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On this page
  • Key Features
  • Usage
  • Available Analyses and Visualizations
  • Performance Plot
  • Signal Decile Plot
  • Stress Test Plot
  • Drawdown Plot
  • Return Distribution Plot
  • Returns Heatmap
  • Signal Autocorrelation Plot
  • Portfolio Turnover Plot
  • Performance Statistics Table
  • Other Core Attributes

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  1. Asset Managment

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.

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Last updated 7 months ago

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Tutorials are the best documentation —

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

Signal Evaluation Tutorial