RevenueRetrieval2 stats approximation tolerance

[namesty]

Background

Just like with #5782 issue with 2013's revenue, not all sources report the numbers with the decimal precision expected by the benchmark string matchers, for all years. Some sources like: https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue and https://www.statista.com/statistics/272120/revenue-of-tesla/ report the results with the approximation that the current benchmarks expect. But several others don't:

• https://backlinko.com/tesla-stats

2008 | $15 million
2009 | $112 million
2010 | $117 million
2011 | $204 million
2012 | $413 million
2013 | $2.01 billion
2014 | $3.2 billion
2015 | $4.05 billion
2016 | $7 billion
2017 | $11.76 billion
2018 | $21.46 billion
2019 | $24.58 billion
2020 | $31.54 billion
2021 (Q1+Q2) | $22.35 billion

• https://www.zippia.com/tesla-careers-11363/revenue/

2009 | $111.9M
2010 | $116.7M
2011 | $204.2M
2012 | $413.3M
2013 | $2.0B
2014 | $3.2B
2015 | $4.0B
2016 | $7.0B
2017 | $11.8B
2018 | $21.5B
2019 | $24.6B
2020 | $31.5B
2021 | $53.8B
2022 | $81.5B

• https://companiesmarketcap.com/tesla/revenue/

2023 (TTM) | $94.02 B
2022 | $81.46 B
2021 | $53.82 B
2020 | $31.53 B
2019 | $24.57 B
2018 | $21.46 B
2017 | $11.75 B
2016 | $7.00 B
2015 | $4.04 B
2014 | $3.19 B
2013 | $2.01 B
2012 | $0.41 B
2011 | $0.20 B
2010 | $0.11 B
2009 | $0.11 B

• https://stockanalysis.com/stocks/tsla/revenue/

Dec 31, 2022 | 81.46B
Dec 31, 2021 | 53.82B
Dec 31, 2020 | 31.54B
Dec 31, 2019 | 24.58B
Dec 31, 2018 | 21.46B
Dec 31, 2017 | 11.76B
Dec 31, 2016 | 7.00B
Dec 31, 2015 | 4.05B
Dec 31, 2014 | 3.20B
Dec 31, 2013 | 2.01B
Dec 31, 2012 | 413.26M
Dec 31, 2011 | 204.24M
Dec 31, 2010 | 116.74M
Dec 31, 2009 | 111.94M
Dec 31, 2008 | 14.74M

Therefore, I think the benchmarks should account for these approximation variances; as this is expected from internet statistics. This has already been done with #5782 's 2013 approximation.

Changes 🏗️

• Make statistic string matchers for tesla's annual revenue more tolerant to approximation variances

PR Quality Scorecard ✨

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• Have you changed or added a feature?   -4 pts
  • Have you added/updated corresponding documentation?   +4 pts
  • Have you added/updated corresponding integration tests?   +5 pts
• Have you changed the behavior of AutoGPT?   -5 pts
  • Have you also run agbenchmark to verify that these changes do not regress performance?   +10 pts

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