Quant research without the guardrails.

# Fill model
long_entry_price       = ask
long_exit_price        = bid
short_entry_price      = bid
short_exit_price       = ask
slippage_applied_at    = tick

# Time model
trade_timestamp_unit   = milliseconds
bar_boundary_snapping  = false
trading_day_timezone   = America/New_York
trading_day_start      = 18:00 prior calendar day

# Replay model
tbbo_replay_source     = parquet bid + ask
ohlc_to_tick_synthesis = none

# Filter model
tag_filter_path        = engine re-run with mask
cascade_preserved      = true

The engine's fill, time, replay, and filter conventions. Not configurable knobs; these are how the engine is built.

from quant_charts import strategy, day_start, input, ta, cross_above, cross_below, Timeframe
import numpy as np

@strategy(name="Optimize", overlay=True, timeframe=Timeframe.M1, data_mode="ohlc",
          required_columns=["high", "low", "close"], uses_sltp=False)
class Optimize:
    fast_ema = input.int(9, "Fast EMA", min=2, max=10000)
    slow_ema = input.int(21, "Slow EMA", min=2, max=10000)
    regime_atr_mult = input.float(1.5, "Regime ATR Mult", min=0.0, max=100.0, step=0.1)

    @day_start
    def prep(self, df):
        close = np.asarray(df["close"], dtype=np.float64)
        atr14 = ta.atr(df["high"], df["low"], df["close"], 14)
        median_atr = float(np.median(atr14[np.isfinite(atr14)]))
        return {"close": close, "atr14": atr14, "median_atr": median_atr}

    def calculate(self, df):
        close = self._day["close"]
        in_regime = self._day["atr14"] > self._day["median_atr"] * self.regime_atr_mult
        fast = ta.ema(close, self.fast_ema)
        slow = ta.ema(close, self.slow_ema)
        return {
            "entry_long":  cross_above(fast, slow) & in_regime,
            "exit_long":   cross_below(fast, slow),
            "entry_short": cross_below(fast, slow) & in_regime,
            "exit_short":  cross_above(fast, slow),
        }

templates/workspace/strategies/built-in/python/optimize.py, abridged.