Original paper

SSRN-id3480923.pdf1607.7KB

Abstract

This paper examines whether there exists a momentum effect after one-day abnormal returns in the cryptocurrency market. For this purpose a number of hypotheses of interest are tested for the BitCoin, Ethereum and LiteCoin exchange rates vis-à-vis the US dollar over the period 01.01.2017-01.09.2019, specifically whether or not: H1) the intraday behaviour of hourly returns is different on overreaction days compared to normal days; H2) there is a momentum effect on overreaction days, and H3) after one-day abnormal returns. The methods used for the analysis include a number of statistical methods as well as a trading simulation approach. The results suggest that hourly returns during the day of positive/negative overreactions are significantly higher/lower than those during the average positive/negative day. Overreactions can usually be detected before the day ends by estimating specific timing parameters. Prices tend to move in the direction of the overreaction till the end of the day when it occurs, which implies the existence of a momentum effect on that day giving rise to exploitable profit opportunities. This effect (together with profit opportunities) is also observed on the following day. In two cases (BTCUSD positive overreactions and ETHUSD negative overreactions) a contrarian effect is detected instead.

Keywords: cryptocurrencies, anomalies, momentum effect, overreactions, abnormal returns, patterns

Trading rules

Focus on: Bitcoin as the trading asset.
Buy on positive extreme fluctuation day at 18:00; Sell on negative overreaction day at 16:00.
Settle all open trades by midnight (00:00).
An "extreme fluctuation day" is defined as when the daily return surpasses the mean daily return by two standard deviations.

Python code

Backtrader

import backtrader as bt
import datetime

class OverreactionStrategy(bt.Strategy):
    params = (
        ("std_mult", 2),
    )

    def __init__(self):
        self.dataclose = self.datas[0].close
        self.dataopen = self.datas[0].open
        self.daily_returns = self.dataclose / self.dataclose(-1) - 1
        self.avg_return = bt.indicators.SimpleMovingAverage(self.daily_returns, period=1)
        self.std_dev = bt.indicators.StdDev(self.daily_returns, period=1)

    def next(self):
        current_time = self.data.datetime.time()
        current_date = self.data.datetime.date()
        overreaction_threshold = self.avg_return[0] + self.params.std_mult * self.std_dev[0]

        if not self.position:
            if self.daily_returns[0] > overreaction_threshold and current_time == datetime.time(18, 0):
                self.buy()
            elif self.daily_returns[0] < -overreaction_threshold and current_time == datetime.time(16, 0):
                self.sell()
        else:
            if current_time == datetime.time(0, 0):
                self.close()

cerebro = bt.Cerebro()
data = bt.feeds.GenericCSVData(dataname='your_bitcoin_data.csv')
cerebro.adddata(data)
cerebro.addstrategy(OverreactionStrategy)
cerebro.run()

Please replace ‘your_bitcoin_data.csv’ with the appropriate Bitcoin data file in the correct format.

Momentum Effects in the Cryptocurrency Market after One-Day Abnormal Returns

Original paper

Abstract

Trading rules

Python code

Backtrader