# API Reference: Point Processes

The point processes module provides Rust-accelerated functions for Hawkes process simulation, fractional Brownian motion, and related special functions — all accessible from Python via PyO3.

## Quick Start

```python
import optimizr
import numpy as np

# Simulate a Hawkes process with power-law kernel
events = optimizr.simulate_hawkes(
    baseline=0.1,
    alpha=0.35,
    beta=0.375,
    t_max=100.0,
    kernel_type="power_law",
    seed=42
)

# Simulate fractional Brownian motion
path = optimizr.simulate_fbm(hurst=0.75, n=1000, dt=0.01, seed=42)

# Estimate Hurst exponent
h_est = optimizr.estimate_hurst(path)
print(f"Estimated H: {h_est:.3f}")
```

---

## Hawkes Process Functions

### `simulate_hawkes(baseline, alpha, beta, t_max, kernel_type="exponential", seed=None)`

Simulate a univariate Hawkes process using Ogata's thinning algorithm.

**Parameters:**

- `baseline` (float): Baseline intensity $\nu > 0$. This is the exogenous event rate in the absence of self-excitation. Higher values produce more events even without clustering.

- `alpha` (float): Kernel amplitude parameter.
  - For `"exponential"`: peak excitation rate $\alpha$ in $\phi(t) = \alpha e^{-\beta t}$
  - For `"power_law"`: scaling constant $K_0$ in $\phi(t) = K_0 (1+t)^{-(1+\alpha_0)}$

- `beta` (float): Kernel decay parameter.
  - For `"exponential"`: decay rate $\beta$ (inverse timescale)
  - For `"power_law"`: tail exponent $\alpha_0 \in (0, 1)$. Connected to Hurst parameter by $H_0 = 2\alpha_0$.

- `t_max` (float): Maximum simulation time $T$. The process runs on $[0, T]$.

- `kernel_type` (str, default=`"exponential"`): Type of excitation kernel.
  - `"exponential"`: Short-memory kernel with exponential decay
  - `"power_law"`: Long-memory kernel with power-law tail

- `seed` (int, optional): Random seed for reproducibility.

**Returns:**
- `np.ndarray`: Array of event times $\{t_1, t_2, \ldots, t_n\}$ sorted in ascending order.

**Stability:**
- Exponential: stable when $\alpha / \beta < 1$
- Power-law: stable when $K_0 / \alpha_0 < 1$

**Examples:**

```python
import optimizr

# Markovian self-exciting process (exponential kernel)
events = optimizr.simulate_hawkes(
    baseline=1.0,
    alpha=0.5,    # branching ratio = 0.5/1.0 = 0.5
    beta=1.0,
    t_max=100.0,
    kernel_type="exponential",
    seed=42
)
print(f"{len(events)} events, expected ≈ {1.0 / (1 - 0.5) * 100:.0f}")

# Long-memory process (power-law kernel, H₀ = 0.75)
events_pl = optimizr.simulate_hawkes(
    baseline=0.1,
    alpha=0.35,    # K₀
    beta=0.375,    # α₀ → H₀ = 0.75
    t_max=1000.0,
    kernel_type="power_law",
    seed=42
)
print(f"{len(events_pl)} events with long-memory clustering")
```

**When to use which kernel:**

| Scenario | Kernel | Typical Parameters |
|----------|--------|--------------------|
| High-frequency order arrivals | Exponential | $\alpha=0.5$, $\beta=2.0$ |
| Market microstructure (unified theory) | Power-law | $\alpha_0=0.375$, $K_0 \leq \alpha_0$ |
| Neural spike trains | Exponential | $\alpha=0.3$, $\beta=5.0$ |
| Seismology (aftershocks) | Power-law | $\alpha_0=0.5$, $K_0=0.4$ |

---

### `simulate_bivariate_hawkes(core_buy_times, core_sell_times, phi1_alpha, phi1_beta, phi2_alpha, phi2_beta, t_max, seed=None)`

Simulate a bivariate Hawkes process modeling buy/sell reaction order flow driven by core order flow.

The model captures how buy orders excite more buy orders (**self-excitation**) and sell orders (**cross-excitation**), and vice versa.

**Parameters:**

- `core_buy_times` (np.ndarray): Core buy order arrival times (driver process $F^+$).
- `core_sell_times` (np.ndarray): Core sell order arrival times (driver process $F^-$).
- `phi1_alpha` (float): Self-excitation kernel amplitude (buy→buy, sell→sell).
- `phi1_beta` (float): Self-excitation kernel decay rate.
- `phi2_alpha` (float): Cross-excitation kernel amplitude (buy→sell, sell→buy).
- `phi2_beta` (float): Cross-excitation kernel decay rate.
- `t_max` (float): Maximum simulation time.
- `seed` (int, optional): Random seed.

**Returns:**
- `tuple[np.ndarray, np.ndarray]`: `(buy_times, sell_times)` — arrays of reaction buy and sell event times.

**Stability condition:**

$$
\frac{\phi_{1,\alpha}}{\phi_{1,\beta}} + \frac{\phi_{2,\alpha}}{\phi_{2,\beta}} < 1
$$

**Example:**

```python
import optimizr
import numpy as np

# Core flow: Poisson arrivals
rng = np.random.default_rng(42)
core_buys = np.sort(rng.uniform(0, 100, 200))
core_sells = np.sort(rng.uniform(0, 100, 180))

# Symmetric reaction with moderate cross-excitation
buys, sells = optimizr.simulate_bivariate_hawkes(
    core_buy_times=core_buys,
    core_sell_times=core_sells,
    phi1_alpha=0.3,  # Self: L¹ = 0.3
    phi1_beta=1.0,
    phi2_alpha=0.15, # Cross: L¹ = 0.15
    phi2_beta=1.0,
    t_max=100.0,
    seed=42
)

# Net order imbalance (price signal)
imbalance = len(buys) - len(sells)
print(f"Reaction buys: {len(buys)}, sells: {len(sells)}")
print(f"Order imbalance: {imbalance:+d}")
print(f"Spectral radius: {0.3 + 0.15:.2f}")  # 0.45 < 1 → stable
```

---

## Fractional Brownian Motion Functions

### `simulate_fbm(hurst, n, dt=1.0, seed=None)`

Simulate a fractional Brownian motion sample path using Hosking's method (Durbin-Levinson algorithm).

**Parameters:**

- `hurst` (float): Hurst parameter $H \in (0, 1)$.
  - $H < 0.5$: Anti-persistent (mean-reverting)
  - $H = 0.5$: Standard Brownian motion
  - $H > 0.5$: Persistent (trending)

- `n` (int): Number of time steps. The output has $n + 1$ values (including $B^H_0 = 0$).

- `dt` (float, default=1.0): Time step size $\Delta t$. Increments are scaled by $(\Delta t)^H$.

- `seed` (int, optional): Random seed.

**Returns:**
- `np.ndarray`: Array of length $n + 1$ representing the fBM path $\{B^H_0, B^H_{\Delta t}, B^H_{2\Delta t}, \ldots, B^H_{n\Delta t}\}$.

**Complexity:** $O(n^2)$ using Hosking's method (vs $O(n^3)$ for Cholesky).

**Example:**

```python
import optimizr
import numpy as np
import matplotlib.pyplot as plt

# Compare three regimes
fig, axes = plt.subplots(1, 3, figsize=(15, 4))

for i, (h, label) in enumerate([
    (0.3, "Anti-persistent"),
    (0.5, "Standard BM"),
    (0.8, "Persistent")
]):
    path = optimizr.simulate_fbm(hurst=h, n=2000, dt=0.001, seed=42)
    axes[i].plot(path, linewidth=0.5, color=['red', 'black', 'blue'][i])
    axes[i].set_title(f"H = {h} ({label})")
    axes[i].set_xlabel("Time step")
    axes[i].set_ylabel("B^H(t)")

plt.suptitle("Fractional Brownian Motion: Three Regimes")
plt.tight_layout()
plt.show()
```
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---

### `simulate_mixed_fbm(a, b, hurst, n, dt=1.0, seed=None)`

Simulate a mixed fractional Brownian motion $M^H(t) = a \cdot B(t) + b \cdot B^H(t)$.

**Parameters:**

- `a` (float): Coefficient for the standard BM component (diffusive).
- `b` (float): Coefficient for the fBM component (persistent).
- `hurst` (float): Hurst parameter $H$ of the fBM component. Typically $H \in (0.5, 1)$ for persistent flow.
- `n` (int): Number of time steps.
- `dt` (float, default=1.0): Time step size.
- `seed` (int, optional): Random seed.

**Returns:**
- `np.ndarray`: Array of length $n + 1$ representing the mixed fBM path.

**Financial interpretation:**
- The BM component captures short-term noise (market making, latency)
- The fBM component captures long-term persistence (informed trading, herding)
- At short timescales, $H_{\text{eff}} \to 1/2$ (BM dominates)
- At long timescales, $H_{\text{eff}} \to H$ (fBM dominates)

**Example:**

```python
import optimizr
import numpy as np

# Unified theory: aggregate order flow as mfBM
path = optimizr.simulate_mixed_fbm(
    a=1.0,       # BM weight
    b=0.5,       # fBM weight
    hurst=0.75,  # H₀ from unified theory
    n=5000,
    dt=0.01,
    seed=42
)

# Semimartingale check: H > 3/4 allows classical stochastic calculus
is_semimartingale = 0.75 > 0.75  # Borderline case
print(f"Path length: {len(path)}")
print(f"Semimartingale: {is_semimartingale}")
```

---

### `estimate_hurst(data)`

Estimate the Hurst exponent from data using Rescaled Range (R/S) analysis.

**Parameters:**
- `data` (np.ndarray): 1-D time series data (path values, not increments).

**Returns:**
- `float`: Estimated Hurst exponent $\hat{H} \in [0.01, 0.99]$.

**Algorithm:**
1. Partition data into subseries of varying lengths $n_1, n_2, \ldots$
2. For each length, compute the average R/S statistic across subseries
3. Fit $\log(R/S) = H \log(n) + c$ by least-squares regression

**Note:** R/S analysis provides a rough estimate. For more precise estimation, consider DFA (Detrended Fluctuation Analysis) or wavelet methods. The method requires at least 20 data points.

**Example:**

```python
import optimizr
import numpy as np

# Verify estimation accuracy
for h_true in [0.3, 0.5, 0.7, 0.9]:
    path = optimizr.simulate_fbm(hurst=h_true, n=5000, dt=1.0, seed=42)
    h_est = optimizr.estimate_hurst(path)
    print(f"H_true = {h_true:.1f}, H_est = {h_est:.3f}, error = {abs(h_true - h_est):.3f}")
```

---

### `scale_dependent_hurst(data, scales=None)`

Compute scale-dependent Hurst exponents using variance ratios at different time scales.

This function identifies whether data follows a pure fBM or a mixed fBM by examining how the effective Hurst exponent varies across scales.

**Parameters:**
- `data` (np.ndarray): 1-D time series data.
- `scales` (list of int, optional): Time scales to analyze. Default: `[10, 50, 100, 500, 1000, 2000, 5000]`.

**Returns:**
- `dict[int, float]`: Mapping from scale to estimated Hurst exponent at that scale.

**Interpretation:**
- **Constant $H(\Delta)$** across scales → pure fBM
- **$H(\Delta)$ increasing from $\sim 0.5$ to $H$** → mixed fBM (BM at short scales, fBM at long scales)
- **$H(\Delta) \approx 0.5$** at all scales → standard BM (no long memory)

**Example:**

```python
import optimizr
import numpy as np

# Generate mixed fBM (should show scale-dependent H)
path = optimizr.simulate_mixed_fbm(a=1.0, b=1.0, hurst=0.8, n=10000, dt=1.0, seed=42)

hurst_scales = optimizr.scale_dependent_hurst(
    data=path,
    scales=[10, 25, 50, 100, 250, 500, 1000, 2500]
)

print("Scale | H_effective")
print("-" * 25)
for s, h in sorted(hurst_scales.items()):
    indicator = "← BM regime" if h < 0.55 else ("← fBM regime" if h > 0.65 else "← transition")
    print(f"{s:>5d} | {h:.4f}  {indicator}")
```

---

## Special Functions

### `mittag_leffler_py(alpha, beta, z)`

Compute the generalized Mittag-Leffler function $E_{\alpha,\beta}(z)$.

**Parameters:**
- `alpha` (float): First parameter $\alpha > 0$.
- `beta` (float): Second parameter $\beta > 0$.
- `z` (float): Real argument.

**Returns:**
- `float`: $E_{\alpha,\beta}(z)$

**Algorithm:**
- $|z| < 10$: Taylor series expansion with 100 terms
- $|z| \geq 10$: Asymptotic expansion

**Example:**

```python
import optimizr
import numpy as np

# Verify: E_{1,1}(z) = exp(z)
for z in [0.5, 1.0, 2.0]:
    ml = optimizr.mittag_leffler_py(1.0, 1.0, z)
    print(f"E_{{1,1}}({z}) = {ml:.8f}, exp({z}) = {np.exp(z):.8f}")

# Compute E_{0.5, 1}(z) (related to complementary error function)
z = -1.0
ml_half = optimizr.mittag_leffler_py(0.5, 1.0, z)
print(f"E_{{0.5,1}}({z}) = {ml_half:.8f}")
```

---

### `f_alpha_lambda_py(alpha0, lambda0, x)`

Compute the scaling function $f_{\alpha_0, \lambda_0}(x)$ from Theorem 3.1.

$$
f_{\alpha_0, \lambda_0}(x) = \lambda_0 \, x^{\alpha_0 - 1} \, E_{\alpha_0, \alpha_0}\!\left(-\lambda_0 \, x^{\alpha_0}\right)
$$

**Parameters:**
- `alpha0` (float): Tail exponent $\alpha_0 \in (0, 1)$.
- `lambda0` (float): Scaling parameter $\lambda_0 > 0$.
- `x` (float): Evaluation point $x > 0$.

**Returns:**
- `float`: $f_{\alpha_0, \lambda_0}(x)$

**Example:**

```python
import optimizr
import numpy as np
import matplotlib.pyplot as plt

# Plot for H₀ = 0.75 → α₀ = 0.375
x = np.linspace(0.01, 20, 500)
f = [optimizr.f_alpha_lambda_py(0.375, 1.0, xi) for xi in x]

plt.figure(figsize=(8, 4))
plt.plot(x, f)
plt.xlabel('x')
plt.ylabel(r'$f_{0.375, 1.0}(x)$')
plt.title('Scaling Function (Theorem 3.1)')
plt.grid(True, alpha=0.3)
plt.show()
```
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---

## Module Architecture

```{mermaid}
graph TD
    MOD["📦 mod.rs\nPublic API re-exports"]
    K["🔧 kernels.rs\nExcitationKernel trait"]
    H["⚡ hawkes.rs\nSimulation & fitting"]
    ML["🔢 mittag_leffler.rs\nSpecial functions"]
    FBM["〰️ mixed_fbm.rs\nFractional Brownian motion"]
    PY["🐍 python_bindings.rs\nPyO3 bindings"]

    MOD --> K
    MOD --> H
    MOD --> ML
    MOD --> FBM
    MOD --> PY

    K --> EK["ExponentialKernel\nφ = α·e^−βt"]
    K --> PLK["PowerLawKernel\nφ = K₀(1+t)^−1−α"]
    K --> CMK["CompletelyMonotoneKernel\nMittag-Leffler"]

    H --> HP["HawkesProcess K\nunivariate · Ogata thinning"]
    H --> BH["BivariateHawkes K\nbuy/sell reaction flow"]

    ML --> mf["mittag_leffler · f_alpha_lambda\ngamma · incomplete_gamma"]

    FBM --> fbm1["FractionalBM\nCholesky & Hosking"]
    FBM --> fbm2["MixedFractionalBM\na·B + b·B^H"]
```

---

## Performance

All computations run in Rust, providing significant speedups over pure Python:

| Operation | n | Rust (optimizr) | Python (pure) | Speedup |
|-----------|---|-----------------|---------------|---------|
| Hawkes simulation (exp) | 10K events | ~2ms | ~150ms | **75×** |
| fBM (Hosking) | 5000 steps | ~15ms | ~800ms | **53×** |
| Hurst estimation (R/S) | 10K points | ~1ms | ~60ms | **60×** |
| Mittag-Leffler | 100 terms | ~5μs | ~300μs | **60×** |

Benchmarks on Apple M1 Pro, single thread. The Hawkes simulation uses Ogata's thinning which depends on the branching ratio — higher branching ratios (closer to 1) produce more events and take longer.
