US Cloud Computing Sector Momentum Spillover (Cardano)
Tier 1

Measures the asset's sensitivity to the US Cloud Computing Index, captures the lead-lag relationship between the two assets.

Overview

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ADA Price with US Cloud Computing Sector Momentum Spillover

Factor Plot

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▆ Very Low▆ Low▆ Moderate▆ High▆ Very High

0.33

Overall

Measures the asset's sensitivity to the US Cloud Computing Index, captures the lead-lag relationship between the two assets.

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Scatter plot - US Cloud Computing Sector Momentum Spillover and ADA 30 and 90 Day Average Returns

Backtest - Strategy Performance

	Benchmark (Cardano)	Strategy
Max Drawdown	-91.9%	-72.6%
Volatility	102.2%	75.4%
Sharpe Ratio	1.05	1.37
CAGR	77.2%	113.3%
Alpha	0.00	0.33
Beta	1.00	0.65

Predictive factors are designed to be translated into simple long-only strategy, with simulated past performance:

100% Long when the predictive factor is close to 1, with a position size equivalent to the predictive factor value.
Flat when the predictive factor is close to 0, with a position size equivalent to the predictive factor value.

The strategy is rebalanced daily, on a continuous basis. There are 0.05% transaction costs applied on each position adjustment.

Replicate the backtest

Get started by replicating the historical performance with our code snippets.

from api import get_normalized_series, get_price_series
from backtest import vectorized_backtest
from plotting import plot_backtest_results


UNRAVEL_API_KEY = "YOUR-API-KEY"   
risk_factor = "momentum_spillover_equities_cloud"
ticker= "ADA"
start_date = "2022-01-01"
end_date = "2024-06-01"
smoothing = 0


risk_factor_signal = get_normalized_series(ticker, risk_factor, start_date, end_date, smoothing, UNRAVEL_API_KEY)
price = get_price_series(ticker, start_date, end_date, UNRAVEL_API_KEY)
price = price[risk_factor_signal.index]
results = vectorized_backtest(price, risk_factor_signal)

plot_backtest_results(results, ticker, risk_factor, smoothing)