Finally, Bitcoin showed strong signs of weakness, following our speculation that it might have topped (outlined in the article from 24th February 2023). Overnight, BTCUSD dropped to $22 000 before erasing some losses. The bearish crossover between DM+ and DM- accompanied the move, which we wanted to see in order to confirm our thesis about the bear market rally. However, despite these bearish developments, we have to reiterate that the confirmation of the short-term trend reversal has not yet been confirmed. Because of that, we remain very cautious and monitor the price action closely. If the price breaks and holds below the “Sloping support,” it will be very bearish; the same applies to the “Support 1”. However, if the price holds above the “Sloping support,” it may suggest that the price will stay in the upward-sloping channel for much longer before the final breakdown. As the majority of developments throughout the recent rally are characteristic of a bear market rally rather than a genuine primary trend reversal, we have no reason to change our bearish outlook beyond the short term. Accordingly, we stick to our view that BTCUSD will revisit its 2022 lows in the current year.
Illustration 1.01 Illustration 1.01 shows the daily chart of BTCUSD. Yellow arrows indicate volume, which suggests that fewer people were willing to buy Bitcoin in the second leg up than in the first one. Moving averages are flattening, which usually accompanies a neutral trend.
Technical analysis Daily time frame = Bearish Weekly time frame = Neutral
Illustration 1.02 The picture above shows the daily chart of BTCUSD. The orange line represents Nasdaq 100 continuous futures (NQ1!). Below this depiction is the correlation coefficient between BTCUSD and NQ1! (using a length of 50 days in the calculation).
*Pine script code used to generate the correlation coefficient* //version=4 study("BTCUSD/NQ1! Correlation Coefficient")
// Define the symbols for the two assets to be tested symbol1 = input("BTCUSD", type=input.symbol) symbol2 = input("NQ1!", type=input.symbol)
// Get the historical data for the two assets asset1 = security(symbol1, timeframe.period, close) asset2 = security(symbol2, timeframe.period, close)
// Calculate the correlation coefficient between the two assets correlation = correlation(asset1, asset2, length=50)
// Plot the correlation coefficient on the chart plot(correlation, title="Correlation", color=color.blue)
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