Streamlining Quantitative Strategies with Qlib Quant#

Data Handler#

At the core of Qlib is its data handling system. Qlib organizes data (e.g., daily stock prices, volumes, fundamental data, etc.) into a convenient format that allows for straightforward slicing and dicing. The platform also offers features like:

• Automatic Data Sourcing: You can download data directly from publicly available sources or integrate your own.
• Efficient Loading: Data for multiple instruments and time periods can be fetched using a consistent interface.
• Caching: Qlib employs caching mechanisms to accelerate repeated data fetches.

Feature Engineering#

Financial modeling often hinges on factors or features extracted from raw data. Qlib provides a flexible pipeline for feature engineering:

• User-Defined Functions: You can define your own transformations (e.g., rolling averages, momentum indicators, or complex machine learning features).
• Pandas-Like Operations: Many feature engineering tasks are kept intuitive, thanks to Qlib’s DataFrame-like data structures.
• Built-In Factor Library: Qlib offers a range of pre-built financial factors for quick experimentation.

Model Interface#

After creating your features, you can pass them into one of Qlib’s model interfaces:

• Traditional Machine Learning: Linear regression, tree-based methods, and more.
• Deep Learning: Neural networks can be integrated, leveraging various backends like PyTorch or TensorFlow.
• Custom Models: Qlib is easily extensible, letting you implement specialized models specific to your domain or strategy.

Evaluation and Metrics#

Qlib goes beyond just raw performance metrics by including:

• Backtesting Framework: Execute your trading strategy on historical data to gauge performance under realistic market conditions.
• Risk Analysis: Evaluate drawdowns, Sharpe ratio, maximum daily losses, and more.
• Custom Assessment: Implement your own metric to measure performance based on specific risk tolerance or alpha generation criteria.

Installation#

A minimal approach to installing Qlib is:

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pip install qlib

For a more thorough setup, especially if you plan on using advanced modules (e.g., GPU-accelerated training), it’s advisable to clone the repository and install dependencies manually:

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git clone https://github.com/microsoft/qlib.git
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cd qlib
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pip install -r requirements.txt
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python setup.py install

Depending on your needs, you could install optional dependencies such as torch or tensorflow for deep learning projects.

Data Preparation#

Before you can train a model or run a backtest, you need data:

1. Obtain Market Data: Use Qlib’s CLI or built-in functions to download sample data:

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   python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/cn_data --region cn
   

   This command downloads sample Chinese stock market data into a directory Qlib can quickly read.

2. Customize Data: You can also supply your own data in CSV format. Qlib can convert CSV files into its internal data format. Pay attention to naming conventions and date formats:

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   python scripts/data_collector/custom_data_convert.py --csv_path your_data.csv --qlib_data_1d_dir ~/.qlib/your_custom_qlib_data
   

3. Check the Data: Always verify the data structure, ensuring that columns for date, open, high, low, close, volume (and any other required fields) are consistent.

Basic Workflow in Qlib#

1. Initialize Qlib:

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   import qlib
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   from qlib.config import REG_CN
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   qlib.init(provider_uri='~/.qlib/qlib_data/cn_data', region=REG_CN)
   

2. Load Datasets: Specify which instruments (stocks) you want to work with, and define your date range:

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   from qlib.data import D
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   data = D.features(instruments='SH600000', fields=['$open', '$close'], start_time='2020-01-01', end_time='2021-01-01')
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   print(data.head())
   

3. Feature Engineering: Use Qlib’s expression engine or define custom factors. For example, a simple factor might be the rate of change over the past 5 days:

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   from qlib.data.dataset import DatasetD
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   from qlib.data.dataset.handler import Alpha158
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   handler = Alpha158(instruments='SH600000', start_time='2020-01-01', end_time='2021-01-01', fit_start_time='2020-01-01', fit_end_time='2021-01-01')
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   dataset = DatasetD(handler, segments={'train':('2020-01-01','2020-06-30'), 'test': ('2020-07-01','2021-01-01')})
   

4. Create and Train Model:

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   from qlib.contrib.model.gbdt import LGBModel
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   model = LGBModel(loss='mse')
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   model.fit(dataset.get_data('train'))
   

5. Evaluate:

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   preds = model.predict(dataset.get_data('test'))
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   print(preds.head())
   

This simple sequence demonstrates the core workflow: initialize, load data, create features, train a model, and evaluate predictions.

Instruments and Datasets#

Instruments are typically identifiers for stocks, ETFs, or indexes. Qlib organizes instruments under different provider URIs. Each provider URI might represent a different data source or region (e.g., China vs. U.S. markets).

Datasets abstract the raw data into train, validation, and test segments. This approach encourages a clean separation of concerns, where raw data is fetched by handlers, then passed to a dataset object that organizes it for modeling.

Calendar and Data Storage#

Qlib’s internal data storage system is built around a concept called the calendar, which defines valid trading days or intervals. This approach ensures:

• Each trading day data is aligned across various instruments.
• It becomes easy to fetch time-aligned slices of data for cross-sectional analysis.

The data is stored in a directory structure that Qlib automatically recognizes. For example:

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~/.qlib/qlib_data/cn_data/
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├── calendar
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├── instruments
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└── stock
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    └── day
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        ├── SH600000
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        ├── SH600010
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        └── ...

Experiment Framework#

An Experiment in Qlib ties together the dataset, model, and evaluation pipeline in a version-controlled manner. You can log experiment results to easily compare different model architectures or parameters. This is especially useful when iterating multiple times, ensuring reproducibility:

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from qlib.workflow import R
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with R.start(experiment_name="my_experiment"):
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    model.fit(dataset.get_data('train'))
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    recorder = R.get_recorder()
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    recorder.save_objects(**{"model": model})
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    # You can also store metrics, plots, etc.

What Are Factors in Quantitative Finance?#

A factor generally represents some characteristic of an asset that explains returns. Some examples:

• Value-Factor: Price-to-book ratio, price-to-earnings ratio, etc.
• Momentum-Factor: The rate of change of returns, 52-week high, etc.
• Quality-Factor: Return on equity, profit margin, etc.

Factors can be combined or weighted to produce more robust signals.

Creating and Testing a Simple Factor#

Suppose you want to create a momentum factor of 20-day returns. You can use Qlib’s built-in expression engine:

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import qlib
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qlib.init()
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from qlib.data.dataset import DatasetD
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from qlib.data.dataset.handler import DataHandlerLP
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class MyHandler(DataHandlerLP):
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    def __init__(self, **kwargs):
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        super().__init__(**kwargs)
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    def _feature_columns(self):
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        return {
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            "ROC20": "Ref($close, 0)/Ref($close, 20) - 1",
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        }
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handler = MyHandler(instruments='SH600000', start_time='2020-01-01', end_time='2021-01-01')
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data_frame = handler.fetch(by_group=False)
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print(data_frame.head())

Here, ROC20 stands for the 20-day rate of change in the closing price.

Backtesting the Factor Strategy#

Once you define your factor, the next step is to implement a backtesting logic. Qlib allows you to create your own backtest logic or use templates:

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from qlib.contrib.strategy.signal_strategy import SignalStrategy
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from qlib.contrib.strategy.model_strategy import TopkDropoutStrategy
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# Basic top-k strategy: select the top 50 stocks with highest factor values
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strategy = TopkDropoutStrategy(n_drop=0, n_top=50, signal='ROC20')

Then simulate the overall performance of this strategy:

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from qlib.backtest import backtest
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positions, trades, performance = backtest(
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    start_time='2020-07-01',
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    end_time='2021-01-01',
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    strategy=strategy
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)
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print(performance)

In a real-world setting, you would elaborate on transaction costs, slippage, risk controls, etc. This example simply demonstrates how quickly you can go from defining a factor to seeing results.

Linear Models in Qlib#

Linear models (like linear or logistic regression) often serve as a starting point for new factors. Qlib includes standard linear modeling interfaces to connect your dataset to a regression pipeline:

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from qlib.contrib.model.linear import LinearModel
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model = LinearModel()
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model.fit(dataset.get_data('train'))
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predictions = model.predict(dataset.get_data('test'))

This approach is straightforward and can be surprisingly effective for many factor-based strategies.

Tree-Based Approaches (LightGBM/XGBoost)#

Decision-tree ensembles often outcompete linear models on many financial datasets due to their ability to capture nonlinearities. Qlib supports popular libraries like LightGBM or XGBoost:

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from qlib.contrib.model.gbdt import LGBModel
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model = LGBModel(
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    loss='mse',
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    num_leaves=64,
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    feature_fraction=0.8,
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    bagging_fraction=0.8,
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    learning_rate=0.05,
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)
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model.fit(dataset.get_data('train'))
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scores = model.predict(dataset.get_data('test'))

Tune hyperparameters for optimal results. You can automate tuning with Qlib’s experiment and hyperparameter search tools.

Neural Network Models#

Deep learning can potentially uncover complex interaction effects within large datasets. Qlib supports PyTorch-based neural networks by providing a consistent model interface:

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from qlib.contrib.model.pytorch_nn import DNNModel
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model = DNNModel(
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    d_hidden=128,
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    num_layers=2,
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    dropout=0.1,
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)
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model.fit(dataset.get_data('train'))
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nn_preds = model.predict(dataset.get_data('test'))

When dealing with neural networks, consider adding some early-stopping criteria or regularization to avoid overfitting, especially in smaller datasets.

Pipeline Automation#

Large-scale experiments often require automated pipelines:

• Auto-Factor Generation: Generate a range of factors by systematically varying parameters.
• Auto-ML: Qlib can integrate with third-party Auto-ML libraries to search for the best model architecture.
• Scheduling: Use tools like Airflow or ZenML to schedule periodic training and rebalancing tasks.

Hyperparameter Tuning#

Qlib’s configuration system allows you to manage a large number of hyperparameters easily:

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from qlib.utils.config import set_config
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config = {
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    "model": {
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        "LGBModel": {
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            "loss": "mse",
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            "num_leaves": [32, 64, 128],
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            "learning_rate": [0.01, 0.05, 0.1]
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        }
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    }
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}
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set_config(config)

You might create multiple experiments that systematically vary over each hyperparameter set.

Custom Model Development#

While Qlib provides a variety of built-in models, you may want to incorporate something entirely new. You can define a custom model by extending its base class:

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from qlib.model.base import Model
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class MyCustomModel(Model):
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    def fit(self, dataset, **kwargs):
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        # Custom training code
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        pass
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    def predict(self, dataset, **kwargs):
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        # Custom inference code
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        pass

This aligns with Qlib’s modular architecture, making it easier to experiment with cutting-edge techniques.

Commonly Used Metrics#

Financial performance evaluation often relies on:

• Annualized Return: How much your strategy returns on a yearly basis.
• Sharpe Ratio: Return per unit of volatility.
• Max Drawdown: Largest peak-to-trough loss during a certain period.

These metrics help you understand the risk-return profile of your strategy. Qlib calculates many of these automatically after a backtest.

Analyzing Trading Results#

A typical analysis might include:

1. Equity Curve: Plots your strategy’s portfolio value over time.
2. Distribution of Returns: Shows how daily or weekly returns are spread out.
3. Sector Performance: Evaluates how well your strategy did in different market sectors (e.g., tech, finance, healthcare).

Risk Management and Drawdown Analysis#

Qlib makes it easy to follow your drawdowns over time:

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import matplotlib.pyplot as plt
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drawdowns = performance['drawdown']
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drawdowns.plot()
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plt.title("Strategy Drawdowns")
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plt.show()

You can further extend this to incorporate advanced risk measures such as Value at Risk (VaR) or Conditional VaR if your strategy demands more rigorous risk analysis.

Integration with Other Libraries#

Qlib’s data structures map nicely to pandas DataFrames, allowing easy integration with the wider Python data science ecosystem:

• Signal Processing with numpy and scipy.
• Deep Learning with PyTorch or TensorFlow.
• Visualization with matplotlib or plotly.

Multi-Factor Strategies and Portfolio Optimization#

As you grow, you might start combining multiple factors to generate a composite signal. You can then feed this signal into a portfolio optimization library (e.g., cvxpy) to balance risk, target specific sector exposures, or maintain a desired beta to the market.

A simplified multi-factor pipeline might look like this:

1. Generate individual factors (value, momentum, quality, etc.).
2. Standardize or normalize each factor.
3. Combine them into a single alpha score.
4. Feed alpha scores into a portfolio optimizer.
5. Execute the strategy via Qlib’s backtesting or a live trading environment.

Deployment and Execution#

Finally, bringing your model into a production environment is often the end goal. For real-time trading:

• Live Data Feeds: Integrate your data provider’s API with Qlib’s data handler interface.
• Low-Latency Execution: Ensure you can operate a stable, low-latency environment if your strategy depends on intraday data.
• Monitoring: Track performance metrics in real time, using a database or logging framework for live metrics.