Under the Hood: How Qlib Manages Feature Extraction#

Data Lifecycle in Qlib#

Qlibs data lifecycle typically follows these steps:

1. Data ingestion: Qlib ingests data in a standardized formatoften using CSV or HDF5 for local storage.
2. Automatic organization: Qlib auto-organizes data by instruments (e.g., stock tickers) and calendar dates, making retrieval efficient for time-series analysis.
3. Expression application: Once the data is organized, Qlibs Expression engine can systematically apply transformations and calculations to generate features.
4. Caching: Intermediate results can be cached to speed up further operations.
5. Feedback loops: Analysts review, refine, and expand feature sets iteratively based on model performance and domain insights.

Basic Terminologies#

• Instruments: These are entities you want to analyzestocks, ETFs, indexes, or any tradeable asset.
• Calendar: A chronological index defining trading days or reference points for your time-series data.
• Feature: A transformed attributelike a moving average or a statistical measureintended for model consumption.
• Label: The target?variable in a supervised learning setting, such as future returns.
• Expression: A symbolic representation of transformations or custom calculations within Qlib.

Knowing how Qlib organizes instruments, calendars, and expressions ensures you can place your features in the correct context. This structure is central to Qlibs design, enabling it to handle large-scale data with relative ease.

What Are Expressions?#

At the heart of Qlibs feature extraction lies the Expression engine. An Expression is a symbolic representation of the transformation you want to apply to a dataset. For example:

• A simple expression might be (Ref($close, 1) - $close) / $close, which calculates yesterdays close price minus todays close price, then divides by today’s close.
• A complex expression might involve multiple chained operators or rolling windows, like Mean($volume, 20) / $volume.

Expressions abstract away the complexities of looping over large datasets. Qlib will compile?these expressions and apply them across instruments and time periods efficiently.

Supported Operators and Functions#

Qlib provides a rich set of built-in functions for you to combine and chain as needed:

1. Arithmetic operators: +, -, *, /, **
2. Statistical functions: Mean, Std, Var, Sum
3. Rolling-window aggregates: Rolling, RollingSum, RollingMean
4. Comparison operators: >, <, >=, <=, ==, !=
5. Logical operators: If, And, Or

The Expression engine is flexible, allowing you to nest and chain these functions in numerous ways. Moreover, you can register new operators to accommodate custom needs (discussed in detail later).

Feature and Labels#

Typically, a feature?is any input to your predictive modelsuch as a technical factor or a fundamental ratio. By contrast, a label?is a quantitative target (like next-day percent change in a stocks price). Qlibs pipeline can seamlessly handle both. Often, you define features and labels together in a configuration or script:

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features = [
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    # Simple moving average
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    Expression("Mean($close, 5)"),
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    # Relative Strength Index
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    Expression("RSI($close, 14)"),
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]
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labels = [
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    # Future 5-day return
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    Expression("(Ref($close, -5) - $close) / $close")
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]

Here, we define two features: a 5-day moving average of closing prices and a 14-day RSI. We also define a label: the 5-day forward return. Qlibs expression syntax (Ref($close, -5) means shift the close price 5 days into the future?if your calendar is in ascending order).

Configuration and YAML Files#

While you can define your features and labels directly in Python, Qlib often uses YAML configuration files to store these definitions for better maintainability. An example portion of a YAML file might look like this:

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features:
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  - name: SMA_5
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    expr: "Mean($close, 5)"
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  - name: RSI_14
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    expr: "RSI($close, 14)"
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labels:
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  - name: FUT_RET_5
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    expr: "(Ref($close, -5) - $close) / $close"

When you load this YAML, Qlib parses these expressions and computes the corresponding features. This approach is particularly beneficial if your research team wants a clear, non-code-based reference for all features in use.

Simple Technical Indicators#

Lets try a hands-on example to illustrate how to define and extract a common technical indicatoran exponential moving average (EMA). Assume you have a local Qlib setup with daily stock data.

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import qlib
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from qlib.data import D
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from qlib.config import REG_CN
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from qlib.workflow import R
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# Initialize Qlib
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qlib.init(provider_uri="~/.qlib/qlib_data/cn_data", region=REG_CN)
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# Define a simple feature: EMA(12)
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# Qlib uses the syntax "EMA($close, window_size)"
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feature_ema_12 = "EMA($close, 12)"
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# Fetch data
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instruments = ["SH600000"]  # Example: a single stock
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fields = [feature_ema_12]
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df_features = D.features(instruments, fields, freq="day", inst_processor=None)
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print(df_features.head(10))

This code snippet accomplishes the following:

1. Initializes Qlib with a specified data provider.
2. Defines a single feature: a 12-day EMA of the closing price.
3. Calls D.features to fetch data for that feature over a given frequency (daily).

Youll get a Pandas DataFrame indexed by datetime and containing the computed EMA for SH600000.

Comparisons and Logical Operations#

You can also use logical operations to shape your features. For example, lets say you want to generate a feature highlighting whether the closing price is above or below the 20-day average:

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feature_close_above_ma20 = "If($close > Mean($close, 20), 1, 0)"

This expression checks if the current close price is greater than its 20-day average. If true, it returns 1; otherwise, 0. This binary feature can help you quickly filter or classify based on price momentum.

Chaining Multiple Expressions#

One of Qlibs strengths is its ability to chain multiple expressions in a single pipeline. For instance, you might wish to first calculate a 10-day standard deviation of returns, then apply a threshold to label high-volatility periods:

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feature_chain_example = "If(Std((Ref($close, 1) - $close)/$close, 10) > 0.02, 1, 0)"

This expression:

1. Computes the daily returns as (Ref($close, 1) - $close)/$close.
2. Takes the 10-day standard deviation of these returns.
3. Compares the result with 0.02 (2% volatility).
4. Generates a binary output indicating if volatility exceeds that threshold.

Rolling Windows and Statistical Features#

Rolling-window operations are vital in feature extraction, especially for time-series data. Qlib comes with rolling-window functions to simplify this process. Consider a 10-day rolling maximum drawdown measure:

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feature_roll_drawdown = "MaxDrawdown($close, 10)"

Under the hood, Qlib calculates the largest peak-to-trough decline within the last 10 days. This feature can signal unusually large price drops, often important in risk management.

You can extend rolling operations with multiple statistical measures:

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feature_roll_stats = "Mean($close, 20) / Std($close, 20)"

This expression, effectively a rolling Sharpe-like ratio (excluding risk-free rate), compares the average price to its standard deviation over 20 days.

Feature Combination Techniques#

Sometimes you need to combine multiple features. Lets say you have a short-term momentum feature Moment_5 = Mean($close, 5) - $close and a long-term momentum feature Moment_20 = Mean($close, 20) - $close. You can combine them into one:

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combined_momentum = "((Mean($close, 5) - $close) + (Mean($close, 20) - $close))/2"

Alternatively, you could compare them to pick whichever momentum is stronger:

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momentum_signal = "If((Mean($close, 5) - $close) > (Mean($close, 20) - $close), 1, 0)"

Creating a Custom Operator#

Qlibs Expression engine also allows developers to build custom operators. Suppose you want a specialized factor called Price Volatility Skew?that requires a combination of advanced mathematical steps not covered by built-in operators. You can do this by:

1. Defining a Python function that calculates your desired result, e.g., price_volatility_skew(prices, window).
2. Registering the function with Qlib so it can be called in expressions like PVS($close, 14).

Heres a simplified skeleton:

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import pandas as pd
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from qlib.data.data import Cal
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def my_custom_operator(series: pd.Series, window: int) -> pd.Series:
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    # Perform custom operation
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    # For example, compute skew:
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    return series.rolling(window).skew()
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# Registering a local function
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from qlib.data.ops import register_numpy_ufunc
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register_numpy_ufunc('SKEW', my_custom_operator)
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# Now you can use it in an expression
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feature_skew_14 = "SKEW($close, 14)"

This approach gives you the power to design unique factors that might give you an edge in the market.

Overriding Built-In Functions#

In some cases, you might want to modify the behavior of built-in functions (for example, to optimize performance for a certain dataset). While not always recommended, Qlibs open architecture makes it possible to override or extend these functions. Youll typically do this only if you have very specific performance or computational requirements.

Data Preparation#

Assume you have daily stock data for a universe of 100 companies. You configure Qlib as follows:

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import qlib
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from qlib.config import REG_US
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qlib.init(provider_uri="~/.qlib/qlib_data/us_data", region=REG_US)

Make sure your data directory (provider_uri) has all the CSVs or the Qlib data format files. Then define an instrument set:

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instruments = [
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    "AAPL", "MSFT", "AMZN", ..., "FB"
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]

Feature Extraction Pipeline#

Next, construct a feature pipeline in Python or YAML. For illustrative purposes, well do it in Python to maintain tight coupling with the code:

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from qlib.data import D
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features = [
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    "Mean($close, 5)",
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    "Mean($close, 20)",
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    "Std($return, 5)",     # rolling std of returns
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    "RSI($close, 14)",
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    "BOLL($close, 20)",   # Bollinger Bands
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]
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labels = [
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    "(Ref($close, -5) - $close) / $close"  # future 5-day return
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]
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# Fetch the data
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df = D.features(instruments, features + labels, freq="day")
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print(df.head())

A few points to note:

• $return is often a built-in alias for (Ref($close, 1) - $close)/$close.
• BOLL($close, 20) might return multiple columns (e.g., upper band, middle band, lower band), depending on your Qlib version.
• The final labels are appended to the feature list for convenience, but you could fetch them separately.

At this stage, you have a DataFrame of input features and labels, indexed by (instrument, date) or a multi-level structure, depending on your Qlib configuration.

Model Training and Evaluation#

While the details of model training can be more elaborate, a basic pipeline might look like:

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import pandas as pd
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from sklearn.ensemble import RandomForestRegressor
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from qlib.utils import flatten_df
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# Flatten multi-level columns if needed
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df_flat = flatten_df(df)
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# Drop rows with missing values
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df_flat = df_flat.dropna()
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# Suppose we store features in X and labels in y
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feature_cols = [col for col in df_flat.columns if col not in ["LABEL0"]]
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X = df_flat[feature_cols]
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y = df_flat["LABEL0"]
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# Split into train and test sets based on date or random
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train_df = df_flat[df_flat.index.get_level_values("datetime") < pd.to_datetime("2020-01-01")]
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test_df = df_flat[df_flat.index.get_level_values("datetime") >= pd.to_datetime("2020-01-01")]
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X_train = train_df[feature_cols]
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y_train = train_df["LABEL0"]
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X_test = test_df[feature_cols]
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y_test = test_df["LABEL0"]
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# Train a simple model
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model = RandomForestRegressor(n_estimators=100)
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model.fit(X_train, y_train)
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# Evaluate
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y_pred = model.predict(X_test)
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# Compute a simple metric like MSE
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from sklearn.metrics import mean_squared_error
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mse = mean_squared_error(y_test, y_pred)
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print("Test MSE:", mse)
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# You could proceed to build a trading strategy with the predictions

Here, your extracted features from Qlib feed directly into a scikit-learn model. Of course, Qlib also provides model training utilities and advanced forecasting wrappers, but the above snippet shows how standard ML libraries can integrate seamlessly with Qlib features.

Caching and Parallelization#

When you have a large universe of instruments or a deep history of daily data, feature extraction can become computationally expensive. Qlib provides caching mechanisms that store intermediate results so that repeated queries for the same data dont trigger time-consuming recomputations. Some tips:

• Enable local caching: Qlib often caches results in memory or disk. Verify that this setting is on for repeated experiments.
• Parallelization: On multi-core systems, Qlib can parallelize feature calculations across instruments or time segments (depending on your configuration). Verify your environment settings (num_workers) to utilize additional cores efficiently.

Memory Management#

Data for thousands of instruments over many years can be massive. Keep an eye on memory usage:

• Chunk your data: If your calculations exceed system memory, consider chunking the data by instrument or date ranges.
• Use sparse representations: For certain features that generate sparse data, you can store them in memory-efficient formats.
• Monitor memory through OS-level tools whenever you run large-scale experiments.

Version Control for Features#

Because features are so central to your success, its crucial to maintain discipline in how you track changes. Some strategies:

• Put your YAML or Python-based feature definitions under version control (e.g., Git).
• Tag or label commits whenever you add or remove features.
• Document the rationale behind each feature addition or removal.

Multi-Modal Data and Custom Datasets#

Qlib isnt limited to just price and volume data. You could integrate:

• Fundamental data such as balance-sheet figures, profitability ratios, industry classification.
• Alternative data like web traffic, satellite imagery, or environmental metrics.
• News sentiment gleaned from textual sources.

Defining a custom dataset is straightforward: once you have structured data, you can register it in Qlibs data provider and apply the Expression engine as usual. Multi-modal setups often unlock deeper insights, but be mindful of data gaps and alignment across various sources.

Feature Importance and Interpretability#

With increasing model complexity, interpretability becomes a challenge. Although Qlib focuses on data handling, you can leverage popular ML frameworks to gauge feature importance:

• Permutation importance: Evaluate how randomizing a features values impacts model performance.
• SHAP (SHapley Additive exPlanations): A method that attributes each prediction to contributions from each feature.

You can store these interpretations in Qlibs workflow system for reference. Tracking feature importance over time helps guide feature engineering priorities.

Integrating Qlib with Other Tools#

Many practitioners adopt a hybrid toolchain, especially in institutional environments. Qlibs modular design lets you integrate with:

• Data version control systems (e.g., DVC) for tracking large datasets.
• Apache Airflow or Luigi for scheduling pipelines.
• RESTful APIs to pull live data or backtesting results.

By incorporating Qlib as the center of your quant workflow, you keep a consistent approach to feature extraction while capitalizing on your institutions existing infrastructure.