Fix API (#68)

Author: lsbardel

app/api/hurst.py

@@ -52,7 +52,7 @@ def _range_std(pdf: Any, range_seconds: float, seconds_in_day: int) -> float:
 
 def _ohlc_hurst(df: pd.DataFrame, serie: str, periods: tuple) -> dict[str, float]:
     template = OHLC(
-        serie=serie,
+        series=serie,
         period="10m",
         rogers_satchell_variance=True,
         parkinson_variance=True,
@@ -97,7 +97,7 @@ async def hurst_wiener(
     gk_vals = []
     rs_vals = []
     template = OHLC(
-        serie="0",
+        series="0",
         period="10m",
         rogers_satchell_variance=True,
         parkinson_variance=True,

docs/api/options/moneyness.md

@@ -0,0 +1,10 @@
+# Moneyness
+
+Conversion utilities between strike, log-strike, and
+[moneyness](../../glossary.md#moneyness) representations.
+
+These functions provide a single authoritative source for the relationships
+between absolute strikes, log-strikes, and time-scaled moneyness used
+throughout the library.
+
+::: quantflow.options.moneyness

docs/api/options/vol_surface.md

@@ -17,8 +17,6 @@
 
 ## Bid/Ask Prices
 
-::: quantflow.options.surface.Price
-
 ::: quantflow.options.surface.SpotPrice
 
 ::: quantflow.options.surface.FwdPrice
@@ -27,8 +25,6 @@
 
 ::: quantflow.options.surface.OptionArrays
 
-::: quantflow.options.surface.OptionMetadata
-
 ::: quantflow.options.surface.OptionPrice
 
 ::: quantflow.options.surface.OptionPrices
@@ -41,6 +37,8 @@
 
 ::: quantflow.options.inputs.OptionType
 
+::: quantflow.options.inputs.OptionMetadata
+
 ::: quantflow.options.inputs.VolSurfaceInputs
 
 ::: quantflow.options.inputs.VolSurfaceInput

docs/api/ta/ewma.md

@@ -1,4 +1,4 @@
 # EWMA
 
 
-::: quantflow.ta.EWMA
+::: quantflow.ta.ewma.EWMA

docs/api/ta/kalman.md

@@ -1,3 +1,3 @@
 # Kalman Filter
 
-::: quantflow.ta.KalmanFilter
+::: quantflow.ta.kalman.KalmanFilter

docs/api/ta/supersmoother.md

@@ -11,4 +11,4 @@ Reference:
     Ehlers, J. (2013). "Cycle Analytics for Traders"
 
 
-::: quantflow.ta.SuperSmoother
+::: quantflow.ta.supersmoother.SuperSmoother

docs/api/utils/price.md

@@ -0,0 +1,5 @@
+## Bid/Ask Prices
+
+::: quantflow.utils.price.Price
+
+::: quantflow.utils.price.PriceVolume

mkdocs.yml

@@ -74,6 +74,7 @@ nav:
           - Black-Scholes: api/options/black.md
           - Calibration: api/options/calibration.md
           - Deep IV Factor Model: api/options/divfm.md
+          - Moneyness: api/options/moneyness.md
           - Put-Call Parity: api/options/parity.md
           - Pricer: api/options/pricer.md
           - SVI Smile: api/options/svi.md
@@ -90,7 +91,7 @@ nav:
           - Poisson Process: api/sp/poisson.md
           - Wiener Process: api/sp/wiener.md
           - Copulas: api/sp/copula.md
-      - Technical Analysis:
+      - Timeseries Analysis:
           - api/ta/index.md
           - EWMA: api/ta/ewma.md
           - Kalman Filter: api/ta/kalman.md
@@ -109,6 +110,7 @@ nav:
           - Distributions: api/utils/distributions.md
           - Marginal 1D: api/utils/marginal1d.md
           - Numbers: api/utils/numbers.md
+          - Price: api/utils/price.md
           - Types: api/utils/types.md
   - Tutorials:
       - tutorials/index.md

pyproject.toml

@@ -33,10 +33,10 @@ readme = "readme.md"
 requires-python = ">=3.11,<3.15"
 dependencies = [
     "scipy>=1.14.1",
+    "pandas>=2.0.0",
     "pydantic>=2.0.2",
     "ccy>=2.0.0",
     "python-dotenv>=1.0.1",
-    "polars[pandas,pyarrow]>=1.11.0",
     "statsmodels>=0.14.6,<0.15.0",
 ]
 

quantflow/options/calibration/base.py

@@ -15,6 +15,7 @@
 from quantflow.utils import plot
 
 from ..bs import implied_black_volatility
+from ..moneyness import log_strike_from_moneyness, moneyness_from_log_strike
 from ..pricer import OptionPricerBase
 from ..surface import OptionPrice, VolSurface
 
@@ -150,7 +151,9 @@ def model_post_init(self, _ctx: Any) -> None:
         entries = tuple(self.options.values())
         self._log_strikes = np.asarray([e.log_strike for e in entries])
         self._ttms = np.asarray([e.ttm for e in entries])
-        self._moneyness = self._log_strikes / np.sqrt(self._ttms)
+        self._moneyness = np.asarray(
+            moneyness_from_log_strike(self._log_strikes, self._ttms)
+        )
         self._mid_prices = np.asarray([e.mid_price() for e in entries])
         self._mid_ivs = np.asarray([e.mid_iv() for e in entries])
 
@@ -223,7 +226,7 @@ def cost_weight(self, ttm: float, log_strike: float) -> float:
         Up-weights wing options via `exp(moneyness_weight * moneyness**2)`,
         capped at `max_cost_weight`. The quadratic form mimics `1/vega`.
         """
-        moneyness = log_strike / np.sqrt(ttm)
+        moneyness = float(moneyness_from_log_strike(log_strike, ttm))
         weight = np.exp(self.moneyness_weight * moneyness * moneyness)
         return float(min(weight, self.max_cost_weight))
 
@@ -281,7 +284,11 @@ def cost_function(self, params: np.ndarray) -> float:
     def _model_grid(
         self, ttm: float, max_moneyness: float, support: int
     ) -> pd.DataFrame:
-        log_strikes = np.linspace(-max_moneyness, max_moneyness, support) * np.sqrt(ttm)
+        log_strikes = np.asarray(
+            log_strike_from_moneyness(
+                np.linspace(-max_moneyness, max_moneyness, support), ttm
+            )
+        )
         return self.pricer.maturity(ttm).prices(log_strikes)
 
     def plot(