Formated and env_setup.sh

.flake8

@@ -5,8 +5,8 @@ select = B,C,E,F,P,T4,W,B9
 # line length is intentionally set to 80 here because black uses Bugbear
 # See https://black.readthedocs.io/en/stable/the_black_code_style/current_style.html#line-length for more details
 max-line-length = 80
-# NOTE (yuting zhao): may change in the future, current setting to see https://github.com/psf/black/blob/main/.flake8
-max-complexity = 18
+# NOTE: may change in the future, current setting to see https://github.com/psf/black/blob/main/.flake8
+max-complexity = 36
 # C408 ignored because we like the dict keyword argument syntax
 # E501 is not flexible enough, we're using B950 instead
 ignore =

.github/CONTRIBUTING.md

@@ -1,5 +1,7 @@
 # Style
 
+> <b>Before you pull request, please run ``./scripts/format.sh --git``.</b>
+
 This project is formatted with [clang-format][fmt] using the style file at the root of the repository. Please run clang-format before sending a pull request.
 
 In general, try to follow the style of surrounding code. We mostly follow the [Google C++ style guide][cpp-style].

.github/scripts/format_check.sh

@@ -2,9 +2,14 @@
 set -euo pipefail
 
 TOP_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd -P)"
+# shellcheck disable=SC1090,SC1091
+source "${TOP_DIR}/scripts/check_gtest_deps.sh"
 
 function check_code_formatted() {
-  readarray -t my_diffs < <(git diff \
+  # shellcheck disable=SC1001,SC2162
+  while IFS=\= read my_diff; do
+    my_diffs+=("$my_diff")
+  done < <(git diff \
     --ignore-submodules --diff-filter=d --name-only -- \
     "*.h" "*.cc" "*.cpp" "*.cu" \
     "*.proto" \

.github/workflows/pylint.yml

@@ -31,5 +31,7 @@ jobs:
         pip install pylint
     - name: Analysing the code with pylint
       run: |
-        source ./.github/scripts/format_check.sh
+        sudo apt install shellcheck
+        sudo apt install protobuf
+        ./.github/scripts/format_check.sh
       shell: bash

.gitignore

@@ -131,5 +131,6 @@ dmypy.json
 .vscode
 .vim
 .nvimrc
+jqdata.json
 
 *.DS_Store

README.md

@@ -1,4 +1,4 @@
-<h1> <img src="https://user-images.githubusercontent.com/29084184/204598632-23c473db-92ee-4e9b-9b57-d6d95c861fdf.png" width="42"/> Qbot </h1>
+<h1> <img src="https://user-images.githubusercontent.com/29084184/204598632-23c473db-92ee-4e9b-9b57-d6d95c861fdf.png" width="42"/> 🤖 Qbot </h1>
 
 <div align="center">
   <img src="https://user-images.githubusercontent.com/29084184/204598632-23c473db-92ee-4e9b-9b57-d6d95c861fdf.png" width="224"/>
@@ -28,19 +28,36 @@
   </p>
 </div>
 
-
-Qbot = 智能交易策略 + 回测系统 + 自动化交易 (+ 可视化分析工具)
-
+```
+🤖 Qbot = 智能交易策略 + 回测系统 + 自动化交易 (+ 可视化分析工具)
+          |           |          |            |
+          |           |          |             \_ quantstats (dashboard\online operate)
+          |           |           \______________ Qbot - vnpy, pytrader, pyfunds
+          |           \__________________________ BackTest - backtrader, easyquant
+          \______________________________________ quant.ai - qlib, deep learning strategies         
+```
 
 ## Installation
 
+[Install Guide](./docs/Install_guide.md)
+
+```
+ ____________________________________
+< Run ``./env_setup.sh`` to say hello >
+ ------------------------------------
+            \   ^__^
+             \  (oo)\_______
+                (__)\       )\/\
+                    ||----w |
+                    ||     ||
+```
 
 ## Get Started
 
 
-## Benchmark and model zoo
+## Benchmark and Model zoo
 
-Results and models are available in the [model zoo](docs/en/model_zoo.md).
+Results and models are available in the [model zoo](docs/model_zoo.md).
 
 <div align="center">
   <b>Components</b>
@@ -176,9 +193,9 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
 |    FCOS3D     |   ✓    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
 
 
-<sup>**Note:** All the about **300+ models, methods of 40+ papers** in 2D detection supported by [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/model_zoo.md) can be trained or used in this codebase.</sup>
-
+<sup>**Note:** All the about **300+ models, methods of 40+ papers** in quant.ai supported by [Model Zoo](./docs/model_zoo.md) can be trained or used in this codebase.</sup>
 
+## Quantstats Report
 ## Contributing
 
 We appreciate all contributions to improve MMDetection3D. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.

docs/Install_guide.md

@@ -20,6 +20,8 @@ bash Anaconda3-5.3.1-Linux-x86_64.sh
 ```bash
 conda create -n Qbot python=3.9
 conda activate Qbot
+
+pip install -r requirements.txt
 ```
 
 ## Prepare

docs/_coverpage.md

@@ -1,12 +1,11 @@
-<div align="center">
+<!-- <div align="center">
 	<img src="https://raw.githubusercontent.com/Charmve/computer-vision-in-action/main/res/ui/maiwei.png" width="100px" alt="L0CV" title="有疑问，跑起来就会变成一朵花 ❀">
-</div>
+</div> -->
 
-# Qbot 人工智能自动化量化交易 🌱
-<!---## AI + 金融 <small>V1.2 </small>-->
-
-> 人工智能、量化交易
+<h1> 🤖 Qbot  </h1>
+<h2> 人工智能自动化量化交易机器人  </h2>
 
+>  <small>🌱 V1.2</small>
 <br>
 
 <span id="busuanzi_container_site_pv" style='display:none'>

docs/index.html

@@ -2,7 +2,7 @@
 <html lang="en">
 <head>
   <meta charset="UTF-8">
-  <title> Qbot 自动化人工智能量化交易工具 🌱</title>
+  <title> Qbot 自动化人工智能量化交易机器人 🌱</title>
   <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1" />
   <meta name="description" content="Description">
   <meta name="viewport" content="width=device-width, user-scalable=no, initial-scale=1.0, maximum-scale=1.0, minimum-scale=1.0">
@@ -113,7 +113,7 @@
     };
 
     window.$docsify = {
-      name: '🔥 自动驾驶感知算法专题',
+      name: '🤖 Qbot',
       // nameLink: 'https://charmve.github.io/L0CV-web/',
       loadSidebar: true,
       maxLevel: 4, // 默认情况下会抓取文档中所有标题渲染成目录，可配置最大支持渲染的标题层级。

docs/model_zoo.md

@@ -0,0 +1,13 @@
+# Model Zoo
+
+## Common settings
+
+- We use distributed training.
+- For fair comparison with other codebases, we report the GPU memory as the maximum value of `torch.cuda.max_memory_allocated()` for all 8 GPUs. Note that this value is usually less than what `nvidia-smi` shows.
+- We report the inference time as the total time of network forwarding and post-processing, excluding the data loading time. Results are obtained with the script [benchmark.py](https://github.com/open-mmlab/mmdetection/blob/master/tools/analysis_tools/benchmark.py) which computes the average time on 2000 images.
+
+## Baselines
+
+### SECOND
+
+Please refer to [SECOND](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/second) for details. We provide SECOND baselines on KITTI and Waymo datasets.

env_setup.sh

@@ -0,0 +1,44 @@
+#!/bin/bash
+set -euo pipefail
+
+wget https://repo.continuum.io/archive/Anaconda3-5.3.1-Linux-x86_64.sh
+bash Anaconda3-5.3.1-Linux-x86_64.sh
+
+echo "export PATH=/home/$USER/anaconda3/bin:$PATH" | sudo tee -a ~/.bashrc
+# shellcheck disable=SC1090
+source ~/.bashrc
+
+conda info --env
+conda create -n Qbot python=3.9
+conda activate Qbot
+conda info --env
+
+pip install -r requirements.txt
+
+export USER_ID="admin"
+export PASSWORD="admin1234."
+
+echo "Successful - Environment is set up!"
+
+echo "Enjoy, Quanter!"
+
+cat << EOF
+
+             ██████╗ ██████╗  ██████╗ ████████╗
+            ██╔═══██╗██╔══██╗██╔═══██╗╚══██╔══╝
+            ██║   ██║██████╔╝██║   ██║   ██║   
+            ██║▄▄ ██║██╔══██╗██║   ██║   ██║   
+            ╚██████╔╝██████╔╝╚██████╔╝   ██║   
+             ╚══▀▀═╝ ╚═════╝  ╚═════╝    ╚═╝   
+                
+                        Alpha Qbot
+          ++++=================================++++
+                 auth: Charmve   --V.0.1   
+
+   🤖 Qbot = 智能交易策略 + 回测系统 + 自动化交易 (+ 可视化分析工具)
+                |           |          |            |
+                |           |          |             \_ quantstats (dashboard\online operate)
+                |           |           \______________ Qbot - vnpy, pytrader, pyfunds
+                |           \__________________________ BackTest - backtrader, easyquant
+                \______________________________________ quant.ai - qlib, deep learning strategies                                   
+EOF

pytrader/analyser/alphafold158.py

@@ -2,9 +2,8 @@
 from qlib.config import REG_CN
 from qlib.contrib.data.handler import Alpha158
 
-
-if __name__ == '__main__':
-    qlib.init(provider_uri='data/cn_data', region=REG_CN)
+if __name__ == "__main__":
+    qlib.init(provider_uri="data/cn_data", region=REG_CN)
 
     data_handler_config = {
         "start_time": "2018-01-01",
@@ -17,11 +16,10 @@
     h = Alpha158(**data_handler_config)
 
     # get all the columns of the data
-    print('columns: \n', h.get_cols())
+    print("columns: \n", h.get_cols())
 
     # fetch all the labels
-    print('labels: \n', h.fetch(col_set="label"))
+    print("labels: \n", h.fetch(col_set="label"))
 
     # fetch all the features
-    print('features: \n', h.fetch(col_set="feature"))
-
+    print("features: \n", h.fetch(col_set="feature"))

pytrader/analyser/highfreq.py

@@ -97,16 +97,16 @@
 
 
 from copy import deepcopy
-import qlib
+
 import fire
 import pandas as pd
-from qlib.config import REG_CN, HIGH_FREQ_CONFIG
-from qlib.data import D
-from qlib.utils import exists_qlib_data, init_instance_by_config, flatten_dict
-from qlib.workflow import R
-from qlib.workflow.record_temp import SignalRecord, PortAnaRecord
-from qlib.tests.data import GetData
+import qlib
 from qlib.backtest import collect_data
+from qlib.config import HIGH_FREQ_CONFIG, REG_CN
+from qlib.tests.data import GetData
+from qlib.utils import flatten_dict, init_instance_by_config
+from qlib.workflow import R
+from qlib.workflow.record_temp import PortAnaRecord, SignalRecord
 
 
 class NestedDecisionExecutionWorkflow:
@@ -172,34 +172,25 @@ class NestedDecisionExecutionWorkflow:
                                 "time_per_step": "5min",
                                 "generate_portfolio_metrics": True,
                                 "verbose": True,
-                                "indicator_config": {
-                                    "show_indicator": True,
-                                },
+                                "indicator_config": {"show_indicator": True},
                             },
                         },
                         "inner_strategy": {
                             "class": "TWAPStrategy",
                             "module_path": "qlib.contrib.strategy.rule_strategy",
                         },
                         "generate_portfolio_metrics": True,
-                        "indicator_config": {
-                            "show_indicator": True,
-                        },
+                        "indicator_config": {"show_indicator": True},
                     },
                 },
                 "inner_strategy": {
                     "class": "SBBStrategyEMA",
                     "module_path": "qlib.contrib.strategy.rule_strategy",
-                    "kwargs": {
-                        "instruments": market,
-                        "freq": "1min",
-                    },
+                    "kwargs": {"instruments": market, "freq": "1min"},
                 },
                 "track_data": True,
                 "generate_portfolio_metrics": True,
-                "indicator_config": {
-                    "show_indicator": True,
-                },
+                "indicator_config": {"show_indicator": True},
             },
         },
         "backtest": {
@@ -220,13 +211,21 @@ class NestedDecisionExecutionWorkflow:
     def _init_qlib(self):
         """initialize qlib"""
         provider_uri_day = "~/.qlib/qlib_data/cn_data"  # target_dir
-        GetData().qlib_data(target_dir=provider_uri_day, region=REG_CN, version="v2", exists_skip=True)
+        GetData().qlib_data(
+            target_dir=provider_uri_day, region=REG_CN, version="v2", exists_skip=True
+        )
         provider_uri_1min = HIGH_FREQ_CONFIG.get("provider_uri")
         GetData().qlib_data(
-            target_dir=provider_uri_1min, interval="1min", region=REG_CN, version="v2", exists_skip=True
+            target_dir=provider_uri_1min,
+            interval="1min",
+            region=REG_CN,
+            version="v2",
+            exists_skip=True,
         )
         provider_uri_map = {"1min": provider_uri_1min, "day": provider_uri_day}
-        qlib.init(provider_uri=provider_uri_map, dataset_cache=None, expression_cache=None)
+        qlib.init(
+            provider_uri=provider_uri_map, dataset_cache=None, expression_cache=None
+        )
 
     def _train_model(self, model, dataset):
         with R.start(experiment_name="train"):
@@ -247,11 +246,7 @@ def backtest(self):
         strategy_config = {
             "class": "TopkDropoutStrategy",
             "module_path": "qlib.contrib.strategy.signal_strategy",
-            "kwargs": {
-                "signal": (model, dataset),
-                "topk": 50,
-                "n_drop": 5,
-            },
+            "kwargs": {"signal": (model, dataset), "topk": 50, "n_drop": 5},
         }
         self.port_analysis_config["strategy"] = strategy_config
         self.port_analysis_config["backtest"]["benchmark"] = self.benchmark
@@ -281,13 +276,11 @@ def collect_data(self):
         strategy_config = {
             "class": "TopkDropoutStrategy",
             "module_path": "qlib.contrib.strategy.signal_strategy",
-            "kwargs": {
-                "signal": (model, dataset),
-                "topk": 50,
-                "n_drop": 5,
-            },
+            "kwargs": {"signal": (model, dataset), "topk": 50, "n_drop": 5},
         }
-        data_generator = collect_data(executor=executor_config, strategy=strategy_config, **backtest_config)
+        data_generator = collect_data(
+            executor=executor_config, strategy=strategy_config, **backtest_config
+        )
         for trade_decision in data_generator:
             print(trade_decision)
 
@@ -303,13 +296,17 @@ def collect_data(self):
     def check_diff_freq(self):
         self._init_qlib()
         exp = R.get_exp(experiment_name="backtest")
-        rec = next(iter(exp.list_recorders().values()))  # assuming this will get the latest recorder
+        rec = next(
+            iter(exp.list_recorders().values())
+        )  # assuming this will get the latest recorder
         for check_key in "account", "total_turnover", "total_cost":
             check_key = "total_cost"
 
             acc_dict = {}
             for freq in ["30minute", "5minute", "1day"]:
-                acc_dict[freq] = rec.load_object(f"portfolio_analysis/report_normal_{freq}.pkl")[check_key]
+                acc_dict[freq] = rec.load_object(
+                    f"portfolio_analysis/report_normal_{freq}.pkl"
+                )[check_key]
             acc_df = pd.DataFrame(acc_dict)
             acc_resam = acc_df.resample("1d").last().dropna()
             assert (acc_resam["30minute"] == acc_resam["1day"]).all()
@@ -362,11 +359,7 @@ def backtest_only_daily(self):
         strategy_config = {
             "class": "TopkDropoutStrategy",
             "module_path": "qlib.contrib.strategy.signal_strategy",
-            "kwargs": {
-                "signal": (model, dataset),
-                "topk": 50,
-                "n_drop": 5,
-            },
+            "kwargs": {"signal": (model, dataset), "topk": 50, "n_drop": 5},
         }
         pa_conf = deepcopy(self.port_analysis_config)
         pa_conf["strategy"] = strategy_config