Bugfix/yfinance in testing

.gitignore

@@ -35,3 +35,8 @@ FinQuant.egg-info/*
 *.aux
 *.out
 *.pdf
+
+#########
+# other #
+#########
+.idea

example/Example-Analysis.py

@@ -20,7 +20,7 @@
 # <codecell>
 
 # plotting style:
-plt.style.use("seaborn-darkgrid")
+plt.style.use("seaborn-v0_8-darkgrid")
 # set line width
 plt.rcParams["lines.linewidth"] = 2
 # set font size for titles

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

@@ -80,7 +80,11 @@
 # e.g. high, low, close, etc, FinQuant will extract the column "Adj. Close" ("Adj Close" if using yfinance).
 
 pf = build_portfolio(
-    names=names, pf_allocation=pf_allocation, start_date=start_date, end_date=end_date
+    names=names,
+    pf_allocation=pf_allocation,
+    start_date=start_date,
+    end_date=end_date,
+    data_api="quandl",
 )
 
 # <markdowncell>

example/Example-Optimisation.py

@@ -38,7 +38,7 @@
 # <codecell>
 
 # plotting style:
-plt.style.use("seaborn-darkgrid")
+plt.style.use("seaborn-v0_8-darkgrid")
 # set line width
 plt.rcParams["lines.linewidth"] = 2
 # set font size for titles

finquant/portfolio.py

@@ -60,7 +60,7 @@
 
 
 class Portfolio(object):
-    """Object that contains information about a investment portfolio.
+    """Object that contains information about an investment portfolio.
     To initialise the object, it does not require any input.
     To fill the portfolio with investment information, the
     function ``add_stock(stock)`` should be used, in which ``stock`` is
@@ -579,7 +579,6 @@ def plot_stocks(self, freq=252):
                 textcoords="offset points",
                 label=i,
             )
-            plt.legend()
 
     def properties(self):
         """Nicely prints out the properties of the portfolio:

setup.py

@@ -28,9 +28,8 @@
         "Intended Audience :: Other Audience",
         "Intended Audience :: Science/Research",
         "Programming Language :: Python :: 3 :: Only",
-        "Programming Language :: Python :: 3.5",
-        "Programming Language :: Python :: 3.6",
-        "Programming Language :: Python :: 3.7",
+        "Programming Language :: Python :: 3.10",
+        "Programming Language :: Python :: 3.11",
         "License :: OSI Approved :: MIT License",
         "Operating System :: OS Independent",
     ],
@@ -45,7 +44,7 @@
         "quantitative",
         "quant",
     ],
-    python_requires=">=3.5",
+    python_requires=">=3.10",
     install_requires=[
         "quandl",
         "yfinance",

tests/Makefile

@@ -3,9 +3,15 @@ default: test
 test:rempltshow
 	@pytest -v .
 
-rempltshow:
+rempltshow:plt_agg_backend
 	@-sed -i 's/plt.show()//g' test_Example*.py
 
+plt_agg_backend:set_api_key_example
+	@-sed -i '/^import matplotlib\.pyplot as plt$$/a \plt.switch_backend("Agg")' test_Example*.py
+
+set_api_key_example:
+	@-sed -i '/^from finquant\.portfolio import build_portfolio$$/a \import os;import quandl;quandl\.ApiConfig\.api_key = os\.getenv("QUANDLAPIKEY")' test_Example-Build-Portfolio-from-web.py
+
 clean:
 	@-rm -rf *.pyc test_Example*.py __pycache__
 

tests/test_moving_average.py

@@ -4,6 +4,8 @@
 from finquant.moving_average import compute_ma, sma, ema, sma_std, ema_std
 from finquant.moving_average import plot_bollinger_band
 
+plt.switch_backend("Agg")
+
 
 def test_sma():
     orig = np.array(
@@ -14,7 +16,7 @@ def test_sma():
     )
     dforig = pd.DataFrame({"0": orig[0], "1": orig[1]}).dropna()
     l1 = range(10)
-    l2 = [i ** 2 for i in range(10)]
+    l2 = [i**2 for i in range(10)]
     df = pd.DataFrame({"0": l1, "1": l2})
     res = sma(df, span=2).dropna()
     assert all((dforig == res).all())
@@ -51,7 +53,7 @@ def test_ema():
     )
     dforig = pd.DataFrame({"0": orig[0], "1": orig[1]}).dropna()
     l1 = range(10)
-    l2 = [i ** 2 for i in range(10)]
+    l2 = [i**2 for i in range(10)]
     df = pd.DataFrame({"0": l1, "1": l2})
     res = ema(df, span=2).dropna()
     assert all((abs(dforig - res) <= 1e-15).all())
@@ -88,7 +90,7 @@ def test_sma_std():
     )
     dforig = pd.DataFrame({"0": orig[0], "1": orig[1]}).dropna()
     l1 = range(10)
-    l2 = [i ** 2 for i in range(10)]
+    l2 = [i**2 for i in range(10)]
     df = pd.DataFrame({"0": l1, "1": l2})
     res = sma_std(df, span=2).dropna()
     assert all((abs(dforig - res) <= 1e-15).all())
@@ -125,7 +127,7 @@ def test_ema_std():
     )
     dforig = pd.DataFrame({"0": orig[0], "1": orig[1]}).dropna()
     l1 = range(10)
-    l2 = [i ** 2 for i in range(10)]
+    l2 = [i**2 for i in range(10)]
     df = pd.DataFrame({"0": l1, "1": l2})
     res = ema_std(df, span=2).dropna()
     assert all((abs(dforig - res) <= 1e-15).all())
@@ -168,12 +170,12 @@ def test_compute_ma():
     )
     x = np.sin(np.linspace(1, 10, 100))
     df = pd.DataFrame({"Stock": x})
-    ma = compute_ma(df, ema, spans=[10, 30])
+    ma = compute_ma(df, ema, spans=[10, 30], plot=False)
     assert all(abs((dforig - ma.describe()) <= 1e-15).all())
 
 
 def test_plot_bollinger_band():
-    labels_orig = ["Stock", "15d", "Bollinger Band"]
+    labels_orig = ["Bollinger Band", "Stock", "15d"]
     xlabel_orig = "Days"
     ylabel_orig = "Price"
     title_orig = (
@@ -187,7 +189,7 @@ def test_plot_bollinger_band():
     # get data from axis object
     ax = plt.gca()
     # ax.lines[0] is the data we passed to plot_bollinger_band
-    # ax.lines[1] is the moving average (tested already)
+    # ax.lines[1] is the moving average (already tested)
     # not sure how to obtain the data of the BollingerBand from
     # the plot.
     # only checking if input data matches data of first line on plot,