Signal Evaluation
This module provides a wide array of analytical tools and visualizations to help quantitative analysts and portfolio managers evaluate the quality, consistency, and robustness of their alpha signals.
Tutorials
are the best documentation — Signal Evaluation Tutorial
Key Features
Comprehensive performance analysis
Risk-adjusted return metrics
Stress testing capabilities
Drawdown analysis
Return distribution analysis
Signal persistence evaluation
Usage
To use the Signal Evaluator, you first need to prepare your signal data. The module expects a DataFrame containing your signal values. Once you have your data ready, you can initialize the Signal Evaluator as follows:
import sovai as sov
# Authenticate
sov.token_auth(token="your_authentication_token")
# Prepare your signal data
df_signal = sov.data("your_signal_data_source")
# Initialize the Signal Evaluator
= df_signal.signal_evaluator()
Available Analyses and Visualizations
Performance Plot
This visualization helps in understanding the overall effectiveness of the signal and its risk-adjusted performance over time.
.performance_plot

This plot provides a comprehensive view of the signal's performance over time. It includes:
Cumulative returns of the strategy
A 95% confidence interval based on random simulations
A rolling Sharpe ratio on a secondary y-axis
Signal Decile Plot
This helps in understanding how different levels of the signal correspond to future returns, providing insights into the signal's predictive power across its range.
.signal_decile_plot

This plot breaks down the signal's performance by strength, showing:
Cumulative returns for each signal decile
Average Sharpe ratios for each decile
Stress Test Plot
.stress_plot

This visualization shows how the signal performs during various historical market stress events, helping to assess:
Strategy robustness during market crises
Potential for drawdowns during extreme market conditions
Comparative performance against benchmark during stress periods
Drawdown Plot
.drawdown_plot

This plot visualizes the drawdowns of the strategy over time, helping to understand:
Magnitude of historical drawdowns
Frequency of drawdowns
Recovery periods
Return Distribution Plot
This plot helps in understanding the risk profile of the strategy and the likelihood of extreme returns.
.distribution_plot

This histogram shows the distribution of strategy returns, typically including:
Mean return
Standard deviation
Skewness and kurtosis
Various risk metrics (e.g., VaR, CVaR)
Returns Heatmap
.returns_heatmap_plot

This heatmap displays strategy returns across different months and years, useful for identifying:
Seasonal patterns in performance
Consistency of returns over time
Years or months of outperformance/underperformance
Signal Autocorrelation Plot
.signal_correlation_plot

This plot shows the autocorrelation of the signal over time, providing insights into:
Signal persistence
Potential for mean reversion
Optimal holding periods
Portfolio Turnover Plot
.turnover_plot

This visualization depicts portfolio turnover over time, separated into long and short positions. It helps in assessing:
Trading costs
Strategy stability
Potential capacity constraints
Performance Statistics Table
.performance_table


This comprehensive table presents key performance statistics, including:
Annualized returns
Sharpe ratio
Sortino ratio
Maximum drawdown
Calmar ratio
Other relevant performance indicators
10. Drawdown Analysis Table
.drawdown_table

This table provides detailed information about the worst drawdown periods, including:
Drawdown magnitude
Duration of drawdowns
Recovery times
Other Core Attributes
The Signal Evaluator also provides access to several core attributes for further analysis:
evaluator.positions
: Initial portfolio holdings derived from the signalevaluator.rebalance_mask
: Boolean mask indicating rebalancing scheduleevaluator.holdings
: Actual portfolio holdings after applying rebalancingevaluator.returns
: Returns of the underlying assetsevaluator.position_returns
: Returns of the portfolio positionsevaluator.resampled_returns
: Returns resampled to match rebalancing frequencyevaluator.portfolio_returns
: Aggregate portfolio returnsevaluator.cumulative_returns
: Cumulative performance of the portfolio
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