raptorbt 0.3.4

High-performance Rust backtesting engine with Python bindings. Drop-in VectorBT replacement with up insanely faster performance at fractional memory footprint.

RaptorBT

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.

5,800x faster · 45x smaller · 100% deterministic

---

Quick Install

pip install raptorbt

30-Second Example

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 team.

Table of Contents

• Overview
• Performance
• Architecture
• Installation
• Quick Start
• Strategy Types
• Metrics
• Indicators
• Stop-Loss & Take-Profit
• VectorBT Comparison
• API Reference
• Building from Source
• 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

pip install raptorbt

From Source

cd raptorbt
maturin develop --release

Verify Installation

import raptorbt
print("RaptorBT installed successfully!")

---

Quick Start

Basic Single Instrument Backtest

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.

# 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.

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.

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.

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.

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.

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:

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

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

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

config.set_trailing_stop(0.02)  # 2% trailing stop

Risk-Reward Target

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.

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)

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)

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

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.

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

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

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

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

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

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

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", "TrailingStop", "EndOfData", "Settlement"

---

Building from Source

Prerequisites

• Rust 1.70+ (install via rustup)
• Python 3.10+
• maturin (pip install maturin)

Development Build

cd raptorbt
maturin develop --release

Production Build

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

---

Testing

Rust Unit Tests

cd raptorbt
cargo test

Python Integration Tests

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)

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 for details.

---

Changelog

v0.3.4

• Add single-leg option spread types: LongCall, LongPut, NakedCall, NakedPut to SpreadType enum
• Add ExitReason::Settlement for option expiry settlement exits
• Add leg_expiry_timestamps parameter to run_spread_backtest for per-leg expiry tracking
• Positions are force-closed at settlement when any leg expires, with premiums replaced by intrinsic value
• Prevent re-entry after all legs have expired

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