Merge pull request #269 from cvxgrp/267-add-name-to-nav-series

267 add name to nav series

Makefile

@@ -47,11 +47,5 @@ jupyter: install ## Run jupyter lab
 	@poetry run jupyter lab
 
 .PHONY: marimo
-marimo: install ## Run jupyter lab
+marimo: install ## Install Marimo
 	@poetry run pip install marimo
-	@poetry run marimo edit
-
-.PHONY: boil
-boil: ## Update the boilerplate code
-	@poetry run pip install cvxcooker
-	@poetry run cook pyproject.toml

README.md

@@ -35,10 +35,10 @@ and initialize the amount of cash used in an experiment:
 
 ```python
 import pandas as pd
-from cvx.simulator import EquityBuilder
+from cvx.simulator import Builder
 
 prices = pd.read_csv("prices.csv", index_col=0, parse_dates=True, header=0)
-b = EquityBuilder(prices=prices, initial_cash=1e6)
+b = Builder(prices=prices, initial_cash=1e6)
 ```
 
 Prices have to be valid, there may be NaNs only at the beginning and the end of
@@ -65,6 +65,8 @@ for t, state in b:
     units[pair] = [state.nav, -state.nav] / state.prices[pair].values
     # update the position
     b.position = 0.1 * units
+    # Do not apply trading costs
+    b.aum = state.aum
 ```
 
 Here t is the growing list of timestamps, e.g. in the first iteration
@@ -81,6 +83,7 @@ implemenent the popular $1/n$ strategy.
 for t, state in b:
     # each day we invest a quarter of the capital in the assets
     b.position = 0.25 * state.nav / state.prices
+    b.aum = state.aum
 ```
 
 Note that we update the position at the last element in the t list
@@ -91,6 +94,7 @@ The builder class also exposes setters for such alternative conventions.
 for t, state in b:
     # each day we invest a quarter of the capital in the assets
     b.weights = np.ones(4)*0.25
+    b.aum = state.aum
 ```
 
 ### Build the portfolio

cvx/simulator/portfolio.py

@@ -85,10 +85,13 @@ def assets(self) -> pd.Index:
     def nav(self):
         """Return a pandas series representing the NAV"""
         if isinstance(self.aum, pd.Series):
-            return self.aum
+            series = self.aum
         else:
             profit = (self.cashposition.shift(1) * self.returns.fillna(0.0)).sum(axis=1)
-            return profit.cumsum() + self.aum
+            series = profit.cumsum() + self.aum
+
+        series.name = "NAV"
+        return series
 
     @property
     def profit(self) -> pd.Series:
@@ -98,7 +101,9 @@ def profit(self) -> pd.Series:
         Returns: pd.Series: A pandas series representing the profit
         gained or lost in the portfolio based on changes in asset prices.
         """
-        return (self.cashposition.shift(1) * self.returns.fillna(0.0)).sum(axis=1)
+        series = (self.cashposition.shift(1) * self.returns.fillna(0.0)).sum(axis=1)
+        series.name = "Profit"
+        return series
 
     @property
     def highwater(self) -> pd.Series:
@@ -115,7 +120,9 @@ def highwater(self) -> pd.Series:
         Min_periods argument is set to 1 to include the minimum period of one day.
         The resulting series will show the highest value the portfolio has reached at each point in time.
         """
-        return self.nav.expanding(min_periods=1).max()
+        series = self.nav.expanding(min_periods=1).max()
+        series.name = "Highwater"
+        return series
 
     @property
     def drawdown(self) -> pd.Series:
@@ -132,7 +139,9 @@ def drawdown(self) -> pd.Series:
         A positive drawdown means the portfolio is currently worth
         less than its high-water mark. A drawdown of 0.1 implies that the nav is currently 0.9 times the high-water mark
         """
-        return 1.0 - self.nav / self.highwater
+        series = 1.0 - self.nav / self.highwater
+        series.name = "Drawdown"
+        return series
 
     @property
     def cashposition(self):

tests/test_applications/test_case.py

@@ -91,5 +91,6 @@ def test_case(columns, index, prices):
     # )
     # nav is the net asset value equity and cash, 1) 2000 2) 2000 3) 2000 4) 1000
     pd.testing.assert_series_equal(
-        portfolio.nav, pd.Series(index=index, data=[2000.0, 2000.0, 2000.0, 1000.0])
+        portfolio.nav,
+        pd.Series(index=index, data=[2000.0, 2000.0, 2000.0, 1000.0], name="NAV"),
     )

tests/test_applications/test_portfolio.py

@@ -34,7 +34,9 @@ def test_from_cash_position_prices(prices):
     )
 
     profit = (portfolio.cashposition.shift(1) * portfolio.returns).sum(axis=1)
-    pd.testing.assert_series_equal(portfolio.nav, profit.cumsum() + portfolio.aum)
+    pd.testing.assert_series_equal(
+        portfolio.nav, profit.cumsum() + portfolio.aum, check_names=False
+    )
 
 
 def test_from_cash_returns(prices):
@@ -46,4 +48,6 @@ def test_from_cash_returns(prices):
     )
 
     profit = (portfolio.cashposition.shift(1) * portfolio.returns).sum(axis=1)
-    pd.testing.assert_series_equal(portfolio.nav, profit.cumsum() + portfolio.aum)
+    pd.testing.assert_series_equal(
+        portfolio.nav, profit.cumsum() + portfolio.aum, check_names=False
+    )

tests/test_builder.py

@@ -32,7 +32,7 @@ def test_build_empty(builder, prices):
     pd.testing.assert_frame_equal(portfolio.prices, prices)
     pd.testing.assert_frame_equal(portfolio.units, np.NaN * prices)
     pd.testing.assert_series_equal(
-        portfolio.profit, pd.Series(index=prices.index, data=0.0)
+        portfolio.profit, pd.Series(index=prices.index, data=0.0, name="Profit")
     )
 
 

tests/test_portfolio.py

@@ -26,14 +26,6 @@ def portfolio(prices, nav):
 
     return Portfolio(prices=prices, units=units, aum=nav)
 
-    # positions = pd.DataFrame(index=prices.index, columns=prices.columns, data=1.0)
-    # b = EquityBuilder(prices, initial_cash=1e6)
-
-    # for t, state in b:
-    #    b.position = positions.loc[t[-1]]
-
-    # return b.build()
-
 
 def test_assets(portfolio, prices):
     """
@@ -107,11 +99,13 @@ def test_drawdown(portfolio):
     :param portfolio: the portfolio object (fixture)
     """
     pd.testing.assert_series_equal(
-        portfolio.highwater, portfolio.nav.expanding(min_periods=1).max()
+        portfolio.highwater,
+        portfolio.nav.expanding(min_periods=1).max(),
+        check_names=False,
     )
 
     drawdown = 1.0 - portfolio.nav / portfolio.highwater
-    pd.testing.assert_series_equal(portfolio.drawdown, drawdown)
+    pd.testing.assert_series_equal(portfolio.drawdown, drawdown, check_names=False)
 
 
 def test_monotonic():
@@ -176,7 +170,9 @@ def test_profit(portfolio):
     :param portfolio: the portfolio object (fixture)
     """
     pd.testing.assert_series_equal(
-        portfolio.profit, portfolio.equity.sum(axis=1).diff().fillna(0.0)
+        portfolio.profit,
+        portfolio.equity.sum(axis=1).diff().fillna(0.0),
+        check_names=False,
     )