more typing improvements

notebooks/Delta_Hedging.ipynb

@@ -27,8 +27,9 @@
     "import pyfeng as pf\n",
     "\n",
     "from yabte.backtest import (\n",
+    "    ADFI_AVAILABLE_AT_CLOSE,\n",
+    "    ADFI_AVAILABLE_AT_OPEN,\n",
     "    Asset,\n",
-    "    AssetDataFieldInfo,\n",
     "    Book,\n",
     "    CashTransaction,\n",
     "    Order,\n",
@@ -73,8 +74,7 @@
     "        dfs.append(\n",
     "            (\n",
     "                \"IVol\",\n",
-    "                AssetDataFieldInfo.AVAILABLE_AT_CLOSE\n",
-    "                | AssetDataFieldInfo.AVAILABLE_AT_OPEN,\n",
+    "                ADFI_AVAILABLE_AT_CLOSE | ADFI_AVAILABLE_AT_OPEN,\n",
     "            )\n",
     "        )\n",
     "        return dfs\n",

yabte/backtest/asset.py

@@ -1,6 +1,6 @@
 from dataclasses import dataclass
 from decimal import Decimal
-from typing import TypeAlias
+from typing import TypeAlias, TypeVar, Union, cast
 
 import pandas as pd
 from mypy_extensions import mypyc_attr
@@ -14,6 +14,7 @@
 ADFI_AVAILABLE_AT_OPEN: int = 2
 ADFI_REQUIRED: int = 4
 
+T = TypeVar("T", bound=Union[pd.Series, pd.DataFrame])
 
 AssetName: TypeAlias = str
 """Asset name string."""
@@ -84,6 +85,12 @@ def _get_fields(self, field_info: AssetDataFieldInfo) -> list[str]:
         """Internal method to get fields from `data_fields` with `field_info`."""
         return [f for f, fi in self.data_fields() if fi & field_info]
 
+    def _filter_data(self, data: T) -> T:
+        """Internal method to filter `data` columns and return only those relevant to
+        pricing."""
+        assert isinstance(self.data_label, str)
+        return cast(T, data[self.data_label])
+
 
 @mypyc_attr(allow_interpreted_subclasses=True)
 @dataclass(kw_only=True)

yabte/backtest/book.py

@@ -101,7 +101,7 @@ def add_transactions(self, transactions: Sequence[Transaction]):
             self.transactions.append(tran)
 
     def eod_tasks(
-        self, ts: pd.Timestamp, day_data: pd.DataFrame, asset_map: Dict[str, Asset]
+        self, ts: pd.Timestamp, day_data: pd.Series, asset_map: Dict[str, Asset]
     ):
         """Run end of day tasks such as book keeping."""
         # accumulate continously compounded interest
@@ -119,7 +119,7 @@ def eod_tasks(
         cash = float(self.cash)
         mtm = float(
             sum(
-                asset.end_of_day_price(day_data[asset.data_label]) * q
+                asset.end_of_day_price(asset._filter_data(day_data)) * q
                 for an, q in self.positions.items()
                 if (asset := asset_map.get(an))
             )

yabte/backtest/order.py

@@ -98,9 +98,7 @@ def post_complete(self, trades: List[Trade]):
         """
         pass
 
-    def apply(
-        self, ts: pd.Timestamp, day_data: pd.DataFrame, asset_map: Dict[str, Asset]
-    ):
+    def apply(self, ts: pd.Timestamp, day_data: pd.Series, asset_map: Dict[str, Asset]):
         """Applies order to `self.book` for time `ts` using provided `day_data` and
         dictionary of asset information `asset_map`."""
         raise NotImplementedError("The apply methods needs to be implemented.")
@@ -128,9 +126,11 @@ def __post_init__(self):
         self.size = ensure_decimal(self.size)
         self.size_type = ensure_enum(self.size_type, OrderSizeType)
 
-    def _calc_quantity_price(self, day_data, asset_map) -> Tuple[Decimal, Decimal]:
+    def _calc_quantity_price(
+        self, day_data: pd.Series, asset_map: Dict[str, Asset]
+    ) -> Tuple[Decimal, Decimal]:
         asset = asset_map[self.asset_name]
-        asset_day_data = day_data[asset.data_label]
+        asset_day_data = asset._filter_data(day_data)
         trade_price = asset.intraday_traded_price(asset_day_data, size=self.size)
 
         if self.size_type == OrderSizeType.QUANTITY:
@@ -159,9 +159,7 @@ def pre_execute_check(
         """
         return None
 
-    def apply(
-        self, ts: pd.Timestamp, day_data: pd.DataFrame, asset_map: Dict[str, Asset]
-    ):
+    def apply(self, ts: pd.Timestamp, day_data: pd.Series, asset_map: Dict[str, Asset]):
         if not self.book or not isinstance(self.book, Book):
             raise RuntimeError("Cannot apply order without book instance")
 
@@ -202,9 +200,7 @@ def __post_init__(self):
         super().__post_init__()
         self.check_type = ensure_enum(self.check_type, PositionalOrderCheckType)
 
-    def apply(
-        self, ts: pd.Timestamp, day_data: pd.DataFrame, asset_map: Dict[str, Asset]
-    ):
+    def apply(self, ts: pd.Timestamp, day_data: pd.Series, asset_map: Dict[str, Asset]):
         if not self.book or not isinstance(self.book, Book):
             raise RuntimeError("Cannot apply order without book instance")
 
@@ -275,10 +271,10 @@ def __post_init__(self):
         self.size_type = ensure_enum(self.size_type, OrderSizeType)
 
     def _calc_quantity_price(
-        self, day_data, asset_map
+        self, day_data: pd.Series, asset_map: Dict[str, Asset]
     ) -> List[Tuple[Decimal, Decimal]]:
         assets = [asset_map[an] for an in self.asset_names]
-        assets_day_data = [day_data[a.data_label] for a in assets]
+        assets_day_data = [a._filter_data(day_data) for a in assets]
         trade_prices = [
             asset.intraday_traded_price(add, size=self.size)
             for asset, add in zip(assets, assets_day_data)
@@ -313,9 +309,7 @@ def _calc_quantity_price(
             for a, q, tp in zip(assets, quantities, trade_prices)
         ]
 
-    def apply(
-        self, ts: pd.Timestamp, day_data: pd.DataFrame, asset_map: Dict[str, Asset]
-    ):
+    def apply(self, ts: pd.Timestamp, day_data: pd.Series, asset_map: Dict[str, Asset]):
         if not self.book or not isinstance(self.book, Book):
             raise RuntimeError("Cannot apply order without book instance")
 
@@ -343,9 +337,7 @@ class PositionalBasketOrder(BasketOrder):
 
     check_type: PositionalOrderCheckType = PositionalOrderCheckType.POS_TQ_DIFFER
 
-    def apply(
-        self, ts: pd.Timestamp, day_data: pd.DataFrame, asset_map: Dict[str, Asset]
-    ):
+    def apply(self, ts: pd.Timestamp, day_data: pd.Series, asset_map: Dict[str, Asset]):
         if not self.book or not isinstance(self.book, Book):
             raise RuntimeError("Cannot apply order without book instance")
 

yabte/tests/test_strategy_runner.py

@@ -106,7 +106,7 @@ def init(self):
 
     def on_close(self):
         p = self.params
-        s = self.data["SPREAD"].Close[-1]
+        s = self.data["SPREAD"].Close.iloc[-1]
         if s < self.mu - 0.5 * self.sigma:
             self.orders.append(PositionalOrder(asset_name=p.s1, size=100))
             self.orders.append(PositionalOrder(asset_name=p.s2, size=p.factor * 100))
@@ -159,7 +159,7 @@ def test_sma_crossover(self):
             th.pivot_table(index="ts", columns="book", values="nc", aggfunc="sum")
             .cumsum()
             .reindex(sr.data.index)
-            .fillna(method="ffill")
+            .ffill()
             .fillna(0)
         )
         self.assertTrue(

yabte/utilities/pandas_extension.py

@@ -33,7 +33,7 @@ def log_returns(self):
 
     @property
     def returns(self):
-        return self._obj.pct_change()[1:]
+        return self._obj.pct_change(fill_method=None)[1:]
 
     @property
     def frequency(self):

yabte/utilities/portopt/hierarchical_risk_parity.py

@@ -36,7 +36,7 @@ def _getClusterVar(cov, cItems):
 
 def _getRecBipart(cov, sortIx):
     # Compute HRP alloc
-    w = pd.Series(1, index=sortIx)
+    w = pd.Series(1., index=sortIx)
     cItems = [sortIx]  # initialize all items in one cluster
     while len(cItems) > 0:
         cItems = [

yabte/utilities/strategy_helpers.py

@@ -9,6 +9,8 @@ def crossover(series1: pd.Series, series2: pd.Series) -> bool:
         True
     """
     try:
-        return series1[-2] < series2[-2] and series1[-1] > series2[-1]
+        return (
+            series1.iloc[-2] < series2.iloc[-2] and series1.iloc[-1] > series2.iloc[-1]
+        )
     except IndexError:
         return False