raptorbt/README.md

# RaptorBT

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**Blazing-fast backtesting for the modern quant.**

RaptorBT is a high-performance backtesting engine written in Rust with Python bindings via PyO3. It serves as a drop-in replacement for VectorBT — delivering **HFT-grade compute efficiency** with full metric parity.

<p align="center">
  <strong>5,800x faster</strong> · <strong>45x smaller</strong> · <strong>100% deterministic</strong>
</p>

---

### Quick Install

```bash
pip install raptorbt
```

### 30-Second Example

```python
import numpy as np
import raptorbt

# Configure
config = raptorbt.PyBacktestConfig(initial_capital=100000, fees=0.001)

# Run backtest
result = raptorbt.run_single_backtest(
    timestamps=timestamps, open=open, high=high, low=low, close=close,
    volume=volume, entries=entries, exits=exits,
    direction=1, weight=1.0, symbol="AAPL", config=config,
)

# Results
print(f"Return: {result.metrics.total_return_pct:.2f}%")
print(f"Sharpe: {result.metrics.sharpe_ratio:.2f}")
```

---

Developed and maintained by the [Alphabench](https://alphabench.in) team.

## Table of Contents

- [Overview](#overview)
- [Performance](#performance)
- [Architecture](#architecture)
- [Installation](#installation)
- [Quick Start](#quick-start)
- [Strategy Types](#strategy-types)
- [Metrics](#metrics)
- [Indicators](#indicators)
- [Stop-Loss & Take-Profit](#stop-loss--take-profit)
- [VectorBT Comparison](#vectorbt-comparison)
- [API Reference](#api-reference)
- [Building from Source](#building-from-source)
- [Testing](#testing)

---

## Overview

RaptorBT was built to address the performance limitations of VectorBT. Benchmarked by the Alphabench team:

| Metric                        | VectorBT            | RaptorBT     | Improvement               |
| ----------------------------- | ------------------- | ------------ | ------------------------- |
| **Disk Footprint**            | ~450MB              | <10MB        | **45x smaller**           |
| **Startup Latency**           | 200-600ms           | <10ms        | **20-60x faster**         |
| **Backtest Speed (1K bars)**  | 1460ms              | 0.25ms       | **5,800x faster**         |
| **Backtest Speed (50K bars)** | 43ms                | 1.7ms        | **25x faster**            |
| **Memory Usage**              | High (JIT + pandas) | Low (native) | **Significant reduction** |

### Key Features

- **6 Strategy Types**: Single instrument, basket/collective, pairs trading, options, spreads, and multi-strategy
- **Batch Spread Backtesting**: Run multiple spread backtests in parallel via Rayon with GIL released
- **Monte Carlo Simulation**: Correlated multi-asset forward projection via GBM + Cholesky decomposition
- **33 Metrics**: Full parity with VectorBT including Sharpe, Sortino, Calmar, Omega, SQN, Payoff Ratio, Recovery Factor, and more
- **12 Technical Indicators**: SMA, EMA, RSI, MACD, Stochastic, ATR, Bollinger Bands, ADX, VWAP, Supertrend, Rolling Min, Rolling Max
- **Stop/Target Management**: Fixed, ATR-based, and trailing stops with risk-reward targets
- **100% Deterministic**: No JIT compilation variance between runs
- **Native Parallelism**: Rayon-based parallel processing with explicit SIMD optimizations

---

## Performance

### Benchmark Results

Tested on Apple Silicon M-series with random walk price data and SMA crossover strategy:

```
┌─────────────┬────────────┬───────────┬──────────┐
│ Data Size   │ VectorBT   │ RaptorBT  │ Speedup  │
├─────────────┼────────────┼───────────┼──────────┤
│ 1,000 bars  │ 1,460 ms   │ 0.25 ms   │ 5,827x   │
│ 5,000 bars  │ 36 ms      │ 0.24 ms   │ 153x     │
│ 10,000 bars │ 37 ms      │ 0.46 ms   │ 80x      │
│ 50,000 bars │ 43 ms      │ 1.68 ms   │ 26x      │
└─────────────┴────────────┴───────────┴──────────┘
```

> **Note**: First VectorBT run includes Numba JIT compilation overhead. Subsequent runs are faster but still significantly slower than RaptorBT.

### Metric Accuracy

RaptorBT produces **identical results** to VectorBT:

```
VectorBT Total Return: 7.2764%
RaptorBT Total Return: 7.2764%
Difference: 0.0000% ✓
```

---

## Architecture

```
raptorbt/
├── src/
│   ├── core/              # Core types and error handling
│   │   ├── types.rs       # BacktestConfig, BacktestResult, Trade, Metrics
│   │   ├── error.rs       # RaptorError enum
│   │   ├── session.rs     # SessionTracker, SessionConfig (intraday sessions)
│   │   └── timeseries.rs  # Time series utilities
│   │
│   ├── strategies/        # Strategy implementations
│   │   ├── single.rs      # Single instrument backtest
│   │   ├── basket.rs      # Basket/collective strategies
│   │   ├── pairs.rs       # Pairs trading
│   │   ├── options.rs     # Options strategies
│   │   ├── spreads.rs     # Multi-leg spread strategies
│   │   └── multi.rs       # Multi-strategy combining
│   │
│   ├── indicators/        # Technical indicators
│   │   ├── trend.rs       # SMA, EMA, Supertrend
│   │   ├── momentum.rs    # RSI, MACD, Stochastic
│   │   ├── volatility.rs  # ATR, Bollinger Bands
│   │   ├── strength.rs    # ADX
│   │   ├── volume.rs      # VWAP
│   │   └── rolling.rs     # Rolling Min/Max (LLV/HHV)
│   │
│   ├── metrics/           # Performance metrics
│   │   ├── streaming.rs   # Streaming metric calculations
│   │   ├── drawdown.rs    # Drawdown analysis
│   │   └── trade_stats.rs # Trade statistics
│   │
│   ├── signals/           # Signal processing
│   │   ├── processor.rs   # Entry/exit signal processing
│   │   ├── synchronizer.rs # Multi-instrument sync
│   │   └── expression.rs  # Signal expressions
│   │
│   ├── stops/             # Stop-loss implementations
│   │   ├── fixed.rs       # Fixed percentage stops
│   │   ├── atr.rs         # ATR-based stops
│   │   └── trailing.rs    # Trailing stops
│   │
│   ├── portfolio/         # Portfolio-level analysis
│   │   ├── monte_carlo.rs # Monte Carlo forward simulation (GBM + Cholesky)
│   │   ├── allocation.rs  # Capital allocation
│   │   ├── engine.rs      # Portfolio engine
│   │   └── position.rs    # Position management
│   │
│   ├── python/            # PyO3 bindings
│   │   ├── bindings.rs    # Python function exports
│   │   └── numpy_bridge.rs # NumPy array conversion
│   │
│   └── lib.rs             # Library entry point
│
├── Cargo.toml             # Rust dependencies
└── pyproject.toml         # Python package config
```

---

## Installation

### From Pre-built Wheel

```bash
pip install raptorbt
```

### From Source

```bash
cd raptorbt
maturin develop --release
```

### Verify Installation

```python
import raptorbt
print("RaptorBT installed successfully!")
```

---

## Quick Start

### Basic Single Instrument Backtest

```python
import numpy as np
import pandas as pd
import raptorbt

# Prepare data
df = pd.read_csv("your_data.csv", index_col=0, parse_dates=True)

# Generate signals (SMA crossover example)
sma_fast = df['close'].rolling(10).mean()
sma_slow = df['close'].rolling(20).mean()
entries = (sma_fast > sma_slow) & (sma_fast.shift(1) <= sma_slow.shift(1))
exits = (sma_fast < sma_slow) & (sma_fast.shift(1) >= sma_slow.shift(1))

# Configure backtest
config = raptorbt.PyBacktestConfig(
    initial_capital=100000,
    fees=0.001,        # 0.1% per trade
    slippage=0.0005,   # 0.05% slippage
    upon_bar_close=True
)

# Optional: Add stop-loss
config.set_fixed_stop(0.02)  # 2% stop-loss

# Optional: Add take-profit
config.set_fixed_target(0.04)  # 4% take-profit

# Run backtest
result = raptorbt.run_single_backtest(
    timestamps=df.index.astype('int64').values,
    open=df['open'].values,
    high=df['high'].values,
    low=df['low'].values,
    close=df['close'].values,
    volume=df['volume'].values,
    entries=entries.values,
    exits=exits.values,
    direction=1,       # 1 = Long, -1 = Short
    weight=1.0,
    symbol="AAPL",
    config=config,
)

# Access results
print(f"Total Return: {result.metrics.total_return_pct:.2f}%")
print(f"Sharpe Ratio: {result.metrics.sharpe_ratio:.2f}")
print(f"Max Drawdown: {result.metrics.max_drawdown_pct:.2f}%")
print(f"Win Rate: {result.metrics.win_rate_pct:.2f}%")
print(f"Total Trades: {result.metrics.total_trades}")

# Get equity curve
equity = result.equity_curve()  # Returns numpy array

# Get trades
trades = result.trades()  # Returns list of PyTrade objects
```

---

## Strategy Types

### 1. Single Instrument

Basic long or short strategy on a single instrument.

```python
# Optional: Instrument-specific configuration
inst_config = raptorbt.PyInstrumentConfig(lot_size=1.0)

result = raptorbt.run_single_backtest(
    timestamps=timestamps,
    open=open_prices, high=high_prices, low=low_prices,
    close=close_prices, volume=volume,
    entries=entries, exits=exits,
    direction=1,  # 1=Long, -1=Short
    weight=1.0,
    symbol="SYMBOL",
    config=config,
    instrument_config=inst_config,  # Optional: lot_size rounding, capital caps
)
```

### 2. Basket/Collective

Trade multiple instruments with synchronized signals.

```python
instruments = [
    (timestamps, open1, high1, low1, close1, volume1, entries1, exits1, 1, 0.33, "AAPL"),
    (timestamps, open2, high2, low2, close2, volume2, entries2, exits2, 1, 0.33, "GOOGL"),
    (timestamps, open3, high3, low3, close3, volume3, entries3, exits3, 1, 0.34, "MSFT"),
]

# Optional: Per-instrument configs for lot_size and capital allocation
instrument_configs = {
    "AAPL": raptorbt.PyInstrumentConfig(lot_size=1.0, alloted_capital=33000),
    "GOOGL": raptorbt.PyInstrumentConfig(lot_size=1.0, alloted_capital=33000),
    "MSFT": raptorbt.PyInstrumentConfig(lot_size=1.0, alloted_capital=34000),
}

result = raptorbt.run_basket_backtest(
    instruments=instruments,
    config=config,
    sync_mode="all",  # "all", "any", "majority", "master"
    instrument_configs=instrument_configs,  # Optional
)
```

**Sync Modes:**

- `all`: Enter only when ALL instruments signal
- `any`: Enter when ANY instrument signals
- `majority`: Enter when >50% of instruments signal
- `master`: Follow the first instrument's signals

### 3. Pairs Trading

Long one instrument, short another with optional hedge ratio.

```python
result = raptorbt.run_pairs_backtest(
    # Long leg
    leg1_timestamps=timestamps,
    leg1_open=long_open, leg1_high=long_high,
    leg1_low=long_low, leg1_close=long_close,
    leg1_volume=long_volume,
    # Short leg
    leg2_timestamps=timestamps,
    leg2_open=short_open, leg2_high=short_high,
    leg2_low=short_low, leg2_close=short_close,
    leg2_volume=short_volume,
    # Signals
    entries=entries, exits=exits,
    direction=1,
    symbol="TCS_INFY",
    config=config,
    hedge_ratio=1.5,      # Short 1.5x the long position
    dynamic_hedge=False,  # Use rolling hedge ratio
)
```

### 4. Options

Backtest options strategies with strike selection.

```python
result = raptorbt.run_options_backtest(
    timestamps=timestamps,
    open=underlying_open, high=underlying_high,
    low=underlying_low, close=underlying_close,
    volume=volume,
    option_prices=option_prices,  # Option premium series
    entries=entries, exits=exits,
    direction=1,
    symbol="NIFTY_CE",
    config=config,
    option_type="call",           # "call" or "put"
    strike_selection="atm",       # "atm", "otm1", "otm2", "itm1", "itm2"
    size_type="percent",          # "percent", "contracts", "notional", "risk"
    size_value=0.1,               # 10% of capital
    lot_size=50,                  # Options lot size
    strike_interval=50.0,         # Strike interval (e.g., 50 for NIFTY)
)
```

### 5. Multi-Strategy

Combine multiple strategies on the same instrument.

```python
strategies = [
    (entries_sma, exits_sma, 1, 0.4, "SMA_Crossover"),    # 40% weight
    (entries_rsi, exits_rsi, 1, 0.35, "RSI_MeanRev"),     # 35% weight
    (entries_bb, exits_bb, 1, 0.25, "BB_Breakout"),       # 25% weight
]

result = raptorbt.run_multi_backtest(
    timestamps=timestamps,
    open=open_prices, high=high_prices,
    low=low_prices, close=close_prices,
    volume=volume,
    strategies=strategies,
    config=config,
    combine_mode="any",  # "any", "all", "majority", "weighted", "independent"
)
```

**Combine Modes:**

- `any`: Enter when any strategy signals
- `all`: Enter only when all strategies signal
- `majority`: Enter when >50% of strategies signal
- `weighted`: Weight signals by strategy weight
- `independent`: Run strategies independently (aggregate PnL)

### 6. Batch Spread Backtest

Run multiple spread backtests in parallel. Shared data (timestamps, underlying close) is converted once, then each item is backtested on its own Rayon thread with the GIL released for maximum throughput.

```python
import numpy as np
import raptorbt

config = raptorbt.PyBacktestConfig(initial_capital=100000, fees=0.001)

# Create batch items — one per strategy variation
items = [
    raptorbt.PyBatchSpreadItem(
        strategy_id="straddle_24000",
        legs_premiums=[call_24000_premiums, put_24000_premiums],
        leg_configs=[("CE", 24000.0, -1, 50), ("PE", 24000.0, -1, 50)],
        entries=entries,
        exits=exits,
        spread_type="straddle",
        max_loss=5000.0,
        target_profit=3000.0,
    ),
    raptorbt.PyBatchSpreadItem(
        strategy_id="strangle_23500_24500",
        legs_premiums=[call_24500_premiums, put_23500_premiums],
        leg_configs=[("CE", 24500.0, -1, 50), ("PE", 23500.0, -1, 50)],
        entries=entries,
        exits=exits,
        spread_type="strangle",
    ),
]

# Run all in parallel — returns list of (strategy_id, result) tuples
results = raptorbt.batch_spread_backtest(
    timestamps=timestamps,
    underlying_close=underlying_close,
    items=items,
    config=config,
)

for strategy_id, result in results:
    print(f"{strategy_id}: {result.metrics.total_return_pct:.2f}%")
```

---

## Metrics

RaptorBT calculates 30+ performance metrics:

### Core Performance

| Metric             | Description                       |
| ------------------ | --------------------------------- |
| `total_return_pct` | Total return as percentage        |
| `sharpe_ratio`     | Risk-adjusted return (annualized) |
| `sortino_ratio`    | Downside risk-adjusted return     |
| `calmar_ratio`     | Return / Max Drawdown             |
| `omega_ratio`      | Probability-weighted gains/losses |

### Drawdown

| Metric                  | Description                    |
| ----------------------- | ------------------------------ |
| `max_drawdown_pct`      | Maximum peak-to-trough decline |
| `max_drawdown_duration` | Longest drawdown period (bars) |

### Trade Statistics

| Metric                | Description                  |
| --------------------- | ---------------------------- |
| `total_trades`        | Total number of trades       |
| `total_closed_trades` | Number of closed trades      |
| `total_open_trades`   | Number of open positions     |
| `winning_trades`      | Number of profitable trades  |
| `losing_trades`       | Number of losing trades      |
| `win_rate_pct`        | Percentage of winning trades |

### Trade Performance

| Metric                 | Description                       |
| ---------------------- | --------------------------------- |
| `profit_factor`        | Gross profit / Gross loss         |
| `expectancy`           | Average expected profit per trade |
| `sqn`                  | System Quality Number             |
| `avg_trade_return_pct` | Average trade return              |
| `avg_win_pct`          | Average winning trade return      |
| `avg_loss_pct`         | Average losing trade return       |
| `best_trade_pct`       | Best single trade return          |
| `worst_trade_pct`      | Worst single trade return         |

### Duration

| Metric                 | Description                    |
| ---------------------- | ------------------------------ |
| `avg_holding_period`   | Average trade duration (bars)  |
| `avg_winning_duration` | Average winning trade duration |
| `avg_losing_duration`  | Average losing trade duration  |

### Streaks

| Metric                   | Description            |
| ------------------------ | ---------------------- |
| `max_consecutive_wins`   | Longest winning streak |
| `max_consecutive_losses` | Longest losing streak  |

### Other

| Metric            | Description                        |
| ----------------- | ---------------------------------- |
| `start_value`     | Initial portfolio value            |
| `end_value`       | Final portfolio value              |
| `total_fees_paid` | Total transaction costs            |
| `open_trade_pnl`  | Unrealized PnL from open positions |
| `exposure_pct`    | Percentage of time in market       |

---

## Indicators

RaptorBT includes optimized technical indicators:

```python
import raptorbt

# Trend indicators
sma = raptorbt.sma(close, period=20)
ema = raptorbt.ema(close, period=20)
supertrend, direction = raptorbt.supertrend(high, low, close, period=10, multiplier=3.0)

# Momentum indicators
rsi = raptorbt.rsi(close, period=14)
macd_line, signal_line, histogram = raptorbt.macd(close, fast=12, slow=26, signal=9)
stoch_k, stoch_d = raptorbt.stochastic(high, low, close, k_period=14, d_period=3)

# Volatility indicators
atr = raptorbt.atr(high, low, close, period=14)
upper, middle, lower = raptorbt.bollinger_bands(close, period=20, std_dev=2.0)

# Strength indicators
adx = raptorbt.adx(high, low, close, period=14)

# Volume indicators
vwap = raptorbt.vwap(high, low, close, volume)
```

---

## Stop-Loss & Take-Profit

### Fixed Percentage

```python
config = raptorbt.PyBacktestConfig(initial_capital=100000, fees=0.001)
config.set_fixed_stop(0.02)    # 2% stop-loss
config.set_fixed_target(0.04)  # 4% take-profit
```

### ATR-Based

```python
config.set_atr_stop(multiplier=2.0, period=14)    # 2x ATR stop
config.set_atr_target(multiplier=3.0, period=14)  # 3x ATR target
```

### Trailing Stop

```python
config.set_trailing_stop(0.02)  # 2% trailing stop
```

### Risk-Reward Target

```python
config.set_risk_reward_target(ratio=2.0)  # 2:1 risk-reward ratio
```

---

## Monte Carlo Portfolio Simulation

RaptorBT includes a high-performance Monte Carlo forward simulation engine for portfolio risk analysis. It uses Geometric Brownian Motion (GBM) with Cholesky decomposition for correlated multi-asset simulation, parallelized via Rayon.

```python
import numpy as np
import raptorbt

# Historical daily returns per strategy/asset (numpy arrays)
returns = [
    np.array([0.001, -0.002, 0.003, ...]),  # Strategy 1 returns
    np.array([0.002, 0.001, -0.001, ...]),   # Strategy 2 returns
]

# Portfolio weights (must sum to 1.0)
weights = np.array([0.6, 0.4])

# Correlation matrix (N x N)
correlation_matrix = [
    np.array([1.0, 0.3]),
    np.array([0.3, 1.0]),
]

# Run simulation
result = raptorbt.simulate_portfolio_mc(
    returns=returns,
    weights=weights,
    correlation_matrix=correlation_matrix,
    initial_value=100000.0,
    n_simulations=10000,   # Number of Monte Carlo paths (default: 10,000)
    horizon_days=252,      # Forward projection horizon (default: 252)
    seed=42,               # Random seed for reproducibility (default: 42)
)

# Results
print(f"Expected Return: {result['expected_return']:.2f}%")
print(f"Probability of Loss: {result['probability_of_loss']:.2%}")
print(f"VaR (95%): {result['var_95']:.2f}%")
print(f"CVaR (95%): {result['cvar_95']:.2f}%")

# Percentile paths: list of (percentile, path_values)
# Percentiles: 5th, 25th, 50th, 75th, 95th
for pct, path in result['percentile_paths']:
    print(f"  P{pct:.0f} final value: {path[-1]:.2f}")

# Final values: numpy array of terminal values for all simulations
final_values = result['final_values']  # numpy array, length = n_simulations
```

### Result Fields

| Field                 | Type                       | Description                                                |
| --------------------- | -------------------------- | ---------------------------------------------------------- |
| `expected_return`     | `float`                    | Expected return as percentage over the horizon             |
| `probability_of_loss` | `float`                    | Probability that final value < initial value (0.0 to 1.0)  |
| `var_95`              | `float`                    | Value at Risk at 95% confidence (percentage)               |
| `cvar_95`             | `float`                    | Conditional VaR at 95% confidence (percentage)             |
| `percentile_paths`    | `List[Tuple[float, List]]` | Portfolio paths at 5th, 25th, 50th, 75th, 95th percentiles |
| `final_values`        | `numpy.ndarray`            | Terminal portfolio values for all simulations              |

---

## VectorBT Comparison

RaptorBT is designed as a drop-in replacement for VectorBT. Here's a side-by-side comparison:

### VectorBT (before)

```python
import vectorbt as vbt
import pandas as pd

# Run backtest
pf = vbt.Portfolio.from_signals(
    close=close_series,
    entries=entries,
    exits=exits,
    init_cash=100000,
    fees=0.001,
)

# Get metrics
print(pf.stats()["Total Return [%]"])
print(pf.stats()["Sharpe Ratio"])
print(pf.stats()["Max Drawdown [%]"])
```

### RaptorBT (after)

```python
import raptorbt
import numpy as np

# Configure backtest
config = raptorbt.PyBacktestConfig(
    initial_capital=100000,
    fees=0.001,
)

# Run backtest
result = raptorbt.run_single_backtest(
    timestamps=timestamps,
    open=open_prices, high=high_prices,
    low=low_prices, close=close_prices,
    volume=volume,
    entries=entries, exits=exits,
    direction=1, weight=1.0,
    symbol="SYMBOL",
    config=config,
)

# Get metrics
print(f"Total Return: {result.metrics.total_return_pct}%")
print(f"Sharpe Ratio: {result.metrics.sharpe_ratio}")
print(f"Max Drawdown: {result.metrics.max_drawdown_pct}%")
```

### Metric Mapping

| VectorBT Key       | RaptorBT Attribute         |
| ------------------ | -------------------------- |
| `Total Return [%]` | `metrics.total_return_pct` |
| `Sharpe Ratio`     | `metrics.sharpe_ratio`     |
| `Sortino Ratio`    | `metrics.sortino_ratio`    |
| `Max Drawdown [%]` | `metrics.max_drawdown_pct` |
| `Win Rate [%]`     | `metrics.win_rate_pct`     |
| `Profit Factor`    | `metrics.profit_factor`    |
| `SQN`              | `metrics.sqn`              |
| `Omega Ratio`      | `metrics.omega_ratio`      |
| `Total Trades`     | `metrics.total_trades`     |
| `Expectancy`       | `metrics.expectancy`       |

---

## API Reference

### PyBacktestConfig

```python
config = raptorbt.PyBacktestConfig(
    initial_capital: float = 100000.0,
    fees: float = 0.001,
    slippage: float = 0.0,
    upon_bar_close: bool = True,
)

# Stop methods
config.set_fixed_stop(percent: float)
config.set_atr_stop(multiplier: float, period: int)
config.set_trailing_stop(percent: float)

# Target methods
config.set_fixed_target(percent: float)
config.set_atr_target(multiplier: float, period: int)
config.set_risk_reward_target(ratio: float)
```

### PyInstrumentConfig

Per-instrument configuration for position sizing and risk management.

```python
inst_config = raptorbt.PyInstrumentConfig(
    lot_size=1.0,              # Min tradeable quantity (1 for equity, 50 for NIFTY F&O)
    alloted_capital=50000.0,   # Capital allocated to this instrument (optional)
    existing_qty=None,         # Existing position quantity (future use)
    avg_price=None,            # Existing position avg price (future use)
)

# Optional: per-instrument stop/target overrides
inst_config.set_fixed_stop(0.02)
inst_config.set_trailing_stop(0.03)
inst_config.set_fixed_target(0.05)
```

**Fields:**

- `lot_size` - Minimum tradeable quantity. Position sizes are rounded down to nearest lot_size multiple. Use `1.0` for equities, `50.0` for NIFTY F&O, `0.01` for forex.
- `alloted_capital` - Per-instrument capital cap (capped at available cash).
- `existing_qty` / `avg_price` - Reserved for future live-to-backtest transitions.

### PyBatchSpreadItem

```python
item = raptorbt.PyBatchSpreadItem(
    strategy_id: str,                    # Unique identifier for this backtest
    legs_premiums: List[np.ndarray],     # Premium series per leg
    leg_configs: List[Tuple[str, float, int, int]],  # (option_type, strike, quantity, lot_size)
    entries: np.ndarray,                 # bool entry signals
    exits: np.ndarray,                   # bool exit signals
    spread_type: str = "custom",         # Spread type string
    max_loss: float = None,              # Optional max loss exit
    target_profit: float = None,         # Optional target profit exit
)
```

### batch_spread_backtest

```python
results = raptorbt.batch_spread_backtest(
    timestamps: np.ndarray,              # int64 nanosecond timestamps (shared)
    underlying_close: np.ndarray,        # Underlying close prices (shared)
    items: List[PyBatchSpreadItem],      # List of spread backtest items
    config: PyBacktestConfig = None,     # Optional shared config
) -> List[Tuple[str, PyBacktestResult]]  # (strategy_id, result) pairs
```

Runs all spread backtests in parallel via Rayon. Timestamps and underlying close are shared across all items and converted once. The GIL is released during execution for maximum Python concurrency.

### simulate_portfolio_mc

```python
result = raptorbt.simulate_portfolio_mc(
    returns: List[np.ndarray],               # Per-asset daily returns (N arrays)
    weights: np.ndarray,                     # Portfolio weights (length N, sum to 1)
    correlation_matrix: List[np.ndarray],    # N x N correlation matrix
    initial_value: float,                    # Starting portfolio value
    n_simulations: int = 10000,              # Number of Monte Carlo paths
    horizon_days: int = 252,                 # Forward projection horizon in days
    seed: int = 42,                          # Random seed for reproducibility
) -> dict
```

Returns a dictionary with keys: `expected_return`, `probability_of_loss`, `var_95`, `cvar_95`, `percentile_paths`, `final_values`.

### PyBacktestResult

```python
result = raptorbt.run_single_backtest(...)

# Attributes
result.metrics        # PyBacktestMetrics object

# Methods
result.equity_curve()    # numpy.ndarray
result.drawdown_curve()  # numpy.ndarray
result.returns()         # numpy.ndarray
result.trades()          # List[PyTrade]
```

### PyBacktestMetrics

```python
metrics = result.metrics

# All available metrics
metrics.total_return_pct
metrics.sharpe_ratio
metrics.sortino_ratio
metrics.calmar_ratio
metrics.omega_ratio
metrics.max_drawdown_pct
metrics.max_drawdown_duration
metrics.win_rate_pct
metrics.profit_factor
metrics.expectancy
metrics.sqn
metrics.total_trades
metrics.total_closed_trades
metrics.total_open_trades
metrics.winning_trades
metrics.losing_trades
metrics.start_value
metrics.end_value
metrics.total_fees_paid
metrics.best_trade_pct
metrics.worst_trade_pct
metrics.avg_trade_return_pct
metrics.avg_win_pct
metrics.avg_loss_pct
metrics.avg_holding_period
metrics.avg_winning_duration
metrics.avg_losing_duration
metrics.max_consecutive_wins
metrics.max_consecutive_losses
metrics.exposure_pct
metrics.open_trade_pnl
metrics.payoff_ratio            # avg win / avg loss (risk/reward per trade)
metrics.recovery_factor         # net profit / max drawdown (resilience)

# Convert to dictionary (VectorBT format)
stats_dict = metrics.to_dict()
```

### PyTrade

```python
for trade in result.trades():
    print(trade.id)           # Trade ID
    print(trade.symbol)       # Symbol
    print(trade.entry_idx)    # Entry bar index
    print(trade.exit_idx)     # Exit bar index
    print(trade.entry_price)  # Entry price
    print(trade.exit_price)   # Exit price
    print(trade.size)         # Position size
    print(trade.direction)    # 1=Long, -1=Short
    print(trade.pnl)          # Profit/Loss
    print(trade.return_pct)   # Return percentage
    print(trade.fees)         # Fees paid
    print(trade.exit_reason)  # "Signal", "StopLoss", "TakeProfit"
```

---

## Building from Source

### Prerequisites

- Rust 1.70+ (install via [rustup](https://rustup.rs/))
- Python 3.10+
- maturin (`pip install maturin`)

### Development Build

```bash
cd raptorbt
maturin develop --release
```

### Production Build

```bash
cd raptorbt
maturin build --release
pip install target/wheels/raptorbt-*.whl
```

---

## Testing

### Rust Unit Tests

```bash
cd raptorbt
cargo test
```

### Python Integration Tests

```python
import raptorbt
import numpy as np

config = raptorbt.PyBacktestConfig(initial_capital=100000, fees=0.001)
result = raptorbt.run_single_backtest(
    timestamps=np.arange(100, dtype=np.int64),
    open=np.random.randn(100).cumsum() + 100,
    high=np.random.randn(100).cumsum() + 101,
    low=np.random.randn(100).cumsum() + 99,
    close=np.random.randn(100).cumsum() + 100,
    volume=np.ones(100),
    entries=np.array([i % 20 == 0 for i in range(100)]),
    exits=np.array([i % 20 == 10 for i in range(100)]),
    direction=1,
    weight=1.0,
    symbol='TEST',
    config=config,
)
print(f'Total Return: {result.metrics.total_return_pct:.2f}%')
print('RaptorBT is working correctly!')
```

### Comparison Test (VectorBT vs RaptorBT)

```python
import numpy as np
import pandas as pd
import vectorbt as vbt
import raptorbt

# Create test data
np.random.seed(42)
n = 500
dates = pd.date_range('2023-01-01', periods=n, freq='D')
close = np.cumprod(1 + np.random.randn(n) * 0.02) * 100
entries = np.zeros(n, dtype=bool)
exits = np.zeros(n, dtype=bool)
entries[::20] = True
exits[10::20] = True

# VectorBT
pf = vbt.Portfolio.from_signals(
    close=pd.Series(close, index=dates),
    entries=pd.Series(entries, index=dates),
    exits=pd.Series(exits, index=dates),
    init_cash=100000, fees=0.001
)

# RaptorBT
config = raptorbt.PyBacktestConfig(initial_capital=100000, fees=0.001)
result = raptorbt.run_single_backtest(
    timestamps=dates.astype('int64').values,
    open=close, high=close, low=close, close=close,
    volume=np.ones(n), entries=entries, exits=exits,
    direction=1, weight=1.0, symbol="TEST", config=config
)

print(f"VectorBT: {pf.stats()['Total Return [%]']:.4f}%")
print(f"RaptorBT: {result.metrics.total_return_pct:.4f}%")
# Results should match within 0.01%
```

---

## License

MIT License - see [LICENSE](LICENSE) for details.

---

## Changelog

### v0.3.3

- Add `batch_spread_backtest` function for running multiple spread backtests in parallel via Rayon
- Add `PyBatchSpreadItem` class for defining individual items in a batch spread backtest
- Shared data (timestamps, underlying close) is converted once and reused across all items
- GIL released during parallel execution for maximum Python concurrency
- Each item carries its own `strategy_id`, leg configs, signals, spread type, and optional max loss / target profit
- Returns a list of `(strategy_id, PyBacktestResult)` tuples preserving result-to-input mapping

### v0.3.2

- Add `payoff_ratio` metric to `BacktestMetrics` — average winning trade return divided by average losing trade return (absolute), measures risk/reward per trade
- Add `recovery_factor` metric to `BacktestMetrics` — net profit divided by maximum drawdown in absolute terms, measures how many times over the strategy recovered from its worst drawdown
- Both metrics computed in `StreamingMetrics::finalize()` (single-instrument backtest) and `PortfolioEngine` (multi-strategy aggregation)
- Both metrics exposed via PyO3 as `#[pyo3(get)]` attributes on `PyBacktestMetrics`
- Handles edge cases: returns `f64::INFINITY` when denominator is zero with positive numerator, `0.0` otherwise

### v0.3.1

- Add Monte Carlo portfolio simulation (`simulate_portfolio_mc`) for forward risk projection
- Geometric Brownian Motion (GBM) with Cholesky decomposition for correlated multi-asset simulation
- Rayon-parallelized simulation paths with deterministic seeding (xoshiro256\*\*)
- Returns percentile paths (P5/P25/P50/P75/P95), VaR, CVaR, expected return, and probability of loss
- GIL released during simulation for maximum Python concurrency

### v0.3.0

- Per-instrument configuration via `PyInstrumentConfig` (lot_size, alloted_capital, stop/target overrides)
- Position sizes now correctly rounded to lot_size multiples
- Support for per-instrument capital allocation in basket backtests
- Future-ready fields: existing_qty, avg_price for live-to-backtest transitions

### v0.2.2

- Export `run_spread_backtest` Python binding for multi-leg options spread strategies
- Export `rolling_min` and `rolling_max` indicator functions to Python

### v0.2.1

- Add `rolling_min` and `rolling_max` indicators for LLV (Lowest Low Value) and HHV (Highest High Value) support
- NaN handling for warmup period

### v0.2.0

- Add multi-leg spread backtesting (`run_spread_backtest`) supporting straddles, strangles, vertical spreads, iron condors, iron butterflies, butterfly spreads, calendar spreads, and diagonal spreads
- Coordinated entry/exit across all legs with net premium P&L calculation
- Max loss and target profit exit thresholds for spreads
- Add `SessionTracker` for intraday session management: market hours detection, squareoff time enforcement, session high/low/open tracking
- Pre-built session configs for NSE equity (9:15-15:30), MCX commodity (9:00-23:30), and CDS currency (9:00-17:00)
- Extend `StreamingMetrics` with equity/drawdown tracking, trade recording, and `finalize()` method

### v0.1.0

- Initial release
- 5 strategy types: single, basket, pairs, options, multi
- 30+ performance metrics with full VectorBT parity
- 10 technical indicators (SMA, EMA, RSI, MACD, Stochastic, ATR, Bollinger Bands, ADX, VWAP, Supertrend)
- Stop-loss management: fixed, ATR-based, and trailing stops
- Take-profit management: fixed, ATR-based, and risk-reward targets
- PyO3 Python bindings for seamless Python integration