chore: Black style adjustments

Author: chilango74

.ipynb_checkpoints/main_notebook-checkpoint.ipynb

@@ -94,7 +94,18 @@
    "source": [
     "from okama.common import make_asset_list\n",
     "\n",
-    "ls = ['SPY.US', 'AGG.US', 'GC.COMM', 'VOO.US', 'MCFTR.INDX', 'RGBITR.INDX', 'MOW_PR.RE', 'MOW_SEC.RE', 'RUCBTRNS.INDX', 'USDRUB.FX']\n"
+    "ls = [\n",
+    "    \"SPY.US\",\n",
+    "    \"AGG.US\",\n",
+    "    \"GC.COMM\",\n",
+    "    \"VOO.US\",\n",
+    "    \"MCFTR.INDX\",\n",
+    "    \"RGBITR.INDX\",\n",
+    "    \"MOW_PR.RE\",\n",
+    "    \"MOW_SEC.RE\",\n",
+    "    \"RUCBTRNS.INDX\",\n",
+    "    \"USDRUB.FX\",\n",
+    "]"
    ]
   },
   {
@@ -256,7 +267,7 @@
     }
    ],
    "source": [
-    "al = ok.AssetList(ls, ccy='RUB', inflation=True)\n",
+    "al = ok.AssetList(ls, ccy=\"RUB\", inflation=True)\n",
     "print(al)"
    ]
   },
@@ -282,9 +293,9 @@
    },
    "outputs": [],
    "source": [
-    "assets = ['MCFTR.INDX', 'GC.COMM', 'RGBITR.INDX', 'RUCBTRNS.INDX']\n",
-    "assets1 = ['MCFTR.INDX', 'GC.COMM']\n",
-    "assets2 = ['SPY.US', 'AGG.US', 'GC.COMM']"
+    "assets = [\"MCFTR.INDX\", \"GC.COMM\", \"RGBITR.INDX\", \"RUCBTRNS.INDX\"]\n",
+    "assets1 = [\"MCFTR.INDX\", \"GC.COMM\"]\n",
+    "assets2 = [\"SPY.US\", \"AGG.US\", \"GC.COMM\"]"
    ]
   },
   {
@@ -525,11 +536,7 @@
    },
    "outputs": [],
    "source": [
-    "pf.dcf.set_mc_parameters(\n",
-    "    distribution=\"norm\",  # Normal distribution (or Gaussian distribution)\n",
-    "    period=10,\n",
-    "    number=100\n",
-    ")"
+    "pf.dcf.set_mc_parameters(distribution=\"norm\", period=10, number=100)  # Normal distribution (or Gaussian distribution)"
    ]
   },
   {
@@ -1116,9 +1123,9 @@
     }
    ],
    "source": [
-    "assets = ['RGBITR.INDX', 'RUCBTRNS.INDX', 'MCFTR.INDX', 'GC.COMM']\n",
-    "weights = [0.16,          0.40,             0.25,        0.19]\n",
-    "pf = ok.Portfolio(assets, weights=weights, ccy='RUB', rebalancing_period='year', inflation=False)\n",
+    "assets = [\"RGBITR.INDX\", \"RUCBTRNS.INDX\", \"MCFTR.INDX\", \"GC.COMM\"]\n",
+    "weights = [0.16, 0.40, 0.25, 0.19]\n",
+    "pf = ok.Portfolio(assets, weights=weights, ccy=\"RUB\", rebalancing_period=\"year\", inflation=False)\n",
     "pf.dcf.discount_rate = 0.09\n",
     "print(pf)"
    ]
@@ -2546,6 +2553,7 @@
     "import okama as ok\n",
     "\n",
     "import warnings\n",
+    "\n",
     "warnings.simplefilter(action=\"ignore\", category=FutureWarning)"
    ]
   },
@@ -2847,11 +2855,11 @@
    "source": [
     "pf = [\"SPY.US\", \"GLD.US\", \"VB.US\"]\n",
     "\n",
-    "curr = 'RUB'\n",
+    "curr = \"RUB\"\n",
     "\n",
-    "last_date=\"2024-07\"\n",
+    "last_date = \"2024-07\"\n",
     "\n",
-    "bounds=((0, 1), (0, 1), (0, 1))\n",
+    "bounds = ((0, 1), (0, 1), (0, 1))\n",
     "\n",
     "ef = ok.EfficientFrontierReb(pf, ccy=curr, last_date=last_date, bounds=bounds)\n",
     "\n",
@@ -2929,7 +2937,7 @@
     "    first_date=\"2004-10\",\n",
     "    last_date=\"2020-10\",\n",
     "    ccy=curr,\n",
-    "    bounds=((0, 0.4), (0, 1), (0, 1)),  # add bounds    \n",
+    "    bounds=((0, 0.4), (0, 1), (0, 1)),  # add bounds\n",
     "    verbose=False,\n",
     ")\n",
     "\n",
@@ -2968,7 +2976,7 @@
    },
    "outputs": [],
    "source": [
-    "ef = ok.EfficientFrontierReb(['SPY.US', 'MCFTR.INDX'], ccy='RUB', last_date='2025-03', bounds=None)"
+    "ef = ok.EfficientFrontierReb([\"SPY.US\", \"MCFTR.INDX\"], ccy=\"RUB\", last_date=\"2025-03\", bounds=None)"
    ]
   },
   {
@@ -2982,7 +2990,7 @@
    },
    "outputs": [],
    "source": [
-    "ef = ok.EfficientFrontierReb(['SPY.US', 'GLD.US'], ccy='USD', last_date='2020-10', bounds=None)"
+    "ef = ok.EfficientFrontierReb([\"SPY.US\", \"GLD.US\"], ccy=\"USD\", last_date=\"2020-10\", bounds=None)"
    ]
   },
   {
@@ -3026,7 +3034,7 @@
     "# ax.plot(df1_not_reb.Risk, df1_not_reb.CAGR, label=\"Not rebalanced\")\n",
     "\n",
     "# Plot the assets\n",
-    "ef.plot_assets(kind=\"cagr\")\n"
+    "ef.plot_assets(kind=\"cagr\")"
    ]
   },
   {

examples/01 howto.ipynb

@@ -49,6 +49,7 @@
    "outputs": [],
    "source": [
     "import warnings\n",
+    "\n",
     "warnings.filterwarnings(\"ignore\")\n",
     "\n",
     "import matplotlib.pyplot as plt\n",

examples/02 index funds perfomance.ipynb

@@ -48,6 +48,7 @@
    "outputs": [],
    "source": [
     "import warnings\n",
+    "\n",
     "warnings.filterwarnings(\"ignore\")\n",
     "\n",
     "import matplotlib.pyplot as plt\n",

examples/03 investment portfolios.ipynb

@@ -60,9 +60,11 @@
    "outputs": [],
    "source": [
     "import warnings\n",
+    "\n",
     "warnings.simplefilter(action=\"ignore\", category=FutureWarning)\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
+    "\n",
     "plt.rcParams[\"figure.figsize\"] = [12.0, 6.0]\n",
     "\n",
     "import okama as ok"

examples/04 investment portfolios with DCF.ipynb

@@ -64,9 +64,11 @@
    "outputs": [],
    "source": [
     "import warnings\n",
+    "\n",
     "warnings.simplefilter(action=\"ignore\", category=FutureWarning)\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
+    "\n",
     "plt.rcParams[\"figure.figsize\"] = [12.0, 6.0]\n",
     "\n",
     "import okama as ok"
@@ -122,7 +124,7 @@
     "    inflation=True,\n",
     "    last_date=\"2024-01\",\n",
     "    rebalancing_strategy=ok.Rebalance(period=\"year\"),\n",
-    "    symbol=\"My_portfolio.PF\"\n",
+    "    symbol=\"My_portfolio.PF\",\n",
     ")\n",
     "pf"
    ]
@@ -172,7 +174,7 @@
    },
    "outputs": [],
    "source": [
-    "ind = ok.IndexationStrategy(pf) # create IndexationStrategy linked to the Portfolio"
+    "ind = ok.IndexationStrategy(pf)  # create IndexationStrategy linked to the Portfolio"
    ]
   },
   {
@@ -551,9 +553,9 @@
    "outputs": [],
    "source": [
     "pf.dcf.set_mc_parameters(\n",
-    "    distribution=\"norm\",  # Normal distribution (or Gaussian distribution) \n",
+    "    distribution=\"norm\",  # Normal distribution (or Gaussian distribution)\n",
     "    period=60,  # the forecasting period is 10 years\n",
-    "    number=400  # generate 400 random simulations\n",
+    "    number=400,  # generate 400 random simulations\n",
     ")"
    ]
   },
@@ -594,7 +596,7 @@
    ],
    "source": [
     "pf.dcf.plot_forecast_monte_carlo(backtest=False)\n",
-    "plt.yscale(\"log\")  # set logarithmic scale for Y-axis to see negative scenarios "
+    "plt.yscale(\"log\")  # set logarithmic scale for Y-axis to see negative scenarios"
    ]
   },
   {
@@ -1441,7 +1443,7 @@
     }
    ],
    "source": [
-    "pf.dcf.wealth_index['My_portfolio.PF'].iat[-1]"
+    "pf.dcf.wealth_index[\"My_portfolio.PF\"].iat[-1]"
    ]
   },
   {
@@ -1619,11 +1621,7 @@
    },
    "outputs": [],
    "source": [
-    "pf.dcf.set_mc_parameters(\n",
-    "    distribution=\"norm\",   \n",
-    "    period=30,   # simulation period in years \n",
-    "    number=400  \n",
-    ")"
+    "pf.dcf.set_mc_parameters(distribution=\"norm\", period=30, number=400)  # simulation period in years"
    ]
   },
   {
@@ -1742,7 +1740,7 @@
     "    percentile=25,  # The percentile of Monte Carlo result distribution where the goal is to be achieved. The 25th percentile is a negative scenario.\n",
     "    threshold=0.10,  # 10% - is the percentage of initial investments when the portfolio balance is considered voided.\n",
     "    iter_max=50,  # The maximum number of iterations to find the solution.\n",
-    "    tolerance_rel=0.15  # The allowed tolerance for the solution. The tolerance is the largest error for the achieved goal.\n",
+    "    tolerance_rel=0.15,  # The allowed tolerance for the solution. The tolerance is the largest error for the achieved goal.\n",
     ")"
    ]
   },
@@ -1818,7 +1816,7 @@
     "    percentile=25,  # The percentile of Monte Carlo result distribution where the goal is to be achieved. The 25th percentile is a negative scenario.\n",
     "    threshold=0.10,  # 10% - is the percentage of initial investments when the portfolio balance is considered voided.\n",
     "    iter_max=50,  # The maximum number of iterations to find the solution.\n",
-    "    tolerance_rel=0.15  # The allowed tolerance for the solution. The tolerance is the largest error for the achieved goal.\n",
+    "    tolerance_rel=0.15,  # The allowed tolerance for the solution. The tolerance is the largest error for the achieved goal.\n",
     ")"
    ]
   },
@@ -1912,10 +1910,7 @@
    },
    "outputs": [],
    "source": [
-    "d = {\n",
-    "    \"2018-02\": 2_000,  # contribution\n",
-    "    \"2024-03\": -4_000  # withdrawal\n",
-    "}"
+    "d = {\"2018-02\": 2_000, \"2024-03\": -4_000}  # contribution  # withdrawal"
    ]
   },
   {
@@ -2419,10 +2414,7 @@
    },
    "outputs": [],
    "source": [
-    "d = {\n",
-    "    \"2027-02\": 2_000,  # contribution\n",
-    "    \"2034-03\": -4_000  # withdrawal\n",
-    "}"
+    "d = {\"2027-02\": 2_000, \"2034-03\": -4_000}  # contribution  # withdrawal"
    ]
   },
   {
@@ -2475,9 +2467,9 @@
    "outputs": [],
    "source": [
     "pf2.dcf.set_mc_parameters(\n",
-    "    distribution=\"norm\",  # Normal distribution (or Gaussian distribution) \n",
+    "    distribution=\"norm\",  # Normal distribution (or Gaussian distribution)\n",
     "    period=30,  # the forecasting period is 30 years\n",
-    "    number=500  # generate 500 random simulations\n",
+    "    number=500,  # generate 500 random simulations\n",
     ")"
    ]
   },

examples/05 macroeconomics - inflation rates.ipynb

@@ -56,6 +56,7 @@
    "outputs": [],
    "source": [
     "import warnings\n",
+    "\n",
     "warnings.simplefilter(action=\"ignore\", category=FutureWarning)\n",
     "\n",
     "import matplotlib.pyplot as plt\n",

examples/06 efficient frontier single period.ipynb

@@ -28,6 +28,7 @@
    "outputs": [],
    "source": [
     "import warnings\n",
+    "\n",
     "warnings.simplefilter(action=\"ignore\", category=FutureWarning)\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
@@ -2226,9 +2227,9 @@
    "source": [
     "df = four_assets.mdp_points\n",
     "fig = plt.figure()\n",
-    "four_assets.plot_assets(kind='cagr')\n",
+    "four_assets.plot_assets(kind=\"cagr\")\n",
     "ax = plt.gca()\n",
-    "ax.plot(df['Risk'], df['CAGR'])\n"
+    "ax.plot(df[\"Risk\"], df[\"CAGR\"])"
    ]
   },
   {
@@ -2264,15 +2265,15 @@
     "df2 = four_assets.ef_points  # the Efficient Frontier points\n",
     "mdp = four_assets.get_most_diversified_portfolio()  # the global Most diversified portfolio\n",
     "fig = plt.figure()\n",
-    "four_assets.plot_assets(kind='cagr')\n",
+    "four_assets.plot_assets(kind=\"cagr\")\n",
     "ax = plt.gca()\n",
-    "ax.plot(df1['Risk'], df1['CAGR'], label='Most diversified portfolios')\n",
-    "ax.plot(df2['Risk'], df2['CAGR'], label='Efficient Frontier')\n",
-    "ax.scatter(mdp['Risk'], mdp['CAGR'], s=30, marker='o', label='MDP')\n",
+    "ax.plot(df1[\"Risk\"], df1[\"CAGR\"], label=\"Most diversified portfolios\")\n",
+    "ax.plot(df2[\"Risk\"], df2[\"CAGR\"], label=\"Efficient Frontier\")\n",
+    "ax.scatter(mdp[\"Risk\"], mdp[\"CAGR\"], s=30, marker=\"o\", label=\"MDP\")\n",
     "# Set title, legend and labels for axes\n",
-    "ax.set_title('Efficient Frontier vs Most diversified portfolios')\n",
-    "ax.set_xlabel('Risk (Standard Deviation)')\n",
-    "ax.set_ylabel('Return (CAGR)')\n",
+    "ax.set_title(\"Efficient Frontier vs Most diversified portfolios\")\n",
+    "ax.set_xlabel(\"Risk (Standard Deviation)\")\n",
+    "ax.set_ylabel(\"Return (CAGR)\")\n",
     "ax.legend();"
    ]
   },