Rename implied vols to iv

app/api/heston.py

@@ -13,7 +13,7 @@
 class VolSurfaceGridResponse(BaseModel):
     moneyness: list[float] = Field(description="Moneyness grid values")
     ttm: list[float] = Field(description="Time to maturity grid values")
-    implied_vol: list[list[float]] = Field(
+    iv: list[list[float]] = Field(
         description="Implied vol grid (rows=ttm, cols=moneyness)"
     )
 
@@ -68,13 +68,13 @@ async def heston_vol_surface(
     implied = np.zeros((len(ttm_arr), len(moneyness_arr)))
     for i, t in enumerate(ttm_arr):
         maturity = pricer.maturity(float(t))
-        vols = maturity.prices(moneyness_arr * np.sqrt(t))["implied_vol"].values
+        vols = maturity.prices(moneyness_arr * np.sqrt(t))["iv"].values
         # replace NaN/Inf/negative with 0
         vols = np.where(np.isfinite(vols) & (vols > 0), vols, 0.0)
         implied[i, :] = vols
 
     return VolSurfaceGridResponse(
         moneyness=[float(m) for m in moneyness_arr],
         ttm=[float(t) for t in ttm_arr],
-        implied_vol=[[float(v) for v in row] for row in implied],
+        iv=[[float(v) for v in row] for row in implied],
     )

app/scripts/heston_divfm_fit.py

@@ -85,7 +85,7 @@ def _sample_day(rng: np.random.Generator, pricer: OptionPricer) -> DayData | Non
             np.float32
         )
         moneyness = m_ttm * np.sqrt(ttm)
-        ivs = np.interp(moneyness, mat.moneyness, mat.implied_vols)
+        ivs = np.interp(moneyness, mat.moneyness, mat.ivs)
 
         # drop any degenerate points (NaN / non-positive IV)
         valid = np.isfinite(ivs) & (ivs > 0)
@@ -102,7 +102,7 @@ def _sample_day(rng: np.random.Generator, pricer: OptionPricer) -> DayData | Non
     return DayData(
         moneyness_ttm=np.concatenate(m_list),
         ttm=np.concatenate(t_list),
-        implied_vols=np.concatenate(iv_list),
+        ivs=np.concatenate(iv_list),
     )
 
 

docs/examples/pricing_method_comparison.py

@@ -59,7 +59,7 @@ class PricingMethodComparison(BaseModel):
         description="Chart properties for each pricing method",
     )
 
-    def _implied_vols(
+    def _ivs(
         self, r: OptionPricingResult, log_strikes: np.ndarray, ttm: float
     ) -> np.ndarray:
         call = np.asarray(r.call_price(log_strikes))
@@ -91,7 +91,7 @@ def run_ttm(self) -> None:
                 self.ref_n + 1, max_log_strike=max_log_strike
             )
             ref = ms.call_option(self.ref_n, max_moneyness=self.max_moneyness)
-            iv_ref = self._implied_vols(ref, log_strikes, ttm)
+            iv_ref = self._ivs(ref, log_strikes, ttm)
             moneyness_ref = log_strikes / np.sqrt(ttm)
             ttm_label = f"TTM={ttm}"
             slug = ttm_label.lower().replace("=", "").replace(".", "_")
@@ -136,7 +136,7 @@ def run_ttm(self) -> None:
                         fig.add_trace(
                             go.Scatter(
                                 x=moneyness_ref,
-                                y=self._implied_vols(r, log_strikes, ttm),
+                                y=self._ivs(r, log_strikes, ttm),
                                 name=method.value,
                                 mode="lines",
                                 line=dict(color=props.color, dash=props.dash),

frontend/src/heston-vol-surface.md

@@ -59,8 +59,8 @@ const data = await fetchJson(`/.api/heston-vol-surface?${params}`);
 
 ```js
 const flat = data.ttm.flatMap((t, i) =>
-  data.moneyness.map((m, j) => ({ttm: t.toFixed(2), moneyness: m, implied_vol: data.implied_vol[i][j]}))
-).filter(d => d.implied_vol > 0);
+  data.moneyness.map((m, j) => ({ttm: t.toFixed(2), moneyness: m, iv: data.iv[i][j]}))
+).filter(d => d.iv > 0);
 
 display(Plot.plot({
   width: 800,
@@ -72,7 +72,7 @@ display(Plot.plot({
   y: {label: "Implied Volatility", percent: true},
   color: {type: "ordinal", scheme: "turbo", legend: true, label: "TTM"},
   marks: [
-    Plot.line(flat, {x: "moneyness", y: "implied_vol", stroke: "ttm", strokeWidth: 1.5, tip: true}),
+    Plot.line(flat, {x: "moneyness", y: "iv", stroke: "ttm", strokeWidth: 1.5, tip: true}),
     Plot.ruleX([0], {stroke: "var(--theme-foreground-muted)", strokeDasharray: "4,4"}),
   ]
 }));
@@ -83,8 +83,8 @@ display(Plot.plot({
 ```js
 const atmByTtm = data.ttm.map((t, i) => {
   const midIdx = Math.floor(data.moneyness.length / 2);
-  return {ttm: t, implied_vol: data.implied_vol[i][midIdx]};
-}).filter(d => d.implied_vol > 0);
+  return {ttm: t, iv: data.iv[i][midIdx]};
+}).filter(d => d.iv > 0);
 
 display(Plot.plot({
   width: 800,
@@ -95,8 +95,8 @@ display(Plot.plot({
   x: {label: "Time to Maturity"},
   y: {label: "ATM Implied Volatility", percent: true},
   marks: [
-    Plot.line(atmByTtm, {x: "ttm", y: "implied_vol", stroke: "var(--theme-foreground-focus)", strokeWidth: 2}),
-    Plot.dot(atmByTtm, {x: "ttm", y: "implied_vol", fill: "var(--theme-foreground-focus)", r: 4, tip: true}),
+    Plot.line(atmByTtm, {x: "ttm", y: "iv", stroke: "var(--theme-foreground-focus)", strokeWidth: 2}),
+    Plot.dot(atmByTtm, {x: "ttm", y: "iv", fill: "var(--theme-foreground-focus)", r: 4, tip: true}),
   ]
 }));
 ```

frontend/src/volatility-surface.md

@@ -41,7 +41,7 @@ const enriched = options.map(d => ({
   log_strike: parseFloat(d.log_strike),
   moneyness: parseFloat(d.moneyness),
   ttm: parseFloat(d.ttm),
-  implied_vol: parseFloat(d.implied_vol),
+  iv: parseFloat(d.iv),
   price_bp: parseFloat(d.price_bp),
   open_interest: parseFloat(d.open_interest),
   volume: parseFloat(d.volume),
@@ -123,7 +123,7 @@ display(Plot.plot({
   marks: [
     Plot.dot(smileData, {
       x: xAxis,
-      y: "implied_vol",
+      y: "iv",
       fill: "maturity",
       r: 3,
       opacity: 0.8,
@@ -142,7 +142,7 @@ display(Plot.plot({
 const atmByMaturity = maturities.map(m => {
   const slice = enriched.filter(d => d.maturity === m);
   const atm = slice.reduce((best, d) => Math.abs(d.moneyness) < Math.abs(best.moneyness) ? d : best);
-  return {maturity: m, implied_vol: atm.implied_vol};
+  return {maturity: m, iv: atm.iv};
 });
 
 display(Plot.plot({
@@ -154,10 +154,10 @@ display(Plot.plot({
   x: {label: "Maturity", type: "point"},
   y: {label: "ATM Implied Volatility", percent: true},
   marks: [
-    Plot.line(atmByMaturity, {x: "maturity", y: "implied_vol", stroke: "var(--theme-foreground-focus)", strokeWidth: 2}),
+    Plot.line(atmByMaturity, {x: "maturity", y: "iv", stroke: "var(--theme-foreground-focus)", strokeWidth: 2}),
     Plot.dot(atmByMaturity, {
       x: "maturity",
-      y: "implied_vol",
+      y: "iv",
       fill: "var(--theme-foreground-focus)",
       r: 5,
       tip: true

notebooks/heston_divfm_fit.py

@@ -96,7 +96,7 @@ def _sample_day(rng: np.random.Generator, pricer: OptionPricer) -> DayData | Non
                 np.float32
             )
             moneyness = m_ttm * np.sqrt(ttm)
-            ivs = np.interp(moneyness, mat.moneyness, mat.implied_vols)
+            ivs = np.interp(moneyness, mat.moneyness, mat.ivs)
 
             # drop any degenerate points (NaN / non-positive IV)
             valid = np.isfinite(ivs) & (ivs > 0)
@@ -113,7 +113,7 @@ def _sample_day(rng: np.random.Generator, pricer: OptionPricer) -> DayData | Non
         return DayData(
             moneyness_ttm=np.concatenate(m_list),
             ttm=np.concatenate(t_list),
-            implied_vols=np.concatenate(iv_list),
+            ivs=np.concatenate(iv_list),
         )
 
 

quantflow/ai/tools/crypto.py

@@ -68,7 +68,7 @@ async def crypto_implied_volatility(currency: str, maturity_index: int = -1) ->
         index = None if maturity_index < 0 else maturity_index
         vs.bs(index=index)
         df = vs.options_df(index=index)
-        df["implied_vol"] = df["implied_vol"].map("{:.2%}".format)
+        df["iv"] = df["iv"].map("{:.2%}".format)
         return df.to_csv(index=False)
 
     @mcp.tool()

quantflow/options/calibration/base.py

@@ -52,10 +52,10 @@ class OptionEntry(BaseModel):
     options: list[OptionPrice] = Field(default_factory=list)
     """Bid and ask option prices for this entry"""
 
-    def implied_vol_range(self) -> Bounds:
+    def iv_range(self) -> Bounds:
         """Get the range of implied volatilities across bid and ask"""
-        implied_vols = tuple(option.implied_vol for option in self.options)
-        return Bounds(min(implied_vols), max(implied_vols))
+        ivs = tuple(option.iv for option in self.options)
+        return Bounds(min(ivs), max(ivs))
 
     def mid_price(self) -> float:
         """Mid price as the average of bid and ask call prices"""
@@ -64,7 +64,7 @@ def mid_price(self) -> float:
 
     def mid_iv(self) -> float:
         """Mid implied volatility as the average of bid and ask"""
-        ivs = tuple(option.implied_vol for option in self.options)
+        ivs = tuple(option.iv for option in self.options)
         return sum(ivs) / len(ivs)
 
 
@@ -175,17 +175,17 @@ def ref_date(self) -> datetime:
         return self.vol_surface.ref_date
 
     @property
-    def implied_vols(self) -> np.ndarray:
+    def ivs(self) -> np.ndarray:
         data: list[float] = []
         for entry in self.options.values():
-            data.extend(option.implied_vol for option in entry.options)
+            data.extend(option.iv for option in entry.options)
         return np.asarray(data)
 
-    def implied_vol_range(self) -> Bounds:
+    def iv_range(self) -> Bounds:
         """Range of implied volatilities across all calibration options"""
         return Bounds(
-            min(option.implied_vol_range().lb for option in self.options.values()),
-            max(option.implied_vol_range().ub for option in self.options.values()),
+            min(option.iv_range().lb for option in self.options.values()),
+            max(option.iv_range().ub for option in self.options.values()),
         )
 
     def fit(self) -> OptimizeResult:

quantflow/options/calibration/bns.py

@@ -31,7 +31,7 @@ class BNSCalibration(VolModelCalibration[B], Generic[B]):
     """
 
     def get_bounds(self) -> Bounds:
-        vol_range = self.implied_vol_range()
+        vol_range = self.iv_range()
         vol_lb = 0.5 * vol_range.lb[0]
         vol_ub = 1.5 * vol_range.ub[0]
         v2 = vol_lb**2
@@ -93,7 +93,7 @@ class BNS2Calibration(VolModelCalibration[B2], Generic[B2]):
     """
 
     def get_bounds(self) -> Bounds:
-        vol_range = self.implied_vol_range()
+        vol_range = self.iv_range()
         vol_lb = 0.5 * vol_range.lb[0]
         vol_ub = 1.5 * vol_range.ub[0]
         v2 = vol_lb**2

quantflow/options/calibration/heston.py

@@ -32,7 +32,7 @@ class HestonCalibration(VolModelCalibration[H], Generic[H]):
     )
 
     def get_bounds(self) -> Bounds:
-        vol_range = self.implied_vol_range()
+        vol_range = self.iv_range()
         vol_lb = 0.5 * vol_range.lb[0]
         vol_ub = 1.5 * vol_range.ub[0]
         return Bounds(
@@ -73,7 +73,7 @@ class HestonJCalibration(HestonCalibration[HestonJ[D]], Generic[D]):
 
     def get_bounds(self) -> Bounds:
         base = super().get_bounds()
-        vol_range = self.implied_vol_range()
+        vol_range = self.iv_range()
         vol_lb = 0.5 * vol_range.lb[0]
         vol_ub = 1.5 * vol_range.ub[0]
         lower = list(base.lb) + [1.0, (0.01 * vol_lb) ** 2]
@@ -149,7 +149,7 @@ def maturity_split(self) -> float:
         return ttms[len(ttms) // 2]
 
     def get_bounds(self) -> Bounds:
-        vol_range = self.implied_vol_range()
+        vol_range = self.iv_range()
         vol_lb = 0.5 * vol_range.lb[0]
         vol_ub = 1.5 * vol_range.ub[0]
         v2 = vol_lb**2
@@ -271,7 +271,7 @@ class DoubleHestonJCalibration(DoubleHestonCalibration[DoubleHestonJ[D]], Generi
 
     def get_bounds(self) -> Bounds:
         base = super().get_bounds()
-        vol_range = self.implied_vol_range()
+        vol_range = self.iv_range()
         vol_lb = 0.5 * vol_range.lb[0]
         vol_ub = 1.5 * vol_range.ub[0]
         lower = list(base.lb) + [1.0, (0.01 * vol_lb) ** 2]

quantflow/options/divfm/pricer.py

@@ -100,7 +100,7 @@ def calibrate(
         self,
         moneyness_ttm: FloatArray,
         ttm: FloatArray,
-        implied_vols: FloatArray,
+        ivs: FloatArray,
         extra: FloatArray | None = None,
     ) -> None:
         """Fit daily OLS coefficients from observed implied volatilities.
@@ -118,7 +118,7 @@ def calibrate(
             Shape (N,). Time-scaled moneyness M = log(K/F) / sqrt(tau).
         ttm:
             Shape (N,). Time-to-maturity tau in years.
-        implied_vols:
+        ivs:
             Shape (N,). Observed implied volatilities.
         extra:
             Shape (N, extra_features) or None. Additional features passed to
@@ -130,7 +130,7 @@ def calibrate(
             np.asarray(ttm, dtype=np.float32),
             extra_arr,
         )
-        self.betas = np.linalg.lstsq(F, implied_vols, rcond=None)[0]
+        self.betas = np.linalg.lstsq(F, ivs, rcond=None)[0]
         # Store the mean X across options as the day-level representative value
         # used when pricing on a grid in _compute_maturity
         self.extra = (

quantflow/options/divfm/trainer.py

@@ -25,7 +25,7 @@ class DayData:
     """Shape (N,). Time-scaled moneyness M = log(K/F) / sqrt(tau)."""
     ttm: np.ndarray
     """Shape (N,). Time-to-maturity tau in years."""
-    implied_vols: np.ndarray
+    ivs: np.ndarray
     """Shape (N,). Observed implied volatilities."""
     extra: np.ndarray | None = None
     """Shape (N, extra_features) or None. Additional observable features X."""
@@ -44,7 +44,7 @@ def _day_loss(
     """
     M = torch.tensor(day.moneyness_ttm, dtype=torch.float32)
     T = torch.tensor(day.ttm, dtype=torch.float32)
-    IV = torch.tensor(day.implied_vols, dtype=torch.float32)
+    IV = torch.tensor(day.ivs, dtype=torch.float32)
     extra = (
         torch.tensor(day.extra, dtype=torch.float32) if day.extra is not None else None
     )

quantflow/options/pricer.py

@@ -186,7 +186,7 @@ def prices(self, log_strikes: FloatArray) -> pd.DataFrame:
                 "log_strike": log_strikes,
                 "moneyness": self.moneyness(log_strikes),
                 "call": call_prices,
-                "implied_vol": ivs,
+                "iv": ivs,
                 "time_value": call_prices - np.maximum(0, 1 - np.exp(log_strikes)),
             }
         )
@@ -283,7 +283,7 @@ def plot3d(
         implied = np.zeros((len(ttm), len(moneyness)))
         for i, t in enumerate(ttm):
             maturity = self.maturity(cast(float, t))
-            implied[i, :] = maturity.prices(moneyness * np.sqrt(t))["implied_vol"]
+            implied[i, :] = maturity.prices(moneyness * np.sqrt(t))["iv"]
         properties: dict = dict(
             xaxis_title="moneyness",
             yaxis_title="TTM",
@@ -292,7 +292,7 @@ def plot3d(
             scene=dict(
                 xaxis=dict(title="moneyness"),
                 yaxis=dict(title="TTM"),
-                zaxis=dict(title="implied_vol"),
+                zaxis=dict(title="iv"),
             ),
             scene_camera=scene_camera or dict(eye=dict(x=1.2, y=-1.8, z=0.3)),
             contours=dict(