Vector Backtesting

Vector backtesting is a high-performance backtesting approach that processes market data in batches rather than tick-by-tick. It is 10-100x faster than event-driven backtesting, making it ideal for testing many strategy variants and parameter combinations.

When to Use Vector Backtesting

• Testing multiple parameter combinations (RSI period, MA length, etc.)
• Running backtests across many time periods
• Large-scale strategy optimization (100+ strategies)
• Fast prototyping and signal research

For realistic simulation with stop losses and take profits, use Event-Driven Backtesting instead.

Quick Start

Single Strategy

from investing_algorithm_framework import create_app, BacktestDateRange, SnapshotInterval
from datetime import datetime, timezone

app = create_app()

backtest_range = BacktestDateRange(
    start_date=datetime(2023, 1, 1, tzinfo=timezone.utc),
    end_date=datetime(2024, 1, 1, tzinfo=timezone.utc)
)

backtest = app.run_vector_backtest(
    backtest_date_range=backtest_range,
    strategy=my_strategy,
    initial_amount=1000,
    market="bitvavo",
    trading_symbol="EUR"
)

Multiple Strategies

Test many strategies simultaneously:

strategies = [
    MyStrategy(rsi_period=10),
    MyStrategy(rsi_period=14),
    MyStrategy(rsi_period=20),
]

backtests = app.run_vector_backtests(
    backtest_date_ranges=[date_range_1, date_range_2],
    strategies=strategies,
    initial_amount=1000,
    snapshot_interval=SnapshotInterval.DAILY,
    market="bitvavo",
    trading_symbol="EUR"
)

Saving and Loading

Save to Directory

backtests = app.run_vector_backtests(
    strategies=strategies,
    backtest_date_ranges=date_ranges,
    backtest_storage_directory="./my_backtests",
    initial_amount=1000
)

Load from Directory

from investing_algorithm_framework import load_backtests_from_directory

backtests = load_backtests_from_directory("./my_backtests")

Checkpointing

Resume interrupted backtests without losing progress:

backtests = app.run_vector_backtests(
    strategies=strategies,
    backtest_date_ranges=date_ranges,
    backtest_storage_directory="./my_backtests",
    use_checkpoints=True,
    initial_amount=1000
)

Filtering Strategies

Progressively eliminate underperforming strategies during backtesting:

def window_filter(backtest_run):
    """Filter after each date range"""
    return backtest_run.backtest_metrics.total_return > 0

def final_filter(backtest):
    """Filter at the end"""
    return backtest.backtest_summary.sharpe_ratio > 1.0

backtests = app.run_vector_backtests(
    strategies=strategies,
    backtest_date_ranges=date_ranges,
    window_filter_function=window_filter,
    final_filter_function=final_filter,
    initial_amount=1000
)

Parallel Processing

Utilize multiple CPU cores for faster backtesting:

import os

backtests = app.run_vector_backtests(
    strategies=strategies,
    backtest_date_ranges=date_ranges,
    n_workers=os.cpu_count() - 1,
    initial_amount=1000
)

Differences from Event-Driven Backtesting

Aspect	Vector	Event-Driven
Speed	10-100x faster	Slower, realistic
Stop Loss / Take Profit	Not supported	Fully supported
Signal Timing	Executes at exact signal timestamp	Executes at next interval boundary
Data Loading	All data loaded at once	Sliding window at each step
Best For	Fast prototyping, parameter sweeps	Final validation, realistic results

With a sufficiently large warmup_window (e.g., 800 bars), both approaches should produce identical signals. Execution timing may differ slightly since vector backtests execute at the exact signal timestamp while event backtests execute at strategy interval boundaries.

Next Steps

• See the Advanced Vector Backtesting guide for batching, storage, and advanced filtering
• Explore Performance Optimization for large-scale testing
• Check out Parallel Processing for multi-core utilization
• Generate Backtest Reports to compare your strategies