Update all changes

CHANGELOG.md

@@ -1,5 +1,14 @@
 # CHANGELOG
 
+## 1.5.0 (2025-12-14)
+
+- Changed `get_pop` to compute the expected profit and expected loss of a strategy with the Black-Scholes model by calling a new function, `_compute_expected_returns_bs`.
+- Updated docstrings.
+- Removed price_array.py (a better Monte Carlo implementation is coming soon).
+- The `expected_return_above_target` and `expected_return_below_target` fields in `PoPOutputs`, in models.py, are no longer optional but floats with default value of 0.0.
+- The same for the `expected_profit` and `expected_loss` fields in `EngineData` and `Outputs`, both also in models.py.
+- Removed test_misc.py.
+
 ## 1.4.3 (2025-04-14)
 
 - Updated docstrings.

examples/black_scholes_calculator.ipynb

@@ -24,7 +24,8 @@
    "source": [
     "from __future__ import print_function\n",
     "from __future__ import division\n",
-    "from optionlab import VERSION, get_bs_info\n",
+    "from optionlab import __version__\n",
+    "from optionlab.black_scholes import get_bs_info\n",
     "import sys"
    ]
   },
@@ -43,13 +44,13 @@
      "output_type": "stream",
      "text": [
       "Python version: 3.11.9 | packaged by Anaconda, Inc. | (main, Apr 19 2024, 16:40:41) [MSC v.1916 64 bit (AMD64)]\n",
-      "OptionLab version: 1.4.3\n"
+      "OptionLab version: 1.5.0\n"
      ]
     }
    ],
    "source": [
     "print(f\"Python version: {sys.version}\")\n",
-    "print(f\"OptionLab version: {VERSION}\")"
+    "print(f\"OptionLab version: {__version__}\")"
    ]
   },
   {
@@ -105,8 +106,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: total: 0 ns\n",
-      "Wall time: 4 ms\n"
+      "CPU times: total: 15.6 ms\n",
+      "Wall time: 7.02 ms\n"
      ]
     }
    ],

examples/calendar_spread.ipynb

@@ -27,7 +27,7 @@
     "import datetime as dt\n",
     "import sys\n",
     "\n",
-    "from optionlab import VERSION, run_strategy, plot_pl\n",
+    "from optionlab import __version__, run_strategy, plot_pl\n",
     "\n",
     "%matplotlib inline"
    ]
@@ -47,13 +47,13 @@
      "output_type": "stream",
      "text": [
       "Python version: 3.11.9 | packaged by Anaconda, Inc. | (main, Apr 19 2024, 16:40:41) [MSC v.1916 64 bit (AMD64)]\n",
-      "OptionLab version: 1.4.3\n"
+      "OptionLab version: 1.5.0\n"
      ]
     }
    ],
    "source": [
     "print(f\"Python version: {sys.version}\")\n",
-    "print(f\"OptionLab version: {VERSION}\")"
+    "print(f\"OptionLab version: {__version__}\")"
    ]
   },
   {
@@ -119,8 +119,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: total: 375 ms\n",
-      "Wall time: 485 ms\n"
+      "CPU times: total: 297 ms\n",
+      "Wall time: 367 ms\n"
      ]
     }
    ],
@@ -179,6 +179,8 @@
      "text": [
       "Probability of profit: 0.599111819020198\n",
       "Profit ranges: [(118.87, 136.15)]\n",
+      "Expected profit: 1383.2\n",
+      "Expected loss: -691.09\n",
       "Per leg cost: [4600.0, -5900.0]\n",
       "Strategy cost: -1300.0\n",
       "Minimum return in the domain: -1300.0000000000146\n",

examples/call_spread.ipynb

@@ -160,12 +160,20 @@
     }
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\rgave\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\optionlab-M7OP2qJc-py3.11\\Lib\\site-packages\\numpy\\lib\\_scimath_impl.py:289: RuntimeWarning: divide by zero encountered in log\n",
+      "  return nx.log(x)\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: total: 39.8 s\n",
-      "Wall time: 42.4 s\n"
+      "CPU times: total: 1min 25s\n",
+      "Wall time: 1min 29s\n"
      ]
     }
    ],
@@ -258,7 +266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-03-13T02:15:17.818384Z",
@@ -269,10 +277,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1d7a0247250>]"
+       "[<matplotlib.lines.Line2D at 0x21b4b9b8250>]"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -306,7 +314,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-03-13T02:17:03.700786Z",
@@ -371,7 +379,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-03-13T02:17:10.924712Z",
@@ -383,8 +391,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: total: 39.5 s\n",
-      "Wall time: 42.3 s\n"
+      "CPU times: total: 1min 26s\n",
+      "Wall time: 1min 31s\n"
      ]
     }
    ],
@@ -395,7 +403,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-03-13T02:17:10.925263Z",
@@ -488,7 +496,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1d7a2447bd0>]"
+       "[<matplotlib.lines.Line2D at 0x21b4b9b1590>]"
       ]
      },
      "execution_count": 15,

examples/covered_call.ipynb

@@ -34,7 +34,7 @@
     "import matplotlib.pyplot as plt\n",
     "from numpy import zeros\n",
     "\n",
-    "from optionlab import VERSION, run_strategy\n",
+    "from optionlab import __version__, run_strategy\n",
     "\n",
     "%matplotlib inline"
    ]
@@ -54,13 +54,13 @@
      "output_type": "stream",
      "text": [
       "Python version: 3.11.9 | packaged by Anaconda, Inc. | (main, Apr 19 2024, 16:40:41) [MSC v.1916 64 bit (AMD64)]\n",
-      "OptionLab version: 1.4.3\n"
+      "OptionLab version: 1.5.0\n"
      ]
     }
    ],
    "source": [
     "print(\"Python version: %s\" % sys.version)\n",
-    "print(\"OptionLab version: %s\" % VERSION)"
+    "print(\"OptionLab version: %s\" % __version__)"
    ]
   },
   {
@@ -119,8 +119,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: total: 281 ms\n",
-      "Wall time: 258 ms\n"
+      "CPU times: total: 234 ms\n",
+      "Wall time: 293 ms\n"
      ]
     }
    ],
@@ -142,7 +142,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x1e0346f7f90>"
+       "<matplotlib.legend.Legend at 0x1909fae4910>"
       ]
      },
      "execution_count": 5,
@@ -195,6 +195,8 @@
      "text": [
       "Probability of profit: 0.522421406650333\n",
       "Profit ranges: [(162.9, inf)]\n",
+      "Expected profit: 787.24\n",
+      "Expected loss: -904.12\n",
       "Per leg cost: [-16404.0, 114.99999999999999]\n",
       "Strategy cost: -16289.0\n",
       "Minimum return in the domain: -8087.0\n",

examples/naked_call.ipynb

@@ -31,7 +31,7 @@
     "import datetime as dt\n",
     "import sys\n",
     "\n",
-    "from optionlab import VERSION, run_strategy, Inputs, plot_pl\n",
+    "from optionlab import __version__, run_strategy, Inputs, plot_pl\n",
     "\n",
     "%matplotlib inline"
    ]
@@ -46,13 +46,13 @@
      "output_type": "stream",
      "text": [
       "Python version: 3.11.9 | packaged by Anaconda, Inc. | (main, Apr 19 2024, 16:40:41) [MSC v.1916 64 bit (AMD64)]\n",
-      "OptionLab version: 1.4.3\n"
+      "OptionLab version: 1.5.0\n"
      ]
     }
    ],
    "source": [
     "print(\"Python version: %s\" % sys.version)\n",
-    "print(\"OptionLab version: %s\" % VERSION)"
+    "print(\"OptionLab version: %s\" % __version__)"
    ]
   },
   {
@@ -116,8 +116,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: total: 328 ms\n",
-      "Wall time: 338 ms\n"
+      "CPU times: total: 141 ms\n",
+      "Wall time: 262 ms\n"
      ]
     }
    ],
@@ -174,31 +174,31 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Maximum loss: 6992.00\n",
-      "Maximum profit: 115.00\n",
-      "Profitable stock price range:\n",
-      "      0.00 ---> 176.14\n",
-      "Probability of Profit (PoP): 83.9%\n",
-      "Probability of reaching 100.00 or more: 82.0%\n",
-      "Probability of losing 100.00 or more: 14.3%\n"
+      "Probability of profit: 0.8389215512144531\n",
+      "Profit ranges: [(0.0, 176.14)]\n",
+      "Expected profit: 113.49\n",
+      "Expected loss: -717.5\n",
+      "Per leg cost: [114.99999999999999]\n",
+      "Strategy cost: 114.99999999999999\n",
+      "Minimum return in the domain: -6991.999999999999\n",
+      "Maximum return in the domain: 114.99999999999999\n",
+      "Implied volatility: [0.256]\n",
+      "In the money probability: [0.1832371984432129]\n",
+      "Delta: [-0.20371918274704337]\n",
+      "Gamma: [0.023104402361599465]\n",
+      "Theta: [0.091289876347897]\n",
+      "Vega: [0.12750177318341913]\n",
+      "Rho: [-0.02417676577711979]\n",
+      "Probability of profit target: 0.8197909190785164\n",
+      "Profit target ranges: [(0.0, 175.15)]\n",
+      "Probability of loss limit: 0.14307836806156238\n",
+      "Loss limit ranges: [(177.15, inf)]\n",
+      "\n"
      ]
     }
    ],
    "source": [
-    "print(f\"Maximum loss: {abs(out.minimum_return_in_the_domain):.2f}\")\n",
-    "print(f\"Maximum profit: {out.maximum_return_in_the_domain:.2f}\")\n",
-    "print(\"Profitable stock price range:\")\n",
-    "\n",
-    "for low, high in out.profit_ranges:\n",
-    "    print(f\"      {low:.2f} ---> {high:.2f}\")\n",
-    "\n",
-    "print(f\"Probability of Profit (PoP): {out.probability_of_profit * 100.0:.1f}%\")\n",
-    "print(\n",
-    "    f\"Probability of reaching {profit_target:.2f} or more: {out.probability_of_profit_target * 100.0:.1f}%\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"Probability of losing {abs(loss_limit):.2f} or more: {out.probability_of_loss_limit * 100.0:.1f}%\"\n",
-    ")"
+    "print(out)"
    ]
   },
   {

optionlab/.gitignore

@@ -1,2 +1,3 @@
 __pycache__
+*.old
 old