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fix_typo (#790)

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Signed-off-by: unknown <lv.linlang@qq.com>
This commit is contained in:
SunsetWolf
2021-12-31 22:14:47 +08:00
committed by GitHub
parent f59cfe51e0
commit dfc0ed3c01
56 changed files with 92 additions and 92 deletions
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6
qlib/backtest/account.py
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@@ -31,7 +31,7 @@ rtn & earning in the Account
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 @@ class Account:
# 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 @@ class Account:
)
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()

2
qlib/backtest/backtest.py
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@@ -13,7 +13,7 @@ from tqdm.auto import tqdm
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`

4
qlib/backtest/decision.py
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@@ -505,8 +505,8 @@ class 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

4
qlib/backtest/exchange.py
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@@ -103,7 +103,7 @@ class Exchange:
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 @@ class Exchange:
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

6
qlib/backtest/executor.py
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@@ -41,7 +41,7 @@ class BaseExecutor:
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 @@ class NestedExecutor(BaseExecutor):
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

2
qlib/backtest/high_performance_ds.py
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@@ -400,7 +400,7 @@ class BaseOrderIndicator:
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.
"""

2
qlib/backtest/position.py
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@@ -152,7 +152,7 @@ class BasePosition:
"""
generate stock weight dict {stock_id : value weight of stock in the position}
it is meaningful in the beginning or the end of each trade step
- During execution of each trading step, the weight may be not consistant with the portfolio value
- During execution of each trading step, the weight may be not consistent with the portfolio value
Parameters
----------

2
qlib/backtest/profit_attribution.py
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@@ -39,7 +39,7 @@ def get_benchmark_weight(
if not path:
path = Path(C.dpm.get_data_uri(freq)).expanduser() / "raw" / "AIndexMembers" / "weights.csv"
# TODO: the storage of weights should be implemented in a more elegent way
# TODO: The benchmark is not consistant with the filename in instruments.
# TODO: The benchmark is not consistent with the filename in instruments.
bench_weight_df = pd.read_csv(path, usecols=["code", "date", "index", "weight"])
bench_weight_df = bench_weight_df[bench_weight_df["index"] == bench]
bench_weight_df["date"] = pd.to_datetime(bench_weight_df["date"])

4
qlib/backtest/report.py
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@@ -73,7 +73,7 @@ class PortfolioMetrics:
self.init_bench(freq=freq, benchmark_config=benchmark_config)
def init_vars(self):
self.accounts = OrderedDict() # account postion value for each trade time
self.accounts = OrderedDict() # account position value for each trade time
self.returns = OrderedDict() # daily return rate for each trade time
self.total_turnovers = OrderedDict() # total turnover for each trade time
self.turnovers = OrderedDict() # turnover for each trade time
@@ -236,7 +236,7 @@ class Indicator:
"""
`Indicator` is implemented in a aggregate way.
All the metrics are calculated aggregately.
All the metrics are calculated for a seperated stock and in a specific step on a specific level.
All the metrics are calculated for a separated stock and in a specific step on a specific level.
| indicator | desc. |
|--------------+--------------------------------------------------------------|

2
qlib/backtest/utils.py
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@@ -93,7 +93,7 @@ class TradeCalendarManager:
About the endpoints:
- Qlib uses the closed interval in time-series data selection, which has the same performance as pandas.Series.loc
# - The returned right endpoints should minus 1 seconds becasue of the closed interval representation in Qlib.
# - The returned right endpoints should minus 1 seconds because of the closed interval representation in Qlib.
# Note: Qlib supports up to minutely decision execution, so 1 seconds is less than any trading time interval.
Parameters