Harnessing Qlibs DataOps: Best Practices Revealed#

What Is DataOps?#

DataOps is the practice of orchestrating data flows in an automated and scalable manner, borrowing from DevOps principles and applying them to the entire data journeyingestion, cleaning, validation, transformation, and utilization. Within finance, DataOps ensures that analysts and quant researchers have high-quality data on time, allowing them to focus on modeling rather than on repetitive data handling tasks.

Why Use Qlib?#

Qlib simplifies data-driven quantitative investment research by providing:

• A unified abstraction for fetching, storing, and processing data.
• Prebuilt modules for feature engineering and model training.
• Tools for evaluating and benchmarking trading strategies.

With Qlib, you can set up your data ingestion once, then concentrate on creating and refining your alpha factors and models. Its designed to handle large historical data sets, various frequencies (daily, minute-level), and keep your pipeline organized.

Prerequisites#

1. A Python environment (3.6+).
2. Basic libraries such as NumPy, pandas, and PyYAML.
3. Git (optional, but recommended for version control).

Installation#

To install Qlib via pip:

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pip install pyqlib

Or clone from GitHub if you want the development version:

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git clone https://github.com/microsoft/qlib.git
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cd qlib
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pip install -e .

Basic Configuration#

Qlib needs to know where your data lives and what type of source it is. Lets set up a quick local path configuration in Python:

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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"  # Example: "/home/user/qlib_data"
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qlib.init(provider_uri=provider_uri)

This snippet initializes Qlib to read data from a local folder. If you havent yet downloaded the data, Qlib can fetch a daily stock dataset by running:

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python scripts/get_data.py qlib_data --target_dir /path/to/qlib_data --region cn

Adapting the ?-region?parameter allows you to fetch data for different markets (e.g., US or CN).

Key Concepts#

• Provider: Manages how Qlib retrieves data.
• Data Handler: Transforms the raw data into standardized pandas DataFrame or NumPy arrays.
• Tasks: These define an end-to-end process, from fetching data to feeding it into a model.

Data Structure in Qlib#

Qlib organizes financial data in .bin files (serialized format) in a hierarchical structure, typically:

• /day or /1min, etc. (contains bar data)
• /features (contains precomputed or intermediate features)
• /instruments (lists available symbols)
• /learned_models (stores trained model files, optional location)

Several files or subfolders within these can represent different market data universes, time intervals, or instrument sets.

Step-by-Step Ingestion Approach#

1. Acquire Raw Data: You can pull from Yahoo Finance, Kaggle, Bloomberg, or local CSV files.
2. Convert to Qlib Format: Use Qlibs scripts or custom code to convert these CSV files into .bin files.
3. Register the Data: Ensure Qlib is aware of the location of your newly ingested data (by setting provider_uri).

Below is an example CSV-to-Qlib ingestion script snippet:

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import pandas as pd
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import qlib
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from qlib.data.data_to_bin import convert_csv_to_bin
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qlib.init(provider_uri='~/.qlib/qlib_data')
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csv_path = "/path/to/your.csv"
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# Load CSV
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df = pd.read_csv(csv_path, parse_dates=["date"])
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df.rename(columns={'ticker': 'symbol'}, inplace=True)
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# The 'symbol' and 'date' columns are critical for Qlib
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convert_csv_to_bin(
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    csv_path      = csv_path,
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    qlib_data_path= '~/.qlib/qlib_data/custom',
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    date_field_name='date',
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    symbol_field_name='symbol'
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)
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print("Data ingestion completed!")

In the above code:

• We rename the ticker?column to symbol?because Qlib expects it.
• We parse the date?column and ensure it is in DateTime format.
• We pass the new data path into convert_csv_to_bin function.

Preprocessing and Validation#

Once data is ingested, you usually want to preprocess it to ensure:

• NaNs are handled appropriately (either filled or removed).
• Duplicate records are dropped.
• Unnecessary columns are cleaned to keep the dataset compact.

For example, after youve generated the .bin files, you can run a quick data validation check:

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from qlib.data import D
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symbol_list = D.list_instruments()
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print(f"Number of instruments: {len(symbol_list)}")
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data = D.features(
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    instruments=symbol_list[:1],  # just checking the first instrument
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    fields=["$close", "$volume"],
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    freq="day",
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    start_time="2020-01-01",
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    end_time="2021-01-01"
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)
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print(data.head())

This snippet retrieves the daily close and volume data for the first symbol in your local dataset, helping you confirm that ingestion was successful.

Using Built-in Data Validation#

Qlib has basic validation functionalities, but you often need custom checks. For example:

• Checking that for each date, the open price is never higher than the high price or lower than the low price.
• Ensuring volumes are non-negative.
• Confirming consecutive day continuity (or as expected for that markets holiday schedule).

Simple Anomaly Detection#

A short code snippet for anomaly checks:

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import numpy as np
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df = D.features(instruments='SH600000', fields=['$open','$high','$low','$close'], freq='day')
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def check_anomalies(df):
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    if (df['$open'] > df['$high']).any():
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        print("Found anomaly: open > high.")
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    if (df['$open'] < df['$low']).any():
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        print("Found anomaly: open < low.")
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    # Additional checks can be performed similarly
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check_anomalies(df)

In production, you may store these anomalies in a logging system (e.g., logging library, or a separate anomaly table in a database) so that your pipeline can flag suspicious entries.

Overview#

Feature engineering is arguably the core of quantitative research. Qlib includes an expression engine that allows you to define transformations (e.g., moving averages, RSI, volatility) in a concise way. By automating this step, you can systematically manage hundreds of new features without manually writing code each time.

Expression Engine Basics#

For instance, to compute a 20-day moving average of a stocks close price, you might define:

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MA_20 = 'Mean($close, 20)'

Then pass it to Qlibs pipeline:

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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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feature_config = {
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    "data_loader": {
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        "instruments": "SH600000",
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        "fields": ["$close", MA_20],
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        "freq": "day"
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    }
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}
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dataset = DatasetD(handler=DataHandlerLP, **feature_config)
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df_features = dataset.prepare("train")
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df_features.head()

Now your df_features will include both the stocks close price and its 20-day moving average.

Common Built-In Features#

• MACD: Ta('MACD($close, 12, 26, 9)')
• Bollinger Bands: Ta('Boll($close, 20, 2)')
• RSI: Ta('RSI($close, 14)')
• Momentum: Ta('Momentum($close, 10)')

Each of these transforms can be applied to large instrument universes, making it easy to generate factor libraries.

Why Orchestration Matters#

Once you scale your data ingestion and feature engineering to multiple symbols, multiple frequencies, and multiple transformations, it becomes essential to schedule tasks. Orchestrating tasks ensures that each stage is processed in the correct order and that dependenciessuch as waiting for the raw data to load before computing the 20-day moving averageare respected.

Options for Scheduling#

1. Cron Jobs: Simple but less robust for complex dependencies.
2. Airflow: A popular choice for orchestrating data pipelines.
3. Prefect / Luigi: Alternatives offering simpler or more advanced capabilities.

Below is an example pseudo-code snippet wiring Qlib ingestion into an Airflow pipeline:

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from airflow import DAG
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from airflow.operators.bash_operator import BashOperator
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from airflow.operators.python_operator import PythonOperator
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from datetime import datetime
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def run_qlib_ingestion(**kwargs):
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    import qlib
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    from qlib.data.data_to_bin import convert_csv_to_bin
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    # Ingestion logic here
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    # ...
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    return "Ingestion completed"
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default_args = {
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    'owner': 'airflow',
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    'start_date': datetime(2023, 1, 1),
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    'retries': 1,
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}
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with DAG(
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    'qlib_data_pipeline',
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    default_args=default_args,
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    schedule_interval='0 2 * * *',  # runs daily at 2 AM
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) as dag:
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    ingestion_task = PythonOperator(
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        task_id='qlib_ingestion',
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        python_callable=run_qlib_ingestion,
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        provide_context=True
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    )
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    # Example command line operation like data cleaning
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    cleaning_task = BashOperator(
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        task_id='clean_data',
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        bash_command='python /path/to/cleanup_script.py'
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    )
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    ingestion_task >> cleaning_task

In this DAG:

• We define a PythonOperator to run the Qlib ingestion process.
• We follow it with a BashOperator that might run additional cleanup or validation.
• The schedule_interval sets the pipeline to run daily, though you can adapt the frequency as needed.

Multi-Symbol Feature Engineering#

Sometimes, you need cross-sectional signals. For instance, you may want to compute a daily ranking of instruments by returns to see if a relative momentum strategy is feasible. Heres how you might define a cross-sectional percentile rank:

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import pandas as pd
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def cross_sectional_rank(df, field="$close"):
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    # group by date, rank instruments
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    df["rank"] = df.groupby("datetime")[field].rank("dense", ascending=True)
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    return df
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# Example usage
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symbols = ["SH600000", "SZ000001"]
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data = D.features(
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    instruments=symbols,
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    fields=["$close"],
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    freq="day"
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)
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df_ranked = cross_sectional_rank(data)
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df_ranked.head()

In an end-to-end pipeline, you would incorporate this function within a custom DataHandler or integrate it via scheduled tasks.

Hints for Better Performance#

1. Chunking: Process instruments in batches.
2. Caching: Store partial results, such as rolling windows, to avoid recomputation every day.
3. Parallel Processing: Use Pythons multiprocessing libraries or a distributed framework like Spark or Dask.
4. Sampling: For tests, sample a subset of instruments or time frames to accelerate feedback loops.

Example: Using Dask for Parallelization#

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import dask.dataframe as dd
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import pandas as pd
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# Convert your Qlib data into a pandas DataFrame
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symbols = D.list_instruments()[:500]  # example subset
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df_list = []
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for symbol in symbols:
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    df_sym = D.features(
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        instruments=symbol,
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        fields=["$close", "$volume"],
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        freq="day"
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    )
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    df_sym["symbol"] = symbol
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    df_list.append(df_sym)
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# Concatenate into a single DataFrame
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df_concat = pd.concat(df_list)
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# Convert to dask
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ddf = dd.from_pandas(df_concat, npartitions=16)
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# Perform parallel computations
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ddf_grouped = ddf.groupby("datetime").agg({"$close": "mean", "$volume": "sum"})
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result = ddf_grouped.compute()
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print(result.head())

Here, we first gather a subset of instruments, concatenate them into a single DataFrame, then transfer it to Dask for parallel group operations. You can adapt this approach to your own transformations.

Database Systems#

If your team relies heavily on relational databases, you might integrate Qlibs .bin files with the following approaches:

• Export daily merges into a PostgreSQL or MySQL database.
• Use a caching layer like Redis for frequently accessed tick-level data.

Cloud Services#

Qlib can be adapted for remote usage:

• AWS S3 or Azure Blob: Host your .bin files in the cloud, referencing them with the provider_uri.
• Serverless: Trigger ingestion tasks with AWS Lambda or Azure Functions.
• Databricks: You can install Qlib within a Databricks cluster to streamline data transformations and ML training.

A typical configuration for S3 might look like:

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qlib.init(provider_uri="s3://your-bucket/qlib_data", enable_cache=True)

Though youll also need to configure proper AWS credentials and permissions.

Stale Data#

When dealing with daily or intraday updates, always confirm:

• The data source updates on schedule.
• The ingestion script is triggered post-update.

Inconsistent Trading Calendars#

Finance data often has irregularities (holidays, half-days). Make sure you configure the correct trading calendar for your region. Qlib uses region-based initialization, but if you merge data from multiple regions, youll need a custom calendar.

Memory Constraints#

Large-scale feature engineering can cause memory spikes. Strategies include:

• Using chunk processing.
• Saving intermediate results to disk.
• Expanding hardware resources (e.g., more RAM or a distributed environment).

Version Mismatches#

Qlib is actively developed. Be mindful of pinning your dependencies in requirements.txt to ensure consistent and reproducible environments.

1. Custom Data Handlers for Alternative Data#

Beyond standard pricing data, you can integrate economic indicators, sentiment data, or even satellite imagery. A custom data handler might fetch these features from a third-party API, unify them with your existing instrument set, and store them as .bin files.

2. Real-Time Data Processing#

Though Qlib is often used for end-of-day strategies, you can adapt it for intraday or near real-time data. Steps involve:

• Setting up a message-queue-based system (e.g., Kafka) for streaming trades/quotes.
• Using a microservice to convert incoming data into Qlibs format on-the-fly.
• Adjusting your pipeline to run in near real-time intervals (minutes or seconds).

3. Model Serving and Monitoring#

With Qlib, you can automate not just data ingestion but also model training, validation, and serving:

• Continuous Model Retraining: At market close each day, retrain the model if new data meets certain thresholds.
• Monitoring: Track model performance metrics (accuracy, returns, drawdowns) over time.
• Auto-Rollback: If a newly retrained model underperforms a baseline, revert to the stable model.

4. Utilization of Containerization#

To maintain consistent environments across your team:

• Wrap Qlib and its dependencies in a Docker container.
• Use Docker Compose or Kubernetes for orchestrating multi-container setups (e.g., ingestion container, feature processing container, etc.).

Sample Dockerfile#

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FROM python:3.9-slim
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RUN pip install pyqlib numpy pandas
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COPY . /app
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WORKDIR /app
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CMD ["python", "run_pipeline.py"]

This minimal Dockerfile can serve as a foundation for your ingestion or feature pipeline tasks.

5. Hyper-Parameter Tuning at Scale#

Once your data pipeline is stable, you can focus on systematically optimizing model hyper-parameters. Qlib integrates with hyper-parameter tuning frameworks like Optuna:

• Store results in a central database to track improvements.
• Use distributed search across multiple machines to speed up the process.

Heres a snippet showing integration with Optuna for a Qlib-based pipeline:

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import optuna
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def objective(trial):
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    param_n_estimators = trial.suggest_int("n_estimators", 50, 500)
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    param_max_depth = trial.suggest_int("max_depth", 3, 20)
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    # Train model with Qlib data
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    model = RandomForestRegressor(
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        n_estimators=param_n_estimators,
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        max_depth=param_max_depth
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    )
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    model.fit(train_features, train_labels)
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    predictions = model.predict(valid_features)
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    score = evaluate(predictions, valid_labels)  # custom evaluation
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    return score
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study = optuna.create_study(direction="maximize")
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study.optimize(objective, n_trials=50)
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print("Best trial:", study.best_trial)

You would integrate this hyper-parameter tuning step in your scheduled pipeline, ensuring each iteration has access to the latest features and data splits.

6. Ensemble Modeling Pipelines#

At a professional scale, multiple models (fundamental factor models, machine learning models, neural networks) can be enqueued in your pipeline. Then you can ensemble their outputs:

• Weighted average predictions.
• Model selection logic (e.g., pick the best daily performer).
• Risk overlay (volatility-based sizing).

By encapsulating ensemble logic as a pipeline stage, you maintain clarity and reusability.