fix_typo (#790)

Signed-off-by: unknown <lv.linlang@qq.com>

CHANGES.rst

@@ -30,7 +30,7 @@ Version 0.2.1
 --------------------
 - Support registering user-defined ``Provider``.
 - Support use operators in string format, e.g. ``['Ref($close, 1)']`` is valid field format.
-- Support dynamic fields in ``$some_field`` format. And exising fields like ``Close()`` may be deprecated in the future.
+- Support dynamic fields in ``$some_field`` format. And existing fields like ``Close()`` may be deprecated in the future.
 
 Version 0.2.2
 --------------------
@@ -78,7 +78,7 @@ Version 0.3.5
 - Support multi-label training, you can provide multiple label in ``handler``. (But LightGBM doesn't support due to the algorithm itself)
 - Refactor ``handler`` code, dataset.py is no longer used, and you can deploy your own labels and features in ``feature_label_config``
 - Handler only offer DataFrame. Also, ``trainer`` and model.py only receive DataFrame
-- Change ``split_rolling_data``, we roll the data on market calender now, not on normal date
+- Change ``split_rolling_data``, we roll the data on market calendar now, not on normal date
 - Move some date config from ``handler`` to ``trainer``
 
 Version 0.4.0
@@ -167,11 +167,11 @@ Version 0.8.0
     - There are lots of changes for daily trading, it is hard to list all of them. But a few important changes could be noticed
         - The trading limitation is more accurate;
             - In `previous version <https://github.com/microsoft/qlib/blob/v0.7.2/qlib/contrib/backtest/exchange.py#L160>`_, longing and shorting actions share the same action.
-            - In `current verison <https://github.com/microsoft/qlib/blob/7c31012b507a3823117bddcc693fc64899460b2a/qlib/backtest/exchange.py#L304>`_, the trading limitation is different between loging and shorting action.
+            - In `current version <https://github.com/microsoft/qlib/blob/7c31012b507a3823117bddcc693fc64899460b2a/qlib/backtest/exchange.py#L304>`_, the trading limitation is different between logging and shorting action.
         - The constant is different when calculating annualized metrics.
             - `Current version <https://github.com/microsoft/qlib/blob/7c31012b507a3823117bddcc693fc64899460b2a/qlib/contrib/evaluate.py#L42>`_ uses more accurate constant than `previous version <https://github.com/microsoft/qlib/blob/v0.7.2/qlib/contrib/evaluate.py#L22>`_
         - `A new version <https://github.com/microsoft/qlib/blob/7c31012b507a3823117bddcc693fc64899460b2a/qlib/tests/data.py#L17>`_ of data is released. Due to the unstability of Yahoo data source, the data may be different after downloading data again.
-        - Users could chec kout the backtesting results between  `Current version <https://github.com/microsoft/qlib/tree/7c31012b507a3823117bddcc693fc64899460b2a/examples/benchmarks>`_ and `previous version <https://github.com/microsoft/qlib/tree/v0.7.2/examples/benchmarks>`_
+        - Users could check out the backtesting results between  `Current version <https://github.com/microsoft/qlib/tree/7c31012b507a3823117bddcc693fc64899460b2a/examples/benchmarks>`_ and `previous version <https://github.com/microsoft/qlib/tree/v0.7.2/examples/benchmarks>`_
 
 
 Other Versions

docs/component/highfreq.rst

@@ -14,7 +14,7 @@ To get the join trading performance of daily and intraday trading, they must int
 In order to support the joint backtest strategies in multiple levels, a corresponding framework is required. None of the publicly available high-frequency trading frameworks considers multi-level joint trading, which make the backtesting aforementioned inaccurate.
 
 Besides backtesting, the optimization of strategies from different levels is not standalone and can be affected by each other.
-For example, the best portfolio management strategy may change with the performance of order executions(e.g. a portfolio with higher turnover may becomes a better choice when we imporve the order execution strategies).
+For example, the best portfolio management strategy may change with the performance of order executions(e.g. a portfolio with higher turnover may becomes a better choice when we improve the order execution strategies).
 To achieve the overall good performance , it is necessary to consider the interaction of strategies in different level. 
 
 Therefore, building a new framework for trading in multiple levels becomes necessary to solve the various problems mentioned above, for which we designed a nested decision execution framework that consider the interaction of strategies.

docs/component/recorder.rst

@@ -37,7 +37,7 @@ Here is a general view of the structure of the system:
         
 This experiment management system defines a set of interface and provided a concrete implementation ``MLflowExpManager``, which is based on the machine learning platform: ``MLFlow`` (`link <https://mlflow.org/>`_). 
 
-If users set the implementation of ``ExpManager`` to be ``MLflowExpManager``, they can use the command `mlflow ui` to visualize and check the experiment results. For more information, pleaes refer to the related documents `here <https://www.mlflow.org/docs/latest/cli.html#mlflow-ui>`_.
+If users set the implementation of ``ExpManager`` to be ``MLflowExpManager``, they can use the command `mlflow ui` to visualize and check the experiment results. For more information, please refer to the related documents `here <https://www.mlflow.org/docs/latest/cli.html#mlflow-ui>`_.
 
 Qlib Recorder
 ===================

docs/hidden/tuner.rst

@@ -31,7 +31,7 @@ Let's see an example,
 
 First make sure you have the latest version of `qlib` installed.
 
-Then, you need to privide a configuration to setup the experiment.
+Then, you need to provide a configuration to setup the experiment.
 We write a simple configuration example as following,
 
 .. code-block:: YAML
@@ -217,13 +217,13 @@ The tuner pipeline contains different tuners, and the `tuner` program will proce
 Each part represents a tuner, and its modules which are to be tuned. Space in each part is the hyper-parameters' space of a certain module, you need to create your searching space and modify it in `/qlib/contrib/tuner/space.py`. We use `hyperopt` package to help us to construct the space, you can see the detail of how to use it in https://github.com/hyperopt/hyperopt/wiki/FMin .
 
 - model
-    You need to provide the `class` and the `space` of the model. If the model is user's own implementation, you need to privide the `module_path`. 
+    You need to provide the `class` and the `space` of the model. If the model is user's own implementation, you need to provide the `module_path`.
 
 - trainer
-    You need to proveide the `class` of the trainer. If the trainer is user's own implementation, you need to privide the `module_path`. 
+    You need to provide the `class` of the trainer. If the trainer is user's own implementation, you need to provide the `module_path`.
 
 - strategy
-    You need to provide the `class` and the `space` of the strategy. If the strategy is user's own implementation, you need to privide the `module_path`. 
+    You need to provide the `class` and the `space` of the strategy. If the strategy is user's own implementation, you need to provide the `module_path`.
 
 - data_label
     The label of the data, you can search which kinds of labels will lead to a better result. This part is optional, and you only need to provide `space`.
@@ -273,7 +273,7 @@ You need to use the same dataset to evaluate your different `estimator` experime
 About the data and backtest
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-`data` and `backtest` are all same in the whole `tuner` experiment. Different `estimator` experiments must use the same data and backtest method. So, these two parts of config are same with that in `estimator` configuration. You can see the precise defination of these parts in `estimator` introduction. We only provide an example here.
+`data` and `backtest` are all same in the whole `tuner` experiment. Different `estimator` experiments must use the same data and backtest method. So, these two parts of config are same with that in `estimator` configuration. You can see the precise definition of these parts in `estimator` introduction. We only provide an example here.
 
 .. code-block:: YAML
 

docs/introduction/quick.rst

@@ -31,7 +31,7 @@ Users can easily intsall ``Qlib`` according to the following steps:
         git clone https://github.com/microsoft/qlib.git && cd qlib
         python setup.py install
 
-To kown more about `installation`, please refer to `Qlib Installation <../start/installation.html>`_.
+To known more about `installation`, please refer to `Qlib Installation <../start/installation.html>`_.
 
 Prepare Data
 ==============
@@ -44,7 +44,7 @@ Load and prepare data by running the following code:
 
 This dataset is created by public data collected by crawler scripts in ``scripts/data_collector/``, which have been released in the same repository. Users could create the same dataset with it.
 
-To kown more about `prepare data`, please refer to `Data Preparation <../component/data.html#data-preparation>`_.
+To known more about `prepare data`, please refer to `Data Preparation <../component/data.html#data-preparation>`_.
 
 Auto Quant Research Workflow
 ====================================

examples/benchmarks/TFT/data_formatters/base.py

@@ -32,7 +32,7 @@
 import enum
 
 
-# Type defintions
+# Type definitions
 class DataTypes(enum.IntEnum):
     """Defines numerical types of each column."""
 

examples/benchmarks/TFT/libs/hyperparam_opt.py

@@ -254,9 +254,9 @@ def __init__(
           param_ranges: Discrete hyperparameter range for random search.
           fixed_params: Fixed model parameters per experiment.
           root_model_folder: Folder to store optimisation artifacts.
-          worker_number: Worker index definining which set of hyperparameters to
+          worker_number: Worker index defining which set of hyperparameters to
             test.
-          search_iterations: Maximum numer of random search iterations.
+          search_iterations: Maximum number of random search iterations.
           num_iterations_per_worker: How many iterations are handled per worker.
           clear_serialised_params: Whether to regenerate hyperparameter
             combinations.
@@ -330,7 +330,7 @@ def load_serialised_hyperparam_df(self):
         if os.path.exists(self.serialised_ranges_folder):
             df = pd.read_csv(self.serialised_ranges_path, index_col=0)
         else:
-            print("Unable to load - regenerating serach ranges instead")
+            print("Unable to load - regenerating search ranges instead")
             df = self.update_serialised_hyperparam_df()
 
         return df

examples/benchmarks/TFT/libs/tft_model.py

@@ -342,7 +342,7 @@ def get(cls, key):
 
     @classmethod
     def contains(cls, key):
-        """Retuns boolean indicating whether key is present in cache."""
+        """Returns boolean indicating whether key is present in cache."""
 
         return key in cls._data_cache
 
@@ -1120,10 +1120,10 @@ def predict(self, df, return_targets=False):
         Args:
           df: Input dataframe
           return_targets: Whether to also return outputs aligned with predictions to
-            faciliate evaluation
+            facilitate evaluation
 
         Returns:
-          Input dataframe or tuple of (input dataframe, algined output dataframe).
+          Input dataframe or tuple of (input dataframe, aligned output dataframe).
         """
 
         data = self._batch_data(df)

examples/benchmarks/TFT/tft.py

@@ -295,7 +295,7 @@ def finetune(self, dataset: DatasetH):
     def to_pickle(self, path: Union[Path, str]):
         """
         Tensorflow model can't be dumped directly.
-        So the data should be save seperatedly
+        So the data should be save separately
 
         **TODO**: Please implement the function to load the files
 

examples/benchmarks/TRA/README.md

@@ -57,7 +57,7 @@ And here are two ways to run the model:
   python example.py --config_file configs/config_alstm.yaml
   ```
 
-Here we trained TRA on a pretrained backbone model. Therefore we run `*_init.yaml` before TRA's scipts.
+Here we trained TRA on a pretrained backbone model. Therefore we run `*_init.yaml` before TRA's scripts.
 
 ### Results
 

examples/benchmarks/TRA/src/model.py

@@ -124,7 +124,7 @@ def train_epoch(self, data_set):
             loss = (pred - label).pow(2).mean()
 
             L = (all_preds.detach() - label[:, None]).pow(2)
-            L -= L.min(dim=-1, keepdim=True).values  # normalize & ensure postive input
+            L -= L.min(dim=-1, keepdim=True).values  # normalize & ensure positive input
 
             data_set.assign_data(index, L)  # save loss to memory
 
@@ -165,7 +165,7 @@ def test_epoch(self, data_set, return_pred=False):
 
             L = (all_preds - label[:, None]).pow(2)
 
-            L -= L.min(dim=-1, keepdim=True).values  # normalize & ensure postive input
+            L -= L.min(dim=-1, keepdim=True).values  # normalize & ensure positive input
 
             data_set.assign_data(index, L)  # save loss to memory
 
@@ -484,7 +484,7 @@ class TRA(nn.Module):
 
     """Temporal Routing Adaptor (TRA)
 
-    TRA takes historical prediction erros & latent representation as inputs,
+    TRA takes historical prediction errors & latent representation as inputs,
     then routes the input sample to a specific predictor for training & inference.
 
     Args:

examples/highfreq/highfreq_ops.py

@@ -150,7 +150,7 @@ def __init__(self, feature, l=None, r=None):
         self.l = l
         self.r = r
         if (self.l is not None and self.l <= 0) or (self.r is not None and self.r >= 0):
-            raise ValueError("Cut operator l shoud > 0 and r should < 0")
+            raise ValueError("Cut operator l should > 0 and r should < 0")
 
         super(Cut, self).__init__(feature)
 

examples/nested_decision_execution/workflow.py

@@ -298,7 +298,7 @@ def collect_data(self):
     #   - Aligning the profit calculation between multiple levels and single levels.
     # 2) comparing different backtest
     # - Basic test idea:
-    #   - the daily backtest will be similar as multi-level(the data quality makes this gap samller)
+    #   - the daily backtest will be similar as multi-level(the data quality makes this gap smaller)
 
     def check_diff_freq(self):
         self._init_qlib()

qlib/__init__.py

@@ -241,7 +241,7 @@ def auto_init(**kwargs):
                     default_exp_name: "Experiment"
 
     Example 2)
-    If you wan to create simple a stand alone config, you can use following config(a.k.a `conf_type: origin`)
+    If you want to create simple a stand alone config, you can use following config(a.k.a `conf_type: origin`)
 
     .. code-block:: python
 

qlib/backtest/account.py

@@ -31,7 +31,7 @@
 class AccumulatedInfo:
     """
     accumulated trading info, including accumulated return/cost/turnover
-    AccumulatedInfo should be shared accross different levels
+    AccumulatedInfo should be shared across different levels
     """
 
     def __init__(self):
@@ -199,7 +199,7 @@ def update_order(self, order, trade_val, cost, trade_price):
 
         # if stock is sold out, no stock price information in Position, then we should update account first, then update current position
         # if stock is bought, there is no stock in current position, update current, then update account
-        # The cost will be substracted from the cash at last. So the trading logic can ignore the cost calculation
+        # The cost will be subtracted from the cash at last. So the trading logic can ignore the cost calculation
         if order.direction == Order.SELL:
             # sell stock
             self._update_state_from_order(order, trade_val, cost, trade_price)
@@ -378,7 +378,7 @@ def update_bar_end(
         )
 
     def get_portfolio_metrics(self):
-        """get the history portfolio_metrics and postions instance"""
+        """get the history portfolio_metrics and positions instance"""
         if self.is_port_metr_enabled():
             _portfolio_metrics = self.portfolio_metrics.generate_portfolio_metrics_dataframe()
             _positions = self.get_hist_positions()

qlib/backtest/backtest.py

@@ -13,7 +13,7 @@
 
 
 def backtest_loop(start_time, end_time, trade_strategy: BaseStrategy, trade_executor: BaseExecutor):
-    """backtest funciton for the interaction of the outermost strategy and executor in the nested decision execution
+    """backtest function for the interaction of the outermost strategy and executor in the nested decision execution
 
     please refer to the docs of `collect_data_loop`
 

qlib/backtest/decision.py

@@ -505,8 +505,8 @@ def mod_inner_decision(self, inner_trade_decision: BaseTradeDecision):
         `inner_trade_decision` will be changed **inplaced**.
 
         Motivation of the `mod_inner_decision`
-        - Leave a hook for outer decision to affact the decision generated by the inner strategy
-            - e.g. the outmost strategy generate a time range for trading. But the upper layer can only affact the
+        - Leave a hook for outer decision to affect the decision generated by the inner strategy
+            - e.g. the outmost strategy generate a time range for trading. But the upper layer can only affect the
               nearest layer in the original design.  With `mod_inner_decision`, the decision can passed through multiple
               layers
 

qlib/backtest/exchange.py

@@ -103,7 +103,7 @@ def __init__(
                                             Necessary fields:
                                                 $close is for calculating the total value at end of each day.
                                             Optional fields:
-                                                $volume is only necessary when we limit the trade amount or caculate PA(vwap) indicator
+                                                $volume is only necessary when we limit the trade amount or calculate PA(vwap) indicator
                                                 $vwap is only necessary when we use the $vwap price as the deal price
                                                 $factor is for rounding to the trading unit
                                                 limit_sell will be set to False by default(False indicates we can sell this
@@ -505,7 +505,7 @@ def generate_order_for_target_amount_position(self, target_position, current_pos
         Note: some future information is used in this function
         Parameter:
         target_position : dict { stock_id : amount }
-        current_postion : dict { stock_id : amount}
+        current_position : dict { stock_id : amount}
         trade_unit : trade_unit
         down sample : for amount 321 and trade_unit 100, deal_amount is 300
         deal order on trade_date

qlib/backtest/executor.py

@@ -41,7 +41,7 @@ def __init__(
         Parameters
         ----------
         time_per_step : str
-            trade time per trading step, used for genreate the trade calendar
+            trade time per trading step, used for generate the trade calendar
         show_indicator: bool, optional
             whether to show indicators, :
             - 'pa', the price advantage
@@ -369,12 +369,12 @@ def _init_sub_trading(self, trade_decision):
         self.inner_strategy.reset(level_infra=sub_level_infra, outer_trade_decision=trade_decision)
 
     def _update_trade_decision(self, trade_decision: BaseTradeDecision) -> BaseTradeDecision:
-        # outter strategy have chance to update decision each iterator
+        # outer strategy have chance to update decision each iterator
         updated_trade_decision = trade_decision.update(self.inner_executor.trade_calendar)
         if updated_trade_decision is not None:
             trade_decision = updated_trade_decision
             # NEW UPDATE
-            # create a hook for inner strategy to update outter decision
+            # create a hook for inner strategy to update outer decision
             self.inner_strategy.alter_outer_trade_decision(trade_decision)
         return trade_decision
 

qlib/backtest/high_performance_ds.py

@@ -400,7 +400,7 @@ def sum_all_indicators(order_indicator, indicators: list, metrics: Union[str, Li
         indicators : List[BaseOrderIndicator]
             the list of all inner indicators.
         metrics : Union[str, List[str]]
-            all metrics needs ot be sumed.
+            all metrics needs to be sumed.
         fill_value : float, optional
             fill np.NaN with value. By default None.
         """