Sample Financial Statements - Boufford, CA

H1 Backtest of ParallaxFX's BBStoch system

Disclaimer: None of this is financial advice. I have no idea what I'm doing. Please do your own research or you will certainly lose money. I'm not a statistician, data scientist, well-seasoned trader, or anything else that would qualify me to make statements such as the below with any weight behind them. Take them for the incoherent ramblings that they are.
TL;DR at the bottom for those not interested in the details.
This is a bit of a novel, sorry about that. It was mostly for getting my own thoughts organized, but if even one person reads the whole thing I will feel incredibly accomplished.

Background

For those of you not familiar, please see the various threads on this trading system here. I can't take credit for this system, all glory goes to ParallaxFX!
I wanted to see how effective this system was at H1 for a couple of reasons: 1) My current broker is TD Ameritrade - their Forex minimum is a mini lot, and I don't feel comfortable enough yet with the risk to trade mini lots on the higher timeframes(i.e. wider pip swings) that ParallaxFX's system uses, so I wanted to see if I could scale it down. 2) I'm fairly impatient, so I don't like to wait days and days with my capital tied up just to see if a trade is going to win or lose.
This does mean it requires more active attention since you are checking for setups once an hour instead of once a day or every 4-6 hours, but the upside is that you trade more often this way so you end up winning or losing faster and moving onto the next trade. Spread does eat more of the trade this way, but I'll cover this in my data below - it ends up not being a problem.
I looked at data from 6/11 to 7/3 on all pairs with a reasonable spread(pairs listed at bottom above the TL;DR). So this represents about 3-4 weeks' worth of trading. I used mark(mid) price charts. Spreadsheet link is below for anyone that's interested.

System Details

I'm pretty much using ParallaxFX's system textbook, but since there are a few options in his writeups, I'll include all the discretionary points here:

And now for the fun. Results!

As you can see, a higher target ended up with higher profit despite a much lower winrate. This is partially just how things work out with profit targets in general, but there's an additional point to consider in our case: the spread. Since we are trading on a lower timeframe, there is less overall price movement and thus the spread takes up a much larger percentage of the trade than it would if you were trading H4, Daily or Weekly charts. You can see exactly how much it accounts for each trade in my spreadsheet if you're interested. TDA does not have the best spreads, so you could probably improve these results with another broker.
EDIT: I grabbed typical spreads from other brokers, and turns out while TDA is pretty competitive on majors, their minors/crosses are awful! IG beats them by 20-40% and Oanda beats them 30-60%! Using IG spreads for calculations increased profits considerably (another 5% on top) and Oanda spreads increased profits massively (another 15%!). Definitely going to be considering another broker than TDA for this strategy. Plus that'll allow me to trade micro-lots, so I can be more granular(and thus accurate) with my position sizing and compounding.

A Note on Spread

As you can see in the data, there were scenarios where the spread was 80% of the overall size of the trade(the size of the confirmation candle that you draw your fibonacci retracements over), which would obviously cut heavily into your profits.
Removing any trades where the spread is more than 50% of the trade width improved profits slightly without removing many trades, but this is almost certainly just coincidence on a small sample size. Going below 40% and even down to 30% starts to cut out a lot of trades for the less-common pairs, but doesn't actually change overall profits at all(~1% either way).
However, digging all the way down to 25% starts to really make some movement. Profit at the -161.8% TP level jumps up to 37.94% if you filter out anything with a spread that is more than 25% of the trade width! And this even keeps the sample size fairly large at 187 total trades.
You can get your profits all the way up to 48.43% at the -161.8% TP level if you filter all the way down to only trades where spread is less than 15% of the trade width, however your sample size gets much smaller at that point(108 trades) so I'm not sure I would trust that as being accurate in the long term.
Overall based on this data, I'm going to only take trades where the spread is less than 25% of the trade width. This may bias my trades more towards the majors, which would mean a lot more correlated trades as well(more on correlation below), but I think it is a reasonable precaution regardless.

Time of Day

Time of day had an interesting effect on trades. In a totally predictable fashion, a vast majority of setups occurred during the London and New York sessions: 5am-12pm Eastern. However, there was one outlier where there were many setups on the 11PM bar - and the winrate was about the same as the big hours in the London session. No idea why this hour in particular - anyone have any insight? That's smack in the middle of the Tokyo/Sydney overlap, not at the open or close of either.
On many of the hour slices I have a feeling I'm just dealing with small number statistics here since I didn't have a lot of data when breaking it down by individual hours. But here it is anyway - for all TP levels, these three things showed up(all in Eastern time):
I don't have any reason to think these timeframes would maintain this behavior over the long term. They're almost certainly meaningless. EDIT: When you de-dup highly correlated trades, the number of trades in these timeframes really drops, so from this data there is no reason to think these timeframes would be any different than any others in terms of winrate.
That being said, these time frames work out for me pretty well because I typically sleep 12am-7am Eastern time. So I automatically avoid the 5am-6am timeframe, and I'm awake for the majority of this system's setups.

Moving stops up to breakeven

This section goes against everything I know and have ever heard about trade management. Please someone find something wrong with my data. I'd love for someone to check my formulas, but I realize that's a pretty insane time commitment to ask of a bunch of strangers.
Anyways. What I found was that for these trades moving stops up...basically at all...actually reduced the overall profitability.
One of the data points I collected while charting was where the price retraced back to after hitting a certain milestone. i.e. once the price hit the -61.8% profit level, how far back did it retrace before hitting the -100% profit level(if at all)? And same goes for the -100% profit level - how far back did it retrace before hitting the -161.8% profit level(if at all)?
Well, some complex excel formulas later and here's what the results appear to be. Emphasis on appears because I honestly don't believe it. I must have done something wrong here, but I've gone over it a hundred times and I can't find anything out of place.
Now, you might think exactly what I did when looking at these numbers: oof, the spread killed us there right? Because even when you move your SL to 0%, you still end up paying the spread, so it's not truly "breakeven". And because we are trading on a lower timeframe, the spread can be pretty hefty right?
Well even when I manually modified the data so that the spread wasn't subtracted(i.e. "Breakeven" was truly +/- 0), things don't look a whole lot better, and still way worse than the passive trade management method of leaving your stops in place and letting it run. And that isn't even a realistic scenario because to adjust out the spread you'd have to move your stoploss inside the candle edge by at least the spread amount, meaning it would almost certainly be triggered more often than in the data I collected(which was purely based on the fib levels and mark price). Regardless, here are the numbers for that scenario:
From a literal standpoint, what I see behind this behavior is that 44 of the 69 breakeven trades(65%!) ended up being profitable to -100% after retracing deeply(but not to the original SL level), which greatly helped offset the purely losing trades better than the partial profit taken at -61.8%. And 36 went all the way back to -161.8% after a deep retracement without hitting the original SL. Anyone have any insight into this? Is this a problem with just not enough data? It seems like enough trades that a pattern should emerge, but again I'm no expert.
I also briefly looked at moving stops to other lower levels (78.6%, 61.8%, 50%, 38.2%, 23.6%), but that didn't improve things any. No hard data to share as I only took a quick look - and I still might have done something wrong overall.
The data is there to infer other strategies if anyone would like to dig in deep(more explanation on the spreadsheet below). I didn't do other combinations because the formulas got pretty complicated and I had already answered all the questions I was looking to answer.

2-Candle vs Confirmation Candle Stops

Another interesting point is that the original system has the SL level(for stop entries) just at the outer edge of the 2-candle pattern that makes up the system. Out of pure laziness, I set up my stops just based on the confirmation candle. And as it turns out, that is much a much better way to go about it.
Of the 60 purely losing trades, only 9 of them(15%) would go on to be winners with stops on the 2-candle formation. Certainly not enough to justify the extra loss and/or reduced profits you are exposing yourself to in every single other trade by setting a wider SL.
Oddly, in every single scenario where the wider stop did save the trade, it ended up going all the way to the -161.8% profit level. Still, not nearly worth it.

Correlated Trades

As I've said many times now, I'm really not qualified to be doing an analysis like this. This section in particular.
Looking at shared currency among the pairs traded, 74 of the trades are correlated. Quite a large group, but it makes sense considering the sort of moves we're looking for with this system.
This means you are opening yourself up to more risk if you were to trade on every signal since you are technically trading with the same underlying sentiment on each different pair. For example, GBP/USD and AUD/USD moving together almost certainly means it's due to USD moving both pairs, rather than GBP and AUD both moving the same size and direction coincidentally at the same time. So if you were to trade both signals, you would very likely win or lose both trades - meaning you are actually risking double what you'd normally risk(unless you halve both positions which can be a good option, and is discussed in ParallaxFX's posts and in various other places that go over pair correlation. I won't go into detail about those strategies here).
Interestingly though, 17 of those apparently correlated trades ended up with different wins/losses.
Also, looking only at trades that were correlated, winrate is 83%/70%/55% (for the three TP levels).
Does this give some indication that the same signal on multiple pairs means the signal is stronger? That there's some strong underlying sentiment driving it? Or is it just a matter of too small a sample size? The winrate isn't really much higher than the overall winrates, so that makes me doubt it is statistically significant.
One more funny tidbit: EUCAD netted the lowest overall winrate: 30% to even the -61.8% TP level on 10 trades. Seems like that is just a coincidence and not enough data, but dang that's a sucky losing streak.
EDIT: WOW I spent some time removing correlated trades manually and it changed the results quite a bit. Some thoughts on this below the results. These numbers also include the other "What I will trade" filters. I added a new worksheet to my data to show what I ended up picking.
To do this, I removed correlated trades - typically by choosing those whose spread had a lower % of the trade width since that's objective and something I can see ahead of time. Obviously I'd like to only keep the winning trades, but I won't know that during the trade. This did reduce the overall sample size down to a level that I wouldn't otherwise consider to be big enough, but since the results are generally consistent with the overall dataset, I'm not going to worry about it too much.
I may also use more discretionary methods(support/resistance, quality of indecision/confirmation candles, news/sentiment for the pairs involved, etc) to filter out correlated trades in the future. But as I've said before I'm going for a pretty mechanical system.
This brought the 3 TP levels and even the breakeven strategies much closer together in overall profit. It muted the profit from the high R:R strategies and boosted the profit from the low R:R strategies. This tells me pair correlation was skewing my data quite a bit, so I'm glad I dug in a little deeper. Fortunately my original conclusion to use the -161.8 TP level with static stops is still the winner by a good bit, so it doesn't end up changing my actions.
There were a few times where MANY (6-8) correlated pairs all came up at the same time, so it'd be a crapshoot to an extent. And the data showed this - often then won/lost together, but sometimes they did not. As an arbitrary rule, the more correlations, the more trades I did end up taking(and thus risking). For example if there were 3-5 correlations, I might take the 2 "best" trades given my criteria above. 5+ setups and I might take the best 3 trades, even if the pairs are somewhat correlated.
I have no true data to back this up, but to illustrate using one example: if AUD/JPY, AUD/USD, CAD/JPY, USD/CAD all set up at the same time (as they did, along with a few other pairs on 6/19/20 9:00 AM), can you really say that those are all the same underlying movement? There are correlations between the different correlations, and trying to filter for that seems rough. Although maybe this is a known thing, I'm still pretty green to Forex - someone please enlighten me if so! I might have to look into this more statistically, but it would be pretty complex to analyze quantitatively, so for now I'm going with my gut and just taking a few of the "best" trades out of the handful.
Overall, I'm really glad I went further on this. The boosting of the B/E strategies makes me trust my calculations on those more since they aren't so far from the passive management like they were with the raw data, and that really had me wondering what I did wrong.

What I will trade

Putting all this together, I am going to attempt to trade the following(demo for a bit to make sure I have the hang of it, then for keeps):
Looking at the data for these rules, test results are:
I'll be sure to let everyone know how it goes!

Other Technical Details

Raw Data

Here's the spreadsheet for anyone that'd like it. (EDIT: Updated some of the setups from the last few days that have fully played out now. I also noticed a few typos, but nothing major that would change the overall outcomes. Regardless, I am currently reviewing every trade to ensure they are accurate.UPDATE: Finally all done. Very few corrections, no change to results.)
I have some explanatory notes below to help everyone else understand the spiraled labyrinth of a mind that put the spreadsheet together.

Insanely detailed spreadsheet notes

For you real nerds out there. Here's an explanation of what each column means:

Pairs

  1. AUD/CAD
  2. AUD/CHF
  3. AUD/JPY
  4. AUD/NZD
  5. AUD/USD
  6. CAD/CHF
  7. CAD/JPY
  8. CHF/JPY
  9. EUAUD
  10. EUCAD
  11. EUCHF
  12. EUGBP
  13. EUJPY
  14. EUNZD
  15. EUUSD
  16. GBP/AUD
  17. GBP/CAD
  18. GBP/CHF
  19. GBP/JPY
  20. GBP/NZD
  21. GBP/USD
  22. NZD/CAD
  23. NZD/CHF
  24. NZD/JPY
  25. NZD/USD
  26. USD/CAD
  27. USD/CHF
  28. USD/JPY

TL;DR

Based on the reasonable rules I discovered in this backtest:

Demo Trading Results

Since this post, I started demo trading this system assuming a 5k capital base and risking ~1% per trade. I've added the details to my spreadsheet for anyone interested. The results are pretty similar to the backtest when you consider real-life conditions/timing are a bit different. I missed some trades due to life(work, out of the house, etc), so that brought my total # of trades and thus overall profit down, but the winrate is nearly identical. I also closed a few trades early due to various reasons(not liking the price action, seeing support/resistance emerge, etc).
A quick note is that TD's paper trade system fills at the mid price for both stop and limit orders, so I had to subtract the spread from the raw trade values to get the true profit/loss amount for each trade.
I'm heading out of town next week, then after that it'll be time to take this sucker live!

Live Trading Results

I started live-trading this system on 8/10, and almost immediately had a string of losses much longer than either my backtest or demo period. Murphy's law huh? Anyways, that has me spooked so I'm doing a longer backtest before I start risking more real money. It's going to take me a little while due to the volume of trades, but I'll likely make a new post once I feel comfortable with that and start live trading again.
submitted by ForexBorex to Forex [link] [comments]

Cases Displaying the Recent Climate of Chinese Economy

This is just a plain list that records the notable cases about China's recent economic woes.
China is rumoured to delay indefinitely its US-China phase one trade deal (fact sheet PDF) implementation that includes the increase of China's purchasing American products & services by at least $200 billion over the next two years, which is almost twice the size of what China purchased before the trade war began.
Okay.
And according to Tianyancha (天眼查), Chinese commercial database that compiles public records; more than 460,000 companies in China closed permanently in Q1 2020, with more than half of them having operated for under three years. [LINK]
Of course, this is mainly caused by the COVID-19 pandemic.
However, what's interesting to me is the following part: "more than half of them having operated for under three years." What happened three years ago?
Once you figure out how big the trade war has played in China's recent economic woes (the article samples listed below may help),
the real question is whose investment or money in China was getting destroyed especially for the last three years.
Other than the article samples listed below, two other data elements that would need to be assessed are: 1) financial loss from the US' blocking Huawei mobile OS & 5G and 2) financial loss from BRI projects.
With CCP, who has been working with Huawei as a team for a long time? [1] [2] [3]
........................................
Tale of How Shanghai clique and Prominent Globalists Got Together.
........................................
Sep 09, 2015 -- Fortune Reports: The real ticking time bomb in China’s economy [LINK]
"[China's] Local governments have borrowed this money with the blessing of China’s central government. In fact, China’s much-lauded $570 billion stimulus package in 2008, which dwarfed the American response to its crisis relative to each country’s respective GDP was funded mostly by local government debt. That program helped power China’s economic growth since 2008, but the dividends are now drying up. As Chinese growth slows, the central government is worried about the local governments’ abilities to finance the debt.
China could continue to kick the can down the road by bailing out its insolvent local governments. But this would run counter to President Xi Jinping’s efforts to curb the power of local officials and shift China’s growth model from investment led to consumption led. Last week, Beijing placed a $16 trillion yuan cap on Chinese government debt, up $600 million yuan from a cap it set last year. And this is after the government has been swapping debt with local governments, buying up real estate-financed local debt in place of government debt officially backed by the Chinese government."
Aug 24, 2016 -- SCMP: Wanted posters for fugitive debtors and runaway bosses symptoms of China’s economic woes [LINK]
"In the first seven months of this year, there were 38 instances of default by 18 bond issuers on the mainland, six of them SOEs. The defaults involved 24.8 billion yuan, more than double the total for the past two years combined. And while only a third of issuers in default this year were SOEs, they accounted for about two-thirds of the amount in default."
Jul 12, 2017 -- The Nikkei Reports: China government auditor flags dodgy books at key state companies [LINK]
"China's National Audit Office) delved into financial statements from 20 of the 101 state enterprises directly controlled by the central government, focusing on filings from the year 2015. The records are notoriously difficult for outsiders to access, as many of the companies are core unlisted units of major state-backed business groups.
Improprieties were unearthed at 18 of the 20, including 200.1 billion yuan ($29.4 billion) in revenue inflation over the last several years and roughly 20.3 billion yuan in improperly booked profit. Culprits included China National Petroleum, one of the country's largest oil producers; China National Chemical, or ChemChina, which recently acquired Switzerland's Syngenta, the world's top maker of agrochemicals; and China Baowu Steel Group."
........................................
2018
........................................
Jul 16: China’s $42-Trillion Debt Bubble Looms Larger than Trade War [LINK]
Oct 10: Financial woes build for HNA Group, forcing sale of subsidiaries and property [LINK]
........................................
2019
........................................
Jan 25: Sinopec Says It Lost $688 Million on ‘Misjudged’ Oil Prices [LINK]
Jun 11: China’s debt disease might wreck its uncrashable housing market [LINK]
Jul 18: More than 50 companies reportedly pull production out of China due to trade war [LINK]
Jul 19: China Minsheng Investment says it cannot repay the principal and interest on US$500 million of bonds as its debt woe deteriorates [LINK]
Sep 06: China Injects $126 Billion Into Its Slowing Economy [LINK]
Oct 06: China's foreign exchange reserves fallen to mere $3.1 trillion USD [LINK]
Nov 06: China Embraces Bankruptcy, U.S.-Style, to Cushion a Slowing Economy [LINK]
Nov 25: China Faces Biggest State Firm Offshore Debt Failure in 20 Years [LINK]
Nov 28: Chinese navy set to build fourth aircraft carrier, but plans for a more advanced ship are put on hold [LINK]
Dec 02: Tech Firm Peking University Founder Welches on USD 284 Million SCP, Has USD 43 Billion Debt [LINK]
Dec 02: Sinopec Group Slims Down Amid Push to Reinvigorate State Firms [LINK]
Dec 13: Fact Sheet: Agreement Between The United States Of America And The People’s Republic Of China Text [PDF LINK]
Dec 19: Money has been leaving China at a record rate. Beijing is battling to stem the tide [LINK]
Money was leaving the country at a record clip earlier this year through unauthorized channels, according to analysts. That's bad news for China, which needs to keep financial reserves high to maintain confidence in its markets.
........................................
2020
........................................
Feb 03: Coronavirus May Delay Hard-Fought U.S. Trade Wins in China [LINK]
Feb 16: China's Evergrande to offer 25% discount for all properties on sale in Feb, March [LINK]
Apr 02: Luckin Coffee stock tanks 80% after discovery that COO fabricated about $310 million in sales [LINK]
Apr 08: Chinese e-learning king TAL Education admits inflated sales [LINK]
Chinese law prohibits Chinese companies from submitting to normal U.S. auditing standards, and four Senators have already introduced a bill requiring them to do so. Should Trump be reelected ... either Beijing will relent on auditing standards or Chinese firms may start to face U.S. delisting threat.
Jul 14: Chinese $2.8bn memory chip project goes bust [LINK]
A Chinese company that launched a $2.8 billion government-backed semiconductor project four years ago is going bankrupt after it failed to attract investors, even as China tries to become self-sufficient in computer chips.
Jul 16: TSMC plans to halt chip supplies to Huawei in 2 months [LINK]
Jul 16: The $52 Trillion Bubble: China Grapples With Epic Property Boom [LINK]
Aug 26: U.S. Penalizes 24 Chinese Companies Over Role in South China Sea [LINK]
Aug 31: China’s Economy Shrinks, Ending a Nearly Half-Century of Growth [LINK]
Sep 09: Hongxin Semiconductor, promised China's first 7 nm chips, has gone bust [LINK]
A government-backed semiconductor manufacturing project based in the central Chinese city of Wuhan has gone belly-up, with key operator HSMC mired in debt. The local government said the project amounts to nearly RMB 128 billion (around $18.7 billion) in investment.
Sep 22: Huawei chairman urges U.S. to reconsider 'attack' on global supply chain [LINK]
Oct 13: EU imposes 48% tariffs on aluminium products from China [LINK]
Oct 18: China's economic growth drops to the lowest level since 1992 [LINK]
Oct 27: China’s Failing Small Banks Are Becoming a Big Problem [LINK]
The reality is that Beijing doesn’t have the wherewithal to guarantee the future of hundreds of smaller, provincial financial institutions that together sit on 73.4 trillion ($11 trillion USD) of yuan of total liabilities.

------------------------
☞ Go Back to the Short Story.
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submitted by vanillabluesea to conspiracy [link] [comments]

Chair for the Council of Trustees of Oxfam, Caroline Thomson, has ties to multiple global scandals, including the Jimmy Saville BBC pedophile scandal, as well as the UK's Lobbygate scandal

According to wikipedia, Caroline Thomson is the eldest "daughter of Labour peer George Thomson, Baron Thomson of Monifieth.[5]"
"She first joined the BBC as a journalist trainee in 1975, ultimately becoming a producer on Analysis (Radio 4) and later Panorama (BBC1) before becoming personal assistant to SDP leader Roy Jenkins in 1982. She spent over a decade at Channel 4 from 1984, initially as a commissioning editor, later as Head of Corporate Affairs from 1990, before rejoining the BBC in 1996 as Deputy Director of the World Service.[8] She became the Corporation's Director of Policy and Legal Affairs in July 2000, a job description later expanded to include Strategy, before being promoted to chief operating officer in 2006. In 2011 she was paid £385,000 by the organisation.[9] The Commons Public Accounts Committee suggested that her £670,000 redundancy pay-off was effectively paid to "compensate" her for missing out on the job of director-general.[10]
In October 2013 she became Executive Director of the English National Ballet [11][12]"
Caroline Thomson is married to the Labour peer Roger Liddle, an advisor to Tony Blair while Blair was Prime Minister.
https://en.wikipedia.org/wiki/Caroline_Thomson
So wikipedia references one of the many scandals connected to Thomson, the BBC Pay-Off Scandal, where chief executives of the BBC were paid excessive amounts of money upon resigning from the public media organization for absolutely no apparent reason whatsoever. Thomson herself received nearly 700,000 dollars of public funds.
http://biasedbbc.proboards.com/thread/2071/bbc-inflated-pay-off-scandal
https://en.wikipedia.org/wiki/BBC_controversies
In the three years to the end of December (2012), 150 departing executives were paid £25 million in compensation settlements. In a sample, one in four payments was found to have been in excess of contractual requirements.
http://www.telegraph.co.uk/culture/tvandradio/bbc/10156111/Im-hanging-on-to-my-680000-pay-off-says-former-BBC-executive.html
This scandal immediately followed the Saville pedophilia scandal, at a time when Thomson was the Chief Operating Officer of the BBC.
https://www.mirror.co.uk/news/uk-news/jimmy-savile-abuse-payouts-120-2195306
http://www.dailymail.co.uk/news/article-2227086/They-angels-Headmistresss-cruel-dismissal-girls-abused-Jimmy-Savile-claims-told-to.html
And then there's this: https://wikileaks.org/syria-files/docs/1110022_paharma-for-great-ssscekx-.html
An email to the syrian government from an email address that appears to be Thomson's that I found on wikileaks. It could be evidence that she was involved in laundering attempts to circumvent UN financial sanctions on Syria. It also could be related to the Forex scandal.
But the biggest scandal associated with Thomson just might be the one that's connected to her husband, Roger Liddle: Lobbygate. In this political scandal of 1998 in which lobbyists Derek Draper and Jonathan Mendelsohn were secretly recorded boasting that they could sell access to government ministers and create tax breaks for their clients, British political figures including Liddle and Tony Blair were exposed as little more than puppets of the lobbying industry an a cabal of 17 elites. Sound familiar? Their organization was informally known as "The Circle".
http://www.gregpalast.com/lobbygate-there-are-17-people-that-count-to-say-that-i-am-intimate-with-every-one-of-them-is-the-understatement-of-the-century/
http://www.nytimes.com/1998/07/10/news/prime-minister-accused-of-favoring-lobbyists-blair-beset-in-scandal.html
http://news.bbc.co.uk/2/hi/uk_news/politics/1621138.stm
Submission Statement: What are the odds that one woman could be so intimately connected to so many scandals, including one that involves a secret elite pedophile ring, AND THEN, be brought on as the head Trustee for an NGO that was ultimately accused of a massive Sexual Scandal?
submitted by Beaustrodamus to conspiracy [link] [comments]

Hump Day Deals: Free Coffee Delivered, Free Haircut, 65 Free Photo Prints

Free potatoes were good but you can't beat free coffee delivered straight to your door.

Freebies

Deals & Coupons

Freebie Redux

In the Herald a couple of months ago, there was a $2 off coupon. Potatoes at Countdown are $1.99 thus making them free. Not sure why these potatoes are $8/kg vs. normal potatoes of around $2.50 but meh it's free.

Wednesday Only Deals

  • Hell Pizza: Free Wedges @ Hell Pizza with code WEDGES
.......
Seems like the Reddit feedback from the previous post was good so thought I'd pick a few freebies & deal from the NZ bargain site I work for. I've put direct links in above but for some it's easier to read the discussion & comments on the bargain site. If you want to find more, ChoiceCheapies (sister site to OzBargain). If you think these posts are crap, tell me and I'll bugger off.
Update 1: Free ice cream from Uber this Friday Thanks Ya_Ya_UrAWoman
submitted by olibird to newzealand [link] [comments]

Can Chatbots be Intelligent?

Can Chatbots be Intelligent?
Businesses devise a billion ways of wooing customers, every day. If a chatbot can be a useful accomplice toward that end, why not give it a try? Afterall, who wouldn’t want a tool that can hold an intelligent conversation with customers, make them feel comfortable and bind them to your business.
Is it possible?
Recall that memorable scene from the award winning 2003 film, Lost in Translation, where an aging American actor, Bob Harris (played by Bill Murray), is on a set in Tokyo to shoot a whiskey commercial. The director, Yutaka Tadokoro, begins instructing Bob in Japanese, and the slapdash interpreter fails to capture the meaning—namely, it gets lost in translation. The process bogs down, and the commercial is a disaster.
You don’t want human-to-computer interactions to end up that way, right? But one-way communications prove to be too exasperating to users. People give up on trying to get a machine understand their intentions in a few clicks and presses. There’s that missing vibe, that interactive component in any human-computer engagement; and it’s the main reason a vast majority feels they must adapt to the technologies they use, rather than technology adapting to them.
https://preview.redd.it/mzxagl6zwrd11.jpg?width=220&format=pjpg&auto=webp&s=49da6f90e91dc9686b28c337b159b74c7f6dd3bf
Enter 2018, and we have artificial intelligence (AI)-driven chatbots that are revolutionizing human-computer interactions just the way the humans want it. Chatbots today are more adaptive to the way people speak and mimic their emotions to the nearest binary. 2018 is paving the way for a great chatbot innovation.
Meanwhile, developers are working tirelessly to bring in new consumer experiences to market. For example, once WhatsApp opens to bots next year, it will unlock direct access to over one billion new users. Chatbots are continuing to push the envelope of new technology further.
To reckon with, a chatbot isn’t an additional handle on your website or a fancy add-on. It’s the need of the hour for every business that’s flourishing or aspires to flourish. In a market that’s fiercely competitive, customers expect to receive accurate information quickly enough to make a decision. As a business owner, you need to cater to that need. If you don’t have funds to recruit more people to answer all the questions customers throw at you, then deploying a smart chatbot can rescue your business in that case.
https://preview.redd.it/vpuz6mb1xrd11.png?width=800&format=png&auto=webp&s=f615929b9f190e38afe38c3d59ba084dbfc9747b
But then intelligence also matters as it determines the kind of tasks or conversations your chatbot can handle. Needless to say, if you have a clear set of activities preconceived in your mind, you can build awesome customized bots.
Let’s take you through a short read about 5 important things that can make a chatbot intelligent.
1. Bots need to understand human conversations:
The bot needs to be quick and intelligent enough to understand the context of the conversation happening in real time. It’s about sense and sensibility, in conversations.
Normal human conversations are replete with instances of switching over context while talking, while at work - resuming a task, discarding the current task and switching to a newer one, or in general hold a task while the other is being executed and work on follow on. Human conversations tend to switch between contexts and variables (intents and entities), often combining multiple things into one.
Sample this response to a flight booking bot for example, "My Destination? San Francisco. But how's the weather over there?"
What should be the bot’s response here - capture the entity and continue booking or check the weather before that?
In this case, chatbots need to
  • have context switching abilities to handle interruptions smartly and provide full control to developers in defining the experience
  • capture unattended interruptions from a conversation flow and keep them accessible
  • be equipped with human conversations and have the ability to hold and resume a dialog for a certain amount of time and execute the tasks in sequential order, and especially while understanding human emotions
You may argue that a bot is after all a machine and cannot absorb emotions, but all said and done, it also depends partly on how much capability you build into it. So, it must be clever enough to filter the feelings of the customer. The bot needs to understand, analyze and respond based on the human emotion. For instance, if a customer messages an online shopping portal saying, “Your service is amazing, the delivery of items are always 2 to 3 days delayed”, none can miss the biting sarcasm intended in the statement. But if the bot isn’t developed to cater to this sort of sentiment, it may end up answering in a horribly awry manner.
https://preview.redd.it/a3p88148xrd11.png?width=800&format=png&auto=webp&s=f589d16a1d1407018f26c460404a69def2a4cf52
Intelligent bots will have: Sentiment analysis, context switching, hold and resume feature.
2. Standardization and uniformity in bot utterances
It’s important to remember that a chatbot must give vibes closest to humans as much as possible. The way humans carry the stamp of their personality and style, bots too need to be enabled to do that. When asked about something, a bot must respond in a particular way and pattern that sounds like a human. This warms the usecustomer and makes him feel at ease during the conversation with a chatbot.
“You must have direct connection with your customers as part of your brand’s identity, even more than your website that doesn’t seem to have an identity, this will have a personality.”- William Meisel
Thus, chatbots need to
  • understand and remember the user context - make all user information available in a single location and accessible
  • store the user profile with information like first name, last name and make it accessible to all the systems for the convenience of the user.
  • remember what a specific user talks to a specific bot, in an enterprise scenario it needs to keep certain features such as prompt for ‘Password length’ / ‘folder for HR information, constant for all the employees in the enterprise
It’s important for a chatbot to keep a current task which is being executed in an active mode and store information.
As a corollary, customers appreciate and connect with the support executives (call support executives/shopping store helpers) who can remember their preferences, can validate their purchases, help them with more information on products, and basically give importance to them while attending to their queries. For example, in a Forex platform the currency against each country is maintained constant across all systems for everyone to access. The platform tends to store the first and last name of the customer, their last transaction and their payment options.
Chatbots now have the responsibility to standardize their understanding of a customer and respond to them accordingly, whether in the manner of communicating or the speed with which they resolve their query. Chatbots need to converse with customers to extract this information and keep up to their pace.
https://preview.redd.it/flps4l6bxrd11.png?width=800&format=png&auto=webp&s=dee88672cef176ed92778e962a9029543ee6cbd9
Intelligent bots will have features like: Small talk, Bot user session, Enterprise context, User context and User session
3. Making the complex conversation sound simple
Chatbots are expected to break the complex structures of conversations into simpler tones and bring to a logical conclusion. Here’s where ‘Artificial Intelligence’ comes into play. Among the many types of chatbots, the most common ones are task specific that cater to a specific job, with pre-loaded answers and information. These type of chatbots have the ability to gather data from the internet, previous company database and other sources. Therefore, these bots are able to reply to diverse queries.
The intelligent bots, in addition, have the potential to mold the conversation the way the customer wants and guide him towards a specific solution. In an office setup, it’s common for a conversation like, “Hey Lisa, set up a meeting with Phani if he’s free”, to be handled between a Boss and Secretary. To enable that, the chatbot needs to first look up the calendar of ‘Phani’, find a suitable time for a meeting in sync with the Boss’s schedule and then reschedule the meeting. Chatbots thus need to break up complex sounding conversation into simpler nuances and then execute the task sequentially and logically.
Intelligent bots can also break down the conversation to its essence and action items. Let’s look at a very common scenario: ‘Customer tries to book tickets for 14th August, confirms on the choice of airlines, origin and destination and navigates to the next page, but feels that the pricing is very high. The customer then asks the bot to check for ‘15th August instead’. Here, based on the situation, the chatbot is acting and will be able to display the new prices by changing the date of journey.
https://preview.redd.it/bzfoasiexrd11.png?width=800&format=png&auto=webp&s=67d134343093daf3d239c9bcfd0b315fb687d333
Intelligent bots will have features like: Amend Entities, Planning.
4. Adapting to human utterances
In the context of human–computer communication, forming assumptions about what a system can do and understand is problematic for most people. In turn, forming assumptions about how users will “talk to” the system is also likely to be problematic for system developers. The potential for variability in how users will communicate with a system is enormous and has been dubbed “The Vocabulary Problem.”
An intelligent chatbot can not only handle queries smartly and remembers them through the session, but also learns new things with every conversation that happens, saves them and uses them appropriately for future instances.
In a human conversation and especially over voice, there are bound to be
  • expectation of elaboration or confirmation (“can you hear me?”, “I do not follow”)
  • request for repeat of sentences (“ I’m sorry I couldn’t hear that, can you please repeat it again?” “Sorry, can you repeat?”)
  • pauses (“can you please hold? [pause] thank you!”)
  • interruptions (“the number is 212-” “sorry can you start over?” )
The simplest thing to do when writing responses to command and inquiry utterances in a conversational UI is to get straight to the point: respond with facts. That’ll remove a lot of the ambiguity and simplify the dialogue.
It’s up to the intelligent chatbot to adapt to the way the human responds - with the referential context (or) pauses (or) specific context (or) synonyms (or) repetition (or) abbreviations (or) variations in dialect. The chatbot needs to map it pre-contextually. But like their human counterparts, chatbots’ conversational skills determine whether they earn you seamless, scalable transactions or just another horde of pissed-off customers. This needs a lot of training by the chatbot to help continue the conversations to the logical ending.
https://preview.redd.it/u5g63jovxrd11.jpg?width=1581&format=pjpg&auto=webp&s=1f394c933c28c1079cf290156de09f285b2054d1
Intelligent bots will have features like: Sync, Repeat, Interruptions and Pauses.
5. When bots are kept simple
Although AI chatbots’ task is complicated and they need to be built up that way, yet the effort should be made to keep it simple. They need to be comprehensive yet detailed. A customer initiating a conversation with a chatbot might already be troubled due to some poor service related issue, hence it’s better not to irk him further with complex interaction. The bot should be answering the already irked usecustomer in a most precise way possible without confusing the person further. It’s easy to figure out if you are talking to a bot or a human. Make sure the customer knows that they are talking to a bot by welcoming them with some sort of welcome message. Nobody likes being told the same thing over and over again, so why do chatbots keep doing it? Bots should detect when they’re about to repeat a previously given answer and switch strategies. If the answer didn’t resolve the user’s needs before, repeating it certainly won’t either. From the user interface, to the dialog flow the experience should be pleasant, and information given to the user needs to be valuable and crisp.
Twitter also provides the option to give your bot a custom name for different sections of the bot, which can be of use. It’s important to show what the chatbot is capable of doing with Quick Replies. The customer needs to be a guided stepwise within the conversation and with enough accessible options to choose from.
Lastly, there must always be a way to end the conversation with the bot and switch to a human agent. Many bots today include a Quick Reply to “Speak to an Agent”. Certain actions, such as open-ended visual search, are challenging to complete in a messaging environment. In those situations, bots can route to a website or app to help the user complete goals they couldn’t execute within the context of chat.
https://preview.redd.it/gc1j3z4sxrd11.jpg?width=578&format=pjpg&auto=webp&s=331b9de9fcfa0c26ddf34eeb93783e909ecab6cf
Intelligent bots will have features like: Simple UI, Simpler steps, Agent Handoff
In a nutshell, a chatbot must be programmed to not just provide optimum solutions to problems, but also converse with customers in an engaging manner. The interaction must be exciting and the bot must appear to be curious enough to answer all queries. People prefer lively interactions and a chatbot needs to meet that expectation.
https://preview.redd.it/erd22jzpxrd11.jpg?width=1505&format=pjpg&auto=webp&s=d83e0de6283b47b1210a912d26e23dd8023d3afc
For example, there are bots aligned with online shopping portals that can actually sense your liking and disliking. They can cancel orders for you accordingly and order the stuff that you actually want. Businesses are now moving way ahead than what anyone had ever thought of earlier. If we have an amazing concept like messenger or Kore.aiBots Platform, then why not use them to the full extent. Their proficiency in collecting massive data in a short period of time can be used to forecast upcoming business. You know it better how to get edgy with this interesting concept. The more you experiment with chatbots, the more you would get to know the wonders you can create with these little machines.
Some of the Global 2,000 companies and large enterprises are using Kore.ai Bots Platform to build their chatbots. How about you?
To get everything you need to build and deploy intelligent, enterprise-grade chatbots — without unnecessary complexity, click on Build your first BOT.
To ask questions, get tips, learn and grow with Kore.AI developer community, click on Ask questions on Developer Community.
Also Read on : Chatbots (of) the Future
Thank You
Phani Marupaka
LinkedIn| Tweet at : @phani_teja
submitted by PhaniTeja4 to u/PhaniTeja4 [link] [comments]

Can Chatbots be Intelligent?

Can Chatbots be Intelligent?
Businesses devise a billion ways of wooing customers, every day. If a chatbot can be a useful accomplice toward that end, why not give it a try? Afterall, who wouldn’t want a tool that can hold an intelligent conversation with customers, make them feel comfortable and bind them to your business.
Is it possible?
Recall that memorable scene from the award winning 2003 film, Lost in Translation, where an aging American actor, Bob Harris (played by Bill Murray), is on a set in Tokyo to shoot a whiskey commercial. The director, Yutaka Tadokoro, begins instructing Bob in Japanese, and the slapdash interpreter fails to capture the meaning—namely, it gets lost in translation. The process bogs down, and the commercial is a disaster.
https://preview.redd.it/6vqwiux3urd11.jpg?width=220&format=pjpg&auto=webp&s=fd5151869d3e932a32f56fc969406633cd3ba623
You don’t want human-to-computer interactions to end up that way, right? But one-way communications prove to be too exasperating to users. People give up on trying to get a machine understand their intentions in a few clicks and presses. There’s that missing vibe, that interactive component in any human-computer engagement; and it’s the main reason a vast majority feels they must adapt to the technologies they use, rather than technology adapting to them.
Enter 2018, and we have artificial intelligence (AI)-driven chatbots that are revolutionizing human-computer interactions just the way the humans want it. Chatbots today are more adaptive to the way people speak and mimic their emotions to the nearest binary. 2018 is paving the way for a great chatbot innovation.
https://preview.redd.it/27twgl16urd11.png?width=800&format=png&auto=webp&s=c2a83408e1f5a9495428fff1f4cc0414d30b8d84
Meanwhile, developers are working tirelessly to bring in new consumer experiences to market. For example, once WhatsApp opens to bots next year, it will unlock direct access to over one billion new users. Chatbots are continuing to push the envelope of new technology further.
To reckon with, a chatbot isn’t an additional handle on your website or a fancy add-on. It’s the need of the hour for every business that’s flourishing or aspires to flourish. In a market that’s fiercely competitive, customers expect to receive accurate information quickly enough to make a decision. As a business owner, you need to cater to that need. If you don’t have funds to recruit more people to answer all the questions customers throw at you, then deploying a smart chatbot can rescue your business in that case.
But then intelligence also matters as it determines the kind of tasks or conversations your chatbot can handle. Needless to say, if you have a clear set of activities preconceived in your mind, you can build awesome customized bots.
Let’s take you through a short read about 5 important things that can make a chatbot intelligent.
1. Bots need to understand human conversations:
The bot needs to be quick and intelligent enough to understand the context of the conversation happening in real time. It’s about sense and sensibility, in conversations.
Normal human conversations are replete with instances of switching over context while talking, while at work - resuming a task, discarding the current task and switching to a newer one, or in general hold a task while the other is being executed and work on follow on. Human conversations tend to switch between contexts and variables (intents and entities), often combining multiple things into one.
Sample this response to a flight booking bot for example, "My Destination? San Francisco. But how's the weather over there?"
What should be the bot’s response here - capture the entity and continue booking or check the weather before that?
In this case, chatbots need to
  • have context switching abilities to handle interruptions smartly and provide full control to developers in defining the experience
  • capture unattended interruptions from a conversation flow and keep them accessible
  • be equipped with human conversations and have the ability to hold and resume a dialog for a certain amount of time and execute the tasks in sequential order, and especially while understanding human emotions
You may argue that a bot is after all a machine and cannot absorb emotions, but all said and done, it also depends partly on how much capability you build into it. So, it must be clever enough to filter the feelings of the customer. The bot needs to understand, analyze and respond based on the human emotion. For instance, if a customer messages an online shopping portal saying, “Your service is amazing, the delivery of items are always 2 to 3 days delayed”, none can miss the biting sarcasm intended in the statement. But if the bot isn’t developed to cater to this sort of sentiment, it may end up answering in a horribly awry manner.
Intelligent bots will have: Sentiment analysis, context switching, hold and resume feature.
2. Standardization and uniformity in bot utterances
It’s important to remember that a chatbot must give vibes closest to humans as much as possible. The way humans carry the stamp of their personality and style, bots too need to be enabled to do that. When asked about something, a bot must respond in a particular way and pattern that sounds like a human. This warms the usecustomer and makes him feel at ease during the conversation with a chatbot.
“You must have direct connection with your customers as part of your brand’s identity, even more than your website that doesn’t seem to have an identity, this will have a personality.”- William Meisel
Thus, chatbots need to
  • understand and remember the user context - make all user information available in a single location and accessible
  • store the user profile with information like first name, last name and make it accessible to all the systems for the convenience of the user.
  • remember what a specific user talks to a specific bot, in an enterprise scenario it needs to keep certain features such as prompt for ‘Password length’ / ‘folder for HR information, constant for all the employees in the enterprise
It’s important for a chatbot to keep a current task which is being executed in an active mode and store information.
As a corollary, customers appreciate and connect with the support executives (call support executives/shopping store helpers) who can remember their preferences, can validate their purchases, help them with more information on products, and basically give importance to them while attending to their queries. For example, in a Forex platform the currency against each country is maintained constant across all systems for everyone to access. The platform tends to store the first and last name of the customer, their last transaction and their payment options.
Chatbots now have the responsibility to standardize their understanding of a customer and respond to them accordingly, whether in the manner of communicating or the speed with which they resolve their query. Chatbots need to converse with customers to extract this information and keep up to their pace.
Intelligent bots will have features like: Small talk, Bot user session, Enterprise context, User context and User session
3. Making the complex conversation sound simple
Chatbots are expected to break the complex structures of conversations into simpler tones and bring to a logical conclusion. Here’s where ‘Artificial Intelligence’ comes into play. Among the many types of chatbots, the most common ones are task specific that cater to a specific job, with pre-loaded answers and information. These type of chatbots have the ability to gather data from the internet, previous company database and other sources. Therefore, these bots are able to reply to diverse queries.
The intelligent bots, in addition, have the potential to mold the conversation the way the customer wants and guide him towards a specific solution. In an office setup, it’s common for a conversation like, “Hey Lisa, set up a meeting with Phani if he’s free”, to be handled between a Boss and Secretary. To enable that, the chatbot needs to first look up the calendar of ‘Phani’, find a suitable time for a meeting in sync with the Boss’s schedule and then reschedule the meeting. Chatbots thus need to break up complex sounding conversation into simpler nuances and then execute the task sequentially and logically.
Intelligent bots can also break down the conversation to its essence and action items. Let’s look at a very common scenario: ‘Customer tries to book tickets for 14th August, confirms on the choice of airlines, origin and destination and navigates to the next page, but feels that the pricing is very high. The customer then asks the bot to check for ‘15th August instead’. Here, based on the situation, the chatbot is acting and will be able to display the new prices by changing the date of journey.
Intelligent bots will have features like: Amend Entities, Planning.
4. Adapting to human utterances
In the context of human–computer communication, forming assumptions about what a system can do and understand is problematic for most people. In turn, forming assumptions about how users will “talk to” the system is also likely to be problematic for system developers. The potential for variability in how users will communicate with a system is enormous and has been dubbed “The Vocabulary Problem.”
An intelligent chatbot can not only handle queries smartly and remembers them through the session, but also learns new things with every conversation that happens, saves them and uses them appropriately for future instances.
In a human conversation and especially over voice, there are bound to be
  • expectation of elaboration or confirmation (“can you hear me?”, “I do not follow”)
  • request for repeat of sentences (“ I’m sorry I couldn’t hear that, can you please repeat it again?” “Sorry, can you repeat?”)
  • pauses (“can you please hold? [pause] thank you!”)
  • interruptions (“the number is 212-” “sorry can you start over?” )
The simplest thing to do when writing responses to command and inquiry utterances in a conversational UI is to get straight to the point: respond with facts. That’ll remove a lot of the ambiguity and simplify the dialogue.
It’s up to the intelligent chatbot to adapt to the way the human responds - with the referential context (or) pauses (or) specific context (or) synonyms (or) repetition (or) abbreviations (or) variations in dialect. The chatbot needs to map it pre-contextually. But like their human counterparts, chatbots’ conversational skills determine whether they earn you seamless, scalable transactions or just another horde of pissed-off customers. This needs a lot of training by the chatbot to help continue the conversations to the logical ending.
Intelligent bots will have features like: Sync, Repeat, Interruptions and Pauses.
📷
5. When bots are kept simple
Although AI chatbots’ task is complicated and they need to be built up that way, yet the effort should be made to keep it simple. They need to be comprehensive yet detailed. A customer initiating a conversation with a chatbot might already be troubled due to some poor service related issue, hence it’s better not to irk him further with complex interaction. The bot should be answering the already irked usecustomer in a most precise way possible without confusing the person further. It’s easy to figure out if you are talking to a bot or a human. Make sure the customer knows that they are talking to a bot by welcoming them with some sort of welcome message. Nobody likes being told the same thing over and over again, so why do chatbots keep doing it? Bots should detect when they’re about to repeat a previously given answer and switch strategies. If the answer didn’t resolve the user’s needs before, repeating it certainly won’t either. From the user interface, to the dialog flow the experience should be pleasant, and information given to the user needs to be valuable and crisp.
Twitter also provides the option to give your bot a custom name for different sections of the bot, which can be of use. It’s important to show what the chatbot is capable of doing with Quick Replies. The customer needs to be a guided stepwise within the conversation and with enough accessible options to choose from.
Lastly, there must always be a way to end the conversation with the bot and switch to a human agent. Many bots today include a Quick Reply to “Speak to an Agent”. Certain actions, such as open-ended visual search, are challenging to complete in a messaging environment. In those situations, bots can route to a website or app to help the user complete goals they couldn’t execute within the context of chat.
Intelligent bots will have features like: Simple UI, Simpler steps, Agent Handoff
In a nutshell, a chatbot must be programmed to not just provide optimum solutions to problems, but also converse with customers in an engaging manner. The interaction must be exciting and the bot must appear to be curious enough to answer all queries. People prefer lively interactions and a chatbot needs to meet that expectation.
https://preview.redd.it/ri36kv7aurd11.jpg?width=800&format=pjpg&auto=webp&s=b8d3f5a1bab5b31dc3617f4bb7645e88bf70f836
For example, there are bots aligned with online shopping portals that can actually sense your liking and disliking. They can cancel orders for you accordingly and order the stuff that you actually want. Businesses are now moving way ahead than what anyone had ever thought of earlier. If we have an amazing concept like messenger or Kore.aiBots Platform, then why not use them to the full extent. Their proficiency in collecting massive data in a short period of time can be used to forecast upcoming business. You know it better how to get edgy with this interesting concept. The more you experiment with chatbots, the more you would get to know the wonders you can create with these little machines.
Some of the Global 2,000 companies and large enterprises are using Kore.ai Bots Platform to build their chatbots. How about you?
To get everything you need to build and deploy intelligent, enterprise-grade chatbots — without unnecessary complexity, click on Build your first BOT.
To ask questions, get tips, learn and grow with Kore.AI developer community, click on Ask questions on Developer Community.
Also Read on : Chatbots (of) the Future
Thank You
Phani Marupaka
LinkedIn| Tweet at : @phani_teja
submitted by PhaniTeja4 to Chatbots [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)
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  29. W1redgh0st (97 points, 70 comments)
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  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)
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  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)

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Math Problem: Find the functional relation between 4 quote pairs and 2 quote pairs. (Journal)

As some may have seen in a post I made a couple days ago, I talked about a formula for determining lot size. That formula was:
R= Risk (In USD) [Found by: (Account Balance/100) x (Risk %) ] ; S= Stop Loss (In Pips) ; U= Unit Size (In $)
(S) x 10,000 = U
Very simple 3 variable Equation, as long as you have 2, you can find the third. Every time. VERY convenient and useful.
However, a problem arises because as it turns out that formula only works for pairs quoted to 4 decimals. When trying it with pairs to two decimals, the function of and relation between these three variables is different.
I've noticed that the ratio is in a flux when working with 2 decimal quote pairs, unlike the consistency that 4 decimal quotes have.. you can actually watch your risk in USD change with the same unit size and SL. In the past 2 days, I think the range has been about .30 cents (when trading a lot). Though this is observation has no real grounds as the sample size is too small.
That being said, I'm hoping to find something that gets me in relatively close, consistently. An accuracy of 1% (about +/- 50 Units on a 5000 Unit, or about +/- 1000 Units on 1lot (100k Units) is all that should be necessary for practical use, given the flux.
Here's what I've done so far:
While typing this I came up with another way to approach this and ultimately the solution, so if you were looking for a math problem to work yourself, stop reading here and get our your scratch paper and get to work. Otherwise, continue!
ex/1) Lets look at two Pairs with the same lot size and stop loss. Pair: GBP/JPY ; Stop Loss = 10pips ; Units = 100,000 ; Risk = 82.95 USD (Currently)
Compared to:
Pair: NZD/USD ; Stop Loss = 10pips ; Units = 100,000 ; Risk = 100 USD
At first, a percentage (~82%) looks like it might help us. So I played with it. No good. Give it a go if you feel so inclined.
So then I tried setting the risks equal and see what sort of percent difference in unit size that gave me; and go from there. Here's that example.
ex/2) NZDUSD ; Risk: 100$ ; SL: 10pips ; Units: 100,000
GBP/JPY ; Risk: 100$ ; SL: 10pips ; Units: 120,555
So, the hypothesis is: When comparing two trades of equal risk across a 2 decimal quote and a 4 decimal quote, the 2 decimal quote trade is roughly 120% larger in unit size than the 4 decimal quote.
Running that math (120,555/100,000) gives us a decimal multiplier of: 1.20555.
Testing that multiplier on a different number set: Risk: 24$ ; SL: 190 ; Units; ?
[(24/190)x10,000]x1.20555= 1523
^ Opening up fxtrade and testing this confirms this statement to be true, as of today.
Looks like we found gold boys!
Well, based on my observations, I'd say that a multiplier of 1.2 will get you close, and taking the percentage difference in the units between a 2 quote pair to a 4 quote pair with equal risk will put you on the money. Though unfortunately finding that takes about as long as it would to just go back and fourth on your trade entry until your close, thus reducing the utility of this equation unless your making more than 1 trade a day. Another point to note: I don't know how much this even changes in a day, or over a course of weeks for that matter.. I'll try to take a sample over a day or two every couple hours, and then take a daily sample over a few weeks to find the real range and variance of the difference, and the accuracy percentage of a 1.2. I'm looking forward to having a stronger conclusion.
Anyway, as of today:
For 4 Decimal Quotes: (S) x 10,000 = Units
For 2 Decimal Quotes: [(S) x 10,000] x 1.20555 = Units
So even though I answer'd my own question here, hopefully this has been an interesting/useful read!
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