  • If you get good economic data that beats analyst estimates surely it should go up? Well, sometimes that is certainly the case.
  • But good economic data might result in the US Central Bank raising interest rates. Raising interest rates will generally make the stock market go down!
So better than expected data could make the S&P go up (“the economy is great”) or down (“the Fed is more likely to raise rates”). It depends. The market can interpret the same data totally differently at different times.
One clue is to look at what happened to the price of risk assets at the last event.
For example, let’s say we looked at unemployment and it came in a lot worse than forecast last month. What happened to the S&P back then?

2% drop last time on a 'worse than expected' number ... so it it is 'better than expected' best guess is we rally 2% higher
So this tells us that - at least for our most recent event - the S&P moved 2% lower on a far worse than expected number. This gives us some guidance as to what it might do next time and the direction. Bad number = lower S&P. For a huge surprise 2% is the size of move we’d expect.
Again - this is a real limitation of online calendars. They should show next to the historic results (expected/actual) the reaction of various instruments.

Buy the rumour, sell the fact

A final example of an unpredictable reaction relates to the old rule of ‘Buy the rumour, sell the fact.’ This captures the tendency for markets to anticipate events and then reverse when they occur.

Buy the rumour, sell the fact
In short: people take profit and close their positions when what they expected to happen is confirmed.
So we have to decide which driver is most important to the market at any point in time. You obviously cannot ask every participant. The best way to do it is to look at what happened recently. Look at the price action during recent releases and you will get a feel for how much the market moves and in which direction.

Trimming or taking off positions

One thing to note is that events sometimes give smart participants information about positioning. This is because many traders take off or reduce positions ahead of big news events for risk management purposes.
Imagine we see GBPUSD rises in the hour before GDP release. That probably indicates the market is short and has taken off / flattened its positions.

The price action before an event can tell you about speculative positioning
If GDP is merely in line with expectations those same people are likely to add back their positions. They avoided a potential banana skin. This is why sometimes the market moves on an event that seemingly was bang on consensus.
But you have learned something. The speculative market is short and may prove vulnerable to a squeeze.

Two kinds of reversals

Fairly often you’ll see the market move in one direction on a release then turn around and go the other way.
These are known as reversals. Traders will often ‘fade’ a move, meaning bet against it and expect it to reverse.

Logical reversals

Sometimes this happens when the data looks good at first glance but the details don’t support it.
For example, say the headline is very bullish on German manufacturing numbers but then a minute later it becomes clear the company who releases the data has changed methodology or believes the number is driven by a one-off event. Or maybe the headline number is positive but buried in the detail there is a very negative revision to previous numbers.
Fading the initial spike is one way to trade news. Try looking at what the price action is one minute after the event and thirty minutes afterwards on historic releases.

Crazy reversals


Some reversals don't make sense
Sometimes a reversal happens for seemingly no fundamental reason. Say you get clearly positive news that is better than anyone expects. There are no caveats to the positive number. Yet the price briefly spikes up and then falls hard. What on earth?
This is a pure supply and demand thing. Even on bullish news the market cannot sustain a rally. The market is telling you it wants to sell this asset. Try not to get in its way.

Some key releases

As we have already discussed, different releases are important at different times. However, we’ll look at some consistently important ones in this final section.

Interest rates decisions

These can sometimes be unscheduled. However, normally the decisions are announced monthly. The exact process varies for each central bank. Typically there’s a headline decision e.g. maintain 0.75% rate.
You may also see “minutes” of the meeting in which the decision was reached and a vote tally e.g. 7 for maintain, 2 for lower rates. These are always top-tier data releases and have capacity to move the currency a lot.
A hawkish central bank (higher rates) will tend to move a currency higher whilst a dovish central bank (lower rates) will tend to move a currency lower.
A central banker speaking is always a big event

Non farm payrolls

These are released once per month. This is another top-tier release that will move all USD pairs as well as equities.
There are three numbers:
  • The headline number of jobs created (bigger is better)
  • The unemployment rate (smaller is better)
  • Average hourly earnings (depends)
Bear in mind these headline numbers are often off by around 75,000. If a report comes in +/- 25,000 of the forecast, that is probably a non event.
In general a positive response should move the USD higher but check recent price action.
Other countries each have their own unemployment data releases but this is the single most important release.

Surveys

There are various types of surveys: consumer confidence; house price expectations; purchasing managers index etc.
Each one basically asks a group of people if they expect to make more purchases or activity in their area of expertise to rise. There are so many we won’t go into each one here.
A really useful tool is the tradingeconomics.com economic indicators for each country. You can see all the major indicators and an explanation of each plus the historic results.

GDP

Gross Domestic Product is another big release. It is a measure of how much a country’s economy is growing.
In general the market focuses more on ‘advance’ GDP forecasts more than ‘final’ numbers, which are often released at the same time.
This is because the final figures are accurate but by the time they come around the market has already seen all the inputs. The advance figure tends to be less accurate but incorporates new information that the market may not have known before the release.
In general a strong GDP number is good for the domestic currency.

Inflation

Countries tend to release measures of inflation (increase in prices) each month. These releases are important mainly because they may influence the future decisions of the central bank, when setting the interest rate.
See the FX fundamentals section for more details.

Industrial data

Things like factory orders or or inventory levels. These can provide a leading indicator of the strength of the economy.
These numbers can be extremely volatile. This is because a one-off large order can drive the numbers well outside usual levels.
Pay careful attention to previous releases so you have a sense of how noisy each release is and what kind of moves might be expected.

Comments

Often there is really good stuff in the comments/replies. Check out 'squitstoomuch' for some excellent observations on why some news sources are noisy but early (think: Twitter, ZeroHedge). The Softbank story is a good recent example: was in ZeroHedge a day before the FT but the market moved on the FT. Also an interesting comment on mistakes, which definitely happen on breaking news, and can cause massive reversals.

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Your Pre Market Brief for 07/16/2020

Pre Market Brief for Thursday July 16th 2020

You can subscribe to the daily 4:00 AM Pre Market Brief on The Twitter Link Here . Alerts in the tweets will direct you to the daily 4:00 AM Pre Market Brief in this sub.
Updated as of 4:45 AM EST
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Stock Futures:
Wednesday 07/15/2020 News and Markets Recap:
Thursday July 16th 2020 Economic Calendar (All times are in EST)
(JOBLESS CLAIMS TODAY)
News Heading into Thursday July 16th 2020:
NOTE: I USUALLY (TRY TO) POST MANY OF THE MOST PROMISING, DRAMATIC, OR BAD NEWS OVERNIGHT STORIES THAT ARE LIKELY IMPORTANT TO THE MEMBERS OF THIS SUB AT THE TOP OF THIS LIST. PLEASE DO NOT YOLO THE VARIOUS TICKERS WITHOUT DOING RESEARCH! THE TIME STAMPS ON THESE MAY BE LATER THAN OTHERS ON THE WEB.
Upcoming Earnings:
Commodities:
COVID-19 Stats and News:
Macro Considerations:
Most Recent SEC Filings
Other
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Subscribe to This Brief and the daily 4:00 AM Pre Market Brief on The Twitter Link Here . Alerts in the tweets will direct you to the daily brief in this sub
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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!
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Hibiscus Petroleum Berhad (5199.KL)


https://preview.redd.it/gp18bjnlabr41.jpg?width=768&format=pjpg&auto=webp&s=6054e7f52e8d52da403016139ae43e0e799abf15
Download PDF of this article here: https://docdro.id/6eLgUPo
In light of the recent fall in oil prices due to the Saudi-Russian dispute and dampening demand for oil due to the lockdowns implemented globally, O&G stocks have taken a severe beating, falling approximately 50% from their highs at the beginning of the year. Not spared from this onslaught is Hibiscus Petroleum Berhad (Hibiscus), a listed oil and gas (O&G) exploration and production (E&P) company.
Why invest in O&G stocks in this particularly uncertain period? For one, valuations of these stocks have fallen to multi-year lows, bringing the potential ROI on these stocks to attractive levels. Oil prices are cyclical, and are bound to return to the mean given a sufficiently long time horizon. The trick is to find those companies who can survive through this downturn and emerge into “normal” profitability once oil prices rebound.
In this article, I will explore the upsides and downsides of investing in Hibiscus. I will do my best to cater this report to newcomers to the O&G industry – rather than address exclusively experts and veterans of the O&G sector. As an equity analyst, I aim to provide a view on the company primarily, and will generally refrain from providing macro views on oil or opinions about secular trends of the sector. I hope you enjoy reading it!
Stock code: 5199.KL
Stock name: Hibiscus Petroleum Berhad
Financial information and financial reports: https://www.malaysiastock.biz/Corporate-Infomation.aspx?securityCode=5199
Company website: https://www.hibiscuspetroleum.com/

Company Snapshot

Hibiscus Petroleum Berhad (5199.KL) is an oil and gas (O&G) upstream exploration and production (E&P) company located in Malaysia. As an E&P company, their business can be basically described as:
· looking for oil,
· drawing it out of the ground, and
· selling it on global oil markets.
This means Hibiscus’s profits are particularly exposed to fluctuating oil prices. With oil prices falling to sub-$30 from about $60 at the beginning of the year, Hibiscus’s stock price has also fallen by about 50% YTD – from around RM 1.00 to RM 0.45 (as of 5 April 2020).
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While the company is domiciled in Malaysia, its two main oil producing fields are located in both Malaysia and the UK. The Malaysian oil field is commonly referred to as the North Sabah field, while the UK oil field is commonly referred to as the Anasuria oil field. Hibiscus has licenses to other oil fields in different parts of the world, notably the Marigold/Sunflower oil fields in the UK and the VIC cluster in Australia, but its revenues and profits mainly stem from the former two oil producing fields.
Given that it’s a small player and has only two primary producing oil fields, it’s not surprising that Hibiscus sells its oil to a concentrated pool of customers, with 2 of them representing 80% of its revenues (i.e. Petronas and BP). Fortunately, both these customers are oil supermajors, and are unlikely to default on their obligations despite low oil prices.
At RM 0.45 per share, the market capitalization is RM 714.7m and it has a trailing PE ratio of about 5x. It doesn’t carry any debt, and it hasn’t paid a dividend in its listing history. The MD, Mr. Kenneth Gerard Pereira, owns about 10% of the company’s outstanding shares.

Reserves (Total recoverable oil) & Production (bbl/day)

To begin analyzing the company, it’s necessary to understand a little of the industry jargon. We’ll start with Reserves and Production.
In general, there are three types of categories for a company’s recoverable oil volumes – Reserves, Contingent Resources and Prospective Resources. Reserves are those oil fields which are “commercial”, which is defined as below:
As defined by the SPE PRMS, Reserves are “… quantities of petroleum anticipated to be commercially recoverable by application of development projects to known accumulations from a given date forward under defined conditions.” Therefore, Reserves must be discovered (by drilling, recoverable (with current technology), remaining in the subsurface (at the effective date of the evaluation) and “commercial” based on the development project proposed.)
Note that Reserves are associated with development projects. To be considered as “commercial”, there must be a firm intention to proceed with the project in a reasonable time frame (typically 5 years, and such intention must be based upon all of the following criteria:)
- A reasonable assessment of the future economics of the development project meeting defined investment and operating criteria; - A reasonable expectation that there will be a market for all or at least the expected sales quantities of production required to justify development; - Evidence that the necessary production and transportation facilities are available or can be made available; and - Evidence that legal, contractual, environmental and other social and economic concerns will allow for the actual implementation of the recovery project being evaluated.
Contingent Resources and Prospective Resources are further defined as below:
- Contingent Resources: potentially recoverable volumes associated with a development plan that targets discovered volumes but is not (yet commercial (as defined above); and) - Prospective Resources: potentially recoverable volumes associated with a development plan that targets as yet undiscovered volumes.
In the industry lingo, we generally refer to Reserves as ‘P’ and Contingent Resources as ‘C’. These ‘P’ and ‘C’ resources can be further categorized into 1P/2P/3P resources and 1C/2C/3C resources, each referring to a low/medium/high estimate of the company’s potential recoverable oil volumes:
- Low/1C/1P estimate: there should be reasonable certainty that volumes actually recovered will equal or exceed the estimate; - Best/2C/2P estimate: there should be an equal likelihood of the actual volumes of petroleum being larger or smaller than the estimate; and - High/3C/3P estimate: there is a low probability that the estimate will be exceeded.
Hence in the E&P industry, it is easy to see why most investors and analysts refer to the 2P estimate as the best estimate for a company’s actual recoverable oil volumes. This is because 2P reserves (‘2P’ referring to ‘Proved and Probable’) are a middle estimate of the recoverable oil volumes legally recognized as “commercial”.
However, there’s nothing stopping you from including 2C resources (riskier) or utilizing 1P resources (conservative) as your estimate for total recoverable oil volumes, depending on your risk appetite. In this instance, the company has provided a snapshot of its 2P and 2C resources in its analyst presentation:
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Basically, what the company is saying here is that by 2021, it will have classified as 2P reserves at least 23.7 million bbl from its Anasuria field and 20.5 million bbl from its North Sabah field – for total 2P reserves of 44.2 million bbl (we are ignoring the Australian VIC cluster as it is only estimated to reach first oil by 2022).
Furthermore, the company is stating that they have discovered (but not yet legally classified as “commercial”) a further 71 million bbl of oil from both the Anasuria and North Sabah fields, as well as the Marigold/Sunflower fields. If we include these 2C resources, the total potential recoverable oil volumes could exceed 100 million bbl.
In this report, we shall explore all valuation scenarios giving consideration to both 2P and 2C resources.
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The company further targets a 2021 production rate of 20,000 bbl (LTM: 8,000 bbl), which includes 5,000 bbl from its Anasuria field (LTM: 2,500 bbl) and 7,000 bbl from its North Sabah field (LTM: 5,300 bbl).
This is a substantial increase in forecasted production from both existing and prospective oil fields. If it materializes, annual production rate could be as high as 7,300 mmbbl, and 2021 revenues (given FY20 USD/bbl of $60) could exceed RM 1.5 billion (FY20: RM 988 million).
However, this targeted forecast is quite a stretch from current production levels. Nevertheless, we shall consider all provided information in estimating a valuation for Hibiscus.
To understand Hibiscus’s oil production capacity and forecast its revenues and profits, we need to have a better appreciation of the performance of its two main cash-generating assets – the North Sabah field and the Anasuria field.

North Sabah oil field
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Hibiscus owns a 50% interest in the North Sabah field together with its partner Petronas, and has production rights over the field up to year 2040. The asset contains 4 oil fields, namely the St Joseph field, South Furious field, SF 30 field and Barton field.
For the sake of brevity, we shall not delve deep into the operational aspects of the fields or the contractual nature of its production sharing contract (PSC). We’ll just focus on the factors which relate to its financial performance. These are:
· Average uptime
· Total oil sold
· Average realized oil price
· Average OPEX per bbl
With regards to average uptime, we can see that the company maintains relative high facility availability, exceeding 90% uptime in all quarters of the LTM with exception of Jul-Sep 2019. The dip in average uptime was due to production enhancement projects and maintenance activities undertaken to improve the production capacity of the St Joseph and SF30 oil fields.
Hence, we can conclude that management has a good handle on operational performance. It also implies that there is little room for further improvement in production resulting from increased uptime.
As North Sabah is under a production sharing contract (PSC), there is a distinction between gross oil production and net oil production. The former relates to total oil drawn out of the ground, whereas the latter refers to Hibiscus’s share of oil production after taxes, royalties and expenses are accounted for. In this case, we want to pay attention to net oil production, not gross.
We can arrive at Hibiscus’s total oil sold for the last twelve months (LTM) by adding up the total oil sold for each of the last 4 quarters. Summing up the figures yields total oil sold for the LTM of approximately 2,075,305 bbl.
Then, we can arrive at an average realized oil price over the LTM by averaging the average realized oil price for the last 4 quarters, giving us an average realized oil price over the LTM of USD 68.57/bbl. We can do the same for average OPEX per bbl, giving us an average OPEX per bbl over the LTM of USD 13.23/bbl.
Thus, we can sum up the above financial performance of the North Sabah field with the following figures:
· Total oil sold: 2,075,305 bbl
· Average realized oil price: USD 68.57/bbl
· Average OPEX per bbl: USD 13.23/bbl

Anasuria oil field
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Doing the same exercise as above for the Anasuria field, we arrive at the following financial performance for the Anasuria field:
· Total oil sold: 1,073,304 bbl
· Average realized oil price: USD 63.57/bbl
· Average OPEX per bbl: USD 23.22/bbl
As gas production is relatively immaterial, and to be conservative, we shall only consider the crude oil production from the Anasuria field in forecasting revenues.

Valuation (Method 1)

Putting the figures from both oil fields together, we get the following data:
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Given that we have determined LTM EBITDA of RM 632m, the next step would be to subtract ITDA (interest, tax, depreciation & amortization) from it to obtain estimated LTM Net Profit. Using FY2020’s ITDA of approximately RM 318m as a guideline, we arrive at an estimated LTM Net Profit of RM 314m (FY20: 230m). Given the current market capitalization of RM 714.7m, this implies a trailing LTM PE of 2.3x.
Performing a sensitivity analysis given different oil prices, we arrive at the following net profit table for the company under different oil price scenarios, assuming oil production rate and ITDA remain constant:
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From the above exercise, it becomes apparent that Hibiscus has a breakeven oil price of about USD 41.8863/bbl, and has a lot of operating leverage given the exponential rate of increase in its Net Profit with each consequent increase in oil prices.
Considering that the oil production rate (EBITDA) is likely to increase faster than ITDA’s proportion to revenues (fixed costs), at an implied PE of 4.33x, it seems likely that an investment in Hibiscus will be profitable over the next 10 years (with the assumption that oil prices will revert to the mean in the long-term).

Valuation (Method 2)

Of course, there are a lot of assumptions behind the above method of valuation. Hence, it would be prudent to perform multiple methods of valuation and compare the figures to one another.
As opposed to the profit/loss assessment in Valuation (Method 1), another way of performing a valuation would be to estimate its balance sheet value, i.e. total revenues from 2P Reserves, and assign a reasonable margin to it.
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From the above, we understand that Hibiscus’s 2P reserves from the North Sabah and Anasuria fields alone are approximately 44.2 mmbbl (we ignore contribution from Australia’s VIC cluster as it hasn’t been developed yet).
Doing a similar sensitivity analysis of different oil prices as above, we arrive at the following estimated total revenues and accumulated net profit:
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Let’s assume that the above average of RM 9.68 billion in total realizable revenues from current 2P reserves holds true. If we assign a conservative Net Profit margin of 15% (FY20: 23%; past 5 years average: 16%), we arrive at estimated accumulated Net Profit from 2P Reserves of RM 1.452 billion. Given the current market capitalization of RM 714 million, we might be able to say that the equity is worth about twice the current share price.
However, it is understandable that some readers might feel that the figures used in the above estimate (e.g. net profit margin of 15%) were randomly plucked from the sky. So how do we reconcile them with figures from the financial statements? Fortunately, there appears to be a way to do just that.
Intangible Assets
I refer you to a figure in the financial statements which provides a shortcut to the valuation of 2P Reserves. This is the carrying value of Intangible Assets on the Balance Sheet.
As of 2QFY21, that amount was RM 1,468,860,000 (i.e. RM 1.468 billion).
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Quite coincidentally, one might observe that this figure is dangerously close to the estimated accumulated Net Profit from 2P Reserves of RM 1.452 billion we calculated earlier. But why would this amount matter at all?
To answer that, I refer you to the notes of the Annual Report FY20 (AR20). On page 148 of the AR20, we find the following two paragraphs:
E&E assets comprise of rights and concession and conventional studies. Following the acquisition of a concession right to explore a licensed area, the costs incurred such as geological and geophysical surveys, drilling, commercial appraisal costs and other directly attributable costs of exploration and appraisal including technical and administrative costs, are capitalised as conventional studies, presented as intangible assets.
E&E assets are assessed for impairment when facts and circumstances suggest that the carrying amount of an E&E asset may exceed its recoverable amount. The Group will allocate E&E assets to cash generating unit (“CGU”s or groups of CGUs for the purpose of assessing such assets for impairment. Each CGU or group of units to which an E&E asset is allocated will not be larger than an operating segment as disclosed in Note 39 to the financial statements.)
Hence, we can determine that firstly, the intangible asset value represents capitalized costs of acquisition of the oil fields, including technical exploration costs and costs of acquiring the relevant licenses. Secondly, an impairment review will be carried out when “the carrying amount of an E&E asset may exceed its recoverable amount”, with E&E assets being allocated to “cash generating units” (CGU) for the purposes of assessment.
On page 169 of the AR20, we find the following:
Carrying amounts of the Group’s intangible assets, oil and gas assets and FPSO are reviewed for possible impairment annually including any indicators of impairment. For the purpose of assessing impairment, assets are grouped at the lowest level CGUs for which there is a separately identifiable cash flow available. These CGUs are based on operating areas, represented by the 2011 North Sabah EOR PSC (“North Sabah”, the Anasuria Cluster, the Marigold and Sunflower fields, the VIC/P57 exploration permit (“VIC/P57”) and the VIC/L31 production license (“VIC/L31”).)
So apparently, the CGUs that have been assigned refer to the respective oil producing fields, two of which include the North Sabah field and the Anasuria field. In order to perform the impairment review, estimates of future cash flow will be made by management to assess the “recoverable amount” (as described above), subject to assumptions and an appropriate discount rate.
Hence, what we can gather up to now is that management will estimate future recoverable cash flows from a CGU (i.e. the North Sabah and Anasuria oil fields), compare that to their carrying value, and perform an impairment if their future recoverable cash flows are less than their carrying value. In other words, if estimated accumulated profits from the North Sabah and Anasuria oil fields are less than their carrying value, an impairment is required.
So where do we find the carrying values for the North Sabah and Anasuria oil fields? Further down on page 184 in the AR20, we see the following:
Included in rights and concession are the carrying amounts of producing field licenses in the Anasuria Cluster amounting to RM668,211,518 (2018: RM687,664,530, producing field licenses in North Sabah amounting to RM471,031,008 (2018: RM414,333,116))
Hence, we can determine that the carrying values for the North Sabah and Anasuria oil fields are RM 471m and RM 668m respectively. But where do we find the future recoverable cash flows of the fields as estimated by management, and what are the assumptions used in that calculation?
Fortunately, we find just that on page 185:
17 INTANGIBLE ASSETS (CONTINUED)
(a Anasuria Cluster)
The Directors have concluded that there is no impairment indicator for Anasuria Cluster during the current financial year. In the previous financial year, due to uncertainties in crude oil prices, the Group has assessed the recoverable amount of the intangible assets, oil and gas assets and FPSO relating to the Anasuria Cluster. The recoverable amount is determined using the FVLCTS model based on discounted cash flows (“DCF” derived from the expected cash in/outflow pattern over the production lives.)
The key assumptions used to determine the recoverable amount for the Anasuria Cluster were as follows:
(i Discount rate of 10%;)
(ii Future cost inflation factor of 2% per annum;)
(iii Oil price forecast based on the oil price forward curve from independent parties; and,)
(iv Oil production profile based on the assessment by independent oil and gas reserve experts.)
Based on the assessments performed, the Directors concluded that the recoverable amount calculated based on the valuation model is higher than the carrying amount.
(b North Sabah)
The acquisition of the North Sabah assets was completed in the previous financial year. Details of the acquisition are as disclosed in Note 15 to the financial statements.
The Directors have concluded that there is no impairment indicator for North Sabah during the current financial year.
Here, we can see that the recoverable amount of the Anasuria field was estimated based on a DCF of expected future cash flows over the production life of the asset. The key assumptions used by management all seem appropriate, including a discount rate of 10% and oil price and oil production estimates based on independent assessment. From there, management concludes that the recoverable amount of the Anasuria field is higher than its carrying amount (i.e. no impairment required). Likewise, for the North Sabah field.
How do we interpret this? Basically, what management is saying is that given a 10% discount rate and independent oil price and oil production estimates, the accumulated profits (i.e. recoverable amount) from both the North Sabah and the Anasuria fields exceed their carrying amounts of RM 471m and RM 668m respectively.
In other words, according to management’s own estimates, the carrying value of the Intangible Assets of RM 1.468 billion approximates the accumulated Net Profit recoverable from 2P reserves.
To conclude Valuation (Method 2), we arrive at the following:

Our estimates Management estimates
Accumulated Net Profit from 2P Reserves RM 1.452 billion RM 1.468 billion

Financials

By now, we have established the basic economics of Hibiscus’s business, including its revenues (i.e. oil production and oil price scenarios), costs (OPEX, ITDA), profitability (breakeven, future earnings potential) and balance sheet value (2P reserves, valuation). Moving on, we want to gain a deeper understanding of the 3 statements to anticipate any blind spots and risks. We’ll refer to the financial statements of both the FY20 annual report and the 2Q21 quarterly report in this analysis.
For the sake of brevity, I’ll only point out those line items which need extra attention, and skip over the rest. Feel free to go through the financial statements on your own to gain a better familiarity of the business.
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Income Statement
First, we’ll start with the Income Statement on page 135 of the AR20. Revenues are straightforward, as we’ve discussed above. Cost of Sales and Administrative Expenses fall under the jurisdiction of OPEX, which we’ve also seen earlier. Other Expenses are mostly made up of Depreciation & Amortization of RM 115m.
Finance Costs are where things start to get tricky. Why does a company which carries no debt have such huge amounts of finance costs? The reason can be found in Note 8, where it is revealed that the bulk of finance costs relate to the unwinding of discount of provision for decommissioning costs of RM 25m (Note 32).
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This actually refers to the expected future costs of restoring the Anasuria and North Sabah fields to their original condition once the oil reserves have been depleted. Accounting standards require the company to provide for these decommissioning costs as they are estimable and probable. The way the decommissioning costs are accounted for is the same as an amortized loan, where the initial carrying value is recognized as a liability and the discount rate applied is reversed each year as an expense on the Income Statement. However, these expenses are largely non-cash in nature and do not necessitate a cash outflow every year (FY20: RM 69m).
Unwinding of discount on non-current other payables of RM 12m relate to contractual payments to the North Sabah sellers. We will discuss it later.
Taxation is another tricky subject, and is even more significant than Finance Costs at RM 161m. In gist, Hibiscus is subject to the 38% PITA (Petroleum Income Tax Act) under Malaysian jurisdiction, and the 30% Petroleum tax + 10% Supplementary tax under UK jurisdiction. Of the RM 161m, RM 41m of it relates to deferred tax which originates from the difference between tax treatment and accounting treatment on capitalized assets (accelerated depreciation vs straight-line depreciation). Nonetheless, what you should take away from this is that the tax expense is a tangible expense and material to breakeven analysis.
Fortunately, tax is a variable expense, and should not materially impact the cash flow of Hibiscus in today’s low oil price environment.
Note: Cash outflows for Tax Paid in FY20 was RM 97m, substantially below the RM 161m tax expense.
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Balance Sheet
The balance sheet of Hibiscus is unexciting; I’ll just bring your attention to those line items which need additional scrutiny. I’ll use the figures in the latest 2Q21 quarterly report (2Q21) and refer to the notes in AR20 for clarity.
We’ve already discussed Intangible Assets in the section above, so I won’t dwell on it again.
Moving on, the company has Equipment of RM 582m, largely relating to O&G assets (e.g. the Anasuria FPSO vessel and CAPEX incurred on production enhancement projects). Restricted cash and bank balances represent contractual obligations for decommissioning costs of the Anasuria Cluster, and are inaccessible for use in operations.
Inventories are relatively low, despite Hibiscus being an E&P company, so forex fluctuations on carrying value of inventories are relatively immaterial. Trade receivables largely relate to entitlements from Petronas and BP (both oil supermajors), and are hence quite safe from impairment. Other receivables, deposits and prepayments are significant as they relate to security deposits placed with sellers of the oil fields acquired; these should be ignored for cash flow purposes.
Note: Total cash and bank balances do not include approximately RM 105 m proceeds from the North Sabah December 2019 offtake (which was received in January 2020)
Cash and bank balances of RM 90m do not include RM 105m of proceeds from offtake received in 3Q21 (Jan 2020). Hence, the actual cash and bank balances as of 2Q21 approximate RM 200m.
Liabilities are a little more interesting. First, I’ll draw your attention to the significant Deferred tax liabilities of RM 457m. These largely relate to the amortization of CAPEX (i.e. Equipment and capitalized E&E expenses), which is given an accelerated depreciation treatment for tax purposes.
The way this works is that the government gives Hibiscus a favorable tax treatment on capital expenditures incurred via an accelerated depreciation schedule, so that the taxable income is less than usual. However, this leads to the taxable depreciation being utilized quicker than accounting depreciation, hence the tax payable merely deferred to a later period – when the tax depreciation runs out but accounting depreciation remains. Given the capital intensive nature of the business, it is understandable why Deferred tax liabilities are so large.
We’ve discussed Provision for decommissioning costs under the Finance Costs section earlier. They are also quite significant at RM 266m.
Notably, the Other Payables and Accruals are a hefty RM 431m. What do they relate to? Basically, they are contractual obligations to the sellers of the oil fields which are only payable upon oil prices reaching certain thresholds. Hence, while they are current in nature, they will only become payable when oil prices recover to previous highs, and are hence not an immediate cash outflow concern given today’s low oil prices.
Cash Flow Statement
There is nothing in the cash flow statement which warrants concern.
Notably, the company generated OCF of approximately RM 500m in FY20 and RM 116m in 2Q21. It further incurred RM 330m and RM 234m of CAPEX in FY20 and 2Q21 respectively, largely owing to production enhancement projects to increase the production rate of the Anasuria and North Sabah fields, which according to management estimates are accretive to ROI.
Tax paid was RM 97m in FY20 and RM 61m in 2Q21 (tax expense: RM 161m and RM 62m respectively).

Risks

There are a few obvious and not-so-obvious risks that one should be aware of before investing in Hibiscus. We shall not consider operational risks (e.g. uptime, OPEX) as they are outside the jurisdiction of the equity analyst. Instead, we shall focus on the financial and strategic risks largely outside the control of management. The main ones are:
· Oil prices remaining subdued for long periods of time
· Fluctuation of exchange rates
· Customer concentration risk
· 2P Reserves being less than estimated
· Significant current and non-current liabilities
· Potential issuance of equity
Oil prices remaining subdued
Of topmost concern in the minds of most analysts is whether Hibiscus has the wherewithal to sustain itself through this period of low oil prices (sub-$30). A quick and dirty estimate of annual cash outflow (i.e. burn rate) assuming a $20 oil world and historical production rates is between RM 50m-70m per year, which considering the RM 200m cash balance implies about 3-4 years of sustainability before the company runs out of cash and has to rely on external assistance for financing.
Table 1: Hibiscus EBITDA at different oil price and exchange rates
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The above table shows different EBITDA scenarios (RM ‘m) given different oil prices (left column) and USD:MYR exchange rates (top row). Currently, oil prices are $27 and USD:MYR is 1:4.36.
Given conservative assumptions of average OPEX/bbl of $20 (current: $15), we can safely say that the company will be loss-making as long as oil remains at $20 or below (red). However, we can see that once oil prices hit $25, the company can tank the lower-end estimate of the annual burn rate of RM 50m (orange), while at RM $27 it can sufficiently muddle through the higher-end estimate of the annual burn rate of RM 70m (green).
Hence, we can assume that as long as the average oil price over the next 3-4 years remains above $25, Hibiscus should come out of this fine without the need for any external financing.
Customer Concentration Risk
With regards to customer concentration risk, there is not much the analyst or investor can do except to accept the risk. Fortunately, 80% of revenues can be attributed to two oil supermajors (Petronas and BP), hence the risk of default on contractual obligations and trade receivables seems to be quite diminished.
2P Reserves being less than estimated
2P Reserves being less than estimated is another risk that one should keep in mind. Fortunately, the current market cap is merely RM 714m – at half of estimated recoverable amounts of RM 1.468 billion – so there’s a decent margin of safety. In addition, there are other mitigating factors which shall be discussed in the next section (‘Opportunities’).
Significant non-current and current liabilities
The significant non-current and current liabilities have been addressed in the previous section. It has been determined that they pose no threat to immediate cash flow due to them being long-term in nature (e.g. decommissioning costs, deferred tax, etc). Hence, for the purpose of assessing going concern, their amounts should not be a cause for concern.
Potential issuance of equity
Finally, we come to the possibility of external financing being required in this low oil price environment. While the company should last 3-4 years on existing cash reserves, there is always the risk of other black swan events materializing (e.g. coronavirus) or simply oil prices remaining muted for longer than 4 years.
Furthermore, management has hinted that they wish to acquire new oil assets at presently depressed prices to increase daily production rate to a targeted 20,000 bbl by end-2021. They have room to acquire debt, but they may also wish to issue equity for this purpose. Hence, the possibility of dilution to existing shareholders cannot be entirely ruled out.
However, given management’s historical track record of prioritizing ROI and optimal capital allocation, and in consideration of the fact that the MD owns 10% of outstanding shares, there is some assurance that any potential acquisitions will be accretive to EPS and therefore valuations.

Opportunities

As with the existence of risk, the presence of material opportunities also looms over the company. Some of them are discussed below:
· Increased Daily Oil Production Rate
· Inclusion of 2C Resources
· Future oil prices exceeding $50 and effects from coronavirus dissipating
Increased Daily Oil Production Rate
The first and most obvious opportunity is the potential for increased production rate. We’ve seen in the last quarter (2Q21) that the North Sabah field increased its daily production rate by approximately 20% as a result of production enhancement projects (infill drilling), lowering OPEX/bbl as a result. To vastly oversimplify, infill drilling is the process of maximizing well density by drilling in the spaces between existing wells to improve oil production.
The same improvements are being undertaken at the Anasuria field via infill drilling, subsea debottlenecking, water injection and sidetracking of existing wells. Without boring you with industry jargon, this basically means future production rate is likely to improve going forward.
By how much can the oil production rate be improved by? Management estimates in their analyst presentation that enhancements in the Anasuria field will be able to yield 5,000 bbl/day by 2021 (current: 2,500 bbl/day).
Similarly, improvements in the North Sabah field is expected to yield 7,000 bbl/day by 2021 (current: 5,300 bbl/day).
This implies a total 2021 expected daily production rate from the two fields alone of 12,000 bbl/day (current: 8,000 bbl/day). That’s a 50% increase in yields which we haven’t factored into our valuation yet.
Furthermore, we haven’t considered any production from existing 2C resources (e.g. Marigold/Sunflower) or any potential acquisitions which may occur in the future. By management estimates, this can potentially increase production by another 8,000 bbl/day, bringing total production to 20,000 bbl/day.
While this seems like a stretch of the imagination, it pays to keep them in mind when forecasting future revenues and valuations.
Just to play around with the numbers, I’ve come up with a sensitivity analysis of possible annual EBITDA at different oil prices and daily oil production rates:
Table 2: Hibiscus EBITDA at different oil price and daily oil production rates
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The left column represents different oil prices while the top row represents different daily oil production rates.
The green column represents EBITDA at current daily production rate of 8,000 bbl/day; the orange column represents EBITDA at targeted daily production rate of 12,000 bbl/day; while the purple column represents EBITDA at maximum daily production rate of 20,000 bbl/day.
Even conservatively assuming increased estimated annual ITDA of RM 500m (FY20: RM 318m), and long-term average oil prices of $50 (FY20: $60), the estimated Net Profit and P/E ratio is potentially lucrative at daily oil production rates of 12,000 bbl/day and above.
2C Resources
Since we’re on the topic of improved daily oil production rate, it bears to pay in mind the relatively enormous potential from Hibiscus’s 2C Resources. North Sabah’s 2C Resources alone exceed 30 mmbbl; while those from the yet undiagnosed Marigold/Sunflower fields also reach 30 mmbbl. Altogether, 2C Resources exceed 70 mmbbl, which dwarfs the 44 mmbbl of 2P Reserves we have considered up to this point in our valuation estimates.
To refresh your memory, 2C Resources represents oil volumes which have been discovered but are not yet classified as “commercial”. This means that there is reasonable certainty of the oil being recoverable, as opposed to simply being in the very early stages of exploration. So, to be conservative, we will imagine that only 50% of 2C Resources are eligible for reclassification to 2P reserves, i.e. 35 mmbbl of oil.
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This additional 35 mmbbl of oil represents an 80% increase to existing 2P reserves. Assuming the daily oil production rate increases similarly by 80%, we will arrive at 14,400 bbl/day of oil production. According to Table 2 above, this would yield an EBITDA of roughly RM 630m assuming $50 oil.
Comparing that estimated EBITDA to FY20’s actual EBITDA:
FY20 FY21 (incl. 2C) Difference
Daily oil production (bbl/day) 8,626 14,400 +66%
Average oil price (USD/bbl) $68.57 $50 -27%
Average OPEX/bbl (USD) $16.64 $20 +20%
EBITDA (RM ‘m) 632 630 -
Hence, even conservatively assuming lower oil prices and higher OPEX/bbl (which should decrease in the presence of higher oil volumes) than last year, we get approximately the same EBITDA as FY20.
For the sake of completeness, let’s assume that Hibiscus issues twice the no. of existing shares over the next 10 years, effectively diluting shareholders by 50%. Even without accounting for the possibility of the acquisition of new oil fields, at the current market capitalization of RM 714m, the prospective P/E would be about 10x. Not too shabby.
Future oil prices exceeding $50 and effects from coronavirus dissipating
Hibiscus shares have recently been hit by a one-two punch from oil prices cratering from $60 to $30, as a result of both the Saudi-Russian dispute and depressed demand for oil due to coronavirus. This has massively increased supply and at the same time hugely depressed demand for oil (due to the globally coordinated lockdowns being implemented).
Given a long enough timeframe, I fully expect OPEC+ to come to an agreement and the economic effects from the coronavirus to dissipate, allowing oil prices to rebound. As we equity investors are aware, oil prices are cyclical and are bound to recover over the next 10 years.
When it does, valuations of O&G stocks (including Hibiscus’s) are likely to improve as investors overshoot expectations and begin to forecast higher oil prices into perpetuity, as they always tend to do in good times. When that time arrives, Hibiscus’s valuations are likely to become overoptimistic as all O&G stocks tend to do during oil upcycles, resulting in valuations far exceeding reasonable estimates of future earnings. If you can hold the shares up until then, it’s likely you will make much more on your investment than what we’ve been estimating.

Conclusion

Wrapping up what we’ve discussed so far, we can conclude that Hibiscus’s market capitalization of RM 714m far undershoots reasonable estimates of fair value even under conservative assumptions of recoverable oil volumes and long-term average oil prices. As a value investor, I hesitate to assign a target share price, but it’s safe to say that this stock is worth at least RM 1.00 (current: RM 0.45). Risk is relatively contained and the upside far exceeds the downside. While I have no opinion on the short-term trajectory of oil prices, I can safely recommend this stock as a long-term Buy based on fundamental research.
submitted by investorinvestor to SecurityAnalysis [link] [comments]

Forecasting the End of Major Corrections, and Accumulating Trend Trading Positions.

Forecasting the End of Major Corrections, and Accumulating Trend Trading Positions.
A prerequisite post to this post can be read here; https://www.reddit.com/Forex/comments/clx0v9/profiting_in_trends_planning_for_the_impulsive/
It will also be beneficial to read this;
https://www.reddit.com/Forex/comments/clbxk2/shorting_noobs_common_trend_following_mistakes_im/

Before getting into the meat of things, you need to understand the 'elastic band' effect of large moves in the market. What this means is most of the time before a market starts to make a big move in the direction it is ultimately going, it will make a strong and usually fast counter move. You know this already in a way. You've been taught from early on (I assume) that pin bars (hammers etc) are indications the market is reversing. You're told the wicks are formed by price pushing into an area and being rejected from it.

In a trend formation, this is what the intra-week price action would tend to look like when there is the formation of reversal candles at the close of the weekly timeframe.


https://preview.redd.it/nv1nbk0c9th31.png?width=909&format=png&auto=webp&s=f87d94ee33f0d07cde211c05d9234a236a487309
Here we would have been in a down trend and then for a week or two seen bullish momentum. The blue swing is the "elastic band" move. Or what I like to call the "ping swing".

The formation I have drawn here is not arbitrary. A lot of specific things are going on in this chart. Here I've highlighted the relevant ones. When we've seen all of these, we know there is a good chance we have reached the end of a C leg correction (read up on basic Elliot wave theory if you do not understand this terminology).


https://preview.redd.it/8u9bg43nath31.png?width=1066&format=png&auto=webp&s=1ddb04a27b9a99ddbcaab5eef4e3ca7eea78e000

There can be variance in the 4 and 5 area. I am being polite, I should be honest. This area is often a bitch to trade in. Sometimes there are deep retracements and sometimes they are really shallow. Personally I've not been able to find ways to get strong ideas of how to forecast which is more likely. It tends to be an area I lose money and one I continue to work on trying to develop better ways of dealing with.

Here are examples of each type from trades I've taken recently.


https://preview.redd.it/6n0x4k43cth31.png?width=744&format=png&auto=webp&s=f03fdbff3176e1df36727f3606dbf6fc67912e53
This is explained in more context at https://www.reddit.com/Forex/comments/cks8q1/shorting_noobs_problems_proofs_and_fine_tuning/

This chart is messy because a lot of positions are being taken rather than a specific strategy being followed, but as I've explained in the 'Shorting Noobs' series of posts, I am mot interested in trading off the 61.8% fib.


https://preview.redd.it/97cb1x0wcth31.png?width=719&format=png&auto=webp&s=d49bbac385242184d9f9ba2708d1e9fe92efba42
Here is one with EURUSD that had very shallow sell-off then made the ping swing.

https://preview.redd.it/dbiujru0fth31.png?width=1025&format=png&auto=webp&s=277682a868af7cb2dc2b612243a8abfef54e9de0
You maybe thinking at this point, "But the range bit looks like it should be the 5". I know! I told you it's a bitch. As you can see here regardless of this I have still sold the best price. I am doing this by having a clear SR level I am forecasting in this sort of move. (Explained in more detail in the shorting noob series [2] [3] )
Note, it is still entirely possible that this can make another ping swing and slightly spike out this high. If it does, we have a great opportunity. At this point, we are wiser to look for the better RR trade with trend continuation by considering we are possibly in this part of the move and we have the next (usually stronger than previous) sell off coming.


https://preview.redd.it/15xd09pzfth31.png?width=730&format=png&auto=webp&s=c5e3a70fbc9411b36d74a7e32ebf5c1aabf1ad05

Which actually fits inside another cycle for a ping swing.

https://preview.redd.it/31craqbkgth31.png?width=1018&format=png&auto=webp&s=4d7cc139aa406673213c62009220a3182e7e9e55

Here is a real time forecast of a ping swing we can watch for and set pending orders (or define areas to watch for reversal patterns)

GBPUSD

https://preview.redd.it/uz93cn53ith31.png?width=1082&format=png&auto=webp&s=3b2d9a7fc12c961dafb7ee3cc7aa4c1aec29c927
(Ignore the buy trades on this, they are from a different type of strategy)

This is a lot of information, and to intrinsically understand this you'd have to go over a lot of trending charts and watch how they have developed. I have spent a hell of a lot of time on this. I will round up with leaving you just a few simple rules we can take from understanding this general pattern that recurs in trends. Some of them will help you win, others will help to prevent you losing.

1 - When it starts to chop, it's time to stop.

When a trend that has been in a free flowing form starts to get choppy, it's time to stop following the trend for the time being. You should be aware the next breakout(s) can be false ones, and the next shallow correction for a "Retest & continue" type trade is likely a trap.

2 - Big corrections rarely feature only one leg.

When you see a really big move against the trend it gets really tempting to rejoin the trend once it starts to form price action reversal candles. Any time you're entering without the market having previously faked and then spiked out early sellers at least a couple of times, you have a more risky trade.

3 - Forecast where early sellers will lose.

Quite simply, if you see a downtrend and then a spike up and what looks like the continuation of a downtrend you can assume there are sellers into what they think will be the new downtrend move. It's also quite likely these sellers have it very wrong on their stop area. It will be just above the previous highs and the consolation range. This is the very area we'd expect the ping swing to spike into and then make the proper trend move after whipsawing those who sold too early.
Where they are getting stopped out, you want to be entering. Not sure where this is? Look in Forex forums, they'll tell you.

4 - Velocity does not mean victory!

As price comes into the reversal area it will usually be carrying a lot of short term momentum and moving fast. Moving quickly into an area is not in any way an indication of a break of that area or a reversal. In fact, once you've identified where you think the ping swing will end, the more parabolic that move is into that area the better for the reversal trade. Plan ahead, do not be caught up in the moment. The moment will be deceptive.

5 - Have excellent exit plans on both sides of this sort of move.

If the move fails, the counter move running against you can be persistent. Stop losses should be around 78% of the swing. Small spike outs of the 61.8% level are to be expected. Breaks of the 76% level are not. Similarly, profits can come lightening quickly. Which can actually be a problem if you've not planned the areas you want to exit or how to trail your stops. So be well prepared to exit before you enter.


The things I have explained in this post have validity on all timeframes. I scalp with it, and I swing with it. It transfers readily to any market with trending properties. If you were to master this (especially at an intraday level - which is harder) , it would be highly likely you significantly beat what most people would think are "good returns" when the markets are trending.
It would be possible for someone who has sufficient skill in doing this to make themselves substantial profits even starting from a small amount of money and using moderate risk over the course of just trading 4 - 5 major trend moves on daily and weekly charts. This is quite an easy setup in my opinion (once it's been highlighted at least) and for as long as you can find trends to use it, it will outperform most strategies I see on public display.

(All bets are off in ranges. This will make a mockery of you if you try to do it in ranges)

Happy trend following :)
submitted by whatthefx to Forex [link] [comments]

Mainfinex: The Panacea to the Future

Mainfinex: The Panacea to the Future

Mainfinex

MAINFINEX offers a trusted exchange that crypto traders can use to make informed trades and participate in the cryptocurrency market. At the time of launch, MAINFINEX offers 15 different cryptocurrency pairs, all of which include USDT. The MAINFINEX cryptocurrency exchange offers something for every type of trader, regardless of experience level. Beginners will appreciate the intuitive interface and the fact that MAINFINEX uses Tradingview charts, which have numerous online tutorials for guidance. Advanced traders will appreciate the hundreds of drawing tools, the vast quantity of indicators, and high level of customization for charts.
Challenges faced by cryptocurrency exchanges today:
● Failure to apply global financial practices, and poor interface
● Large number of exchanges with little differentiation which complicates the choice of platform for operations
● Large number of unsuccessful traders losing money
● Pain points that are still there.

Exchange
Our understanding of the needs of the key trading parties in digital exchanges comes down to the concept “Traders seek liquidity and investors need profitability.”
  1. Liquidity and profitability
A mechanism we could build in to solve the problems of traders and long-term investors based on the exchange policy related to
trading fees:
  • Flexible interest rate depending on the volume, thus reducing the trading fee. The more activity in a trading section, the cheaper it is for that section
  • Fees reduced in case of severe price deviation. To reduce volatility and slippage and thus increase liquidity, market-making traders creating liquidity will be charged at a lower rate. The increase in volumes triggered by the reduced fee in case of price deviation will help smoothen out volatility.
  1. Reliability
Traders bearing losses have a regressive fee scale depending on the volume of the loss. This mechanism serves to mitigate the consequences of unfavorable deals for a trader.
  1. Sustainability “Back to the battle” Traders who have lost money but made it to the daily TOP 100 based on the volume will receive tokens compensating all the fees they paid or part of the losses. This will help stimulate liquidity in the exchange and create best cryptocurrency market conditions for arbitrage funds. Such funds account for up 80% of transaction in fiat exchanges.
  2. Concept: gaming elements of the exchange, buttons, etc. “Titles and statuses” With the emergence of cryptocurrencies, the world of finance has been transformed. It has to be clear and relevant for our users since the key audience of the exchange is 25-38 years old. Which means they played DOOM 2 when they were school students (in 1994). Why can’t we give simple names to complex financial instruments? It was the stunts and dirty tricks that guys in suits from investment banks played that eventually caused the mortgage crisis. We have selected the most popular financial instruments that we can provide. They can be understood and activated in one click. We have chosen simple names for them:
● "Forecast”
This button activates an analytical indicator used by most profitable traders
● "Call for help”
Activates a trading robot that will close transactions for you based on algorithms. Trading robots will be provided by successful third party funds
● "Stop me”
Block trading activity for two days. This is a mechanism that successful traders recommend to newbies. Breaks in trading activity help increase the accuracy of decisions and overall profitability
● "Join the group”
This function lets the user transfer money to a pool of professional traders. Similar to PAMM accounts in forex companies
● “Saving up for retirement”
10% from each profitable transaction will be automatically transferred to the annual/call deposit. Many experienced traders who work for themselves do not care about savings because trading is a constant source of big income. Having such a long-term deposit is one of the key ways to ensure security and can even save a family in the bad times
● “Work for us”
Traders without substantial deposits but with free working hours can make money by performing important tasks for the exchange, like in Amazon Mechanical Turk
● “Vanity fair”
Most successful traders may share their divine trading strategies in a master class for traders, with payment in our tokens.
  1. To benefit from certain options like the trading robot or funds management, users will be required to perform specific actions, e.g.: Purchasing exchange tokens. Equivalent free options: e.g., reposting our news daily throughout a month, which will also help expand the user’s subscriber base.
  2. Purchasing liquidity from “mini exchanges”
A partner exchange that will provide liquidity for trading in our exchange or display our depth of market diagram on its website will receive all the relevant fees collected in our tokens. This is how this mechanism works. Mini exchanges have a permanent audience of traders creating liquidity but due to the small volumes, the mutual liquidity among the participants is low and transactions are infrequent. This is a case of “the chicken or the egg” problem. The more users there are, the more frequently the transactions occur between the same users. Accordingly, a mini exchange will be able to increase the volume of fees collected by 3-4 times by using this opportunity.
  1. IEO sale
A shopping cart with all kinds of tokens. Includes both potentially successful and unsuccessful coins that cannot afford to pay the listing fee on their own. We collect the entire pool in a cart and sell it as one portfolio at a greatly reduced price. This gives unsuccessful ICO projects an opportunity to return part of the invested funds. And the users buying such assets at a rate below the cost level have more chances of profiting from price growth. The higher risk of unsuccessful projects in the portfolio compensated by the low price and the potentially high profitability is the key incentive.
  1. Exchange Tutorial
Just like in complex computer games such as urban construction simulators or turn-based strategies, at the first stage the player is taught how to use the game’s functionalities before he starts playing it in the full mode. Finance and cryptocurrencies have never been simple. Every individual financial instrument is based on a complex concept. The simplicity of starting to trade cryptocurrencies and the lack of regulation in the market result in a situation when most traders lose their money and investments. The tutorial works the same simple way, providing prompts on the sequence of the steps in the exchange. We will cooperate with several financial regulators to improve this instrument in order to develop new instruments that will help mitigate the risk of losses for each individual trader. At the end, many of the regulators’ tasks come down to managing the consequences of the great financial gap between trading parties.

Information correct at time of going to type. For updated information, go to Mainfinex Exchange web platform (Mainfinex Exchange website).
Note: In the event of conflict between this information and the information on the Mainfinex Exchange Website, the information on the Mainfinex Exchange Website will prevail.
Here, I present to you Mainfinex- The Future of Cryptocurrency Exchange, Mainfinex!!!
Mainfinex Exchange website
Mainfinex Exchange WhitePaper
ETH Address: 0x49d576e54C78e17E4451E7eF9f1d9C8e55360661
Email Address: [[email protected]](mailto:[email protected])
submitted by Busganda to CryptoCurrency [link] [comments]

free Forex signals via SMS EMAIL WhatsApp

free Forex signals via SMS EMAIL WhatsApp
Currency recommendations, currency analysis today, and the GBP / USD outlook from the best Forex Gold Pattern recommendation provider The direction of the GBP USD pair on the global currency exchange Forex trend is bearish in the near term The GBPUSD is trading near the 1.3460 support level, which is the buyer's house The GBPUSD price also formed the bearish measured move pattern and the pattern ended near the 1.3460 support level The bullish divergence on the RSI has appeared on the hourly frame GBP USD buy @ 1.3470 tp @ 1.3540 sl @ 1.3430 Forex trading signals , free forex recommendations and GBPUSD forecasts The GBP / USD pair is preferable to buy today as long as the pair is above 1.3430 Targeting the 1.3540 level of profit
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submitted by Athardewidar to u/Athardewidar [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
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