Backtesting

Run a Backtest

from datetime import datetime, timezone

import vibetrading.backtest
import vibetrading.tools

start = datetime(2025, 1, 1, tzinfo=timezone.utc)
end = datetime(2025, 7, 1, tzinfo=timezone.utc)

data = vibetrading.tools.download_data(
    ["BTC"],
    exchange="binance",
    start_time=start,
    end_time=end,
    interval="1h",
)

engine = vibetrading.backtest.BacktestEngine(
    start_time=start,
    end_time=end,
    interval="1h",
    exchange="binance",
    initial_balances={"USDC": 10000},
    data=data,
)

results = engine.run(strategy_code)

print(results["metrics"])

Quick Backtest with run()

For a simpler interface, use the run() shortcut:

results = vibetrading.backtest.run(
    code,
    start_time=start,
    end_time=end,
    interval="1h",
    data=data,
)

Backtest Results

engine.run() returns a dictionary containing:

results["metrics"]          # Performance metrics dict
results["trades"]           # List of all executed trades
results["final_balances"]   # Final asset balances
results["results"]          # Time-series DataFrame of portfolio values
results["simulation_info"]  # Metadata (steps, time range, liquidation status)

Metrics

Metric	Description
total_return	Total portfolio return (decimal)
max_drawdown	Maximum peak-to-trough drawdown
sharpe_ratio	Annualized Sharpe ratio
win_rate	Percentage of profitable closed trades
number_of_trades	Total number of trades executed
funding_revenue	Net funding payments received/paid
total_tx_fees	Total transaction fees paid
average_trade_duration_hours	Mean holding period

Supported Intervals

1s, 1m, 5m, 15m, 30m, 1h, 6h, 1d

Supported Exchanges

Data is fetched from exchanges via CCXT. Download data first, then pass it to the backtest engine:

import vibetrading.tools

data = vibetrading.tools.download_data(["BTC", "ETH"], exchange="binance", ...)
data = vibetrading.tools.download_data(["BTC"], exchange="bybit", ...)
data = vibetrading.tools.download_data(["BTC"], exchange="okx", ...)

Next Steps

After backtesting, you can:

• Analyze results — Use an LLM to score performance and get actionable improvement suggestions.

• Iterate manually — Use vibetrading.strategy.generate() and vibetrading.strategy.analyze() in a loop to refine based on feedback.