backtesting.py Oracle Validation for Range Bar Patterns

Configuration and anti-patterns for using backtesting.py to validate ClickHouse SQL sweep results. Ensures bit-atomic replicability between SQL and Python trade evaluation.

Companion skills: clickhouse-antipatterns (SQL correctness, AP-16) | sweep-methodology (sweep design) | rangebar-eval-metrics (evaluation metrics)

Validated: Gen600 oracle verification (2026-02-12) — 3 assets, 5 gates, ALL PASS.

Critical Configuration (NEVER omit)

from backtesting import Backtest

bt = Backtest(
    df,
    Strategy,
    cash=100_000,
    commission=0,
    hedging=True,           # REQUIRED: Multiple concurrent positions
    exclusive_orders=False,  # REQUIRED: Don't auto-close on new signal
)

Why: SQL evaluates each signal independently (overlapping trades allowed). Without hedging=True, backtesting.py skips signals while a position is open, producing fewer trades than SQL. This was discovered when SOLUSDT produced 105 Python trades vs 121 SQL trades — 16 signals were silently skipped.

Anti-Patterns (Ordered by Severity)

BP-01: Missing Multi-Position Mode (CRITICAL)

Symptom: Python produces fewer trades than SQL. Gate 1 (signal count) fails.

Root Cause: Default exclusive_orders=True prevents opening new positions while one is active.

Fix: Always use hedging=True, exclusive_orders=False.

BP-02: ExitTime Sort Order (CRITICAL)

Symptom: Entry prices appear mismatched (Gate 3 fails) even though both SQL and Python use the same price source.

Root Cause: stats._trades is sorted by ExitTime, not EntryTime. When overlapping trades exit in a different order than they entered, trade[i] no longer maps to signal[i].

Fix:

trades = stats._trades.sort_values("EntryTime").reset_index(drop=True)

BP-03: NaN Poisoning in Rolling Quantile (CRITICAL)

Symptom: Cross-asset tests fail with far fewer Python trades. Feature quantile becomes NaN and propagates forward indefinitely.

Root Cause: np.percentile with NaN inputs returns NaN. If even one NaN feature value enters the rolling window, all subsequent quantiles become NaN, making all subsequent filter comparisons fail.

Fix: Skip NaN values when building the signal window:

def _rolling_quantile_on_signals(feature_arr, is_signal_arr, quantile_pct, window=1000):
    result = np.full(len(feature_arr), np.nan)
    signal_values = []
    for i in range(len(feature_arr)):
        if is_signal_arr[i]:
            if len(signal_values) > 0:
                window_data = signal_values[-window:]
                result[i] = np.percentile(window_data, quantile_pct * 100)
            # Only append non-NaN values (matches SQL quantileExactExclusive NULL handling)
            if not np.isnan(feature_arr[i]):
                signal_values.append(feature_arr[i])
    return result

BP-04: Data Range Mismatch (MODERATE)

Symptom: Different signal counts between SQL and Python for assets with early data (BNB, XRP).

Root Cause: load_range_bars() defaults to start='2020-01-01' but SQL has no lower bound.

Fix: Always pass start='2017-01-01' to cover all available data.

BP-05: Margin Exhaustion with Overlapping Positions (MODERATE)

Symptom: Orders canceled with insufficient margin. Fewer trades than expected.

Root Cause: With hedging=True and default full-equity sizing, overlapping positions exhaust available margin.

Fix: Use fixed fractional sizing:

self.buy(size=0.01)  # 1% equity per trade

BP-06: Signal Timestamp vs Entry Timestamp (LOW)

Symptom: Gate 2 (timestamp match) fails because SQL uses signal bar timestamps while Python uses entry bar timestamps.

Root Cause: SQL outputs the signal detection bar's timestamp_ms. Python's EntryTime is the fill bar (next bar after signal). These differ by 1 bar.

Fix: Record signal bar timestamps in the strategy's next() method:

# Before calling self.buy()
self._signal_timestamps.append(int(self.data.index[-1].timestamp() * 1000))

5-Gate Oracle Validation Framework

Gate	Metric	Threshold	What it catches
1	Signal Count	<5% diff	Missing signals, filter misalignment
2	Timestamp Match	>95%	Timing offset, warmup differences
3	Entry Price	>95%	Price source mismatch, sort ordering
4	Exit Type	>90%	Barrier logic differences
5	Kelly Fraction	<0.02	Aggregate outcome alignment

Expected residual: 1-2 exit type mismatches per asset at TIME barrier boundary (bar 50). SQL uses fwd_closes[max_bars], backtesting.py closes at current bar price. Impact on Kelly < 0.006.

Strategy Architecture: Single vs Multi-Position

Mode	Constructor	Use Case	Position Sizing
Single-position	`hedging=False` (default)	Champion 1-bar hold	Full equity
Multi-position	`hedging=True, exclusive_orders=False`	SQL oracle validation	Fixed fractional (`size=0.01`)

Multi-Position Strategy Template

class Gen600Strategy(Strategy):
    def next(self):
        current_bar = len(self.data) - 1

        # 1. Register newly filled trades and set barriers
        for trade in self.trades:
            tid = id(trade)
            if tid not in self._known_trades:
                self._known_trades.add(tid)
                self._trade_entry_bar[tid] = current_bar
                actual_entry = trade.entry_price
                if self.tp_mult > 0:
                    trade.tp = actual_entry * (1.0 + self.tp_mult * self.threshold_pct)
                if self.sl_mult > 0:
                    trade.sl = actual_entry * (1.0 - self.sl_mult * self.threshold_pct)

        # 2. Check time barrier for each open trade
        for trade in list(self.trades):
            tid = id(trade)
            entry_bar = self._trade_entry_bar.get(tid, current_bar)
            if self.max_bars > 0 and (current_bar - entry_bar) >= self.max_bars:
                trade.close()
                self._trade_entry_bar.pop(tid, None)

        # 3. Check for new signal (no position guard — overlapping allowed)
        if self._is_signal[current_bar]:
            self.buy(size=0.01)

Data Loading

from data_loader import load_range_bars

df = load_range_bars(
    symbol="SOLUSDT",
    threshold=1000,
    start="2017-01-01",      # Cover all available data
    end="2025-02-05",        # Match SQL cutoff
    extra_columns=["volume_per_trade", "lookback_price_range"],  # Gen600 features
)

Project Artifacts (rangebar-patterns repo)

Artifact	Path
Oracle comparison script	`scripts/gen600_oracle_compare.py`
Gen600 strategy (reference)	`backtest/backtesting_py/gen600_strategy.py`
SQL oracle query template	`sql/gen600_oracle_trades.sql`
Oracle validation findings	`findings/2026-02-12-gen600-oracle-validation.md`
Backtest CLAUDE.md	`backtest/CLAUDE.md`
ClickHouse AP-16	`.claude/skills/clickhouse-antipatterns/SKILL.md`
Fork source	`~/fork-tools/backtesting.py/`

backtesting-py-oracle