Chore/auto formatting workflow (fmilthaler#109)

This PR adds a new GitHub workflow. Purpose of that is, for every commit
to either of the branches master or develop as well as any commit pushed
to a branch that is in a PR with a base branch of either master or
develop the new workflow should run and automatically apply code
formatting with black, import sorting with isort as well as update the
README.tex.md (porting changes over from README.md).

---------

Co-authored-by: Pietropaolo Frisoni <[email protected]>
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

Author: 3 people

.github/workflows/auto-format.yml

@@ -0,0 +1,49 @@
+name: AutoUpdateFormatting
+on:
+  push:
+    branches:
+      - master
+      - develop
+  pull_request:
+    branches:
+      - master
+      - develop
+
+jobs:
+  code-formatting:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v3
+        with:
+          ref: ${{ github.head_ref }}
+          fetch-depth: 0
+
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.10'  # Specify the desired Python version
+
+      - name: Install dependencies
+        run: |
+          pip install -r requirements_test.txt
+
+      - name: Updating README files
+        id: update_readme
+        run: |
+          bash scripts/update-readme.sh
+          bash scripts/auto-commit.sh "Updating README files"
+        continue-on-error: true
+
+      - name: Code formatting and committing changes
+        id: code_format
+        run: |
+          bash scripts/auto-format.sh
+          bash scripts/auto-commit.sh "Automated formatting changes"
+        continue-on-error: true
+
+      - name: Push changes
+        if: ${{ steps.update_readme.outcome == 'success' || steps.code_format.outcome == 'success' }}
+        uses: ad-m/github-push-action@master
+        with:
+          branch: ${{ github.head_ref }}

README.md

@@ -268,4 +268,3 @@ Furthermore, it is also shown how the entire *Efficient Frontier* and the optima
 Also, the optimisation of a portfolio and its visualisation based on a *Monte Carlo* is shown.
 
 Finally, *FinQuant*'s visualisation methods allow for overlays, if this is desired. Thus, with only the following few lines of code, one can create an overlay of the *Monte Carlo* run, the *Efficient Frontier*, its optimised portfolios for *Minimum Volatility* and *Maximum Sharpe Ratio*, as well as the portfolio's individual stocks.
-

example/Example-Analysis.py

@@ -9,10 +9,11 @@
 
 # <codecell>
 
+import datetime
 import pathlib
+
 import matplotlib.pyplot as plt
 import pandas as pd
-import datetime
 
 # importing FinQuant's function to automatically build the portfolio
 from finquant.portfolio import build_portfolio

example/Example-Build-Portfolio-from-file.py

@@ -9,9 +9,10 @@
 
 # <codecell>
 
+import datetime
 import pathlib
+
 import pandas as pd
-import datetime
 
 # importing FinQuant's function to automatically build the portfolio
 from finquant.portfolio import build_portfolio

example/Example-Build-Portfolio-from-web.py

@@ -9,9 +9,10 @@
 
 # <codecell>
 
-import pandas as pd
 import datetime
 
+import pandas as pd
+
 # importing some custom functions/objects
 from finquant.portfolio import build_portfolio
 

example/Example-Optimisation.py

@@ -26,11 +26,12 @@
 
 # <codecell>
 
+import datetime
 import pathlib
+
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-import datetime
 
 # importing FinQuant's function to automatically build the portfolio
 from finquant.portfolio import build_portfolio

finquant/asset.py

@@ -45,7 +45,9 @@ class Asset:
 
     """
 
-    def __init__(self, data: pd.Series, name: str, asset_type: str = 'Market index') -> None:
+    def __init__(
+        self, data: pd.Series, name: str, asset_type: str = "Market index"
+    ) -> None:
         """
         :Input:
          :data: ``pandas.Series``, of asset prices
@@ -119,4 +121,4 @@ def properties(self):
     def __str__(self):
         # print short description
         string = f"Contains information about {self.asset_type}: {self.name}."
-        return string
+        return string

finquant/market.py

@@ -14,6 +14,7 @@
 
 import numpy as np
 import pandas as pd
+
 from finquant.asset import Asset
 from finquant.returns import daily_returns, historical_mean_return
 
@@ -43,4 +44,4 @@ def comp_daily_returns(self) -> pd.Series:
         """Computes the daily returns (percentage change) of the market index.
         See ``finquant.returns.daily_returns``.
         """
-        return daily_returns(self.data)
+        return daily_returns(self.data)

finquant/portfolio.py

@@ -49,8 +49,8 @@
 - Individual stocks of the portfolio (Expected Return over Volatility)
 """
 
-from typing import List
 import datetime
+from typing import List
 
 import matplotlib.pylab as plt
 import numpy as np

finquant/stock.py

@@ -25,6 +25,7 @@
 
 import numpy as np
 import pandas as pd
+
 from finquant.asset import Asset
 from finquant.returns import daily_returns, historical_mean_return