Citadel-Luna Backtest Tearsheet: Short-Window Results Versus SPY
Document Lumibot
Summary
This document is a QuantStats tear sheet for a strategy labeled citadel-luna, compared with SPY over January 4–15, 2026. It reports a 2% strategy total return versus 1% for the benchmark, with a maximum drawdown near 0.7% for both. The strategy’s reported Sharpe ratio is 5.23, and its correlation to SPY is negative 0.47. It also lists risk, return, and drawdown measures, plus three dated drawdown episodes. The document provides metrics but no description of the trading rules, holdings, or portfolio construction.
The evidence is a very short backtest report generated from Yahoo data, with only a handful of trading days reflected in its win and loss counts. Annualized figures therefore extrapolate a brief observation window and should not be read as durable expected performance. The report offers no out-of-sample results, transaction-cost assumptions, or explanation of how the strategy was developed. It can serve as a snapshot of reported results, but cannot establish a repeatable edge or explain the source of returns.
Key ideas
- The report compares citadel-luna with SPY over January 4–15, 2026.\nIt reports higher total return and Sharpe ratio for the strategy during the displayed period.\nThe strategy’s reported correlation with SPY is negative 0.47.\nThe document omits the strategy rules and provides no out-of-sample or transaction-cost analysis.\nAnnualized performance figures rely on a very short backtest window.
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# citadel sector pods
Tearsheet (generated by QuantStats)
citadel-luna Compared to SPY 4 Jan, 2026 - 15 Jan, 2026
Benchmark is SPY | LumiBot 4.6.0 | DataSource yahoo | Backtest time 50:23 | Generated by QuantStats (Lumiwealth Version) (v.1.1.5)
Annual Return ⓘ
73.87%
Total Return ⓘ
2%
Max Drawdown ⓘ
-0.7%
RoMaD ⓘ
105.78
Longest DD Days ⓘ
3
Sharpe ⓘ
5.23
Sortino ⓘ
10.19
Key Performance Metrics
MetricSPYStrategy
Risk-Free Rate3.56%3.56%
Time in Market67.0%75.0%
Total Return1%2%
CAGR% (Annual Return)24.3%73.87%
Sharpe2.595.23
RoMaD35.19105.78
Corr to Benchmark1.0-0.47
Prob. Sharpe Ratio67.18%81.0%
Smart Sharpe2.214.47
Sortino4.7310.19
Smart Sortino4.048.7
Sortino/√23.357.2
Smart Sortino/√22.866.15
Omega1.52.16
Max Drawdown-0.69%-0.7%
Longest DD Days33
Volatility (ann.)6.41%9.09%
R^20.220.22
Information Ratio0.120.12
Calmar35.19105.78
Skew0.450.05
Kurtosis0.14-0.41
Expected Daily%0.05%0.14%
Expected Monthly%0.66%1.68%
Expected Yearly%0.66%1.68%
Daily Value-at-Risk-0.5%-0.64%
Expected Shortfall (cVaR)-0.5%-0.7%
MTD0.66%1.68%
3M0.66%1.68%
6M0.66%1.68%
YTD0.66%1.68%
1Y0.66%1.68%
3Y (ann.)24.3%73.87%
5Y (ann.)24.3%73.87%
10Y (ann.)24.3%73.87%
All-time (ann.)24.3%73.87%
Best Day0.66%0.89%
Worst Day-0.49%-0.7%
Best Month0.66%1.68%
Worst 1-Month Return0.66%1.68%
Best Year0.66%1.68%
Worst Year0.66%1.68%
Avg. Drawdown-0.51%-0.43%
Avg. Drawdown Days22
Recovery Factor0.962.4
Ulcer Index0.00.0
Serenity Index-4.9-3.12
Annualized Return on Risk Capital2,895.24%7,316.31%
Worst 3-Month Return--
Time to Recovery (Days)12
5th Percentile Tail Loss-0.4%-0.54%
Time Underwater (Days)55
Percent Positive Months100.0100.0
Avg. Up Month0.66%1.68%
Avg. Down Month--
Win Days6.08.0
Loss Days6.04.0
Win Days%50.0%66.67%
Win Month%100.0%100.0%
Win Quarter%100.0%100.0%
Win Year%100.0%100.0%
Beta--0.66
Alpha-0.64
Correlation--46.79%
Treynor Ratio-2.83%
EOY Returns vs Benchmark
YearSPYStrategyMultiplierWon
20260.661.682.56+
Worst 10 Drawdowns
StartedRecoveredDrawdownDays
2026-01-092026-01-11-0.703
2026-01-152026-01-15-0.411
2026-01-052026-01-05-0.181
2026-09-24T12:52:15.487779
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Matplotlib v3.10.9, https://matplotlib.org/
2026-09-24T12:52:15.527668
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Matplotlib v3.10.9, https://matplotlib.org/
2026-09-24T12:52:15.566981
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Matplotlib v3.10.9, https://matplotlib.org/
2026-09-24T12:52:15.621137
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Matplotlib v3.10.9, https://matplotlib.org/
2026-09-24T12:52:15.661631
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Matplotlib v3.10.9, https://matplotlib.org/
2026-09-24T12:52:15.707315
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-24T12:52:15.755402
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-24T12:52:15.808210
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-24T12:52:15.960554
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-24T12:52:16.002029
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-24T12:52:16.037396
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-24T12:52:16.082880
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-24T12:52:16.126566
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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
universe['XLK', 'XLF', 'XLV', 'XLE', 'XLY', 'XLI', 'XLP', 'XLU', 'XLB', 'XLRE', 'XLC', 'SHV']
min_positions3
agent_max_model_calls120
agent_technology_pod_modelopenai/gpt-6-luna
agent_financials_pod_modelopenai/gpt-6-luna
agent_healthcare_pod_modelopenai/gpt-6-luna
agent_energy_pod_modelopenai/gpt-6-luna
agent_consumer_pod_modelopenai/gpt-6-luna
agent_risk_manager_modelopenai/gpt-6-luna
agent_portfolio_manager_modelopenai/gpt-6-luna
agent_model_calls63
agent_technology_pod_calls9
agent_technology_pod_cache_hits0
agent_technology_pod_tool_calls55
agent_technology_pod_input_tokens751396
agent_technology_pod_output_tokens18956
agent_technology_pod_total_tokens770352
agent_technology_pod_thinking_tokens12291
agent_technology_pod_cached_input_tokens666005
agent_technology_pod_cache_write_input_tokens0
agent_technology_pod_uncached_input_tokens85391
agent_technology_pod_tool_use_input_tokens0
agent_technology_pod_latency_ms_total310167
agent_technology_pod_latency_ms_avg34463.0
agent_technology_pod_first_event_latency_ms_avg4497.44
agent_technology_pod_detail_parquetlogs/citadel-luna_2026-09-24_12-01_Y9abv8_agent_detail.parquet
agent_financials_pod_calls9
agent_financials_pod_cache_hits0
agent_financials_pod_tool_calls62
agent_financials_pod_input_tokens958990
agent_financials_pod_output_tokens17851
agent_financials_pod_total_tokens976841
agent_financials_pod_thinking_tokens10443
agent_financials_pod_cached_input_tokens856363
agent_financials_pod_cache_write_input_tokens0
agent_financials_pod_uncached_input_tokens102627
agent_financials_pod_tool_use_input_tokens0
agent_financials_pod_latency_ms_total309716
agent_financials_pod_latency_ms_avg34412.89
agent_financials_pod_first_event_latency_ms_avg3638.22
agent_financials_pod_detail_parquetlogs/citadel-luna_2026-09-24_12-01_Y9abv8_agent_detail.parquet
agent_healthcare_pod_calls9
agent_healthcare_pod_cache_hits0
agent_healthcare_pod_tool_calls58
agent_healthcare_pod_input_tokens816253
agent_healthcare_pod_output_tokens21991
agent_healthcare_pod_total_tokens838244
agent_healthcare_pod_thinking_tokens12586
agent_healthcare_pod_cached_input_tokens709783
agent_healthcare_pod_cache_write_input_tokens0
agent_healthcare_pod_uncached_input_tokens106470
agent_healthcare_pod_tool_use_input_tokens0
agent_healthcare_pod_latency_ms_total352340
agent_healthcare_pod_latency_ms_avg39148.89
agent_healthcare_pod_first_event_latency_ms_avg3334.22
agent_healthcare_pod_detail_parquetlogs/citadel-luna_2026-09-24_12-01_Y9abv8_agent_detail.parquet
agent_energy_pod_calls9
agent_energy_pod_cache_hits0
agent_energy_pod_tool_calls55
agent_energy_pod_input_tokens845464
agent_energy_pod_output_tokens19965
agent_energy_pod_total_tokens865429
agent_energy_pod_thinking_tokens12458
agent_energy_pod_cached_input_tokens738455
agent_energy_pod_cache_write_input_tokens0
agent_energy_pod_uncached_input_tokens107009
agent_energy_pod_tool_use_input_tokens0
agent_energy_pod_latency_ms_total271901
agent_energy_pod_latency_ms_avg30211.22
agent_energy_pod_first_event_latency_ms_avg3810.33
agent_energy_pod_detail_parquetlogs/citadel-luna_2026-09-24_12-01_Y9abv8_agent_detail.parquet
agent_consumer_pod_calls9
agent_consumer_pod_cache_hits0
agent_consumer_pod_tool_calls59
agent_consumer_pod_input_tokens808942
agent_consumer_pod_output_tokens20735
agent_consumer_pod_total_tokens829677
agent_consumer_pod_thinking_tokens10982
agent_consumer_pod_cached_input_tokens705266
agent_consumer_pod_cache_write_input_tokens0
agent_consumer_pod_uncached_input_tokens103676
agent_consumer_pod_tool_use_input_tokens0
agent_consumer_pod_latency_ms_total290230
agent_consumer_pod_latency_ms_avg32247.78
agent_consumer_pod_first_event_latency_ms_avg3547.78
agent_consumer_pod_detail_parquetlogs/citadel-luna_2026-09-24_12-01_Y9abv8_agent_detail.parquet
agent_risk_manager_calls9
agent_risk_manager_cache_hits0
agent_risk_manager_tool_calls15
agent_risk_manager_input_tokens415306
agent_risk_manager_output_tokens26683
agent_risk_manager_total_tokens441989
agent_risk_manager_thinking_tokens17926
agent_risk_manager_cached_input_tokens315413
agent_risk_manager_cache_write_input_tokens0
agent_risk_manager_uncached_input_tokens99893
agent_risk_manager_tool_use_input_tokens0
agent_risk_manager_latency_ms_total356187
agent_risk_manager_latency_ms_avg39576.33
agent_risk_manager_first_event_latency_ms_avg5403.22
agent_risk_manager_detail_parquetlogs/citadel-luna_2026-09-24_12-01_Y9abv8_agent_detail.parquet
agent_portfolio_manager_calls9
agent_portfolio_manager_cache_hits0
agent_portfolio_manager_tool_calls307
agent_portfolio_manager_input_tokens4742682
agent_portfolio_manager_output_tokens73090
agent_portfolio_manager_total_tokens4815772
agent_portfolio_manager_thinking_tokens55413
agent_portfolio_manager_cached_input_tokens4535376
agent_portfolio_manager_cache_write_input_tokens0
agent_portfolio_manager_uncached_input_tokens207306
agent_portfolio_manager_tool_use_input_tokens0
agent_portfolio_manager_latency_ms_total1127300
agent_portfolio_manager_latency_ms_avg125255.56
agent_portfolio_manager_first_event_latency_ms_avg4791.89
agent_portfolio_manager_detail_parquetlogs/citadel-luna_2026-09-24_12-01_Y9abv8_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.