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:

1. evaluator.positions: Initial portfolio holdings derived from the signal

2. evaluator.rebalance_mask: Boolean mask indicating rebalancing schedule

3. evaluator.holdings: Actual portfolio holdings after applying rebalancing

4. evaluator.returns: Returns of the underlying assets

5. evaluator.position_returns: Returns of the portfolio positions

6. evaluator.resampled_returns: Returns resampled to match rebalancing frequency

7. evaluator.portfolio_returns: Aggregate portfolio returns

8. evaluator.cumulative_returns: Cumulative performance of the portfolio