Fast-Track Your Stock Predictions with Qlib Quant#

Configuring Qlib#

By default, Qlib tries to use local data if it’s available. If you want to use pre-built data from remote sources, you’ll need some configurations. For instance, you can specify environmental variables or configuration files to link Qlib to your data directory.

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import qlib
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from qlib.config import C
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provider_uri = "/path/to/qlib_data"
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qlib.init(provider_uri=provider_uri)
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print(f"Qlib data directory is set to: {C['provider_uri']}")

You will see a message verifying that Qlib is initialized. You can always re-initialize Qlib with qlib.init() whenever you modify configurations.

Data Structure in Qlib#

Qlib organizes its data around a provider, usually a directory containing instruments and corresponding factors. A typical directory structure includes:

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qlib_data/
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├─ intraday/
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│  ├─ stocks/
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│  └─ ...
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└─ daily/
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   ├─ stocks/
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   │  ├─ SH600519/
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   │  ├─ SH000300/
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   │  └─ ...
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   └─ fields/
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      ├─ close/
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      ├─ open/
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      ├─ ...

Within each instrument folder, you will find binary files designed to load efficiently. Qlib also provides utilities to fetch or convert data from raw CSVs into its internal format.

Supported Markets#

While Qlib was first designed for the Chinese stock market, it can also be used for other regions. Community contributions extend coverage to NASDAQ, NYSE, and more. The official documentation includes instructions to prepare and convert data from Yahoo Finance or local data sources.

Local vs. Remote Data#

You can store all required data locally or connect to a remote data provider for large-scale usage. When remote data is used, Qlib can periodically fetch market updates, letting you keep your local store up-to-date without re-downloading everything.

Creating Custom Factors#

In quantitative research, factors are crucial to capturing meaningful signals. Qlib provides a factor interface and built-in transformations like Ref, Mean, Std, etc. An example of creating a custom factor might look like this in Qlib’s expression-based syntax:

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from qlib.data.dataset import DatasetD, TSDatasetH
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# Expression-based factor
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expression = "(Mean($close, 5) - Mean($close, 15))/Mean($close, 15)"
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# This expression calculates the difference between the 5-day and 15-day
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# moving average, normalized by the 15-day average price.

You can chain multiple expressions to build more sophisticated features. Alternatively, you can define Python-based transformations if you need maximum flexibility.

Rolling Features and Statistical Analysis#

Rolling or window-based features like moving averages, standard deviation, RSI, or Bollinger Bands provide short-term signals about momentum and volatility. Qlib’s interface makes it easy to define window-based features:

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short_term_ma = "Mean($close, 5)"
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long_term_ma = "Mean($close, 20)"
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rsi = "100 - (100 / (1 + (Mean($close,5)/Mean($close,5).shift(1))))"

A wide range of statistical and technical features can be created similarly. You can further combine them to construct composite signals.

Transformations and Normalizations#

Financial data often benefits from normalization or scaling. Qlib’s dataset pipeline allows transformations like MinMaxScaler or StandardScaler on your chosen features:

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from qlib.data.dataset.handler import DataHandlerLP
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from qlib.data.dataset.pipeline import MinMaxNorm
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class CustomDataHandler(DataHandlerLP):
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    def __init__(self, **kwargs):
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        super().__init__(instruments=kwargs["instruments"], fields=kwargs["fields"])
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    def get_data(self, start_time, end_time, fields=None, freq='day'):
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        # load data using Qlib's D interface or internal methods
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        data = super().get_data(start_time, end_time, fields, freq)
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        # Additional transformations
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        data = MinMaxNorm(feature_fields=["$close", "$volume"])(data)
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        return data

This approach helps keep all transformations in a single pipeline so that training, validation, and backtest phases are consistently using the same scaling procedures.

Built-in Models: LightGBM, XGBoost, etc.#

Qlib includes wrappers around common gradient boosting frameworks such as LightGBM and XGBoost. These wrappers simplify model training and reduce boilerplate code:

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from qlib.contrib.model.gbdt import LGBModel
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config = {
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    "learning_rate": 0.01,
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    "num_leaves": 31,
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    "num_threads": 4,
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}
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model = LGBModel(**config)
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model.fit(X_train, y_train, eval_set=[(X_val, y_val)])

You can also integrate neural network models via PyTorch or TensorFlow, though that requires additional configuration and familiarity with deep learning frameworks.

Model Configuration#

For more advanced usage, create YAML or JSON files to hold your configuration:

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model:
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  class: LGBModel
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  module_path: qlib.contrib.model.gbdt
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  kwargs:
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    learning_rate: 0.01
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    num_leaves: 31
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    num_threads: 4

Then load this configuration in your Python script:

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from qlib.config import load_yaml_conf
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model_conf = load_yaml_conf("path/to/model_config.yaml")["model"]
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model_cls = getattr(import_module(model_conf["module_path"]), model_conf["class"])
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model_kwargs = model_conf.get("kwargs", {})
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model = model_cls(**model_kwargs)

This design approach keeps code tidy and allows quick reconfiguration.

Training and Hyperparameter Tuning#

Qlib integrates with parameter tuning libraries, enabling you to systematically test different hyperparameters:

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from qlib.contrib.model.tuner import LightGBMTuner
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param_space = {
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    'num_leaves': [31, 63, 127],
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    'learning_rate': [0.1, 0.01, 0.001],
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}
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tuner = LightGBMTuner(model, param_space, X_train, y_train, X_val, y_val)
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best_params, best_score = tuner.tune()
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model.set_params(**best_params)
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model.fit(X_train, y_train)

By automating the search for optimal hyperparameters, you save critical time and ensure your models are more robust.

How Qlib Handles Backtesting#

Qlib provides a Collector and Executor framework to handle backtesting. You define how signals are generated (by your model) and how they translate into trades. For instance, if your model predicts future returns, you can pick the top 10 stocks each day to buy.

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from qlib.workflow.online.strategy import TopkDropoutStrategy
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from qlib.workflow.online.executor import SimulatorExecutor
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strategy = TopkDropoutStrategy(topk=10, n_drop=3)  # example
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executor = SimulatorExecutor(
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    time_per_step='day',
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    generate_report=True
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)
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# Run the backtest with your signals
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executor.run(strategy)

Performance Metrics and Evaluation Techniques#

Common evaluation metrics in Qlib include:

• Annualized return
• Information ratio (IR)
• Max drawdown (MDD)
• Sharpe ratio

You can retrieve these via Qlib’s BacktestIndicator or Evaluator classes:

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from qlib.contrib.evaluate import backtest, indicator_analysis
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report_df, positions = backtest(...)
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analysis_df = indicator_analysis(report_df, positions["strategy"])
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print(analysis_df)

Analyzing Results#

Evaluation often goes beyond just computing numbers. Visualizations of equity curves, drawdown over time, factor exposure, and risk decomposition are crucial. Qlib’s framework offers integrated plotting or you can easily export your metrics to further libraries like Matplotlib or Plotly:

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import matplotlib.pyplot as plt
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report_df["account_value"].plot()
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plt.title("Portfolio Equity Curve")
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plt.show()

Alpha Research and Factor Investing#

Alpha generation relies on discovering novel factors that can anticipate price movements. Once you’ve mastered simple technical indicators, you can move to advanced signals:

• Fundamental factors: Combining financial statements, intangible metrics, or alternative data sources.
• Sentiment and alternative data: Using news sentiment, social media, or satellite imagery.
• Timing and momentum combinations: Combining multiple horizons of momentum signals.

Qlib’s flexible pipeline can incorporate these inputs, letting you systematically evaluate which factors hold predictive power.

Ensemble Models and Stacking#

Beyond standalone models, Qlib supports stacking and blending multiple models to reduce overfitting and diversify factor exposures:

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from qlib.contrib.model.ensemble import EnsembleModel
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ensemble_model = EnsembleModel(
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    models=[LGBModel(**config1), LGBModel(**config2)],
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    ensemble_method="average"
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)
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ensemble_model.fit(X_train, y_train)
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predictions = ensemble_model.predict(X_test)

You can also expand into more sophisticated ensemble techniques like gradient blending or meta-learners to boost performance in volatile markets.

Event-Driven Signals and Intraday Analysis#

While daily data is a common starting point, intraday or event-based approaches can yield more nuanced signals:

• Intraday bars: 1-minute, 5-minute intervals, or tick feeds.
• Event detection: Generating signals when an economic indicator is released or when unusual volume is detected.

Qlib’s workflow modules help handle these contexts so that your pipeline can capture fleeting market inefficiencies.

Scheduling Model Retraining#

In a production environment, you typically need to re-train models periodically. Tools like cron jobs or continuous integration (CI) can trigger Qlib scripts:

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# cron entry: retrain every Monday at 6 AM
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0 6 * * 1 /path/to/env/bin/python /path/to/your_retrain_script.py

Within your retrain script:

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qlib.init()
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# fetch new data
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# reprocess features
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# retrain model
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# backtest
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# store model

Continuous Data Collection#

If you rely on external data (like Yahoo Finance or a commercial data feed), set up an ingestion pipeline that updates your local Qlib data daily. Qlib bundles scripts for Yahoo data ingestion, or you can write your own for custom data.

Pipeline Integration with Airflow, Luigi, or Others#

For more complex workflows involving multiple dependencies (e.g., updated fundamentals, sentiment analysis, factor engineering, model training, backtesting, deployment), a pipeline orchestrator like Airflow or Luigi offers features for scheduling, monitoring, and retrying tasks.

On-Premise vs. Cloud Deployments#

As your data grows, you may need more computing resources:

• On-Premise: If you have in-house servers with GPUs and low-latency networking to data sources, setting up an internal cluster can be a cost-effective long-term solution.
• Cloud (AWS, Azure, GCP): For burst computing, flexible scaling, and distributed storage, spinning up containers or clusters in the cloud is often simpler. Qlib runs smoothly on standard VM instances or managed Kubernetes clusters.

Running Qlib on Big Data Clusters#

For extremely large datasets (e.g., intraday data across thousands of stocks):

• Consider Apache Spark-based ingestion to process raw data in parallel.
• You can store factor outputs in distributed file systems like HDFS.
• Use Qlib’s custom data handlers that read from big data file formats (Parquet, ORC) if needed.

Using Docker and Containerization#

Dockerizing your Qlib setup ensures consistency and reproducibility across environments. You can define a Dockerfile that installs Python, Qlib, and all dependencies:

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FROM python:3.9-slim
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RUN apt-get update && apt-get install -y --no-install-recommends build-essential
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RUN pip install pyqlib
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WORKDIR /app
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COPY . /app
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CMD ["python", "your_script.py"]

Then run a container:

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docker build -t qlib_env .
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docker run --name qlib_container qlib_env

With containers, you can easily deploy your pipeline across various servers and orchestrators like Kubernetes.

Risk Management and Hedging Techniques#

Professional-level quantitative strategies need robust risk management. Though Qlib focuses on alpha generation and backtesting, you can integrate risk overlays in your models or backtesting logic:

• Position sizing: Adjust proportionally based on volatility or risk budgets.
• Stop-loss or trailing stops: Automated exit logic for losing positions.
• Factor hedging: If your strategy is heavily exposed to a single factor (e.g., momentum), you can apply hedging with futures or inversely correlated assets.

These advanced risk overlays require careful integration but significantly reduce downside risk.