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Merge branch 'main' into bugfix

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BookSword
2023-03-23 16:11:19 +08:00
parent 4fc8a5f25f 0e8bfcb5d3
commit d010219ba6
35 changed files with 624 additions and 314 deletions
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3
.github/workflows/test_qlib_from_source.yml vendored
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@@ -86,12 +86,11 @@ jobs:
# W1309: f-string-without-interpolation
# E1102: not-callable
# E1136: unsubscriptable-object
# FIXME: Due to the version change of Pylint, some code will cause W0719 error after PR 1417. W0719 is temporarily disabled in PR 1417 and should be fixed.
# References for parameters: https://github.com/PyCQA/pylint/issues/4577#issuecomment-1000245962
# We use sys.setrecursionlimit(2000) to make the recursion depth larger to ensure that pylint works properly (the default recursion depth is 1000).
- name: Check Qlib with pylint
run: |
pylint --disable=C0104,C0114,C0115,C0116,C0301,C0302,C0411,C0413,C1802,R0401,R0801,R0902,R0903,R0911,R0912,R0913,R0914,R0915,R1720,W0105,W0123,W0201,W0511,W0613,W1113,W1514,E0401,E1121,C0103,C0209,R0402,R1705,R1710,R1725,R1735,W0102,W0212,W0221,W0223,W0231,W0237,W0612,W0621,W0622,W0703,W1309,E1102,E1136,W0719 --const-rgx='[a-z_][a-z0-9_]{2,30}$' qlib --init-hook "import astroid; astroid.context.InferenceContext.max_inferred = 500; import sys; sys.setrecursionlimit(2000)"
pylint --disable=C0104,C0114,C0115,C0116,C0301,C0302,C0411,C0413,C1802,R0401,R0801,R0902,R0903,R0911,R0912,R0913,R0914,R0915,R1720,W0105,W0123,W0201,W0511,W0613,W1113,W1514,E0401,E1121,C0103,C0209,R0402,R1705,R1710,R1725,R1735,W0102,W0212,W0221,W0223,W0231,W0237,W0612,W0621,W0622,W0703,W1309,E1102,E1136 --const-rgx='[a-z_][a-z0-9_]{2,30}$' qlib --init-hook "import astroid; astroid.context.InferenceContext.max_inferred = 500; import sys; sys.setrecursionlimit(2000)"
# The following flake8 error codes were ignored:
# E501 line too long

2
.gitignore vendored
View File

@@ -27,6 +27,8 @@ examples/estimator/estimator_example/
examples/rl/data/
examples/rl/checkpoints/
examples/rl/outputs/
examples/rl_order_execution/data/
examples/rl_order_execution/outputs/
*.egg-info/

4
examples/benchmarks/LightGBM/multi_freq_handler.py
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@@ -29,13 +29,13 @@ class Avg15minHandler(DataHandlerLP):
fit_end_time=None,
process_type=DataHandlerLP.PTYPE_A,
filter_pipe=None,
inst_processor=None,
inst_processors=None,
**kwargs,
):
infer_processors = check_transform_proc(infer_processors, fit_start_time, fit_end_time)
learn_processors = check_transform_proc(learn_processors, fit_start_time, fit_end_time)
data_loader = Avg15minLoader(
config=self.loader_config(), filter_pipe=filter_pipe, freq=freq, inst_processor=inst_processor
config=self.loader_config(), filter_pipe=filter_pipe, freq=freq, inst_processors=inst_processors
)
super().__init__(
instruments=instruments,

2
examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158_multi_freq.yaml
View File

@@ -18,7 +18,7 @@ data_handler_config: &data_handler_config
label: day
feature: 1min
# with label as reference
inst_processor:
inst_processors:
feature:
- class: Resample1minProcessor
module_path: features_sample.py

2
examples/benchmarks/LightGBM/workflow_config_lightgbm_multi_freq.yaml
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@@ -19,7 +19,7 @@ data_handler_config: &data_handler_config
feature_15min: 1min
feature_day: day
# with label as reference
inst_processor:
inst_processors:
feature_15min:
- class: ResampleNProcessor
module_path: features_resample_N.py

60
examples/rl/README.md
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@@ -1,60 +0,0 @@
This folder contains a simple example of how to run Qlib RL. It contains:
```
.
├── experiment_config
│ ├── backtest # Backtest config
│ └── training # Training config
├── README.md # Readme (the current file)
└── scripts # Scripts for data pre-processing
```
## Data preparation
Use [AzCopy](https://learn.microsoft.com/en-us/azure/storage/common/storage-use-azcopy-v10) to download data:
```
azcopy copy https://qlibpublic.blob.core.windows.net/data/rl/qlib_rl_example_data ./ --recursive
mv qlib_rl_example_data data
```
The downloaded data will be placed at `./data`. The original data are in `data/csv`. To create all data needed by the case, run:
```
bash scripts/data_pipeline.sh
```
After the execution finishes, the `data/` directory should be like:
```
data
├── backtest_orders.csv
├── bin
├── csv
├── pickle
├── pickle_dataframe
└── training_order_split
```
## Run training
Run:
```
python -m qlib.rl.contrib.train_onpolicy --config_path ./experiment_config/training/config.yml
```
After training, checkpoints will be stored under `checkpoints/`.
## Run backtest
```
python -m qlib.rl.contrib.backtest --config_path ./experiment_config/backtest/config.yml
```
The backtest workflow will use the trained model in `checkpoints/`. The backtest summary can be found in `outputs/`.
## Others
The RL module is designed in a loosely-coupled way. Currently, RL examples are integrated with concrete business logic.
But the core part of RL is much simpler than what you see.
To demonstrate the simple core of RL, [a dedicated notebook](./simple_example.ipynb) for RL without business loss is created.

57
examples/rl/experiment_config/backtest/config.yml
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@@ -1,57 +0,0 @@
order_file: ./data/backtest_orders.csv
start_time: "9:45"
end_time: "14:44"
qlib:
provider_uri_1min: ./data/bin
feature_root_dir: ./data/pickle
feature_columns_today: [
"$open", "$high", "$low", "$close", "$vwap", "$volume",
]
feature_columns_yesterday: [
"$open_v1", "$high_v1", "$low_v1", "$close_v1", "$vwap_v1", "$volume_v1",
]
exchange:
limit_threshold: ['$close == 0', '$close == 0']
deal_price: ["If($close == 0, $vwap, $close)", "If($close == 0, $vwap, $close)"]
volume_threshold:
all: ["cum", "0.2 * DayCumsum($volume, '9:45', '14:44')"]
buy: ["current", "$close"]
sell: ["current", "$close"]
strategies:
30min:
class: TWAPStrategy
module_path: qlib.contrib.strategy.rule_strategy
kwargs: {}
1day:
class: SAOEIntStrategy
module_path: qlib.rl.order_execution.strategy
kwargs:
state_interpreter:
class: FullHistoryStateInterpreter
module_path: qlib.rl.order_execution.interpreter
kwargs:
max_step: 8
data_ticks: 240
data_dim: 6
processed_data_provider:
class: PickleProcessedDataProvider
module_path: qlib.rl.data.pickle_styled
kwargs:
data_dir: ./data/pickle_dataframe/feature
action_interpreter:
class: CategoricalActionInterpreter
module_path: qlib.rl.order_execution.interpreter
kwargs:
values: 14
max_step: 8
network:
class: Recurrent
module_path: qlib.rl.order_execution.network
kwargs: {}
policy:
class: PPO
module_path: qlib.rl.order_execution.policy
kwargs:
lr: 1.0e-4
weight_file: ./checkpoints/latest.pth
concurrency: 5

14
examples/rl/scripts/data_pipeline.sh
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@@ -1,14 +0,0 @@
# Generate `bin` format data
set -e
python ../../scripts/dump_bin.py dump_all --csv_path ./data/csv --qlib_dir ./data/bin --include_fields open,close,high,low,vwap,volume --symbol_field_name symbol --date_field_name date --freq 1min
# Generate pickle format data
python scripts/gen_pickle_data.py -c scripts/pickle_data_config.yml
if [ -e stat/ ]; then
rm -r stat/
fi
python scripts/collect_pickle_dataframe.py
# Sample orders
python scripts/gen_training_orders.py
python scripts/gen_backtest_orders.py

55
examples/rl/scripts/gen_backtest_orders.py
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@@ -1,55 +0,0 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import argparse
import os
import pandas as pd
import numpy as np
import pickle
parser = argparse.ArgumentParser()
parser.add_argument("--seed", type=int, default=20220926)
parser.add_argument("--num_order", type=int, default=10)
args = parser.parse_args()
np.random.seed(args.seed)
path = os.path.join("data", "pickle", "backtesttest.pkl")
df = pickle.load(open(path, "rb")).reset_index()
df["date"] = df["datetime"].dt.date.astype("datetime64")
instruments = sorted(set(df["instrument"]))
# TODO: The example is expected to be able to handle data containing missing values.
# TODO: Currently, we just simply skip dates that contain missing data. We will add
# TODO: this feature in the future.
skip_dates = {}
for instrument in instruments:
csv_df = pd.read_csv(os.path.join("data", "csv", f"{instrument}.csv"))
csv_df = csv_df[csv_df["close"].isna()]
dates = set([str(d).split(" ")[0] for d in csv_df["date"]])
skip_dates[instrument] = dates
df_list = []
for instrument in instruments:
print(instrument)
cur_df = df[df["instrument"] == instrument]
dates = sorted(set([str(d).split(" ")[0] for d in cur_df["date"]]))
dates = [date for date in dates if date not in skip_dates[instrument]]
n = args.num_order
df_list.append(
pd.DataFrame(
{
"date": sorted(np.random.choice(dates, size=n, replace=False)),
"instrument": [instrument] * n,
"amount": np.random.randint(low=3, high=11, size=n) * 100.0,
"order_type": np.random.randint(low=0, high=2, size=n),
}
).set_index(["date", "instrument"]),
)
total_df = pd.concat(df_list)
total_df.to_csv("data/backtest_orders.csv")

39
examples/rl/scripts/gen_training_orders.py
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@@ -1,39 +0,0 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import argparse
import os
import pandas as pd
import numpy as np
import pickle
parser = argparse.ArgumentParser()
parser.add_argument("--seed", type=int, default=20220926)
parser.add_argument("--stock", type=str, default="AAPL")
parser.add_argument("--train_size", type=int, default=10)
parser.add_argument("--valid_size", type=int, default=2)
parser.add_argument("--test_size", type=int, default=2)
args = parser.parse_args()
np.random.seed(args.seed)
os.makedirs(os.path.join("data", "training_order_split"), exist_ok=True)
for group, n in zip(("train", "valid", "test"), (args.train_size, args.valid_size, args.test_size)):
path = os.path.join("data", "pickle", f"backtest{group}.pkl")
df = pickle.load(open(path, "rb")).reset_index()
df["date"] = df["datetime"].dt.date.astype("datetime64")
dates = sorted(set([str(d).split(" ")[0] for d in df["date"]]))
data_df = pd.DataFrame(
{
"date": sorted(np.random.choice(dates, size=n, replace=False)),
"instrument": [args.stock] * n,
"amount": np.random.randint(low=3, high=11, size=n) * 100.0,
"order_type": [0] * n,
}
).set_index(["date", "instrument"])
os.makedirs(os.path.join("data", "training_order_split", group), exist_ok=True)
pickle.dump(data_df, open(os.path.join("data", "training_order_split", group, f"{args.stock}.pkl"), "wb"))

100
examples/rl_order_execution/README.md Normal file
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@@ -0,0 +1,100 @@
# RL Example for Order Execution
This folder comprises an example of Reinforcement Learning (RL) workflows for order execution scenario, including both training workflows and backtest workflows.
## Data Processing
### Get Data
```
python -m qlib.run.get_data qlib_data qlib_data --target_dir ./data/bin --region hs300 --interval 5min
```
### Generate Pickle-Style Data
To run codes in this example, we need data in pickle format. To achieve this, run following commands (might need a few minutes to finish):
```
python scripts/gen_pickle_data.py -c scripts/pickle_data_config.yml
python scripts/collect_pickle_dataframe.py
python scripts/gen_training_orders.py
python scripts/merge_orders.py
```
When finished, the structure under `data/` should be:
```
data
├── bin
├── orders
├── pickle
└── pickle_dataframe
```
## Training
Each training task is specified by a config file. The config file for task `TASKNAME` is `exp_configs/train_TASKNAME.yml`. This example provides two training tasks:
- **PPO**: Method proposed by IJCAL 2020 paper "[An End-to-End Optimal Trade Execution Framework based on Proximal Policy Optimization](https://www.ijcai.org/proceedings/2020/0627.pdf)".
- **OPDS**: Method proposed by AAAI 2021 paper "[Universal Trading for Order Execution with Oracle Policy Distillation](https://arxiv.org/abs/2103.10860)".
The main differece between these two methods is their reward functions. Please see their config files for details.
Take OPDS as an example, to run the training workflow, run:
```
python -m qlib.rl.contrib.train_onpolicy --config_path exp_configs/train_opds.yml --run_backtest
```
Metrics, logs, and checkpoints will be stored under `outputs/opds` (configured by `exp_configs/train_opds.yml`).
## Backtest
Once the training workflow has completed, the trained model can be used for the backtesting workflow. Still taking OPDS as an example, once training is finished, the latest checkpoint of the model can be found at `outputs/opds/checkpoints/latest.pth`. To run backtest workflow:
1. Uncomment the `weight_file` parameter in `exp_configs/train_opds.yml` (it is commented by default). While it is possible to run the backtesting workflow without setting a checkpoint, this will lead to randomly initialized model results, thus making them meaningless.
2. Run `python -m qlib.rl.contrib.backtest --config_path exp_configs/backtest_opds.yml`.
The backtest result is stored in `outputs/checkpoints/backtest_result.csv`.
In addition to OPDS and PPO, we also provide TWAP ([Time-weighted average price](https://en.wikipedia.org/wiki/Time-weighted_average_price)) as a weak baseline. The config file for TWAP is `exp_configs/backtest_twap.yml`.
### Gap between backtest and training pipeline's testing
It is worthy to notice that the results of the backtesting process may differ from the results of the testing process used during training.
This is because different simulators are used to simulate market conditions during training and backtesting.
In training pipeline, the simplified simulator called `SingleAssetOrderExecutionSimple` is used for efficiency reasons.
`SingleAssetOrderExecutionSimple` makes no restriction to trading amounts.
No matter what the amount of the order is, it can be completely executed.
However, during backtesting, a more realistic simulator called `SingleAssetOrderExecution` is used.
It takes into account practical constraints in more real-world scenarios (for example, the trading volume must be a multiple of the smallest trading unit).
As a result, the amount of an order that is actually executed during backtesting may differ from the amount expected to be executed.
If you would like to obtain results that are exactly the same as those obtained during testing in the training pipeline, you could run training pipeline with only backtest phrase.
In order to do this:
- Modify the training config. Add the path of the checkpoint you want to use (see following for an example).
- Run `python -m qlib.rl.contrib.train_onpolicy --config_path PATH/TO/CONFIG --run_backtest --no_training`
```yaml
...
policy:
class: PPO # PPO, DQN
kwargs:
lr: 0.0001
weight_file: PATH/TO/CHECKPOINT
module_path: qlib.rl.order_execution.policy
...
```
## Benchmarks (TBD)
To accurately evaluate the performance of models using Reinforcement Learning algorithms, it's best to run experiments multiple times and compute the average performance across all trials. However, given the time-consuming nature of model training, this is not always feasible. An alternative approach is to run each training task only once, selecting the 10 checkpoints with the highest validation performance to simulate multiple trials. In this example, we use "Price Advantage (PA)" as the metric for selecting these checkpoints. The average performance of these 10 checkpoints on the testing set is as follows:
| **Model** | **PA mean with std.** |
|-----------------------------|-----------------------|
| OPDS (with PPO policy) | 0.4785 ± 0.7815 |
| OPDS (with DQN policy) | -0.0114 ± 0.5780 |
| PPO | -1.0935 ± 0.0922 |
| TWAP | ≈ 0.0 ± 0.0 |
The table above also includes TWAP as a rule-based baseline. The ideal PA of TWAP should be 0.0, however, in this example, the order execution is divided into two steps: first, the order is split equally among each half hour, and then each five minutes within each half hour. Since trading is forbidden during the last five minutes of the day, this approach may slightly differ from traditional TWAP over the course of a full day (as there are 5 minutes missing in the last "half hour"). Therefore, the PA of TWAP can be considered as a number that is close to 0.0. To verify this, you may run a TWAP backtest and check the results.

59
examples/rl_order_execution/exp_configs/backtest_opds.yml Executable file
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@@ -0,0 +1,59 @@
order_file: ./data/orders/test_orders.pkl
start_time: "9:30"
end_time: "14:54"
qlib:
provider_uri_5min: ./data/bin/
feature_root_dir: ./data/pickle/
feature_columns_today: [
"$open", "$high", "$low", "$close", "$vwap", "$bid", "$ask", "$volume",
"$bidV", "$bidV1", "$bidV3", "$bidV5", "$askV", "$askV1", "$askV3", "$askV5"
]
feature_columns_yesterday: [
"$open_1", "$high_1", "$low_1", "$close_1", "$vwap_1", "$bid_1", "$ask_1", "$volume_1",
"$bidV_1", "$bidV1_1", "$bidV3_1", "$bidV5_1", "$askV_1", "$askV1_1", "$askV3_1", "$askV5_1"
]
exchange:
limit_threshold: null
deal_price: ["$close", "$close"]
volume_threshold: null
strategies:
1day:
class: SAOEIntStrategy
kwargs:
data_granularity: 5
action_interpreter:
class: CategoricalActionInterpreter
kwargs:
max_step: 8
values: 4
module_path: qlib.rl.order_execution.interpreter
network:
class: Recurrent
kwargs: {}
module_path: qlib.rl.order_execution.network
policy:
class: PPO # PPO, DQN
kwargs:
lr: 0.0001
# Restore `weight_file` once the training workflow finishes. You can change the checkpoint file you want to use.
# weight_file: outputs/opds/checkpoints/latest.pth
module_path: qlib.rl.order_execution.policy
state_interpreter:
class: FullHistoryStateInterpreter
kwargs:
data_dim: 5
data_ticks: 48
max_step: 8
processed_data_provider:
class: PickleProcessedDataProvider
kwargs:
data_dir: ./data/pickle_dataframe/feature
module_path: qlib.rl.data.pickle_styled
module_path: qlib.rl.order_execution.interpreter
module_path: qlib.rl.order_execution.strategy
30min:
class: TWAPStrategy
kwargs: {}
module_path: qlib.contrib.strategy.rule_strategy
concurrency: 16
output_dir: outputs/opds/

59
examples/rl_order_execution/exp_configs/backtest_ppo.yml Executable file
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@@ -0,0 +1,59 @@
order_file: ./data/orders/test_orders.pkl
start_time: "9:30"
end_time: "14:54"
qlib:
provider_uri_5min: ./data/bin/
feature_root_dir: ./data/pickle/
feature_columns_today: [
"$open", "$high", "$low", "$close", "$vwap", "$bid", "$ask", "$volume",
"$bidV", "$bidV1", "$bidV3", "$bidV5", "$askV", "$askV1", "$askV3", "$askV5"
]
feature_columns_yesterday: [
"$open_1", "$high_1", "$low_1", "$close_1", "$vwap_1", "$bid_1", "$ask_1", "$volume_1",
"$bidV_1", "$bidV1_1", "$bidV3_1", "$bidV5_1", "$askV_1", "$askV1_1", "$askV3_1", "$askV5_1"
]
exchange:
limit_threshold: null
deal_price: ["$close", "$close"]
volume_threshold: null
strategies:
1day:
class: SAOEIntStrategy
kwargs:
data_granularity: 5
action_interpreter:
class: CategoricalActionInterpreter
kwargs:
max_step: 8
values: 4
module_path: qlib.rl.order_execution.interpreter
network:
class: Recurrent
kwargs: {}
module_path: qlib.rl.order_execution.network
policy:
class: PPO # PPO, DQN
kwargs:
lr: 0.0001
# Restore `weight_file` once the training workflow finishes. You can change the checkpoint file you want to use.
# weight_file: outputs/ppo/checkpoints/latest.pth
module_path: qlib.rl.order_execution.policy
state_interpreter:
class: FullHistoryStateInterpreter
kwargs:
data_dim: 5
data_ticks: 48
max_step: 8
processed_data_provider:
class: PickleProcessedDataProvider
kwargs:
data_dir: ./data/pickle_dataframe/feature
module_path: qlib.rl.data.pickle_styled
module_path: qlib.rl.order_execution.interpreter
module_path: qlib.rl.order_execution.strategy
30min:
class: TWAPStrategy
kwargs: {}
module_path: qlib.contrib.strategy.rule_strategy
concurrency: 16
output_dir: outputs/ppo/

29
examples/rl_order_execution/exp_configs/backtest_twap.yml Executable file
View File

@@ -0,0 +1,29 @@
order_file: ./data/orders/test_orders.pkl
start_time: "9:30"
end_time: "14:54"
qlib:
provider_uri_5min: ./data/bin/
feature_root_dir: ./data/pickle/
feature_columns_today: [
"$open", "$high", "$low", "$close", "$vwap", "$bid", "$ask", "$volume",
"$bidV", "$bidV1", "$bidV3", "$bidV5", "$askV", "$askV1", "$askV3", "$askV5"
]
feature_columns_yesterday: [
"$open_1", "$high_1", "$low_1", "$close_1", "$vwap_1", "$bid_1", "$ask_1", "$volume_1",
"$bidV_1", "$bidV1_1", "$bidV3_1", "$bidV5_1", "$askV_1", "$askV1_1", "$askV3_1", "$askV5_1"
]
exchange:
limit_threshold: null
deal_price: ["$close", "$close"]
volume_threshold: null
strategies:
1day:
class: TWAPStrategy
kwargs: {}
module_path: qlib.contrib.strategy.rule_strategy
30min:
class: TWAPStrategy
kwargs: {}
module_path: qlib.contrib.strategy.rule_strategy
concurrency: 16
output_dir: outputs/twap/

38
examples/rl/experiment_config/training/config.yml → examples/rl_order_execution/exp_configs/train_opds.yml Normal file → Executable file
View File

@@ -1,20 +1,21 @@
simulator:
data_granularity: 5
time_per_step: 30
vol_limit: null
env:
concurrency: 1
parallel_mode: dummy
concurrency: 48
parallel_mode: shmem
action_interpreter:
class: CategoricalActionInterpreter
kwargs:
values: 14
values: 4
max_step: 8
module_path: qlib.rl.order_execution.interpreter
state_interpreter:
class: FullHistoryStateInterpreter
kwargs:
data_dim: 6
data_ticks: 240
data_dim: 5
data_ticks: 48 # 48 = 240 min / 5 min
max_step: 8
processed_data_provider:
class: PickleProcessedDataProvider
@@ -25,23 +26,24 @@ state_interpreter:
reward:
class: PAPenaltyReward
kwargs:
penalty: 100.0
penalty: 4.0
scale: 0.01
module_path: qlib.rl.order_execution.reward
data:
source:
order_dir: ./data/training_order_split
order_dir: ./data/orders
data_dir: ./data/pickle_dataframe/backtest
total_time: 240
default_start_time: 0
default_end_time: 240
proc_data_dim: 6
default_start_time_index: 0
default_end_time_index: 235
proc_data_dim: 5
num_workers: 0
queue_size: 20
network:
class: Recurrent
module_path: qlib.rl.order_execution.network
policy:
class: PPO
class: PPO # PPO, DQN
kwargs:
lr: 0.0001
module_path: qlib.rl.order_execution.policy
@@ -49,11 +51,11 @@ runtime:
seed: 42
use_cuda: false
trainer:
max_epoch: 2
repeat_per_collect: 5
earlystop_patience: 2
episode_per_collect: 20
batch_size: 16
val_every_n_epoch: 1
checkpoint_path: ./checkpoints
max_epoch: 500
repeat_per_collect: 25
earlystop_patience: 50
episode_per_collect: 10000
batch_size: 1024
val_every_n_epoch: 4
checkpoint_path: ./outputs/opds
checkpoint_every_n_iters: 1

62
examples/rl_order_execution/exp_configs/train_ppo.yml Executable file
View File

@@ -0,0 +1,62 @@
simulator:
data_granularity: 5
time_per_step: 30
vol_limit: null
env:
concurrency: 48
parallel_mode: shmem
action_interpreter:
class: CategoricalActionInterpreter
kwargs:
values: 4
max_step: 8
module_path: qlib.rl.order_execution.interpreter
state_interpreter:
class: FullHistoryStateInterpreter
kwargs:
data_dim: 5
data_ticks: 48 # 48 = 240 min / 5 min
max_step: 8
processed_data_provider:
class: PickleProcessedDataProvider
module_path: qlib.rl.data.pickle_styled
kwargs:
data_dir: ./data/pickle_dataframe/feature
module_path: qlib.rl.order_execution.interpreter
reward:
class: PPOReward
kwargs:
max_step: 8
start_time_index: 0
end_time_index: 46 # 46 = (240 - 5) min / 5 min - 1
module_path: qlib.rl.order_execution.reward
data:
source:
order_dir: ./data/orders
data_dir: ./data/pickle_dataframe/backtest
total_time: 240
default_start_time_index: 0
default_end_time_index: 235
proc_data_dim: 5
num_workers: 0
queue_size: 20
network:
class: Recurrent
module_path: qlib.rl.order_execution.network
policy:
class: PPO # PPO, DQN
kwargs:
lr: 0.0001
module_path: qlib.rl.order_execution.policy
runtime:
seed: 42
use_cuda: false
trainer:
max_epoch: 500
repeat_per_collect: 25
earlystop_patience: 50
episode_per_collect: 10000
batch_size: 1024
val_every_n_epoch: 4
checkpoint_path: ./outputs/ppo
checkpoint_every_n_iters: 1

15
examples/rl/scripts/collect_pickle_dataframe.py → examples/rl_order_execution/scripts/collect_pickle_dataframe.py Normal file → Executable file
View File

@@ -4,10 +4,17 @@
import os
import pickle
import pandas as pd
from tqdm import tqdm
from joblib import Parallel, delayed
os.makedirs(os.path.join("data", "pickle_dataframe"), exist_ok=True)
def _collect(df: pd.DataFrame, instrument: str, tag: str) -> None:
cur = df[df["instrument"] == instrument].sort_values(by=["datetime"])
cur = cur.set_index(["instrument", "datetime", "date"])
pickle.dump(cur, open(os.path.join("data", "pickle_dataframe", tag, f"{instrument}.pkl"), "wb"))
for tag in ("backtest", "feature"):
df = pickle.load(open(os.path.join("data", "pickle", f"{tag}.pkl"), "rb"))
df = pd.concat(list(df.values())).reset_index()
@@ -15,7 +22,5 @@ for tag in ("backtest", "feature"):
instruments = sorted(set(df["instrument"]))
os.makedirs(os.path.join("data", "pickle_dataframe", tag), exist_ok=True)
for instrument in tqdm(instruments):
cur = df[df["instrument"] == instrument].sort_values(by=["datetime"])
cur = cur.set_index(["instrument", "datetime", "date"])
pickle.dump(cur, open(os.path.join("data", "pickle_dataframe", tag, f"{instrument}.pkl"), "wb"))
Parallel(n_jobs=-1, verbose=10)(delayed(_collect)(df, instrument, tag) for instrument in instruments)

3
examples/rl/scripts/gen_pickle_data.py → examples/rl_order_execution/scripts/gen_pickle_data.py
View File

@@ -4,6 +4,7 @@
import yaml
import argparse
import os
import shutil
from copy import deepcopy
from qlib.contrib.data.highfreq_provider import HighFreqProvider
@@ -41,3 +42,5 @@ if __name__ == "__main__":
if args.split == "stock" or args.split == "both":
provider._gen_stock_dataset(deepcopy(provider.feature_conf), "feature")
provider._gen_stock_dataset(deepcopy(provider.backtest_conf), "backtest")
shutil.rmtree("stat/", ignore_errors=True)

42
examples/rl_order_execution/scripts/gen_training_orders.py Executable file
View File

@@ -0,0 +1,42 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import os
import numpy as np
import pandas as pd
from tqdm import tqdm
from pathlib import Path
DATA_PATH = Path(os.path.join("data", "pickle_dataframe", "backtest"))
OUTPUT_PATH = Path(os.path.join("data", "orders"))
def generate_order(stock: str, start_idx: int, end_idx: int) -> None:
df = pd.read_pickle(DATA_PATH / f"{stock}.pkl")
df = df.groupby("date").take(range(start_idx, end_idx)).droplevel(level=0)
div = df["$volume0"].rolling((end_idx - start_idx) * 60).mean().shift(1).groupby(level="date").transform("first")
order_all = pd.DataFrame(df.groupby(level=(2, 0)).mean().dropna())
order_all["amount"] = np.random.lognormal(-3.28, 1.14) * order_all["$volume0"]
order_all = order_all[order_all["amount"] > 0.0]
order_all["order_type"] = 0
order_all = order_all.drop(columns=["$volume0"])
order_train = order_all[order_all.index.get_level_values(0) <= pd.Timestamp("2021-06-30")]
order_test = order_all[order_all.index.get_level_values(0) > pd.Timestamp("2021-06-30")]
order_valid = order_test[order_test.index.get_level_values(0) <= pd.Timestamp("2021-09-30")]
order_test = order_test[order_test.index.get_level_values(0) > pd.Timestamp("2021-09-30")]
for order, tag in zip((order_train, order_valid, order_test, order_all), ("train", "valid", "test", "all")):
path = OUTPUT_PATH / tag
os.makedirs(path, exist_ok=True)
if len(order) > 0:
order.to_pickle(path / f"{stock}.pkl.target")
np.random.seed(1234)
file_list = sorted(os.listdir(DATA_PATH))
stocks = [f.replace(".pkl", "") for f in file_list]
stocks = sorted(np.random.choice(stocks, size=100, replace=False))
for stock in tqdm(stocks):
generate_order(stock, 0, 240 // 5 - 1)

15
examples/rl_order_execution/scripts/merge_orders.py Executable file
View File

@@ -0,0 +1,15 @@
import pickle
import os
import pandas as pd
from tqdm import tqdm
for tag in ["test", "valid"]:
files = os.listdir(os.path.join("data/orders/", tag))
dfs = []
for f in tqdm(files):
df = pickle.load(open(os.path.join("data/orders/", tag, f), "rb"))
df = df.drop(["$close0"], axis=1)
dfs.append(df)
total_df = pd.concat(dfs)
pickle.dump(total_df, open(os.path.join("data", "orders", f"{tag}_orders.pkl"), "wb"))

36
examples/rl/scripts/pickle_data_config.yml → examples/rl_order_execution/scripts/pickle_data_config.yml
View File

@@ -1,15 +1,16 @@
# start & end time for training/validation/test datasets
start_time: !!str &start 2020-01-01
end_time: !!str &end 2020-07-31
train_end_time: !!str &tend 2020-03-31
valid_start_time: !!str &vstart 2020-04-01
valid_end_time: !!str &vend 2020-05-31
test_start_time: !!str &tstart 2020-06-01
end_time: !!str &end 2021-12-31
train_end_time: !!str &tend 2021-06-30
valid_start_time: !!str &vstart 2021-07-01
valid_end_time: !!str &vend 2021-09-30
test_start_time: !!str &tstart 2021-10-01
# the instrument set
instruments: &ins all
instruments: &ins csi300s19_22
# qlib related configuration
qlib_conf:
provider_uri: ./data/bin # path to generated qlib bin
provider_uri:
5min: ./data/bin # path to generated qlib bin
redis_port: 233
feature_conf:
path: ./data/pickle/feature.pkl # output path of feature
@@ -26,14 +27,23 @@ feature_conf:
fit_end_time: *tend
instruments: *ins
day_length: 240 # how many minutes in one trading day
freq: 5min
columns: ["$open", "$high", "$low", "$close"]
infer_processors:
- class: HighFreqNorm
module_path: qlib.contrib.data.highfreq_processor
kwargs:
feature_save_dir: ./stat/ # output path of statistics of features (for feature normalization)
norm_groups:
price: 10
price: 8
volume: 2
inst_processors:
- class: TimeRangeFlt
module_path: qlib.data.dataset.processor
kwargs:
start_time: "2020-01-01"
end_time: "2021-12-31"
freq: 5min
segments:
train: !!python/tuple [*start, *tend]
valid: !!python/tuple [*vstart, *vend]
@@ -51,7 +61,17 @@ backtest_conf:
end_time: *end
instruments: *ins
day_length: 240
freq: 5min
columns: ["$close", "$volume"]
inst_processors:
- class: TimeRangeFlt
module_path: qlib.data.dataset.processor
kwargs:
start_time: "2020-01-01"
end_time: "2021-12-31"
freq: 5min
segments:
train: !!python/tuple [*start, *tend]
valid: !!python/tuple [*vstart, *vend]
test: !!python/tuple [*tstart, *end]
freq: 5min

8
qlib/contrib/data/handler.py
View File

@@ -56,7 +56,7 @@ class Alpha360(DataHandlerLP):
fit_start_time=None,
fit_end_time=None,
filter_pipe=None,
inst_processor=None,
inst_processors=None,
**kwargs
):
infer_processors = check_transform_proc(infer_processors, fit_start_time, fit_end_time)
@@ -71,7 +71,7 @@ class Alpha360(DataHandlerLP):
},
"filter_pipe": filter_pipe,
"freq": freq,
"inst_processor": inst_processor,
"inst_processors": inst_processors,
},
}
@@ -152,7 +152,7 @@ class Alpha158(DataHandlerLP):
fit_end_time=None,
process_type=DataHandlerLP.PTYPE_A,
filter_pipe=None,
inst_processor=None,
inst_processors=None,
**kwargs
):
infer_processors = check_transform_proc(infer_processors, fit_start_time, fit_end_time)
@@ -167,7 +167,7 @@ class Alpha158(DataHandlerLP):
},
"filter_pipe": filter_pipe,
"freq": freq,
"inst_processor": inst_processor,
"inst_processors": inst_processors,
},
}
super().__init__(

10
qlib/contrib/data/highfreq_handler.py
View File

@@ -44,7 +44,7 @@ class HighFreqHandler(DataHandlerLP):
names = []
template_if = "If(IsNull({1}), {0}, {1})"
template_paused = "Select(Gt($hx_paused_num, 1.001), {0})"
template_paused = "Select(Gt($paused_num, 1.001), {0})"
def get_normalized_price_feature(price_field, shift=0):
# norm with the close price of 237th minute of yesterday.
@@ -115,6 +115,7 @@ class HighFreqGeneralHandler(DataHandlerLP):
day_length=240,
freq="1min",
columns=["$open", "$high", "$low", "$close", "$vwap"],
inst_processors=None,
):
self.day_length = day_length
self.columns = columns
@@ -128,6 +129,7 @@ class HighFreqGeneralHandler(DataHandlerLP):
"config": self.get_feature_config(),
"swap_level": False,
"freq": freq,
"inst_processors": inst_processors,
},
}
super().__init__(
@@ -257,6 +259,7 @@ class HighFreqGeneralBacktestHandler(DataHandler):
day_length=240,
freq="1min",
columns=["$close", "$vwap", "$volume"],
inst_processors=None,
):
self.day_length = day_length
self.columns = set(columns)
@@ -266,6 +269,7 @@ class HighFreqGeneralBacktestHandler(DataHandler):
"config": self.get_feature_config(),
"swap_level": False,
"freq": freq,
"inst_processors": inst_processors,
},
}
super().__init__(
@@ -311,6 +315,7 @@ class HighFreqOrderHandler(DataHandlerLP):
learn_processors=[],
fit_start_time=None,
fit_end_time=None,
inst_processors=None,
drop_raw=True,
):
@@ -323,6 +328,7 @@ class HighFreqOrderHandler(DataHandlerLP):
"config": self.get_feature_config(),
"swap_level": False,
"freq": "1min",
"inst_processors": inst_processors,
},
}
super().__init__(
@@ -482,7 +488,7 @@ class HighFreqBacktestOrderHandler(DataHandler):
names = []
template_if = "If(IsNull({1}), {0}, {1})"
template_paused = "Select(Gt($hx_paused_num, 1.001), {0})"
template_paused = "Select(Gt($paused_num, 1.001), {0})"
template_fillnan = "FFillNan({0})"
fields += [
template_fillnan.format(template_paused.format("$close")),

38
qlib/contrib/data/highfreq_provider.py
View File

@@ -128,7 +128,7 @@ class HighFreqProvider:
raise ValueError("Must specify the path to save the dataset.") from e
if os.path.isfile(path):
start = time.time()
self.logger.info("Dataset exists, load from disk.", __name__)
self.logger.info(f"[{__name__}]Dataset exists, load from disk.")
# res = dataset.prepare(['train', 'valid', 'test'])
with open(path, "rb") as f:
@@ -137,11 +137,11 @@ class HighFreqProvider:
res = [data[i] for i in datasets]
else:
res = data.prepare(datasets)
self.logger.info(f"Data loaded, time cost: {time.time() - start:.2f}", __name__)
self.logger.info(f"[{__name__}]Data loaded, time cost: {time.time() - start:.2f}")
else:
if not os.path.exists(os.path.dirname(path)):
os.makedirs(os.path.dirname(path))
self.logger.info("Generating dataset", __name__)
self.logger.info(f"[{__name__}]Generating dataset")
start_time = time.time()
self._prepare_calender_cache()
dataset = init_instance_by_config(config)
@@ -160,7 +160,7 @@ class HighFreqProvider:
with open(path[:-4] + "test.pkl", "wb") as f:
pkl.dump(testset, f)
res = [data[i] for i in datasets]
self.logger.info(f"Data generated, time cost: {(time.time() - start_time):.2f}", __name__)
self.logger.info(f"[{__name__}]Data generated, time cost: {(time.time() - start_time):.2f}")
return res
def _gen_data(self, config, datasets=["train", "valid", "test"]):
@@ -170,7 +170,7 @@ class HighFreqProvider:
raise ValueError("Must specify the path to save the dataset.") from e
if os.path.isfile(path):
start = time.time()
self.logger.info("Dataset exists, load from disk.", __name__)
self.logger.info(f"[{__name__}]Dataset exists, load from disk.")
# res = dataset.prepare(['train', 'valid', 'test'])
with open(path, "rb") as f:
@@ -179,18 +179,18 @@ class HighFreqProvider:
res = [data[i] for i in datasets]
else:
res = data.prepare(datasets)
self.logger.info(f"Data loaded, time cost: {time.time() - start:.2f}", __name__)
self.logger.info(f"[{__name__}]Data loaded, time cost: {time.time() - start:.2f}")
else:
if not os.path.exists(os.path.dirname(path)):
os.makedirs(os.path.dirname(path))
self.logger.info("Generating dataset", __name__)
self.logger.info(f"[{__name__}]Generating dataset")
start_time = time.time()
self._prepare_calender_cache()
dataset = init_instance_by_config(config)
dataset.config(dump_all=True, recursive=True)
dataset.to_pickle(path)
res = dataset.prepare(datasets)
self.logger.info(f"Data generated, time cost: {(time.time() - start_time):.2f}", __name__)
self.logger.info(f"[{__name__}]Data generated, time cost: {(time.time() - start_time):.2f}")
return res
def _gen_dataset(self, config):
@@ -200,21 +200,21 @@ class HighFreqProvider:
raise ValueError("Must specify the path to save the dataset.") from e
if os.path.isfile(path):
start = time.time()
self.logger.info("Dataset exists, load from disk.", __name__)
self.logger.info(f"[{__name__}]Dataset exists, load from disk.")
with open(path, "rb") as f:
dataset = pkl.load(f)
self.logger.info(f"Data loaded, time cost: {time.time() - start:.2f}", __name__)
self.logger.info(f"[{__name__}]Data loaded, time cost: {time.time() - start:.2f}")
else:
start = time.time()
if not os.path.exists(os.path.dirname(path)):
os.makedirs(os.path.dirname(path))
self.logger.info("Generating dataset", __name__)
self.logger.info(f"[{__name__}]Generating dataset")
self._prepare_calender_cache()
dataset = init_instance_by_config(config)
self.logger.info(f"Dataset init, time cost: {time.time() - start:.2f}", __name__)
self.logger.info(f"[{__name__}]Dataset init, time cost: {time.time() - start:.2f}")
dataset.prepare(["train", "valid", "test"])
self.logger.info(f"Dataset prepared, time cost: {time.time() - start:.2f}", __name__)
self.logger.info(f"[{__name__}]Dataset prepared, time cost: {time.time() - start:.2f}")
dataset.config(dump_all=True, recursive=True)
dataset.to_pickle(path)
return dataset
@@ -227,15 +227,15 @@ class HighFreqProvider:
if os.path.isfile(path + "tmp_dataset.pkl"):
start = time.time()
self.logger.info("Dataset exists, load from disk.", __name__)
self.logger.info(f"[{__name__}]Dataset exists, load from disk.")
else:
start = time.time()
if not os.path.exists(os.path.dirname(path)):
os.makedirs(os.path.dirname(path))
self.logger.info("Generating dataset", __name__)
self.logger.info(f"[{__name__}]Generating dataset")
self._prepare_calender_cache()
dataset = init_instance_by_config(config)
self.logger.info(f"Dataset init, time cost: {time.time() - start:.2f}", __name__)
self.logger.info(f"[{__name__}]Dataset init, time cost: {time.time() - start:.2f}")
dataset.config(dump_all=False, recursive=True)
dataset.to_pickle(path + "tmp_dataset.pkl")
@@ -268,15 +268,15 @@ class HighFreqProvider:
if os.path.isfile(path + "tmp_dataset.pkl"):
start = time.time()
self.logger.info("Dataset exists, load from disk.", __name__)
self.logger.info(f"[{__name__}]Dataset exists, load from disk.")
else:
start = time.time()
if not os.path.exists(os.path.dirname(path)):
os.makedirs(os.path.dirname(path))
self.logger.info("Generating dataset", __name__)
self.logger.info(f"[{__name__}]Generating dataset")
self._prepare_calender_cache()
dataset = init_instance_by_config(config)
self.logger.info(f"Dataset init, time cost: {time.time() - start:.2f}", __name__)
self.logger.info(f"[{__name__}]Dataset init, time cost: {time.time() - start:.2f}")
dataset.config(dump_all=False, recursive=True)
dataset.to_pickle(path + "tmp_dataset.pkl")

30
qlib/data/dataset/handler.py
View File

@@ -7,6 +7,7 @@ from typing import Callable, Union, Tuple, List, Iterator, Optional
import pandas as pd
from qlib.typehint import Literal
from ...log import get_module_logger, TimeInspector
from ...utils import init_instance_by_config
from ...utils.serial import Serializable
@@ -49,6 +50,8 @@ class DataHandler(Serializable):
- Fetching data with `col_set=CS_RAW` will return the raw data and may avoid pandas from copying the data when calling `loc`
"""
_data: pd.DataFrame # underlying data.
def __init__(
self,
instruments=None,
@@ -155,6 +158,11 @@ class DataHandler(Serializable):
"""
fetch data from underlying data source
Design motivation:
- providing a unified interface for underlying data.
- Potential to make the interface more friendly.
- User can improve performance when fetching data in this extra layer
Parameters
----------
selector : Union[pd.Timestamp, slice, str]
@@ -328,6 +336,9 @@ class DataHandler(Serializable):
yield cur_date, self.fetch(selector, **kwargs)
DATA_KEY_TYPE = Literal["raw", "infer", "learn"]
class DataHandlerLP(DataHandler):
"""
DataHandler with **(L)earnable (P)rocessor**
@@ -353,10 +364,15 @@ class DataHandlerLP(DataHandler):
- `drop_raw=True`: this will modify the data inplace on raw data;
"""
# based on `self._data`, _infer and _learn are genrated after processors
_infer: pd.DataFrame # data for inference
_learn: pd.DataFrame # data for learning models
# data key
DK_R = "raw"
DK_I = "infer"
DK_L = "learn"
DK_R: DATA_KEY_TYPE = "raw"
DK_I: DATA_KEY_TYPE = "infer"
DK_L: DATA_KEY_TYPE = "learn"
# map data_key to attribute name
ATTR_MAP = {DK_R: "_data", DK_I: "_infer", DK_L: "_learn"}
# process type
@@ -600,7 +616,7 @@ class DataHandlerLP(DataHandler):
# TODO: Be able to cache handler data. Save the memory for data processing
def _get_df_by_key(self, data_key: str = DK_I) -> pd.DataFrame:
def _get_df_by_key(self, data_key: DATA_KEY_TYPE = DK_I) -> pd.DataFrame:
if data_key == self.DK_R and self.drop_raw:
raise AttributeError(
"DataHandlerLP has not attribute _data, please set drop_raw = False if you want to use raw data"
@@ -613,7 +629,7 @@ class DataHandlerLP(DataHandler):
selector: Union[pd.Timestamp, slice, str] = slice(None, None),
level: Union[str, int] = "datetime",
col_set=DataHandler.CS_ALL,
data_key: str = DK_I,
data_key: DATA_KEY_TYPE = DK_I,
squeeze: bool = False,
proc_func: Callable = None,
) -> pd.DataFrame:
@@ -647,7 +663,7 @@ class DataHandlerLP(DataHandler):
proc_func=proc_func,
)
def get_cols(self, col_set=DataHandler.CS_ALL, data_key: str = DK_I) -> list:
def get_cols(self, col_set=DataHandler.CS_ALL, data_key: DATA_KEY_TYPE = DK_I) -> list:
"""
get the column names
@@ -655,7 +671,7 @@ class DataHandlerLP(DataHandler):
----------
col_set : str
select a set of meaningful columns.(e.g. features, columns).
data_key : str
data_key : DATA_KEY_TYPE
the data to fetch: DK_*.
Returns

22
qlib/data/dataset/loader.py
View File

@@ -153,7 +153,7 @@ class QlibDataLoader(DLWParser):
filter_pipe: List = None,
swap_level: bool = True,
freq: Union[str, dict] = "day",
inst_processor: dict = None,
inst_processors: Union[dict, list] = None,
):
"""
Parameters
@@ -167,16 +167,19 @@ class QlibDataLoader(DLWParser):
freq: dict or str
If type(config) == dict and type(freq) == str, load config data using freq.
If type(config) == dict and type(freq) == dict, load config[<group_name>] data using freq[<group_name>]
inst_processor: dict
If inst_processor is not None and type(config) == dict; load config[<group_name>] data using inst_processor[<group_name>]
inst_processors: dict | list
If inst_processors is not None and type(config) == dict; load config[<group_name>] data using inst_processors[<group_name>]
If inst_processors is a list, then it will be applied to all groups.
"""
self.filter_pipe = filter_pipe
self.swap_level = swap_level
self.freq = freq
# sample
self.inst_processor = inst_processor if inst_processor is not None else {}
assert isinstance(self.inst_processor, dict), f"inst_processor(={self.inst_processor}) must be dict"
self.inst_processors = inst_processors if inst_processors is not None else {}
assert isinstance(
self.inst_processors, (dict, list)
), f"inst_processors(={self.inst_processors}) must be dict or list"
super().__init__(config)
@@ -187,8 +190,8 @@ class QlibDataLoader(DLWParser):
if _gp not in freq:
raise ValueError(f"freq(={freq}) missing group(={_gp})")
assert (
self.inst_processor
), f"freq(={self.freq}), inst_processor(={self.inst_processor}) cannot be None/empty"
self.inst_processors
), f"freq(={self.freq}), inst_processors(={self.inst_processors}) cannot be None/empty"
def load_group_df(
self,
@@ -208,9 +211,10 @@ class QlibDataLoader(DLWParser):
warnings.warn("`filter_pipe` is not None, but it will not be used with `instruments` as list")
freq = self.freq[gp_name] if isinstance(self.freq, dict) else self.freq
df = D.features(
instruments, exprs, start_time, end_time, freq=freq, inst_processors=self.inst_processor.get(gp_name, [])
inst_processors = (
self.inst_processors if isinstance(self.inst_processors, list) else self.inst_processors.get(gp_name, [])
)
df = D.features(instruments, exprs, start_time, end_time, freq=freq, inst_processors=inst_processors)
df.columns = names
if self.swap_level:
df = df.swaplevel().sort_index() # NOTE: if swaplevel, return <datetime, instrument>

43
qlib/data/dataset/processor.py
View File

@@ -2,7 +2,7 @@
# Licensed under the MIT License.
import abc
from typing import Union, Text
from typing import Union, Text, Optional
import numpy as np
import pandas as pd
@@ -11,6 +11,8 @@ from ...constant import EPS
from .utils import fetch_df_by_index
from ...utils.serial import Serializable
from ...utils.paral import datetime_groupby_apply
from qlib.data.inst_processor import InstProcessor
from qlib.data import D
def get_group_columns(df: pd.DataFrame, group: Union[Text, None]):
@@ -378,3 +380,42 @@ class HashStockFormat(Processor):
from .storage import HashingStockStorage # pylint: disable=C0415
return HashingStockStorage.from_df(df)
class TimeRangeFlt(InstProcessor):
"""
This is a filter to filter stock.
Only keep the data that exist from start_time to end_time (the existence in the middle is not checked.)
WARNING: It may induce leakage!!!
"""
def __init__(
self,
start_time: Optional[Union[pd.Timestamp, str]] = None,
end_time: Optional[Union[pd.Timestamp, str]] = None,
freq: str = "day",
):
"""
Parameters
----------
start_time : Optional[Union[pd.Timestamp, str]]
The data must start earlier (or equal) than `start_time`
None indicates data will not be filtered based on `start_time`
end_time : Optional[Union[pd.Timestamp, str]]
similar to start_time
freq : str
The frequency of the calendar
"""
# Align to calendar before filtering
cal = D.calendar(start_time=start_time, end_time=end_time, freq=freq)
self.start_time = None if start_time is None else cal[0]
self.end_time = None if end_time is None else cal[-1]
def __call__(self, df: pd.DataFrame, instrument, *args, **kwargs):
if (
df.empty
or (self.start_time is None or df.index.min() <= self.start_time)
and (self.end_time is None or df.index.max() >= self.end_time)
):
return df
return df.head(0)

5
qlib/rl/contrib/backtest.py
View File

@@ -357,7 +357,10 @@ def backtest(backtest_config: dict, with_simulator: bool = False) -> pd.DataFram
if not output_path.exists():
os.makedirs(output_path)
res.to_csv(output_path / "summary.csv")
if "pa" in res.columns:
res["pa"] = res["pa"] * 10000.0 # align with training metrics
res.to_csv(output_path / "backtest_result.csv")
return res

54
qlib/rl/order_execution/policy.py
View File

@@ -12,11 +12,11 @@ import torch
import torch.nn as nn
from gym.spaces import Discrete
from tianshou.data import Batch, ReplayBuffer, to_torch
from tianshou.policy import BasePolicy, PPOPolicy
from tianshou.policy import BasePolicy, PPOPolicy, DQNPolicy
from qlib.rl.trainer.trainer import Trainer
__all__ = ["AllOne", "PPO"]
__all__ = ["AllOne", "PPO", "DQN"]
# baselines #
@@ -158,6 +158,56 @@ class PPO(PPOPolicy):
set_weight(self, Trainer.get_policy_state_dict(weight_file))
DQNModel = PPOActor # Reuse PPOActor.
class DQN(DQNPolicy):
"""A wrapper of tianshou DQNPolicy.
Differences:
- Auto-create model network. Supports discrete action space only.
- Support a ``weight_file`` that supports loading checkpoint.
"""
def __init__(
self,
network: nn.Module,
obs_space: gym.Space,
action_space: gym.Space,
lr: float,
weight_decay: float = 0.0,
discount_factor: float = 0.99,
estimation_step: int = 1,
target_update_freq: int = 0,
reward_normalization: bool = False,
is_double: bool = True,
clip_loss_grad: bool = False,
weight_file: Optional[Path] = None,
) -> None:
assert isinstance(action_space, Discrete)
model = DQNModel(network, action_space.n)
optimizer = torch.optim.Adam(
model.parameters(),
lr=lr,
weight_decay=weight_decay,
)
super().__init__(
model,
optimizer,
discount_factor=discount_factor,
estimation_step=estimation_step,
target_update_freq=target_update_freq,
reward_normalization=reward_normalization,
is_double=is_double,
clip_loss_grad=clip_loss_grad,
)
if weight_file is not None:
set_weight(self, Trainer.get_policy_state_dict(weight_file))
# utilities: these should be put in a separate (common) file. #

14
qlib/rl/order_execution/reward.py
View File

@@ -70,7 +70,19 @@ class PPOReward(Reward[SAOEState]):
def reward(self, simulator_state: SAOEState) -> float:
if simulator_state.cur_step == self.max_step - 1 or simulator_state.position < 1e-6:
vwap_price = cast(dict, simulator_state.metrics)["trade_price"]
if simulator_state.history_exec["deal_amount"].sum() == 0.0:
vwap_price = cast(
float,
np.average(simulator_state.history_exec["market_price"]),
)
else:
vwap_price = cast(
float,
np.average(
simulator_state.history_exec["market_price"],
weights=simulator_state.history_exec["deal_amount"],
),
)
twap_price = simulator_state.backtest_data.get_deal_price().mean()
if simulator_state.order.direction == OrderDir.SELL:

8
qlib/rl/order_execution/strategy.py
View File

@@ -7,6 +7,7 @@ import collections
from types import GeneratorType
from typing import Any, Callable, cast, Dict, Generator, List, Optional, Tuple, Union
import warnings
import numpy as np
import pandas as pd
import torch
@@ -137,7 +138,12 @@ class SAOEStateAdapter:
exec_vol[idx - last_step_range[0]] = order.deal_amount
if exec_vol.sum() > self.position and exec_vol.sum() > 0.0:
assert exec_vol.sum() < self.position + 1, f"{exec_vol} too large"
if exec_vol.sum() > self.position + 1.0:
warnings.warn(
f"Sum of execution volume is {exec_vol.sum()} which is larger than "
f"position + 1.0 = {self.position} + 1.0 = {self.position + 1.0}. "
f"All execution volume is scaled down linearly to ensure that their sum does not position."
)
exec_vol *= self.position / (exec_vol.sum())
market_volume = cast(

2
qlib/utils/__init__.py
View File

@@ -224,7 +224,7 @@ def requests_with_retry(url, retry=5, **kwargs):
except Exception as e:
log.warning("exception encountered {}".format(e))
continue
raise Exception("ERROR: requests failed!")
raise TimeoutError("ERROR: requests failed!")
#################### Parse ####################

2
qlib/workflow/exp.py
View File

@@ -333,7 +333,7 @@ class MLflowExperiment(Experiment):
recorder = self._get_recorder(recorder_name=recorder_name)
self._client.delete_run(recorder.id)
except MlflowException as e:
raise Exception(
raise ValueError(
f"Error: {e}. Something went wrong when deleting recorder. Please check if the name/id of the recorder is correct."
) from e

2
qlib/workflow/expm.py
View File

@@ -415,7 +415,7 @@ class MLflowExpManager(ExpManager):
raise MlflowException("No valid experiment has been found.")
self.client.delete_experiment(experiment.experiment_id)
except MlflowException as e:
raise Exception(
raise ValueError(
f"Error: {e}. Something went wrong when deleting experiment. Please check if the name/id of the experiment is correct."
) from e

4
qlib/workflow/recorder.py
View File

@@ -324,7 +324,7 @@ class MLflowRecorder(Recorder):
raise RuntimeError("This recorder is not saved in the local file system.")
else:
raise Exception(
raise ValueError(
"Please make sure the recorder has been created and started properly before getting artifact uri."
)
@@ -464,7 +464,7 @@ class MLflowRecorder(Recorder):
if self.artifact_uri is not None:
return self.artifact_uri
else:
raise Exception(
raise ValueError(
"Please make sure the recorder has been created and started properly before getting artifact uri."
)