Original paper
Abstract
What predicts returns on assets with "hard-to-value" fundamentals, such as Bitcoin and stocks in new industries? We propose an equilibrium model that shows how rational learning enables return predictability through technical analysis. We document that ratios of prices to their moving averages forecast daily Bitcoin returns in- and out-of sample. Trading strategies based on these ratios generate an economically significant alpha and Sharpe ratio gains relative to a buy-and-hold position. Similar results hold for small-cap, young-firm, and low-analyst-coverage stocks as well as NASDAQ stocks during the dotcom era.
Keywords:Â Bitcoin, Cryptocurrency, Technical Analysis
Trading rules
- Target asset for investment: Exclusively Bitcoin.
- Obtain daily Bitcoin price data from: Coindesk.com.
- Determine the 10-day moving average on day t: Average of the prices from the last 10 days (not counting day t).
- Purchase indicator: Buy Bitcoin when the ratio of (price on day t-1) to (10-day moving average) exceeds 1.
- Sale indicator: Liquidate Bitcoin and move to a risk-free asset when the ratio of (price on day t-1) to (10-day moving average) is less than 1.
- Adjust positions daily.
- Only trade actively with 10% of the total portfolio.
Python code
Backtrader
import backtrader as bt
class MovingAverageStrategy(bt.Strategy):
params = dict(
ma_period=10,
active_pct=0.1
)
def __init__(self):
self.data_close = self.datas[0].close
self.sma = bt.indicators.SimpleMovingAverage(
self.datas[0], period=self.params.ma_period)
def next(self):
position_size = self.broker.get_value() * self.params.active_pct
if not self.position:
if self.data_close[-1] > self.sma[-1]:
self.buy(size=position_size / self.data_close[0])
else:
if self.data_close[-1] < self.sma[-1]:
self.sell(size=self.position.size)
if __name__ == '__main__':
cerebro = bt.Cerebro()
data = bt.feeds.GenericCSVData(
dataname='path/to/your/bitcoin_data.csv',
dtformat=('%Y-%m-%d'),
datetime=0,
high=2,
low=3,
open=1,
close=4,
volume=5,
openinterest=-1
)
cerebro.adddata(data)
cerebro.addstrategy(MovingAverageStrategy)
cerebro.broker.setcash(100000.0)
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
cerebro.run()
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())Remember to replace ‘path/to/your/bitcoin_data.csv’ with the actual path to your Bitcoin daily price data downloaded from Coindesk.com.