Fig 1.
The close price of (a) BTC, (b) ETH and (c) ETH/BTC during the period 1/1/2019 to 07/06/2022 (source: https://www.cryptodatadownload.com).
Table 1.
Comparing the obtained benefits between the conventional cost variation and implementation of coin conversion.
Fig 2.
The framework of CCPM.
Fig 3.
The average of hourly ETH/BTC prices in each day of estimation step (a) and obtained T2j statistics (b). The current day is at the end of the chart (26/02/2022). The green line in panel (a) is the average of ETH/BTC prices in the estimation step which is called RP (0.0702). The red lines in panel (b) are the LCLT and UCLT.
Fig 4.
The average of hourly ETH/BTC prices in each day of Phase II data (a) and obtained T2j statistics (b). Due to 6 (NOC + 1) consecutive signals, a decision about a buy or sell action is needed at 15/04/2022. The green line in panel (a) is the average of ETH/BTC prices in Phase II which is called CP (0.0705). The red lines in panel (b) are the LCLT and UCLT.
Fig 5.
The average ETH/BTC hourly price after the end of Phase I to the end of dataset.
The first vertical line is the time of a sell signal (15/04/2022) and the second indicates the time that the price relative decrement would be smaller than TR (01/05/2022). The subsequent days (after the second vertical line) are only plotted to show the general decreasing patterns.
Table 2.
The sensitivity analysis of NPH through different values of TR.
Table 3.
Sensitivity analysis of NOC through different values of TR.
Table 4.
The performance of MACD, RSI and CCPM (NPH and NOC were set at 20 and 10 respectively) approaches through different values of TR.
Fig 6.
Comparing CCPM with random strategy in term of RUS (a) and ADDP (b) criteria.
Table 5.
The performance of Hoteling T2 control chart in the existence of within and between profile auto-correlation in term of ARL, SDRL and MRL criteria when there are artificial shifts in intercept, slope and standard deviation parameters.