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TOP ASSETS of the AI NARRATIVE | PART 2In the comments of “Top AI assets part 1” you mentioned some more promising projects, the main product of which is AI. We decided to tell you more about them and check their metrics iExec RLC iExec is considered as a project with the AI narrative, but it is partly wrong. The main specialization of iExec is providing computing power and organizing the market around this sector. iExec forms large volumes of data and if we check their products, we will see that these volumes of data are being used actively but we have to understand that this is a side line of their business. In general, iExec as a project is more like Flux than any project in the AI narrative. Metrics of the $RLC token: Price: $1.75 ATH price: $11.6 Market.cap: $141m ATH market.cap: $800m FDMC: $152m Over the past 2 months, the $RLC token has grown more than 2 times. We do like iExec as a project with its own goals and values and that’s why we listed it on our platform for trading Vectorspace AI The team focuses on creating AI and ML solutions in space biosciences, general life science and capital markets. So far the team has launched two products: A financial product for protecting investment portfolios and finding stock and cryptocurrency market correlations for long or short trades. A product for biosciences in a Protein Relationship Networks area. Metrics of $VXV token: Price: $0.57 ATH price: $18.1 Market.cap: $27m ATH market.cap: $347m FDMC: $28m Over the past 2 months, the $VXV token has grown more than 2 times. Matrix AI Network Project that focuses on an AI integration directly into the crypto. Matrix has 4 main products: Mania - a platform for trading AI algorithms in an NFT type Airtist - a generative art creation platform for NFT Manta - an automatic machine learning platform Matrix - an AI service platform Metrics of $MAN token: Price: $0.02254 ATH price: $1.7 Market.cap: $4.8m ATH market.cap: $6m FDMC: $22.5m Over the past 2 months, the $MAN token has grown more than 4 times. Numeraire Platform for Data Science and Machine Learning specialists. Project supports DS and ML specialists, conducts predictive ML contests and builds its own progressive community. Metrics of $NMR token: Price: $16.7 ATH price: $84 Market.cap: $98m ATH market.cap: $487m FDMC: $183m Over the past 2 months $NMR has grown by 64% Streamr A project for data transferring within web3. Streamr is primarily an infrastructure project, preparing the basis for the data economy. Metrics of $DATA token: Price: $0.03308 ATH price: $0.3102 Market.cap: $25m ATH market.cap: $223m FDMC: $28m Over the past 2 months $DATA has grown by 50% Conclusion As we’ve told you earlier, the benefits that AI offers, along with its increasing adoption and application, guarantee the expansion of AI projects and a profitable market. Let us know in the comments about more AI projects we should look at. Share your investing or trading experience with such projects.Thanks for reading!
Education
by Samura0x
6
Neo Wave Learner doubtWhat is the difference between projection overlapping and "obviously different in price and/or time" in NEO Wave? In Pre-Constructive Rules of Logic, Rule#1, Condition_b, Paraghraph_4, it states "If part of m2's price range is shared by m0 and m3 is longer and more vertical than ml during a time span equal to (or less than) ml and m(-1) is shorter than ml and m0 and m2 are obviously different in price or time or both and m4 (or m4 through m6) returns to the beginning of ml in a time period 50% of that consumed by ml through m3, a 5th Extension Terminal pattern may have completed with m3; add ":c3" to ml's Structure list." Here, m0 and m2 should share price range, which means projection overlapping, at the same time, it is mentioned, m0 and m2 should be obviously different in price&/time. Experts please help.. Also, Mr. Neely mentioned, "m1 is longer than m3" in few other places, does the length means, by means of distance between 2 price points or by means of time distance or should i consider a multiple..
TVC:DJIEducation
by SmArT_NEO_WAVE
Q&As: non-market dataThere's some curious personalities that trade (at least claim to trade) based on news, fundamental metrics, alt data n stuff. I don't mean invest, I mean trade. Well that looks like a skill to be proud off, superstimuli always feels cool aye? Good thing tho there no real reason in doing it all. The most precise term to explain non-market data is, well, everything that ain't have a direct involvement with what happens inside the order matching servers of a given exchange. So open interest is in fact a great example of non-market data. The one & only real purpose for using all this data is to know (not to guess/predict/forecast, not to even anticipate), but to understand when the ACTION is going to happen. If you think deeper, ultimately it's all about asset selection to satisfy whatever purpose you got. if you ever got caught yourself feeling fooled when media release a bad info but prices go up, or media release a good info but prices go down, it's ok. It doesn't work that way, direction of prices can't be affected this way. Direction of prices is the result of how buyers meet sellers which is based on +inf number of factors, where a non-market data is simply just one of these +inf factors. It exclusively provokes action, meat, hype, momentum, volatility, whatever you call it. What's happening is that things start to happen very fast. Without a trigger event, the trading activity would've been the same, it just would've take longer to unwind. News don't change the structure, they make it all happen faster, that's it. Examples of non-market data that can be used to expect action: 1) Trading schedule, eg the US, EU opening times; 2) Economic releases; 3) Commitment of traders reports; 4) Significant news; 5) Changes in yield curves; 6) "Fundamental" stock data; 7) Open interest; 8) etc etc etc One really important thing to add is that, just like trading activity is understood in context (other resolutions), sizing also includes context (equity control, market impact), the same way every non-market data event lives in the context (previous releases, other releases, overall economy). You're interesting not in a new per se, but rather in what does it mean in the world. For example, inflation reports don't mean much when the rates are low, but when the rates are high, they trigger significant activity. That's the area where statistical learning, automated learning, "machine" learning, 'Really' starts to make sense business-wise. The ultimate goal is to create a system that will process every kind of data you have (NLP and TDA should help) and output the tickers with raising/already risen levels of interest.
CBOT:ZN1!Education
by gorx1
Risk:Reward Ratio. What is it?Risk to reward ratio. What is it? What does it mean and how do we use it? Now, if you made it to the point where you're here on TradingView, there's a good chance that you have heard about Risk to Reward ratio. Today, I want to dive into what it really means and how to actually utilize it. I see so many beginners missing out on huge profits and opportunities because of their risk reward ratio and I want to share my knowledge of this tool and how to actually use it in the future. Firstly, let's dive into what is the risk/reward ratio? The RR ratio is a tool that can accurately predict by expected returns based off of previous results. This tool measures how much reward you are estimated to gain based off of the dollar amount you risk. For example, if you have a risk to reward ratio of 1:3, it means for every $1 you risk, you will gain a return of $3 in the event of a positive trade. Using the same example in the FX market, let's say you're risking 10 pips on EURUSD, your take profit is at 30 pips. This means you gain 30 pips in the event of a win, lose 10 pips in the event of a loss, giving you a 1:3 risk/reward ratio. This is a very powerful tool because compared with the win rate and in correlation, you can actually predict based off of your previous results, you're expected returns on investment. Being able to predict what you're expected returns are are great way of giving you milestone targets, but also when you're looking at getting funded with prop firms, you also know what you are actually able to achieve in what time frame. Now, it goes without saying, the higher your risk to reward ratio, the less you need to win in order to maintain profitability. The opposite, the lower your risk reward ratio, the higher win rate is required to maintain profitability. But this is where we get into where I find beginners struggle. A lot of people will base their strategies on their risk/reward ratios, which is understandable if you're building the strategy from scratch. If you're using a prebuilt strategy or something that doesn't really correlate with risk/reward ratio. Then it makes it obsolete and just confusing. Going back to my first point, risk to reward ratio is a tool that you can use to estimate future potential returns based off of previous results. Let's say you have 100 trades worth of data. You can accurately have a look at what is your risk to reward ratio is and compare that with your win rate. From there you can make a decision whether or not that is a profitable strategy. On top of that, you can then start to look to improve either your win rate and risk to reward ratio, knowing that that is an area that needs improvement. When it comes to improving your risk to reward ratio, one thing that always grinds my gears with traders, is when they enter a trade, they'll set their stop loss and take profits based on their risk to reward ratio not based on the actual analytics of the trade. While I understand this and with some strategies, this can work. For most, they end up setting those take profits in areas that is just realistically is going to be really hard for the price to get to. What professionals do when trying to improve the risks of reward ratio is only take those setups where a good take profit is viable around that level of risk to reward. For example, in this chart, we are looking at buying the USDCAD over the next couple of weeks. We like this setup. We've had our entry signal and we're going to place a stop loss below that recent low, which was created early last week. We are not happy with our risk to reward ratio. We think we're leaving too much profit on the table and want to increase our overall results. So I'm only taking trades that have close to a three to one risk to reward ratio. But as you can see by this chart that dotted lines are areas of resistance which we are going to have to break in order to achieve that level of profitability. There are 5 different zones we are going to have to get through in order for my take profit to be hit, it is fair to say the odds are not in my favor. Now a beginner Trader will still enter this trade with the same take profit and the same stop loss and just hold on. The reason they'll do that is because they want the 1:3 risk reward ratio. They don't care where the profit target is. What matters is it is 3 times worth what they're risking. On the other hand, A professional trader will actually either let this trade go and not enter it, or look for another entry point later on on smaller timeframes to where you can fit that risk to reward ratio and you're not going to hit the high levels of resistance. To sum up what my point is, risk to reward ratio is a very powerful tool to understand what you are capable of the trader and also where you can improve. It is not a valid take profit selection strategy. Yes, it can definitely help with guidelines on where to set your take profit, but it should not be the sole reason your take profit is set at a certain price just because it is X amount whatever you are risking. Have a look at what the chart is telling you and what your analysis is telling you. Then, only take the trades which coincide with the risk to reward ratio. You want to achieve. I hope you enjoyed this insight and I hope it was beneficial to you. I recommend highly diving into your previous trading data. Have a look at your win rate. Have a look at your risk reward ratio and understand what your profitability expectation really is and base your future decisions off of that data. Have a fantastic trading we can I look forward to seeing your comments. - Jordon
FOREXCOM:XAUUSDEducation
by the5erstrading
5
How to crawl history price or options from TD Ameritrade API?I crawl options data from Yahoo Finance but actually, Yahoo returned fake data if I send a thousand requests. I research and use TD Ameritrade API to get options, both github.com and github.com is a good rating. However, every time I send a request a new token is generated then the TD Ameritrade API team noticed me. I like tdameritrade package than td-ameritrade-python-api because of the handled tokens. ====================== Major changes in the v0.1.0 update to the way tokens are handled. You will still need the original authentication instructions, but the TDClient now takes the refresh token and client id, not the access token. A new session class handles token expiration and will automatically call a new token as needed. ====================== And I used Redis to keep tokens in 30 minutes, then my project works smoothly. You have a job similar please reference github.com you can download source code and use (the package I am testing) I hope this source saves your time.
NASDAQ:AAPLEducation
by binancash
How to import Quandl data into TradingViewQuandl is a data library with all sorts of useful macroeconomic data. Unfortunately a lot of it you have to pay for, but there are also many data series you can access for free, including the "Blockchain" library with lots of useful data such as Bitcoin transaction fees. To access Quandl data, go to quandl.com. In the left-hand column, check the "Free" box to ensure your search results include only free data sets. Then type what you're looking for in the text box (e.g. "US wages" or "Bitcoin transaction cost"). Click the name of a data set in the search results. You should now see a chart. On the right hand side of the chart, click the "TradingView" button to import to TradingView. For an example of how to use this data, watch the video or check out my previous idea on Bitcoin transaction fees as a predictor of Bitcoin's price:
CEducation
by ChristopherCarrollSmith
42
Accessing FRED data via TradingViewHey guys. As a macro trader, I really need to look at a wide variety of indicators and market data, whether economic or price based. However, I HATE many of the other charts that are out there. So I'd like to introduce you to a little known trick. Have you heard of Quandl? They provide alternate data sets for loads of different types of things, whether it's COT data, corporate debt, carbon emissions... you name it, you can probably find it. The problem is that many of these cost. Enter FRED, or 'Federal Reserve Economic Data', compiled by the Federal Reserve Bank of St Louis. They provide data on their website, but the charts aren't very intuitive and you can't manipulate, compare or add in other assets to try to visualise a thesis. So, there's a great solution. In the chart above, I have shown the 10-Year Treasury Constant Maturity Minus 3-Month Treasury Constant Maturity. This is an interest rate spread - and specifically, it's identifying a steepener trade (this shows that the yield on longer term bonds are rising faster than shorter term bonds, so could be indicative of an increase in inflation expectations). So what I did was go onto FRED (give it a Google), copy the data code (T10Y3M), head over the Quandl (again, give it a Google), then pop that code into the search box. You'll then see the data set pop up. Click through and you will see a standard boring orange line chart. But over to the right, you'll see 'Excel/TradingView' buttons. Click TradingView and you can view the data in TV charts! FRED data is all totally free and it's pretty comprehensive. Check it out and let me know what you think.
FRED:T10Y3MEducation
by FinkPro
30