QSTrader update version 0.2.4 (#386)

* updated performance.py for future warnings and fixed get_loc deprecation

* updated tearsheet.py for future warnings

* Updated package requirements

* updated tests

* Updated version number

CHANGELOG.md

@@ -1,3 +1,9 @@
+# 0.2.4
+
+* Fixed bug involving NaN at Timestamp in sixty_forty example.
+* Removed support for python 3.7 and 3.8
+* Updated the python package requirements to work with matplotlib 3.8, numpy 1.26 and pandas 2.2.0
+
 # 0.2.3
 
 * Updated the python package requirements to work with matplotlib 3.4, numpy 1.21 and pandas 1.3

README.md

@@ -34,14 +34,19 @@ Any issues with installation should be reported to the development team as issue
 The following command will create a brand new environment called `backtest`.
 
 ```
-conda create -n backtest
+conda create -n backtest python
+```
+This will use the conda default Python version. At time of writing this was Python 3.12. QSTrader currently supports Python 3.9, 3.10, 3.11 and 3.12. Optionally you can specify a python version by substituting python==3.9 into the command as follows:
+
+```
+conda create -n backtest python==3.9
 ```
 
 In order to start using QSTrader, you need to activate this new environment and install QSTrader using pip.
 
 ```
 conda activate backtest
-pip install qstrader
+pip3 install qstrader
 ```
 
 ## pip
@@ -51,7 +56,7 @@ Alternatively, you can use [venv](https://docs.python.org/3/tutorial/venv.html#c
 ```
 python -m venv backtest
 source backtest/bin/activate  # Need to activate environment before installing package
-pip install qstrader
+pip3 install qstrader
 ```
 
 # Full Documentation

qstrader/data/daily_bar_csv.py

@@ -174,7 +174,7 @@ def _convert_bar_frame_into_bid_ask_df(self, bar_df):
         dp_df = seq_oc_df[['Date', 'Price']]
         dp_df['Bid'] = dp_df['Price']
         dp_df['Ask'] = dp_df['Price']
-        dp_df = dp_df.loc[:, ['Date', 'Bid', 'Ask']].fillna(method='ffill').set_index('Date').sort_index()
+        dp_df = dp_df.loc[:, ['Date', 'Bid', 'Ask']].ffill().set_index('Date').sort_index()
         return dp_df
 
     def _convert_bars_into_bid_ask_dfs(self):
@@ -215,8 +215,9 @@ def get_bid(self, dt, asset):
             The bid price.
         """
         bid_ask_df = self.asset_bid_ask_frames[asset]
+        bid_series = bid_ask_df.iloc[bid_ask_df.index.get_indexer([dt], method='pad')]['Bid']
         try:
-            bid = bid_ask_df.iloc[bid_ask_df.index.get_loc(dt, method='pad')]['Bid']
+            bid = bid_series.iloc[0]
         except KeyError:  # Before start date
             return np.NaN
         return bid
@@ -239,8 +240,9 @@ def get_ask(self, dt, asset):
             The ask price.
         """
         bid_ask_df = self.asset_bid_ask_frames[asset]
+        ask_series = bid_ask_df.iloc[bid_ask_df.index.get_indexer([dt], method='pad')]['Ask']
         try:
-            ask = bid_ask_df.iloc[bid_ask_df.index.get_loc(dt, method='pad')]['Ask']
+            ask = ask_series.iloc[0]
         except KeyError:  # Before start date
             return np.NaN
         return ask

qstrader/statistics/performance.py

@@ -9,7 +9,7 @@ def aggregate_returns(returns, convert_to):
     Aggregates returns by day, week, month, or year.
     """
     def cumulate_returns(x):
-        return np.exp(np.log(1 + x).cumsum())[-1] - 1
+        return np.exp(np.log(1 + x).cumsum()).iloc[-1] - 1
 
     if convert_to == 'weekly':
         return returns.groupby(
@@ -38,7 +38,7 @@ def create_cagr(equity, periods=252):
     periods - Daily (252), Hourly (252*6.5), Minutely(252*6.5*60) etc.
     """
     years = len(equity) / float(periods)
-    return (equity[-1] ** (1.0 / years)) - 1.0
+    return (equity.iloc[-1] ** (1.0 / years)) - 1.0
 
 
 def create_sharpe_ratio(returns, periods=252):
@@ -89,7 +89,7 @@ def create_drawdowns(returns):
     # Calculate the drawdown and duration statistics
     perf = pd.DataFrame(index=idx)
     perf["Drawdown"] = (hwm - returns) / hwm
-    perf["Drawdown"].iloc[0] = 0.0
+    perf.loc[perf.index[0], 'Drawdown'] = 0.0
     perf["DurationCheck"] = np.where(perf["Drawdown"] == 0, 0, 1)
     duration = max(
         sum(1 for i in g if i == 1)

qstrader/statistics/tearsheet.py

@@ -195,7 +195,7 @@ def format_perc(x, pos):
         # Strategy statistics
         returns = stats["returns"]
         cum_returns = stats['cum_returns']
-        tot_ret = cum_returns[-1] - 1.0
+        tot_ret = cum_returns.iloc[-1] - 1.0
         cagr = perf.create_cagr(cum_returns, self.periods)
         sharpe = perf.create_sharpe_ratio(returns, self.periods)
         sortino = perf.create_sortino_ratio(returns, self.periods)
@@ -205,7 +205,7 @@ def format_perc(x, pos):
         if bench_stats is not None:
             bench_returns = bench_stats["returns"]
             bench_cum_returns = bench_stats['cum_returns']
-            bench_tot_ret = bench_cum_returns[-1] - 1.0
+            bench_tot_ret = bench_cum_returns.iloc[-1] - 1.0
             bench_cagr = perf.create_cagr(bench_cum_returns, self.periods)
             bench_sharpe = perf.create_sharpe_ratio(bench_returns, self.periods)
             bench_sortino = perf.create_sortino_ratio(bench_returns, self.periods)

qstrader/system/rebalance/end_of_month.py

@@ -65,7 +65,7 @@ def _generate_rebalances(self):
         rebalance_dates = pd.date_range(
             start=self.start_dt,
             end=self.end_dt,
-            freq='BM'
+            freq='BME'
         )
 
         rebalance_times = [

requirements/base.txt

@@ -1,5 +1,5 @@
 Click>=7.0
-matplotlib==3.4.3
-numpy==1.21.2
-pandas==1.3.3
-seaborn==0.11.2
+matplotlib==3.8.2
+numpy==1.26.4
+pandas==2.2.0
+seaborn==0.13.2

setup.py

@@ -5,7 +5,7 @@
 
 setup(
     name="qstrader",
-    version="0.2.3",
+    version="0.2.4",
     description="QSTrader backtesting simulation engine",
     long_description=long_description,
     long_description_content_type="text/markdown",
@@ -17,17 +17,18 @@
         "License :: OSI Approved :: MIT License",
         "Programming Language :: Python",
         "Programming Language :: Python :: 3",
-        "Programming Language :: Python :: 3.7",
-        "Programming Language :: Python :: 3.8",
         "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
+        "Programming Language :: Python :: 3.11",
+        "Programming Language :: Python :: 3.12",
     ],
     packages=find_packages(exclude=("tests",)),
     include_package_data=True,
     install_requires=[
         "Click==7.1.2",
-        "matplotlib>=3.3.4",
-        "numpy>=1.18.4",
-        "pandas>=1.3.3",
-        "seaborn>=0.10.1"
+        "matplotlib>=3.8.2",
+        "numpy>=1.26.4",
+        "pandas>=2.2.0",
+        "seaborn>=0.13.2"
     ]
 )

tests/integration/trading/test_backtest_e2e.py

@@ -69,10 +69,7 @@ def test_backtest_sixty_forty(etf_filepath):
     # Pandas 1.1.5 and 1.2.0 very slightly
     for symbol in expected_dict.keys():
         for metric in expected_dict[symbol].keys():
-            assert pytest.approx(
-                portfolio_dict[symbol][metric],
-                expected_dict[symbol][metric]
-            )
+            assert portfolio_dict[symbol][metric] == pytest.approx(expected_dict[symbol][metric])
 
 
 def test_backtest_long_short_leveraged(etf_filepath):