ArBinance is a professional arbitrage trading platform that aims to revolutionize cryptocurrency trading by allowing its users to conduct crypto arbitrage through an easy-to-use, all-in-one platform. By employing ArBinance’s cutting edge software and technology, its users can instantly capitalize on arbitrage opportunities between a wide array of cryptocurrency exchanges.
Cryptocurrency traders no longer need to worry about managing logins and maintaining balances on multiple exchanges in order to execute arbitrage trades, as all of this is handled directly on the ArBinance platform safely and securely.
How Arbitrage Works with Cryptocurrency
The art of arbitrage has been around for a long time, and when executed properly can yield surefire profits for its traders. As applied to cryptocurrency, arbitrage revolves around the simple premise of exploiting differences in prices of coins between different cryptocurrency exchanges.
It is simply a matter of buying low on one exchange and selling high on another, and being able to do so within a matter of seconds. These price differentials exist because the crypto market and its exchanges are young and still somewhat disconnected.
Even if there are only a few pennies of a difference in coin prices between one exchange and another, this profit adds up quickly when conducted hundreds – or even thousands – of times per day.
The Problems with Crypto Arbitrage
Arbitraging cryptocurrency may seem like easy money, but competition is becoming fiercer by the day. New exchanges are opening all the time, existing exchanges are constantly adding new pairings, and maintaining login information as well as balances on all these exchanges can be daunting.
In 2019, an individual trader manually buying
Even among automated arbitrage, lightning quick speed is a must, which is where ArBinance is set to succeed. Not only does ArBinance have accounts on all of these exchanges, they have coins there ready to be bought and sold, which is another factor that makes crypto arbitrage difficult for the average trader.
How ArBinance Stands Above the Competition
The ArBinance arbitrage system uses state-of-the-art software to continually seek out the best arbitrage deals for its users, 24/7. Active across over one hundred cryptocurrency exchanges, their innovative platform instantly locates and takes advantage of price discrepancies via their proprietary automation techniques before they are identified by competitors, putting the resulting profits directly into the accounts of their users.
One of the main benefits of using ArBinance is its simplicity. ArBinance can be used to make deals for its users, meaning that all that is necessarily required of the user is to choose the best arbitrage plan for them, and ArBinance will handle the rest.
In addition, users don’t need to have their own assets on each exchange, as the ArBinance system distributes user assets over their list of registered exchanges so their money is always in right place, at the right time. Built with speed, security and stability in mind, ArBinance is changing the game for those looking to yield steady profits from the exciting arena of crypto arbitrage.
For more information on ArBinance and to get started making the most in crypto arbitrage opportunities today
BitMex sued by initial investor for $300 million share settlement
The claims made by the plaintiff also addressed concerns about how BitMex was not able to raise money through traditional methods and was at risk of failing.
Cryptocurrency exchanges have had their fair share of controversies in the past months, and the latest platform, tog et hit by one is BitMex. Being one of the largest and more popular exchanges on the planet, the company, as well as its founder, Arthur Hayes, have been subjects of scrutiny multiple times. This time around, BitMex and Hayes were sued for $300 million by one of its initial investors, Frank Amato and RGB Coin Ltd.
Amato revealed that he was one of the first believers of BitMex and had given seed money for its conception in 2015. The total investment of $30,000 was then supposed to be converted to equity within BitMex, a process that would place the value of the stocks at $50 million today. Amato and his partners added that Hayes did not provide any equity and gave out only false information to them. The filing submitted by Amato in the Superior Court of the State of California in San Francisco stated:
“Through this action, Plaintiffs seek damages representing the value of their equity interest in BitMEX, which is conservatively
According to the case file, Hayes had repeatedly pitched Amato to invest in BitMex’s nascent and struggling cryptocurrency exchange platform. The cryptocurrency exchange needed the money to pay engineers, procure equipment, develop the necessary algorithms, and to help promote the platform.
The claims made by the plaintiff also addressed concerns about how BitMex was not able to raise money through traditional methods and was at risk of failing. The suit filed by Amato talked about how the defendants mislead the plaintiff, a move that came right after a $30,000 investment at a $600,000 valuation. This investment was made from SOSV, a startup accelerator, and a multi-stage venture capital investor with offices in San Francisco.
The latest lawsuit comes a month after BitMex witnessed a significant data leak within their system. The event caused several email addresses of users being sent across servers en masse. Viven Khoo, BitMex’s deputy chief operating officer, had then said:
“We are deeply sorry for the concern this has caused to our users. The issue was caused by an error in the software used to send emails. As soon as we were made aware of the issue, we immediately prevented further emails from being sent and have since addressed the issue to ensure this does not happen again.”
BTC, EOS, TRX Price Prediction: Signals of Bullish Continuation on the Market
Have the bulls capitul
The cryptocurrency market is finishing the week with uncertainty. While the local bullish trend is coming down, the bears have not seized on the initiative.
Below is key information for Bitcoin (BTC), TRON (TRX), and EOS (EOS).
|Name||Ticker||Market Cap||Price||Volume (24H)||Change (24H)|
The 1H time frame shows a breakdown of the ascending channel’s lower boundary, after which the price is likely to visit a three-week low of $7,072 and then the psychological level of $7,000.
The likelihood of such a scenario will increase if the relative strength index (RSI) supports line crashes.
The overall picture will remain bearish until the $ 7,870 mark (reached on November 29th) is passed.
At press time, BTC is trading at $7,146.
The bulls could not hold the rate of EOS at its resistance level, pushing the price down to $2.59.
Looking at the 4H chart, the short-term bullish mood is coming down as the bears are soon about to seize on the initiative. This is confirmed by the lines of the Moving Average Convergence/Divergence (MACD), which will switch to a bearish trend soon. In this case, the nearest support mark will be the at $2.55.
At press time, EOS is trading at $2.5987.
Our recent TRON price forecast came true as the rate rolled back and touched the expected $0.135.
According to the chart, the RSI indicator bounced off the oversold area and is currently moving upward. What is more, there is a high trading volume index located around the $0.015 resistance mark. If the volume remains at its previous levels or potentially increases, then that mark might be achieved through the end of the current year.
At press time, TRX is trading at $0.01420.
Myths and Realities: Sentiment Analysis for Crypto Assets
Jesus Rodriguez is the CTO and co-founder of IntoTheBlock, a platform focused on enabling an intelligent infrastructure for the crypto markets, as well as chief scientist of AI firm Invector Labs and an active investor, speaker and author in crypto and artificial intelligence. This article originally appeared in CoinDesk’s Institutional Crypto newsletter.
One of the established beliefs in the cryptocurrency market is its susceptibility to news and social media. Like any other nascent and still irrational financial market, unexpected developments captured in news or social media tend to impact price. As a result, there is increasing interest in leveraging machine learning techniques such as sentiment analysis to detect possible correlations with the price of cryptocurrencies and digital tokens. Despite its importance, most attempts to leverage sentiment analysis are too basic to output any tangible intelligence and quite often produce misleading results.
The challenges of efficiently leveraging sentiment analysis to evaluate the behavior of an asset are not unique to the crypto space. Producing true insights based on textual sentiment is a very difficult task that, most of the time, requires natural language processing (NLP) models optimized for a specific financial domain. Large quantitative hedge funds use armies of machine learning experts to train NLP models in a very specific task like analyzing earning reports in order to get an edge in a medium frequency trade. Efficiently leveraging sentiment analysis for crypto assets requires machine learning depth and rigor.
To understand that statement, let’s start by diving a bit deeper into the characteristics of sentiment analysis methods.
A gentle introduction to sentiment analysis
In Act II, Scene II of the famous play Richelieu; Or the Conspiracy, British playwright Edward Bulwer-Lytton coined a phrase that has transcended generations: “The pen is mightier than the sword.” Centuries after, that famous quote brilliantly encapsulates the importance of sentiment analysis. Emotions in textual communication are sometimes more conducive to actions than physical actions themselves.
Conceptually, sentiment analysis is a subdiscipline of NLP that focuses on identifying the affective states of textual communications. Contrary to popular beliefs, sentiment analysis is not a single technique but rather a subdiscipline of the deep learning space that covers different types of affection detection in textual data. From that perspective, there are several types of sentiment analysis that could be relevant in the context of crypto-asset intelligence:
- Polarity Analysis: This type of sentiment analysis ranks textual sentiment in positive, negative and neutral. For instance, the sentence “the bitcoin price rally has reenergized the market” would likely be classified as positive by most models.
- Emotion/Tone Analysis: Instead of an overall qualifier for the text, this type of analysis centers on scoring the different types of emotions present in a particular text. Emotions such as sadness, happiness or anger are a common focus of emotion analysis algorithms. For instance, the sentence “this bitcoin rally is crazy,” will show high levels of excitement and joy.
- Aspect Sentiment Analysis: This type of sentiment analysis focuses on interpreting the sentiment about specific subjects within a sentence rather than a sentence as a whole. For instance, in the sentence “Bakkt futures are a major milestone for the bitcoin market,” aspect analysis will determine the sentiment related to “Bakkt futures” instead of the complete sentence.
Looking at the previous list, we can clearly see the benefits of sentiment analysis for crypto assets. However, there are also plenty of challenges that should be considered before venturing into using these types of techniques. Contextualization, subjectivity, irony or even bad grammar are among the factors that can easily trick the best NLP algorithms.
Sentiment analysis for crypto assets
Crypto is a nascent asset class that is still vulnerable to the irrationality of financial markets and the lack of proper disclosure channels. From that perspective, it is only logical to assume NLP techniques such as sentiment analysis can identify alpha or smart beta generator factors to predict the behavior of crypto assets. Reality is a bit different.
When applying sentiment analysis to crypto assets, we are likely to encounter two main types of challenges:
- Limitations of mainstream NLP technologies when applied to a domain-specific problem such as crypto asset analysis.
- Incorrect assumptions about how sentiment is reflected in news and social media.
The first challenge can almost be seen as an unexpected side effect of the rapid growth of NLP technologies. Today, it is relatively easy for a developer to incorporate sentiment analysis into applications using simple APIs that don’t require any deep learning expertise.
While NLP APIs can be effective analyzing the sentiment of a generic sentence, they perform extremely poorly
The second challenge is related to misconceptions about how sentiment is reflected in news and social media commentary. As a source of intelligence, news can be highly informative but quite useless when comes to sentiment analysis. The reason is obvious: the sentiment in well-written news should trend around neutral. Social media behaves in the exact opposite way. Conversations about cryptocurrencies in Twitter or Telegram tend to contain relevant sentiment but, for the most part, are based on a reaction to public material information, which means that they are unlikely to generate any informational edge. Additionally, social media threads tend to be noisy and relatively subjective, which can produce misleading sentiment analysis results.
From a purely technological standpoint, building effective sentiment analysis models for crypto assets requires models trained in the terminology of crypto markets, but that also analyze news as sources of information and social media feeds as amplifiers of sentiment. However, if we get past this technological challenge, we are now faced with one of the biggest psychological misconceptions when comes to sentiment analysis models in the crypto space.
The sentiment-market impact fallacy
The sentiment-market impact fallacy describes a phenomenon that is notorious or irrational, such as nascent financial markets in which investors assume a direct correlation between a sentiment score and a price movement. To explain this behavioral economics dynamic, let’s imagine that you are using an analytics tool that analyze the sentiment of recent bitcoin tweets. Psychologically, most investors are inclined to interpret the sentiment as a leading indicator based on the following rules:
- If the sentiment is positive that’s a bullish indicator for the price of bitcoin.
- If the sentiment is negative that’s a bearish indicator for the price of bitcoin.
However, if your model is analyzing public, material information, the sentiment should be interpreted as a lagging indicator following some non-intuitive rules:
- If the sentiment is positive and the price of bitcoin does not go up, that is a bearish signal.
- If the sentiment is negative and the price of bitcoin does not go down, that is a bullish signal.
Being aware of sentiment-price bias positions sentiment analysis not as a leading indicator but as an often relevant factor in a trading strategy.
From sentiment analysis to market impact analysis
From an informational standpoint, the crypto market is noisy and full of unexpected events. In terms of sentiment analysis, that combination of factors is a nightmare. Instead of narrowly focusing on sentiment analysis, we should probably develop a more holistic approach. A sentiment-market impact indicator would be a combination of polarity (negative, positive, neutral), emotion (anxious, excited, sad…) and aspect-based (topics, entities…) analysis over long periods of time. This approach would require the training of models specialized in the dynamics of crypto assets to evaluate the sentiment in the context of specific market conditions.
The idea of sentiment-market impact models is conceptually trivial: quantify the impact that combinations of sentiment, emotions and topics can have on a crypto asset during specific market conditions. Part of the beauty of this approach is that it doesn’t have to be completely unsupervised like most sentiment models today; it can be trained on domain-specific knowledge of crypto markets. For instance, we could train a model to learn that positive articles about Chinese investment in crypto can have a positive impact in a market that had been relatively bearish for the last week. The core principle of sentiment-market impact analysis models would be to contextualize the knowledge of sentiment models to the specifics of the crypto market.
Sentiment analysis is likely to keep sparking flashy headlines in the crypto market. However, in order to be effective, the models require deeper machine learning rigor and the building of knowledge based on the specific dynamics of crypto markets. As the markets evolve, we are likely to see a transition from plain sentiment analysis techniques to more holistic market impact models that quantify the relevance of specific topics in the behavior of the crypto markets