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alphalens-reloaded

repository·main·Indexed 18 days ago

https://github.com/stefan-jansen/alphalens-reloaded

A Python library for the performance analysis of predictive (alpha) stock factors. It provides tools to evaluate factor quality through returns analysis, information coefficient (IC) analysis, turnover analysis, and grouped analysis. The library includes modules for calculating performance and risk metrics, visualization via plotting functions, and the generation of comprehensive factor 'tear sheets'.

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What's inside alphalens-reloaded

  1. Overview of Alphalens analysis capabilities

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    Alphalens is a Python library designed for the performance analysis of predictive (alpha) stock factors. It surfaces relevant statistics and plots through four main types of analysis:

    • Returns Analysis: Evaluating the returns associated with the factor.
    • Information Coefficient Analysis: Measuring the predictive power of the factor.
    • Turnover Analysis: Assessing how frequently the factor requires rebalancing.
    • Grouped Analysis: Analyzing factor performance across different groups (e.g., sectors).

  2. Overview of the alphalens API modules

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    The alphalens-reloaded API is organized into four primary modules, each serving a specific stage of the factor analysis pipeline:

    1. Tear Sheets (alphalens.tears): Used to generate thematic and summary plots that combine key performance metrics.
    2. Performance Metrics (alphalens.performance): Provides the underlying mathematical calculations for performance and risk metrics.
    3. Plotting Functions (alphalens.plotting): Facilitates the visualization of the metrics calculated by the performance module.
    4. Utilities (alphalens.utils): Contains helper functions, specifically for formatting factor data into the required input formats for analysis.

  3. Create a factor tear sheet

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    Creating a factor tear sheet is a two-step process: first, ingest and format your signal and pricing data using alphalens.utils.get_clean_factor_and_forward_returns, then run the analysis using alphalens.tears.create_full_tear_sheet.

    import alphalens

# Ingest and format data
factor_data = alphalens.utils.get_clean_factor_and_forward_returns(my_factor,
                                                                   pricing,
                                                                   quantiles=5,
                                                                   groupby=ticker_sector,
                                                                   groupby_labels=sector_names)

# Run analysis
alphalens.tears.create_full_tear_sheet(factor_data)

  4. Install alphalens-reloaded

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    You can install alphalens-reloaded using pip, conda, or directly from the GitHub master branch for development purposes.

    # Install with pip
pip install alphalens-reloaded

# Install with conda
conda install -c ml4t alphalens-reloaded

# Install from the master branch (development code)
pip install git+https://github.com/stefan-jansen/alphalens-reloaded

  5. Configure Event Study parameters

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    When running an event study, pay attention to these configuration settings to ensure correct statistical behavior:

    • filter_zscore: Set to None if you do not want any filtering to be performed on the signal.
    • quantiles vs bins: For a single event study, you typically want only one bin/quantile. Set quantiles=None and bins=1.
      • Note: In older pandas versions, using bins=1 with identical values might cause a ValueError. A workaround is to provide a custom range, e.g., bins=[-1000000, 1000000].
    • long_short: When using alphalens.tears.create_event_study_tear_sheet, you do not need to set this explicitly. However, if using other Alphalens functions for event studies, set long_short=False. Setting it to True performs forward return demeaning, which is intended for dollar-neutral portfolios, not typical event studies.
    • Signal Polarity: To analyze a short signal, ensure your event values are negative (events = -events).

  6. Customize plotting context and axes style

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    Alphalens provides utilities to manage the visual style of plots using Seaborn contexts and axes styles. You can use plotting_context and axes_style within a with statement to apply custom settings like font scale, color palettes, or grid styles to your plots.

    Additionally, many Alphalens plotting functions are decorated with @customize, which automatically applies a default colorblind palette and specific axes styles unless explicitly disabled via the set_context=False argument.

    import alphalens

# Use custom context for all plots in this block
with alphalens.plotting.plotting_context(font_scale=2.0):
    with alphalens.plotting.axes_style(style='whitegrid'):
        # Call plotting functions here
        # Note: pass set_context=False to prevent the decorator from overriding your context
        alphalens.create_full_tear_sheet(..., set_context=False)

  7. Configure event study parameters in Alphalens

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    When preparing data for an event study, pay attention to these configuration settings to ensure correct statistical behavior:

    • filter_zscore: Set to None if you do not want any filtering to be performed on your events.
    • quantiles vs bins: For an event study where you are looking at a single specific event rather than a ranked universe, you typically want only one group. Set quantiles=None and bins=1 (or vice versa).
    • long_short: When using Alphalens functions for event studies, set long_short=False. Setting it to True performs forward return demeaning, which is intended for dollar-neutral portfolios. Event-style signals usually cannot be treated as dollar-neutral.
    • Pandas Compatibility: In older pandas versions (< 0.20.0), qcut or cut might throw a ValueError if identical values are present. A workaround is to provide a custom range for bins that covers all values, e.g., bins=[-1000000, 1000000].

  8. Understand the return format of create_pyfolio_input()

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    The create_pyfolio_input() function returns a tuple of three elements:

    1. returns (pd.Series): Daily non-cumulative strategy returns as decimal values.

      • Example: 2015-07-16 -0.012143

    2. positions (pd.DataFrame): A time series of the amount invested in each position and cash.

      • If capital is not provided, these are percentages.
      • If capital is provided, these are dollar amounts.
      • Includes a 'cash' column to represent non-working capital.
      • Example:
        index         'AAPL'         'MSFT'          cash
2004-01-09    13939.3800     -14012.9930     711.5585

    3. benchmark_rets (pd.Series): Daily benchmark returns. If the benchmark_period is not found in the factor_data columns, this returns None.

  9. Avoid lookahead bias when preparing pricing and factor data

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    When preparing data for Alphalens, ensure that the pricing DataFrame contains the entry price that is available after the factor value is observed.

    Key Rules:

    1. Entry Price: Must reflect the next available price after a factor value is observed at a given timestamp. If you observe a factor at the close of day $T$, the entry price should be the open of day $T+1$.
    2. Exit Price: For a period of $N$, the exit price will be the price $N$ timestamps after the entry price.
    3. Alignment: Factor and price DataFrames must be properly aligned to ensure that factor values are not calculated using information from the prices used for entry/exit.

  10. Prepare data for intraday factor analysis

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    When analyzing intraday factors, you must ensure the pricing DataFrame contains both the entry and exit prices and that the factor is aligned with the entry price timestamp to avoid lookahead bias.

    Key requirements:

    • Pricing Data: Must contain the entry price (the price at the exact timestamp the factor is observed) and the exit price (the price used to compute forward returns).
    • Alignment: The factor index must match the pricing index at the moment of trade execution (e.g., market open).
    • Lookahead Bias: Ensure the prices used to calculate the factor are not included in the pricing DataFrame at the same timestamp used for the factor's observation.

    Example of aligning an intraday factor to market open and combining open/close prices:

    # Adjust timestamps to specific market times
today_open.index += pd.Timedelta('9h30m')
today_close.index += pd.Timedelta('16h')

# Combine into a single pricing DataFrame
pricing = pd.concat([today_open, today_close]).sort_index()

# Align factor to the open price timestamp
factor.index += pd.Timedelta('9h30m')
factor = factor.stack()
factor.index = factor.index.set_names(['date', 'asset'])

  11. Analyze factor returns and performance metrics

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    Returns analysis evaluates the predictive power of a factor in terms of currency/percentage returns.

    Key Metrics:

    • Mean Return by Quantile: Measures how well the factor differentiates returns across signal quantiles. Ideally, higher quantiles should show higher returns.
    • Returns Spread: The basis point spread between the top and bottom quantiles over time.
    • Cumulative Returns: Shows the growth of returns for specific quantiles or the entire long/short factor.
    • Alpha and Beta: Determines the annualized alpha and beta of the factor.

    Tear Sheets: Use alphalens.tears.create_returns_tear_sheet(factor_data) to generate a comprehensive visual report of all returns-based metrics.

    # Compute mean returns by quantile (not by date)
mean_return_by_q, std_err_by_q = alphalens.performance.mean_return_by_quantile(factor_data, by_date=False)

# Plotting results
alphalens.plotting.plot_quantile_returns_bar(mean_return_by_q)

# Create a full returns tear sheet
alphalens.tears.create_returns_tear_sheet(factor_data)

  12. Generate Alphalens tear sheets

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    Alphalens provides several high-level functions to generate comprehensive visual reports (tear sheets) for different types of analysis. These functions take the factor_data (the output from get_clean_factor_and_forward_returns) as input.

    Available Tear Sheets:

    • create_returns_tear_sheet(factor_data): Focuses on returns and quantile performance.
    • create_information_tear_sheet(factor_data): Focuses on Information Coefficient (IC) and predictive skill.
    • create_turnover_tear_sheet(factor_data): Focuses on signal stability and turnover.
    • create_event_returns_tear_sheet(factor_data, pricing, by_group=True): Analyzes cumulative returns in a window before and after a factor event. Requires pricing data.
    • create_summary_tear_sheet(factor_data): A quick snapshot of key performance indicators.
    • create_full_tear_sheet(factor_data): Combines all the above analyses into one massive report.
    # For a quick overview
alphalens.tears.create_summary_tear_sheet(factor_data)

# For everything at once
alphalens.tears.create_full_tear_sheet(factor_data)