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Bitcoin Intraday Momentum

Academic paper

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

AuthorsGuglielmo Maria Caporale; Alex Plastun

Institute

Brunel University of London
London South Bank University
DEGerman Institute for Economic Research
DEIfo Institute for Economic Research
?Brunel University London - Department of Economics and Finance
?CESifo (Center for Economic Studies and Ifo Institute)
?German Institute for Economic Research (DIW Berlin)
UASumy State University

Paper linkhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=3480923

Strategy in a nutshell

The strategy trades Bitcoin by buying on positive overreaction days and selling on negative ones. An overreaction day occurs when returns exceed the average plus two standard deviations. Positions are opened at 18:00 (positive) or 16:00 (negative) and closed at 00:00, capturing significant intraday price movements.

Economic rationale

Overreaction-driven momentum persists intraday, driven by behavioral biases such as herding, overreaction, and confirmation bias. These effects are amplified in markets with high retail participation, making Bitcoin particularly responsive to momentum-based strategies.

Backtest performance

Annualised return28.62%

Volatility30.26%

Beta-0.052

Sharpe ratio0.95

Sortino ratio0.051

Win rate52%

Full Python code

1from AlgorithmImports import *
2from pandas.core.frame import DataFrame
3from math import floor
4#endregion
5class BitcoinIntradayMomentum(QCAlgorithm):
6def Initialize(self):
7self.SetStartDate(2015, 1, 1)
8self.SetCash('USD', 100000)
9self.closes:List[float] = []
10self.period:int = 12 * 21
11self.SetWarmup(self.period, Resolution.Daily)
12
13self.percentage_traded:float = .9
14self.std_threshold:float = 2.
15self.signal_hours:List[int] = [16, 18]
16
17self.symbol:Symbol = self.AddCrypto('BTCUSD', Resolution.Minute, Market.Bitfinex).Symbol
18def OnData(self, data: Slice) -> None:
19if not (self.symbol in data and data[self.symbol]):
20    return
21
22current_price:float = data[self.symbol].Value
23if self.Time.hour == 23 and self.Time.minute == 59:
24    # store daily price
25    self.closes.append(current_price)
26    if self.Portfolio[self.symbol].Invested:
27        self.Liquidate(self.symbol)
28if self.IsWarmingUp: return
29if len(self.closes) < self.period: return
30if (self.Time.hour in self.signal_hours and self.Time.minute == 0):
31
32    # daily return return calculation
33    last_close:float = self.closes[-1]
34    performance:float = current_price / last_close - 1
35    
36    # daily return average
37    closes:np.ndarray = np.array(self.closes)
38    daily_returns:np.ndarray = closes[1:] / closes[:-1] - 1
39    ret_mean:float = np.mean(daily_returns)
40    ret_std:float = np.std(daily_returns)
41    q:float = floor(self.Portfolio.TotalPortfolioValue * self.percentage_traded / current_price)
42    if q >= self.Securities[self.symbol].SymbolProperties.MinimumOrderSize:
43        # overreaction handling
44        if self.Time.hour == self.signal_hours[0] and self.Time.minute == 0:
45            if not self.Portfolio[self.symbol].Invested:
46                if performance < ret_mean - self.std_threshold * ret_std:
47                    self.MarketOrder(self.symbol, -q)
48        
49        elif self.Time.hour == self.signal_hours[1] and self.Time.minute == 0:
50            if not self.Portfolio[self.symbol].Invested:
51                if performance > ret_mean + self.std_threshold * ret_std:
52                    self.MarketOrder(self.symbol, q)