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Help!Should I convert USD to EUR now or later?

I’m going to be a student in Europe very soon. I’m lucky to be able to go despite corona, but with the USD falling in relation to the Euro, I’m very scared that I will lose a lot of money when I get there and need to convert my funds. The USD is worth .85 euros, the lowest it’s been in a few years. This January, $1 = .92 euros. That rate doesn’t look like it’s improving at all judging by the economic situation of the US, but I am not sure if there will be a dip or something. I don’t know much about forex.
I have a multicurrency account with TransferWise and was thinking of converting my savings to euros ahead of time. Every penny counts for me, though, and I don’t want to convert at the wrong time. Does anyone have any insights about how the forex market is performing - near-term predictors that would indicate it’s better to convert now or just to wait it out?
submitted by electra982 to personalfinance [link] [comments]

What factors predict the success of a Steam game? (An analysis)

What factors predict the success of a Steam game?

I've seen quite a few discussions, comments and questions on /gamedev about what determines a game's success. How much does quality matter? Is establishing market awareness before launch the only thing that matters? Does a demo help or hurt? If your game has a poor launch, how likely is it to recover? Is it possible to roughly predict the sales of a game before launch?
In preparation for my game's launch, I spent a lot of time monitoring upcoming releases trying to find the answer to these questions. I compiled a spreadsheet, noted followers, whether it was Early Access or not, and saw how many reviews it received in the first week, month and quarter.
I'm sharing this data now in the hopes that it helps other developers understand and predict their games' sales.
First some notes on the data:
Game Price Launch Discount Week Guess Week actual 3 Month 3 Month/week Followers Early Access Demo Review Score
Pit of Doom 9.99 0 7 27 43 1.592592593 295 Y N 0.8
Citrouille 9.99 0.2 16 8 12 1.5 226 N N
Corspe Party: Book 14.99 0.1 32 40 79 1.975 1015 N N 0.95
Call of Cthulhu 44.99 0 800 875 1595 1.822857143 26600 N N 0.74
On Space 0.99 0.4 0 0 0 4 N N
Orphan 14.99 0 50 0 8 732 N N
Black Bird 19.99 0 20 13 34 2.615384615 227 N N
Gloom 6.99 0 20 8 17 2.125 159 N N
Gilded Rails 5.99 0.35 2 3 7 2.333333333 11 N Y
The Quiet Man 14.99 0.1 120 207 296 1.429951691 5596 N N 0.31
KartKraft 19.99 0.1 150 90 223 2.477777778 7691 Y N 0.84
The Other Half 7.99 0 2 3 27 9 91 N Y 0.86
Parabolus 14.99 0.15 0 0 0 16 N Y
Yet Another Tower Defense 1.99 0.4 20 22 38 1.727272727 396 N N 0.65
Galaxy Squad 9.99 0.25 8 42 5.25 3741 Y N 0.87
Swords and Soldiers 2 14.99 0.1 65 36 63 1.75 1742 N N 0.84
SpitKiss 2.99 0 3 1 2 2 63 N N
Holy Potatoes 14.99 0 24 11 22 2 617 N N 0.7
Kursk 29.99 0.15 90 62 98 1.580645161 2394 N N 0.57
SimpleRockets 2 14.99 0.15 90 142 272 1.915492958 3441 Y N 0.85
Egress 14.99 0.15 160 44 75 1.704545455 7304 Y N 0.67
Kynseed 9.99 0 600 128 237 1.8515625 12984 Y N 0.86
11-11 Memories 29.99 0 30 10 69 6.9 767 N N 0.96
Rage in Peace 12.99 0.1 15 10 42 4.2 377 N N 0.85
One Hour One Life 19.99 0 12 153 708 4.62745098 573 N N 0.81
Optica 9.99 0 0 2 3 1.5 18 N N
Cybarian 5.99 0.15 8 4 18 4.5 225 N N
Zeon 25 3.99 0.3 3 11 12 1.090909091 82 Y N
Of Gods and Men 7.99 0.4 3 10 18 1.8 111 N Y
Welcome to Princeland 4.99 0.1 1 15 55 3.666666667 30 N N 0.85
Zero Caliber VR 24.99 0.1 100 169 420 2.485207101 5569 Y N 0.73
HellSign 14.99 0 100 131 334 2.549618321 3360 Y N 0.85
Thief Simulator 19.99 0.15 400 622 1867 3.001607717 10670 N N 0.81
Last Stanza 7.99 0.1 8 2 4 2 228 N Y
Evil Bank Manager 11.99 0.1 106 460 4.339622642 8147 Y N 0.78
Oppai Puzzle 0.99 0.3 36 93 2.583333333 54 N N 0.92
Hexen Hegemony 9.99 0.15 3 1 5 5 55 Y N
Blokin 2.99 0 0 0 0 0 10 N N
Light Fairytale Ep 1 9.99 0.1 80 23 54 2.347826087 4694 Y N 0.89
The Last Sphinx 2.99 0.1 0 0 1 0 17 N N
Glassteroids 9.99 0.2 0 0 0 0 5 Y N
Hitman 2 59.99 0 2000 2653 3677 1.385978138 52226 N N 0.88
Golf Peaks 4.99 0.1 1 8 25 3.125 46 N N 1
Sipho 13.99 0 24 5 14 2.8 665 Y N
Distraint 2 8.99 0.1 40 104 321 3.086538462 1799 N N 0.97
Healing Harem 12.99 0.1 24 10 15 1.5 605 N N
Spark Five 2.99 0.3 0 0 0 0 7 N N
Bad Dream: Fever 9.99 0.2 30 78 134 1.717948718 907 N N 0.72
Underworld Ascendant 29.99 0.15 200 216 288 1.333333333 8870 N N 0.34
Reentry 19.99 0.15 8 24 78 3.25 202 Y N 0.95
Zvezda 5.99 0 2 0 0 0 25 Y Y
Space Gladiator 2.99 0 0 1 2 2 5 N N
Bad North 14.99 0.1 500 360 739 2.052777778 15908 N N 0.8
Sanctus Mortem 9.99 0.15 3 3 3 1 84 N Y
The Occluder 1.99 0.2 1 1 1 1 13 N N
Dark Fantasy: Jigsaw 2.99 0.2 1 9 36 4 32 N N 0.91
Farming Simulator 19 34.99 0 1500 3895 5759 1.478562259 37478 N N 0.76
Don't Forget Our Esports Dream 14.99 0.13 3 16 22 1.375 150 N N 1
Space Toads Mayhem 3.99 0.15 1 2 3 1.5 18 N N
Cattle Call 11.99 0.1 10 19 53 2.789473684 250 Y N 0.71
Ralf 9.99 0.2 0 0 2 0 6 N N
Elite Archery 0.99 0.4 0 2 3 1.5 5 Y N
Evidence of Life 4.99 0 0 2 4 2 10 N N
Trinity VR 4.99 0 2 8 15 1.875 61 N N
Quiet as a Stone 9.99 0.1 1 1 4 4 42 N N
Overdungeon 14.99 0 3 86 572 6.651162791 77 Y N 0.91
Protocol 24.99 0.15 60 41 117 2.853658537 1764 N N 0.68
Scraper: First Strike 29.99 0 3 3 15 5 69 N N
Experiment Gone Rogue 16.99 0 1 1 5 5 27 Y N
Emerald Shores 9.99 0.2 0 1 2 2 12 N N
Age of Civilizations II 4.99 0 600 1109 2733 2.464382326 18568 N N 0.82
Dereliction 4.99 0 0 0 0 #DIV/0! 18 N N
Poopy Philosophy 0.99 0 0 6 10 1.666666667 6 N N
NOCE 17.99 0.1 1 3 4 1.333333333 35 N N
Qu-tros 2.99 0.4 0 3 7 2.333333333 4 N N
Mosaics Galore. Challenging Journey 4.99 0.2 1 1 8 8 14 N N
Zquirrels Jump 2.99 0.4 0 1 4 4 9 N N
Dark Siders III 59.99 0 2400 1721 2708 1.573503777 85498 N N 0.67
R-Type Dimensions Ex 14.99 0.2 10 48 64 1.333333333 278 N N 0.92
Artifact 19.99 0 7000 9700 16584 1.709690722 140000 N N 0.53
Crimson Keep 14.99 0.15 20 5 6 1.2 367 N N
Rival Megagun 14.99 0 35 26 31 1.192307692 818 N N
Santa's Workshop 1.99 0.1 3 1 1 1 8 N N
Hentai Shadow 1.99 0.3 2 12 6 14 N N
Ricky Runner 12.99 0.3 3 6 13 2.166666667 66 Y N 0.87
Pro Fishing Simulator 39.99 0.15 24 20 19 0.95 609 N N 0.22
Broken Reality 14.99 0.1 60 58 138 2.379310345 1313 N Y 0.98
Rapture Rejects 19.99 0 200 82 151 1.841463415 9250 Y N 0.64
Lost Cave 19.99 0 3 8 11 1.375 43 Y N
Epic Battle Fantasy 5 14.99 0 300 395 896 2.26835443 4236 N N 0.97
Ride 3 49.99 0 75 161 371 2.304347826 1951 N N 0.74
Escape Doodland 9.99 0.2 25 16 19 1.1875 1542 N N
Hillbilly Apocalypse 5.99 0.1 0 1 2 2 8 N N
X4 49.99 0 1500 2638 4303 1.63115997 38152 N N 0.7
Splotches 9.99 0.15 0 2 1 0.5 10 N N
Above the Fold 13.99 0.15 5 2 6 3 65 Y N
The Seven Chambers 12.99 0.3 3 0 0 #DIV/0! 55 N N
Terminal Conflict 29.99 0 5 4 11 2.75 125 Y N
Just Cause 4 59.99 0 2400 2083 3500 1.680268843 50000 N N 0.34
Grapple Force Rena 14.99 0 11 12 29 2.416666667 321 N Y
Beholder 2 14.99 0.1 479 950 1.983298539 16000 N N 0.84
Blueprint Word 1.99 0 12 15 1.25 244 N Y
Aeon of Sands 19.99 0.1 20 12 25 2.083333333 320 N N
Oakwood 4.99 0.1 32 68 2.125 70 N N 0.82
Endhall 4.99 0 4 22 42 1.909090909 79 N N 0.84
Dr. Cares - Family Practice 12.99 0.25 6 3 8 2.666666667 39 N N
Treasure Hunter 16.99 0.15 200 196 252 1.285714286 4835 N N 0.6
Forex Trading 1.99 0.4 7 10 14 1.4 209 N N
Ancient Frontier 14.99 0 24 5 16 3.2 389 N N
Fear the Night 14.99 0.25 25 201 440 2.189054726 835 Y N 0.65
Subterraneus 12.99 0.1 4 0 3 #DIV/0! 82 N N
Starcom: Nexus 14.99 0.15 53 119 2.245283019 1140 Y N 0.93
Subject 264 14.99 0.2 25 2 3 1.5 800 N N
Gris 16.9 0 100 1484 4650 3.133423181 5779 N N 0.96
Exiled to the Void 7.99 0.3 9 4 11 2.75 84 Y N
Column Explanations
For the columns that are not self-explanatory:

Question 1: Does Quality Predict Success?

There was a recent blog post stating that the #1 metric for indie games' success is how good it is.
Quality is obviously a subjective metric. The most obvious objective measure of quality for Steam games is their % Favorable Review score. This is the percentage of reviews by purchasers of the game that gave the game a positive rating. I excluded any game that did not have at least 20 user reviews in the first month, which limited the sample size to 56.
The (Pearson) correlation of a game's review score to its number of reviews three months after its release was -0.2. But 0.2 (plus or minus) isn't a very strong correlation at all. More importantly, Pearson correlation can be swayed if the data contains some big outliers. Looking at the actual games, we can see that the difference is an artifact of an outlier. Literally. Valve's Artifact by far had the most reviews after three months and had one of the lowest review scores (53% at the time). Removing this game from the data changed the correlation to essentially zero.
Spearman's Rho, an alternative correlation model that correlates rank position and minimizes the effect of huge outliers produced a similar result.
Conclusion: If there is correlation between a game's quality (as measured by Steam review score) and first quarter sales (as measured by total review count), it is too subtle to be detected in this data.

Question 2: Do Demos, Early Access or Launch Discounts Affect Success/Failure?

Unfortunately, there were so few games that had demos prior to release (10) that only a very strong correlation would really tell us anything. As it happens, there was no meaningful correlation one way or another.
There were more Early Access titles (28), but again the correlation was too small to be meaningful.
More than half the titles had a launch week discount and there was actually a moderate negative correlation of -0.3 between having a launch discount and first week review count. However it appears that this is primarily the result of the tendency of AAA titles (which sell the most copies) to not do launch discounts. Removing the titles that likely grossed over a $1 million in the first week reduced the correlation to basically zero.
Conclusion: Insufficient data. No clear correlation between demos, Early Access or launch discount and review counts: if they help or hurt the effect is not consistent enough to be seen here.

Question 3: Does pre-launch awareness (i.e., Steam followers) predict success?

You can see the number of "followers" for any game on Steam by searching for its automatically-created Community Group. Prior to launch, this is a good rough indicator of market awareness.
The correlation between group followers shortly before launch and review count at 3 months was 0.89. That's a very strong positive correlation. The rank correlation was also high (0.85) suggesting that this wasn't the result of a few highly anticipated games.
Save for a single outlier (discussed later), the ratio of 3 month review counts to pre-launch followers ranged from 0 (for the handful of games that never received any reviews) to 1.8, with a median value of 0.1. If you have 1000 followers just prior to launch, then at the end of the first quarter you should expect "about" 100 reviews.
One thing I noticed was that there were a few games that had follower counts that seemed too high compared to secondary indicators of market awareness, such as discussion forum threads and Twitter engagement. After some investigation I came to the conclusion that pre-launch key activations are treated as followers by Steam. If a game gave away a lot of Steam keys before launch (say as Kickstarter rewards or part of beta testing) this would cause the game to appear to have more followers than it had gained "organically."
Conclusion: Organic followers prior to launch are a strong predictor of a game's eventual success.

Question 4: What about price?

The correlation between price and review count at 3 month is 0.36, which is moderate correlation. I'm not sure how useful that data point is: it is somewhat obvious that higher budget games have larger marketing budgets.
There is a correlation between price and review score of -0.41. It seems likely that players do factor price into their reviews and a game priced at $60 has a higher bar to clear to earn a thumbs up review than a game priced at $10.

Question 5: Do first week sales predict first quarter results?

The correlation between number of reviews after 1 week and number of reviews after 3 months was 0.99. The Spearman correlation was 0.97. This is the highest correlation I found in the data.
Excluding games that sold very few copies (fewer than 5 reviews after the first week), most games had around twice as many reviews after 3 months as they did after 1 week. This suggests that games sell about as many copies in their first week as they do in the next 12 weeks combined. The vast majority of games had a tail ratio (ratio of reviews at 3 months to 1 week) of between 1.3 to 3.2.
I have seen a number of questions from developers whose game had a poor launch on Steam and wanted to know what they can do to improve sales. While I'm certain post-launch marketing can have an effect on continuing sales, your first week does seem to set hard bounds on your results.
Conclusion: ALL SIGNS POINT TO YES

Question 6: Does Quality Help with a Game's "Tail"?

As discussed in the last question while first week sales are very strongly correlated with first quarter, there's still quite a wide range of ratios. Defining a game's Tail Ratio as the ratio of reviews after 3 months to after 1 week, the lowest value was 0.95 for "Pro Fishing Simulator" which actually managed to lose 1 review. The highest ratio was 6.9, an extreme outlier that I'll talk about later. It is perhaps not a coincidence that the worst tail had a Steam score of 22% and the best tail had a Steam score of 96%.
The overall correlation between the Tail Ratio and Steam score was 0.42.
Conclusion: Even though there is no clear correlation between quality and overall review count/sales, there is a moderate correlation between a game's review score and its tail. This suggests that "good games" do better in the long run than "bad games," but the effect is small compared to the more important factor of pre-launch awareness.

Question 7: Is it possible to predict a game's success before launch without knowing its wishlists?

While I was compiling the data for each game, sometime prior to its scheduled launch date, I would make a prediction of how many reviews I thought it would receive in its first week and add that prediction to the spreadsheet.
The #1 factor I used in making my prediction was group follower count. In some cases I would adjust my prediction if I thought that value was off, using secondary sources such as Steam forum activity and Twitter engagement.
The correlation between my guess and the actual value was 0.96, which is a very strong correlation. As you can see in the data, the predictions are, for the most part, in the right ballpack with a few cases where I was way off.
Based on my experience, multiplying the group follower count by 0.1 will, in most cases, give you a ballpark sense of the first week quarter review count. If a game doesn't have at least one question in the discussion forum for every 100 followers, that may indicate that there are large number of "inorganic" followers and you may need to adjust your estimate.
Conclusion: Yes, with a few exceptions, using follower data and other indicators you can predict first week results approximately. Given the strong correlation between first week and quarter sales, it should also be possible to have a ballpark idea of first quarter results before launch.

Final Question: What about the outliers you mentioned?

There were a few games in the data that stood out significantly in one way or another.
Outlier #1: Overdungeon. This game had 77 group followers shortly before launch, a fairly small number and based solely on that number I would have expected fewer than a dozen reviews in the first week. It ended up with 86. Not only that, it had a strong tail and finished its first quarter with 572 reviews. This was by a wide margin the highest review count to follower ratio in the sample.
Based on the reviews, it appears to basically be Slay the Spire, but huge in Asia. 90% of the reviews seem to be in Japanese or Chinese. If anyone has some insight to this game's unusual apparent success, I'm very curious.
This seems to be the only clear example in the data of a game with minimal following prior to launch going on to having a solid first quarter.
Outlier #2: 11-11 Memories Retold. This game had 767 group followers shortly before launch, ten times as many as Overdungeon. That's still not a large number for even a small indie title. It had a fair amount going for it, though: it was directed by Yoan Fanise, who co-directed the critally acclaimed Valiant Hearts, a game with a similar theme. It was animated by Aardman Studios of "Wallace and Gromit" fame. Its publisher was Bandai Namco Europe, a not inexperienced publisher. The voice acting was by Sebastian Koch and Elijah Wood. It has dozens of good reviews in both gaming and traditional press. It currently has a 95% positive review rating on Steam.
Despite all that, nobody bought it. 24 hours after it came out it had literally zero reviews on Steam. One week after it came out it had just 10. Three months later it had demonstrated the largest tail in the data, but even then it had only climbed to 69 reviews. Now it's at about 100, an incredible tail ratio, but almost certainly a commercial failure.
This is a solid example that good game + good production values does necessarily equal good sales.

Final notes:
The big take-aways from this analysis are:
Thanks for reading!
submitted by justkevin to gamedev [link] [comments]

After 9 months of obsession, here is my open source Node.js framework for backtesting forex trading strategies

TL;DR There's lots more to the story. But the code is all open source now. Have at it. I'm too exhausted to continue with this. If you'd like more details, feel free to message me. If you happen to carry on with this project or use any ideas from it, I would greatly appreciate it if you could keep in touch on your findings. If anyone has any insights, please feel free to comment or message me.
I've spent the last nine months working furiously on this. I started a project for backtesting strategies against data I exported from MetaTrader. I had a very powerful computer crunching numbers constantly, trying to find the most optimal configuration of strategy indicator inputs that would results in the highest win rate and profit possible.
Eventually, after talking with a data scientist, I realized my backtesting optimizer was suffering from something called overfitting. He then recommend using the k-fold cross-validation technique. So, I modified things (in the "k-fold" forex-backtesting branch), and in fact it provided very optimistic results when backtested against MetaTrader data (60 - 70% win rate for 3 years). However, I had collected 3 months of data from a trading site (by intercepting their Web Socket data), and when I performed validation tests against that data using the k-fold results created from the MetaTrader data, I only got a ~57% win rate or so. In order to break even with Binary Options trading, you need at least a 58% win rate. So in short, the k-fold optimization results produce a good result when validation tested against data exported from MetaTrader, but they do not produce a good result when validation tested against the trading site's data.
I have two theories on why this ended up not working with the trading site's data:
For the strategy I use the following indicators: SMA (Simple Moving Average), EMA (Exponential Moving Average), RSI (Relative Strength Index), Stochastic Oscillator, and Polynomial Regression Channel. forex-backtesting has an optimizer which tries hundreds of thousands of combinations of values for each of these indicators, combined, and saves the results to a MongoDB database. It can take days to run depending on how many configurations there are.
Basically the strategy tries to detect price reversals and trade with those. So if it "thinks" the price is going to go down within the next five minutes, it places a 5 minutes PUT trade. The Polynomial Regression Channel indicator is the most important indicator; if the price deviates outside the upper or lower value for this indicator (and other indicators meet their criteria for the strategy), then a trade is initiated. The optimizer tries to find the best values for the upper and lower values (standard deviations from the middle regression line).
Additionally, I think it might be best to enter trades at the 59th or 00th second of each minute. So I have used minute tick data for backtesting.
Also, I apologize that some of the code is messy. I tried to keep it clean but ended up hacking some of it in desperation toward the end :)
gulpfile.js is a good place to start as far as figuring out how to use the tools available. Look through the available tasks, and see how various "classes" are used ("classes" in quotes because ES5 doesn't have real class support).
The best branches to look at are "k-fold" and "master", and "validation".
One word of advice: never, ever create an account with Tradorax. They will call you every other day, provide very bad customer support, hang up the phone on you, and they will make it almost impossible to withdraw your money.
submitted by chaddjohnson to algotrading [link] [comments]

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