Update docs and fix tabnet

qlib/contrib/evaluate.py

@@ -26,9 +26,9 @@ def risk_analysis(r, N=252):
     Parameters
     ----------
     r : pandas.Series
-        daily return series
+        daily return series.
     N: int
-        scaler for annualizing information_ratio (day: 250, week: 50, month: 12)
+        scaler for annualizing information_ratio (day: 250, week: 50, month: 12).
     """
     mean = r.mean()
     std = r.std(ddof=1)
@@ -61,7 +61,7 @@ def get_strategy(
     ----------
 
     strategy : Strategy()
-        strategy used in backtest
+        strategy used in backtest.
     topk : int (Default value: 50)
         top-N stocks to buy.
     margin : int or float(Default value: 0.5)
@@ -73,14 +73,14 @@ def get_strategy(
 
             sell_limit = pred_in_a_day.count() * margin
 
-        buffer margin, in single score_mode, continue holding stock if it is in nlargest(sell_limit)
-        sell_limit should be no less than topk
+        buffer margin, in single score_mode, continue holding stock if it is in nlargest(sell_limit).
+        sell_limit should be no less than topk.
     n_drop : int
-        number of stocks to be replaced in each trading date
+        number of stocks to be replaced in each trading date.
     risk_degree: float
-        0-1, 0.95 for example, use 95% money to trade
+        0-1, 0.95 for example, use 95% money to trade.
     str_type: 'amount', 'weight' or 'dropout'
-        strategy type: TopkAmountStrategy ,TopkWeightStrategy or TopkDropoutStrategy
+        strategy type: TopkAmountStrategy ,TopkWeightStrategy or TopkDropoutStrategy.
 
     Returns
     -------
@@ -126,21 +126,21 @@ def get_exchange(
     ----------
 
     # exchange related arguments
-    exchange: Exchange()
+    exchange: Exchange().
     subscribe_fields: list
-        subscribe fields
+        subscribe fields.
     open_cost : float
-        open transaction cost
+        open transaction cost.
     close_cost : float
-        close transaction cost
+        close transaction cost.
     min_cost : float
-        min transaction cost
+        min transaction cost.
     trade_unit : int
-        100 for China A
+        100 for China A.
     deal_price: str
-        dealing price type: 'close', 'open', 'vwap'
+        dealing price type: 'close', 'open', 'vwap'.
     limit_threshold : float
-        limit move 0.1 (10%) for example, long and short with same limit
+        limit move 0.1 (10%) for example, long and short with same limit.
     extract_codes: bool
         will we pass the codes extracted from the pred to the exchange.
         NOTE: This will be faster with offline qlib.
@@ -193,20 +193,20 @@ def backtest(pred, account=1e9, shift=1, benchmark="SH000905", verbose=True, **k
     - **backtest workflow related or commmon arguments**
 
     pred : pandas.DataFrame
-        predict should has <datetime, instrument> index and one `score` column
+        predict should has <datetime, instrument> index and one `score` column.
     account : float
-        init account value
+        init account value.
     shift : int
-        whether to shift prediction by one day
+        whether to shift prediction by one day.
     benchmark : str
-        benchmark code, default is SH000905 CSI 500
+        benchmark code, default is SH000905 CSI 500.
     verbose : bool
-        whether to print log
+        whether to print log.
 
     - **strategy related arguments**
 
     strategy : Strategy()
-        strategy used in backtest
+        strategy used in backtest.
     topk : int (Default value: 50)
         top-N stocks to buy.
     margin : int or float(Default value: 0.5)
@@ -218,33 +218,33 @@ def backtest(pred, account=1e9, shift=1, benchmark="SH000905", verbose=True, **k
 
             sell_limit = pred_in_a_day.count() * margin
 
-        buffer margin, in single score_mode, continue holding stock if it is in nlargest(sell_limit)
-        sell_limit should be no less than topk
+        buffer margin, in single score_mode, continue holding stock if it is in nlargest(sell_limit).
+        sell_limit should be no less than topk.
     n_drop : int
-        number of stocks to be replaced in each trading date
+        number of stocks to be replaced in each trading date.
     risk_degree: float
-        0-1, 0.95 for example, use 95% money to trade
+        0-1, 0.95 for example, use 95% money to trade.
     str_type: 'amount', 'weight' or 'dropout'
-        strategy type: TopkAmountStrategy ,TopkWeightStrategy or TopkDropoutStrategy
+        strategy type: TopkAmountStrategy ,TopkWeightStrategy or TopkDropoutStrategy.
 
     - **exchange related arguments**
 
     exchange: Exchange()
         pass the exchange for speeding up.
     subscribe_fields: list
-        subscribe fields
+        subscribe fields.
     open_cost : float
         open transaction cost. The default value is 0.002(0.2%).
     close_cost : float
         close transaction cost. The default value is 0.002(0.2%).
     min_cost : float
-        min transaction cost
+        min transaction cost.
     trade_unit : int
-        100 for China A
+        100 for China A.
     deal_price: str
-        dealing price type: 'close', 'open', 'vwap'
+        dealing price type: 'close', 'open', 'vwap'.
     limit_threshold : float
-        limit move 0.1 (10%) for example, long and short with same limit
+        limit move 0.1 (10%) for example, long and short with same limit.
     extract_codes: bool
         will we pass the codes extracted from the pred to the exchange.
 
@@ -291,17 +291,17 @@ def long_short_backtest(
     """
     A backtest for long-short strategy
 
-    :param pred:        The trading signal produced on day `T`
-    :param topk:       The short topk securities and long topk securities
-    :param deal_price:  The price to deal the trading
+    :param pred:        The trading signal produced on day `T`.
+    :param topk:       The short topk securities and long topk securities.
+    :param deal_price:  The price to deal the trading.
     :param shift:       Whether to shift prediction by one day.  The trading day will be T+1 if shift==1.
-    :param open_cost:   open transaction cost
-    :param close_cost:  close transaction cost
-    :param trade_unit:  100 for China A
-    :param limit_threshold: limit move 0.1 (10%) for example, long and short with same limit
-    :param min_cost:    min transaction cost
-    :param subscribe_fields: subscribe fields
-    :param extract_codes:  bool
+    :param open_cost:   open transaction cost.
+    :param close_cost:  close transaction cost.
+    :param trade_unit:  100 for China A.
+    :param limit_threshold: limit move 0.1 (10%) for example, long and short with same limit.
+    :param min_cost:    min transaction cost.
+    :param subscribe_fields: subscribe fields.
+    :param extract_codes:  bool.
                        will we pass the codes extracted from the pred to the exchange.
                        NOTE: This will be faster with offline qlib.
     :return:            The result of backtest, it is represented by a dict.