Short-Period Buffett-Style Stock Strategy Backtest Against SPY
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Summary
This document presents a QuantStats tear sheet for a strategy labeled “buffett-plain,” compared with SPY over January 4–15, 2026. It lists returns, drawdowns, risk-adjusted statistics, market exposure, daily outcomes, and two drawdown episodes. The reported strategy return is 2%, versus 1% for SPY, with a 4.25 Sharpe ratio and a maximum drawdown of 0.92%. The report also shows near-zero correlation to the benchmark and a 75% time in market.
The document provides performance output rather than an explanation of how the strategy selects or trades stocks. Its very short test period, sparse observations, and lack of trading rules make the annualized figures and risk metrics poor evidence of durable performance. The results are historical calculations from Yahoo data, and the document itself cautions that past performance does not predict future results. The Buffett label alone does not establish that the method follows Buffett’s investing principles.
Key ideas
- The report compares a strategy labeled “buffett-plain” with SPY over a short January 2026 period.
- The strategy shows a 2% total return and a 0.92% maximum drawdown in the reported interval.
- The tear sheet includes risk-adjusted metrics, exposure, benchmark relationship, and drawdown episodes.
- No stock-selection rules or implementation details are supplied, limiting what can be inferred about the strategy.
- The short sample makes annualized results an unreliable guide to future performance.
Tags
Full text
# warren buffett ai stock picker
Tearsheet (generated by QuantStats)
buffett-plain Compared to SPY 4 Jan, 2026 - 15 Jan, 2026
Benchmark is SPY | LumiBot 4.6.3 | DataSource yahoo | Backtest time 17:59 | Generated by QuantStats (Lumiwealth Version) (v.1.1.5)
Annual Return ⓘ
72.72%
Total Return ⓘ
2%
Max Drawdown ⓘ
-0.92%
RoMaD ⓘ
79.02
Longest DD Days ⓘ
6
Sharpe ⓘ
4.25
Sortino ⓘ
8.35
Key Performance Metrics
MetricSPYStrategy
Risk-Free Rate3.56%3.56%
Time in Market67.0%75.0%
Total Return1%2%
CAGR% (Annual Return)24.3%72.72%
Sharpe2.594.25
RoMaD35.1979.02
Corr to Benchmark1.0-0.03
Prob. Sharpe Ratio67.18%76.54%
Smart Sharpe1.382.26
Sortino4.738.35
Smart Sortino2.514.44
Sortino/√23.355.9
Smart Sortino/√21.783.14
Omega1.52.07
Max Drawdown-0.69%-0.92%
Longest DD Days36
Volatility (ann.)6.41%11.11%
R^20.00.0
Information Ratio0.120.12
Calmar35.1979.02
Skew0.450.62
Kurtosis0.142.1
Expected Daily%0.05%0.14%
Expected Monthly%0.66%1.66%
Expected Yearly%0.66%1.66%
Daily Value-at-Risk-0.5%-0.82%
Expected Shortfall (cVaR)-0.5%-0.92%
MTD0.66%1.66%
3M0.66%1.66%
6M0.66%1.66%
YTD0.66%1.66%
1Y0.66%1.66%
3Y (ann.)24.3%72.72%
5Y (ann.)24.3%72.72%
10Y (ann.)24.3%72.72%
All-time (ann.)24.3%72.72%
Best Day0.66%1.45%
Worst Day-0.49%-0.92%
Best Month0.66%1.66%
Worst 1-Month Return0.66%1.66%
Best Year0.66%1.66%
Worst Year0.66%1.66%
Avg. Drawdown-0.51%-0.69%
Avg. Drawdown Days24
Recovery Factor0.961.81
Ulcer Index0.00.0
Serenity Index-4.9-3.17
Annualized Return on Risk Capital2,895.24%5,484.79%
Worst 3-Month Return--
Time to Recovery (Days)12
5th Percentile Tail Loss-0.4%-0.64%
Time Underwater (Days)59
Percent Positive Months100.0100.0
Avg. Up Month0.66%1.66%
Avg. Down Month--
Win Days6.06.67
Loss Days6.05.33
Win Days%50.0%55.56%
Win Month%100.0%100.0%
Win Quarter%100.0%100.0%
Win Year%100.0%100.0%
Beta--0.06
Alpha-0.52
Correlation--3.48%
Treynor Ratio-31.43%
EOY Returns vs Benchmark
YearSPYStrategyMultiplierWon
20260.661.662.53+
Worst 10 Drawdowns
StartedRecoveredDrawdownDays
2026-01-132026-01-15-0.923
2026-01-062026-01-11-0.466
2026-09-29T23:13:56.414634
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:13:56.466526
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:13:56.515033
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:13:56.577556
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:13:56.621180
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:13:56.673034
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:13:56.731941
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:13:56.795016
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:13:56.849308
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:13:56.907090
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:13:56.953490
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:13:57.015438
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:13:57.075701
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
universe['AAPL', 'MSFT', 'GOOGL', 'COST', 'V', 'MA', 'KO', 'AXP', 'JPM', 'PG']
agent_max_model_calls160
agent_researcher_modelopenai/gpt-6-luna
agent_skeptic_modelopenai/gpt-6-luna
agent_trader_modelopenai/gpt-6-luna
agent_model_calls27
agent_researcher_calls9
agent_researcher_cache_hits0
agent_researcher_tool_calls309
agent_researcher_input_tokens1666217
agent_researcher_output_tokens45314
agent_researcher_total_tokens1711531
agent_researcher_thinking_tokens26080
agent_researcher_cached_input_tokens1329550
agent_researcher_cache_write_input_tokens0
agent_researcher_uncached_input_tokens336667
agent_researcher_tool_use_input_tokens0
agent_researcher_latency_ms_total440750
agent_researcher_latency_ms_avg48972.22
agent_researcher_first_event_latency_ms_avg3593.22
agent_researcher_detail_parquetlogs/buffett-plain_2026-09-29_22-55_E6pcU9_agent_detail.parquet
agent_skeptic_calls9
agent_skeptic_cache_hits0
agent_skeptic_tool_calls164
agent_skeptic_input_tokens1243654
agent_skeptic_output_tokens27639
agent_skeptic_total_tokens1271293
agent_skeptic_thinking_tokens13556
agent_skeptic_cached_input_tokens1057210
agent_skeptic_cache_write_input_tokens0
agent_skeptic_uncached_input_tokens186444
agent_skeptic_tool_use_input_tokens0
agent_skeptic_latency_ms_total322533
agent_skeptic_latency_ms_avg35837.0
agent_skeptic_first_event_latency_ms_avg2953.89
agent_skeptic_detail_parquetlogs/buffett-plain_2026-09-29_22-55_E6pcU9_agent_detail.parquet
agent_trader_calls9
agent_trader_cache_hits0
agent_trader_tool_calls114
agent_trader_input_tokens1480602
agent_trader_output_tokens20977
agent_trader_total_tokens1501579
agent_trader_thinking_tokens14229
agent_trader_cached_input_tokens1377145
agent_trader_cache_write_input_tokens0
agent_trader_uncached_input_tokens103457
agent_trader_tool_use_input_tokens0
agent_trader_latency_ms_total312848
agent_trader_latency_ms_avg34760.89
agent_trader_first_event_latency_ms_avg4288.78
agent_trader_detail_parquetlogs/buffett-plain_2026-09-29_22-55_E6pcU9_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.