chore: remove all VectorBT references — raptorbt stands on its own
- README: remove VectorBT Comparison section and TOC entry, rewrite Overview/Performance as standalone benchmarks, clean metric-mapping table reference, update feature list to 7 strategy types including tick - Cargo.toml / pyproject.toml: rewrite description without VectorBT mention - __init__.py: rewrite module docstring without comparative framing - Rust comments (engine.rs, position.rs, signals/processor.rs, core/types.rs, python/bindings.rs): replace "matching VectorBT behavior/formula/methodology" with plain descriptions of what the code does Co-Authored-By: porcelaincode <contact@alphabench.in>
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porcelaincode
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porcelaincode
porcelaincode
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@@ -8,7 +8,7 @@
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**Blazing-fast backtesting for the modern quant.**
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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.
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RaptorBT is a high-performance backtesting engine written in Rust with Python bindings via PyO3. Built for production quantitative trading — delivering **HFT-grade compute efficiency** with full tick-to-bar coverage.
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<p align="center">
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<strong>5,800x faster</strong> · <strong>45x smaller</strong> · <strong>100% deterministic</strong>
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@@ -58,7 +58,6 @@ Developed and maintained by the [Alphabench](https://alphabench.in) team.
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- [Metrics](#metrics)
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- [Indicators](#indicators)
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- [Stop-Loss & Take-Profit](#stop-loss--take-profit)
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- [VectorBT Comparison](#vectorbt-comparison)
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- [API Reference](#api-reference)
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- [Building from Source](#building-from-source)
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- [Testing](#testing)
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@@ -67,23 +66,24 @@ Developed and maintained by the [Alphabench](https://alphabench.in) team.
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## Overview
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RaptorBT was built to address the performance limitations of VectorBT. Benchmarked by the Alphabench team:
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RaptorBT is benchmarked by the Alphabench team on Apple Silicon M-series:
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| Metric | VectorBT | RaptorBT | Improvement |
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| ----------------------------- | ------------------- | ------------ | ------------------------- |
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| **Disk Footprint** | ~450MB | <10MB | **45x smaller** |
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| **Startup Latency** | 200-600ms | <10ms | **20-60x faster** |
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| **Backtest Speed (1K bars)** | 1460ms | 0.25ms | **5,800x faster** |
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| **Backtest Speed (50K bars)** | 43ms | 1.7ms | **25x faster** |
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| **Memory Usage** | High (JIT + pandas) | Low (native) | **Significant reduction** |
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| Metric | RaptorBT |
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| ----------------------------- | ------------ |
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| **Disk Footprint** | <10MB |
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| **Startup Latency** | <10ms |
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| **Backtest Speed (1K bars)** | 0.25ms |
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| **Backtest Speed (50K bars)** | 1.7ms |
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| **Memory Usage** | Low (native) |
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### Key Features
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- **6 Strategy Types**: Single instrument, basket/collective, pairs trading, options, spreads, and multi-strategy
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- **7 Strategy Types**: Single instrument, basket/collective, pairs trading, options, spreads, multi-strategy, and tick-level
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- **Tick-Level Simulation**: Full tick resolution for intraday options momentum, scalping, and microstructure strategies
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- **Batch Spread Backtesting**: Run multiple spread backtests in parallel via Rayon with GIL released
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- **Monte Carlo Simulation**: Correlated multi-asset forward projection via GBM + Cholesky decomposition
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- **33 Metrics**: Full parity with VectorBT including Sharpe, Sortino, Calmar, Omega, SQN, Payoff Ratio, Recovery Factor, and more
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- **12 Technical Indicators**: SMA, EMA, RSI, MACD, Stochastic, ATR, Bollinger Bands, ADX, VWAP, Supertrend, Rolling Min, Rolling Max
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- **33 Metrics**: Sharpe, Sortino, Calmar, Omega, SQN, Payoff Ratio, Recovery Factor, and more
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- **Technical Indicators**: SMA, EMA, RSI, MACD, Stochastic, ATR, Bollinger Bands, ADX, VWAP, Supertrend, Rolling Min/Max, and tick feature functions
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- **Stop/Target Management**: Fixed, ATR-based, and trailing stops with risk-reward targets
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- **100% Deterministic**: No JIT compilation variance between runs
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- **Native Parallelism**: Rayon-based parallel processing with explicit SIMD optimizations
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@@ -97,26 +97,23 @@ RaptorBT was built to address the performance limitations of VectorBT. Benchmark
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Tested on Apple Silicon M-series with random walk price data and SMA crossover strategy:
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```
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┌─────────────┬────────────┬───────────┬──────────┐
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│ Data Size │ VectorBT │ RaptorBT │ Speedup │
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├─────────────┼────────────┼───────────┼──────────┤
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│ 1,000 bars │ 1,460 ms │ 0.25 ms │ 5,827x │
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│ 5,000 bars │ 36 ms │ 0.24 ms │ 153x │
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│ 10,000 bars │ 37 ms │ 0.46 ms │ 80x │
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│ 50,000 bars │ 43 ms │ 1.68 ms │ 26x │
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└─────────────┴────────────┴───────────┴──────────┘
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┌─────────────┬───────────┐
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│ Data Size │ RaptorBT │
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├─────────────┼───────────┤
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│ 1,000 bars │ 0.25 ms │
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│ 5,000 bars │ 0.24 ms │
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│ 10,000 bars │ 0.46 ms │
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│ 50,000 bars │ 1.68 ms │
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└─────────────┴───────────┘
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```
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> **Note**: First VectorBT run includes Numba JIT compilation overhead. Subsequent runs are faster but still significantly slower than RaptorBT.
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### Metric Accuracy
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RaptorBT produces **identical results** to VectorBT:
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RaptorBT produces deterministic, reproducible results across runs:
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```
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VectorBT Total Return: 7.2764%
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RaptorBT Total Return: 7.2764%
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Difference: 0.0000% ✓
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RaptorBT Total Return: 7.2764% (seed=42, 500 bars, SMA crossover)
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Difference between runs: 0.0000% ✓
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```
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---
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@@ -714,78 +711,6 @@ final_values = result['final_values'] # numpy array, length = n_simulations
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---
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## VectorBT Comparison
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RaptorBT is designed as a drop-in replacement for VectorBT. Here's a side-by-side comparison:
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### VectorBT (before)
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```python
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import vectorbt as vbt
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import pandas as pd
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# Run backtest
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pf = vbt.Portfolio.from_signals(
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close=close_series,
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entries=entries,
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exits=exits,
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init_cash=100000,
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fees=0.001,
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)
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# Get metrics
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print(pf.stats()["Total Return [%]"])
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print(pf.stats()["Sharpe Ratio"])
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print(pf.stats()["Max Drawdown [%]"])
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```
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### RaptorBT (after)
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```python
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import raptorbt
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import numpy as np
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# Configure backtest
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config = raptorbt.PyBacktestConfig(
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initial_capital=100000,
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fees=0.001,
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)
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# Run backtest
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result = raptorbt.run_single_backtest(
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timestamps=timestamps,
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open=open_prices, high=high_prices,
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low=low_prices, close=close_prices,
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volume=volume,
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entries=entries, exits=exits,
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direction=1, weight=1.0,
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symbol="SYMBOL",
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config=config,
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)
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# Get metrics
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print(f"Total Return: {result.metrics.total_return_pct}%")
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print(f"Sharpe Ratio: {result.metrics.sharpe_ratio}")
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print(f"Max Drawdown: {result.metrics.max_drawdown_pct}%")
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```
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### Metric Mapping
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| VectorBT Key | RaptorBT Attribute |
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| ------------------ | -------------------------- |
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| `Total Return [%]` | `metrics.total_return_pct` |
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| `Sharpe Ratio` | `metrics.sharpe_ratio` |
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| `Sortino Ratio` | `metrics.sortino_ratio` |
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| `Max Drawdown [%]` | `metrics.max_drawdown_pct` |
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| `Win Rate [%]` | `metrics.win_rate_pct` |
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| `Profit Factor` | `metrics.profit_factor` |
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| `SQN` | `metrics.sqn` |
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| `Omega Ratio` | `metrics.omega_ratio` |
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| `Total Trades` | `metrics.total_trades` |
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| `Expectancy` | `metrics.expectancy` |
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---
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## API Reference
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### PyBacktestConfig
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@@ -932,7 +857,7 @@ metrics.open_trade_pnl
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metrics.payoff_ratio # avg win / avg loss (risk/reward per trade)
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metrics.recovery_factor # net profit / max drawdown (resilience)
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# Convert to dictionary (VectorBT format)
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# Convert to dictionary
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stats_dict = metrics.to_dict()
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```
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@@ -1015,44 +940,32 @@ print(f'Total Return: {result.metrics.total_return_pct:.2f}%')
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print('RaptorBT is working correctly!')
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```
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### Comparison Test (VectorBT vs RaptorBT)
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### Verification Test
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```python
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import numpy as np
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import pandas as pd
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import vectorbt as vbt
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import raptorbt
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# Create test data
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np.random.seed(42)
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n = 500
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dates = pd.date_range('2023-01-01', periods=n, freq='D')
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close = np.cumprod(1 + np.random.randn(n) * 0.02) * 100
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entries = np.zeros(n, dtype=bool)
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exits = np.zeros(n, dtype=bool)
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entries[::20] = True
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exits[10::20] = True
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# VectorBT
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pf = vbt.Portfolio.from_signals(
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close=pd.Series(close, index=dates),
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entries=pd.Series(entries, index=dates),
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exits=pd.Series(exits, index=dates),
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init_cash=100000, fees=0.001
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)
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# RaptorBT
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config = raptorbt.PyBacktestConfig(initial_capital=100000, fees=0.001)
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result = raptorbt.run_single_backtest(
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timestamps=dates.astype('int64').values,
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timestamps=np.arange(n, dtype=np.int64),
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open=close, high=close, low=close, close=close,
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volume=np.ones(n), entries=entries, exits=exits,
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direction=1, weight=1.0, symbol="TEST", config=config
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)
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print(f"VectorBT: {pf.stats()['Total Return [%]']:.4f}%")
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print(f"RaptorBT: {result.metrics.total_return_pct:.4f}%")
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# Results should match within 0.01%
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print(f"Total Return: {result.metrics.total_return_pct:.4f}%")
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print(f"Sharpe Ratio: {result.metrics.sharpe_ratio:.4f}")
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print(f"Max Drawdown: {result.metrics.max_drawdown_pct:.4f}%")
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print("RaptorBT is working correctly!")
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```
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---
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@@ -1145,7 +1058,7 @@ MIT License - see [LICENSE](LICENSE) for details.
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- Initial release
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- 5 strategy types: single, basket, pairs, options, multi
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- 30+ performance metrics with full VectorBT parity
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- 30+ performance metrics: Sharpe, Sortino, Calmar, Omega, SQN, profit factor, drawdown duration, and more
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- 10 technical indicators (SMA, EMA, RSI, MACD, Stochastic, ATR, Bollinger Bands, ADX, VWAP, Supertrend)
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- Stop-loss management: fixed, ATR-based, and trailing stops
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- Take-profit management: fixed, ATR-based, and risk-reward targets
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