W08 · example_weights_08_intraday_1m_ema_scalp.py

W08 Intraday EMA Scalp1m

Hold each name only while its fast EMA(9) is above its slow EMA(21) on 1-minute bars. A fast, high-turnover trend-follow / scalp template.

Review the source on GitHub or return to all strategies.

What this example shows

This example shows how to build a 1m-bar portfolio-weight 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 period2026-07-05 → 2026-07-11

BenchmarkSPY · SPDR S&P 500 ETF Trust

Initial capital$100,000

Data sourceyfinance

Engine / snapshot0.10.0 · 924f61a0fc070eb9

Weight ModeMode

1mGranularity

3Instruments

21Published plots

passedRun status

Dashboard

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

-0.24% Max DD

-0.23% Total return

$-233 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.

W08 Intraday EMA Scalp1m Cumulative Returns — Cumulative Returns PNG

W08 Intraday EMA Scalp1m Portfolio Drawdown — Portfolio Drawdown PNG

W08 Intraday EMA Scalp1m Nav Composition — Nav Composition Pro PNG

W08 Intraday EMA Scalp1m Percentage Weights — Percentage Weights PNG

W08 Intraday EMA Scalp1m Blotter Trade Pnl — Blotter Trade Pnl Pro PNG

W08 Intraday EMA Scalp1m Blotter Cumulative Pnl — Blotter Cumulative Pnl Pro PNG

W08 Intraday EMA Scalp1m Blotter Costs — Blotter Costs Pro PNG

W08 Intraday EMA Scalp1m Blotter Frequency — Blotter Frequency Pro PNG

W08 Intraday EMA Scalp1m Blotter Holding Periods — Blotter Holding Periods Pro PNG

W08 Intraday EMA Scalp1m Blotter Instrument Pnl — Blotter Instrument Pnl Pro PNG

Execution

W08 Intraday EMA Scalp1m Blotter Trade Size — Blotter Trade Size Pro PNG

W08 Intraday EMA Scalp1m Blotter Trade Timeline — Blotter Trade Timeline Pro PNG

Portfolio accounting

W08 Intraday EMA Scalp1m Composition — Composition PNG

Performance

W08 Intraday EMA Scalp1m Cumulative Log Returns — Cumulative Log Returns PNG

Drawdown

W08 Intraday EMA Scalp1m Time Underwater — Time Underwater PNG

Distribution and optimization

W08 Intraday EMA Scalp1m Return Quantiles — Return Quantiles PNG

W08 Intraday EMA Scalp1m Returns Distribution — Returns Distribution Pro PNG

Strategy traces

W08 Intraday EMA Scalp1m Strategy Trace IWM — Strategy Trace IWM Pro PNG

W08 Intraday EMA Scalp1m Strategy Trace QQQ — Strategy Trace QQQ Pro PNG

W08 Intraday EMA Scalp1m 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_08_intraday_1m_ema_scalp.py

Run log

Tail of the execution log for this packet.

Running strategy: example_weights_08_intraday_1m_ema_scalp
File: <QJ_BACKTESTER_REPO>/strategies/example_weights_08_intraday_1m_ema_scalp.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 12:11:20,785 - quantjourney_ti._utils - WARNING - 2026-07-11 12:11:20,785 - quantjourney_ti._utils - WARNING - Series contains NaN values; they will be handled in calculations
2026-07-11 12:11:20,789 - quantjourney_ti._utils - WARNING - 2026-07-11 12:11:20,789 - quantjourney_ti._utils - WARNING - Series contains NaN values; they will be handled in calculations
2026-07-11 12:11:20,793 - quantjourney_ti._utils - WARNING - 2026-07-11 12:11:20,793 - quantjourney_ti._utils - WARNING - Series contains NaN values; they will be handled in calculations
2026-07-11 12:11:20,797 - quantjourney_ti._utils - WARNING - 2026-07-11 12:11:20,797 - quantjourney_ti._utils - WARNING - Series contains NaN values; they will be handled in calculations
2026-07-11 12:11:20,801 - quantjourney_ti._utils - WARNING - 2026-07-11 12:11:20,801 - quantjourney_ti._utils - WARNING - Series contains NaN values; they will be handled in calculations
2026-07-11 12:11:20,804 - quantjourney_ti._utils - WARNING - 2026-07-11 12:11:20,804 - quantjourney_ti._utils - WARNING - Series contains NaN values; they will be handled in calculations
[2026-07-11 12:11:24] [ERROR] [backtester] Failed to compute r_squared: Benchmark overlap 0.00% is below required 95.00%
[2026-07-11 12:11:24] [ERROR] [backtester] Failed to compute alpha: Benchmark overlap 0.00% is below required 95.00%
[2026-07-11 12:11:24] [ERROR] [backtester] Failed to compute beta: Benchmark overlap 0.00% is below required 95.00%
[2026-07-11 12:11:24] [ERROR] [backtester] Failed to compute down_capture: Benchmark overlap 0.00% is below required 95.00%
[2026-07-11 12:11:24] [ERROR] [backtester] Failed to compute up_capture: Benchmark overlap 0.00% is below required 95.00%
[2026-07-11 12:11:24] [ERROR] [backtester] Failed to compute tracking_error: Benchmark overlap 0.00% is below required 95.00%
[2026-07-11 12:11:25] [ERROR] [backtester] Failed to generate cumulative_returns_with_regime plot: Insufficient data for regime cumulative returns.
<QJ_BACKTESTER_REPO>/backtester/portfolio/portfolio_plots.py:1887: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all Axes decorations.
  fig.tight_layout()
<QJ_BACKTESTER_REPO>/.venv/lib/python3.14/site-packages/seaborn/matrix.py:202: RuntimeWarning: All-NaN slice encountered
  vmin = np.nanmin(calc_data)
<QJ_BACKTESTER_REPO>/.venv/lib/python3.14/site-packages/seaborn/matrix.py:207: RuntimeWarning: All-NaN slice encountered
  vmax = np.nanmax(calc_data)
[2026-07-11 12:11:26] [ERROR] [backtester] Failed to generate portfolio_drawdown_with_regime plot: Insufficient data for regime drawdown plot.
<QJ_BACKTESTER_REPO>/.venv/lib/python3.14/site-packages/numpy/lib/_histograms_impl.py:897: RuntimeWarning: divide by zero encountered in divide
  return n / db / n.sum(), bin_edges
<QJ_BACKTESTER_REPO>/.venv/lib/python3.14/site-packages/numpy/lib/_histograms_impl.py:897: RuntimeWarning: invalid value encountered in divide
  return n / db / n.sum(), bin_edges
<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 12:11:28] [ERROR] [backtester] Failed to generate rolling_beta plot: No valid beta values calculated.
[2026-07-11 12:11:28] [ERROR] [backtester] Failed to generate rolling_beta_with_regime plot: No valid beta values calculated.
[2026-07-11 12:11:28] [ERROR] [backtester] Failed to generate rolling_alpha plot: index -1 is out of bounds for axis 0 with size 0
[2026-07-11 12:11:28] [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 12:11:28] [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 12:11:28] [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 12:11:28] [ERROR] [backtester] Failed to generate relative_performance plot: Insufficient data for relative performance plot.
[2026-07-11 12:11:30] [ERROR] [backtester] Failed to generate factor_exposure plot: Insufficient data for factor exposure proxy.
[2026-07-11 12:11:30] [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 12:11:32] [ERROR] [backtester] Failed to generate asset_risk_contribution plot: Portfolio variance is non-positive.
<QJ_BACKTESTER_REPO>/backtester/portfolio/portfolio_plots.py:3705: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all Axes decorations.
  fig.tight_layout()

============================================================
  Strategy: ExampleWeights08_IntradayEMAScalp1m
  Backtester: v0.10.0
  Period:   2026-07-06 → 2026-07-10
  Assets:   SPY, QQQ, IWM
────────────────────────────────────────────────────────────
  Initial NAV:  $    100,000.00
  Final NAV:    $     99,767.45
  Total Return:         -0.23%
  Ann. Volatility:       0.33%
  Max Drawdown:         -0.24%
  Sharpe Ratio:      -41.2717
────────────────────────────────────────────────────────────
  Runtime Total:        29.89s
  Data Fetch:            4.70s
  Data Prep:             0.25s
  Calculation:           1.64s
  Reporting:            23.28s
────────────────────────────────────────────────────────────
  Rebalance Policy: RebalancePolicy(freq=None, signal_change)
  Rebalances:              32
  Avg Days Btw:         150.0
============================================================

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