Vectorbt

vectorbt pulls in pandas, NumPy, and Plotly automatically. For technical indicators, also install pandas-ta:

uv pip install vectorbt pandas-ta

Core Concepts

1. Signals — Boolean Entry/Exit Arrays

Strategies in vectorbt are expressed as boolean pandas Series (or arrays) indicating where to enter and exit positions:

import vectorbt as vbt
import pandas as pd

# Entry: buy when fast EMA crosses above slow EMA
entries = fast_ema > slow_ema
# Exit: sell when fast EMA crosses below slow EMA
exits = fast_ema < slow_ema

vectorbt resolves conflicting signals automatically (you can't enter while already in a position).

2. Portfolio — The Backtesting Engine

vbt.Portfolio.from_signals() is the primary backtesting function. It takes price data and entry/exit signals, simulates trades, and computes performance:

pf = vbt.Portfolio.from_signals(
    close=close_prices,
    entries=entries,
    exits=exits,
    init_cash=10_000,
    fees=0.003,       # 0.3% per trade
    slippage=0.005,   # 0.5% slippage
    freq="1h",        # hourly data
)

3. Metrics — Built-in Performance Analysis

# Full stats summary
print(pf.stats())

# Individual metrics
print(f"Total Return: {pf.total_return():.2%}")
print(f"Sharpe Ratio: {pf.sharpe_ratio():.3f}")
print(f"Max Drawdown: {pf.max_drawdown():.2%}")
print(f"Win Rate:     {pf.trades.win_rate():.2%}")

4. Parameter Optimization — Grid Search in Seconds

Pass arrays instead of scalars to test many parameter combos simultaneously:

import numpy as np

fast_periods = np.arange(5, 25, 2)   # 10 values
slow_periods = np.arange(20, 60, 5)  # 8 values

fast_ma = vbt.MA.run(close, fast_periods, short_name="fast")
slow_ma = vbt.MA.run(close, slow_periods, short_name="slow")

# This creates 80 parameter combinations automatically
entries = fast_ma.ma_crossed_above(slow_ma)
exits = fast_ma.ma_crossed_below(slow_ma)

Basic Workflow

Step 1: Load OHLCV Data

import pandas as pd

# From CSV
df = pd.read_csv("ohlcv.csv", parse_dates=["timestamp"], index_col="timestamp")
close = df["close"]

# From Yahoo Finance (traditional markets)
btc = vbt.YFData.download("BTC-USD", start="2023-01-01", end="2025-01-01")
close = btc.get("Close")

For Solana tokens, fetch data via the birdeye-api skill and load into a DataFrame.

Step 2: Compute Indicators

import pandas_ta as ta

# Using pandas-ta (see pandas-ta skill)
df.ta.ema(length=12, append=True)
df.ta.ema(length=26, append=True)
df.ta.rsi(length=14, append=True)
df.ta.bbands(length=20, std=2, append=True)

# Or using vectorbt built-ins
rsi = vbt.RSI.run(close, window=14)
bbands = vbt.BBANDS.run(close, window=20, alpha=2)

Step 3: Generate Entry/Exit Signals

# EMA crossover
entries = df["EMA_12"] > df["EMA_26"]
exits = df["EMA_12"] < df["EMA_26"]

# RSI mean reversion
entries = rsi.rsi_below(30)
exits = rsi.rsi_above(70)

Step 4: Run Backtest

pf = vbt.Portfolio.from_signals(
    close=close,
    entries=entries,
    exits=exits,
    init_cash=10_000,
    fees=0.003,
    slippage=0.005,
    size=0.95,               # use 95% of available cash
    size_type="percent",
    freq="1h",
)

Step 5: Analyze Results

# Summary statistics
print(pf.stats())

# Trade-level analysis
trades = pf.trades.records_readable
print(f"\nTrade count: {len(trades)}")
print(f"Avg holding period: {trades['Duration'].mean()}")

# Equity curve
pf.plot().show()

# Drawdown chart
pf.drawdowns.plot().show()

Key Portfolio Parameters

Performance Metrics

Returns

• total_return() — cumulative return over the period
• annualized_return() — annualized compound return
• daily_returns() — Series of daily returns

Risk

• max_drawdown() — maximum peak-to-trough decline
• annualized_volatility() — annualized standard deviation of returns
• value_at_risk() — VaR at specified confidence level

Risk-Adjusted

• sharpe_ratio() — excess return per unit volatility
• sortino_ratio() — excess return per unit downside deviation
• calmar_ratio() — annualized return / max drawdown
• omega_ratio() — probability-weighted gain/loss ratio

Trade Statistics

• trades.win_rate() — fraction of profitable trades
• trades.profit_factor() — gross profit / gross loss
• trades.expectancy() — average P&L per trade
• trades.avg_winning_trade() — mean profit on winners
• trades.avg_losing_trade() — mean loss on losers
• trades.count() — total number of completed trades

Parameter Optimization

Grid Search

fast_windows = [5, 8, 12, 15, 20]
slow_windows = [20, 26, 30, 40, 50]

# Run all 25 combos at once
fast_ma = vbt.MA.run(close, fast_windows, short_name="fast")
slow_ma = vbt.MA.run(close, slow_windows, short_name="slow")

entries = fast_ma.ma_crossed_above(slow_ma)
exits = fast_ma.ma_crossed_below(slow_ma)

pf = vbt.Portfolio.from_signals(close, entries, exits, fees=0.003)

# Find best params by Sharpe
sharpe = pf.sharpe_ratio()
best_idx = sharpe.idxmax()
print(f"Best params: {best_idx}, Sharpe: {sharpe[best_idx]:.3f}")

Walk-Forward Validation

Always validate optimized parameters on out-of-sample data:

# Split: 70% train, 30% test
split_idx = int(len(close) * 0.7)
train_close = close.iloc[:split_idx]
test_close = close.iloc[split_idx:]

# Optimize on training data
# ... (run grid search on train_close)

# Validate best params on test data
# ... (run single backtest on test_close with best params)

See references/optimization_guide.md for detailed walk-forward methodology and overfitting prevention.

Crypto-Specific Considerations

24/7 Markets

Crypto markets never close. Use hourly or minute-based frequencies, not business-day frequencies:

# Correct for crypto
pf = vbt.Portfolio.from_signals(close, entries, exits, freq="1h")

# Wrong — business days assume market closures
# pf = vbt.Portfolio.from_signals(close, entries, exits, freq="1B")

Realistic Fees

DEX swaps on Solana typically cost 0.25–1% including AMM fees. CEX spot fees are 0.05–0.1%.

# Solana DEX (conservative)
pf = vbt.Portfolio.from_signals(close, entries, exits, fees=0.005)

# CEX spot
pf = vbt.Portfolio.from_signals(close, entries, exits, fees=0.001)

Slippage

Low-liquidity tokens can have 1–5% slippage. Always model this:

# High-liquidity (SOL, ETH): 0.1–0.5%
pf = vbt.Portfolio.from_signals(close, entries, exits, slippage=0.003)

# Low-liquidity memecoins: 1–3%
pf = vbt.Portfolio.from_signals(close, entries, exits, slippage=0.02)

Short History

Many tokens have less than 1 year of data. Be cautious about annualizing metrics from short samples.

Common Strategy Patterns

EMA Crossover

fast = vbt.MA.run(close, 12, short_name="fast")
slow = vbt.MA.run(close, 26, short_name="slow")
entries = fast.ma_crossed_above(slow)
exits = fast.ma_crossed_below(slow)

RSI Mean Reversion

rsi = vbt.RSI.run(close, 14)
entries = rsi.rsi_crossed_below(30)
exits = rsi.rsi_crossed_above(70)

Bollinger Band Breakout

bb = vbt.BBANDS.run(close, window=20, alpha=2)
entries = close > bb.upper
exits = close < bb.lower

Stop-Loss and Take-Profit

pf = vbt.Portfolio.from_signals(
    close, entries, exits,
    sl_stop=0.05,    # 5% stop-loss
    tp_stop=0.10,    # 10% take-profit
)

Related Skills

• pandas-ta — Technical indicator computation (feeds vectorbt signals)
• birdeye-api — Fetch Solana token OHLCV data for backtesting
• trading-visualization — Advanced chart generation for backtest results
• portfolio-analytics — Deeper portfolio-level risk/return analysis
• position-sizing — Optimal position sizing methodology
• risk-management — Portfolio-level risk guardrails
• regime-detection — Market regime awareness for adaptive strategies

Files

References

• references/api_guide.md — Complete vectorbt API reference for Portfolio, indicators, plotting, and data loading
• references/optimization_guide.md — Grid search, walk-forward validation, overfitting prevention, and optimization best practices

Scripts

• scripts/backtest_example.py — Three-strategy backtest comparison using synthetic data (EMA crossover, RSI mean reversion, Bollinger breakout)
• scripts/parameter_sweep.py — EMA crossover parameter grid search with walk-forward validation