Market News Bot Backtest: Short-Window Results Against SPY
Document Lumibot
Summary
This report presents a brief backtest of a market-news trading bot against SPY, covering January 4–15, 2026. It lists return, drawdown, risk, correlation, and other performance statistics, along with model-call and data-source details. The strategy reports a 0% total return and a negative Sharpe ratio, while SPY returns 1% over the period; the displayed statistics also show low strategy volatility and a small maximum drawdown.
The report does not explain how news is interpreted, how trades are chosen, or how orders are executed, so it offers little guidance for reproducing the strategy. Its short evaluation window and inconsistent-looking annualized figures limit what can be inferred from the results. The figures describe this particular backtest and do not establish durable performance or predictive value.
Key ideas
- The report compares a market-news bot with SPY over a short January 2026 period.
- It shows zero total strategy return and a negative Sharpe ratio for the test window.
- The report provides performance statistics but no details of the bot’s signal or trade rules.
- The short sample and annualized metrics limit conclusions about the strategy’s longer-term behavior.
Tags
Full text
# market news ai trading bot
Tearsheet (generated by QuantStats)
market-news-generic Compared to SPY 4 Jan, 2026 - 15 Jan, 2026
Benchmark is SPY | LumiBot 4.6.3 | DataSource yahoo | Backtest time 6:32 | Generated by QuantStats (Lumiwealth Version) (v.1.1.5)
Annual Return ⓘ
2.11%
Total Return ⓘ
0%
Max Drawdown ⓘ
-0.02%
RoMaD ⓘ
117.42
Longest DD Days ⓘ
1
Sharpe ⓘ
-8.52
Sortino ⓘ
-8.56
Key Performance Metrics
MetricSPYStrategy
Risk-Free Rate3.56%3.56%
Time in Market67.0%67.0%
Total Return1%0%
CAGR% (Annual Return)24.3%2.11%
Sharpe2.59-8.52
RoMaD35.19117.42
Corr to Benchmark1.0-0.42
Prob. Sharpe Ratio67.18%90.43%
Smart Sharpe2.44-8.03
Sortino4.73-8.56
Smart Sortino4.46-8.06
Sortino/√23.35-6.05
Smart Sortino/√23.15-5.7
Omega1.50.24
Max Drawdown-0.69%-0.02%
Longest DD Days31
Volatility (ann.)6.41%0.19%
R^20.180.18
Information Ratio-0.15-0.15
Calmar35.19117.42
Skew0.45-1.03
Kurtosis0.142.05
Expected Daily%0.05%0.01%
Expected Monthly%0.66%0.06%
Expected Yearly%0.66%0.06%
Daily Value-at-Risk-0.5%-0.01%
Expected Shortfall (cVaR)-0.5%-0.02%
MTD0.66%0.06%
3M0.66%0.06%
6M0.66%0.06%
YTD0.66%0.06%
1Y0.66%0.06%
3Y (ann.)24.3%2.11%
5Y (ann.)24.3%2.11%
10Y (ann.)24.3%2.11%
All-time (ann.)24.3%2.11%
Best Day0.66%0.02%
Worst Day-0.49%-0.02%
Best Month0.66%0.06%
Worst 1-Month Return0.66%0.06%
Best Year0.66%0.06%
Worst Year0.66%0.06%
Avg. Drawdown-0.51%-0.02%
Avg. Drawdown Days21
Recovery Factor0.963.5
Ulcer Index0.00.0
Serenity Index-4.9-349.66
Annualized Return on Risk Capital2,895.24%10,647.57%
Worst 3-Month Return--
Time to Recovery (Days)10
5th Percentile Tail Loss-0.4%-0.01%
Time Underwater (Days)51
Percent Positive Months100.0100.0
Avg. Up Month0.66%0.06%
Avg. Down Month--
Win Days6.010.5
Loss Days6.01.5
Win Days%50.0%87.5%
Win Month%100.0%100.0%
Win Quarter%100.0%100.0%
Win Year%100.0%100.0%
Beta--0.01
Alpha-0.02
Correlation--41.85%
Treynor Ratio-288.03%
EOY Returns vs Benchmark
YearSPYStrategyMultiplierWon
20260.660.060.10-
Worst 10 Drawdowns
StartedRecoveredDrawdownDays
2026-01-062026-01-06-0.021
2026-09-29T23:21:38.896109
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:21:39.028304
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:21:39.176938
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:21:39.258009
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:21:39.322909
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:21:39.400717
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:21:39.485340
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:21:39.615561
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:21:39.698106
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:21:39.773040
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:21:39.831083
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:21:39.895926
image/svg+xml
Matplotlib v3.10.9, https://matplotlib.org/
2026-09-29T23:21:39.966445
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_calls132
agent_trader_input_tokens2018235
agent_trader_output_tokens29214
agent_trader_total_tokens2047449
agent_trader_thinking_tokens19072
agent_trader_cached_input_tokens1896003
agent_trader_cache_write_input_tokens0
agent_trader_uncached_input_tokens122232
agent_trader_tool_use_input_tokens0
agent_trader_latency_ms_total391119
agent_trader_latency_ms_avg43457.67
agent_trader_first_event_latency_ms_avg3443.78
agent_trader_detail_parquetlogs/market-news-generic_2026-09-29_23-15_vn91au_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.