polars-backtest

polars-backtest

High-performance portfolio backtesting for Polars long-format DataFrames. Rust core, Finlab-compatible semantics (parity ~2e-16), T+1 execution.

pip install polars-backtest    # or: uv add polars-backtest

Reference files — read when needed

File	When to read
references/api.md	Full parameter reference, BacktestReport surface, trades schema, wide-format API, plugin expressions
references/execution-semantics.md	Exact timing/price/fee/stop mechanics — read before answering "when does it trade / at what price / how are fees applied"
references/finlab-migration.md	Porting finlab sim() code; param and Report mapping; unsupported params
references/recipes.md	Data prep, top-N ranking, masks, long/short, sweeps, live-trading actions

Data requirements

Long format: one row per (date, symbol). Required columns (names remappable via parameters, values can also be pl.Expr):

Column	Dtype	Notes
date	pl.Date preferred (ISO strings accepted)	Sort by date; unsorted input is auto-sorted but slower
symbol	str
close (= trade_at_price)	Float64	Use adjusted prices; NaN/≤0 treated as invalid
weight (= position)	Boolean or Float64	Bool → equal weights among True. Float → Finlab normalization. Nulls auto-filled with False/0.0

Optional columns picked up when present / when features need them:

• factor — adjustment factor, raw_price = adj_price / factor. Missing column silently means factor=1.0.
• open, high, low — required only when touched_exit=True.
• limit_up, limit_down, trading_value — enable liquidity metrics (buyHigh, sellLow, capacity). Column names are configurable via the limit_up/limit_down/trading_value parameters of backtest_with_report.

Quick start

import polars as pl
import polars_backtest as pl_bt   # registers the df.bt namespace

# Boolean signal -> equal-weight portfolio, monthly rebalance
df = df.with_columns(
    (pl.col("close") >= pl.col("close").rolling_max(60).over("symbol"))
    .alias("weight")
)

report = df.bt.backtest_with_report(position="weight", resample="M")
report.stats          # 1-row DataFrame: total_return, cagr, max_drawdown, daily_sharpe, ...
report.creturn        # DataFrame [date, creturn]
report.trades         # per-trade records with mae/gmfe/bmfe/mdd/pdays

# Equity curve only (faster, for optimization loops)
curve = df.bt.backtest(position="weight", resample="M")   # DataFrame [date, creturn]

# Function form is identical: pl_bt.backtest(df, ...) / pl_bt.backtest_with_report(df, ...)

Parameters at a glance

Exact signature — see references/api.md for full semantics.

Parameter	Default	Meaning
trade_at_price	"close"	Price column/Expr for valuation and execution
position	"weight"	Weight column/Expr (bool signals or float weights)
date, symbol	"date", "symbol"	Column names/Exprs
open, high, low	"open"…	Used only with touched_exit=True
factor	"factor"	str only; raw = adj / factor; missing → 1.0
resample	"D"	None, D, W, W-MON…W-SUN, M, ME, Q, QE, Y, YE, A. MS/QS raise ValueError (no start-of-period support). None = trade only on position change
resample_offset	None	Delay rebalance by calendar days: "1d", "2d", "1W". Non-negative only; bad strings silently ignored
fee_ratio	0.001425	Fee on buy AND sell notional (TW default)
tax_ratio	0.003	Tax on sell side only (TW default)
stop_loss	1.0 = off	Positive fraction: 0.1 exits at −10%
take_profit	inf = off	0.2 exits at +20%
trail_stop	inf = off	0.08 exits 0.08 below peak cr since entry
touched_exit	False	Intraday OHLC stop detection, same-day exit at the stop level
stop_trading_next_period	True	Stopped stock excluded from the next rebalance
position_limit	1.0	Max weight per stock (clip; float-weight excess → cash)
retain_cost_when_rebalance	False	True: stops keep measuring from original entry across rebalances
benchmark (report only)	None	Symbol string in your data, or DataFrame with date+creturn → enables alpha/beta/m12WinRate
limit_up, limit_down (report only)	"limit_up", "limit_down"	Raw limit-price columns for liquidity metrics

Execution model (the 20-second version)

Full mechanics: references/execution-semantics.md.

• T+1: a signal on date T trades on the next trading day at that day's trade_at_price. No same-bar fills. Last-date signals become pending trades (null entry_date/exit_date in report.trades).
• Delta trading: rebalances trade only target_value − current_value per stock. Fees: fee_ratio on the traded amount both ways, + tax_ratio when reducing/closing. Full round trip ≈ 0.585% at TW defaults.
• Stops: evaluated on cumulative return since entry at each close; triggered stocks exit next day at next day's price (gaps can overshoot the threshold). With touched_exit=True, OHLC is checked intraday and the exit happens same day at the touched level (priority open > high > low).
• Weights per date: bool → equal weight (sum=1). Float → Σ|w|>1 scaled down to 1; Σ|w|<1 kept as-is, remainder in cash. Negative weights = shorts.
• creturn starts at 1.0 from the first signal date.

Report essentials

Full surface: references/api.md.

report.get_stats(riskfree_rate=0.02)     # = report.stats
report.get_monthly_stats()
report.get_return_table()                # year x month returns
report.get_metrics(sections=["backtest", "profitability", "risk",
                             "ratio", "winrate", "liquidity"])
report.actions()        # enter/exit/hold + weight/next_weight  (live trading)
report.weights()        # current holdings, normalized, sum <= 1
report.next_weights()   # next-period targets, sum <= 1
report.current_trades() # open + pending + just-exited trades
report.is_stop_triggered()
report.benchmark = bm_df                 # attach later; needs date + creturn

Common pitfalls

1. stop_loss sign: pass stop_loss=0.1 for a 10% stop. The threshold formula is 1 - stop_loss, so a negative value (e.g. -0.1) makes the threshold > 1 and stops every position out immediately.
2. Look-ahead bias: any feature must use only data available at signal time. Use .shift(k).over("symbol") for time series and never rank on same-day future info. The engine's T+1 handles execution lag, not your feature construction.
3. Unadjusted prices: raw close across ex-dividend/split dates fabricates losses. Feed adjusted prices to trade_at_price and supply factor so raw prices are still available for trade records and liquidity metrics.
4. Weights that don't sum to 1: float weights with Σ|w| < 1 are NOT scaled up — the rest sits in cash. If you want full investment, normalize per date: w / w.abs().sum().over("date"), or just use a Boolean column.
5. Nulls in position: auto-filled with 0/False, which is usually right for rolling-window warmups. But for ranking, mask excluded stocks to null before ranking so they leave the cross-section, then fill_null(False) on the final signal.
6. Silent no-ops: a misspelled resample_offset (e.g. "-1d", "5days") is ignored without error; a missing factor column means factor=1.0 without error. Verify both when results look suspicious.
7. Duplicate (date, symbol) rows: dedupe with .unique(subset=["symbol", "date"]) before backtesting. 7b. Sparse weights + resample="D" liquidate: a symbol missing its row (or null/NaN weight) on a day is sold the next day — weights are NOT forward-filled. Emit weights on every held date or use a coarser resample.
8. Nulls in date/symbol raise: fill or drop them first. Null prices are treated as missing data (converted to NaN internally) — no error, the position is simply carried without a return that day.
9. Empty universe: if masking leaves zero weights everywhere, backtest_with_report returns an empty report (height-0 creturn/trades); thin universes in sweeps may raise — wrap sweep iterations in try/except.
10. Costs eat the alpha: check gross first (fee_ratio=0, tax_ratio=0), then reduce turnover (resample="M" or None) rather than dropping the cost assumptions.

Finlab migration

polars-backtest reproduces Finlab sim() bit-for-bit in parity tests. Wide pandas position → long Boolean/float column; rolling → .over("symbol"); cross-sectional ops → .over("date"). Same parameter names for fee/tax/stops/resample; mae_mfe_window*, fast_mode, and upload/notification params are unsupported. Full mapping and verification recipe: references/finlab-migration.md.

Wide-format API

pl_bt.backtest_wide(prices, position, ...) and pl_bt.backtest_with_report_wide(close, position, ...) accept Finlab-style wide DataFrames (first column date, one column per symbol). Prefer long format — wide exists mainly for Finlab parity. Details in references/api.md.