Fibonacci crypto total market cap

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Despite the volatility that has ruled the market this year, Stoltzfus describes a situation that nevertheless still brings high potential for investors willing to shoulder the risk. It's unclear if the billionaire has it. Markets closed. Dow 30 34, Nasdaq 13, Russell 2, Crude Oil Gold 1, Silver Vix The Fibonacci tool tends to capture price movements of energy stocks relatively better than energy cryptos. Despite the higher volatility found in cryptos, relative to energy stocks, the energy cryptos, like most major cryptos, such as Bitcoin, Ethereum, and Ripple, witnessed their highest peaks between November and December Comparatively, energy stocks fluctuated within more defined price ranges between November and January , allowing tools, such as Fibonacci retracements, to better capture price movements.

Similarly, around January , all energy stocks witnessed price increases. Energy stock prices and Fibonacci retracements. Note : This figure represents the Energy crypto prices and Fibonacci retracements. While Fibonacci retracement levels tend to capture energy stock prices relatively well compared to energy crypto prices, it is worthwhile to analyze the existence of price violations during an uptrend or downtrend. Figure 10 displays the price violations which occurred against the five retracement levels.

During an uptrend, energy cryptos witnessed the least price violations SNC and TSL with no price violations ; however, KMI accumulated the highest number of violations, with 48 violations at different support levels. Relatively, the number of price violations during a downtrend was higher than during uptrends. Energy cryptos followed the same trend, i. More importantly, during uptrends, the highest number of violations occurred at the In contrast, during downtrends, numerous violations occurred at the These findings suggest that while the Fibonacci retracement tool captured most of the downward movements in energy stock and crypto prices during an uptrend, price increases during downtrends were omitted.

Noticeably, constituents with more price violations at a particular level of retracement tend to have price violations at other levels. This raises the critical question of whether violations during an uptrend downtrend , e. We looked back for up to 3 days to determine whether another price violation preceded price violations at a specific, different retracement level.

We analyzed more than 1 day back to allow the energy stock and crypto prices to fluctuate and potentially cross retracement levels. For example, we investigated if a price violation during an uptrend or downtrend, e. The analysis was decomposed into uptrend and downtrend periods for both stocks and cryptos.

Table 2 reports the existence of price violations, where a current price violation at a specific retracement level was preceded by another price violation at the prior descending or ascending retracement level. We analyzed violations at the We assumed prices could not have broken a higher retracement level 1, 2, or 3 days back when the Most price violations e.

This was more noticeable during downtrends, when retracement levels were broken more frequently 1 day before, including the current retracement break. There were fourteen instances when a Energy cryptos did not seem to witness consecutive violations in retracement levels, whether during an uptrend or downtrend. The highest number of consecutive price violations for energy cryptos occurred during downtrends. As we moved from 1 to 2 and 3 days back, fewer consecutive retracement breaks occurred, suggesting that most retracement levels were broken consecutively within 1 day.

This suggests that price violations tend to occur more frequently when the In other words, the number of consecutive price violations at the During an uptrend, prices are expected to rise, following which price violations are likely to occur.

Similarly, during downtrends, prices are expected to fall, after which price violations tend to occur. The only exception to this was during downtrends when most of the price violations took place consecutively at the lower retracement levels of Based on the above findings in which retracements tend to witness lesser price violations at lower retracement levels, we put together a trading strategy to test the use of Fibonacci retracement levels on energy stock and crypto prices.

During an uptrend, a long position was pursued when the price crossed over the Similarly, a short position was pursued during downtrends when the price crossed under the Table 3 provides a summary of the retracement levels. Assuming that a transaction is based on the purchase or sale of one stock and that long or short energy stocks can be transacted without restrictions, like a buy sell followed by a sell buy , the total net profit, or loss, during periods of uptrends and downtrends is calculated as follows:.

Due to the approach taken to calculate the return, the average risk was proxied using the average SD of energy prices. All positions were closed at the end to allow for comparison with the buy-and-hold strategy. Buy-and-hold returns were based on a buy on November 28, , and a subsequent sale on January 17, Assuming that a transaction is based on the purchase or sale of one stock and that long or short energy stocks can be transacted without restrictions, like a buy sell followed by a sell buy , we can calculate the total net profit or loss during uptrends and downtrends.

Sharpe values were relatively low; the highest value was 0. This was consistent with the highest Sharpe per trade value of 0. Compared to the Fibonacci-based trading strategy, buy-and-hold returns reported negative returns for six of the energy stocks.

The highest lowest return of nearly For the energy cryptos, the use of our Fibonacci-based strategy resulted in very few trades. Only GRID reported net long positions during uptrends, while POWR reported net short positions, with the other two energy cryptos showcasing no transactions.

During periods of downtrends, all four energy cryptos reported net short positions. TSL had a very high total return relative to all stocks and cryptos, primarily because cryptos have only net short positions during downtrends. These open positions were all closed at the end of the studied trading horizon. The low amount and type of transactions short or long resulted in the abnormally high Sharpe value for TSL. Buy-and-hold returns were negative for all cryptos instead of the positive performance observed under the Fibonacci-based strategy for four stocks.

Table 4 shows the results of a trading strategy based solely on the use of Fibonacci retracements. However, it is interesting to examine whether complementing the Fibonacci tool with a price crossover strategy results in a superior trading model for the energy commodities. Table 5 provides the findings of a Fibonacci retracement strategy complemented with price crossover rules. Due to the addition of price crossover rules to the existing model, fewer trading opportunities are expected.

During uptrends, energy stocks tend to display relatively more short net positions, with only XOM reporting one net long position. Comparatively, five of the energy stocks had net long positions during downtrends, while EOG reported a net short position. This suggests that energy stocks tend to attract more sales buys during uptrends downtrends , based on traders following a Fibonacci retracement strategy complemented with a price cross strategy. Assuming that a transaction is based on the purchase or sale of one stock and that long or short energy stocks can be transacted without restrictions, like a buy sell followed by a sell buy , we can calculate the total net profit or loss during periods of uptrends and downtrends.

While the negative performance of XOM can be attributed to losses during both uptrend and downtrend periods, the negative returns observed for KMI and CVX were due to the closure of the open positions at lower prices at the end of the trading horizon. This was closely consistent with the second-highest Sharpe per trade value of 0.

Compared to the Fibonacci-based trading and the buy-and-hold strategy, the model that complemented both the Fibonacci and price crossover strategy did not result in superior total returns; no transaction occurred for SLB due to the latter strategy.

Using our Fibonacci-based strategy in conjunction with the price crossover strategy resulted in even fewer or no trading signals for the energy cryptos. During uptrends, no energy cryptos reported net long positions. The low amount and type of transactions short or long resulted in the abnormally high Sharpe value for energy cryptos.

Buy-and-hold returns were negative for all cryptos compared to the Fibonacci-based strategy, which yielded positive returns only for one crypto. This suggests that using the Fibonacci retracement tool complemented with the price crossover strategy is not warranted, potentially due to the significant down trending periods since January , which allowed for no position during relatively small pockets of eventual uptrends.

This resulted in performance measures, such as the Sharpe or Sharpe per trade being less reliable, due to very few or zero transactions. This study investigates the use of Fibonacci retracements as a technical analysis tool, which the extant literature has not sufficiently documented, particularly regarding i its application on energy stocks and cryptos, ii its usage as a strategy when complemented with a price crossover strategy, and iii its performance relative to a buy-and-hold trading strategy.

With positive correlations ranging from 0. The Fibonacci retracement tool tended to capture energy stock prices better than energy cryptos. A possible explanation resides in the fact that energy stock prices fluctuate within a more defined range, allowing the technical analysis tool to capture the price movements better.

To refine the use of the Fibonacci tool, we applied the difference between the highest and lowest prices during a prior trend and used it for future price movements. Price violations tended to occur more during downtrends compared to uptrends for both energy stocks and cryptos.

While most down movements were captured during uptrends, price increases during downtrends were largely omitted. Constituents with relatively more price violations at a particular retracement level also tended to have more price violations at other retracement levels. The highest number of consecutive price violations occurred during downtrends. Less consecutive retracement breaks took place as we moved from 1 day to 2 and 3 days prior. Price violations tended to occur more when the We found similar results for energy cryptos.

While superior to the buy-and-hold model, the Fibonacci-based trading strategy did not significantly improve when complemented with a price crossover strategy, resulting in fewer or no trades in most instances and consequently unimpressive Sharpe values.

This provides some insights to regulating agencies, like the Commodity Futures Trading Commission and the Securities Exchange Commission, that, despite the substantial fall in the prices of commodity markets, like oil, which affected energy-based entities, traders in those energy stocks still enjoyed significant profits.

Nevertheless, this does not necessarily mean losses for speculators skilled in using technical analysis indicators, like the Fibonacci retracement tool. Alternatively, in our study, financialization brought some benefits to commodity speculators with access to energy stocks. Future avenues of research are warranted in terms of frequency, which can be modified to a higher e. More importantly, future studies need to assess the period that defines a trend.

The study by Duan et al. Fibonacci arcs, fans, and retracements are the popular methods used by traders. The role of Fibonacci arcs is to highlight potential support or reversal when prices pull back after an earlier advance. Fibonacci fans, constructed to confirm support levels or potential reversal zones, are also beneficial for measuring the speed of a trend's direction.

Fibonacci retracement levels can be used to highlight support and resistance levels when prices retrace. Fibonacci extensions are applicable when stock prices reach new highs or lows without support or resistance levels Bhandari Aggarwal R Stock index futures and cash market volatility. Rev Futures Mark 7 2 — Google Scholar. Ball R Filter rules: interpretation of market efficiency, experimental problems and Australian evidence.

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Box , Dubai, United Arab Emirates. You can also search for this author in PubMed Google Scholar. IG carried out the data analysis, interpreted the results and drafted the manuscript. MN reviewed and revised the manuscript and was a major contributor in the discussion of the results. RKB wrote the introduction and literature review. All authors read and approved the final manuscript.

His research interests are in energy finance, macroeconomic analysis and financial markets, portfolio management, and cryptocurrencies markets. In , he completed his Ph. He has mainly focused on efficiency performance, financial technology, risk and insurance, banking, financial economics and industrial economics.

Rajesh Kumar Bhaskaran received his Ph.

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This is all in spite of earlier speculation about a Flippening, in which other digital currencies would permanently take over the No. That has yet to transpire. Ethereum, the digital token which prompted the rise of the initial coin offering ICO , comes in second on our list of cryptocurrencies by market cap.

There are just over The No. Unlike the top two digital currencies as well as the one directly following it , the price of each XRP token is very small. As of this writing, it's just over 50 cents per token. On the other hand, the total number of XRP in circulation is quite high. There are over 39 billion tokens in circulation now. Bitcoin cash, the spin-off of bitcoin which launched as a result of a hard fork , comes in fourth in our ranking.

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