linting also in notebooks

Author: tschm

cookbook/2-Mean-Variance-Optimisation.ipynb

@@ -84,7 +84,23 @@
    },
    "outputs": [],
    "source": [
-    "tickers = [\"MSFT\", \"AMZN\", \"KO\", \"MA\", \"COST\", \"LUV\", \"XOM\", \"PFE\", \"JPM\", \"UNH\", \"ACN\", \"DIS\", \"GILD\", \"F\", \"TSLA\"]"
+    "tickers = [\n",
+    "    \"MSFT\",\n",
+    "    \"AMZN\",\n",
+    "    \"KO\",\n",
+    "    \"MA\",\n",
+    "    \"COST\",\n",
+    "    \"LUV\",\n",
+    "    \"XOM\",\n",
+    "    \"PFE\",\n",
+    "    \"JPM\",\n",
+    "    \"UNH\",\n",
+    "    \"ACN\",\n",
+    "    \"DIS\",\n",
+    "    \"GILD\",\n",
+    "    \"F\",\n",
+    "    \"TSLA\",\n",
+    "]"
    ]
   },
   {
@@ -1117,7 +1133,9 @@
     "from pypfopt import DiscreteAllocation\n",
     "\n",
     "latest_prices = prices.iloc[-1]  # prices as of the day you are allocating\n",
-    "da = DiscreteAllocation(weights, latest_prices, total_portfolio_value=20000, short_ratio=0.3)\n",
+    "da = DiscreteAllocation(\n",
+    "    weights, latest_prices, total_portfolio_value=20000, short_ratio=0.3\n",
+    ")\n",
     "alloc, leftover = da.lp_portfolio()\n",
     "print(f\"Discrete allocation performed with ${leftover:.2f} leftover\")\n",
     "alloc"
@@ -6854,7 +6872,9 @@
     }
    ],
    "source": [
-    "ax = plotting.plot_efficient_frontier(ef, ef_param=\"risk\", ef_param_range=np.linspace(0.2, 0.5, 50), showfig=False);"
+    "ax = plotting.plot_efficient_frontier(\n",
+    "    ef, ef_param=\"risk\", ef_param_range=np.linspace(0.2, 0.5, 50), showfig=False\n",
+    ");"
    ]
   },
   {

cookbook/3-Advanced-Mean-Variance-Optimisation.ipynb

@@ -113,7 +113,23 @@
    },
    "outputs": [],
    "source": [
-    "tickers = [\"BLK\", \"BAC\", \"AAPL\", \"TM\", \"WMT\", \"JD\", \"INTU\", \"MA\", \"UL\", \"CVS\", \"DIS\", \"AMD\", \"NVDA\", \"PBI\", \"TGT\"]"
+    "tickers = [\n",
+    "    \"BLK\",\n",
+    "    \"BAC\",\n",
+    "    \"AAPL\",\n",
+    "    \"TM\",\n",
+    "    \"WMT\",\n",
+    "    \"JD\",\n",
+    "    \"INTU\",\n",
+    "    \"MA\",\n",
+    "    \"UL\",\n",
+    "    \"CVS\",\n",
+    "    \"DIS\",\n",
+    "    \"AMD\",\n",
+    "    \"NVDA\",\n",
+    "    \"PBI\",\n",
+    "    \"TGT\",\n",
+    "]"
    ]
   },
   {
@@ -1170,7 +1186,11 @@
    "source": [
     "# Deviation risk parity with weight bound on the first asset\n",
     "ef = EfficientFrontier(mu, S)\n",
-    "ef.nonconvex_objective(deviation_risk_parity, ef.cov_matrix, constraints=[{\"type\": \"eq\", \"fun\": lambda w: w[0] - 0.1}])\n",
+    "ef.nonconvex_objective(\n",
+    "    deviation_risk_parity,\n",
+    "    ef.cov_matrix,\n",
+    "    constraints=[{\"type\": \"eq\", \"fun\": lambda w: w[0] - 0.1}],\n",
+    ")\n",
     "weights = ef.clean_weights()\n",
     "weights"
    ]

cookbook/4-Black-Litterman-Allocation.ipynb

@@ -620,7 +620,13 @@
    },
    "outputs": [],
    "source": [
-    "bl = BlackLittermanModel(S, pi=market_prior, absolute_views=viewdict, omega=\"idzorek\", view_confidences=confidences)"
+    "bl = BlackLittermanModel(\n",
+    "    S,\n",
+    "    pi=market_prior,\n",
+    "    absolute_views=viewdict,\n",
+    "    omega=\"idzorek\",\n",
+    "    view_confidences=confidences,\n",
+    ")"
    ]
   },
   {
@@ -768,7 +774,14 @@
    "outputs": [],
    "source": [
     "# We are using the shortcut to automatically compute market-implied prior\n",
-    "bl = BlackLittermanModel(S, pi=\"market\", market_caps=mcaps, risk_aversion=delta, absolute_views=viewdict, omega=omega)"
+    "bl = BlackLittermanModel(\n",
+    "    S,\n",
+    "    pi=\"market\",\n",
+    "    market_caps=mcaps,\n",
+    "    risk_aversion=delta,\n",
+    "    absolute_views=viewdict,\n",
+    "    omega=omega,\n",
+    ")"
    ]
   },
   {
@@ -942,7 +955,9 @@
     }
    ],
    "source": [
-    "rets_df = pd.DataFrame([market_prior, ret_bl, pd.Series(viewdict)], index=[\"Prior\", \"Posterior\", \"Views\"]).T\n",
+    "rets_df = pd.DataFrame(\n",
+    "    [market_prior, ret_bl, pd.Series(viewdict)], index=[\"Prior\", \"Posterior\", \"Views\"]\n",
+    ").T\n",
     "rets_df"
    ]
   },

cookbook/5-Hierarchical-Risk-Parity.ipynb

@@ -297,7 +297,23 @@
     }
    ],
    "source": [
-    "tickers = [\"BLK\", \"BAC\", \"AAPL\", \"TM\", \"WMT\", \"JD\", \"INTU\", \"MA\", \"UL\", \"CVS\", \"DIS\", \"AMD\", \"NVDA\", \"PBI\", \"TGT\"]\n",
+    "tickers = [\n",
+    "    \"BLK\",\n",
+    "    \"BAC\",\n",
+    "    \"AAPL\",\n",
+    "    \"TM\",\n",
+    "    \"WMT\",\n",
+    "    \"JD\",\n",
+    "    \"INTU\",\n",
+    "    \"MA\",\n",
+    "    \"UL\",\n",
+    "    \"CVS\",\n",
+    "    \"DIS\",\n",
+    "    \"AMD\",\n",
+    "    \"NVDA\",\n",
+    "    \"PBI\",\n",
+    "    \"TGT\",\n",
+    "]\n",
     "\n",
     "ohlc = yf.download(tickers, period=\"max\")\n",
     "prices = ohlc[\"Adj Close\"]\n",