Short-Window Trend Strategy Backtest Compared with SPY
Summary
This QuantStats tearsheet compares a strategy labeled as a generic trend system with SPY over a short January 2026 backtest window, using Yahoo data. The strategy report shows a 1% total return and 59.35% annualized return, alongside a 1.75% maximum drawdown, 20.92% annualized volatility, and a 1.97 Sharpe ratio. SPY also returned 1% over the reported period, with lower volatility and drawdown but a higher Sharpe ratio. The report gives additional risk, correlation, and drawdown statistics.
The figures are descriptive rather than evidence of a robust trading edge: the stated window covers only a small number of trading days, and annualized metrics extrapolate from that brief sample. The document does not describe the strategy’s actual signal rules, portfolio construction, transaction costs, or slippage. It therefore supports a limited comparison of reported backtest metrics, not a reproducible assessment of the underlying method or its likely live performance.
Key ideas
- The report compares a generic trend-labeled strategy with SPY over a brief January 2026 period.
- Both the strategy and benchmark show a 1% total return in the displayed results.
- The strategy has higher reported volatility and maximum drawdown than SPY, with a lower Sharpe ratio.
- Annualized performance statistics are highly uncertain when derived from a short sample.
- The report omits the trading rules and cost assumptions needed to reproduce or validate the backtest.
Tags
Full text
# trend ai trading bot
Tearsheet (generated by QuantStats)
trend-generic Compared to SPY 4 Jan, 2026 - 15 Jan, 2026
Benchmark is SPY | LumiBot 4.6.3 | DataSource yahoo | Backtest time 8:43 | Generated by QuantStats (Lumiwealth Version) (v.1.1.5)
Annual Return ⓘ
59.35%
Total Return ⓘ
1%
Max Drawdown ⓘ
-1.75%
RoMaD ⓘ
33.84
Longest DD Days ⓘ
5
Sharpe ⓘ
1.97
Sortino ⓘ
3.67
Key Performance Metrics
MetricSPYStrategy
Risk-Free Rate3.56%3.56%
Time in Market67.0%75.0%
Total Return1%1%
CAGR% (Annual Return)24.3%59.35%
Sharpe2.591.97
RoMaD35.1933.84
Corr to Benchmark1.0-0.27
Prob. Sharpe Ratio67.18%60.42%
Smart Sharpe1.931.47
Sortino4.733.67
Smart Sortino3.522.73
Sortino/√23.352.6
Smart Sortino/√22.491.93
Omega1.51.42
Max Drawdown-0.69%-1.75%
Longest DD Days35
Volatility (ann.)6.41%20.92%
R^20.070.07
Information Ratio0.050.05
Calmar35.1933.84
Skew0.450.76
Kurtosis0.141.39
Expected Daily%0.05%0.12%
Expected Monthly%0.66%1.41%
Expected Yearly%0.66%1.41%
Daily Value-at-Risk-0.5%-1.68%
Expected Shortfall (cVaR)-0.5%-1.75%
MTD0.66%1.41%
3M0.66%1.41%
6M0.66%1.41%
YTD0.66%1.41%
1Y0.66%1.41%
3Y (ann.)24.3%59.35%
5Y (ann.)24.3%59.35%
10Y (ann.)24.3%59.35%
All-time (ann.)24.3%59.35%
Best Day0.66%2.42%
Worst Day-0.49%-1.75%
Best Month0.66%1.41%
Worst 1-Month Return0.66%1.41%
Best Year0.66%1.41%
Worst Year0.66%1.41%
Avg. Drawdown-0.51%-1.05%
Avg. Drawdown Days23
Recovery Factor0.960.84
Ulcer Index0.00.01
Serenity Index-4.9-2.05
Annualized Return on Risk Capital2,895.24%2,450.7%
Worst 3-Month Return--
Time to Recovery (Days)10
5th Percentile Tail Loss-0.4%-1.27%
Time Underwater (Days)59
Percent Positive Months100.0100.0
Avg. Up Month0.66%1.41%
Avg. Down Month--
Win Days6.05.33
Loss Days6.06.67
Win Days%50.0%44.44%
Win Month%100.0%100.0%
Win Quarter%100.0%100.0%
Win Year%100.0%100.0%
Beta--0.88
Alpha-0.62
Correlation--26.9%
Treynor Ratio-2.44%
EOY Returns vs Benchmark
YearSPYStrategyMultiplierWon
20260.661.412.15+
Worst 10 Drawdowns
StartedRecoveredDrawdownDays
2026-01-052026-01-07-1.753
2026-01-092026-01-13-0.925
2026-01-152026-01-15-0.481
2026-09-29T23:23:48.774882
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Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:23:48.818074
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Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:23:48.860628
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Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:23:48.919717
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:23:48.961040
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:23:49.005660
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:23:49.190218
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:23:49.248352
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:23:49.316453
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:23:49.369366
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:23:49.411939
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:23:49.465400
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:23:49.519717
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
Disclaimer: This report is for informational purposes only and
should not be considered as investment advice. Past performance
is not indicative of future results.
Parameters Used
ParameterValue
agent_max_model_calls40
agent_trader_modelopenai/gpt-6-luna
agent_model_calls9
agent_trader_calls9
agent_trader_cache_hits0
agent_trader_tool_calls87
agent_trader_input_tokens984743
agent_trader_output_tokens9965
agent_trader_total_tokens994708
agent_trader_thinking_tokens4242
agent_trader_cached_input_tokens920587
agent_trader_cache_write_input_tokens0
agent_trader_uncached_input_tokens64156
agent_trader_tool_use_input_tokens0
agent_trader_latency_ms_total521972
agent_trader_latency_ms_avg57996.89
agent_trader_first_event_latency_ms_avg38863.56
agent_trader_detail_parquetlogs/trend-generic_2026-09-29_23-15_Uic1Mw_agent_detail.parquet
BACKTESTING_DATA_SOURCEyahooShown in full with attribution under the source's licence. Licence: GPL-3.0
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.