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. For a high-level comparison of backtesting modes, see the Backtesting overview.

Quick Start

Single Strategy

from investing_algorithm_framework import (
    create_app, BacktestDateRange, SnapshotInterval, Study, Universe,
    BacktestWindow, BacktestEngine, BacktestRunConfiguration,
)
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)
)

study = Study(
    universe=Universe(market="bitvavo", trading_symbol="EUR"),
    initial_capital=1000,
    backtest_windows=[BacktestWindow(train_range=backtest_range)],
    engines=[BacktestEngine.VECTOR],
)

backtests = app.run_backtest(
    strategy=my_strategy,
    study=study,
)
backtest = backtests[0]

Multiple Strategies

Test many strategies simultaneously:

from investing_algorithm_framework import BacktestRunConfiguration
strategies = [
    MyStrategy(rsi_period=10),
    MyStrategy(rsi_period=14),
    MyStrategy(rsi_period=20),
]

study = Study(
    universe=Universe(market="bitvavo", trading_symbol="EUR"),
    initial_capital=1000,
    backtest_windows=[
        BacktestWindow(train_range=date_range_1),
        BacktestWindow(train_range=date_range_2),
    ],
    engines=[BacktestEngine.VECTOR],
)

backtests = app.run_backtests(
    strategies=strategies,
    study=study,
    run_configuration=BacktestRunConfiguration(
        snapshot_interval=SnapshotInterval.DAILY,
    ),
)

Saving and Loading

Save to Directory

from investing_algorithm_framework import BacktestRunConfiguration
study = Study(
    universe=Universe(market="bitvavo", trading_symbol="EUR"),
    initial_capital=1000,
    backtest_windows=[BacktestWindow(train_range=dr) for dr in date_ranges],
    engines=[BacktestEngine.VECTOR],
)

backtests = app.run_backtests(
    strategies=strategies,
    study=study,
    run_configuration=BacktestRunConfiguration(
        backtest_storage_directory="./my_backtests",
    ),
)

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:

study = Study(
    universe=Universe(market="bitvavo", trading_symbol="EUR"),
    initial_capital=1000,
    backtest_windows=[BacktestWindow(train_range=dr) for dr in date_ranges],
    engines=[BacktestEngine.VECTOR],
)

backtests = app.run_backtests(
    strategies=strategies,
    study=study,
    run_configuration=BacktestRunConfiguration(
        backtest_storage_directory="./my_backtests",
        n_workers=8,
        memory_budget_mb=16_384,
        min_available_memory_mb=4_096,
    ),
)

BacktestRunConfiguration enables checkpoints, progress output and continue-on-error by default. Window summaries are always current. Use BacktestRunConfiguration.from_env() to read the same settings from IAF_BACKTEST_* environment variables.

Environment variable	Field
IAF_BACKTEST_CONTINUE_ON_ERROR	continue_on_error
IAF_BACKTEST_USE_CHECKPOINTS	use_checkpoints
IAF_BACKTEST_STORAGE_DIRECTORY	backtest_storage_directory
IAF_BACKTEST_SHOW_PROGRESS	show_progress
IAF_BACKTEST_N_WORKERS	n_workers
IAF_BACKTEST_MEMORY_BUDGET_MB	memory_budget_mb
IAF_BACKTEST_MIN_AVAILABLE_MEMORY_MB	min_available_memory_mb
IAF_BACKTEST_SNAPSHOT_INTERVAL	snapshot_interval (DAILY or STRATEGY_ITERATION)
IAF_BACKTEST_SKIP_DATA_SOURCES_INITIALIZATION	skip_data_sources_initialization
IAF_BACKTEST_DYNAMIC_POSITION_SIZING	dynamic_position_sizing
IAF_BACKTEST_FILL_MISSING_DATA	fill_missing_data
IAF_BACKTEST_MAX_TASKS_PER_CHILD	max_tasks_per_child (None disables recycling)

Filtering Strategies

Progressively eliminate underperforming strategies during backtesting:

def window_filter(index, date_range):
    """Keep algorithms with positive cumulative returns so far."""
    return index.filter(lambda row: row["summary.total_return"] > 0)

def final_filter(index):
    """Select completed results."""
    return index.filter(lambda row: row["summary.sharpe_ratio"] > 1.0)

study = Study(
    universe=Universe(market="bitvavo", trading_symbol="EUR"),
    initial_capital=1000,
    backtest_windows=[BacktestWindow(train_range=dr) for dr in date_ranges],
    engines=[BacktestEngine.VECTOR],
)

backtests = app.run_backtests(
    strategies=strategies,
    window_metrics_filter_function=window_filter,
    final_metrics_filter_function=final_filter,
    study=study,
)

Parallel Processing

Utilize multiple CPU cores for faster backtesting:

from investing_algorithm_framework import BacktestRunConfiguration
import os

study = Study(
    universe=Universe(market="bitvavo", trading_symbol="EUR"),
    initial_capital=1000,
    backtest_windows=[BacktestWindow(train_range=dr) for dr in date_ranges],
    engines=[BacktestEngine.VECTOR],
)

backtests = app.run_backtests(
    strategies=strategies,
    study=study,
    run_configuration=BacktestRunConfiguration(
        n_workers=os.cpu_count() - 1,
    ),
)

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