Bitcoin Historical Data Kaggle

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]

Annual Exchange Rate for 20 Currencies (2000 - 2020)

I have 20 years of funding data in the period 2000 - 2020. Every year there are at most 20 currencies that I would need to convert to USD. Is there a dataset that would allow me to do this easily? I guess this page has all I need: https://www.ofx.com/en-au/forex-news/historical-exchange-rates/monthly-average-rates/ but there's no export option.
submitted by vladproex to datasets [link] [comments]

How to use a Forex Robot

How to use a Forex Robot
https://preview.redd.it/p9ga08w641121.jpg?width=600&format=pjpg&auto=webp&s=2e6efec7a84f437fab19c8d2e65a737bfbc3d38f
What Is an Algorithm or Forex Robot?
In its simplest form, an algorithm is a list of steps needed to solve a problem. When referring to algorithmic trading, we refer to steps written in machine language so that a computer can understand what you want and execute trades on behalf of you and your goals. An algorithm spans multiple functions outside of trading but either way the algorithmis used; it has a clear purpose to help compute large datasets in an efficient manner while abiding by key rules to help ensure the desired outcome. Algorithms accomplish this feat without having to worry about human biases or mental fatigue and high-level and high-frequency decision-making.
-Algorithm Trading Styles
The following list is not inclusive but does cover many commonly used strategies and styles in algorithmic trading:
Mean Reversion: Reverting to the mean takes the idea that an extended move away from a long-term average is likely short-term and due for a reversion or retracement. Algorithms that quantify extended moves based on an oscillator will utilize the average price over a set time and use that level as a target. There many popular tools and calculations for quantifying an extension that is due to revert but risk management must also be included in the algorithm encasing new trend is developing.
Trend following: Trend following is the first, and still very popular technique of algorithmic-based momentum investing. Trends are easy to see, but can be hard to trade without the help of an algorithm. Because algorithms take over for the mind and the minds inherent biases, many of the fears that plague discretionary trend followers do not effect algorithms. A common fear when riding a strong trend is that it is about to turn or end, but that fear is often unfounded. One of the first widely followed trend following algorithms looked to buy a 20-day price breakout and hold that trade until a 20-day price low took them out of the trade. The traders who have and still do employ this algorithmic approach and other similar approaches are often amazed at how long the strongest trends extend that they would have likely exited had their algorithms not managed the trade and exit on their behalf.
News Trading: Another popular style of trading in the archaic world of discretionary trading that now belongs to the Quants is news trading. These strategies scan high important news events and calculate what type of print relative to prior news events and expectations would be needed to place a trade. As you can imagine, the efficiency of receiving the data and calculating whether a trade should be placed in entering that trade is of key focus. This form of algorithmic trading often gets the lion share of media’s attention.
Arbitrage:Arbitrage is a word that has multiple meetings and strategies built around the concept. Historically, you could have euros trading in London at a different price than in New York so that a trader could buy the lower and sell the higher until equilibrium had been established. Nowadays, arbitrage algorithm strategies are more geared to highly correlated assets whose underlying fundamental effects are very similar. When a wide spread in value between the highly correlated assets are recognized, the algorithm will either by the lower and or sell the higher until an equilibrium is met similar to the mean reversion strategy.
High-Frequency Trading and Scalping: For our purposes, will look at these as synonymous even though trading desks and hedge funds view them separately. True high-frequency trading attempts to beat out other traders to the thousand of a second and to do so some firms position their computers next door to an exchange to see in one millisecond faster than a competitor if something is rising by a penny.
Unless you’re looking to buy a house next to the New York Stock Exchange to compete with billion-dollar hedge funds, short-term trading or scalping is likely more up your alley. Even this term has evolved over time whereas traders use to look to make profits on the difference in the bid-ask spread but now has taken a wider meeting for very short-term traits.
For more information about algorithmic trading, click here
submitted by iforexrobot to u/iforexrobot [link] [comments]

Little Known AI Company Makes World Debut In Front Of United Nations

Founded by William J D West around 5 years ago and developed in stealth mode since then, Invacio is a tech company unlike any other in the world and because of this, it is attracting attention from some fairly high up places. Governmental discussions are ongoing around the world to implement their technology in various ways.
But what is it exactly that invacio do that instigated the invitation to make their worldwide debut presentation (coming out if you will) in front of world leaders at the UNESCAP FDI meeting in Thailand on November the 3rd last year?
The answer to that is quite hard to formulate without going off on multiple tangents all at once, so, I will give a brief outline and then try to cover some more specifics in order that you can try to paint the picture for yourself more clearly…All I will say before launching into this is open your mind and try to see all the potential uses of this technology.
Invacio is primarily an applied artificial intelligence company that has created a multi-agent neural network that analyzes the world’s informational output, digests it, correlates the various disparate facts and ultimately delivers actionable intelligence across any sector of commerce, industry, society or government possible.
Regardless of the source of this data be it: NASAs satellite feeds, live news, digital radio, video broadcasts, social media twitterings etc or live market data from world exchanges or even historical digitized data banks like the National Library of Congress and thousands of others, they are all relentlessly probed and itemized by a system which is self learning and always hungry for more. Since the system was switched on, an ever growing number of proprietary datasets have been created well over 2 billion as this story is being written. Ok, so Invacio has a lot of information from all over the world: but what exactly is its use? And, what can Invacio do that any other information rich organisation with a decent search engine can’t?
Well, this is where it truly comes into its own you see, it doesn’t pull the information together, stick a label on it and forget about it until someone asks for it. No, Invacio actively seeks to place all the information it holds into context with all the rest so that comparisons, parallels and connections can be created. Why? Because their system is designed to understand the world and guide us where it can. An example of this which was given during the presentation to the United Nations was that of crisis management during the Californian wildfires last year – Within a second of the first tweet relating to the fires starting being sent, Jean (that is what they call the full operational system) was aware and rapidly pulling together every type of relevant information available. Everything from geographical data, historically relevant time based records of vineyards (soil composition, state of vines or ripeness of fruits), meteorological details (live conditions and historical in relation to similar events globally)…… you get the picture.
In the second second Jean would have been able to direct the emergency response and do as much as possible to limit the ongoing damage being caused by the fire by predicting the way the emergency would develop having analysed all available information and extrapolating.
As a bonus, in the 3rd second Jean could have told you whether it was worth buying wine futures or no.
That is one example,imagine the benefits of having that kind of analysing power in your sector:
Hedge funds… highly accurate predictions on shares, indices, commodities and forex (Agnes, Archimedes, Aquila and Tomahawk), Market intelligence… knowing what the world is thinking about your brand, competitor, client or product in real time (Alise), Security… maintaining secure facilities through the application of advanced facial recognition through security cameras(Sauron) or alternatively simply plug your organisation straight into Invacio API and pull out the information you need to make your world a better place.
Invacio ICO Invacio are currently undergoing an ICO (initial coin offering) in order to fund the roll out of various divisions that are underpinned by Jean. The coins sold during the sale will be directly connected to the use of Invacios raft of products. Whether they are used to pay whole subscription fees for AI fintech predictive services or to attract a discount against a corporate market intelligence monitoring account through the Alise division these coins will provide a great amount of utility within the Invacio group.
submitted by InvacioOfficial to u/InvacioOfficial [link] [comments]

Invacio versus the world

British born entrepreneur William West is set to go “toe to toe” with major institutions around the world with his, one of a kind, applied artificial intelligence organisation Invacio. Created over the last 5 years William’s brainchild is far more than your average chatbot or sentiment scraper creating tech company. In point of fact it is such a powerful system that Invacio were invited to make their inaugural presentation in front of the United Nations during a UNESCAP FDI meeting in Thailand last year.
The main elements that have made the elite sit up and take notice are Invacio’s flexibility and shere data processing capabilities. When you have a system that is plugged into thousands upon thousands of data sources with the capacity to analyse and correlate everything from historical market exchange data, and live news feeds with satellite data, and social media interactions, to formulate comprehensive reports and predictions for virtually any industry on earth, it tends to make an impact when people become aware of it.
Data crunching leviathans are ten a penny, in this day and age, so what is so unique about invacio that world leaders invite them to elaborate the details in front of them? That is the secret sauce: a multi agent deep neural network that constantly learns from the data coming in and its own self created distinct datasets. A system that is aware enough of its own data requirements that it literally sourced its own hacking software to gain access to some data it really wanted to see (that got shut down immediately and new rules were implemented “no entry means no entry”).
Wealth generation and crisis management were two of the areas explored during the initial UN presentation and since then further, more detailed, discussions have continued behind closed doors.
First off the bat the sector which is going to feel the full force of Invacio, muscling its way in, is the finance sector, initially they will be putting “Agnes” into the ring. A subscription based service which monitors 2995 stocks/shares and the main forex pairs, Agnes will provide highly accurate short term price predictions to whomever pays the fees be that professionals looking to get ahead of the game or hobbyist day traders looking to put a lump sum away for their future. With accuracy levels regularly running between 92 & 98% on any given trade, with the correct type of equity management trading might just become fun again, even during downturns.
Next up will be an onslaught to capture institutional money through the application of AI directly into the hedge fund market, Aquila, Archimedes and Tomahawk are the names given to these funds. Archimedes will be a human/AI hybrid fund that applies predictions made by Agnes and actioned by a human fund manager. In a 16 week experiment, with real money, Archimedes showed growth of 79%. Tomahawk is a long term forecasting system which looks anywhere from 6 months to 2 years into the future. Aquila will be a combination of all of these with the addition of invacio’s full market oversight (all commodities, shares, indices and forex pairs)
Other markets that will feel the wrath of Invacio are, Market intelligence, communications, social networking, data provision and Global security but they are a different story altogether.
Invacio are currently undergoing an ICO (initial coin offering) in order to fund the roll out of Their various divisions. The coins sold during the sale will be directly connected to the use of Invacios products find out more here www.invest.invacio.com
submitted by InvacioOfficial to u/InvacioOfficial [link] [comments]

INVACIO AI Company Makes World Debut In Front Of United Nations

Little Known AI Company Makes World Debut In Front Of United Nations
Founded by William J D West around 5 years ago and developed in stealth mode since then, Invacio is a tech company unlike any other in the world and because of this, it is attracting attention from some fairly high up places. Governmental discussions are ongoing around the world to implement their technology in various ways.
But what is it exactly that invacio do that instigated the invitation to make their worldwide debut presentation (coming out if you will) in front of world leaders at the UNESCAP FDI meeting in Thailand on November the 3rd last year?
The answer to that is quite hard to formulate without going off on multiple tangents all at once, so, I will give a brief outline and then try to cover some more specifics in order that you can try to paint the picture for yourself more clearly…All I will say before launching into this is open your mind and try to see all the potential uses of this technology.
Invacio is primarily an applied artificial intelligence company that has created a multi-agent neural network that analyzes the world’s informational output, digests it, correlates the various disparate facts and ultimately delivers actionable intelligence across any sector of commerce, industry, society or government possible.
Regardless of the source of this data be it: NASAs satellite feeds, live news, digital radio, video broadcasts, social media twitterings etc or live market data from world exchanges or even historical digitized data banks like the National Library of Congress and thousands of others, they are all relentlessly probed and itemized by a system which is self learning and always hungry for more. Since the system was switched on, an ever growing number of proprietary datasets have been created well over 2 billion as this story is being written. Ok, so Invacio has a lot of information from all over the world: but what exactly is its use? And, what can Invacio do that any other information rich organisation with a decent search engine can’t?
Well, this is where it truly comes into its own you see, it doesn’t pull the information together, stick a label on it and forget about it until someone asks for it. No, Invacio actively seeks to place all the information it holds into context with all the rest so that comparisons, parallels and connections can be created. Why? Because their system is designed to understand the world and guide us where it can. An example of this which was given during the presentation to the United Nations was that of crisis management during the Californian wildfires last year – Within a second of the first tweet relating to the fires starting being sent, Jean (that is what they call the full operational system) was aware and rapidly pulling together every type of relevant information available. Everything from geographical data, historically relevant time based records of vineyards (soil composition, state of vines or ripeness of fruits), meteorological details (live conditions and historical in relation to similar events globally)…… you get the picture.
In the second second Jean would have been able to direct the emergency response and do as much as possible to limit the ongoing damage being caused by the fire by predicting the way the emergency would develop having analysed all available information and extrapolating.
As a bonus, in the 3rd second Jean could have told you whether it was worth buying wine futures or no.
That is one example,imagine the benefits of having that kind of analysing power in your sector:
Hedge funds… highly accurate predictions on shares, indices, commodities and forex (Agnes, Archimedes, Aquila and Tomahawk), Market intelligence… knowing what the world is thinking about your brand, competitor, client or product in real time (Alise), Security… maintaining secure facilities through the application of advanced facial recognition through security cameras(Sauron) or alternatively simply plug your organisation straight into Invacio API and pull out the information you need to make your world a better place.
submitted by InvacioOfficial to u/InvacioOfficial [link] [comments]

How can I get this code to work, I want to have a closure function return an object to access private functions?

Ok so I am pretty new to Go since I have been learning for about 2 days, so I apologize for the super basic question.
So anyways right now I am trying to build a micro service that streams fake simulated stock data via web sockets to a client. The data used is essentially a months worth of historical m1(minute bar data) i got here. The data is stored in redis as a JSON object that is an Array of floats [1081.8, 1101.2, 1060.1, 1090.2] which is the exchange rate of EUUSD (multiplied by 1000) of that current minutes Open value, Highest value, Lowest value, Closing Value, but I stream these points 10 times a second from a Node.js service I spun up, the timestamps are created on the client side since it's a simulation.
Here Is what I do with the data Fake early build of sample trading platform
For the Go Service here is what I have:
This is the Main package on github
This is the closure object thing I am trying to make SymbolSock I uploaded it in github so that comments are easier to read, since it explains what each function/struct does.
symbolHandler explained:
When a client sends in a request to subscribe to a Feed such as GBP/JPY a new struct instance is created called symbolPush
type symbolPush struct{ symbol string clients int feed string lastV float32 atIndex int } 
a SymbolPush instance stores an index atIndex which is the index of the shared dataset, it's symbol (which is like it's namespace/websocket channel) and the last closing price.
Also I have a scale-like case class: Which is used by a function I append to the symbolPush struct to send out an instance to be eventually stringified like this conn.WriteJSON(the return value)
 type seriesPoint struct { Symbol string `json:"symb"` LastValue float32 `json:"lastVal"` MinValue float32 `json:"min"` PointData OHLC `json:"data"` } func (s *symbolPush) GetPoint() seriesPoint { s.atIndex++ lastVV := s.lastV if(s.atIndex == (len(dataPoints) -1)){ s.atIndex = 0 } //index 3 is the close value s.lastV = dataPoints[s.atIndex][3] minVal := dataPoints[s.atIndex][2] return seriesPoint{ Symbol: s.symbol, LastValue: lastVV, MinValue: minVal, PointData: dataPoints[s.atIndex], } } 
Now right now it's obvious I am not returning anything that can be used from symbolsock since last night I deleted my old code since it wasn't working and just left it incomplete since I feel I would like to hear some input before I do a sloppy solution.
What I want to have returned from symbolsock is essentially an object that can do this:
Step1:
call the closure function with the JSONblob and return an object like seriesSockets := symbolsock.SymbolStream(theData)
Step2:
Use this object to seriesSockets.joinStream("APPL") which will call newStream function in symbol sockets, either creating a new struct or incrementing the clients field.
Also:
signal when a user has unsubscribed to a feed, which will delete the feed if clients == 0
Also:
get a point from these structs to be broadcasted to subscribed clients like seriesSockets.getSeriesPoint("APPL")
So my main question is how can I return an object like seriesSockets that accomplishes these things?
Oh here are the Go Playgrounds: main symbolSock
Also for readability here are is the github repo
Thanks!
Also I should mention I asked a question earlier which I will get back to once I have more information, but they're related.
submitted by TheBeardofGilgamesh to golang [link] [comments]

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