W09 · example_weights_09_intraday_1h_sma_trend.py

W09 Intraday SMA Trend1h

On hourly bars, hold each name only while its SMA(10) is above its SMA(30), equal weight across active names. A slower intraday-to-swing trend template.

Review the source on GitHub or return to all strategies.

What this example shows

This example shows how to build a 1h-bar moving-average trend weight-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, weight construction, risk overlays, execution assumptions, 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 period2026-05-11 → 2026-07-11

BenchmarkSPY · SPDR S&P 500 ETF Trust

Initial capital$100,000

Data sourceyfinance

Engine / snapshot0.10.0 · 5387e17299744403

Weight ModeMode

1hGranularity

3Instruments

21Published plots

passedRun status

Dashboard

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

-3.49% Max DD

1.50% Total return

$1,502 Net profit

Selected plot gallery

21 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.

W09 Intraday SMA Trend1h Cumulative Returns — Cumulative Returns PNG

W09 Intraday SMA Trend1h Portfolio Drawdown — Portfolio Drawdown PNG

W09 Intraday SMA Trend1h Nav Composition — Nav Composition Pro PNG

W09 Intraday SMA Trend1h Percentage Weights — Percentage Weights PNG

W09 Intraday SMA Trend1h Blotter Trade Pnl — Blotter Trade Pnl Pro PNG

W09 Intraday SMA Trend1h Blotter Cumulative Pnl — Blotter Cumulative Pnl Pro PNG

W09 Intraday SMA Trend1h Blotter Costs — Blotter Costs Pro PNG

W09 Intraday SMA Trend1h Blotter Frequency — Blotter Frequency Pro PNG

W09 Intraday SMA Trend1h Blotter Holding Periods — Blotter Holding Periods Pro PNG

W09 Intraday SMA Trend1h Blotter Instrument Pnl — Blotter Instrument Pnl Pro PNG

Execution

W09 Intraday SMA Trend1h Blotter Trade Size — Blotter Trade Size Pro PNG

W09 Intraday SMA Trend1h Blotter Trade Timeline — Blotter Trade Timeline Pro PNG

Portfolio accounting

W09 Intraday SMA Trend1h Composition — Composition PNG

Performance

W09 Intraday SMA Trend1h Cumulative Log Returns — Cumulative Log Returns PNG

Drawdown

W09 Intraday SMA Trend1h Time Underwater — Time Underwater PNG

Distribution and optimization

W09 Intraday SMA Trend1h Return Quantiles — Return Quantiles PNG

W09 Intraday SMA Trend1h Returns Distribution — Returns Distribution Pro PNG

Strategy traces

W09 Intraday SMA Trend1h Strategy Trace IWM — Strategy Trace IWM Pro PNG

W09 Intraday SMA Trend1h Strategy Trace QQQ — Strategy Trace QQQ Pro PNG

W09 Intraday SMA Trend1h Strategy Trace SPY — Strategy Trace SPY 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_weights_09_intraday_1h_sma_trend.py

Run log

Tail of the execution log for this packet.

Running strategy: example_weights_09_intraday_1h_sma_trend
File: <QJ_BACKTESTER_REPO>/strategies/example_weights_09_intraday_1h_sma_trend.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 12:11:50] [ERROR] [backtester] Failed to compute r_squared: Benchmark overlap 0.00% is below required 95.00%
[2026-07-11 12:11:50] [ERROR] [backtester] Failed to compute alpha: Benchmark overlap 0.00% is below required 95.00%
[2026-07-11 12:11:50] [ERROR] [backtester] Failed to compute beta: Benchmark overlap 0.00% is below required 95.00%
[2026-07-11 12:11:50] [ERROR] [backtester] Failed to compute tracking_error: Benchmark overlap 0.00% is below required 95.00%
[2026-07-11 12:11:50] [ERROR] [backtester] Failed to compute up_capture: Benchmark overlap 0.00% is below required 95.00%
[2026-07-11 12:11:50] [ERROR] [backtester] Failed to compute down_capture: Benchmark overlap 0.00% is below required 95.00%
[2026-07-11 12:11:51] [ERROR] [backtester] Failed to generate cumulative_returns_with_regime plot: Insufficient data for regime cumulative returns.
[2026-07-11 12:11:51] [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 12:11:53] [ERROR] [backtester] Failed to generate rolling_beta plot: No valid beta values calculated.
[2026-07-11 12:11:53] [ERROR] [backtester] Failed to generate rolling_beta_with_regime plot: No valid beta values calculated.
[2026-07-11 12:11:53] [ERROR] [backtester] Failed to generate rolling_alpha plot: index -1 is out of bounds for axis 0 with size 0
[2026-07-11 12:11:53] [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 12:11:53] [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 12:11:53] [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 12:11:53] [ERROR] [backtester] Failed to generate relative_performance plot: Insufficient data for relative performance plot.
[2026-07-11 12:11:53] [ERROR] [backtester] Failed to generate factor_exposure plot: Insufficient data for factor exposure proxy.
[2026-07-11 12:11:53] [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()

============================================================
  Strategy: ExampleWeights09_IntradaySMATrend1h
  Backtester: v0.10.0
  Period:   2026-05-11 → 2026-07-10
  Assets:   SPY, QQQ, IWM
────────────────────────────────────────────────────────────
  Initial NAV:  $    100,000.00
  Final NAV:    $    101,501.67
  Total Return:          1.50%
  Ann. Volatility:       8.23%
  Max Drawdown:         -3.49%
  Sharpe Ratio:        0.8858
────────────────────────────────────────────────────────────
  Runtime Total:        15.61s
  Data Fetch:            1.23s
  Data Prep:             0.22s
  Calculation:           0.78s
  Reporting:            13.35s
────────────────────────────────────────────────────────────
  Rebalance Policy: RebalancePolicy(freq=None, signal_change)
  Rebalances:              67
  Avg Days Btw:          10.7
============================================================

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