diff --git a/app/volatility_surface.py b/app/volatility_surface.py
index 7c30ceb..b0ac27e 100644
--- a/app/volatility_surface.py
+++ b/app/volatility_surface.py
@@ -101,12 +101,16 @@ def _(index, pd, surface):
 
 @app.cell
 def _(surface):
-    surface.term_structure()
-    return
+    ts = surface.term_structure()
+    ts
+    return (ts,)
 
 
 @app.cell
-def _():
+def _(ts):
+    from quantflow.utils import plot
+
+    plot.plot_lines(ts, x="ttm", y="rate_percent")
     return
 
 
diff --git a/docs/api/options/svi.md b/docs/api/options/svi.md
new file mode 100644
index 0000000..38d8899
--- /dev/null
+++ b/docs/api/options/svi.md
@@ -0,0 +1,3 @@
+# SVI Volatility Smile
+
+::: quantflow.options.svi.SVI
diff --git a/docs/api/options/vol_surface.md b/docs/api/options/vol_surface.md
index cc52999..666a753 100644
--- a/docs/api/options/vol_surface.md
+++ b/docs/api/options/vol_surface.md
@@ -19,6 +19,8 @@
 
 ::: quantflow.options.surface.FwdPrice
 
+::: quantflow.options.surface.ImpliedFwdPrice
+
 ::: quantflow.options.surface.Strike
 
 ::: quantflow.options.surface.OptionArrays
diff --git a/mkdocs.yml b/mkdocs.yml
index 794c1a4..610fbdc 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -72,6 +72,7 @@ nav:
           - Calibration: api/options/calibration.md
           - Deep IV Factor Model: api/options/divfm.md
           - Pricer: api/options/pricer.md
+          - SVI Smile: api/options/svi.md
           - Volatility Surface: api/options/vol_surface.md
       - Stochastic Processes:
           - api/sp/index.md
diff --git a/pyproject.toml b/pyproject.toml
index 80d8961..8615de7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "quantflow"
-version = "0.6.3"
+version = "0.7.0"
 description = "quantitative analysis"
 authors = [ { name = "Luca Sbardella", email = "luca@quantmind.com" } ]
 license = "BSD-3-Clause"
@@ -91,6 +91,11 @@ theme = "dark"
 asyncio_mode = "auto"
 testpaths = [ "quantflow_tests" ]
 
+[tool.coverage.report]
+exclude_also = [
+    "@abstractmethod",
+]
+
 [tool.isort]
 profile = "black"
 
diff --git a/quantflow/options/surface.py b/quantflow/options/surface.py
index 0bfd16c..fc9447b 100644
--- a/quantflow/options/surface.py
+++ b/quantflow/options/surface.py
@@ -20,7 +20,9 @@
     ZERO,
     DecimalNumber,
     Number,
+    Rounding,
     normalize_decimal,
+    round_to_step,
     sigfig,
     to_decimal,
     to_decimal_or_none,
@@ -40,6 +42,7 @@
     VolSurfaceInputs,
     VolSurfaceSecurity,
 )
+from .svi import SVI
 
 INITIAL_VOL = 0.5
 default_day_counter = DayCounter.ACTACT
@@ -93,7 +96,7 @@ def bp_spread(self) -> Decimal:
         price and multiplied by 10000"""
         mid = self.mid
         if mid > ZERO:
-            return 10000 * self.spread / mid
+            return round(10000 * self.spread / mid, 2)
         else:
             return Decimal("inf")
 
@@ -155,124 +158,94 @@ def moneyness(self, ttm: float) -> float:
     @classmethod
     def aggregate(
         cls,
-        forwards: list[Self],
-        ttm: float,
-        default: FwdPrice[S] | None = None,
-        previous_forward: Decimal | None = None,
+        implied_forwards: Annotated[
+            list[Self], Doc("Implied forward prices from put-call parity")
+        ],
+        ttm: Annotated[float, Doc("Time to maturity in years")],
+        default: Annotated[
+            FwdPrice[S] | None,
+            Doc("Market forward (e.g. from futures) used as fallback or for blending"),
+        ] = None,
+        previous_forward: Annotated[
+            Decimal | None,
+            Doc(
+                "Anchor forward for proximity weighting, "
+                "typically the previous maturity"
+            ),
+        ] = None,
+        tick_size: Annotated[
+            Decimal | None, Doc("Tick size for rounding the implied forward bid/ask")
+        ] = None,
     ) -> FwdPrice[S] | None:
         r"""Aggregate implied forward prices extracted from put-call parity into a
         single best-estimate forward price.
 
-        Each implied forward is an independent noisy estimate of the true forward,
-        obtained at a different strike. Strikes near the money tend to produce the
-        most reliable estimates (tightest bid-ask spreads, smallest put-call parity
-        error), so the aggregation weights each estimate by three independent factors
-        that all reward quality:
+        **Selection**: valid implied forwards are sorted by bid-ask spread in basis
+        points and the tightest 5 are retained as candidates. Let $c$ denote the
+        tightest bp spread among the candidates.
 
-        \begin{equation}
-            w_i = w^{\text{moneyness}}_i
-                  \cdot w^{\text{spread}}_i
-                  \cdot w^{\text{proximity}}_i
-        \end{equation}
+        **Default priority**: if a default forward is provided and its bp spread is
+        tighter than $c$, it is returned immediately as the most reliable price.
 
-        **Moneyness weight** rewards strikes close to the current mid price.
-        It uses the same Gaussian shape as the standard normal density, where
-        $m_i = \log(K_i / F_i) / \sqrt{\tau}$ is the normalised log-moneyness:
+        **Default inclusion**: if the default's bp spread is wider than $c$ but
+        narrower than the worst candidate, it is appended to the candidate pool
+        and weighted on equal footing with the implied forwards.
 
-        \begin{equation}
-            w^{\text{moneyness}}_i = \exp\!\left(-\tfrac{m_i^2}{2}\right)
-        \end{equation}
-
-        **Spread weight** penalises wide bid-ask markets, which indicate either
-        low liquidity or high uncertainty in the put-call parity estimate. It
-        decays exponentially with the bid-ask spread relative to the adaptive
-        cutoff $c$:
+        **Weighting**: each candidate $i$ receives weight
 
         \begin{equation}
-            w^{\text{spread}}_i
-                = \exp\!\left(-\frac{\text{bp\_spread}_i}{c}\right)
+            w_i = w^{\text{spread}}_i \cdot w^{\text{proximity}}_i
         \end{equation}
 
-        **Proximity weight** anchors the result to the previous maturity's forward
-        $F_{\text{prev}}$ when provided. It down-weights estimates whose mid is
-        far from the known anchor, acting as a soft prior that limits how much the
-        result can deviate from a recent reliable observation:
-
-        \begin{equation}
-            w^{\text{proximity}}_i
-                = \exp\!\left(
-                    -\frac{1}{2}
-                    \left(\frac{F_i - F_{\text{prev}}}{F_{\text{prev}}}\right)^{\!2}
-                  \right)
-        \end{equation}
-
-        **Adaptive spread filter**: the cutoff $c$ starts at 10 bp and doubles
-        until at least half the valid forwards survive. This ensures that in
-        illiquid markets with universally wide spreads, the algorithm still
-        produces a result rather than discarding everything.
-
-        **Default blending**: the actual market forward (e.g. from futures) is
-        optionally blended into the weighted average using only the spread weight.
-        This forward may be unreliable (wide spread, stale price), so it receives
-        no moneyness or proximity weight.
-
-        **Default fallback**: if the computed mid lies within one default
-        spread-width of the default mid, the default is returned unchanged,
-        avoiding unnecessary deviation from the observable market price when
-        the implied estimate is consistent with it.
+        where the spread weight is a Gaussian on the normalised distance from the
+        best spread $c$ and the proximity weight, applied only when
+        `previous_forward` is provided.
+        The result is the weighted average of the candidate mid prices, with the
+        bid/ask spread computed as the weighted average of candidate spreads.
+        When `tick_size` is provided the output bid is rounded down and the ask
+        is rounded up to the nearest tick.
         """
-        forwards = [f for f in forwards if f.is_valid()]
+        forwards: list[FwdPrice[S]] = [f for f in implied_forwards if f.is_valid()]
         if not forwards:
             return default
+        forwards = sorted(forwards, key=lambda f: f.bp_spread)[:5]
+        best_bp_spread = forwards[0].bp_spread
+        if (
+            default is not None
+            and default.is_valid()
+            and default.bp_spread < best_bp_spread
+        ):
+            return default
         weights = 0.0
         values = 0.0
         spreads = 0.0
-        target_len = max(1, len(forwards) // 2)
-        cleaned: list[Self] = []
-        spread_bp_cutoff = 10
-        while True:
-            cleaned = [
-                forward for forward in forwards if forward.bp_spread < spread_bp_cutoff
-            ]
-            if len(cleaned) < target_len:
-                spread_bp_cutoff *= 2
-            else:
-                forwards = cleaned
-                break
-        for forward in forwards:
-            m = forward.moneyness(ttm)
-            moneyness_weight = math.exp(-(m**2) / 2)
-            spread_weight = math.exp(-forward.bp_spread / spread_bp_cutoff)
-            if previous_forward is not None:
-                d = float((forward.mid - previous_forward) / previous_forward)
-                proximity_weight = math.exp(-(d**2) / 2)
-            else:
-                proximity_weight = 1.0
-            w = moneyness_weight * spread_weight * proximity_weight
-            weights += w
-            values += w * float(forward.mid)
-            spreads += w * float(forward.spread)
+        worse_bp_spread = forwards[-1].bp_spread
         if (
             default is not None
             and default.is_valid()
-            and default.bp_spread < spread_bp_cutoff
+            and default.bp_spread < worse_bp_spread
         ):
-            w = math.exp(-10000 * float(default.spread) / float(default.mid))
-            weights += w
-            values += w * float(default.mid)
-            spreads += w * float(default.spread)
+            forwards.append(default)
+        for forward in forwards:
+            s = (forward.bp_spread - best_bp_spread) / best_bp_spread
+            weight = math.exp(-s * s)
+            if previous_forward is not None:
+                d = (forward.mid - previous_forward) / previous_forward
+                weight *= math.exp(-d * d)
+            weights += weight
+            values += weight * float(forward.mid)
+            spreads += weight * float(forward.spread)
         mid = to_decimal(values / weights)
         spread = to_decimal(spreads / weights)
-        if (
-            default is not None
-            and default.is_valid()
-            and abs(mid - default.mid) / default.spread < 1
-        ):
-            return default
+        bid = mid - spread / 2
+        ask = mid + spread / 2
+        if tick_size is not None:
+            bid = round_to_step(bid, tick_size, Rounding.DOWN)
+            ask = round_to_step(ask, tick_size, Rounding.UP)
         return FwdPrice(
             security=forwards[0].security.forward(),
-            bid=mid - spread / 2,
-            ask=mid + spread / 2,
+            bid=bid,
+            ask=ask,
             maturity=forwards[0].maturity,
         )
 
@@ -534,6 +507,11 @@ def mid(self) -> Decimal:
         """Calculate the mid option price by averaging the bid and ask prices"""
         return (self.bid.price + self.ask.price) / 2
 
+    @property
+    def spread(self) -> Decimal:
+        """Calculate the bid-ask spread"""
+        return self.ask.price - self.bid.price
+
     def iv_bid_ask_spread(self) -> float:
         """Calculate the bid-ask spread of the implied volatility"""
         return self.ask.implied_vol - self.bid.implied_vol
@@ -604,10 +582,18 @@ class Strike(BaseModel, Generic[S]):
         default=None, description="Put option prices for the strike"
     )
 
-    def implied_forward(self) -> ImpliedFwdPrice[S] | None:
+    def implied_forward(
+        self,
+        tick_size: Annotated[
+            Decimal | None, Doc("Tick size for rounding the implied forward bid/ask")
+        ] = None,
+    ) -> ImpliedFwdPrice[S] | None:
         r"""Extract the implied forward price from put-call parity.
 
-        Requires both a call and a put at this strike. Uses mid prices.
+        Requires both a call and a put at this strike. Uses bid/ask prices
+        to construct the bid/ask of the implied forward. When `tick_size` is
+        provided, bid is rounded down and ask is rounded up to the nearest tick.
+
         For inverse options (prices quoted in the underlying currency)
         put-call parity reads
 
@@ -642,6 +628,9 @@ def implied_forward(self) -> ImpliedFwdPrice[S] | None:
                 return None
         if bid > ask:
             return None
+        if tick_size is not None:
+            bid = round_to_step(bid, tick_size, Rounding.DOWN)
+            ask = round_to_step(ask, tick_size, Rounding.UP)
         return ImpliedFwdPrice(
             security=self.call.security.forward(),
             strike=self.strike,
@@ -781,6 +770,7 @@ def info_dict(self, ref_date: datetime, spot: SpotPrice[S]) -> dict:
             maturity=self.maturity,
             ttm=self.ttm(ref_date),
             forward=self.forward.mid,
+            bid_ask_spread=self.forward.spread,
             basis=self.forward.mid - spot.mid,
             rate_percent=self.forward_rate(ref_date, spot).percent,
             fwd_spread_pct=round(100 * self.forward_spread_fraction(), 4),
@@ -859,17 +849,23 @@ def option_securities(
     def disable_outliers(
         self,
         *,
-        bid_ask_spread: Annotated[
+        ttm: Annotated[float, Doc("Time to maturity in years, used for SVI fitting")],
+        bid_ask_spread_fraction: Annotated[
             float,
             Doc(
-                "Maximum allowed bid/ask spread as a fraction of the mid "
-                "implied volatility. A value of 0.3 means that options with a bid/ask"
-                "spread greater than 30% of the mid implied volatility will be "
-                "considered outliers and have their implied volatility convergence "
-                "set to False",
+                "Maximum allowed bid/ask spread as a fraction of the mid implied "
+                "volatility. A value of 0.2 means options with a spread greater than "
+                "20% of the mid vol are disabled."
             ),
-        ] = 0.3,
-        quantile: Annotated[float, Doc("Quantile for determining outliers")] = 0.99,
+        ] = 0.2,
+        svi_residual_fraction: Annotated[
+            float,
+            Doc(
+                "Maximum allowed SVI residual as a fraction of the mid implied "
+                "volatility. A value of 0.2 means options whose mid vol deviates "
+                "from the SVI fit by more than 20% of their mid vol are disabled."
+            ),
+        ] = 0.2,
         repeat: Annotated[
             int, Doc("Number of times to repeat the outlier removal process")
         ] = 2,
@@ -879,17 +875,17 @@ def disable_outliers(
 
         Two passes are applied:
 
-
         First pass: options where the bid/ask spread in implied vol space exceeds
-        `bid_ask_spread` as a fraction of the mid implied vol are disabled.
-        For example, a value of 0.3 disables options where the spread is more
-        than 30% of the mid vol. Options with a zero mid vol are also disabled.
-
-        Second pass: a degree-2 polynomial is fitted to the smile (mid implied vol
-        vs log-moneyness). Options whose residual from the fit exceeds the
-        `quantile` threshold of all residuals are disabled. This is repeated up
-        to `repeat` times, refitting after each removal. The loop stops early if
-        no outliers are found or fewer than 4 options remain.
+        `bid_ask_spread_fraction` of the mid implied vol are disabled.
+        For example, a value of 0.2 disables options where the spread is more
+        than 20% of the mid vol. Options with a zero mid vol are also disabled.
+
+        Second pass: an [SVI][quantflow.options.svi.SVI] smile is fitted to the
+        surviving options (mid implied vol vs log-moneyness). Options whose
+        residual from the SVI fit exceeds `svi_residual_fraction` of their mid
+        implied vol are disabled. This is repeated up to `repeat` times,
+        refitting after each removal. The loop stops early if no outliers are
+        found or fewer than 5 options remain.
         """
         options = list(self.option_securities(converged=True))
         # first remove options with high bid/offer spread
@@ -897,24 +893,27 @@ def disable_outliers(
             spread = option.iv_bid_ask_spread()
             mid = option.iv_mid()
             if mid > 0:
-                if spread / mid > bid_ask_spread:
+                if spread / mid > bid_ask_spread_fraction:
                     option.disable()
             else:
                 option.disable()
-        # remove outliers based on residuals from a quadratic smile fit
+        # remove outliers based on residuals from an SVI smile fit
         forward = float(self.forward.mid)
         for _ in range(repeat):
             options = list(self.option_securities(converged=True))
-            if len(options) < 4:
+            if len(options) < 5:
                 break
             log_m = np.array([np.log(float(o.meta.strike) / forward) for o in options])
             iv_mid = np.array([o.iv_mid() for o in options])
-            coeffs = np.polyfit(log_m, iv_mid, 2)
-            residuals = np.abs(iv_mid - np.polyval(coeffs, log_m))
-            threshold = np.quantile(residuals, quantile)
+            try:
+                svi = SVI.fit(log_m, iv_mid, ttm)
+            except Exception:
+                break
+            iv_fit = svi.implied_vol(log_m, ttm)
+            residuals = np.abs(iv_mid - iv_fit) / iv_mid
             found = False
             for option, residual in zip(options, residuals):
-                if residual > threshold:
+                if residual > svi_residual_fraction:
                     option.disable()
                     found = True
             if not found:
@@ -1250,17 +1249,22 @@ def reset_convergence(self) -> None:
     def disable_outliers(
         self,
         *,
-        bid_ask_spread: Annotated[
+        bid_ask_spread_fraction: Annotated[
             float,
             Doc(
-                "Maximum allowed bid/ask spread as a fraction of the mid "
-                "implied volatility. A value of 0.3 means that options with a bid/ask"
-                "spread greater than 30% of the mid implied volatility will be "
-                "considered outliers and have their implied volatility convergence "
-                "set to False",
+                "Maximum allowed bid/ask spread as a fraction of the mid implied "
+                "volatility. A value of 0.2 means options with a spread greater than "
+                "20% of the mid vol are disabled."
             ),
-        ] = 0.3,
-        quantile: Annotated[float, Doc("Quantile for determining outliers")] = 0.99,
+        ] = 0.2,
+        svi_residual_fraction: Annotated[
+            float,
+            Doc(
+                "Maximum allowed SVI residual as a fraction of the mid implied "
+                "volatility. A value of 0.2 means options whose mid vol deviates "
+                "from the SVI fit by more than 20% of their mid vol are disabled."
+            ),
+        ] = 0.2,
         repeat: Annotated[
             int, Doc("Number of times to repeat the outlier removal process")
         ] = 2,
@@ -1274,74 +1278,13 @@ def disable_outliers(
         """
         for maturity in self.maturities:
             maturity.disable_outliers(
-                bid_ask_spread=bid_ask_spread, quantile=quantile, repeat=repeat
+                ttm=maturity.ttm(self.ref_date),
+                bid_ask_spread_fraction=bid_ask_spread_fraction,
+                svi_residual_fraction=svi_residual_fraction,
+                repeat=repeat,
             )
         return self
 
-    def calibrate_forwards(
-        self,
-        *,
-        max_spread_fraction: Annotated[
-            float,
-            Doc(
-                "Maximum allowed forward bid-ask spread as a fraction of the mid "
-                "price. Forwards exceeding this threshold are considered unreliable "
-                "and replaced with a synthetic price derived from interpolated rates. "
-                "A value of 0.05 flags forwards whose spread is more than 5% of mid."
-            ),
-        ] = 0.05,
-    ) -> Self:
-        """Replace forwards with wide bid-ask spreads with synthetic prices
-        interpolated from the smooth rate term structure.
-
-        For each maturity the implied continuous rate is computed as
-        `r = log(F_mid / S) / T`. Maturities whose forward bid-ask spread
-        exceeds `max_spread_fraction` of the mid are treated as unreliable.
-        A piecewise-linear interpolation (with flat extrapolation at the
-        boundaries) is fitted through the reliable `(T, r)` pairs, and the
-        synthetic forward is:
-
-        `F_synth = S * exp(r_interp * T)`
-
-        The synthetic bid and ask are both set to this value, giving a
-        zero spread, and the cross-section forward is replaced accordingly.
-        Returns a new `VolSurface` instance leaving the original unchanged.
-        """
-        spot = self.spot.mid
-        max_spread = to_decimal(max_spread_fraction)
-        good_ttms: list[float] = []
-        good_rates: list[float] = []
-        bad_indices: list[int] = []
-
-        for i, cross in enumerate(self.maturities):
-            ttm = cross.ttm(self.ref_date)
-            spread_frac = cross.forward_spread_fraction()
-            rate = cross.forward_rate(self.ref_date, self.spot)
-            if ttm > 0 and spread_frac <= max_spread:
-                good_ttms.append(ttm)
-                good_rates.append(float(rate.rate))
-            else:
-                bad_indices.append(i)
-
-        if not good_ttms or not bad_indices:
-            return self
-
-        ttm_arr = np.array(good_ttms)
-        rate_arr = np.array(good_rates)
-
-        new_maturities = list(self.maturities)
-        for i in bad_indices:
-            cross = self.maturities[i]
-            ttm = cross.ttm(self.ref_date)
-            if ttm <= 0:
-                continue
-            r_synth = float(np.interp(ttm, ttm_arr, rate_arr))
-            f_synth = to_decimal(float(spot) * math.exp(r_synth * ttm))
-            new_fwd = cross.forward.model_copy(update=dict(bid=f_synth, ask=f_synth))
-            new_maturities[i] = cross.model_copy(update=dict(forward=new_fwd))
-
-        return self.model_copy(update=dict(maturities=tuple(new_maturities)))
-
     def plot(
         self,
         *,
@@ -1443,6 +1386,10 @@ def cross_section(
                 "or unreliable forwards"
             ),
         ] = None,
+        tick_size: Annotated[
+            Decimal | None,
+            Doc("Tick size for rounding implied forward bid/ask prices"),
+        ] = None,
     ) -> VolCrossSection[S] | None:
         strikes = []
         implied_forwards = []
@@ -1450,7 +1397,7 @@ def cross_section(
             sk = self.strikes[strike]
             if sk.call is None and sk.put is None:
                 continue
-            if implied_forward := sk.implied_forward():
+            if implied_forward := sk.implied_forward(tick_size=tick_size):
                 implied_forwards.append(implied_forward)
             strikes.append(sk)
         forward = self.forward
@@ -1461,6 +1408,7 @@ def cross_section(
                 ttm,
                 default=self.forward,
                 previous_forward=previous_forward,
+                tick_size=tick_size,
             )
         if forward is None or not forward.is_valid():
             return None
@@ -1618,6 +1566,7 @@ def surface(
             if section := self.maturities[maturity].cross_section(
                 ref_date=ref_date,
                 previous_forward=previous_forward,
+                tick_size=self.tick_size_forwards,
             ):
                 previous_forward = section.forward.mid
                 maturities.append(section)
diff --git a/quantflow/options/svi.py b/quantflow/options/svi.py
new file mode 100644
index 0000000..1215552
--- /dev/null
+++ b/quantflow/options/svi.py
@@ -0,0 +1,144 @@
+from __future__ import annotations
+
+from decimal import Decimal
+
+import numpy as np
+from numpy.typing import ArrayLike
+from pydantic import BaseModel, Field
+from scipy.optimize import least_squares
+from typing_extensions import Annotated, Doc
+
+from quantflow.utils.numbers import ONE, ZERO, to_decimal
+
+
+class SVI(BaseModel, extra="forbid"):
+    r"""Gatheral's Stochastic Volatility Inspired (SVI) parametrisation of the
+    implied volatility smile.
+
+    The raw SVI parametrisation expresses the total implied variance
+    $w(k) = \sigma^2(k) \cdot \tau$ as a function of log-strike
+    $k = \log(K/F)$:
+
+    \begin{equation}
+        w(k) = a + b \left[\rho (k - m) + \sqrt{(k - m)^2 + \theta^2}\right]
+    \end{equation}
+
+    References:
+        Gatheral, J. (2004). A parsimonious arbitrage-free implied volatility
+        parametrization with application to the valuation of volatility
+        derivatives. Global Derivatives, Madrid.
+
+        Gatheral, J. and Jacquier, A. (2014). Arbitrage-free SVI volatility
+        surfaces. Quantitative Finance, 14(1), 59-71.
+    """
+
+    a: Decimal = Field(
+        description=(
+            "Vertical shift of the smile: overall level of total implied variance. "
+            "Must satisfy $a + b \\theta \\sqrt{1 - \\rho^2} \\geq 0$ to "
+            "avoid negative variance."
+        )
+    )
+    b: Decimal = Field(
+        ge=ZERO,
+        description=(
+            "Angle between the left and right asymptotes of the smile. "
+            "Controls the overall steepness of the wings. Must be non-negative."
+        ),
+    )
+    rho: Decimal = Field(
+        gt=-ONE,
+        lt=ONE,
+        description=(
+            "Correlation parameter controlling the skew of the smile. "
+            "Negative values produce a left-skewed smile (typical for equities), "
+            "positive values produce a right skew. Must satisfy $|\\rho| < 1$."
+        ),
+    )
+    m: Decimal = Field(
+        description=(
+            "Location parameter: the log-moneyness at the vertex of the smile. "
+            "Shifts the smile horizontally. A value of zero centres the smile "
+            "at the forward."
+        )
+    )
+    theta: Decimal = Field(
+        gt=ZERO,
+        description=(
+            "Smoothness parameter controlling the curvature of the smile around "
+            "the vertex. Larger values produce a flatter region near $m$. "
+            "Must be strictly positive."
+        ),
+    )
+
+    def total_variance(
+        self,
+        k: Annotated[ArrayLike, Doc("Log-moneyness log(K/F), scalar or array")],
+    ) -> np.ndarray:
+        r"""Total implied variance $w(k)$.
+
+        Returns an array of the same shape as $k$.
+        """
+        k_arr = np.asarray(k, dtype=float)
+        a = float(self.a)
+        b = float(self.b)
+        rho = float(self.rho)
+        m = float(self.m)
+        theta = float(self.theta)
+        km = k_arr - m
+        return a + b * (rho * km + np.sqrt(km**2 + theta**2))
+
+    def implied_vol(
+        self,
+        k: Annotated[ArrayLike, Doc("Log-moneyness log(K/F), scalar or array")],
+        ttm: Annotated[float, Doc("Time to maturity in years")],
+    ) -> np.ndarray:
+        r"""Implied volatility $\sigma(k) = \sqrt{w(k) / \tau}$.
+
+        Returns an array of the same shape as $k$. Values are set to zero where
+        total variance is non-positive.
+        """
+        w = self.total_variance(k)
+        return np.where(w > 0, np.sqrt(np.maximum(w, 0.0) / ttm), 0.0)
+
+    @classmethod
+    def fit(
+        cls,
+        k: Annotated[ArrayLike, Doc("Log-moneyness log(K/F) for each option")],
+        iv: Annotated[ArrayLike, Doc("Observed implied volatilities")],
+        ttm: Annotated[float, Doc("Time to maturity in years")],
+    ) -> SVI:
+        """Fit SVI smile to observed implied volatilities via non-linear least squares.
+
+        Minimises the sum of squared differences between observed and model
+        total variances.
+        """
+        k_arr = np.asarray(k, dtype=float)
+        iv_arr = np.asarray(iv, dtype=float)
+        w_obs = iv_arr**2 * ttm
+
+        atm_var = float(np.interp(0.0, k_arr, w_obs)) if k_arr.size else w_obs.mean()
+        x0 = [atm_var * 0.9, 0.1, 0.0, 0.0, 0.1]
+
+        def residuals(x: list[float]) -> np.ndarray:
+            a, b, rho, m, theta = x
+            km = k_arr - m
+            w_fit = a + b * (rho * km + np.sqrt(km**2 + theta**2))
+            return w_fit - w_obs
+
+        result = least_squares(
+            residuals,
+            x0,
+            bounds=(
+                [-np.inf, 0.0, -1.0 + 1e-6, -np.inf, 1e-6],
+                [np.inf, np.inf, 1.0 - 1e-6, np.inf, np.inf],
+            ),
+        )
+        a, b, rho, m, theta = result.x
+        return cls(
+            a=to_decimal(round(a, 10)),
+            b=to_decimal(round(b, 10)),
+            rho=to_decimal(round(rho, 10)),
+            m=to_decimal(round(m, 10)),
+            theta=to_decimal(round(theta, 10)),
+        )
diff --git a/quantflow_tests/test_disable_outliers.py b/quantflow_tests/test_disable_outliers.py
new file mode 100644
index 0000000..aa33638
--- /dev/null
+++ b/quantflow_tests/test_disable_outliers.py
@@ -0,0 +1,215 @@
+"""Tests for VolCrossSection.disable_outliers with synthetic cross sections."""
+
+from __future__ import annotations
+
+from datetime import datetime, timezone
+from decimal import Decimal
+
+import numpy as np
+
+from quantflow.options.inputs import DefaultVolSecurity
+from quantflow.options.surface import (
+    FwdPrice,
+    OptionMetadata,
+    OptionPrice,
+    OptionPrices,
+    OptionType,
+    Side,
+    Strike,
+    VolCrossSection,
+)
+from quantflow.options.svi import SVI
+
+MATURITY = datetime(2026, 12, 31, tzinfo=timezone.utc)
+TTM = 1.0
+FORWARD = Decimal("100")
+SECURITY = DefaultVolSecurity.option()
+FWD_SECURITY = DefaultVolSecurity.forward()
+
+
+def _make_option(
+    strike: float,
+    iv_mid: float,
+    iv_spread_fraction: float,
+    option_type: OptionType = OptionType.call,
+) -> OptionPrices[DefaultVolSecurity]:
+    iv_bid = iv_mid * (1 - iv_spread_fraction / 2)
+    iv_ask = iv_mid * (1 + iv_spread_fraction / 2)
+    meta = OptionMetadata(
+        strike=Decimal(str(strike)),
+        option_type=option_type,
+        maturity=MATURITY,
+        forward=FORWARD,
+        ttm=TTM,
+        inverse=False,
+    )
+    bid = OptionPrice(
+        price=Decimal("0.01"),
+        meta=meta,
+        implied_vol=iv_bid,
+        side=Side.bid,
+        converged=True,
+    )
+    ask = OptionPrice(
+        price=Decimal("0.02"),
+        meta=meta,
+        implied_vol=iv_ask,
+        side=Side.ask,
+        converged=True,
+    )
+    return OptionPrices(security=SECURITY, meta=meta, bid=bid, ask=ask)
+
+
+def _make_cross(
+    strikes: list[float],
+    iv_mids: list[float],
+    iv_spread_fractions: list[float],
+) -> VolCrossSection[DefaultVolSecurity]:
+    strike_objs = [
+        Strike(
+            strike=Decimal(str(k)),
+            call=_make_option(k, iv, sp),
+        )
+        for k, iv, sp in zip(strikes, iv_mids, iv_spread_fractions)
+    ]
+    fwd = FwdPrice(
+        security=FWD_SECURITY,
+        bid=FORWARD,
+        ask=FORWARD,
+        maturity=MATURITY,
+    )
+    return VolCrossSection(maturity=MATURITY, forward=fwd, strikes=tuple(strike_objs))
+
+
+def _converged_count(cross: VolCrossSection) -> int:
+    return sum(1 for _ in cross.option_securities(converged=True))
+
+
+# ---------------------------------------------------------------------------
+# pass 1: spread filter
+# ---------------------------------------------------------------------------
+
+
+def test_pass1_disables_wide_spread_option() -> None:
+    strikes = [90.0, 95.0, 100.0, 105.0, 110.0]
+    iv_mids = [0.25, 0.22, 0.20, 0.22, 0.25]
+    # last option has spread fraction 0.4 — above the 0.2 threshold
+    iv_spreads = [0.05, 0.05, 0.05, 0.05, 0.40]
+    cross = _make_cross(strikes, iv_mids, iv_spreads)
+    assert _converged_count(cross) == 5
+    cross.disable_outliers(ttm=TTM, svi_residual_fraction=100.0)
+    assert _converged_count(cross) == 4
+
+
+def test_pass1_disables_all_options_above_threshold() -> None:
+    strikes = [90.0, 95.0, 100.0, 105.0, 110.0]
+    iv_mids = [0.25, 0.22, 0.20, 0.22, 0.25]
+    iv_spreads = [0.50, 0.50, 0.05, 0.50, 0.50]
+    cross = _make_cross(strikes, iv_mids, iv_spreads)
+    cross.disable_outliers(ttm=TTM, svi_residual_fraction=100.0)
+    assert _converged_count(cross) == 1
+
+
+def test_pass1_keeps_options_below_threshold() -> None:
+    strikes = [90.0, 95.0, 100.0, 105.0, 110.0]
+    iv_mids = [0.25, 0.22, 0.20, 0.22, 0.25]
+    # all at 0.10 fraction — well below the 0.20 threshold
+    iv_spreads = [0.10, 0.10, 0.10, 0.10, 0.10]
+    cross = _make_cross(strikes, iv_mids, iv_spreads)
+    cross.disable_outliers(ttm=TTM, svi_residual_fraction=100.0)
+    assert _converged_count(cross) == 5
+
+
+def test_pass1_disables_zero_mid_vol() -> None:
+    strikes = [90.0, 95.0, 100.0, 105.0, 110.0]
+    # one option has zero mid vol
+    iv_mids = [0.25, 0.22, 0.0, 0.22, 0.25]
+    iv_spreads = [0.05, 0.05, 0.05, 0.05, 0.05]
+    cross = _make_cross(strikes, iv_mids, iv_spreads)
+    cross.disable_outliers(ttm=TTM, svi_residual_fraction=100.0)
+    assert _converged_count(cross) == 4
+
+
+# ---------------------------------------------------------------------------
+# pass 2: SVI outlier filter
+# ---------------------------------------------------------------------------
+
+
+def _svi_smile(
+    strikes: list[float],
+    ttm: float = TTM,
+    forward: float = float(FORWARD),
+) -> list[float]:
+    svi = SVI(
+        a=Decimal("0.04"),
+        b=Decimal("0.1"),
+        rho=Decimal("-0.2"),
+        m=Decimal("0.0"),
+        theta=Decimal("0.15"),
+    )
+    k = np.log(np.array(strikes) / forward)
+    return svi.implied_vol(k, ttm).tolist()
+
+
+def test_pass2_disables_svi_outlier() -> None:
+    strikes = [80.0, 85.0, 90.0, 95.0, 100.0, 105.0, 110.0, 115.0, 120.0]
+    iv_mids = _svi_smile(strikes)
+    # inject a large outlier at strike 100 (ATM)
+    iv_mids[4] *= 2.0
+    iv_spreads = [0.05] * len(strikes)
+    cross = _make_cross(strikes, iv_mids, iv_spreads)
+    assert _converged_count(cross) == 9
+    cross.disable_outliers(
+        ttm=TTM, bid_ask_spread_fraction=1.0, svi_residual_fraction=0.2
+    )
+    assert _converged_count(cross) == 8
+
+
+def test_pass2_clean_smile_not_disabled() -> None:
+    strikes = [80.0, 85.0, 90.0, 95.0, 100.0, 105.0, 110.0, 115.0, 120.0]
+    iv_mids = _svi_smile(strikes)
+    iv_spreads = [0.05] * len(strikes)
+    cross = _make_cross(strikes, iv_mids, iv_spreads)
+    cross.disable_outliers(
+        ttm=TTM, bid_ask_spread_fraction=1.0, svi_residual_fraction=0.2
+    )
+    assert _converged_count(cross) == 9
+
+
+def test_pass2_does_not_run_with_fewer_than_5_options() -> None:
+    strikes = [95.0, 97.5, 100.0, 102.5, 105.0]
+    iv_mids = _svi_smile(strikes)
+    # discard one so only 4 survive pass 1
+    iv_spreads = [0.05, 0.05, 0.05, 0.05, 0.90]
+    cross = _make_cross(strikes, iv_mids, iv_spreads)
+    # 4 remain after pass 1 — pass 2 should not run
+    cross.disable_outliers(ttm=TTM, svi_residual_fraction=0.01)
+    # the remaining 4 should be untouched by pass 2
+    assert _converged_count(cross) == 4
+
+
+def test_pass2_repeat_removes_multiple_outliers() -> None:
+    strikes = [80.0, 85.0, 90.0, 95.0, 100.0, 105.0, 110.0, 115.0, 120.0]
+    iv_mids = _svi_smile(strikes)
+    # inject two large outliers
+    iv_mids[1] *= 3.0
+    iv_mids[7] *= 3.0
+    iv_spreads = [0.05] * len(strikes)
+    cross = _make_cross(strikes, iv_mids, iv_spreads)
+    cross.disable_outliers(
+        ttm=TTM, bid_ask_spread_fraction=1.0, svi_residual_fraction=0.2, repeat=3
+    )
+    # at least the two injected outliers must be removed
+    assert _converged_count(cross) <= 7
+
+
+def test_pass2_huge_threshold_removes_nothing() -> None:
+    strikes = [80.0, 85.0, 90.0, 95.0, 100.0, 105.0, 110.0, 115.0, 120.0]
+    iv_mids = _svi_smile(strikes)
+    iv_mids[4] *= 5.0  # extreme outlier
+    iv_spreads = [0.05] * len(strikes)
+    cross = _make_cross(strikes, iv_mids, iv_spreads)
+    cross.disable_outliers(
+        ttm=TTM, bid_ask_spread_fraction=1.0, svi_residual_fraction=100.0
+    )
+    assert _converged_count(cross) == 9
diff --git a/quantflow_tests/test_implied_fwd.py b/quantflow_tests/test_implied_fwd.py
index 132b8d9..fbb71cd 100644
--- a/quantflow_tests/test_implied_fwd.py
+++ b/quantflow_tests/test_implied_fwd.py
@@ -5,6 +5,7 @@
 from datetime import datetime, timezone
 from decimal import Decimal
 
+import pytest
 from hypothesis import given
 from hypothesis import strategies as st
 
@@ -132,13 +133,15 @@ def test_aggregate_with_previous_forward(
     assert min(mids) - 1e-4 <= float(result.mid) <= max(mids) + 1e-4
 
 
-def test_aggregate_default_returned_when_implied_agrees() -> None:
-    # implied forwards all near 100, default also at 100 with tight spread
-    # result mid ≈ 100, |result - default.mid| / default.spread < 1 → return default
+def test_aggregate_implied_tighter_than_default_uses_implied() -> None:
+    # implied forwards (5 bp) are tighter than the default (20 bp)
+    # the default is not included in the candidate pool, result comes from implied
     forwards = [make_implied(100, 5), make_implied(100, 5)]
     default = make_fwd(100, 20)
     result = ImpliedFwdPrice.aggregate(forwards, ttm=1.0, default=default)
-    assert result is default
+    assert result is not None
+    assert result is not default
+    assert float(result.mid) == pytest.approx(100.0, rel=1e-3)
 
 
 def test_aggregate_previous_forward_pulls_result_toward_anchor() -> None:
diff --git a/quantflow_tests/test_options.py b/quantflow_tests/test_options.py
index 6700a5c..7337d4e 100644
--- a/quantflow_tests/test_options.py
+++ b/quantflow_tests/test_options.py
@@ -85,6 +85,7 @@ def test_term_structure(vol_surface: VolSurface) -> None:
         "maturity",
         "ttm",
         "forward",
+        "bid_ask_spread",
         "basis",
         "rate_percent",
         "fwd_spread_pct",
diff --git a/quantflow_tests/test_surface_methods.py b/quantflow_tests/test_surface_methods.py
new file mode 100644
index 0000000..966dd5d
--- /dev/null
+++ b/quantflow_tests/test_surface_methods.py
@@ -0,0 +1,82 @@
+"""Tests for VolCrossSection.disable_outliers and VolSurface.calibrate_forwards."""
+
+from __future__ import annotations
+
+import pytest
+
+from quantflow.options.surface import VolSurface, VolSurfaceInputs, surface_from_inputs
+from quantflow_tests.utils import load_fixture_dict
+
+
+@pytest.fixture
+def vol_surface() -> VolSurface:
+    inputs = load_fixture_dict("volsurface.json")
+    return surface_from_inputs(VolSurfaceInputs(**inputs))
+
+
+# ---------------------------------------------------------------------------
+# disable_outliers
+# ---------------------------------------------------------------------------
+
+
+def test_disable_outliers_runs_without_error(vol_surface: VolSurface) -> None:
+    for cross in vol_surface.maturities:
+        cross.disable_outliers(ttm=cross.ttm(vol_surface.ref_date))
+
+
+def test_disable_outliers_never_increases_converged_count(
+    vol_surface: VolSurface,
+) -> None:
+    for cross in vol_surface.maturities:
+        before = sum(1 for _ in cross.option_securities(converged=True))
+        cross.disable_outliers(ttm=cross.ttm(vol_surface.ref_date))
+        after = sum(1 for _ in cross.option_securities(converged=True))
+        assert after <= before
+
+
+def test_disable_outliers_surface_level(vol_surface: VolSurface) -> None:
+    before = sum(
+        1
+        for cross in vol_surface.maturities
+        for _ in cross.option_securities(converged=True)
+    )
+    vol_surface.disable_outliers()
+    after = sum(
+        1
+        for cross in vol_surface.maturities
+        for _ in cross.option_securities(converged=True)
+    )
+    assert after <= before
+
+
+def test_disable_outliers_removes_zero_mid_vol(vol_surface: VolSurface) -> None:
+    cross = vol_surface.maturities[0]
+    # inject a zero mid vol by disabling then re-enabling a strike whose call
+    # has zero prices — just verify that any already-zero mid options get caught
+    cross.disable_outliers(ttm=cross.ttm(vol_surface.ref_date))
+    for option in cross.option_securities(converged=True):
+        assert option.iv_mid() > 0
+
+
+def test_disable_outliers_removes_wide_spread_options(vol_surface: VolSurface) -> None:
+    cross = vol_surface.maturities[0]
+    # with a very tight bid_ask_spread_fraction almost everything gets removed
+    cross.disable_outliers(
+        ttm=cross.ttm(vol_surface.ref_date), bid_ask_spread_fraction=0.001
+    )
+    for option in cross.option_securities(converged=True):
+        spread = option.iv_bid_ask_spread()
+        mid = option.iv_mid()
+        assert mid == 0 or spread / mid <= 0.001 + 1e-9
+
+
+def test_disable_outliers_svi_pass_respects_threshold(vol_surface: VolSurface) -> None:
+    cross = vol_surface.maturities[0]
+    ttm = cross.ttm(vol_surface.ref_date)
+    # with a huge svi_residual_fraction nothing is removed in pass 2
+    before = sum(1 for _ in cross.option_securities(converged=True))
+    cross.disable_outliers(
+        ttm=ttm, bid_ask_spread_fraction=1.0, svi_residual_fraction=100.0
+    )
+    after = sum(1 for _ in cross.option_securities(converged=True))
+    assert after == before
diff --git a/quantflow_tests/test_svi.py b/quantflow_tests/test_svi.py
new file mode 100644
index 0000000..450d7cd
--- /dev/null
+++ b/quantflow_tests/test_svi.py
@@ -0,0 +1,136 @@
+from __future__ import annotations
+
+from decimal import Decimal
+
+import numpy as np
+import pytest
+
+from quantflow.options.svi import SVI
+
+TTM = 1.0
+K = np.linspace(-0.5, 0.5, 21)
+
+
+def make_svi(**kwargs: float) -> SVI:
+    defaults = dict(a=0.04, b=0.1, rho=-0.3, m=0.0, theta=0.2)
+    defaults.update(kwargs)
+    return SVI(**{k: Decimal(str(v)) for k, v in defaults.items()})
+
+
+# ---------------------------------------------------------------------------
+# total_variance
+# ---------------------------------------------------------------------------
+
+
+def test_total_variance_is_positive() -> None:
+    svi = make_svi()
+    w = svi.total_variance(K)
+    assert np.all(w > 0)
+
+
+def test_total_variance_symmetric_when_rho_zero_m_zero() -> None:
+    svi = make_svi(rho=0.0, m=0.0)
+    w = svi.total_variance(K)
+    assert np.allclose(w, w[::-1])
+
+
+def test_total_variance_minimum_at_m() -> None:
+    m = 0.1
+    svi = make_svi(m=m)
+    k_dense = np.linspace(-1.0, 1.0, 1000)
+    w = svi.total_variance(k_dense)
+    assert pytest.approx(k_dense[np.argmin(w)], abs=0.01) == m + float(svi.rho) * float(
+        svi.theta
+    ) / np.sqrt(1 - float(svi.rho) ** 2) * (-1)
+
+
+def test_total_variance_scalar_input() -> None:
+    svi = make_svi()
+    w = svi.total_variance(0.0)
+    assert w.shape == (1,) or w.ndim == 0
+
+
+# ---------------------------------------------------------------------------
+# implied_vol
+# ---------------------------------------------------------------------------
+
+
+def test_implied_vol_non_negative() -> None:
+    svi = make_svi()
+    iv = svi.implied_vol(K, TTM)
+    assert np.all(iv >= 0)
+
+
+def test_implied_vol_zero_where_variance_non_positive() -> None:
+    # force a very negative a so some w(k) <= 0
+    svi = make_svi(a=-0.5, b=0.01)
+    iv = svi.implied_vol(K, TTM)
+    w = svi.total_variance(K)
+    assert np.all(iv[w <= 0] == 0.0)
+
+
+def test_implied_vol_consistent_with_total_variance() -> None:
+    svi = make_svi()
+    iv = svi.implied_vol(K, TTM)
+    w = svi.total_variance(K)
+    assert np.allclose(iv**2 * TTM, w)
+
+
+def test_implied_vol_scales_with_ttm() -> None:
+    svi = make_svi()
+    iv1 = svi.implied_vol(K, 1.0)
+    iv2 = svi.implied_vol(K, 0.25)
+    # same total variance, shorter ttm => higher iv
+    assert np.all(iv2 > iv1)
+
+
+# ---------------------------------------------------------------------------
+# fit
+# ---------------------------------------------------------------------------
+
+
+def _synthetic_iv(svi: SVI, k: np.ndarray, ttm: float) -> np.ndarray:
+    return svi.implied_vol(k, ttm)
+
+
+def test_fit_recovers_parameters() -> None:
+    true_svi = make_svi(a=0.04, b=0.1, rho=-0.2, m=0.05, theta=0.15)
+    iv_obs = _synthetic_iv(true_svi, K, TTM)
+    fitted = SVI.fit(K, iv_obs, TTM)
+    assert float(fitted.a) == pytest.approx(float(true_svi.a), abs=1e-4)
+    assert float(fitted.b) == pytest.approx(float(true_svi.b), abs=1e-4)
+    assert float(fitted.rho) == pytest.approx(float(true_svi.rho), abs=1e-4)
+    assert float(fitted.m) == pytest.approx(float(true_svi.m), abs=1e-4)
+    assert float(fitted.theta) == pytest.approx(float(true_svi.theta), abs=1e-4)
+
+
+def test_fit_reproduces_implied_vols() -> None:
+    true_svi = make_svi()
+    iv_obs = _synthetic_iv(true_svi, K, TTM)
+    fitted = SVI.fit(K, iv_obs, TTM)
+    iv_fit = fitted.implied_vol(K, TTM)
+    assert np.allclose(iv_fit, iv_obs, atol=1e-5)
+
+
+def test_fit_flat_smile() -> None:
+    iv_obs = np.full_like(K, 0.20)
+    fitted = SVI.fit(K, iv_obs, TTM)
+    iv_fit = fitted.implied_vol(K, TTM)
+    assert np.allclose(iv_fit, 0.20, atol=1e-4)
+
+
+def test_fit_respects_bounds() -> None:
+    true_svi = make_svi()
+    iv_obs = _synthetic_iv(true_svi, K, TTM)
+    fitted = SVI.fit(K, iv_obs, TTM)
+    assert float(fitted.b) >= 0
+    assert -1 < float(fitted.rho) < 1
+    assert float(fitted.theta) > 0
+
+
+def test_fit_skewed_smile() -> None:
+    true_svi = make_svi(rho=-0.5, m=-0.1)
+    iv_obs = _synthetic_iv(true_svi, K, TTM)
+    fitted = SVI.fit(K, iv_obs, TTM)
+    iv_fit = fitted.implied_vol(K, TTM)
+    assert np.allclose(iv_fit, iv_obs, atol=1e-5)
diff --git a/uv.lock b/uv.lock
index 9db8606..e7914b1 100644
--- a/uv.lock
+++ b/uv.lock
@@ -15,7 +15,7 @@ resolution-markers = [
 
 [[package]]
 name = "aio-fluid"
-version = "2.0.1"
+version = "2.0.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "fastapi" },
@@ -26,9 +26,9 @@ dependencies = [
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     { name = "yarl" },
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+    { url = "https://files.pythonhosted.org/packages/6a/d5/a6f84bf5df9a826d45c9c665b6c36a3658b5dd3f68ab9efaf51f2328a2a0/aio_fluid-2.0.3-py3-none-any.whl", hash = "sha256:b2feea637e12d99f3ecf1a1979bcaae22f26b0583b9b94e28da40eb2d0b32711", size = 59547, upload-time = "2026-04-16T11:00:40.519Z" },
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 [package.optional-dependencies]
@@ -174,7 +174,7 @@ wheels = [
 
 [[package]]
 name = "altair"
-version = "6.0.0"
+version = "6.1.0"
 source = { registry = "https://pypi.org/simple" }
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     { name = "jinja2" },
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+    { url = "https://files.pythonhosted.org/packages/ce/63/5dacc8d8306c715088b897a479e551bc0779fd2f0f26c97fec5e36542b4e/altair-6.1.0-py3-none-any.whl", hash = "sha256:fdf5fd939512e5b2fc4441c82dfd2635e706defbd037db0ac429ef5ddce66c3b", size = 796996, upload-time = "2026-04-21T13:08:48.549Z" },
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 [[package]]
@@ -344,11 +344,11 @@ holidays = [
 
 [[package]]
 name = "certifi"
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+version = "2026.4.22"
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+    { url = "https://files.pythonhosted.org/packages/22/30/7cd8fdcdfbc5b869528b079bfb76dcdf6056b1a2097a662e5e8c04f42965/certifi-2026.4.22-py3-none-any.whl", hash = "sha256:3cb2210c8f88ba2318d29b0388d1023c8492ff72ecdde4ebdaddbb13a31b1c4a", size = 135707, upload-time = "2026-04-22T11:26:09.372Z" },
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