O15 · example_orders_15_intraday_5m_bracket_reversion.py

O15 Intraday Bracket Reversion5m

On 5-minute bars, buy oversold RSI(14) dips and wrap each entry in a tight intraday bracket (+0.6% take-profit / -0.4% stop-loss).

Review the source on GitHub or return to all strategies.

What this example shows

This example shows how to build a 5m-bar RSI mean-reversion order-mode example with intraday signal generation, execution timing, cost assumptions and reporting at non-daily granularity.

The goal is educational: to demonstrate the full workflow for universe definition, signal generation, order construction, fill simulation, cash and position accounting, diagnostics and reproducible reporting.

Short-window execution example. Short-window intraday execution example. The dashboard emphasizes raw total return and drawdown; annualized CAGR and Sharpe are intentionally omitted because this sample is too short for a credible annual comparison.

Run setup

The ordinary configuration used by the published run—kept visible here and in the Python source.

UniverseSPY · QQQ · IWM

Configured period2026-06-10 → 2026-07-11

BenchmarkSPY · SPDR S&P 500 ETF Trust

Initial capital$100,000

Data sourceyfinance

Engine / snapshot0.10.0 · 85fd55372484856d

Order ModeMode

5mGranularity

3Instruments

18Published plots

passedRun status

Dashboard

Compact evidence view built from the same run packet: metrics, plots and run metadata.

-1.28% Max DD

1.12% Total return

$1,117 Net profit

Selected plot gallery

18 published PNG artifacts selected for raw short-window interpretation. Charts marked Pro are generated by the hosted platform report pack; the rest come from the open-source engine output.

O15 Intraday Bracket Reversion5m Cumulative Returns — Cumulative Returns PNG

O15 Intraday Bracket Reversion5m Portfolio Drawdown — Portfolio Drawdown PNG

O15 Intraday Bracket Reversion5m Nav Composition — Nav Composition Pro PNG

O15 Intraday Bracket Reversion5m Percentage Weights — Percentage Weights PNG

O15 Intraday Bracket Reversion5m Blotter Trade Pnl — Blotter Trade Pnl Pro PNG

O15 Intraday Bracket Reversion5m Blotter Cumulative Pnl — Blotter Cumulative Pnl Pro PNG

O15 Intraday Bracket Reversion5m Blotter Costs — Blotter Costs Pro PNG

O15 Intraday Bracket Reversion5m Blotter Frequency — Blotter Frequency Pro PNG

O15 Intraday Bracket Reversion5m Blotter Holding Periods — Blotter Holding Periods Pro PNG

O15 Intraday Bracket Reversion5m Blotter Instrument Pnl — Blotter Instrument Pnl Pro PNG

Execution

O15 Intraday Bracket Reversion5m Blotter Trade Size — Blotter Trade Size Pro PNG

O15 Intraday Bracket Reversion5m Blotter Trade Timeline — Blotter Trade Timeline Pro PNG

Portfolio accounting

O15 Intraday Bracket Reversion5m Composition — Composition PNG

Performance

O15 Intraday Bracket Reversion5m Cumulative Log Returns — Cumulative Log Returns PNG

Drawdown

O15 Intraday Bracket Reversion5m Time Underwater — Time Underwater PNG

Distribution and optimization

O15 Intraday Bracket Reversion5m Return Quantiles — Return Quantiles PNG

O15 Intraday Bracket Reversion5m Returns Distribution — Returns Distribution Pro PNG

Metrics

Raw short-window KPIs are shown above; annualized ratios are intentionally not promoted.

Artifacts

Stable links that can be referenced from GitHub README files or external docs.

Run log run.log Raw text report performance_report.txt Published source snapshot strategy.py Structured report performance_report.json GitHub source example_orders_15_intraday_5m_bracket_reversion.py

Run log

Tail of the execution log for this packet.

Running strategy: example_orders_15_intraday_5m_bracket_reversion
File: <QJ_BACKTESTER_REPO>/strategies/example_orders_15_intraday_5m_bracket_reversion.py
Python: <QJ_BACKTESTER_REPO>/.venv/bin/python
Backtester: v0.10.0
Theme: quantjourney
Plot DPI: 300
Log Level: ERROR
Output: reports/canonical-suite-20260711

2026-07-11 12:02:59,165 - quantjourney_ti._utils - WARNING - 2026-07-11 12:02:59,165 - quantjourney_ti._utils - WARNING - Series contains NaN values; they will be handled in calculations
2026-07-11 12:02:59,185 - quantjourney_ti._utils - WARNING - 2026-07-11 12:02:59,185 - quantjourney_ti._utils - WARNING - Series contains NaN values; they will be handled in calculations
[2026-07-11 12:03:02] [ERROR] [backtester] Failed to compute r_squared: Benchmark overlap 0.00% is below required 95.00%
[2026-07-11 12:03:02] [ERROR] [backtester] Failed to compute alpha: Benchmark overlap 0.00% is below required 95.00%
[2026-07-11 12:03:02] [ERROR] [backtester] Failed to compute beta: Benchmark overlap 0.00% is below required 95.00%
[2026-07-11 12:03:02] [ERROR] [backtester] Failed to compute tracking_error: Benchmark overlap 0.00% is below required 95.00%
[2026-07-11 12:03:02] [ERROR] [backtester] Failed to compute up_capture: Benchmark overlap 0.00% is below required 95.00%
[2026-07-11 12:03:02] [ERROR] [backtester] Failed to compute down_capture: Benchmark overlap 0.00% is below required 95.00%
[2026-07-11 12:03:03] [ERROR] [backtester] Failed to generate cumulative_returns_with_regime plot: Insufficient data for regime cumulative returns.
[2026-07-11 12:03:04] [ERROR] [backtester] Failed to generate portfolio_drawdown_with_regime plot: Insufficient data for regime drawdown plot.
<QJ_BACKTESTER_REPO>/backtester/portfolio/portfolio_plots.py:203: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
  regime = regime.reindex(target_index).ffill()
<QJ_BACKTESTER_REPO>/backtester/portfolio/portfolio_plots.py:203: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
  regime = regime.reindex(target_index).ffill()
[2026-07-11 12:03:06] [ERROR] [backtester] Failed to generate rolling_asset_correlations plot: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
[2026-07-11 12:03:06] [ERROR] [backtester] Failed to generate rolling_beta plot: No valid beta values calculated.
[2026-07-11 12:03:06] [ERROR] [backtester] Failed to generate rolling_beta_with_regime plot: No valid beta values calculated.
[2026-07-11 12:03:06] [ERROR] [backtester] Failed to generate rolling_alpha plot: index -1 is out of bounds for axis 0 with size 0
[2026-07-11 12:03:06] [ERROR] [backtester] Failed to generate rolling_alpha_with_regime plot: index -1 is out of bounds for axis 0 with size 0
[2026-07-11 12:03:06] [ERROR] [backtester] Failed to generate rolling_information_ratio plot: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
[2026-07-11 12:03:06] [ERROR] [backtester] Failed to generate rolling_information_ratio_with_regime plot: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
[2026-07-11 12:03:06] [ERROR] [backtester] Failed to generate relative_performance plot: Insufficient data for relative performance plot.
[2026-07-11 12:03:08] [ERROR] [backtester] Failed to generate factor_exposure plot: Insufficient data for factor exposure proxy.
[2026-07-11 12:03:08] [ERROR] [backtester] Failed to generate benchmark_regime_analysis plot: Insufficient data for benchmark regime analysis.
<QJ_BACKTESTER_REPO>/backtester/portfolio/portfolio_plots.py:203: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
  regime = regime.reindex(target_index).ffill()
[2026-07-11 12:03:10] [ERROR] [backtester] Failed to generate asset_risk_contribution plot: Portfolio variance is non-positive.

============================================================
  Strategy: ExampleOrders15_IntradayBracketReversion5m
  Backtester: v0.10.0
  Period:   2026-06-10 → 2026-07-10
  Assets:   SPY, QQQ, IWM
────────────────────────────────────────────────────────────
  Initial NAV:  $    100,000.00
  Final NAV:    $    101,116.75
  Total Return:          1.12%
  Ann. Volatility:       6.76%
  Max Drawdown:         -1.28%
  Sharpe Ratio:        1.7093
────────────────────────────────────────────────────────────
  Runtime Total:        21.76s
  Data Fetch:            4.00s
  Data Prep:             0.21s
  Calculation:           1.76s
  Reporting:            15.77s
============================================================

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