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3339913 2d2e42a 3339913 2d2e42a 3339913 2d2e42a 3339913 2d2e42a 3339913 2d2e42a 3339913 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | """algotrader 2.0 — a backtester that tries to prove itself wrong.
Most backtesting libraries answer "how much would this have made?". This one
answers the question that actually matters before you risk money: "how much of
that was luck?"
Quick start::
from algotrader import LabConfig, run_lab
report = run_lab(LabConfig(symbol="SPY", strategy="sma_cross"))
print(report.verdict["verdict"])
"""
from .attribution import build_style_factors, factor_attribution
from .cross_sectional import XS_REGISTRY, get_xs_strategy, list_xs_strategies
from .data import load_ohlcv, simulate_ohlcv
from .engine import run_backtest
from .lab import LabConfig, LabReport, run_arena, run_lab
from .metrics import compute_metrics
from .panel import Panel, load_panel
from .portfolio import rebalance_schedule, run_portfolio_backtest
from .portfolio_lab import PortfolioLabConfig, PortfolioLabReport, run_portfolio_arena, run_portfolio_lab
from .strategies import REGISTRY, get_strategy, list_strategies
from .types import BacktestResult, CostModel, MarketData
from .verdict import reality_score
__version__ = "2.1.0"
__all__ = [
"__version__",
# single asset
"LabConfig",
"LabReport",
"run_lab",
"run_arena",
"run_backtest",
"get_strategy",
"list_strategies",
"REGISTRY",
# multi asset
"Panel",
"load_panel",
"run_portfolio_backtest",
"rebalance_schedule",
"PortfolioLabConfig",
"PortfolioLabReport",
"run_portfolio_lab",
"run_portfolio_arena",
"get_xs_strategy",
"list_xs_strategies",
"XS_REGISTRY",
"build_style_factors",
"factor_attribution",
# shared
"compute_metrics",
"load_ohlcv",
"simulate_ohlcv",
"BacktestResult",
"CostModel",
"MarketData",
"reality_score",
]
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