Fix ttm issue (#24)

* Update version in pyproject.toml

* Add 1 day to data.days_to_target to account for the expiration day

* Add 1 day to data._days_to_maturity to account for the expiration day

* Update test_core.py

* Update .gitignore

* Update models.py

* Update calendar_spread.ipynb, .gitignore, CHANGELOG.md, poetry.lock and pyproject.toml

* Add 1 to `time_to_target` and `time_to_maturity` in `engine.py`

* Update Jupyter notebooks, replacing the `StrategyEngine` class by the `run_strategy()` function

* Update CHANGELOG.md

* Black files in `tests`

* Black `engine.py`, `models.py` and `support.py`

* Update CHANGELOG.md

* Change variable `project_target_ranges` in `models.py` and `engines.py` to `profit_target_ranges`

* Black again `engine.py`

* Update version in `__init__.py`

.gitignore

@@ -1 +1,4 @@
-dist/
+dist/
+__pycache__
+run_*.py
+.ipynb_checkpoints

CHANGELOG.md

@@ -1,13 +1,20 @@
 # CHANGELOG
 
+## 1.2.1 (2024-06-03)
+
+- Add 1 to `time_to_target` and `time_to_maturity` in `engine.py` to consider the target and expiration dates as  trading days in the calculations
+- Change Jupyter notebooks in the `examples` directory to utilize the `run_strategy()` function for performing options strategy calculations, instead of using the `StrategyEngine` class (deprecated) 
+- Correct the PoP Calculator notebook
+- Change the name of variable `project_target_ranges` in `models.py` and `engine.py` to `profit_target_ranges`
+
 ## 1.2.0 (2024-03-31)
 
-- Add functions to run engine
+- Add functions to run engine.
 
 ## 1.1.0 (2024-03-24)
 
-- Refactor the engine's `run` method for readability.
-- Accept dictionary of inputs to `StratgyEngine` init.
+- Refactor the engine's `run` method for readability
+- Accept dictionary of inputs to `StratgyEngine` init
 
 ## 1.0.1 (2024-03-18)
 

examples/pop_calculator.ipynb

@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-03-15T17:51:40.653496Z",
@@ -29,7 +29,7 @@
     "\n",
     "from scipy import stats\n",
     "\n",
-    "from optionlab import VERSION, get_d1_d2, get_pop"
+    "from optionlab import VERSION, get_d1_d2, get_pop, ProbabilityOfProfitInputs"
    ]
   },
   {
@@ -46,8 +46,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Python version: 3.10.13 (main, Aug 24 2023, 12:59:26) [Clang 15.0.0 (clang-1500.0.40.1)]\n",
-      "optionlab version: 1.0.0\n"
+      "Python version: 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n",
+      "optionlab version: 1.2.0\n"
      ]
     }
    ],
@@ -76,12 +76,12 @@
    },
    "outputs": [],
    "source": [
-    "stockprice = 100.0\n",
+    "stock_price = 100.0\n",
     "s1, s2 = 95, 105\n",
-    "interestrate = 1.0\n",
-    "dividendyield = 0.0\n",
+    "interest_rate = 1.0\n",
+    "dividend_yield = 0.0\n",
     "volatility = 20.0\n",
-    "days2maturity = 60"
+    "days_to_maturity = 60"
    ]
   },
   {
@@ -102,10 +102,10 @@
    },
    "outputs": [],
    "source": [
-    "interestrate = interestrate / 100\n",
+    "interest_rate = interest_rate / 100\n",
     "volatility = volatility / 100\n",
-    "dividendyield = dividendyield / 100\n",
-    "time_to_maturity = days2maturity / 365"
+    "dividend_yield = dividend_yield / 100\n",
+    "time_to_maturity = days_to_maturity / 365"
    ]
   },
   {
@@ -136,10 +136,10 @@
    "source": [
     "d2 = [\n",
     "    get_d1_d2(\n",
-    "        stockprice, s1, interestrate, volatility, time_to_maturity, dividendyield\n",
+    "        stock_price, s1, interest_rate, volatility, time_to_maturity, dividend_yield\n",
     "    )[1],\n",
     "    get_d1_d2(\n",
-    "        stockprice, s2, interestrate, volatility, time_to_maturity, dividendyield\n",
+    "        stock_price, s2, interest_rate, volatility, time_to_maturity, dividend_yield\n",
     "    )[1],\n",
     "]\n",
     "pop1 = stats.norm.cdf(d2[0]) - stats.norm.cdf(d2[1])\n",
@@ -174,20 +174,29 @@
    "source": [
     "pop2 = get_pop(\n",
     "    [[s1, s2]],\n",
-    "    \"black-scholes\",\n",
-    "    stock_price=stockprice,\n",
-    "    volatility=volatility,\n",
-    "    interest_rate=interestrate,\n",
-    "    years_to_maturity=time_to_maturity,\n",
-    "    dividend_yield=dividendyield,\n",
+    "    ProbabilityOfProfitInputs(\n",
+    "        source=\"black-scholes\",\n",
+    "        stock_price=stock_price,\n",
+    "        volatility=volatility,\n",
+    "        interest_rate=interest_rate,\n",
+    "        years_to_maturity=time_to_maturity,\n",
+    "        dividend_yield=dividend_yield,\n",
+    "    ),\n",
     ")\n",
     "print(\"===> Probability of Profit (PoP) from getPoP(): %.2f\" % (pop2 * 100))"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -201,7 +210,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.11.7"
   }
  },
  "nbformat": 4,

optionlab/__init__.py

@@ -1,7 +1,7 @@
 import typing
 
 
-VERSION = "1.2.0"
+VERSION = "1.2.1"
 
 
 if typing.TYPE_CHECKING:

optionlab/engine.py

@@ -82,7 +82,6 @@ def _init_inputs(inputs: Inputs) -> EngineData:
                 data._days_to_maturity.append(data.days_to_target)
                 data._use_bs.append(False)
             elif isinstance(strategy.expiration, dt.date) and inputs.start_date:
-
                 if inputs.discard_nonbusiness_days:
                     n_discarded_days = get_nonbusiness_days(
                         inputs.start_date, strategy.expiration, inputs.country
@@ -141,7 +140,9 @@ def _run(data: EngineData) -> EngineData:
     """
     inputs = data.inputs
 
-    time_to_target = data.days_to_target / data._days_in_year
+    time_to_target = (
+        data.days_to_target + 1
+    ) / data._days_in_year  # To consider the target date as a trading day
     data.cost = [0.0] * len(data.type)
 
     data.profit = zeros((len(data.type), data.stock_price_array.shape[0]))
@@ -198,7 +199,7 @@ def _run(data: EngineData) -> EngineData:
         data._profit_target_range = get_profit_range(
             data.stock_price_array, data.strategy_profit, inputs.profit_target
         )
-        data.project_target_probability = get_pop(data._profit_target_range, pop_inputs)
+        data.profit_target_probability = get_pop(data._profit_target_range, pop_inputs)
 
     if inputs.loss_limit is not None:
         data._loss_limit_rangesm = get_profit_range(
@@ -236,7 +237,9 @@ def _run_option_calcs(data: EngineData, i: int) -> EngineData:
 
         return data
 
-    time_to_maturity = data._days_to_maturity[i] / data._days_in_year
+    time_to_maturity = (
+        data._days_to_maturity[i] + 1
+    ) / data._days_in_year  # To consider the expiration date as a trading day
     bs = get_bs_info(
         inputs.stock_price,
         data.strike[i],
@@ -279,8 +282,8 @@ def _run_option_calcs(data: EngineData, i: int) -> EngineData:
 
     if data._use_bs[i]:
         target_to_maturity = (
-            data._days_to_maturity[i] - data.days_to_target
-        ) / data._days_in_year
+            data._days_to_maturity[i] - data.days_to_target + 1
+        ) / data._days_in_year  # To consider the expiration date as a trading day
 
         data.profit[i], data.cost[i] = get_pl_profile_bs(
             type,
@@ -410,9 +413,9 @@ def _generate_outputs(data: EngineData) -> Outputs:
 
     if inputs.profit_target is not None:
         optional_outputs["probability_of_profit_target"] = (
-            data.project_target_probability
+            data.profit_target_probability
         )
-        optional_outputs["project_target_ranges"] = data._profit_target_range
+        optional_outputs["profit_target_ranges"] = data._profit_target_range
 
     if inputs.loss_limit is not None:
         optional_outputs["probability_of_loss_limit"] = data.loss_limit_probability

optionlab/models.py

@@ -48,6 +48,7 @@ class StockStrategy(BaseStrategy):
         negative, it means that the position is closed and the
         difference between this price and the current price is
         considered in the payoff calculation.
+
     """
 
     type: Literal["stock"] = "stock"
@@ -194,6 +195,7 @@ class Inputs(BaseModel):
     mc_prices_number : int, optional
         Number of random terminal prices to be generated when calculationg
         the average profit and loss of a strategy. Default is 100,000.
+
     """
 
     stock_price: float = Field(gt=0)
@@ -323,7 +325,7 @@ class EngineData(EngineDataResults):
     theta: list[float] = []
     cost: list[float] = []
     profit_probability: float = 0.0
-    project_target_probability: float = 0.0
+    profit_target_probability: float = 0.0
     loss_limit_probability: float = 0.0
 
 
@@ -358,7 +360,7 @@ class Outputs(BaseModel):
         Maximum return of the strategy within the stock price domain.
     probability_of_profit_target : float, optional
         Probability of the strategy yielding at least the profit target.
-    project_target_ranges : list, optional
+    profit_target_ranges : list, optional
         A list of minimum and maximum stock prices defining
         ranges in which the strategy makes at least the profit
         target.
@@ -390,7 +392,7 @@ class Outputs(BaseModel):
     theta: list[float]
     vega: list[float]
     probability_of_profit_target: float | None = None
-    project_target_ranges: list[Range] | None = None
+    profit_target_ranges: list[Range] | None = None
     probability_of_loss_limit: float | None = None
     average_profit_from_mc: float | None = None
     average_loss_from_mc: float | None = None

optionlab/support.py

@@ -279,7 +279,6 @@ def get_pop(
         return pop
 
     if isinstance(inputs, ProbabilityOfProfitInputs):
-
         stock_price = inputs.stock_price
         volatility = inputs.volatility
         years_to_maturity = inputs.years_to_maturity