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Crypto Tech Analysis: SEC Influenced Bearish Market Rally in Play



Cryptocurrency market over the course of this week has lost nearly US$ 17 Billion in overall market capitalisation and most of the decline stems from Bitcoins sharp decline. The progenitor of cryptocurrency suffered loss of nearly seven percent since Monday. This decline was triggered by delay from US SEC to respond to three Bitcoin ETF proposals submitted by top players in the cryptocurrency market yet again. Bitwise, SolidX are some of the long standing players aiming to become first Bitcoin ETF in the market but SEC’s delay in response while mostly expected by the market came at the time when market was stagnating and looking for directional cues to make a breakout resulting in bears gaining the upper hand. Altcoins remain passive and relatively immune to volatility in Bitcoin causing them to see comparatively lesser declines when considering Bitcoin’s losses.

Bitcoin: Bitcoin is currently trading well near mid- 10K handle having suffered sharp declines over influence from SEC’s decision to delay Bitcoin ETF’s yet again. Bitcoin lost more than US$ 14 Billion in market cap since last Monday and has a current market cap of US$ 189.62 Billion. The BTCUSD pair is currently trading at $10639 down by 6.58% on the day as of writing this article. When looking from technical perspective, the price momentum favors further decline in immediate future given prevalent bearish bias. The price is well below 9 and 50 SMA’s in most intra-day and daily charts while 30 min and 1 hr chart see price below all three SMA’s – 9, 50 and 100. RSI momentum used to measure momentum of price is currently well below oversold level at 29 with signal line seemingly flattening out suggesting bear

ish bias is likely to remain steady in immediate future. Bitcoin is likely to rebound if it managed to stay above $10500 but failure to stay above could result in further steep declines towards 10000 handle before Friday.

Ethereum: Ethereum has had a relatively safer price action compared to Bitcoin. We could even say that Ethereum remains relatively unchanged despite declines when compared with Bitcoin as Ethereum’s market capitalization still remains above 22 billion since Monday. Ethereum has lost nearly US$ 610 Million but still has a market cap of US$22.25 Billion and ETHUSD pair is trading at $208.95 down by 0.41% on the day as of writing this article. When looking from technical perspective, bears’ grip on price momentum of ETH seems clearly evident but bears lack enough strength to create an upset or breakout rally. The price is below all three SMA’s – 9, 50 and 100 across all intra-day and daily charts but the RSI indicator used to measure momentum is seeing signal trade remain well in neutral levels at 48-40 across intra-day and daily charts suggesting breakout is clearly unlikely in immediate future. Expected support and resistance for the pair are at 206, 204, 202 and 210, 213, 214 handles respectively.

Ripple: Ripple similar to Ethereum suffered relatively less declines and remains well within familiar levels albeit falling below 0.30 handle – a key psychological price level. Ripple lost over US$ 290 Million in market cap since Monday and has a current market cap of US$ 12.62 Billion. As of writing this article, XRPUSD pair is trading at 0.2961 down by 0.77% on the day. The technical picture mirrors Ethereum, the price is moving well below all three SMA’s – 9, 50 and 100 across all intra-day and daily charts while RSI indicator used to measure price momentum is moving with downward incline but remains above oversold territory and is moving within 45 to 43 levels across intra-day charts. Expected support and resistance for the pair are at 0.2950, 0.2945, 0.2930 and 0.2970, 0.2990, 0.3000 handles respectively.

.Source: .cryptopotato


BTC, EOS, TRX Price Prediction: Signals of Bullish Continuation on the Market



Have the bulls capitul

ated against the bears in a short-term projection?

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.

Top 10 coins by Coinstats

Top 10 coins by Coinstats

Below is key information for Bitcoin (BTC), TRON (TRX), and EOS (EOS).

NameTickerMarket CapPrice 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. 

BTC/USD chart by TradingView

BTC/USD chart by TradingView

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.

EOS/USD chart by TradingView

EOS/USD chart by TradingView

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.

TRX/USD chart by TradingView

TRX/USD chart by TradingView

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.

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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: 

  1. Limitations of mainstream NLP technologies when applied to a domain-specific problem such as crypto asset analysis. 
  2. 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

when trying to extrapolate domain-specific knowledge of a specific sentence. For instance, analyzing the sentence “a bitcoin ETF approval could be imminent” requires NLP models that are specialized in the semantics of market-specific terminology and that are able to extrapolate sentiment at a more granular level than from just a sentence. 

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

.Source: fxstreet

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It seems that not a day goes by without another central bank jumping on the crypto train. Yesterday it was South Korea, today it is Sweden as plans to roll out the crypto krona gather pace.


Sweden’s central bank is poised to sign a deal with multinational professional services company Accenture to begin pilot testing its CBDC, the e-krona. According to reports the partnership is for an initial year-long run to the end of 2020.

A Riksbank statement added that;

“The primary objective of the e-krona pilot project is to broaden the bank’s understanding of the technological possibilities for the e-krona,”

Cash usage has already started to decline in the Scandinavian country which spurred the central bank to begin researching a digital option in 2017.

The Accenture partnership will involve the development of a payment platform with a user interface that enables e-krona transactions from cards and smartphones. There has been no confirmation from the bank that a crypto krona will see the light of day, but it is highly

likely to given the circumstances.

Industry observer ‘Rhythm Trader’ likened cryptocurrencies to the ‘space race of our generation’, but still maintained that bitcoin was the king of them all.


Sweden has been more proactive towards crypto assets than other regional countries. Earlier this week the Swedish Financial Supervisory Authority (SFSA) approved Swiss crypto ETP provider Amun.

According to the announcement, Amun is the first issuer to deliver fully collateralized, passive investment products with cryptocurrencies as the underlying asset.

Amun’s President Ms. Ophelia Snyder added;

“Our mission is clear and that is to help investors more safely, cost effectively and easily invest in crypto asset classes through our crypto ETPs. We recognise that the regulatory framework in Sweden has been supportive of such initiatives and we welcome its deliberation.”

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