Fast Momentum (Filecoin)
Tier 1

Measures the momentum of an asset, calculated on shorter timeframes.

Overview

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FIL Price with Fast Momentum

Factor Plot

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

0.18

Overall

Fast Momentum Factor is a measure of the momentum of an asset, calculated based on a range different lookback periods.

Potential Edge

Mechanical Feedback Loops From Leveraged Trading

Leveraged positions and stop-loss orders create cascading buy/sell pressure. For example:

Rising prices trigger margin calls on short positions, forcing liquidations that accelerate upward momentum
Falling prices similarly amplify downward moves via long-position liquidations
Fast Momentum indicators detect these feedback loops early, as they manifest in abrupt price-rate changes on intraday charts

Network Effects & Asymmetric Information Flow

Cryptocurrency valuations often depend on viral adoption cycles and developer activity spikes. Faster Momentum indicators:

Detect early accumulation phases before network growth becomes public knowledge
Identify "hot" assets benefiting from protocol upgrades or partnerships not yet priced in
Studies show crypto momentum strategies generate alpha in 7-30 day windows, aligning with typical news dissemination lags

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Scatter plot - Fast Momentum and FIL 30 and 90 Day Average Returns

Backtest - Strategy Performance

	Benchmark (Filecoin)	Strategy
Max Drawdown	-98.9%	-91.7%
Volatility	152.5%	85.6%
Sharpe Ratio	0.65	0.76
CAGR	-5.7%	39.3%
Alpha	0.00	0.18
Beta	1.00	0.47

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_fast"
ticker= "FIL"
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)