# Optimal Control

This module provides advanced optimal control algorithms for financial applications, including Hamilton-Jacobi-Bellman (HJB) equation solvers, regime-switching models, parameter estimation, and state-space filtering. All algorithms are implemented in high-performance Rust with Python bindings.

## Mathematical Foundations

### Hamilton-Jacobi-Bellman (HJB) Equation

The HJB equation is a fundamental result in optimal control theory that provides the necessary and sufficient conditions for optimality of a control policy. For a stochastic control problem:

$$
V(x) = \sup_{\alpha \in \mathcal{A}} \mathbb{E}\left[\int_0^\infty e^{-\rho t} L(X_t, \alpha_t) dt \mid X_0 = x\right]
$$

where $V(x)$ is the value function, $\rho$ is the discount rate, $L$ is the running cost, and $X_t$ follows a controlled stochastic process. The HJB equation is:

$$
\rho V(x) = \sup_{\alpha \in \mathcal{A}} \left\{ \mathcal{L}^\alpha V(x) + L(x, \alpha) \right\}
$$

where $\mathcal{L}^\alpha$ is the infinitesimal generator of the controlled process.

#### Application to Mean-Reverting Spreads

For pairs trading with an Ornstein-Uhlenbeck (OU) spread process:

$$
dX_t = \kappa(\theta - X_t)dt + \sigma dW_t
$$

with transaction costs $c > 0$, the HJB equation becomes:

$$
\rho V(x) = \kappa(\theta - x)V'(x) + \frac{\sigma^2}{2}V''(x) + \sup_{\alpha \in \{-1, 0, 1\}} \{ -c|\alpha| + \alpha x \}
$$

The optimal control is a threshold policy: buy when $x < x_L$, sell when $x > x_U$, hold otherwise.

### Viscosity Solutions

Classical solutions to HJB equations rarely exist due to:
1. **Non-smoothness at boundaries**: The value function $V(x)$ has kinks where the optimal control switches
2. **Lack of regularity**: Second derivatives $V''(x)$ may not exist everywhere
3. **Free boundary problems**: The optimal switching thresholds $(x_L, x_U)$ are unknown

**Viscosity solutions** generalize the notion of solution to allow for non-smooth value functions. A function $V$ is a viscosity solution if:

1. **Subsolution property**: For any smooth test function $\phi$ such that $V - \phi$ has a local maximum at $x_0$:
   $$\rho V(x_0) \leq \mathcal{H}(x_0, V(x_0), D\phi(x_0), D^2\phi(x_0))$$

2. **Supersolution property**: For any smooth test function $\psi$ such that $V - \psi$ has a local minimum at $x_0$:
   $$\rho V(x_0) \geq \mathcal{H}(x_0, V(x_0), D\psi(x_0), D^2\psi(x_0))$$

where $\mathcal{H}$ is the Hamiltonian.

**Key properties:**
- **Uniqueness**: Under suitable conditions (coercivity, proper discount), the viscosity solution is unique
- **Stability**: Viscosity solutions are stable under uniform convergence
- **Numerical convergence**: Monotone finite difference schemes converge to the viscosity solution

### Finite Difference Methods

We discretize the HJB equation on a spatial grid $x_i = x_{\min} + ih$, $i = 0, \ldots, N$, with grid spacing $h$.

#### Upwind Schemes

For the OU drift term $\kappa(\theta - x)V'(x)$, we use **upwind finite differences** to ensure monotonicity and stability:

- If $\kappa(\theta - x_i) > 0$ (rightward drift): use forward difference
  $$V'(x_i) \approx \frac{V_{i+1} - V_i}{h}$$

- If $\kappa(\theta - x_i) < 0$ (leftward drift): use backward difference
  $$V'(x_i) \approx \frac{V_i - V_{i-1}}{h}$$

The diffusion term uses centered differences:
$$V''(x_i) \approx \frac{V_{i+1} - 2V_i + V_{i-1}}{h^2}$$

#### Policy Iteration Algorithm

The HJB equation with control is solved via **policy iteration**:

1. **Initialize**: Start with policy $\alpha^{(0)}$ (e.g., always hold)
2. **Policy evaluation**: Solve the linear system for value function $V^{(k)}$:
   $$\rho V^{(k)}_i = \mathcal{L}^{\alpha^{(k)}} V^{(k)}_i + L(x_i, \alpha^{(k)}_i)$$
3. **Policy improvement**: Update policy by maximizing Hamiltonian:
   $$\alpha^{(k+1)}_i = \arg\max_{\alpha} \{ \mathcal{L}^\alpha V^{(k)}_i + L(x_i, \alpha) \}$$
4. **Convergence check**: If $\|\alpha^{(k+1)} - \alpha^{(k)}\|_\infty < \epsilon$, stop; otherwise return to step 2

**Convergence properties:**
- Typically 10-50 iterations for practical problems
- Geometric convergence rate
- Numerical solution converges to viscosity solution as $h \to 0$

## Implemented Algorithms

### 1. HJB Solver for OU Process

Solves the optimal switching problem for mean-reverting spreads with transaction costs.

**Implementation**: `src/optimal_control/hjb_solver.rs`

**Python API**:
```python
from optimizr import solve_hjb_py, solve_hjb_full_py

# Basic solver - returns optimal thresholds
lower, upper, residual, iters = solve_hjb_py(
    kappa=3.0,           # Mean reversion speed
    theta=0.0,           # Long-run mean
    sigma=0.2,           # Volatility
    rho=0.04,            # Discount rate
    transaction_cost=0.001,  # Transaction cost per trade
    n_points=400,        # Number of grid points
    max_iter=4000,       # Maximum policy iterations
    tolerance=1e-7,      # Convergence tolerance
    n_std=5.0,          # Grid extent in standard deviations
)

print(f"Optimal bounds: ({lower:.3f}, {upper:.3f})")
print(f"Residual: {residual:.2e}, Iterations: {iters}")

# Full solver - also returns value function and derivatives
lower, upper, residual, iters, V, V_x, V_xx = solve_hjb_full_py(
    kappa=3.0, theta=0.0, sigma=0.2, rho=0.04,
    transaction_cost=0.001, n_points=400
)

# Plot value function derivatives for diagnostics
import matplotlib.pyplot as plt
plt.plot(V_x)
plt.axvline(lower, color='r', linestyle='--', label='Lower bound')
plt.axvline(upper, color='g', linestyle='--', label='Upper bound')
plt.legend()
plt.title("Value function derivative V'(x)")
```

**Parameters**:
- `kappa`: Mean reversion speed (typical range: 0.1-10). Higher values → faster reversion → narrower bands
- `theta`: Long-run mean (typically 0 for normalized spreads)
- `sigma`: Volatility (typical range: 0.1-1.0). Higher values → wider bands
- `rho`: Discount rate (typical: 0.01-0.1). Higher values → more myopic strategy
- `transaction_cost`: Per-trade cost (typical: 0.0001-0.01). Higher values → wider bands, fewer trades
- `n_points`: Grid resolution (recommended: 200-500). Higher → more accurate but slower
- `n_std`: Grid extent (recommended: 3-6). Should cover 99%+ of spread distribution

**Returns**:
- `lower`: Optimal buy threshold (negative value)
- `upper`: Optimal sell threshold (positive value)
- `residual`: Maximum policy change in last iteration (should be < tolerance)
- `iters`: Number of policy iterations (typically 10-50)
- `V`, `V_x`, `V_xx`: (full solver only) Value function and derivatives on grid

**When to use**:
- Pairs trading with mean-reverting spreads
- Statistical arbitrage with transaction costs
- Optimal entry/exit for mean-reverting assets
- Requires reliable OU parameter estimates (see OU estimation below)

**Diagnostics**:
- Plot $V'(x)$ to check smoothness near thresholds
- Verify `residual < tolerance` for convergence
- Check that thresholds are within grid bounds
- If not converged: increase `max_iter` or adjust grid parameters

### 2. Viscosity Solution Solver

General-purpose viscosity solution solver for HJB equations with arbitrary Hamiltonians.

**Implementation**: `src/optimal_control/viscosity.rs`

**Usage**: Advanced users can extend this for custom control problems beyond OU switching.

### 3. Regime Switching Models

Optimal control with multiple market regimes, each with different dynamics.

**Implementation**: `src/optimal_control/regime_switching.rs`

**Approach**:
1. Use HMM to identify hidden regimes (see HMM section)
2. Estimate OU parameters per regime
3. Solve HJB per regime to get regime-specific thresholds
4. Switch control policy based on decoded regime

**Example workflow**:
```python
from optimizr import HMM, estimate_ou_params_py, solve_hjb_py
import numpy as np

# Step 1: Train HMM on spread returns
returns = np.diff(spread)
hmm = HMM(n_states=2)
hmm.fit(returns.reshape(-1, 1), n_iterations=100)
regimes = hmm.predict(returns.reshape(-1, 1))

# Step 2: Estimate OU parameters per regime
params = []
for regime_id in range(2):
    mask = (regimes == regime_id)
    spread_regime = spread[1:][mask]  # Align with returns
    kappa, theta, sigma, half_life = estimate_ou_params_py(
        spread_regime, dt=1/252
    )
    params.append((kappa, theta, sigma))
    print(f"Regime {regime_id}: κ={kappa:.2f}, θ={theta:.3f}, σ={sigma:.3f}")

# Step 3: Solve HJB per regime
thresholds = []
for kappa, theta, sigma in params:
    lower, upper, _, _ = solve_hjb_py(
        kappa=kappa, theta=theta, sigma=sigma,
        rho=0.04, transaction_cost=0.001
    )
    thresholds.append((lower, upper))
    print(f"Thresholds: ({lower:.3f}, {upper:.3f})")

# Step 4: Apply regime-specific control
current_regime = regimes[-1]
lower, upper = thresholds[current_regime]
if spread[-1] < lower:
    action = "BUY"
elif spread[-1] > upper:
    action = "SELL"
else:
    action = "HOLD"
print(f"Current regime: {current_regime}, Action: {action}")
```

### 4. Jump Diffusion Models

Extension of OU process with Poisson jumps for modeling sudden price shocks.

**Implementation**: `src/optimal_control/jump_diffusion.rs`

**Model**:
$$
dX_t = \kappa(\theta - X_t)dt + \sigma dW_t + J_t dN_t
$$

where $N_t$ is a Poisson process with intensity $\lambda$, and $J_t \sim \mathcal{N}(\mu_J, \sigma_J^2)$ are jump sizes.

**Use case**: Markets with flash crashes, earnings announcements, or other discontinuous events.

### 5. Multi-Regime Switching Jump Diffusion (MRSJD)

Combines regime switching with jump diffusion for maximum flexibility.

**Implementation**: `src/optimal_control/mrsjd.rs`

**Model**: Each regime has its own OU parameters AND jump process parameters.

**Use case**: Complex markets with both regime changes and sudden shocks (e.g., crypto, emerging markets).

### 6. OU Parameter Estimation

Estimates Ornstein-Uhlenbeck process parameters from time series data.

**Implementation**: `src/optimal_control/ou_estimator.rs`

**Python API**:
```python
from optimizr import estimate_ou_params_py
import numpy as np
import matplotlib.pyplot as plt

# Simulate OU process (for testing)
dt = 1/252  # Daily data
T = 1000
kappa_true, theta_true, sigma_true = 3.0, 0.0, 0.2
rng = np.random.default_rng(0)
spread = [0.0]
for _ in range(T-1):
    dx = kappa_true * (theta_true - spread[-1]) * dt + \
         sigma_true * np.sqrt(dt) * rng.standard_normal()
    spread.append(spread[-1] + dx)

spread = np.array(spread)

# Estimate parameters
kappa, theta, sigma, half_life = estimate_ou_params_py(spread, dt=dt)

print(f"True:      κ={kappa_true:.2f}, θ={theta_true:.3f}, σ={sigma_true:.3f}")
print(f"Estimated: κ={kappa:.2f}, θ={theta:.3f}, σ={sigma:.3f}")
print(f"Half-life: {half_life:.1f} periods ({half_life*252:.1f} days)")

# Visualise the simulated path together with the estimated mean-reversion
# level and the decay envelope implied by the fitted half-life.
t_axis = np.arange(len(spread)) * dt * 252  # in days
fig, axes = plt.subplots(1, 2, figsize=(11, 4))
axes[0].plot(t_axis, spread, lw=0.7, label="simulated path")
axes[0].axhline(theta_true, color="k", ls=":", label="true θ")
axes[0].axhline(theta, color="red", ls="--", label="estimated θ")
axes[0].set_xlabel("days"); axes[0].set_ylabel("spread")
axes[0].set_title("OU simulation vs estimated long-run mean")
axes[0].legend(); axes[0].grid(alpha=0.3)

# Empirical autocorrelation vs theoretical exp(-κ τ).
lags = np.arange(0, 60)
x = spread - spread.mean()
acf = np.array([
    (x[: len(x) - k] @ x[k:]) / (x @ x) for k in lags
])
axes[1].plot(lags, acf, "o-", label="empirical ACF")
axes[1].plot(lags, np.exp(-kappa * lags * dt), "--",
             label=r"theoretical $e^{-\kappa\,\tau}$")
axes[1].set_xlabel("lag (days)"); axes[1].set_ylabel("autocorrelation")
axes[1].set_title("Mean-reversion fingerprint")
axes[1].legend(); axes[1].grid(alpha=0.3)
fig.tight_layout(); plt.show()
```
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**Method**: Maximum likelihood estimation (MLE) using analytical formulas for discrete-time OU process.

**Parameters**:
- `spread`: Time series of spread values (1D numpy array)
- `dt`: Time step in years (e.g., 1/252 for daily data, 1/52 for weekly)

**Returns**:
- `kappa`: Mean reversion speed (annualized)
- `theta`: Long-run mean
- `sigma`: Volatility (annualized)
- `half_life`: Half-life in time step units ($\ln(2)/\kappa \cdot dt^{-1}$)

**Practical tips**:
- Use at least 500-1000 observations for stable estimates
- Check half-life: typical pairs have half-life 5-60 days
- Winsorize extreme outliers (e.g., clip at ±5σ) if needed
- For rolling estimates, use expanding or rolling windows of 250-500 periods

### 7. Kalman Filtering

State-space filtering for latent variable estimation and forecasting.

**Implementation**: `src/optimal_control/kalman_filter.rs`, `src/optimal_control/kalman_py_bindings.rs`

#### Linear Kalman Filter

For linear Gaussian state-space models:
$$
\begin{aligned}
x_{t+1} &= F x_t + B u_t + w_t, \quad w_t \sim \mathcal{N}(0, Q) \\
y_t &= H x_t + v_t, \quad v_t \sim \mathcal{N}(0, R)
\end{aligned}
$$

**Python API**:
```python
from optimizr import LinearKalmanFilter
import numpy as np

# Define system matrices
F = [[1.0, 1.0], [0.0, 1.0]]  # State transition (2×2)
H = [[1.0, 0.0]]              # Observation matrix (1×2)
Q = [[1e-4, 0.0], [0.0, 1e-4]]  # Process noise covariance
R = [[1e-2]]                  # Measurement noise covariance

# Initialize filter
kf = LinearKalmanFilter(
    f_matrix=F,
    h_matrix=H,
    q_matrix=Q,
    r_matrix=R,
    initial_state=[0.0, 0.0],
    initial_covariance=[[1.0, 0.0], [0.0, 1.0]],
)

# Online filtering loop
observations = np.random.randn(100)
states = []
for obs in observations:
    kf.predict(control=[0.0, 0.0])  # Prediction step
    kf.update(observation=[obs])     # Correction step
    state = kf.get_state()
    states.append(state)

states = np.array(states)
print(f"Final state estimate: {states[-1]}")
```

**Use cases**:
- Tracking latent spread dynamics with noise
- State estimation for control (e.g., estimate velocity from noisy position)
- Online parameter adaptation

#### Extended Kalman Filter (EKF)

For nonlinear systems with local linearization.

**Use case**: Nonlinear spread dynamics, regime probabilities as states.

#### Unscented Kalman Filter (UKF)

For highly nonlinear systems using sigma-point approximation.

**Python API**:
```python
from optimizr import UnscentedKalmanFilter

ukf = UnscentedKalmanFilter(
    state_dim=2,
    obs_dim=1,
    q_matrix=Q,
    r_matrix=R,
    initial_state=[0.0, 0.0],
    initial_covariance=[[1.0, 0.0], [0.0, 1.0]],
)
# Similar predict/update interface
```

**Use case**: Jump diffusion models, volatility estimation, option pricing.

### 8. Backtesting Framework

Backtests optimal switching strategies on historical data.

**Implementation**: `src/optimal_control/backtest.rs`

**Python API**:
```python
from optimizr import backtest_optimal_switching_py

# First, get optimal thresholds
lower, upper, _, _ = solve_hjb_py(
    kappa=3.0, theta=0.0, sigma=0.2,
    rho=0.04, transaction_cost=0.001
)

# Backtest on historical spread
metrics = backtest_optimal_switching_py(
    spread=spread,              # Historical spread data
    lower_bound=lower,          # Optimal buy threshold
    upper_bound=upper,          # Optimal sell threshold
    transaction_cost=0.001,     # Must match HJB solver
)

(
    total_return,    # Cumulative return
    sharpe,          # Annualized Sharpe ratio
    max_dd,          # Maximum drawdown
    n_trades,        # Number of round-trip trades
    win_rate,        # Fraction of profitable trades
    pnl_path,        # P&L time series
) = metrics

print(f"Return: {total_return:.2%}, Sharpe: {sharpe:.2f}")
print(f"Max DD: {max_dd:.2%}, Trades: {n_trades}, Win rate: {win_rate:.2%}")

# Plot P&L path
import matplotlib.pyplot as plt
plt.figure(figsize=(12, 6))
plt.subplot(2, 1, 1)
plt.plot(spread, label='Spread')
plt.axhline(lower, color='r', linestyle='--', label='Lower')
plt.axhline(upper, color='g', linestyle='--', label='Upper')
plt.legend()
plt.subplot(2, 1, 2)
plt.plot(pnl_path, label='P&L')
plt.legend()
plt.tight_layout()
```
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**Metrics interpretation**:
- `total_return`: Should be positive with low transaction costs
- `sharpe`: Good values > 1.0, excellent > 2.0
- `max_dd`: Risk metric, compare to expected return
- `n_trades`: Too many → excessive costs; too few → missing opportunities
- `win_rate`: Typically 40-60% for mean-reversion strategies

**Parameter tuning**:
- If `win_rate` low but `max_dd` high → bands too narrow, increase `transaction_cost` or `rho`
- If `n_trades` low → bands too wide, decrease `transaction_cost` or `rho`
- Compare Sharpe ratios across different parameter settings

## Complete Workflow Example

Here's a complete optimal control pipeline for pairs trading:

```python
import numpy as np
import pandas as pd
from optimizr import (
    estimate_ou_params_py,
    solve_hjb_py,
    backtest_optimal_switching_py,
    HMM
)

# 1. Load price data (example with simulated data)
np.random.seed(42)
T = 5000
dt = 1/252

# Simulate cointegrated pair
price_A = 100 * np.exp(np.cumsum(0.0001 + 0.01*np.sqrt(dt)*np.random.randn(T)))
price_B = 100 * np.exp(np.cumsum(0.0001 + 0.01*np.sqrt(dt)*np.random.randn(T)))
spread = np.log(price_A) - np.log(price_B)

# Split into train/test
train_spread = spread[:3000]
test_spread = spread[3000:]

# 2. Estimate OU parameters
kappa, theta, sigma, half_life = estimate_ou_params_py(train_spread, dt=dt)
print(f"OU parameters: κ={kappa:.2f}, θ={theta:.3f}, σ={sigma:.3f}")
print(f"Half-life: {half_life:.1f} days")

# 3. Solve HJB for optimal thresholds
lower, upper, residual, iters = solve_hjb_py(
    kappa=kappa,
    theta=theta,
    sigma=sigma,
    rho=0.04,
    transaction_cost=0.001,
    n_points=400,
    max_iter=2000,
    tolerance=1e-7,
)
print(f"Optimal thresholds: ({lower:.3f}, {upper:.3f})")
print(f"Converged in {iters} iterations, residual={residual:.2e}")

# 4. Backtest on out-of-sample data
metrics = backtest_optimal_switching_py(
    spread=test_spread,
    lower_bound=lower,
    upper_bound=upper,
    transaction_cost=0.001,
)
total_return, sharpe, max_dd, n_trades, win_rate, pnl_path = metrics

print(f"\nBacktest Results:")
print(f"  Total Return: {total_return:.2%}")
print(f"  Sharpe Ratio: {sharpe:.2f}")
print(f"  Max Drawdown: {max_dd:.2%}")
print(f"  # Trades: {n_trades}")
print(f"  Win Rate: {win_rate:.2%}")

# 5. Optional: Regime-aware control with HMM
returns = np.diff(train_spread)
hmm = HMM(n_states=2)
hmm.fit(returns.reshape(-1, 1), n_iterations=100)
regimes = hmm.predict(returns.reshape(-1, 1))

# Estimate OU per regime and get regime-specific thresholds
for regime_id in range(2):
    mask = (regimes == regime_id)
    spread_regime = train_spread[1:][mask]
    k, t, s, _ = estimate_ou_params_py(spread_regime, dt=dt)
    l, u, _, _ = solve_hjb_py(k, t, s, 0.04, 0.001)
    print(f"Regime {regime_id}: κ={k:.2f}, thresholds=({l:.3f}, {u:.3f})")
```

## Performance Characteristics

### Computational Complexity

- **HJB Solver**: $O(N \cdot K)$ where $N$ is `n_points`, $K$ is policy iterations (~10-50)
- **OU Estimation**: $O(T)$ where $T$ is time series length (closed-form MLE)
- **Kalman Filter**: $O(T \cdot d^3)$ where $d$ is state dimension (matrix inversion per step)
- **Backtesting**: $O(T)$ single pass through data

### Typical Runtimes (on modern CPU)

- HJB solve (400 points): ~10-50ms
- OU estimation (5000 samples): ~1ms
- Kalman filter (1000 steps, 2D state): ~10ms
- Backtest (5000 samples): ~5ms

### Memory Requirements

- HJB solver: $O(N)$ for grid storage (~few KB)
- Kalman filter: $O(d^2)$ for covariance matrices (~few KB for small $d$)
- Backtesting: $O(T)$ for P&L path storage (~few MB for long histories)

## Integration with Other Modules

### With HMM (Hidden Markov Models)
- Use HMM to detect market regimes
- Estimate OU parameters per regime
- Apply regime-specific optimal controls
- See `api/hmm.md` for HMM documentation

### With Mean Field Games
- Use optimal control as individual agent strategy
- Aggregate across population for mean-field dynamics
- See `algorithms/mean_field_games.md` for MFG theory

### With Sparse Optimization
- Use Kalman-filtered states as inputs to sparse controllers
- Combine L1-regularized control with HJB thresholds
- See `algorithms/sparse_optimization.md`

## Troubleshooting

### HJB solver not converging
- **Symptom**: `residual > tolerance` after `max_iter`
- **Fix**: Increase `max_iter` (try 5000-10000); reduce `tolerance` requirement; check that OU parameters are reasonable

### Thresholds outside grid bounds
- **Symptom**: Optimal thresholds at grid edges
- **Fix**: Increase `n_std` (try 6-8); check OU parameter estimates (very high σ needs wider grid)

### OU estimates unstable
- **Symptom**: Negative `kappa` or extreme `half_life`
- **Fix**: Use more data (>1000 samples); check for non-stationarity; consider winsorizing outliers

### Backtest Sharpe ratio low
- **Symptom**: Sharpe < 0.5 despite positive thresholds
- **Fix**: Check for regime changes (use HMM); verify spread is actually mean-reverting; adjust `transaction_cost` in HJB solver

### Kalman filter diverging
- **Symptom**: State estimates exploding
- **Fix**: Check process noise `Q` is not too large; verify observations are scaled properly; use UKF for strong nonlinearity

## References

### Optimal Control Theory
- **Fleming, W. H., & Soner, H. M.** (2006). *Controlled Markov Processes and Viscosity Solutions*. Springer.
- **Øksendal, B.** (2003). *Stochastic Differential Equations: An Introduction with Applications* (6th ed.). Springer.
- **Pham, H.** (2009). *Continuous-time Stochastic Control and Optimization with Financial Applications*. Springer.

### Viscosity Solutions
- **Barles, G., & Souganidis, P. E.** (1991). Convergence of approximation schemes for fully nonlinear second order equations. *Asymptotic Analysis*, 4(3), 271-283.
- **Crandall, M. G., Ishii, H., & Lions, P.-L.** (1992). User's guide to viscosity solutions of second order partial differential equations. *Bulletin of the American Mathematical Society*, 27(1), 1-67.

### Kalman Filtering
- **Kalman, R. E.** (1960). A new approach to linear filtering and prediction problems. *Journal of Basic Engineering*, 82(1), 35-45.
- **Julier, S. J., & Uhlmann, J. K.** (1997). New extension of the Kalman filter to nonlinear systems. *Signal Processing, Sensor Fusion, and Target Recognition VI*, 3068, 182-193.

### Financial Applications
- **Avellaneda, M., & Lee, J.-H.** (2010). Statistical arbitrage in the US equities market. *Quantitative Finance*, 10(7), 761-782.
- **Gatev, E., Goetzmann, W. N., & Rouwenhorst, K. G.** (2006). Pairs trading: Performance of a relative-value arbitrage rule. *The Review of Financial Studies*, 19(3), 797-827.

## See Also

- [HMM API Reference](../api/hmm.md) - Hidden Markov Models for regime detection
- [Mean Field Games](mean_field_games.md) - Population-level optimal control
- [Optimal Control API](../api/optimal_control.md) - Complete function signatures and types
