Increased financepy version and file formatting

Author: saeedamen

README.md

@@ -60,8 +60,8 @@ Calculate event study around events for asset (see examples/events_examples.py)
 # Requirements
 
 Major requirements
-* Required: Python 3.8
-* Required: pandas 1.2.3, numpy etc.
+* Required: Python 3.10
+* Required: pandas, numpy etc.
 * Required: findatapy for downloading market data (https://github.com/cuemacro/findatapy)
 * Required: chartpy for funky interactive plots (https://github.com/cuemacro/chartpy)
 
@@ -187,6 +187,9 @@ In finmarketpy/examples you will find several examples, including some simple tr
 
 # finmarketpy log
 
+* 07 Mar 2025
+  * Merge changes for pyproject.toml etc.
+  * Formatting towards PEP8
 * 09 Nov 2024
   * Changed trend following example to use FRED
 * 19 May 2024

finmarketpy/backtest/backtestcomparison.py

@@ -1,16 +1,20 @@
+__author__ = 'saeedamen'  # Saeed Amen
+
 #
 # Copyright 2016-2020 Cuemacro - https://www.cuemacro.com / @cuemacro
 #
-# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the
-# License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not
+# use this file except in compliance with the License. You may obtain a copy of
+# the License at http://www.apache.org/licenses/LICENSE-2.0
 #
-# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# Unless required by applicable law or agreed to in writing, software distributed
+# under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
+# CONDITIONS OF ANY KIND, either express or implied.
 #
-# See the License for the specific language governing permissions and limitations under the License.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 #
 
-
 import numpy as np
 import pandas as pd
 from finmarketpy.util.marketconstants import MarketConstants
@@ -20,8 +24,8 @@
 from finmarketpy.backtest.backtestengine import TradingModel
 from chartpy import Chart, Style, ChartConstants
 
-class BacktestComparison(object):
 
+class BacktestComparison(object):
     DEFAULT_PLOT_ENGINE = ChartConstants().chartfactory_default_engine
     SCALE_FACTOR = ChartConstants().chartfactory_scale_factor
     CHART_SOURCE = ChartConstants().chartfactory_source
@@ -34,16 +38,19 @@ def __init__(self, models, ref_index=0,
         :param models: iterable of TradingModel instances.
         :param ref_index: index of the reference model in the list (for difference).
         """
-        if hasattr(models, "__iter__") and all([isinstance(x, TradingModel) for x in models]):
+        if hasattr(models, "__iter__") and all(
+                [isinstance(x, TradingModel) for x in models]):
             self.models = models
             self.ref_index = ref_index
         else:
-            raise AttributeError("Models need to be an iterable of TradingModel instances.")
+            raise AttributeError(
+                "Models need to be an iterable of TradingModel instances.")
 
         self.labels = labels
 
     def plot_pnl(self, diff=True, silent_plot=False, reduce_plot=True):
-        style = self.models[self.ref_index]._create_style("", "Strategy PnL", reduce_plot=reduce_plot)
+        style = self.models[self.ref_index]._create_style("", "Strategy PnL",
+                                                          reduce_plot=reduce_plot)
 
         models = self.models
         ref = self.ref_index
@@ -57,14 +64,16 @@ def plot_pnl(self, diff=True, silent_plot=False, reduce_plot=True):
         if self.labels is not None:
             df.columns = self.labels
 
-        chart = Chart(df, engine=self.DEFAULT_PLOT_ENGINE, chart_type='line', style=style)
+        chart = Chart(df, engine=self.DEFAULT_PLOT_ENGINE, chart_type='line',
+                      style=style)
         if not silent_plot:
             chart.plot()
 
         return chart
 
     def plot_sharpe(self, silent_plot=False, reduce_plot=True):
-        style = self.models[self.ref_index]._create_style("", "Sharpe Curve", reduce_plot=reduce_plot)
+        style = self.models[self.ref_index]._create_style("", "Sharpe Curve",
+                                                          reduce_plot=reduce_plot)
 
         models = self.models
         ref = self.ref_index
@@ -76,18 +85,23 @@ def plot_sharpe(self, silent_plot=False, reduce_plot=True):
 
         df = pd.concat(annualized_sharpe, axis=1)
 
-        chart = Chart(df, engine=self.DEFAULT_PLOT_ENGINE, chart_type='bar', style=style)
+        chart = Chart(df, engine=self.DEFAULT_PLOT_ENGINE, chart_type='bar',
+                      style=style)
         if not silent_plot:
             chart.plot()
         return chart
 
-    def plot_strategy_trade_notional(self, diff=True, silent_plot=False, reduce_plot=True):
-        style = self.models[self.ref_index]._create_style("", "Trades (Scaled by Notional)", reduce_plot=reduce_plot)
+    def plot_strategy_trade_notional(self, diff=True, silent_plot=False,
+                                     reduce_plot=True):
+        style = self.models[self.ref_index]._create_style("",
+                                                          "Trades (Scaled by Notional)",
+                                                          reduce_plot=reduce_plot)
 
         models = self.models
         ref = self.ref_index
 
-        strategy_trade_notional = [model._strategy_trade_notional for model in models]
+        strategy_trade_notional = [model._strategy_trade_notional for model in
+                                   models]
 
         df = pd.concat(strategy_trade_notional, axis=1)
 
@@ -96,7 +110,8 @@ def plot_strategy_trade_notional(self, diff=True, silent_plot=False, reduce_plot
         if self.labels is not None:
             df.columns = self.labels
 
-        chart = Chart(df, engine=self.DEFAULT_PLOT_ENGINE, chart_type='bar', style=style)
+        chart = Chart(df, engine=self.DEFAULT_PLOT_ENGINE, chart_type='bar',
+                      style=style)
         if not silent_plot:
             chart.plot()
 

finmarketpy/backtest/backtestengine.py

@@ -1,17 +1,19 @@
-__author__ = 'saeedamen'
+__author__ = 'saeedamen'  # Saeed Amen
 
 #
-# Copyright 2016 Cuemacro
+# Copyright 2016-2020 Cuemacro - https://www.cuemacro.com / @cuemacro
 #
-# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the
-# License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not
+# use this file except in compliance with the License. You may obtain a copy of
+# the License at http://www.apache.org/licenses/LICENSE-2.0
 #
-# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# Unless required by applicable law or agreed to in writing, software distributed
+# under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
+# CONDITIONS OF ANY KIND, either express or implied.
 #
-# See the License for the specific language governing permissions and limitations under the License.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 #
-
 import numpy as np
 import pandas as pd
 
@@ -273,23 +275,23 @@ def calculate_trading_PnL(
             # weighting each signal separately)
             portfolio_signal_before_weighting = pd.DataFrame(
                 data=(
-                            portfolio_signal_before_weighting.values * position_clip_adjustment_matrix),
+                        portfolio_signal_before_weighting.values * position_clip_adjustment_matrix),
                 index=portfolio_signal_before_weighting.index,
                 columns=portfolio_signal_before_weighting.columns)
 
             # Recalculate portfolio signal after adjustment (for portfolio
             # level positions)
             portfolio_signal = pd.DataFrame(
                 data=(
-                            portfolio_signal.values * position_clip_adjustment_matrix),
+                        portfolio_signal.values * position_clip_adjustment_matrix),
                 index=portfolio_signal.index,
                 columns=portfolio_signal.columns)
 
             # Recalculate portfolio leverage with position constraint
             # (multiply vectors elementwise)
             portfolio_leverage_df = pd.DataFrame(
                 data=(
-                            portfolio_leverage_df.values * position_clip_adjustment.values),
+                        portfolio_leverage_df.values * position_clip_adjustment.values),
                 index=portfolio_leverage_df.index,
                 columns=portfolio_leverage_df.columns)
 
@@ -1037,7 +1039,8 @@ def load_model(path):
         return pkl
 
     ####### Generic functions for every backtest
-    def construct_strategy(self, br: BacktestRequest = None, run_in_parallel: bool = False) -> pd.DataFrame:
+    def construct_strategy(self, br: BacktestRequest = None,
+                           run_in_parallel: bool = False) -> pd.DataFrame:
         """Constructs the returns for all the strategies which have been specified.
 
         It gets backtesting parameters from fill_backtest_request (although
@@ -2479,7 +2482,7 @@ def _create_style(self, title, file_add, reduce_plot=True):
         style.height = self.HEIGHT
         style.source = self.CHART_SOURCE
         style.silent_display = not (self.SHOW_CHARTS)
-        style.plotly_plot_mode = self.PLOTLY_PLOT_MODE # Smaller file sizes
+        style.plotly_plot_mode = self.PLOTLY_PLOT_MODE  # Smaller file sizes
 
         style.legend_bgcolor = 'rgba(0,0,0,0)'
 
@@ -2521,8 +2524,8 @@ def optimize_portfolio_weights(
             signal_df: pd.DataFrame,
             signal_pnl_cols: List[str],
             br: BacktestRequest = None) -> (
-    pd.DataFrame, pd.DataFrame, pd.DataFrame,
-    pd.DataFrame, pd.DataFrame, pd.DataFrame):
+            pd.DataFrame, pd.DataFrame, pd.DataFrame,
+            pd.DataFrame, pd.DataFrame, pd.DataFrame):
 
         if br is None:
             br = self._br