/Daily Trending Bitcoin News and Market Sentiment: Macro Bear Over, Neural BTC Price Predictions?

Daily Trending Bitcoin News and Market Sentiment: Macro Bear Over, Neural BTC Price Predictions?


  • Asian profit-taking has bumped Bitcoin price down but USD 7,200 is stubbornly holding
  • Bullish daily charts, MACD and oversold RSI align to suggest the macro bear market is over
  • Indian data scientist Abhinav Sagar claims to have cracked neural price prediction in crypto markets

Bitcoin markets are fighting hard to ensure they do not lose the gains of Thanksgiving weekend. So far, there is a lot of resistance surprisingly at USD 7,300, where bulls may perhaps not want to relinquish USD 7,000 so quickly into the new month. With a low only achieved by Asian sellers at USD 7,162 (CoinDesk), there is plenty of time for Central Europe to regain lost momentum.

Altcoin markets are also doing their best to stay in tune with Bitcoin’s resilience, though other than EOS and ETH holding 1% gains today, the rest of the top 10 by market capitalization don’t seem to be faring too well.

There is still plenty to play for in the markets at this price, no matter the outlook. And if you’re a speculator looking for the bottom, then this piece of news from Cointelegraph’s Keith Wareing will cheer you up. According to the crypto analyst, the macro bear period for the crypto market could be approaching its end, if not already so.

He uses three key indicators to come to this conclusion of the imminent rally, beginning with the daily chart that has turned a corner since November 2019. Pointing to a failure to break USD 9,500, and the following three weeks of decline, Bitcoin experienced a bounce that restored USD 1,300 from its monthly low, changing the trend on the dailies from bearish to decidedly bullish. Bollinger Bands Indicator shows the likelihood to break the USD 8,000 moving average barrier. If that happens, USD 9,000 will be on the horizon.

Wareing then follows this up with a positive assessment of the Moving Average Divergence Convergence (MACD) indicator. Here, he shows how Bitcoin is right on track for a bullish cross when daily candles close. He explains:

“This will result in the first green candle to be printed on the MACD histogram, and history shows that this results in a reversal period, how long that period will last is difficult to answer, but it’s a buying signal to traders nonetheless.”

A third indicator would normally be the CME futures gap, something we also discussed in past analyses. However, Wareing says that this will not be the case in the new week, although a 7% price bump is to be expected if the gap fills next week. He proceeds to show the third indicator of a change of trend, which he believes is the oversold signal on the weekly Relative Strength Indicator (RSI).

The final November week was signaling an extremely oversold reading with 17.65 on 25 November, and currently is still in the mid-30s, a usual sign for traders to say that an asset is oversold and a correction is imminent. Should we look for confirmation in Wareing’s conclusion? He says:

“It isn’t often that traders get so many tangible bullish signs lining up like this so could this be the beginning of the next Bitcoin parabola? Or is there something we’re not seeing?”

Meanwhile, an Indian data scientist from the Vellore Institute of Technology has published his method of predicting crypto price in real time, through the use of a neural network called Long Short-Term Memory (LSTM).

Abinhav Sagar outlined in his blog post a process of only four steps in using machine learning in the crypto markets to make for accurate price forecasting. He began by saying how machine learning applications in the past have been unable to replicate the success of traditional stock markets, being quite restrictive in the digital assets market. He said:

“The reason behind this is obvious as prices of cryptocurrencies depend on a lot of factors like technological progress, internal competition, pressure on the markets to deliver, economic problems, security issues, political factor etc. Their high volatility leads to the great potential of high profit if intelligent inventing strategies are taken.”

Sagar says first, data has to be collected in real time. Then, the data needs to be prepped for neural network training. The LSTM neural network then tests the predictions before outputting the results in a visual manner.

He demonstrates datasets from a Canadian exchange, and even shares the code behind his algorithm. In essence, LSTM is able to “remember important information and at the same time forget irrelevant information”.

Has Sagar cracked the code to automatic profits? Unlikely, but there will always be believers!


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