RaptorBT

Blazing-fast backtesting for the modern quant.

RaptorBT is a high-performance backtesting engine written in Rust with Python bindings via PyO3. It runs single-instrument, basket, pairs, options, spread, multi-strategy, and tick-level backtests over any OHLCV or tick arrays — from any broker, market, or asset class — and returns a full performance report in sub-millisecond time.

Sub-millisecond backtests · <1 MB compiled engine · Bit-for-bit deterministic

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Quick Install

pip install raptorbt

30-Second Example

import numpy as np
import raptorbt

# Configure
config = raptorbt.PyBacktestConfig(initial_capital=100000, fees=0.001)

# Run backtest
result = raptorbt.run_single_backtest(
    timestamps=timestamps,
    open=open,
    high=high,
    low=low,
    close=close,
    volume=volume,
    entries=entries,
    exits=exits,
    direction=1,
    weight=1.0,
    symbol="AAPL",
    config=config,
)

# Results
print(f"Return: {result.metrics.total_return_pct:.2f}%")
print(f"Sharpe: {result.metrics.sharpe_ratio:.2f}")

RaptorBT is open source (MIT) and developed by the Alphabench team.

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Table of Contents

• Overview
• Performance
• Class-Based Strategies
• Strategy Types
• Metrics
• Indicators
• Stop-Loss & Take-Profit
• Monte Carlo Portfolio Simulation
• API Reference
• Building from Source

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Overview

RaptorBT compiles to a single native extension and runs entirely in Rust, so a full backtest with all 33 metrics finishes in well under a millisecond on typical bar counts. Measured on an Apple M4 (raptorbt 0.4.0):

Metric	RaptorBT
Compiled engine size	<1 MB
Backtest speed (1K bars)	~0.03 ms
Backtest speed (10K bars)	~0.25 ms
Backtest speed (50K bars)	~1.4 ms
Memory usage	Low (native)

See Performance for the full method and how to reproduce these numbers on your own hardware.

Key Features

• 7 Strategy Types: Single instrument, basket/collective, pairs trading, options, spreads, multi-strategy, and tick-level
• Asset- and broker-agnostic: Pass NumPy OHLCV or tick arrays from any source — equities, futures, FX, crypto, options — RaptorBT never assumes a market or data vendor
• Tick-Level Simulation: Full tick resolution for intraday options momentum, scalping, and microstructure strategies
• Batch Spread Backtesting: Run multiple spread backtests in parallel via Rayon with GIL released
• Monte Carlo Simulation: Correlated multi-asset forward projection via GBM + Cholesky decomposition
• 33 Metrics: Sharpe, Sortino, Calmar, Omega, SQN, Payoff Ratio, Recovery Factor, and more
• 20 Indicator & Tick Functions: 12 classic technical indicators (SMA, EMA, RSI, MACD, Stochastic, ATR, Bollinger Bands, ADX, VWAP, Supertrend, Rolling Min/Max) plus 8 tick microstructure/feature functions
• Stop/Target Management: Fixed, ATR-based, and trailing stops with risk-reward targets
• Deterministic: Identical inputs produce bit-for-bit identical results across runs — no JIT compilation variance
• Native Parallelism: Rayon-based parallel processing with explicit SIMD optimizations

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Performance

Benchmark Results

Measured on an Apple M4 (raptorbt 0.4.0, Python 3.11) with random-walk price data and an SMA-crossover strategy. Each figure is the fastest of several hundred repetitions of run_single_backtest (so it reflects engine time, not scheduler noise):

┌─────────────┬───────────┐
│ Data Size   │ RaptorBT  │
├─────────────┼───────────┤
│ 1,000 bars  │ 0.03 ms   │
│ 5,000 bars  │ 0.13 ms   │
│ 10,000 bars │ 0.25 ms   │
│ 50,000 bars │ 1.37 ms   │
└─────────────┴───────────┘

Timings scale roughly linearly with bar count and will vary with your CPU, data, and signal density. Reproduce them with the Verification Test below, swapping in your own array sizes.

Determinism

RaptorBT is fully deterministic: the same inputs produce bit-for-bit identical results across runs (no JIT warmup, no nondeterministic reductions). Running the Verification Test five times in a row on this machine produced the same total return every time, to the last decimal:

Total return:           -30.6192%  (seed=42, 500 bars, periodic entries/exits)
Max difference across 5 runs: 0.0000000000%

(The exact return depends on your data and signals — the point is that it does not change between runs.)

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Class-Based Strategies

New in 0.5.0: strategies can be written as event-driven classes instead of precomputed signal arrays. Subclass raptorbt.Strategy, override lifecycle hooks, and emit order intents; the engine simulates fills and routes events back into your hooks. Both paths share one execution core, so identical decisions produce identical results — the class contract is the recommended way to write new strategies, while the array runners remain the fast path for vectorized workloads.

import numpy as np
import raptorbt


class SmaCross(raptorbt.Strategy):
    def on_start(self, ctx):
        # Full OHLCV arrays are available for indicator precomputation.
        self.fast = raptorbt.sma(ctx.close, 10)
        self.slow = raptorbt.sma(ctx.close, 30)

    def on_bar(self, ctx):
        i = ctx.idx
        if i == 0 or np.isnan(self.slow[i]) or np.isnan(self.slow[i - 1]):
            return
        crossed_up = self.fast[i] > self.slow[i] and self.fast[i - 1] <= self.slow[i - 1]
        crossed_dn = self.fast[i] < self.slow[i] and self.fast[i - 1] >= self.slow[i - 1]
        if crossed_up and ctx.position is None:
            self.enter()                      # optional: size_frac=, stop_price=, target_price=
        elif crossed_dn and ctx.position is not None:
            self.close_position()

    def on_position_closed(self, ctx, event):
        self.log.info("closed: pnl=%.2f", event.trade.pnl)


result = raptorbt.run_strategy_backtest(
    SmaCross(), timestamps, open_, high, low, close, volume,
    symbol="EXAMPLE", config=raptorbt.PyBacktestConfig(fees=0.001),
)
print(result.metrics.total_return_pct, len(result.trades()))

Hooks: on_start, on_bar, on_stop, on_order_filled, on_order_rejected, on_position_opened, on_position_closed. Inside on_bar, ctx provides the current bar, position snapshot, equity, cash, history(n), and set_stop_price() / set_target_price() for programmatic exits. Decision logic must only read array values at ctx.idx or earlier — indexing past the current bar reads the future.

Engine-level stop/target/sizing configuration (PyBacktestConfig, PyInstrumentConfig) applies to both paths. run_strategy_backtest returns the same PyBacktestResult as run_single_backtest. For advanced drivers (live feeds, custom loops), PyKernelSession exposes the per-bar engine step directly.

Note: one Python hook call per bar makes the class path slower than the array path — fine for typical bar counts, but prefer arrays for large parameter sweeps.

Instrument Definitions

New in 0.5.0: InstrumentSpec describes the market being traded — tick size, lot size, contract multiplier, expiry — separately from the per-run allocation knobs in PyInstrumentConfig. Attach one to a class-based run via run_strategy_backtest(..., instrument=...) (or directly on PyKernelSession):

import raptorbt

# NIFTY monthly future: 50-unit lots, expiry settlement at the contract's
# expiration timestamp, entries refused before activation / after expiry.
fut = raptorbt.InstrumentSpec.futures_contract(
    "NIFTY24AUGFUT",
    expiration_ns=1724839200_000_000_000,
    lot_size=50.0,
    price_increment=0.05,
    underlying="NIFTY",
)

result = raptorbt.run_strategy_backtest(
    MyStrategy, ts, o, h, l, c, v, instrument=fut,
)

Constructors: equity, futures_contract, perpetual, option (vanilla and binary; settles to intrinsic value when an underlying price is known), currency_pair, and index (non-tradable reference). With a spec attached the engine:

• scales notional by the contract multiplier — sizing, cash, PnL, and value-based fees charge on price * size * multiplier, while per-share/per-contract fee models keep charging per contract;
• floors sizes to lot_size / size_increment (an explicit PyInstrumentConfig.lot_size still wins — it is the per-run override);
• rounds engine-derived stop/target prices onto the price_increment grid, conservatively (never in the strategy's favor);
• force-settles open positions at expiry (Settlement exit reason) and rejects entries outside the activation/expiration window.

Without a spec, behavior is unchanged — existing results reproduce bit-for-bit. margin_init/margin_maint/maker_fee/taker_fee are carried on the spec for the account layer that consumes them in a later 0.5.x release.

Choosing a side

New in 0.6.0. enter() opens in the session's configured direction, as it always has. To decide the side in code, call enter_long() / enter_short() (or enter(side="buy"/"sell")) — they take the same arguments and ignore the configured direction, so one run can hold long and short legs and a leg can flip side once it is flat:

class CrossSectional(raptorbt.Strategy):
    def on_bar(self, ctx):
        if ctx.position is not None:
            self.close_position()      # flat before flipping
            return
        if ctx.symbol in winners:
            self.enter_long(size_frac=0.1)
        elif ctx.symbol in losers:
            self.enter_short(size_frac=0.1)

Under the default netting policy an order's side is authoritative for opening: with no position it opens in that side, while an order opposing an open position closes it (so bracket legs and take-profits behave as before). Mark an order reduce_only to guarantee it can only ever close.

Typed Orders

New in 0.5.0: alongside the enter()/close_position() sugar, strategies can submit typed orders that rest across bars and report a full lifecycle:

from raptorbt.strategy import orders

class Breakout(raptorbt.Strategy):
    def on_bar(self, ctx):
        if ctx.idx == 20 and ctx.position is None:
            # Buy stop above the market, protective stop attached.
            self.oid = self.submit_order(orders.StopMarket(
                side="buy",
                trigger=float(ctx.high[:20].max()),
                size_frac=0.5,
                stop_price=float(ctx.close[ctx.idx] * 0.97),
                tif="day",
            ))
        if ctx.position is not None:
            self.submit_order(orders.Limit(side="sell", price=ctx.position.entry_price * 1.1))

• Kinds: orders.Market, orders.Limit (with post_only=), orders.StopMarket, orders.StopLimit (trigger fires, then rests as a limit from the next bar), orders.MarketIfTouched / orders.LimitIfTouched (favorable-touch triggers — a buy fires when price falls to the trigger), orders.MarketToLimit (fills at the next bar's open), and orders.TrailingStopMarket / orders.TrailingStopLimit (trigger trails the running favorable extreme; offset_kind is "price", "bps", or "ticks" — ticks need an instrument price_increment).
• Time-in-force: gtc (default), day (UTC-date rollover), gtd (with expire_ns), ioc, fok, plus at_open / at_close for market orders queued to a bar phase.
• Flags: post_only (limit rejects if marketable at its first resting open), reduce_only (a fill may never increase exposure).
• Brackets: self.submit_bracket(entry, stop_trigger=…, target_price=…, stop_limit_price=None) — the protective legs are held until the entry fills (one-triggers-other), then linked one-cancels-other: the first leg to fill cancels its sibling, and both die if the entry never fills. Generic linkage: submit_order(order, parent=other_id) and self.link_oco(id_a, id_b, …). One-updates-other reduces to one-cancels-other while fills are all-or-nothing (partial fills arrive with book depth). Netting policy only — under hedging every order opens, so protect positions with per-position stop_price/target_price attachments instead.
• Sizing: units= (explicit contracts; refused if it exceeds available capital) or size_frac= (fraction of capital, resolved at fill time); omit both on a closing-side order to close the full position.
• Semantics: market orders fill on the submission bar at the configured fill-price model — the same contract as enter(). Resting orders begin matching on the next bar (an order cannot rest into a bar that had already closed), with gap-throughs filling at the open.
• Lifecycle hooks: on_order_accepted, on_order_triggered, on_order_filled, on_order_canceled, on_order_expired, on_order_rejected, plus catch-all on_order_event. Events carry client_order_id (deterministic "{order_id_tag}-{seq}").
• Management: self.cancel_order(client_id), self.cancel_all_orders(), self.modify_order(client_id, limit_price=…, trigger_price=…, units=…).
• Order-driven exits report exit_reason == "Order" on the trade record. One position at a time: an opening order while a position is open rejects with "position_open" (independent concurrent positions arrive in a later 0.5.x release).

The signal-array runners do not interact with the order book and are unaffected.

Bar Aggregation and Multi-Timeframe Strategies

New in 0.5.0. Streaming and batch aggregation of bars (and raw ticks) into coarser bars — time ("ms"/"s"/"m"/"h"/"d"/"w"), "tick", "volume", and "value" units. Time bars use left-open epoch-aligned windows and are stamped with the window-end timestamp, so a bar labeled t contains only data strictly before t — no look-ahead by construction. Beyond time, tick, volume and value windows, two families sample on something other than the clock:

Renko ("renko") emits a brick per full brick-height price move and ignores time and volume entirely — a quiet hour produces nothing, a fast move produces several bricks at once. Set the height with brick_size; without it, step reads as whole price units. Because one record can complete several bricks, push returns only the first and the rest must be drained:

agg = raptorbt.BarAggregator(1, "renko", brick_size=0.05)
bar = agg.push_trade(ts, price, size)
while bar is not None:
    handle(bar)
    bar = agg.next_pending()      # drain, or bricks are silently lost

Bricks carry no wicks, and a partial brick is discarded at end of data rather than flushed — an incomplete brick is not a brick.

Signed-flow bars ("{tick,volume,value}_imbalance" and "{tick,volume,value}_runs") sample by order-flow direction. Imbalance closes on net signed flow, so balanced two-sided trading never closes a bar however heavy it is; runs closes on the larger one-sided accumulation, so the same tape does close bars. step is the threshold — fixed, rather than the adaptive estimate in the literature, so runs stay reproducible.

Direction comes from the buy/sell quantity deltas when you supply them (bars_from_ticks), and otherwise from the tick rule, which is what lets these units work over plain OHLC bars.

# Batch: 1-minute bars -> 5-minute bars (or ticks -> bars).
ts5, o5, h5, l5, c5, v5 = raptorbt.aggregate_bars(ts, o, h, l, c, v, 5, "m")
bts, bo, bh, bl, bc, bv = raptorbt.bars_from_ticks(ts, ltp, buys, sells, 1000, "volume")
# Signed flow: close a bar every 10,000 shares of net buying or selling.
its = raptorbt.bars_from_ticks(ts, ltp, buys, sells, 10_000, "volume_imbalance")

# In a strategy: a 5-minute trend filter gating 1-minute entries.
class TrendGated(raptorbt.Strategy):
    def on_start(self, ctx):
        self.h5 = self.subscribe_bars(5, "m")
        self.trend_up = False

    def on_composite_bar(self, ctx, bar):   # fires when a 5m bar completes
        self.trend_up = bar.close > bar.open

    def on_bar(self, ctx):                  # every 1m bar
        if self.trend_up and ctx.position is None:
            self.enter()

on_composite_bar dispatches before the on_bar of the primary bar that completed the window — the composite closed strictly earlier. A partial final window is not dispatched to strategies (it never closed); the batch helpers do include it, flushed at end of data.

In a portfolio run one subscribe_bars declaration yields one aggregated stream per symbol, each built only from that symbol's bars. The symbol that completed a bar arrives as bar.symbol (and ctx.symbol); the dispatch-before-on_bar guarantee holds per symbol, while ordering across symbols follows the merged schedule.

Calendar "month"/"year" units aggregate on civil UTC dates. Passing tz_offset_ns (e.g. raptorbt.IST_OFFSET_NS) aligns day/week/month/year windows to that timezone's civil dates — an NSE day bar covers one IST trading date (a 23:30 IST print stays on its trading date instead of rolling into the next UTC day).

Streaming Indicators, Clock, and Cache

New in 0.5.0:

• Streaming indicators — raptorbt.Indicator.sma(14), .ema, .wilder_ma, .wma, .roc, .stddev, .rsi, .atr, .donchian (value (upper, lower)), .bollinger ((middle, upper, lower)), .macd ((macd, signal, histogram)). Rust incremental cores, O(1)-ish per bar, producing values identical to the batch array functions (equivalence-tested). Register for auto-update:

  class Cross(raptorbt.Strategy):
      def on_start(self, ctx):
          self.fast = self.register_indicator(raptorbt.Indicator.ema(10))
          self.slow = self.register_indicator(raptorbt.Indicator.ema(30))
          # Or feed a subscribed higher timeframe instead:
          h5 = self.subscribe_bars(5, "m")
          self.trend = self.register_indicator(raptorbt.Indicator.sma(20), stream_id=h5)
  
      def on_bar(self, ctx):
          if not self.indicators_initialized():
              return
          if self.fast.value > self.slow.value and ctx.position is None:
              self.enter()
  

  In portfolio runs an indicator tracks one symbol, so register one per symbol with symbol= — an unrouted registration is fed every symbol's bars interleaved (and warns):

  class Cross(raptorbt.Strategy):
      def on_start(self, ctx):
          self.fast = self.register_indicators(
              lambda: raptorbt.Indicator.ema(10), ctx.symbols
          )
          # Equivalently, explicit per symbol:
          self.slow = {
              s: self.register_indicator(raptorbt.Indicator.ema(30), symbol=s)
              for s in ctx.symbols
          }
  
      def on_bar(self, ctx):
          fast, slow = self.fast[ctx.symbol], self.slow[ctx.symbol]
          if self.indicators_initialized() and fast.value > slow.value:
              self.enter()
  

  Registered indicators update before handlers see the bar.

• Clock — self.clock.set_time_alert(name, at_ns) (one-shot) and set_timer(name, interval_ns, start_ns=None, stop_ns=None) (recurring; one firing per bar, gaps collapse); due events reach on_time_event before the bar's data handlers. Bar-granular by design: events carry ts_scheduled and ts_fired.

• Cache — self.cache, an event-sourced mirror (no per-query engine calls): order(client_id) / orders_open() / is_order_open(), closed_trades(), realized_pnl(symbol=None).

• Portfolio view — ctx.net_position / is_net_long / is_net_short / is_flat (signed across hedged positions) — properties on every context; per-symbol lookups via position_for(symbol) / positions(symbol) / net_position_for(symbol) on the portfolio context.

Multi-Instrument Strategies

New in 0.5.0: one class-based strategy trading N instruments against a single shared cash pool. Bars from all instruments merge into one deterministic schedule (by timestamp, then registration order); on_bar fires once per event with ctx.symbol naming the instrument whose bar closed. Capital committed to one symbol is unavailable to the rest.

class Rotation(raptorbt.Strategy):
    def on_bar(self, ctx):
        if ctx.idx == 0:
            self.enter(size_frac=0.4)          # routes to ctx.symbol
        if ctx.symbol == "INFY" and ctx.idx == 50:
            # Orders and closes can route across symbols explicitly.
            self.submit_order(orders.Limit(side="buy", price=2400.0,
                                           units=10.0), symbol="TCS")
            if ctx.position_for("RELIANCE") is not None:
                self.close_position(symbol="RELIANCE")

result = raptorbt.run_portfolio_strategy(
    Rotation,
    data={sym: dict(timestamps=..., open=..., high=..., low=..., close=...,
                    volume=...) for sym in symbols},
    instruments={...},        # optional per-symbol InstrumentSpec
    oms_type="netting",       # or "hedging", per instrument
    account_type="cash",      # or "margin", shared across all instruments
    leverage=1.0,             # portfolio-wide under account_type="margin"
)
result.result.equity_curve()  # portfolio curve, sampled per merged event
result.per_instrument         # per-symbol trades / pnl / rejections

ctx in portfolio runs is a PortfolioContext: ctx.bar / ctx.symbol / ctx.idx (local to the symbol), ctx.series(symbol) for full arrays, ctx.position / ctx.is_flat (properties for the current symbol, same as the single-instrument context), ctx.position_for(symbol) / ctx.positions(symbol), and portfolio-level ctx.equity / ctx.cash. Composite-bar subscriptions are single-instrument for now, and one-cancels-other links cannot span symbols.

Risk limits on the config are portfolio-wide, matching run_portfolio_backtest: max_positions counts open positions across all symbols (including entries from resting orders), and max_drawdown_pct trips on portfolio equity and halts entries on every symbol. Capital allocation is the strategy's own — each entry is offered the full free balance, so size it with size_frac; there is no EqualWeight budget on this path yet.

With account_type="margin" the instruments share one account: leverage applies portfolio-wide, sizing draws on the portfolio's free capital (balance less every instrument's locked margin), and equity marks the balance plus direction-aware unrealized PnL so winning shorts price upward. The maintenance requirement is the sum of each instrument's own requirement, so per-symbol margin_maint rates apply rather than one blended rate. A breach fires on_margin_call once and halts new entries on every instrument — subsequent entries are rejected with MarginCall, including on symbols that never traded. Results carry halted and halted_at; in portfolio runs halted_at is a schedule-event ordinal (the session interleaves N streams), not the bar index the array runners report. result.rejected_entries is the sum across instruments, whereas the array portfolio runner reports its single shared risk gate's counter.

Execution Realism Knobs

Four opt-in settings, all off by default:

config.limit_slippage = 0.0005          # adverse adjustment on limit fills
config.liquidate_on_margin_call = True  # broker closes you out, vs only halting
spec.settlement_fee = 0.001             # charged on the settled notional at expiry

limit_slippage models adverse selection on a resting order — you tend to be filled when the market is about to move through you. It is suppressed when queue_fill_model granted the fill, since volume observed trading ahead of your order is evidence you genuinely held that price.

liquidate_on_margin_call turns a margin call from a latching halt into a forced close. Unlike expiry settlement or end-of-data finalization, which close free, a liquidation prices through the fill model and pays exit costs, reporting exit_reason == "Liquidation".

Options settle at their own last close unless you supply an underlying — an option's bars carry the option's price, so intrinsic value has to come from somewhere else:

class Hold(raptorbt.Strategy):
    def on_bar(self, ctx):
        # From whatever index series the strategy already tracks.
        ctx.set_underlying_price(spot_close[ctx.idx])

Without it a 100-strike call whose own price decayed to 0.50 settles at 0.50, even with spot at 112 — where intrinsic is 12.00.

Execution Algorithms

orders.Twap slices an order into equal parts released at a fixed interval, each an ordinary order with its own fill:

class Accumulate(raptorbt.Strategy):
    def on_bar(self, ctx):
        if ctx.idx == 0:
            self.submit_order(orders.Twap(
                side="buy", units=1_000, slices=10,
                every=60_000_000_000,        # one slice per minute
            ))

    def on_order_filled(self, ctx, event):
        # client ids are "<parent>#0", "<parent>#1", ...
        ...

The interval is a duration rather than a bar count, because a bar index means different things in bar and tick sessions — "every 1 bar" would silently become "every 1 print" on a tick feed. Use every_bars=N, bar_ns=... if you prefer to think in bars.

A schedule is not an order: cancelling it (cancel_twap) stops the remaining slices but does not unwind the ones that already traded, and on_algo_completed means fully released, not fully filled. Only explicit units can be sliced — size_frac resolves against equity at fill time, so each slice would size against a different account.

Position Policies, Margin Accounts, and Fill Realism

New in 0.5.0, all default-off (defaults reproduce prior results bit-for-bit; a committed golden-fixture suite enforces this):

• Hedging — run_strategy_backtest(..., oms_type="hedging"): every typed order opens an independent position in its own direction (buy → long, sell → short), so longs and shorts coexist, each with its own protective stop/target and trailing state. Inspect them via ctx.positions (each has a position_id) and close one with self.close_position(position_id). The default "netting" keeps the one-position-at-a-time behavior.
• Margin accounts — account_type="margin", leverage=N: entries lock initial margin (the instrument's margin_init, else 1/leverage) instead of full notional, equity marks balance plus direction-aware unrealized PnL (shorts price correctly), and an equity breach of the maintenance requirement (margin_maint, else half initial) fires on_margin_call and halts new entries — no forced liquidation. ctx.free_capital reports unlocked cash.
• Stochastic fills — PyBacktestConfig(fill_prob_limit=0.9, fill_prob_slippage=0.1, fill_seed=42): a marketable resting limit may be passed over (it stays working and retries), and stop/market fills may slip one tick against the trader (needs an instrument price_increment). Seeded and fully deterministic: same seed, same fills.
• Adaptive bar path — PyBacktestConfig(bar_path_adaptive=True): when a stop and target are both touched inside one bar, infer the traversal from candle geometry (up-candle: open→low→high→close) instead of the conservative stop-first default.

Strategy Types

All strategy entrypoints take NumPy arrays directly. Signals (entries / exits) are boolean arrays you compute however you like — pandas, the built-in indicators, or your own model. The engine is asset- and broker-agnostic: timestamps are int64 (nanoseconds for tick data; any monotonic int for bars), prices are float64.

1. Single Instrument

Long or short on one instrument. This is the canonical example — the other strategy types follow the same shape.

import numpy as np
import pandas as pd
import raptorbt

df = pd.read_csv("your_data.csv", index_col=0, parse_dates=True)

# Signals (SMA crossover) — any boolean arrays work here
sma_fast = df["close"].rolling(10).mean()
sma_slow = df["close"].rolling(20).mean()
entries = (sma_fast > sma_slow) & (sma_fast.shift(1) <= sma_slow.shift(1))
exits = (sma_fast < sma_slow) & (sma_fast.shift(1) >= sma_slow.shift(1))

config = raptorbt.PyBacktestConfig(initial_capital=100000, fees=0.001, slippage=0.0005)
config.set_fixed_stop(0.02)    # optional 2% stop-loss
config.set_fixed_target(0.04)  # optional 4% take-profit

result = raptorbt.run_single_backtest(
    timestamps=df.index.astype("int64").values,
    open=df["open"].values,
    high=df["high"].values,
    low=df["low"].values,
    close=df["close"].values,
    volume=df["volume"].values,
    entries=entries.values,
    exits=exits.values,
    direction=1,   # 1 = long, -1 = short
    weight=1.0,
    symbol="AAPL",
    config=config,
    instrument_config=raptorbt.PyInstrumentConfig(lot_size=1.0),  # optional: lot rounding, capital cap
)

print(f"Return {result.metrics.total_return_pct:.2f}%  "
      f"Sharpe {result.metrics.sharpe_ratio:.2f}  "
      f"MaxDD {result.metrics.max_drawdown_pct:.2f}%  "
      f"Trades {result.metrics.total_trades}")

equity = result.equity_curve()  # np.ndarray
trades = result.trades()        # list[PyTrade]

2. Basket/Collective

Trade multiple instruments with synchronized signals.

instruments = [
    (timestamps, open1, high1, low1, close1, volume1, entries1, exits1, 1, 0.33, "AAPL"),
    (timestamps, open2, high2, low2, close2, volume2, entries2, exits2, 1, 0.33, "GOOGL"),
    (timestamps, open3, high3, low3, close3, volume3, entries3, exits3, 1, 0.34, "MSFT"),
]

# Optional: Per-instrument configs for lot_size and capital allocation
instrument_configs = {
    "AAPL": raptorbt.PyInstrumentConfig(lot_size=1.0, alloted_capital=33000),
    "GOOGL": raptorbt.PyInstrumentConfig(lot_size=1.0, alloted_capital=33000),
    "MSFT": raptorbt.PyInstrumentConfig(lot_size=1.0, alloted_capital=34000),
}

result = raptorbt.run_basket_backtest(
    instruments=instruments,
    config=config,
    sync_mode="all",  # "all", "any", "majority", "master"
    instrument_configs=instrument_configs,  # Optional
)

Sync Modes:

• all: Enter only when ALL instruments signal
• any: Enter when ANY instrument signals
• majority: Enter when >50% of instruments signal
• master: Follow the first instrument's signals

3. Pairs Trading

Long one instrument, short another with optional hedge ratio.

result = raptorbt.run_pairs_backtest(
    # Long leg
    leg1_timestamps=timestamps,
    leg1_open=long_open,
    leg1_high=long_high,
    leg1_low=long_low,
    leg1_close=long_close,
    leg1_volume=long_volume,
    # Short leg
    leg2_timestamps=timestamps,
    leg2_open=short_open,
    leg2_high=short_high,
    leg2_low=short_low,
    leg2_close=short_close,
    leg2_volume=short_volume,
    # Signals
    entries=entries,
    exits=exits,
    direction=1,
    symbol="TCS_INFY",
    config=config,
    hedge_ratio=1.5,      # Short 1.5x the long position
    dynamic_hedge=False,  # Use rolling hedge ratio
)

4. Options

Backtest options strategies with strike selection.

result = raptorbt.run_options_backtest(
    timestamps=timestamps,
    open=underlying_open,
    high=underlying_high,
    low=underlying_low,
    close=underlying_close,
    volume=volume,
    option_prices=option_prices,  # Option premium series
    entries=entries,
    exits=exits,
    direction=1,
    symbol="NIFTY_CE",
    config=config,
    option_type="call",           # "call" or "put"
    strike_selection="atm",       # "atm", "otm1", "otm2", "itm1", "itm2"
    size_type="percent",          # "percent", "contracts", "notional", "risk"
    size_value=0.1,               # 10% of capital
    lot_size=50,                  # Options lot size
    strike_interval=50.0,         # Strike interval (e.g., 50 for NIFTY)
)

5. Multi-Strategy

Combine multiple strategies on the same instrument.

strategies = [
    (entries_sma, exits_sma, 1, 0.4, "SMA_Crossover"),    # 40% weight
    (entries_rsi, exits_rsi, 1, 0.35, "RSI_MeanRev"),     # 35% weight
    (entries_bb, exits_bb, 1, 0.25, "BB_Breakout"),       # 25% weight
]

result = raptorbt.run_multi_backtest(
    timestamps=timestamps,
    open=open_prices,
    high=high_prices,
    low=low_prices,
    close=close_prices,
    volume=volume,
    strategies=strategies,
    config=config,
    combine_mode="any",  # "any", "all", "majority", "weighted", "independent"
)

Combine Modes:

• any: Enter when any strategy signals
• all: Enter only when all strategies signal
• majority: Enter when >50% of strategies signal
• weighted: Weight signals by strategy weight
• independent: Run strategies independently (aggregate PnL)

6. Batch Spread Backtest

Run multiple spread backtests in parallel. Shared data (timestamps, underlying close) is converted once, then each item is backtested on its own Rayon thread with the GIL released for maximum throughput.

import numpy as np
import raptorbt

config = raptorbt.PyBacktestConfig(initial_capital=100000, fees=0.001)

# Create batch items — one per strategy variation
items = [
    raptorbt.PyBatchSpreadItem(
        strategy_id="straddle_24000",
        legs_premiums=[call_24000_premiums, put_24000_premiums],
        leg_configs=[("CE", 24000.0, -1, 50), ("PE", 24000.0, -1, 50)],
        entries=entries,
        exits=exits,
        spread_type="straddle",
        max_loss=5000.0,
        target_profit=3000.0,
    ),
    raptorbt.PyBatchSpreadItem(
        strategy_id="strangle_23500_24500",
        legs_premiums=[call_24500_premiums, put_23500_premiums],
        leg_configs=[("CE", 24500.0, -1, 50), ("PE", 23500.0, -1, 50)],
        entries=entries,
        exits=exits,
        spread_type="strangle",
    ),
]

# Run all in parallel — returns list of (strategy_id, result) tuples
results = raptorbt.batch_spread_backtest(
    timestamps=timestamps,
    underlying_close=underlying_close,
    items=items,
    config=config,
)

for strategy_id, result in results:
    print(f"{strategy_id}: {result.metrics.total_return_pct:.2f}%")

7. Tick-Level Backtest

Simulate intraday strategies at full tick resolution — no bar resampling, no intra-bar path approximation. Designed for options momentum, scalping, and any setup where the exact fill tick matters.

import numpy as np
import raptorbt

# Raw tick arrays (one element per tick, same length N)
# buy_qty_delta / sell_qty_delta must be per-tick deltas, NOT Zerodha cumulative sums
result = raptorbt.run_tick_backtest(
    timestamps=timestamps_ns,       # int64 nanoseconds-since-epoch
    ltp=ltp_arr,                    # last traded price
    bid=bid_arr,
    ask=ask_arr,
    buy_qty_delta=buy_delta,        # pre-converted from cumulative: np.diff(buy_cum).clip(0)
    sell_qty_delta=sell_delta,
    oi=oi_arr,
    entries=entry_signals,          # bool array — True where entry is allowed
    exits=exit_signals,             # bool array — True where position should exit
    symbol="NIFTY26APR24600PE",
    initial_capital=100_000.0,
    fees=0.001,
    slippage=0.0005,
    stop_loss_pct=5.0,
    take_profit_pct=10.0,
    max_hold_seconds=1800,          # 30-minute maximum hold
    entry_cooldown_ticks=10,        # minimum ticks between entries
    max_trades=50,
)

print(f"trades: {result.metrics.total_trades}")
print(f"profit_factor: {result.metrics.profit_factor:.2f}")
print(f"win_rate: {result.metrics.win_rate_pct:.1f}%")

Class-Contract Tick Strategies

run_tick_backtest above is the array runner: precomputed signal arrays, one long-only position at a time. For the full class contract — typed orders, multiple positions, margin accounts, portfolio risk gates — drive the event session from ticks instead:

class Scalper(raptorbt.Strategy):
    def on_quote(self, ctx, quote):
        # Quotes are observation only: nothing fills here.
        self.wide = quote.spread > 0.05

    def on_trade_tick(self, ctx, tick):
        # ctx.best_bid / ctx.best_ask are the book observed BEFORE this
        # print — the quote from the same feed row arrives next.
        if not self.wide and ctx.is_flat and ctx.best_bid is not None:
            self.submit_order(orders.Limit(side="buy", price=ctx.best_bid, units=10))

    def on_bar(self, ctx):
        # Only fires when primary_bars is set. A view, not a venue.
        ...

result = raptorbt.run_tick_strategy(
    Scalper,
    ticks={"NIFTY24600PE": dict(timestamps=ts, ltp=ltp, bid=bid, ask=ask)},
    primary_bars=(1, "m"),     # aggregate prints into 1m bars for on_bar
    account_type="margin", leverage=5.0,
)

Three semantics to know:

• Quotes do not fill orders, move trailing stops, or mark equity. Filling against a quote asserts a counterparty the engine has no evidence for; the print that follows is that evidence. Orders submitted from on_quote rest and match on the next print. Quotes also do not sample the equity curve, so metrics do not shift with how chatty the feed is.
• primary_bars builds bars from prints as a view. They fire on_bar and feed indicators registered without a stream_id; subscribe_bars composites work too. Order matching still happens against ticks only.
• AT_OPEN/AT_CLOSE market orders keep resting on a print — a print has no bar phase to queue against. Trailing stops ratchet off every print, so they resolve at tick resolution; a tick run and a bar run over the same data legitimately differ there, since a bar can trigger a stop against a low that preceded the high which set the watermark.

Order Book and Queue-Position Fills

Pass depth= to run_tick_strategy for five-level book snapshots, which arrive via on_order_book and persist on ctx.book:

depth = {"NIFTY24600PE": dict(
    timestamps=ts,                      # int64 ns, one row per snapshot
    bid_prices=bp, bid_sizes=bs,        # (n_snapshots, levels), best first
    ask_prices=ap, ask_sizes=asz,
)}

class Maker(raptorbt.Strategy):
    def on_order_book(self, ctx, book):
        if book.imbalance and book.imbalance > 0.7:      # bid-heavy
            self.submit_order(orders.Limit(side="buy", price=book.best_bid, units=10))

config.queue_fill_model = True          # opt-in
result = raptorbt.run_tick_strategy(Maker, ticks, config=config, depth=depth)

queue_fill_model replaces fill_prob_limit's coin flip with the tape. The size queued ahead is estimated once when the order rests, then consumed by print volume at that price; a print through the level fills unconditionally. Progress is monotone, so an order passed over repeatedly genuinely advances — the probability model has no such memory.

It does not claim a real queue rank. Market-by-price data cannot tell you where you stand in line, nor separate size that executed ahead of you from size that was cancelled, so the model falls back to fill_prob_limit rather than guessing: on bar events (a bar's volume is not volume at the limit price) and on a quote-only book (a quote gives the price, not the size). A level outside the visible five reads as unknown, never as empty.

Books, like quotes, are observation only — they never fill an order, move a trailing stop, or mark equity. Displayed size is intent, not a trade.

Tick Signal & Feature Helpers

Precompute entry/exit signal arrays and tick microstructure features before calling run_tick_backtest:

# Signal arrays
entries = raptorbt.compute_tick_entry_signals(
    spread_pct=raptorbt.tick_spread_pct(bid, ask),
    bsi_delta=raptorbt.buy_sell_imbalance_delta(buy_cum, sell_cum),  # pass raw cumulative
    return_1m=raptorbt.return_window(timestamps_ns, ltp, window_seconds=60.0),
    spread_pct_max=3.0,
    bsi_min=0.55,           # minimum buy-side delta fraction
    return_1m_min_abs=0.3,  # minimum 1-min return % (abs)
    return_direction=1,     # +1 long, -1 short
    cooldown_ticks=10,
)
exits = raptorbt.compute_tick_exit_signals(
    timestamps_ns=timestamps_ns,
    eod_exit_time_ns=eod_ns,   # force exit at/after this timestamp; 0 = disabled
)

# Feature arrays (all return Vec<f64> of same length as input)
spread   = raptorbt.tick_spread_pct(bid, ask)               # (ask-bid)/mid * 100
bsi      = raptorbt.buy_sell_imbalance_delta(buy_cum, sell_cum)  # delta BSI per tick
ret_1m   = raptorbt.return_window(ts_ns, ltp, 60.0)         # 1-min lookback return %
vol      = raptorbt.realized_vol_rolling(ts_ns, ltp, 300.0)  # 5-min realized vol %
oi_pos   = raptorbt.oi_position_pct(oi, oi_day_high, oi_day_low)  # [0, 100]
velocity = raptorbt.tick_velocity(ts_ns, 60.0)              # ticks/min over last 60s

Important for Zerodha data: total_buy_qty and total_sell_qty from KiteTicker are cumulative session running sums, not per-tick values. Pass them as-is to buy_sell_imbalance_delta (it computes deltas internally). For run_tick_backtest, convert first: buy_delta = np.diff(buy_cum, prepend=0).clip(min=0).

---

Metrics

Every backtest returns a PyBacktestMetrics object exposing 33 metric fields (listed in full under PyBacktestMetrics). metrics.to_dict() returns a subset of 24 of them under human-readable labels (e.g. "Sharpe Ratio", "Total Return [%]") for quick display; read fields directly off the object to access all 33. The most useful are grouped below.

Core Performance

Metric	Description
total_return_pct	Total return as percentage
sharpe_ratio	Risk-adjusted return (annualized)
sortino_ratio	Downside risk-adjusted return
calmar_ratio	Return / Max Drawdown
omega_ratio	Probability-weighted gains/losses

Drawdown

Metric	Description
max_drawdown_pct	Maximum peak-to-trough decline
max_drawdown_duration	Longest drawdown period (bars)

Trade Statistics

Metric	Description
total_trades	Total number of trades
total_closed_trades	Number of closed trades
total_open_trades	Number of open positions
winning_trades	Number of profitable trades
losing_trades	Number of losing trades
win_rate_pct	Percentage of winning trades

Trade Performance

Metric	Description
profit_factor	Gross profit / Gross loss
expectancy	Average expected profit per trade
sqn	System Quality Number
avg_trade_return_pct	Average trade return
avg_win_pct	Average winning trade return
avg_loss_pct	Average losing trade return
best_trade_pct	Best single trade return
worst_trade_pct	Worst single trade return

Duration

Metric	Description
avg_holding_period	Average trade duration (bars)
avg_winning_duration	Average winning trade duration
avg_losing_duration	Average losing trade duration

Streaks

Metric	Description
max_consecutive_wins	Longest winning streak
max_consecutive_losses	Longest losing streak

Other

Metric	Description
start_value	Initial portfolio value
end_value	Final portfolio value
total_fees_paid	Total transaction costs
open_trade_pnl	Unrealized PnL from open positions
exposure_pct	Percentage of time in market

---

Indicators

RaptorBT exports 12 classic technical indicators, computed in native Rust and operating on (and returning) NumPy arrays:

import raptorbt

# Trend indicators
sma = raptorbt.sma(close, period=20)
ema = raptorbt.ema(close, period=20)
supertrend, direction = raptorbt.supertrend(high, low, close, period=10, multiplier=3.0)

# Momentum indicators
rsi = raptorbt.rsi(close, period=14)
macd_line, signal_line, histogram = raptorbt.macd(close, 12, 26, 9)  # fast, slow, signal (positional)
stoch_k, stoch_d = raptorbt.stochastic(high, low, close, k_period=14, d_period=3)

# Volatility indicators
atr = raptorbt.atr(high, low, close, period=14)
upper, middle, lower = raptorbt.bollinger_bands(close, period=20, std_dev=2.0)

# Strength indicators
adx = raptorbt.adx(high, low, close, period=14)

# Volume indicators
vwap = raptorbt.vwap(high, low, close, volume)

# Rolling indicators (LLV / HHV)
rolling_low = raptorbt.rolling_min(low, period=20)    # Lowest Low Value
rolling_high = raptorbt.rolling_max(high, period=20)  # Highest High Value

In addition, 8 tick microstructure / feature functions are available for tick-level work (tick_spread_pct, buy_sell_imbalance_delta, return_window, realized_vol_rolling, oi_position_pct, tick_velocity, compute_tick_entry_signals, compute_tick_exit_signals) — see Tick-Level Backtest.

---

Stop-Loss & Take-Profit

Fixed Percentage

config = raptorbt.PyBacktestConfig(initial_capital=100000, fees=0.001)
config.set_fixed_stop(0.02)    # 2% stop-loss
config.set_fixed_target(0.04)  # 4% take-profit

ATR-Based

config.set_atr_stop(multiplier=2.0, period=14)    # 2x ATR stop
config.set_atr_target(multiplier=3.0, period=14)  # 3x ATR target

Trailing Stop

config.set_trailing_stop(0.02)  # 2% trailing stop

Risk-Reward Target

config.set_risk_reward_target(ratio=2.0)  # 2:1 risk-reward ratio

---

Monte Carlo Portfolio Simulation

RaptorBT includes a high-performance Monte Carlo forward simulation engine for portfolio risk analysis. It uses Geometric Brownian Motion (GBM) with Cholesky decomposition for correlated multi-asset simulation, parallelized via Rayon.

import numpy as np
import raptorbt

# Historical daily returns per strategy/asset (numpy arrays)
returns = [
    np.array([0.001, -0.002, 0.003, ...]),  # Strategy 1 returns
    np.array([0.002, 0.001, -0.001, ...]),   # Strategy 2 returns
]

# Portfolio weights (must sum to 1.0)
weights = np.array([0.6, 0.4])

# Correlation matrix (N x N)
correlation_matrix = [
    np.array([1.0, 0.3]),
    np.array([0.3, 1.0]),
]

# Run simulation
result = raptorbt.simulate_portfolio_mc(
    returns=returns,
    weights=weights,
    correlation_matrix=correlation_matrix,
    initial_value=100000.0,
    n_simulations=10000,   # Number of Monte Carlo paths (default: 10,000)
    horizon_days=252,      # Forward projection horizon (default: 252)
    seed=42,               # Random seed for reproducibility (default: 42)
)

# Results
print(f"Expected Return: {result['expected_return']:.2f}%")
print(f"Probability of Loss: {result['probability_of_loss']:.2%}")
print(f"VaR (95%): {result['var_95']:.2f}%")
print(f"CVaR (95%): {result['cvar_95']:.2f}%")

# Percentile paths: list of (percentile, path_values)
# Percentiles: 5th, 25th, 50th, 75th, 95th
for pct, path in result['percentile_paths']:
    print(f"  P{pct:.0f} final value: {path[-1]:.2f}")

# Final values: numpy array of terminal values for all simulations
final_values = result['final_values']  # numpy array, length = n_simulations

Result Fields

Field	Type	Description
expected_return	float	Expected return as percentage over the horizon
probability_of_loss	float	Probability that final value < initial value (0.0 to 1.0)
var_95	float	Value at Risk at 95% confidence (percentage)
cvar_95	float	Conditional VaR at 95% confidence (percentage)
percentile_paths	List[Tuple[float, List]]	Portfolio paths at 5th, 25th, 50th, 75th, 95th percentiles
final_values	numpy.ndarray	Terminal portfolio values for all simulations

---

API Reference

PyBacktestConfig

config = raptorbt.PyBacktestConfig(
    initial_capital: float = 100000.0,
    fees: float = 0.001,
    slippage: float = 0.0,
    upon_bar_close: bool = True,
)

# Stop methods
config.set_fixed_stop(percent: float)
config.set_atr_stop(multiplier: float, period: int)
config.set_trailing_stop(percent: float)

# Target methods
config.set_fixed_target(percent: float)
config.set_atr_target(multiplier: float, period: int)
config.set_risk_reward_target(ratio: float)

PyInstrumentConfig

Per-instrument configuration for position sizing and risk management.

inst_config = raptorbt.PyInstrumentConfig(
    lot_size=1.0,              # Min tradeable quantity (1 for equity, 50 for NIFTY F&O)
    alloted_capital=50000.0,   # Capital allocated to this instrument (optional)
    existing_qty=None,         # Existing position quantity (future use)
    avg_price=None,            # Existing position avg price (future use)
)

# Optional: per-instrument stop/target overrides
inst_config.set_fixed_stop(0.02)
inst_config.set_trailing_stop(0.03)
inst_config.set_fixed_target(0.05)

Fields:

• lot_size - Minimum tradeable quantity. Position sizes are rounded down to nearest lot_size multiple. Use 1.0 for equities, 50.0 for NIFTY F&O, 0.01 for forex.
• alloted_capital - Per-instrument capital cap (capped at available cash).
• existing_qty / avg_price - Reserved for future live-to-backtest transitions.

PyBatchSpreadItem

item = raptorbt.PyBatchSpreadItem(
    strategy_id: str,                    # Unique identifier for this backtest
    legs_premiums: List[np.ndarray],     # Premium series per leg
    leg_configs: List[Tuple[str, float, int, int]],  # (option_type, strike, quantity, lot_size)
    entries: np.ndarray,                 # bool entry signals
    exits: np.ndarray,                   # bool exit signals
    spread_type: str = "custom",         # Spread type string
    max_loss: float = None,              # Optional max loss exit
    target_profit: float = None,         # Optional target profit exit
)

batch_spread_backtest

results = raptorbt.batch_spread_backtest(
    timestamps: np.ndarray,              # int64 nanosecond timestamps (shared)
    underlying_close: np.ndarray,        # Underlying close prices (shared)
    items: List[PyBatchSpreadItem],      # List of spread backtest items
    config: PyBacktestConfig = None,     # Optional shared config
) -> List[Tuple[str, PyBacktestResult]]  # (strategy_id, result) pairs

Runs all spread backtests in parallel via Rayon. Timestamps and underlying close are shared across all items and converted once. The GIL is released during execution for maximum Python concurrency.

simulate_portfolio_mc

result = raptorbt.simulate_portfolio_mc(
    returns: List[np.ndarray],               # Per-asset daily returns (N arrays)
    weights: np.ndarray,                     # Portfolio weights (length N, sum to 1)
    correlation_matrix: List[np.ndarray],    # N x N correlation matrix
    initial_value: float,                    # Starting portfolio value
    n_simulations: int = 10000,              # Number of Monte Carlo paths
    horizon_days: int = 252,                 # Forward projection horizon in days
    seed: int = 42,                          # Random seed for reproducibility
) -> dict

Returns a dictionary with keys: expected_return, probability_of_loss, var_95, cvar_95, percentile_paths, final_values.

PyBacktestResult

result = raptorbt.run_single_backtest(...)

# Attributes
result.metrics        # PyBacktestMetrics object

# Methods
result.equity_curve()    # numpy.ndarray
result.drawdown_curve()  # numpy.ndarray
result.returns()         # numpy.ndarray
result.trades()          # List[PyTrade]

PyBacktestMetrics

33 read-only fields — see the Metrics section for the full table with descriptions. metrics.to_dict() returns 24 of them under human-readable labels (e.g. "Sharpe Ratio") for quick display; read fields off the object directly for the complete set.

m = result.metrics
m.total_return_pct, m.sharpe_ratio, m.max_drawdown_pct   # etc. — 33 fields total
stats = m.to_dict()

PyTrade

for trade in result.trades():
    print(trade.id)           # Trade ID
    print(trade.symbol)       # Symbol
    print(trade.entry_idx)    # Entry bar index
    print(trade.exit_idx)     # Exit bar index
    print(trade.entry_price)  # Entry price
    print(trade.exit_price)   # Exit price
    print(trade.size)         # Position size
    print(trade.direction)    # 1=Long, -1=Short
    print(trade.pnl)          # Profit/Loss
    print(trade.return_pct)   # Return percentage
    print(trade.fees)         # Fees paid
    print(trade.exit_reason)  # "Signal", "StopLoss", "TakeProfit", "TrailingStop", "EndOfData", "Settlement", "TimeExit"

---

Building from Source

Most users should pip install raptorbt. To build the engine yourself you need Rust 1.70+, Python 3.10+, and maturin:

cd raptorbt
maturin develop --release   # editable install into the active venv
cargo test                  # run the Rust test suite

Verification Test

A seeded smoke test — run it twice and the result is identical to the last decimal (the determinism guarantee):

import numpy as np
import raptorbt

np.random.seed(42)
n = 500
close = np.cumprod(1 + np.random.randn(n) * 0.02) * 100
entries = np.zeros(n, dtype=bool); entries[::20] = True
exits = np.zeros(n, dtype=bool);  exits[10::20] = True

config = raptorbt.PyBacktestConfig(initial_capital=100000, fees=0.001)
result = raptorbt.run_single_backtest(
    timestamps=np.arange(n, dtype=np.int64),
    open=close,
    high=close,
    low=close,
    close=close,
    volume=np.ones(n),
    entries=entries,
    exits=exits,
    direction=1,
    weight=1.0,
    symbol="TEST",
    config=config,
)
print(f"Total Return: {result.metrics.total_return_pct:.4f}%")  # -30.6192%
print(f"Sharpe Ratio: {result.metrics.sharpe_ratio:.4f}")       # -0.9086

---

License

MIT License - see LICENSE for details.

---

Changelog

v0.4.0

Tick-level backtesting — full tick resolution, no bar resampling.

• Add TickData struct — parallel arrays of timestamps, ltp, bid, ask, buy_qty_delta, sell_qty_delta, oi (one element per tick). Callers must pre-convert Zerodha cumulative session totals to per-tick deltas before passing.
• Add ExitReason::TimeExit — max hold-time exceeded exit for tick strategies.
• Add run_tick_backtest — tick-native simulation engine. Entry fills at ask+slippage; stop/target checked against ltp on every tick (not OHLC approximation); max-hold-seconds time exit; configurable cooldown between entries. Returns the same PyBacktestResult / PyBacktestMetrics (33 fields) as all other strategy types.
• Add compute_tick_entry_signals — compute momentum entry bool array from precomputed feature arrays (spread gate, delta BSI gate, 1-min return gate, cooldown enforcement). O(N) single pass.
• Add compute_tick_exit_signals — time-based (EOD) exit bool array from tick timestamps.
• Add tick_spread_pct — per-tick bid/ask spread as percentage of mid price.
• Add buy_sell_imbalance_delta — per-tick delta BSI from Zerodha cumulative running sums. Fixes the raw-cumulative BSI artefact (~0.95 all day regardless of order flow).
• Add return_window — per-tick lookback return over a configurable time window using binary search (O(N log N)). Returns NaN where history is insufficient — correctly gates the entry filter rather than silently passing.
• Add realized_vol_rolling — rolling realized volatility proxy (stddev of log-returns) over a time window.
• Add oi_position_pct — OI position within the day's high/low range, per tick: [0, 100].
• Add tick_velocity — rolling tick count per minute over a configurable time window.
• Expose compute_backtest_metrics as a public free function in portfolio::engine — non-OHLCV strategy types can produce identical metrics without duplicating the calculation logic.

v0.3.4

• Add single-leg option spread types: LongCall, LongPut, NakedCall, NakedPut to SpreadType enum
• Add ExitReason::Settlement for option expiry settlement exits
• Add leg_expiry_timestamps parameter to run_spread_backtest for per-leg expiry tracking
• Positions are force-closed at settlement when any leg expires, with premiums replaced by intrinsic value
• Prevent re-entry after all legs have expired

v0.3.3

• Add batch_spread_backtest function for running multiple spread backtests in parallel via Rayon
• Add PyBatchSpreadItem class for defining individual items in a batch spread backtest
• Shared data (timestamps, underlying close) is converted once and reused across all items
• GIL released during parallel execution for maximum Python concurrency
• Each item carries its own strategy_id, leg configs, signals, spread type, and optional max loss / target profit
• Returns a list of (strategy_id, PyBacktestResult) tuples preserving result-to-input mapping

v0.3.2

• Add payoff_ratio metric to BacktestMetrics — average winning trade return divided by average losing trade return (absolute), measures risk/reward per trade
• Add recovery_factor metric to BacktestMetrics — net profit divided by maximum drawdown in absolute terms, measures how many times over the strategy recovered from its worst drawdown
• Both metrics computed in StreamingMetrics::finalize() (single-instrument backtest) and PortfolioEngine (multi-strategy aggregation)
• Both metrics exposed via PyO3 as #[pyo3(get)] attributes on PyBacktestMetrics
• Handles edge cases: returns f64::INFINITY when denominator is zero with positive numerator, 0.0 otherwise

v0.3.1

• Add Monte Carlo portfolio simulation (simulate_portfolio_mc) for forward risk projection
• Geometric Brownian Motion (GBM) with Cholesky decomposition for correlated multi-asset simulation
• Rayon-parallelized simulation paths with deterministic seeding (xoshiro256**)
• Returns percentile paths (P5/P25/P50/P75/P95), VaR, CVaR, expected return, and probability of loss
• GIL released during simulation for maximum Python concurrency

v0.3.0

• Per-instrument configuration via PyInstrumentConfig (lot_size, alloted_capital, stop/target overrides)
• Position sizes now correctly rounded to lot_size multiples
• Support for per-instrument capital allocation in basket backtests
• Future-ready fields: existing_qty, avg_price for live-to-backtest transitions

v0.2.2

• Export run_spread_backtest Python binding for multi-leg options spread strategies
• Export rolling_min and rolling_max indicator functions to Python

v0.2.1

• Add rolling_min and rolling_max indicators for LLV (Lowest Low Value) and HHV (Highest High Value) support
• NaN handling for warmup period

v0.2.0

• Add multi-leg spread backtesting (run_spread_backtest) supporting straddles, strangles, vertical spreads, iron condors, iron butterflies, butterfly spreads, calendar spreads, and diagonal spreads
• Coordinated entry/exit across all legs with net premium P&L calculation
• Max loss and target profit exit thresholds for spreads
• Add SessionTracker for intraday session management: market hours detection, squareoff time enforcement, session high/low/open tracking
• Pre-built session configs for NSE equity (9:15-15:30), MCX commodity (9:00-23:30), and CDS currency (9:00-17:00)
• Extend StreamingMetrics with equity/drawdown tracking, trade recording, and finalize() method

v0.1.0

• Initial release
• 5 strategy types: single, basket, pairs, options, multi
• 30+ performance metrics: Sharpe, Sortino, Calmar, Omega, SQN, profit factor, drawdown duration, and more
• 10 technical indicators (SMA, EMA, RSI, MACD, Stochastic, ATR, Bollinger Bands, ADX, VWAP, Supertrend)
• Stop-loss management: fixed, ATR-based, and trailing stops
• Take-profit management: fixed, ATR-based, and risk-reward targets
• PyO3 Python bindings for seamless Python integration