Merge branch 'main' into main

.github/stale.yml

@@ -0,0 +1,62 @@
+# Configuration for probot-stale - https://github.com/probot/stale
+
+# Number of days of inactivity before an Issue or Pull Request becomes stale
+daysUntilStale: 60
+
+# Number of days of inactivity before an Issue or Pull Request with the stale label is closed.
+# Set to false to disable. If disabled, issues still need to be closed manually, but will remain marked as stale.
+daysUntilClose: 7
+
+# Only issues or pull requests with all of these labels are check if stale. Defaults to `[]` (disabled)
+onlyLabels: []
+
+# Issues or Pull Requests with these labels will never be considered stale. Set to `[]` to disable
+exemptLabels:
+  - bug
+  - pinned
+  - security
+  - "[Status] Maybe Later"
+
+# Set to true to ignore issues in a project (defaults to false)
+exemptProjects: false
+
+# Set to true to ignore issues in a milestone (defaults to false)
+exemptMilestones: false
+
+# Set to true to ignore issues with an assignee (defaults to false)
+exemptAssignees: false
+
+# Label to use when marking as stale
+staleLabel: wontfix
+
+# Comment to post when marking as stale. Set to `false` to disable
+markComment: >
+  This issue has been automatically marked as stale because it has not had
+  recent activity. It will be closed if no further activity occurs. Thank you
+  for your contributions.
+
+# Comment to post when removing the stale label.
+# unmarkComment: >
+#   Your comment here.
+
+# Comment to post when closing a stale Issue or Pull Request.
+# closeComment: >
+#   Your comment here.
+
+# Limit the number of actions per hour, from 1-30. Default is 30
+limitPerRun: 30
+
+# Limit to only `issues` or `pulls`
+# only: issues
+
+# Optionally, specify configuration settings that are specific to just 'issues' or 'pulls':
+# pulls:
+#   daysUntilStale: 30
+#   markComment: >
+#     This pull request has been automatically marked as stale because it has not had
+#     recent activity. It will be closed if no further activity occurs. Thank you
+#     for your contributions.
+
+# issues:
+#   exemptLabels:
+#     - confirmed

.github/workflows/test.yml

@@ -22,9 +22,58 @@ jobs:
       uses: actions/setup-python@v2
       with:
         python-version: ${{ matrix.python-version }}
+
+    - name: Lint with Black
+      run: |
+        cd ..
+        if [ "$RUNNER_OS" == "Windows" ]; then
+          $CONDA\\python.exe -m pip install black
+          $CONDA\\python.exe -m black qlib -l 120 --check --diff
+        else
+          sudo $CONDA/bin/python -m pip install black
+          $CONDA/bin/python -m black qlib -l 120 --check --diff
+        fi
+      shell: bash
+
+    # Test Qlib installed with pip
+    - name: Install Qlib with pip
+      run: |
+        if [ "$RUNNER_OS" == "Windows" ]; then
+          $CONDA\\python.exe -m pip install pyqlib --ignore-installed ruamel.yaml --user
+        else
+          sudo $CONDA/bin/python -m pip install pyqlib --ignore-installed ruamel.yaml
+        fi
+      shell: bash
     
-    - name: Install dependencies
-      run:   |
+    - name: Install Lightgbm for MacOS
+      if: runner.os == 'macOS'
+      run: |
+        /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Microsoft/qlib/main/.github/brew_install.sh)"
+        HOMEBREW_NO_AUTO_UPDATE=1 brew install lightgbm
+
+    - name: Test data downloads
+      run: |
+        if [ "$RUNNER_OS" == "Windows" ]; then
+          $CONDA\\python.exe scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/cn_data --interval 1d --region cn
+        else
+          $CONDA/bin/python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/cn_data --interval 1d --region cn
+        fi
+      shell: bash
+
+    - name: Test workflow by config (install from pip)
+      run: |
+        if [ "$RUNNER_OS" == "Windows" ]; then
+          $CONDA\\python.exe qlib\\workflow\\cli.py examples\\benchmarks\\LightGBM\\workflow_config_lightgbm_Alpha158.yaml 
+          $CONDA\\python.exe -m pip uninstall -y pyqlib
+        else
+          $CONDA/bin/python qlib/workflow/cli.py examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml 
+          sudo $CONDA/bin/python -m pip uninstall -y pyqlib
+        fi
+      shell: bash
+      
+    # Test Qlib installed from source
+    - name: Install Qlib from source
+      run: |
         if [ "$RUNNER_OS" == "Windows" ]; then
           $CONDA\\python.exe -m pip install --upgrade cython
           $CONDA\\python.exe -m pip install numpy jupyter jupyter_contrib_nbextensions
@@ -36,13 +85,7 @@ jobs:
           sudo $CONDA/bin/python -m pip install -U scipy scikit-learn # installing without this line will cause errors on GitHub Actions, while instsalling locally won't
           sudo $CONDA/bin/python setup.py install
         fi
-      shell: bash        
-
-    - name: Install Lightgbm for MacOS
-      if: runner.os == 'macOS'
-      run: |
-        /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Microsoft/qlib/main/.github/brew_install.sh)"
-        HOMEBREW_NO_AUTO_UPDATE=1 brew install lightgbm
+      shell: bash
 
     - name: Install test dependencies
       run: |
@@ -54,16 +97,6 @@ jobs:
           sudo $CONDA/bin/python -m pip install black pytest
         fi
       shell: bash 
-        
-    - name: Lint with Black
-      run: |
-        cd ..
-        if [ "$RUNNER_OS" == "Windows" ]; then
-          $CONDA\\python.exe -m black qlib -l 120 --check --diff
-        else
-          $CONDA/bin/python -m black qlib -l 120 --check --diff
-        fi
-      shell: bash 
 
     - name: Unit tests with Pytest
       run: |
@@ -73,22 +106,13 @@ jobs:
         else
           $CONDA/bin/python -m pytest . --durations=0
         fi
-      shell: bash 
+      shell: bash
 
-    - name: Test data downloads
-      run: |
-        if [ "$RUNNER_OS" == "Windows" ]; then
-          $CONDA\\python.exe scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/cn_data --interval 1d --region cn
-        else
-          $CONDA/bin/python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/cn_data --interval 1d --region cn
-        fi
-      shell: bash 
-
-    - name: Test workflow by config
+    - name: Test workflow by config (install from source)
       run: |
         if [ "$RUNNER_OS" == "Windows" ]; then
           $CONDA\\python.exe qlib\\workflow\\cli.py examples\\benchmarks\\LightGBM\\workflow_config_lightgbm_Alpha158.yaml 
         else
           $CONDA/bin/python qlib/workflow/cli.py examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml 
         fi
-      shell: bash
+      shell: bash

README.md

@@ -61,17 +61,22 @@ At the module level, Qlib is a platform that consists of the above components. T
 
 This quick start guide tries to demonstrate
 1. It's very easy to build a complete Quant research workflow and try your ideas with _Qlib_.
-1. Though with *public data* and *simple models*, machine learning technologies **work very well** in practical Quant investment.
+2. Though with *public data* and *simple models*, machine learning technologies **work very well** in practical Quant investment.
+
+Here is a quick **[demo](https://terminalizer.com/view/3f24561a4470)** shows how to install ``Qlib``, and run LightGBM with ``qrun``. **But**, please make sure you have already prepared the data following the [instruction](#data-preparation).
+
 
 ## Installation
 
-Users can easily install ``Qlib`` by pip according to the following command
+Users can easily install ``Qlib`` by pip according to the following command(Currently, Qlib only support Python 3.6, 3.7 and 3.8). 
 
 ```bash
   pip install pyqlib
 ```
 
-Also, users can install ``Qlib`` by the source code according to the following steps:
+**Note**: pip will install the latest stable qlib. However, the main branch of qlib is in active development. If you want to test the latest scripts or functions in the main branch. Please install qlib with the methods below.
+
+Also, users can install the latest dev version ``Qlib`` by the source code according to the following steps:
 
 * Before installing ``Qlib`` from source, users need to install some dependencies:
 
@@ -79,15 +84,20 @@ Also, users can install ``Qlib`` by the source code according to the following s
   pip install numpy
   pip install --upgrade  cython
   ```
-  **Note**: please pay attention that installing cython in Python 3.6 will raise some error when installing ``Qlib`` from source. If users use Python 3.6 on their machines, it is recommended to *upgrade* Python to version 3.7 or use `conda`'s Python to install ``Qlib`` from source.
-
-* Clone the repository and install ``Qlib``:
-
-  ```bash
-  git clone https://github.com/microsoft/qlib.git && cd qlib
-  python setup.py install
-  ```
+  **Note**: Please pay attention that installing cython in Python 3.6 will raise some error when installing ``Qlib`` from source. If users use Python 3.6 on their machines, it is recommended to *upgrade* Python to version 3.7 or use `conda`'s Python to install ``Qlib`` from source.
 
+* Clone the repository and install ``Qlib`` as follows.
+  * If you haven't installed qlib by the command ``pip install pyqlib`` before:
+    ```bash
+    git clone https://github.com/microsoft/qlib.git && cd qlib
+    python setup.py install
+    ```
+  * If you have already installed the stable version by the command ``pip install pyqlib``:
+    ```bash
+    git clone https://github.com/microsoft/qlib.git && cd qlib
+    pip install .
+    ```
+  **Note**: **Only** the command ``pip install .`` **can** overwrite the stable version installed by ``pip install pyqlib``, while the command ``python setup.py install`` **can't**.
 
 ## Data Preparation
 Load and prepare data by running the following code:
@@ -131,10 +141,10 @@ Users could create the same dataset with it.
 ## Auto Quant Research Workflow
 Qlib provides a tool named `qrun` to run the whole workflow automatically (including building dataset, training models, backtest and evaluation). You can start an auto quant research workflow and have a graphical reports analysis according to the following steps: 
 
-1. Quant Research Workflow: Run  `qrun` with lightgbm workflow config ([workflow_config_lightgbm.yaml](examples/benchmarks/LightGBM/workflow_config_lightgbm.yaml)) as following.
+1. Quant Research Workflow: Run  `qrun` with lightgbm workflow config ([workflow_config_lightgbm_Alpha158.yaml](examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml) as following.
     ```bash
       cd examples  # Avoid running program under the directory contains `qlib`
-      qrun benchmarks/LightGBM/workflow_config_lightgbm.yaml
+      qrun benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml
     ```
     The result of `qrun` is as follows, please refer to please refer to [Intraday Trading](https://qlib.readthedocs.io/en/latest/component/backtest.html) for more details about the result. 
 
@@ -191,16 +201,16 @@ The automatic workflow may not suite the research workflow of all Quant research
 # [Quant Model Zoo](examples/benchmarks)
 
 Here is a list of models built on `Qlib`.
-- [GBDT based on LightGBM](qlib/contrib/model/gbdt.py)
-- [GBDT based on Catboost](qlib/contrib/model/catboost_model.py)
-- [GBDT based on XGBoost](qlib/contrib/model/xgboost.py)
+- [GBDT based on LightGBM (Guolin Ke, et al.)](qlib/contrib/model/gbdt.py)
+- [GBDT based on Catboost (Liudmila Prokhorenkova, et al.)](qlib/contrib/model/catboost_model.py)
+- [GBDT based on XGBoost (Tianqi Chen, et al.)](qlib/contrib/model/xgboost.py)
 - [MLP based on pytorch](qlib/contrib/model/pytorch_nn.py)
-- [GRU based on pytorch](qlib/contrib/model/pytorch_gru.py)
-- [LSTM based on pytorcn](qlib/contrib/model/pytorch_lstm.py)
-- [ALSTM based on pytorcn](qlib/contrib/model/pytorch_alstm.py)
-- [GATs based on pytorch](qlib/contrib/model/pytorch_gats.py)
-- [SFM based on pytorch](qlib/contrib/model/pytorch_sfm.py)
-- [TFT based on tensorflow](examples/benchmarks/TFT/tft.py)
+- [GRU based on pytorch (Kyunghyun Cho, et al.)](qlib/contrib/model/pytorch_gru.py)
+- [LSTM based on pytorcn (Sepp Hochreiter, et al.)](qlib/contrib/model/pytorch_lstm.py)
+- [ALSTM based on pytorcn (Yao Qin, et al.)](qlib/contrib/model/pytorch_alstm.py)
+- [GATs based on pytorch (Petar Velickovic, et al.)](qlib/contrib/model/pytorch_gats.py)
+- [SFM based on pytorch (Liheng Zhang, et al.)](qlib/contrib/model/pytorch_sfm.py)
+- [TFT based on tensorflow (Bryan Lim, et al.)](examples/benchmarks/TFT/tft.py)
 
 Your PR of new Quant models is highly welcomed.
 
@@ -296,4 +306,4 @@ provided by the bot. You will only need to do this once across all repos using o
 
 This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
 For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
-contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
+contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.

docs/_static/demo.sh

@@ -0,0 +1,12 @@
+#!/bin/sh
+git clone https://github.com/microsoft/qlib.git
+cd qlib
+ls
+pip install pyqlib
+# or
+# pip install numpy
+# pip install --upgrade cython
+# python setup.py install
+cd examples
+ls
+qrun benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml

docs/component/recorder.rst

@@ -91,7 +91,7 @@ Record Template
 
 The ``RecordTemp`` class is a class that enables generate experiment results such as IC and backtest in a certain format. We have provided three different `Record Template` class:
 
-- ``SignalRecord``: This class generates the `preidction` results of the model.
+- ``SignalRecord``: This class generates the `prediction` results of the model.
 - ``SigAnaRecord``: This class generates the `IC`, `ICIR`, `Rank IC` and `Rank ICIR` of the model.
 - ``PortAnaRecord``: This class generates the results of `backtest`. The detailed information about `backtest` as well as the available `strategy`, users can refer to `Strategy <../component/strategy.html>`_ and `Backtest <../component/backtest.html>`_.
 

examples/README.md

@@ -1,6 +1,6 @@
 # Requirements
 
-Here is the minimal hardware requirements to run the example.
+Here is the minimal hardware requirements to run the `workflow_by_code` example.
 - Memory: 16G
 - Free Disk: 5G
 

examples/benchmarks/README.md

@@ -29,4 +29,4 @@ The numbers shown below demonstrate the performance of the entire `workflow` of
 | GRU (Kyunghyun Cho, et al.) | Alpha158 (with selected 20 features) | 0.0311±0.00 | 0.2418±0.04| 0.0425±0.00 | 0.3434±0.02 | 0.0330±0.02 | 0.4805±0.30| -0.1021±0.02 |
 | LSTM (Sepp Hochreiter, et al.) | Alpha158 (with selected 20 features) | 0.0312±0.00 | 0.2394±0.04| 0.0418±0.00 | 0.3324±0.03 | 0.0298±0.02 | 0.4198±0.33| -0.1348±0.03 |
 | ALSTM (Yao Qin, et al.) | Alpha158 (with selected 20 features) | 0.0385±0.01 | 0.3022±0.06| 0.0478±0.00 | 0.3874±0.04 | 0.0486±0.03 | 0.7141±0.45| -0.1088±0.03 |
-| GATs (Petar Velickovic, et al.) | Alpha158 (with selected 20 features) | 0.0349±0.00 | 0.2511±0.01| 0.0457±0.00 | 0.3537±0.01 | 0.0578±0.02 | 0.8221±0.25| -0.0824±0.02 |
+| GATs (Petar Velickovic, et al.) | Alpha158 (with selected 20 features) | 0.0349±0.00 | 0.2511±0.01| 0.0457±0.00 | 0.3537±0.01 | 0.0578±0.02 | 0.8221±0.25| -0.0824±0.02 |

examples/benchmarks/SFM/README.md

@@ -1,3 +1,3 @@
 # State-Frequency-Memory
 - State Frequency Memory (SFM) is a novel recurrent network that uses Discrete Fourier Transform to decompose the hidden states of memory cells and capture the multi-frequency trading patterns from past market data to make stock price predictions. 
-- Paper: Stock Price Prediction via Discovering Multi-Frequency Trading Patterns. [https://www.cs.ucf.edu/~gqi/publications/kdd2017_stock.pdf.](https://www.cs.ucf.edu/~gqi/publications/kdd2017_stock.pdf.)
+- Paper: Stock Price Prediction via Discovering Multi-Frequency Trading Patterns. [http://www.eecs.ucf.edu/~gqi/publications/kdd2017_stock.pdf.](http://www.eecs.ucf.edu/~gqi/publications/kdd2017_stock.pdf)

examples/benchmarks/TFT/expt_settings/configs.py

@@ -25,7 +25,7 @@ import os
 import data_formatters.qlib_Alpha158
 
 
-class ExperimentConfig(object):
+class ExperimentConfig:
     """Defines experiment configs and paths to outputs.
 
     Attributes:

examples/benchmarks/TFT/libs/tft_model.py

@@ -320,7 +320,7 @@ class InterpretableMultiHeadAttention:
         return outputs, attn
 
 
-class TFTDataCache(object):
+class TFTDataCache:
     """Caches data for the TFT."""
 
     _data_cache = {}
@@ -348,7 +348,7 @@ class TFTDataCache(object):
 
 
 # TFT model definitions.
-class TemporalFusionTransformer(object):
+class TemporalFusionTransformer:
     """Defines Temporal Fusion Transformer.
 
     Attributes:
@@ -972,7 +972,7 @@ class TemporalFusionTransformer(object):
             valid_quantiles = self.quantiles
             output_size = self.output_size
 
-            class QuantileLossCalculator(object):
+            class QuantileLossCalculator:
                 """Computes the combined quantile loss for prespecified quantiles.
 
                 Attributes:

examples/run_all_model.py

@@ -69,9 +69,9 @@ def handler(signum, frame):
     os.system("kill -9 %d" % os.getpid())
 
 
-signal.signal(signal.SIGTSTP, handler)
 signal.signal(signal.SIGINT, handler)
 
+
 # function to calculate the mean and std of a list in the results dictionary
 def cal_mean_std(results) -> dict:
     mean_std = dict()

qlib/__init__.py

@@ -2,7 +2,7 @@
 # Licensed under the MIT License.
 
 
-__version__ = "0.6.0.dev"
+__version__ = "0.6.1.dev"
 
 
 import os

qlib/contrib/backtest/__init__.py

@@ -1,7 +1,6 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 
-# -*- coding: utf-8 -*-
 from .order import Order
 from .account import Account
 from .position import Position

qlib/contrib/model/pytorch_nn.py

@@ -296,7 +296,7 @@ class DNNModelPytorch(Model):
         self._fitted = True
 
 
-class AverageMeter(object):
+class AverageMeter:
     """Computes and stores the average and current value"""
 
     def __init__(self):

qlib/contrib/model/pytorch_sfm.py

@@ -464,7 +464,7 @@ class SFM(Model):
         return pd.Series(np.concatenate(preds), index=index)
 
 
-class AverageMeter(object):
+class AverageMeter:
     """Computes and stores the average and current value"""
 
     def __init__(self):

qlib/contrib/online/operator.py

@@ -21,7 +21,7 @@ from .executor import SimulatorExecutor
 from .executor import save_score_series, load_score_series
 
 
-class Operator(object):
+class Operator:
     def __init__(self, client: str):
         """
         Parameters

qlib/contrib/report/graph.py

@@ -17,7 +17,7 @@ from plotly.figure_factory import create_distplot
 from ...utils import get_module_by_module_path
 
 
-class BaseGraph(object):
+class BaseGraph:
     """"""
 
     _name = None
@@ -204,7 +204,7 @@ class HistogramGraph(BaseGraph):
         return _data
 
 
-class SubplotsGraph(object):
+class SubplotsGraph:
     """Create subplots same as df.plot(subplots=True)
 
     Simple package for `plotly.tools.subplots`

qlib/contrib/tuner/config.py

@@ -6,7 +6,7 @@ import copy
 import os
 
 
-class TunerConfigManager(object):
+class TunerConfigManager:
     def __init__(self, config_path):
 
         if not config_path:
@@ -27,7 +27,7 @@ class TunerConfigManager(object):
         self.qlib_client_config = config.get("qlib_client", dict())
 
 
-class PipelineExperimentConfig(object):
+class PipelineExperimentConfig:
     def __init__(self, config, TUNER_CONFIG_MANAGER):
         """
         :param config:  The config dict for tuner experiment
@@ -53,7 +53,7 @@ class PipelineExperimentConfig(object):
             yaml.dump(TUNER_CONFIG_MANAGER.config, fp)
 
 
-class OptimizationConfig(object):
+class OptimizationConfig:
     def __init__(self, config, TUNER_CONFIG_MANAGER):
 
         self.report_type = config.get("report_type", "pred_long")

qlib/contrib/tuner/pipeline.py

@@ -11,7 +11,7 @@ from ...log import get_module_logger, TimeInspector
 from ...utils import get_module_by_module_path
 
 
-class Pipeline(object):
+class Pipeline:
 
     GLOBAL_BEST_PARAMS_NAME = "global_best_params.json"
 

qlib/contrib/tuner/tuner.py

@@ -19,7 +19,7 @@ from hyperopt import fmin, tpe
 from hyperopt import STATUS_OK, STATUS_FAIL
 
 
-class Tuner(object):
+class Tuner:
     def __init__(self, tuner_config, optim_config):
 
         self.logger = get_module_logger("Tuner", sh_level=logging.INFO)

qlib/data/_libs/expanding.pyx

@@ -8,7 +8,7 @@ from libc.math cimport sqrt, isnan, NAN
 from libcpp.vector cimport vector
 
 
-cdef class Expanding(object):
+cdef class Expanding:
     """1-D array expanding"""
     cdef vector[double] barv
     cdef int na_count

qlib/data/_libs/rolling.pyx

@@ -8,7 +8,7 @@ from libc.math cimport sqrt, isnan, NAN
 from libcpp.deque cimport deque
 
 
-cdef class Rolling(object):
+cdef class Rolling:
     """1-D array rolling"""
     cdef int window
     cdef deque[double] barv

qlib/data/cache.py

@@ -68,7 +68,7 @@ class MemCacheUnit(OrderedDict):
                 self.popitem(last=False)
 
 
-class MemCache(object):
+class MemCache:
     """Memory cache."""
 
     def __init__(self, mem_cache_size_limit=None, limit_type="length"):
@@ -140,7 +140,7 @@ class MemCacheExpire:
         return value, expire
 
 
-class CacheUtils(object):
+class CacheUtils:
     LOCK_ID = "QLIB"
 
     @staticmethod
@@ -224,7 +224,7 @@ class CacheUtils(object):
             current_cache_wlock.release()
 
 
-class BaseProviderCache(object):
+class BaseProviderCache:
     """Provider cache base class"""
 
     def __init__(self, provider):

qlib/data/client.py

@@ -12,7 +12,7 @@ from ..log import get_module_logger
 import pickle
 
 
-class Client(object):
+class Client:
     """A client class
 
     Provide the connection tool functions for ClientProvider.

qlib/data/ops.py

@@ -18,7 +18,9 @@ try:
     from ._libs.rolling import rolling_slope, rolling_rsquare, rolling_resi
     from ._libs.expanding import expanding_slope, expanding_rsquare, expanding_resi
 except ImportError as err:
-    print("Do not import qlib package in the repository directory!")
+    print(
+        "#### Do not import qlib package in the repository directory in case of importing qlib from . without compiling #####"
+    )
     raise
 
 __all__ = (
@@ -143,6 +145,15 @@ class Sign(ElemOperator):
     def __init__(self, feature):
         super(Sign, self).__init__(feature, "sign")
 
+    def _load_internal(self, instrument, start_index, end_index, freq):
+        """
+        To avoid error raised by bool type input, we transform the data into float32.
+        """
+        series = self.feature.load(instrument, start_index, end_index, freq)
+        # TODO:  More precision types should be configurable
+        series = series.astype(np.float32)
+        return getattr(np, self.func)(series)
+
 
 class Log(ElemOperator):
     """Feature Log

qlib/log.py

@@ -36,7 +36,7 @@ def get_module_logger(module_name, level=None):
     return module_logger
 
 
-class TimeInspector(object):
+class TimeInspector:
 
     timer_logger = get_module_logger("timer", level=logging.WARNING)
 

qlib/portfolio/optimizer.py

@@ -9,7 +9,7 @@ import scipy.optimize as so
 from typing import Optional, Union, Callable, List
 
 
-class PortfolioOptimizer(object):
+class PortfolioOptimizer:
     """Portfolio Optimizer
 
     The following optimization algorithms are supported:

qlib/utils/__init__.py

@@ -686,7 +686,7 @@ def flatten_dict(d, parent_key="", sep="."):
 
 
 #################### Wrapper #####################
-class Wrapper(object):
+class Wrapper:
     """Wrapper class for anything that needs to set up during qlib.init"""
 
     def __init__(self):

scripts/collect_info.py

@@ -1,28 +1,66 @@
-import sys, platform
+import sys
+import platform
 import qlib
+import fire
+import pkg_resources
+from pathlib import Path
+
+QLIB_PATH = Path(__file__).absolute().resolve().parent.parent
 
 
-def linux_distribution():
-    try:
-        return platform.linux_distribution()
-    except:
-        return "N/A"
+class InfoCollector:
+    def sys(self):
+        """collect system related info"""
+        for method in ["system", "machine", "platform", "version"]:
+            print(getattr(platform, method)())
+
+    def py(self):
+        """collect Python related info"""
+        print("Python version: {}".format(sys.version.replace("\n", " ")))
+
+    def qlib(self):
+        """collect qlib related info"""
+        print("Qlib version: {}".format(qlib.__version__))
+        REQUIRED = [
+            "numpy",
+            "pandas",
+            "scipy",
+            "requests",
+            "sacred",
+            "pymongo",
+            "python-socketio",
+            "redis",
+            "python-redis-lock",
+            "schedule",
+            "cvxpy",
+            "hyperopt",
+            "fire",
+            "statsmodels",
+            "xlrd",
+            "plotly",
+            "matplotlib",
+            "tables",
+            "pyyaml",
+            "mlflow",
+            "tqdm",
+            "loguru",
+            "lightgbm",
+            "tornado",
+            "joblib",
+            "fire",
+            "ruamel.yaml",
+        ]
+
+        for package in REQUIRED:
+            version = pkg_resources.get_distribution(package).version
+            print(f"{package}=={version}")
+
+    def all(self):
+        """collect all info"""
+        for method in ["sys", "py", "qlib"]:
+            getattr(self, method)()
+            print()
 
 
-print("Qlib version: {} \n".format(qlib.__version__))
-print(
-    """Python version: {} \n
-linux_distribution: {}
-system: {}
-machine: {}
-platform: {}
-version: {}
-""".format(
-        sys.version.split("\n"),
-        linux_distribution(),
-        platform.system(),
-        platform.machine(),
-        platform.platform(),
-        platform.version(),
-    )
-)
+if __name__ == "__main__":
+    fire.Fire(InfoCollector)

setup.py

@@ -11,7 +11,7 @@ NAME = "pyqlib"
 DESCRIPTION = "A Quantitative-research Platform"
 REQUIRES_PYTHON = ">=3.5.0"
 
-VERSION = "0.6.0.dev"
+VERSION = "0.6.1.dev"
 
 # Detect Cython
 try: