The Ultimate Guide to Qlib Quant for Beginners#

1.1 What Is Quantitative Finance?#

Quantitative finance applies statistical and mathematical models to financial markets. In practical terms, it means automating your investment rules, using algorithms and historical data to drive decision-making. The goal is to remove subjective biases, maintain consistency in trading, and ideally capitalize on market inefficiencies.

1.2 Basics of a Quantitative Strategy#

A typical quant workflow involves:

• Gathering and cleaning financial data.
• Creating features (predictors) from this data.
• Training models that forecast asset prices or returns.
• Conducting simulations (backtests) to see how strategies would have performed historically.
• Deploying the models in live trading environments, assuming they meet performance benchmarks.

1.3 Why Automate?#

Automation:

• Helps reduce emotional biases in trading.
• Ensures consistent execution of strategies.
• Scales more efficiently as you diversify across assets or markets.

At the same time, moving into automated trading requires robust data pipelines, reliable model training, and thorough performance evaluations. Tools like Qlib drastically simplify this entire process.

2.1 Overview of Qlib#

Qlib is an open-source research platform developed by Microsoft Research. It is built in Python and designed to facilitate the end-to-end workflow of quantitative investment research, from data ingestion to online deployment. With Qlib, you can:

• Easily manage and preprocess finance data.
• Generate advanced features (factors) for your models.
• Construct, train, and test predictive models.
• Evaluate trading performance with backtests.
• Deploy models into a live trading or simulation environment.

2.2 Key Features#

Below is a summary of some of Qlib’s most compelling functionalities:

Using Qlib, you can focus on building a successful strategy rather than wrestling with data, or custom-coding entire frameworks yourself.

3.1 Prerequisites#

You will need:

• A reasonable Python environment (version 3.6+).
• Basic familiarity with Python libraries like numpy, pandas, and scikit-learn.

It’s recommended (although not strictly necessary) to use a virtual environment, such as conda or venv, to keep your project dependencies isolated.

3.2 Installation Steps#

Below are straightforward steps to get Qlib up and running:

1. Create a new virtual environment (optional but recommended):

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   conda create -n qlib-env python=3.8
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   conda activate qlib-env
   

   or

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   python -m venv qlib-env
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   source qlib-env/bin/activate  # On Linux/Mac
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   qlib-env\Scripts\activate     # On Windows
   

2. Install Qlib:

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   pip install pyqlib
   

3. (Optional) If you want to use advanced functionality (like Neural Networks or GPU acceleration), ensure you have the relevant deep learning frameworks (e.g., PyTorch, TensorFlow) installed.

3.3 Setting Up the Data#

Qlib provides a convenient command to download sample datasets (e.g., for Chinese or U.S. markets). For example:

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python -m qlib.data --target_dir ~/.qlib/qlib_data/cn_data --region cn --interval 1d

This downloads the Chinese market data (daily frequency) to a local folder. Adjust the target directory or region as needed.

Once the data is downloaded, you can initialize Qlib with:

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import qlib
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qlib.init(provider_uri="~/.qlib/qlib_data/cn_data", region="cn")

If you plan to work with U.S. market data, you can specify region="us" and download the corresponding dataset.

4.1 The Qlib Workflow#

A simple Qlib workflow might look like this:

1. Data Ingestion: Load daily (or intraday) stock price data into Qlib.
2. Feature Engineering: Transform raw OHLCV data (Open, High, Low, Close, Volume) into features.
3. Model Construction: Select a model structure (e.g., linear regression, XGBoost, or deep neural networks).
4. Model Training: Train using historical data, typically a rolling window or train-validation split.
5. Backtesting: Evaluate the strategy’s performance and metrics like Sharpe ratio or drawdown.
6. Refinement & Deployment: Refine features and models. Potentially deploy them in a live or paper trading environment.

4.2 Data Structure in Qlib#

Qlib organizes data by instruments (e.g., stock tickers), fields (e.g., close, volume, custom factors), and frequency (daily, 1-min, etc.). This structure makes it easy to slice data by date range or ticker.

You can retrieve data with:

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import pandas as pd
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from qlib.data import D
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df = D.features(
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    instruments='SH600519',
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    fields=['$close', '$volume'],
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    start_time='2020-01-01',
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    end_time='2020-12-31'
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)
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print(df.head())

Here:

• SH600519 refers to the ticker for Kweichow Moutai stock in China’s A-share market.
• $close and $volume are raw data fields.

4.3 Common Indicators and Features#

Commonly used technical indicators might include:

• Moving averages (SMA, EMA).
• Volume-based indicators (Volume Weighted Average Price, OBV).
• Momentum indicators (RSI, MACD).
• Custom factors using wearable or fundamental data (if available).

Within Qlib, you can quickly define these features by configuring them in data or factor definitions.

5.1 Model Definition in Qlib#

Qlib includes a variety of model templates, many of which are in the qlib.contrib.model module. Examples:

• Linear models like Ordinary Least Squares.
• Tree-based models like LightGBM/XGBoost.
• Deep learning models like LSTM or GRU (if PyTorch or TensorFlow is installed).

To define a simple LightGBM model:

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from qlib.contrib.model.gbdt import LGBModel
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from qlib.contrib.strategy.strategy import TopkDropoutStrategy
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# Model settings
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model = LGBModel(
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    loss='mse',
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    num_leaves=64,
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    feature_name=['feature1', 'feature2', 'feature3']
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)
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# Strategy settings (how we pick top stocks once predictions are made)
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strategy = TopkDropoutStrategy(
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    N=50,          # top 50 stocks
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    topk=10,       # choose top 10 out of the 50
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    n_drop=2,      # drop 2 from the old positions
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)

5.2 Training and Validation#

The typical training plan in Qlib is to specify a data handler for training (with a start and end date), a handler for validation, and a handler for testing. The easiest way to get started is to define a YAML-like configuration, but you can do it directly in Python as well:

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from qlib.data.dataset import DatasetH
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from qlib.data.dataset.handler import DataHandlerLP
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# Data & Dataset
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handler = {
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    "class": "Alpha158",
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    "module_path": "qlib.contrib.data.handler",
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    "kwargs": {
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        "start_time": "2017-01-01",
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        "end_time": "2020-12-31",
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        "fit_start_time": "2017-01-01",
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        "fit_end_time": "2019-12-31",
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        "instruments": "csi300"
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    }
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}
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dataset = DatasetH(handler, segments={
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    "train": ("2017-01-01", "2018-12-31"),
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    "valid": ("2019-01-01", "2019-12-31"),
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    "test": ("2020-01-01", "2020-12-31")
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})
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model.fit(dataset.get_data("train"), dataset.get_data("valid"))

5.3 Making Predictions#

Once you’ve trained your model, you can obtain predictions:

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predictions = model.predict(dataset.get_data("test"))
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print(predictions.head())

These predictions typically represent some form of return forecast. Qlib can help you feed those forecasts into a backtest strategy.

6.1 Customizing Factors#

Qlib supports easy generation of custom factors if the built-in ones don’t suit your strategy. For instance, you can create a factor that calculates a 10-day momentum:

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from qlib.data.dataset.handler import DataHandlerLP
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class CustomHandler(DataHandlerLP):
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    def feature(self, df):
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        # Calculate a 10-day momentum factor
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        df['MOM_10'] = df['close'] / df['close'].shift(10) - 1
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        return df

You can then load this CustomHandler into Qlib and incorporate the new factor in your model.

6.2 Rolling Windows and Walk-Forward Analysis#

Walk-forward analysis is crucial for ensuring your strategy generalizes. Qlib lets you update your dataset segments programmatically (e.g., train on 2017-2018, validate on 2019, test on 2020, then roll forward). This rolling approach mimics real-world scenarios where you retrain models on the most recent data.

6.3 Hyperparameter Tuning#

For model-based strategies, hyperparameter tuning can be done via:

• Grid or random search.
• Bayesian optimization (e.g., Optuna).
• Automated processes integrated into Qlib or external libraries.

You can integrate these quickly with Qlib by repeatedly calling model.fit(dataset) with different configurations and tracking performance metrics.

6.4 Deep Learning Pipelines#

If you install PyTorch, Qlib offers prebuilt neural network models like GRU, LSTM, and Transformer-based structures. Deep learning can capture nonlinear relationships or temporal patterns in the data. However, it also increases complexity—ensure you have enough data and domain knowledge before diving into deep networks.

7.1 Key Performance Metrics#

Common performance metrics for quant strategies include:

• Annualized Return (AR): Measures overall returns over a year.
• Sharpe Ratio: Indicates risk-adjusted return. Higher is typically better.
• Max Drawdown (MDD): Largest peak-to-trough drop. Lower is better.
• Information Ratio or Calmar Ratio: Variations of risk-adjusted return.

7.2 Backtest Example#

Qlib provides a backtest module that integrates with your model’s predictions. Below is a high-level example:

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from qlib.backtest import backtest, executor
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from qlib.contrib.strategy.strategy import TopkDropoutStrategy
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from qlib.rl.order_execution import Execution
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# predictions is the output from model.predict
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account, performance = backtest(
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    strategy=strategy,
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    executor=executor.SimulatorExecutor(Execution()),
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    start_time='2020-01-01',
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    end_time='2020-12-31',
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    account=None,
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    verbose=True,
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    verbose_detail=False,
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)
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print(performance)

Here, strategy is how you choose which assets to buy or sell, and executor simulates ordering and transaction costs.

7.3 Visualizing Results#

It’s often beneficial to generate plots of cumulative returns, daily returns, or drawdowns. Qlib might generate some of these automatically, or you can export data to libraries like matplotlib or plotly:

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import matplotlib.pyplot as plt
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plt.figure(figsize=(10, 6))
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performance['return'].cumsum().plot()
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plt.title("Cumulative Returns")
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plt.show()

8.1 Data Quality#

For quantitative models, “garbage in, garbage out” applies. Even the best algorithms will fail on poorly cleaned or incorrectly labeled data. Always verify your data, handle outliers, and ensure everything is aligned (e.g., matching trading days, consistent time zones).

8.2 Overfitting#

Overfitting is a major concern in quantitative finance. A strategy can appear outstanding on historical data but falter in real time. Mitigation techniques:

• Use a robust train-validation-test split.
• Regularize your models with methods like ridge, L1 penalty, or dropout (in neural nets).
• Conduct walk-forward testing.
• Keep your features well-supported by financial theory.

8.3 Transaction Costs and Liquidity#

When you backtest, include:

• Slippage assumptions (the difference between expected and actual fill price).
• Commission and fees.
• Realistic trading volume constraints.

Failing to account for these can lead to overly optimistic backtests that don’t hold up in production.

8.4 Model Interpretability#

While interpretability might not be mandatory, understanding why a model is making certain predictions can help you build confidence in its real-world viability and detect potential data leaks or spurious correlations.

9.1 Research Pipeline#

A typical research pipeline in Qlib:

1. Data acquisition: Download or ingest market data into Qlib format.
2. Feature engineering: Use built-in or custom factors to create signals.
3. Modeling: Train and validate across multiple models.
4. Backtesting: Evaluate your strategies with robust metrics.
5. Refinement: Adjust or replace the model, or do further feature engineering.

9.2 Demo: Building a Complete Strategy#

Below is a sample skeleton code for putting these pieces together:

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import qlib
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from qlib.data.dataset import DatasetH
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from qlib.contrib.data.handler import Alpha158
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from qlib.contrib.strategy.strategy import TopkDropoutStrategy
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from qlib.contrib.model.gbdt import LGBModel
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from qlib.backtest import backtest, executor
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from qlib.rl.order_execution import Execution
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qlib.init(provider_uri="~/.qlib/qlib_data/cn_data", region="cn")
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# Define dataset
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dataset = DatasetH(
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    handler=Alpha158(
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        start_time='2017-01-01',
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        end_time='2020-12-31',
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        fit_start_time='2017-01-01',
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        fit_end_time='2019-06-30',
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        instruments='csi300'
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    ),
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    segments={
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        'train': ('2017-01-01', '2018-12-31'),
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        'valid': ('2019-01-01', '2019-06-30'),
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        'test': ('2019-07-01', '2020-12-31')
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    }
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)
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# Define model
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model = LGBModel(
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    loss='mse',
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    num_leaves=64,
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    feature_name=Alpha158.get_feature_names()
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)
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# Train model
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model.fit(dataset.get_data('train'), dataset.get_data('valid'))
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# Predict
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predictions = model.predict(dataset.get_data('test'))
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# Strategy
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strategy = TopkDropoutStrategy(
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    signal=predictions,
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    N=50,
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    topk=10,
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    n_drop=2,
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)
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# Backtest
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account, performance = backtest(
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    strategy=strategy,
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    executor=executor.SimulatorExecutor(Execution()),
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    start_time='2019-07-01',
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    end_time='2020-12-31',
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    account=None,
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    freq='day'
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)
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print(performance)

In a real setup, you’d add additional complexity (transaction costs, more advanced signal processing, etc.), but this demonstrates the end-to-end flow.

10.1 Incorporating Alternative Data#

Professional quantitative funds often augment price and volume data with external or alternative data sources:

• Financial statements and fundamental indicators.
• News sentiment or social media data.
• Satellite data (e.g., shipping, store traffic).
• ESG indicators (Environmental, Social, and Governance metrics).

Qlib handles multiple data sources by letting you define your own data handlers and factor computations. You can store alternative data in CSV or a database, then feed it into Qlib’s pipeline.

10.2 Multi-Factor Models#

Professional strategies often rely on multi-factor models—combining technical factors, fundamental factors, and sentiment factors. Qlib makes it straightforward to combine these into a single dataset. The synergy of complementary factors often yields more robust predictions.

10.3 Hierarchical Models and Risk Parity#

Once you have predictions for multiple asset classes (equities, bonds, commodities), you can build more complex portfolio allocations like:

• Risk parity approaches (each asset or asset class contributes equally to the portfolio’s risk).
• Factor-based allocations (grouping assets by factors such as value, momentum, quality).
• Hierarchical risk parity or hierarchical clustering for asset grouping.

These advanced portfolio constructions can be integrated with Qlib’s backtest engine, though you may need to do some custom coding.

10.4 Automated Hyperparameter Tuning#

At scale, idle machine time translates into opportunity cost. Setting up automated hyperparameter tuning via frameworks like Optuna or Hyperopt can speed up your research significantly. You can integrate them with Qlib’s model fitting routine to systematically test combinations of:

• Learning rates.
• Number of trees or layers.
• Regularization coefficients.
• Feature subsets.

10.5 Online Learning and Live Trading#

For real production usage, you’ll often want to:

• Update your models daily or weekly with new data.
• Use an API to place live trades or at least paper trades in real time.
• Monitor model drift and performance decay.

Qlib offers an “online training” feature that allows you to incrementally retrain or update your models. For live trading, you can integrate Qlib with broker APIs or use custom bridging scripts.