ammBT - AMM Backtesting Engine

High-performance vectorized backtesting engine for AMM liquidity provider positions.

Inspired by vectorbt, ammBT enables testing thousands of LP strategies simultaneously using vectorized operations and Numba compilation.

Features

• Multi-AMM Support: Uniswap v2 (CPAMM), Uniswap v3 (CLMM), Meteora (DLMM)
• Vectorized Backtesting: Test thousands of strategy variants in seconds
• Path-Dependent Simulation: Accurate swap-by-swap pool state simulation
• Comprehensive Metrics: IL, fees, PnL, Sharpe ratio, capital efficiency
• Interactive Visualization: Plotly-based charts and heatmaps

Architecture

ammBT uses a hybrid approach:

• Sequential (Numba): Swap processing through time (path-dependent)
• Vectorized (NumPy): Parallel strategy evaluation across parameter space

This allows testing N strategies with time complexity of O(swaps) instead of O(swaps × N).

Supported AMM Types

Uniswap v2 (CPAMM)

• Constant product formula: x × y = k
• Full-range liquidity
• Fixed 0.3% fee

Uniswap v3 (CLMM)

• Concentrated liquidity with tick ranges
• Multiple fee tiers (0.05%, 0.3%, 1%)
• Capital efficiency through range orders

Meteora DLMM

• Bin-based liquidity distribution
• Dynamic fees based on volatility
• Solana-native implementation

Status

⚠️ Work in Progress - This project is under active development.

Installation (Development)

git clone <repo>
cd ammbt
pip install -r requirements.txt
pip install -e .
python -c "import ammbt; print('ammbt installed successfully')"

Quick Start

import pandas as pd
import numpy as np
import ammbt as amm

# Create swap data (or load from your data source)
np.random.seed(42)
n_swaps = 1000
prices = 1000 * np.exp(np.cumsum(np.random.randn(n_swaps) * 0.01))
swaps = pd.DataFrame({
    'amount0': np.random.randn(n_swaps) * 10,
    'amount1': np.random.randn(n_swaps) * 10000,
    'price': prices,
})

# Define strategy space
strategies = {
    'initial_capital': [10000, 50000, 100000],
    'rebalance_threshold': [0.0, 0.05, 0.10],
    'rebalance_frequency': [0, 100, 500],
}

# Run backtest
backtester = amm.LPBacktester(amm_type='v2')
results = backtester.run(swaps, strategies)

# Analyze
print(results.summary())
amm.plot_performance(results, strategy_idx=0).show()

See examples/uniswap_v2_demo.ipynb for full walkthrough.

Using Real Swap Data

Currently, users should source their own swap data from The Graph, Dune Analytics, or similar providers. The swap DataFrame should contain at minimum:

• amount0: Token 0 amount (positive = buy, negative = sell)
• amount1: Token 1 amount
• price: Current pool price

For Uniswap V3, additional fields enable tick-by-tick fee tracking:

• tick: Current tick after swap
• liquidity: Pool liquidity at time of swap
• sqrt_price_x96: Precise price representation

Note: Data loader classes (UniswapV3Loader, UniswapV2Loader) are planned for a future release.

Simulation Capabilities

ammBT provides comprehensive LP position simulation:

• Impermanent Loss (IL): Calculated by comparing LP value vs hold value
• Rebalancing: Threshold and frequency-based rebalancing with gas tracking
• In-range Fee Accrual: V3 has complete tick-based fee tracking
• Comprehensive Metrics: Sharpe ratio, Sortino ratio, max drawdown, capital efficiency
• Tick-by-tick V3 Processing: Accurate fee growth simulation per swap

Project Status

• Architecture design
• Uniswap v2 implementation (COMPLETE)
• Uniswap v3 implementation (COMPLETE)
• Meteora DLMM implementation (COMPLETE)
• Data loaders for real swap data (Planned)
• Record system for event tracking

License

MIT