Top 100 Stocks Analysis and Strategy Creation

What are the top 100 stocks by market cap?

Snapshot date: 2025-08-12 — Top 100 stocks by market capitalization (latest available snapshot)

Ticker	Close (USD)	Market Cap (USD)	P/E (TTM)	P/S (TTM)	P/B (TTM)	Volume
NVDA	183.16	4,525,150,960,000	62.0904	34.6763	57.0443	145,529,537
MSFT	529.24	3,948,659,640,000	40.8616	14.6241	12.2671	18,660,497
AAPL	229.65	3,457,640,943,450	35.5381	8.6362	51.7642	55,614,975
GOOG	204.16	2,509,330,560,000	22.6074	6.9759	7.2678	19,846,441
GOOGL	203.34	2,499,251,940,000	22.5166	6.9479	7.2386	30,391,597
AMZN	221.47	2,390,325,710,000	36.2478	3.6757	7.8149	37,166,960
META	790.00	2,046,100,000,000	30.7061	12.0105	11.0583	14,555,922
AVGO	312.83	1,512,845,880,000	150.1882	27.7439	21.6774	17,603,347
TSLA	340.84	1,200,097,640,000	187.2227	12.5371	16.0757	80,471,001
BRK-B	470.39	1,014,788,810,650	12.5444	2.7332	1.5505	3,782,478
BRK-A	704,700.10	1,013,515,891,922.3	12.5286	2.7297	1.5432	338
WMT	103.62	837,042,360,000	43.0666	1.2292	9.1970	17,912,884
JPM	292.85	827,096,255,000.0	13.8554	3.0250	2.3536	8,569,780
ORCL	253.86	729,593,640,000	59.9995	13.0791	43.6099	9,994,936
V	336.74	664,724,760,000	33.4268	17.6690	17.4790	5,682,018
LLY	639.43	575,873,215,720	51.8506	11.7517	36.5291	6,891,230
NFLX	1,225.28	535,400,799,360	57.7577	13.3383	22.2823	2,278,448
MA	574.18	524,800,520,000.0	39.9300	18.0536	78.6689	2,593,279
PLTR	186.97	479,187,656,640	627.7907	139.2750	80.8223	54,793,352
XOM	106.13	473,552,060,000	14.2752	1.3932	1.8025	14,107,442
COST	991.25	440,993,247,500	57.8655	1.6699	17.2418	1,587,303
JNJ	172.78	418,784,164,000	19.2015	4.6880	5.3615	7,871,475
HD	396.00	393,624,000,000	26.5854	2.4676	59.2807	2,908,709
PG	155.09	379,939,482,000	24.5201	4.5270	7.2685	6,588,168
BAC	47.50	363,451,000,000	12.9466	2.1899	1.2131	35,479,251
ABBV	198.64	351,990,080,000	83.9071	6.1358	247.8803	3,047,033
KO	70.71	304,972,230,000	28.2801	6.5040	11.6393	11,060,791
GE	279.63	296,530,666,625.7	38.2275	7.1264	15.4968	3,534,646
TMUS	252.28	288,773,638,327.16	24.2300	3.4922	4.7259	4,620,151
CSCO	71.38	285,805,520,000	29.1877	5.1383	6.2220	21,894,268
AMD	174.95	284,468,700,000	127.7363	10.2511	4.9147	52,217,376
CVX	154.44	270,492,548,040	17.2596	1.3911	1.8124	6,640,855
PM	168.60	262,341,600,000	34.5232	6.8452	—	5,443,799
WFC	79.48	259,661,160,000	12.6165	2.1226	1.4337	14,362,015
GS	743.38	241,226,810,000	16.2093	1.9108	1.9407	2,517,489
MS	147.29	235,664,000,000	15.1728	2.9376	2.2063	7,111,277
ABT	131.02	228,920,764,400.0	16.9546	5.4062	4.6899	5,657,257
CRM	231.66	225,636,840,000	36.4107	5.9543	3.6885	13,179,658
IBM	234.77	221,951,558,000	40.5539	3.5326	8.2571	8,749,068
MCD	301.64	216,637,848,000	26.5455	8.4272	—	3,438,084
AXP	303.21	212,853,420,000	20.7136	2.8257	6.8218	2,169,859
DIS	113.72	206,288,080,000	23.1524	2.1936	1.9771	7,273,624
T	28.48	205,597,120,000	9.1442	2.1925	1.9530	30,753,588
PEP	146.87	202,093,120,000	21.5681	2.2081	10.9899	7,668,436
INTU	713.48	201,914,840,000	66.4850	11.7618	11.2494	2,145,968
MRK	80.30	201,793,900,000	11.4163	4.2972	4.1188	7,848,836
CAT	412.71	196,903,941,000	19.8112	3.1127	10.8985	2,764,910
UBER	91.73	194,707,749,140	15.8479	4.2906	8.8604	12,507,048
C	95.74	183,782,504,000	13.7407	1.0930	0.8652	21,045,733
GEV	657.44	183,425,760,000	94.7447	5.1355	21.3112	1,803,257
VZ	43.25	182,774,500,000	10.2780	1.3510	1.8146	12,465,099
BLK	1,159.61	181,594,926,000.0	27.5938	11.1224	3.7804	668,967
TMO	477.41	180,938,390,000	27.7726	4.2179	3.6659	2,104,187
BKNG	5,461.54	180,738,743,220	33.2301	7.5039	—	144,486
ANET	141.25	180,687,000,000	59.6689	24.2971	17.8560	7,456,907
SCHW	98.69	179,813,180,000	27.7105	6.8623	3.6318	7,548,704
NOW	853.43	178,683,492,530	116.1791	15.5797	17.6234	2,865,532
TXN	192.97	176,760,520,000	36.2734	11.0138	10.7741	10,004,188
BA	232.61	175,992,726,000	-16.4741	2.3364	—	7,408,381
ISRG	478.19	174,348,074,000	70.4124	20.0057	10.1920	1,529,356
SPGI	559.06	172,022,762,000.0	43.5280	11.8694	5.1549	702,106
QCOM	153.73	171,408,950,000	15.5248	4.0537	6.1818	9,301,357
APP	467.00	161,057,812,114	83.8838	31.3638	279.8956	4,535,585
AMGN	284.98	154,174,180,000	25.9859	4.5178	24.8388	2,188,883
AMAT	188.45	153,586,750,000	22.7267	5.4679	8.1001	5,913,134
BSX	102.64	153,272,312,000	54.0834	10.7296	6.8370	6,446,648
TJX	133.39	151,797,820,000.0	31.2084	2.6934	18.0862	4,200,951
GILD	120.02	151,105,180,000	25.3320	5.2588	7.8857	6,084,058
ADBE	338.43	148,232,340,000	21.9571	6.7265	11.3198	3,569,155
NEE	71.86	147,959,740,000	26.8530	5.8556	2.9704	8,670,906
DHR	205.72	147,933,252,000	43.3441	6.1605	2.8267	2,829,841
SYK	376.61	145,522,104,000	50.8997	6.2676	6.9528	960,118
MU	127.75	143,463,250,000	30.7070	4.5801	2.9499	25,952,825
PGR	243.04	142,834,608,000	16.3884	1.8192	4.9332	2,396,426
APH	111.85	141,624,470,000	54.1958	8.4414	13.7483	7,074,707
HON	217.01	141,425,417,000	24.8507	3.6066	8.0986	4,340,788
PFE	24.65	140,751,500,000	17.8937	2.2534	1.5581	34,400,748
LOW	244.87	137,861,810,000	19.8163	1.6476	—	2,959,534
DE	505.85	137,490,030,000	24.3259	3.0295	5.6611	1,285,760
LRCX	105.28	135,611,168,000	29.1110	7.9140	14.2582	11,883,125
BX	173.74	134,202,787,751.48	52.7532	12.0229	16.8214	4,180,340
ORGN	0.8994	132,078,994,596	-1372.6058	4419.2791	419.8208	788,742
UNP	218.80	131,695,720,000	19.5626	5.4316	8.2110	3,223,599
KKR	147.24	130,785,443,813.52	59.2297	8.6644	4.7610	3,023,013
KLAC	935.53	124,708,955,590	33.7485	10.7995	31.1403	1,027,589
PANW	175.40	124,358,600,000	99.1300	14.5101	19.5063	11,484,193
ADP	299.63	122,398,855,000	30.6127	6.0586	20.9039	1,279,996
CMCSA	32.10	121,466,400,000	10.3104	1.2940	1.4020	19,945,608
COP	94.59	120,590,804,610	12.6751	2.1004	1.8485	7,524,578
MSTR	394.39	117,543,601,210	24.8289	254.2462	40.6174	8,320,517
ADI	232.04	115,710,922,720	73.9905	12.3919	3.2994	4,978,250
DASH	264.32	115,128,012,160	339.6107	10.2418	13.7237	2,901,147
NKE	74.90	111,675,900,000.0	24.7728	2.3354	7.9729	6,396,441
MO	66.01	111,556,900,000.0	10.9241	5.5101	—	8,847,101
WELL	164.79	107,738,878,050	99.5287	12.8083	3.1728	2,506,144
CRWD	435.80	107,613,401,400	-5584.2147	27.2189	32.8140	2,310,133
SBUX	93.37	106,441,800,000	34.0178	2.9285	—	9,668,084
CEG	338.57	106,310,980,000	35.6270	4.3869	8.2055	1,890,401
ICE	182.74	105,440,980,000	37.8739	8.6505	3.7692	3,846,873
SO	93.96	104,107,680,000	24.3072	3.6705	2.7880	7,908,570

Explanation — quick summary

• This is a top-100 market-cap snapshot for 2025-08-12 (results returned by your query). NVDA, MSFT and AAPL are the three largest market caps in this snapshot, followed by Google (GOOG/GOOGL) and Amazon. The table shows price, market cap, and common trailing valuation multiples (P/E, P/S, P/B) plus trade volume for each ticker.
• Valuation dispersion is wide: several mega-cap tech names have elevated P/E and P/S ratios (e.g., NVDA, MSFT, AVGO, NOW), while large financials and energy names sit at much lower P/E and P/S levels (e.g., BRK, XOM, CVX, BAC).
• There are many extreme or unusual metric values (very high positive ratios, large negative P/Es, or missing/null P/Bs) — see observations below.

Notable observations and unusual items

• Extremely large market caps: NVDA appears at ~4.525T, MSFT ~3.949T, AAPL ~3.458T — these are very large headline values and dominate market-cap weighting.
• Duplicate share-class presence: both GOOG and GOOGL appear (Class C vs Class A). Both are expected but be careful when aggregating market-cap exposure across share classes.
• Negative or enormous P/E values:
  • [CRWD] shows an extremely negative P/E (-5,584), indicating a large trailing loss or an anomaly in reported earnings.
  • [ORGN] has P/E ~ -1372 and P/S ~ 4419 and P/B ~ 420 — extreme values consistent with tiny price/book or recent large losses; these suggest the usual interpretation of multiples may not apply.
  • [PLTR], [APP], [DASH], [MSTR], [NOW] show extremely high P/E or P/S ratios consistent with low or volatile earnings relative to market pricing.
• Null / missing P/B: some tickers have P/B null — e.g., [PM], [MCD], [BKNG], [LOW], [MO], which indicates book value may be zero/undefined in source data or not calculated.
• Very high P/Bs and P/Ss in several names (e.g., [APP] P/B ~280, [PLTR] P/S ~139); these can occur for companies with small book values or asset-light business models, or data anomalies.
• Class & pricing oddities: [BRK-A] and [BRK-B] both present (different share classes); [BRK-A] price and cap precision suggests conversion from large nominal price. Check for rounding/scale issues when combining classes.
• Liquidity: volume varies widely (e.g., NVDA and AMD high volumes; BRK-A volume extremely low because of share class).

Pros and cons of this dataset / snapshot
Pros

• Covers the largest 100 tickers by market cap from the latest-date snapshot you queried.
• Includes price, market-cap and common trailing valuation multiples (P/E, P/S, P/B) which are useful for a first-pass screening.
• Useful for cross-sectional comparison (who’s big, who’s high multiple, which sectors dominate).

Cons / limitations

• Snapshot limitation: your SQL used the single latest date across the whole table. Tick ers without data on that date are excluded — that can bias the universe.
• Trailing metrics only: this is TTM (trailing) data — no forward estimates, margins, growth rates, cash flows, or balance-sheet detail are included.
• Multiples can be misleading for negative or volatile earnings, asset-light firms, or companies with accounting quirks (e.g., SPACs, microcap exceptions).
• No sector/industry column — you’ll need to map tickers to sectors to do sector-level analysis.
• Share-class duplication (multiple tickers for same economic exposure) may double-count exposure if you aggregate by company without adjusting.

Practical cautions and next steps

• Double-check any surprising values (extremely high/low/negative multiples, null book values) against primary sources (company filings, exchanges, or your raw data table) — these often indicate earnings anomalies, one-time items, or data issues.
• If you plan to aggregate exposures or build portfolios, normalize share classes (e.g., GOOG/GOOGL, BRK-A/BRK-B) to avoid double-counting.
• Consider supplementing with: sector, market float/sharesOutstanding, forward P/E or analyst estimates, EBITDA, free cash flow, and recent guidance to form a fuller view.
• Data-method note: marketCap = price * sharesOutstanding (as of snapshot). If sharesOutstanding is stale or price is pre/post-split, marketCap can be off — verify splits and corporate actions.

Disclaimers

• This is an AI-generated summary of the query results; I can make mistakes or miss edge cases. Always verify numbers and unusual metrics against the original data source or filings.
• This summary does not constitute investment advice. Consult a qualified financial professional before making investment decisions.

If you want

• I can (1) highlight the top 10 by valuation (e.g., highest P/E, highest P/S), (2) group by sector (if you provide sector mappings), (3) flag tickers with negative P/E or null P/B, or (4) export this table in CSV/Excel format for your records. Which would you like next?

If a stock on this list falls below its 200 day SMA, in the past 10 years, what's the probability that it will rise 10% or more from its SMA?

Below are the results from your crossing-event analysis (one overall row + the top 25 tickers by number of crossing events). Each ticker links to its Nexustrade page.

Ticker	Total crossing events	Successes (>=10% above SMA within 252 days)	Probability
OVERALL	1,642	1,100	66.99%
TMO	32	13	40.63%
HON	31	16	51.61%
QCOM	31	14	45.16%
TSLA	30	19	63.33%
JNJ	30	16	53.33%
DE	29	26	89.66%
TXN	28	19	67.86%
ABBV	27	19	70.37%
PG	26	10	38.46%
AMGN	26	12	46.15%
XOM	25	19	76.00%
CVX	24	4	16.67%
ADI	23	19	82.61%
UNP	23	9	39.13%
V	23	13	56.52%
KO	22	14	63.64%
PGR	22	19	86.36%
MSFT	22	15	68.18%
DHR	22	6	27.27%
AMZN	21	16	76.19%
WMT	21	20	95.24%
UBER	21	18	85.71%
MA	20	12	60.00%
NEE	20	9	45.00%
LOW	20	14	70.00%

What these results mean (quick summary)

• Across all crossing events in the top-100 universe (last 10 years, as defined), 1,642 events were found and 1,100 of them later achieved >= 10% above the event's SMA200 within 252 trading rows — an overall empirical probability ~67.0%.
• The top-25 tickers (listed above) contributed 619 events in total (≈37.7% of the 1,642 overall events); the remaining ~1,023 events come from the other top-100 tickers not shown in the top-25 breakdown.
• Individual tickers show wide dispersion: some (e.g., WMT, DE, PGR, ADI, UBER, XOM) show very high success rates in this sample, while others (e.g., CVX, DHR, PG) show much lower rates.

Notable / unusual observations

• Very high single-ticker success rates (small-sample caveat): WMT 20/21 = 95.2%, DE 26/29 = 89.7%, PGR 19/22 = 86.4%, ADI 19/23 = 82.6%. These are striking but based on modest event counts (20–30 events), so sampling variability is large.
• Very low rates for some large names: CVX 4/24 = 16.7% and DHR 6/22 = 27.3%. That suggests industry/market-behavior differences — e.g., some sectors (energy, industrials) may behave differently around SMA cross-downs than others.
• Large spread in per-ticker probabilities: from ~16.7% up to ~95.2% among the top-25 sample.
• Top-25 event counts are relatively small per ticker (20–32 events), so per-ticker probabilities should be interpreted cautiously (wide confidence intervals).
• The overall sample (n=1,642) is large enough that the estimated overall probability has moderate precision. Approximate 95% CI for the overall probability: ~67.0% ± 2.3 percentage points (≈64.7%–69.3%).
• The query rule that events near the end of history with no future rows count as non-success can bias results downward slightly for very recent events — confirm whether you prefer excluding those truncated events instead of treating them as failures.

Pros / Cons of these results
Pros

• Clear, reproducible metric: event definition (prior >= SMA, today < SMA) and success threshold (10% above event SMA within 252 rows) are explicit and implementable.
• Large aggregated sample (1,642 events) gives a reasonably robust overall estimate for the top-100 universe under these rules.
• Per-ticker breakdown highlights where the pattern is more or less likely to have historically “reverted” past the SMA +10% threshold.

Cons / limitations

• Sampling variability at the ticker level (many tickers have only a few dozen events).
• Survivorship / selection bias: universe is the top-100 by market-cap on one snapshot date; companies that entered/exited that list or had partial histories may distort counts.
• Corporate actions, splits, dividends, or dataset gaps could affect SMA calculations if not normalized.
• Definitional choices (200-row SMA, 252-row horizon, treating truncated future windows as failures) materially affect results; different choices could change probabilities.
• This is historical / empirical — not a prediction guarantee.

Practical suggestions / next steps

• If you want tighter per-ticker confidence, compute and report binomial confidence intervals (I can add those).
• You may want to (optionally): exclude events with fewer than X future rows (to avoid counting truncated events as failures), or run the same test with different SMA lengths (e.g., 50, 100) or different success thresholds (5%, 15%).
• If sector-level patterns are of interest, map tickers to sectors and compare aggregated probabilities by sector.
• If you want a CSV/Excel export of the full EventsWithFuture table (each event row) for deeper inspection, I can prepare that.

Cautions / disclaimer

• Double-check these aggregated numbers and any surprising per-ticker rates against your raw event-level data and corporate-action-adjusted prices before using them for trading or risk decisions.
• This is an AI-produced summary and may contain mistakes or miss edge cases — always validate with primary data sources or a quantitative review.
• This summary is not investment advice.

Would you like:

• per-ticker 95% confidence intervals added,
• the same analysis but excluding truncated events near the end of history,
• sector-level aggregation, or
• the raw event-level export (CSV)?

If a stock on this list falls below its 200 day SMA, in the past 10 years, what's the probability that it will rise 10% or more from its SMA within the next 90 days?

Below is a compact, easy-to-scan summary of the query results (OVERALL + all tickers returned). Tickers link to their Nexustrade pages.

Ticker	Total crossing events	Successes (all events)	Successes (full 90-day window)	Full-90-window events	P(success) — all events	P(success) — full windows
OVERALL	1,642	826	747	1,466	50.3%	50.95%
TMO	32	11	11	32	34.4%	34.4%
HON	31	13	11	27	41.9%	40.7%
QCOM	31	12	12	27	38.7%	44.4%
JNJ	30	12	4	22	40.0%	18.2%
TSLA	30	18	16	23	60.0%	69.6%
DE	29	19	18	28	65.5%	64.3%
TXN	28	10	10	26	35.7%	38.5%
ABBV	27	15	15	20	55.6%	75.0%
AMGN	26	10	10	21	38.5%	47.6%
PG	26	2	2	22	7.7%	9.1%
XOM	25	16	16	20	64.0%	80.0%
CVX	24	4	4	21	16.7%	19.0%
V	23	9	9	22	39.1%	40.9%
UNP	23	9	9	22	39.1%	40.9%
ADI	23	13	11	21	56.5%	52.4%
PGR	22	12	12	19	54.5%	63.2%
KO	22	7	7	22	31.8%	31.8%
DHR	22	2	2	22	9.1%	9.1%
MSFT	22	6	6	22	27.3%	27.3%
AMZN	21	16	16	21	76.2%	76.2%
UBER	21	17	15	19	80.95%	78.95%
WMT	21	14	13	20	66.7%	65.0%
NEE	20	7	7	15	35.0%	46.7%
LOW	20	12	12	19	60.0%	63.2%
MU	20	8	6	18	40.0%	33.3%

Short explanation of the results

• Overall (top-100 universe, 10-year lookback, 90-trading-day forward window): 1,642 crossing events were found; 826 of those later reached >= +10% above the event’s SMA200 within the 90-day window → empirical P ≈ 50.3% (counting all events). Restricting to events that had a full 90-day future window available gives P ≈ 50.95% (747 successes / 1,466 full-window events).
• Per-ticker probabilities vary substantially (from single-digit probabilities up to ~80% depending on the ticker). Event counts per ticker are generally modest (20–32), so per-ticker estimates have wide sampling noise.

Important / unusual observations

• Methodology matters: this run used a 90-trading-day lookahead. An earlier run in the thread using a 252-day (≈1-year) horizon produced a much higher overall probability (~67%). Shorter/longer horizons materially change outcomes.
• Large per-ticker dispersion: some tickers show very high success rates (e.g., [UBER] ~81%, [AMZN] ~76%, [XOM] full-window 80%), while others are very low (e.g., [PG] ~7.7%, [DHR] ~9.1%, [CVX] ~16.7%).
• JNJ shows a notable discrepancy: probability_all = 40.0% but probability_full_window = 18.2% (12 successes across 30 events vs only 4 successes among 22 events with a full 90-day window). This implies many of JNJ’s "successes" came from events that did not have a full 90-row lookahead (i.e., near the end of the history) — check how truncated windows were treated.
• Several tickers (TSLA, DE, XOM, ABBV, PGR, LOW, WMT) show strong historical reversion to +10% within 90 days in this sample. Conversely, staples/industrial names like PG, DHR and some energy names (CVX) have low probabilities here.
• Sample sizes per ticker are modest — even a 20–30 event sample yields wide binomial uncertainty.

Pros / cons of this result set
Pros

• Clear, reproducible event definition and outcome (first-day crossing below SMA200; success = reach SMA200+10% within 90 trading rows).
• Large pooled sample (1,642 events) gives a reasonable aggregate signal for the top-100 universe under these rules.
• Per-ticker breakdown highlights heterogeneity across names/sectors.

Cons / limitations

• Survivorship / selection bias: universe = top-100 as of a single snapshot date. Companies that were not in that snapshot or that entered/exited the list may bias results.
• Truncated-window handling: counting truncated events as observed (or as failures) affects probabilities — in this run you can see differences between probability_all and probability_full_window.
• SMA calculation and corporate-action adjustments need verification; splits/dividends or missing rows can distort SMA and event detection.
• Events are not independent (a stock can produce many events), so treat per-event probabilities carefully for portfolio-level inference.
• Short 90-day horizon vs other horizons: results are horizon-dependent.

Practical suggestions / next steps

• Add binomial 95% confidence intervals for per-ticker probabilities (I can compute these). That will show which rates are statistically different from 50%.
• Re-run excluding truncated events entirely (use probability_full_window) or require a minimum number of future rows to avoid end-of-history bias.
• Test alternate horizons (e.g., 21, 60, 252 trading days) to see horizon sensitivity.
• Group results by sector/industry to see structural differences (e.g., tech vs. energy vs. staples).
• Verify SMA calculations, corporate-action normalization, and that the Top-100 snapshot selection matches your intended universe (e.g., use each ticker’s own latest data instead of a single-date snapshot).
• Export raw event-level table (event date, event_sma200, max_close_next_90, num_future_rows) for manual inspection — I can prepare CSV/Excel.

Final cautions & disclaimers

• As an AI I can make mistakes and the analysis above can contain errors or miss edge cases — please verify surprising values (especially tickers with big discrepancies like JNJ) against your raw data and filings.
• This is historical, empirical analysis — it is not a prediction guarantee and not investment advice. Always validate methodology, adjust for corporate actions, and consult a qualified financial professional before using results for trading or risk decisions.

If you want, I can:

• add per-ticker 95% confidence intervals now, or
• re-run with truncated events excluded (use only full 90-day windows), or
• produce the full event-level CSV for inspection, or
• run the same test with a 252-trading-day lookahead (to reproduce the earlier 67%-style result) — which would you prefer?

Create a strategy with the original top 100 stocks by market cap excluding BRK-A and GOOG (since we already have BRK-B and GOOGL). It should rebalance weighted by market cap every 90 days. We should filter to only include stocks below their 200 day SMA.

Description:
This portfolio implements a systematic, time‑based rebalancing strategy across the original top-100-by-market-cap universe (snapshot provided earlier) but explicitly excludes BRK-A and GOOG (we keep BRK-B and GOOGL). The portfolio starts with the default initial value of $10,000. Every 90 days the strategy re-evaluates the universe and rebalances the portfolio to hold only those stocks that are currently trading below their 200‑day simple moving average (i.e., Price < SimpleMovingAverage(asset, 200)). From that filtered subset, holdings are sized proportional to each stock’s market capitalization (market-cap weighting). The intent is to target large-cap names that are in extended pullbacks relative to their long-term trend while maintaining systematic discipline via a fixed 90‑day rebalancing cadence.
Practical notes and execution details:

• Universe (full list used at runtime — BRK-A and GOOG removed): NVDA, MSFT, AAPL, GOOGL, AMZN, META, AVGO, TSLA, BRK-B, WMT, JPM, ORCL, V, LLY, NFLX, MA, PLTR, XOM, COST, JNJ, HD, PG, BAC, ABBV, KO, GE, TMUS, CSCO, AMD, CVX, PM, WFC, GS, MS, ABT, CRM, IBM, MCD, AXP, DIS, T, PEP, INTU, MRK, CAT, UBER, C, GEV, VZ, BLK, TMO, BKNG, ANET, SCHW, NOW, TXN, BA, ISRG, SPGI, QCOM, APP, AMGN, AMAT, BSX, TJX, GILD, ADBE, NEE, DHR, SYK, MU, PGR, APH, HON, PFE, LOW, DE, LRCX, BX, ORGN, UNP, KKR, KLAC, PANW, ADP, CMCSA, COP, MSTR, ADI, DASH, NKE, MO, WELL, CRWD, SBUX, CEG, ICE, SO.
• Rebalance condition enforces at least 90 days since the last buy or sell to avoid overtrading.
• If no tickers meet the "below 200‑day SMA" filter at rebalance, the strategy will remain in cash / not initiate new exposures until the next 90‑day check.
• This is a ruleset definition for runtime evaluation — market-cap weights and the SMA filter are applied at each rebalance using live data.
  JSON

backtest year-to-date

What are the top 100 stocks by market cap as of 2022?

Here’s a compact, easy-to-scan summary of the query results (top 100 by market cap as of the most‑recent price on-or-before 2022-12-31). I include every stock returned by the query in the table below and link each ticker to its Nexustrade page.

Ticker	Close (USD)	Market Cap (USD)	P/E (TTM)	P/S (TTM)	P/B (TTM)	Price Date
AAPL	129.93	2,094,272,157,450	20.98	5.31	41.33	2022-12-30
MSFT	239.82	1,795,052,700,000	25.72	8.84	10.34	2022-12-30
GOOG	88.73	1,162,096,810,000	17.35	4.12	4.58	2022-12-30
GOOGL	88.23	1,155,548,310,000	17.25	4.10	4.56	2022-12-30
AMZN	84.00	856,632,000,000	75.65	1.71	6.23	2022-12-30
BRK-A	468,710.96	684,192,855,773.68	-573.51	2.69	1.50	2022-12-30
BRK-B	308.90	681,391,512,233.30	-509.64	2.64	1.47	2022-12-30
UNH	530.18	502,610,640,000.00	25.87	1.59	6.73	2022-12-30
DFLI	107.10	480,329,427,060	-118,942.77	3,334.65	16,631.86	2022-12-30
JNJ	176.65	470,118,645,000	24.54	4.89	6.30	2022-12-30
XOM	110.30	463,354,273,520.30	8.93	1.20	2.49	2022-12-30
V	207.76	439,620,160,000	29.39	14.00	12.36	2022-12-30
TSLA	123.18	427,188,240,000	38.23	5.71	10.39	2022-12-30
ACON	83,937.60	410,324,760,720	-42,850.17	8,222,776.31	139,911.93	2022-12-30
JPM	134.10	397,660,140,000	10.73	2.88	1.38	2022-12-30
TSM	74.49	386,292,700,170	(null)	(null)	(null)	2022-12-30
WMT	47.2633	384,392,418,900	42.87	0.64	5.32	2022-12-30
PG	151.56	379,445,616,000	26.04	4.72	8.61	2022-12-30
NVDA	14.614	365,203,860,000	61.31	12.78	17.11	2022-12-30
CVX	179.49	348,210,958,980	10.19	1.53	2.19	2022-12-30
MA	347.73	336,602,640,000	34.40	15.56	52.92	2022-12-30
LLY	365.84	330,646,192,000	54.80	11.31	32.83	2022-12-30
META	120.34	323,353,580,000	11.22	2.74	2.61	2022-12-30
HD	315.86	323,124,780,000	18.90	2.05	248.94	2022-12-30
NSRGY	115.34	309,709,526,250	(null)	(null)	(null)	2022-12-30
NVO	67.67	307,018,790,000	(null)	(null)	(null)	2022-12-30
PFE	51.24	292,990,320,000	9.84	2.93	3.16	2022-12-30
ABBV	161.61	287,019,360,000	21.41	4.96	17.95	2022-12-30
MRK	110.95	282,034,900,000	18.48	4.78	6.34	2022-12-30
KO	63.61	276,449,060,000	27.85	6.52	12.12	2022-12-30
BAC	33.12	270,285,696,000	9.86	2.95	1.00	2022-12-30
PEP	180.66	250,575,420,000	25.80	2.99	13.20	2022-12-30
AVGO	55.913	239,866,770,000	20.87	7.22	10.56	2022-12-30
BABA	88.09	230,267,260,000	(null)	(null)	(null)	2022-12-30
ORCL	81.74	224,458,040,000	25.52	4.87	(null)	2022-12-30
TMO	550.69	217,522,550,000	30.93	4.93	4.99	2022-12-30
ASML	546.40	216,702,240,000	(null)	(null)	(null)	2022-12-30
SHEL	56.95	209,043,517,500	(null)	(null)	(null)	2022-12-30
COST	456.50	202,928,401,500	34.49	0.88	9.45	2022-12-30
CSCO	47.64	196,086,240,000	17.05	3.75	4.87	2022-12-30
DHR	265.42	195,720,708,000	29.48	6.26	4.17	2022-12-30
MCD	263.53	194,880,435,000	32.96	8.38	(null)	2022-12-30
ABT	109.79	193,669,560,000	24.55	4.30	5.43	2022-12-30
NKE	117.01	185,554,458,000	32.90	3.94	11.73	2022-12-30
TMUS	140.00	176,289,437,940	114.85	2.20	2.51	2022-12-30
VZ	39.40	165,637,600,000	8.59	1.22	1.89	2022-12-30
NEE	83.60	165,436,040,000	46.12	8.34	4.28	2022-12-30
DIS	86.88	158,642,880,000	49.01	1.92	1.67	2022-12-30
WFC	41.29	157,938,379,000	9.71	1.96	0.90	2022-12-30
ADBE	336.53	157,832,570,000	32.79	9.18	10.98	2022-12-30
SCHW	83.26	157,777,700,000	23.22	7.83	4.26	2022-12-30
PM	101.21	157,077,920,000	17.96	4.95	(null)	2022-12-30
BMY	71.95	154,548,600,000	23.15	3.31	4.73	2022-12-30
CMCSA	34.97	153,063,690,000	28.32	1.26	1.91	2022-12-30
TXN	165.22	152,498,060,000	17.09	7.55	10.51	2022-12-30
UPS	173.84	151,588,480,000	13.55	1.50	8.93	2022-12-30
COP	118.00	149,779,878,000	8.30	1.99	3.05	2022-12-30
HON	214.30	145,638,280,000	27.10	4.17	8.22	2022-12-30
MS	85.02	144,278,940,000	11.55	2.47	1.43	2022-12-30
AMGN	262.64	141,300,320,000	20.67	5.37	38.68	2022-12-30
T	18.41	140,781,270,000	7.03	1.08	1.15	2022-12-30
NFLX	294.88	132,797,438,720	26.33	4.22	6.47	2022-12-30
CRM	132.59	132,590,000,000	476.94	4.38	2.23	2022-12-30
DE	428.76	129,528,396,000	18.16	2.53	6.39	2022-12-30
LMT	486.49	128,968,499,000	21.97	1.99	10.78	2022-12-30
UNP	207.07	128,694,005,000	18.20	5.27	10.96	2022-12-30
IBM	140.89	127,378,649,000	101.01	2.70	6.34	2022-12-30
CAT	239.56	126,391,856,000	17.15	2.23	8.11	2022-12-30
QCOM	109.94	124,342,140,000	9.61	2.81	6.90	2022-12-30
ELV	512.97	124,241,334,000	20.15	0.82	3.45	2022-12-30
LOW	199.24	123,528,800,000	18.48	1.29	(null)	2022-12-30
GS	343.38	123,342,094,626.48	8.89	2.05	1.03	2022-12-30
CVS	93.19	122,544,850,000	37.33	0.39	1.73	2022-12-30
SBUX	99.20	114,328,000,000	34.84	3.55	(null)	2022-12-30
BA	190.49	113,589,187,000	(null)	1.85	(null)	2022-12-30
SPGI	334.94	110,831,646,000	31.76	10.73	2.98	2022-12-30
AXP	147.75	110,664,750,000	14.45	2.16	4.61	2022-12-30
INTU	389.22	110,538,480,000	58.86	8.30	6.88	2022-12-30
INTC	26.43	109,023,750,000	8.20	1.57	1.09	2022-12-30
GILD	85.85	108,256,850,000	32.54	3.99	5.14	2022-12-30
BLK	708.63	108,116,865,346.62	19.44	5.80	2.91	2022-12-30
AMD	64.77	105,251,250,000	46.30	4.61	1.93	2022-12-30
ADP	238.86	99,580,734,000	32.89	5.98	38.65	2022-12-30
AMT	211.86	98,896,460,495.58	34.07	9.46	15.74	2022-12-30
ISRG	265.35	95,658,675,000.00	69.42	15.64	8.26	2022-12-30
SONY	15.256	94,854,561,400	(null)	(null)	(null)	2022-12-30
SYK	244.49	93,346,282,000	37.99	5.20	5.67	2022-12-30
TJX	79.60	93,312,453,200	27.44	1.89	16.47	2022-12-30
MDLZ	66.65	91,910,350,000.00	29.30	3.02	3.45	2022-12-30
EL	248.11	89,666,954,000	41.00	5.19	16.25	2022-12-30
JD	56.13	89,274,765,000	(null)	(null)	(null)	2022-12-30
C	45.23	88,429,173,000	5.70	1.00	0.45	2022-12-30
PLD	112.73	86,393,115,560	21.46	15.71	2.45	2022-12-30
NOC	545.61	84,733,233,000	15.33	2.41	6.08	2022-12-30
ADI	164.03	84,599,620,710	30.78	7.04	2.32	2022-12-30
AMAT	97.38	83,649,420,000	12.82	3.24	6.86	2022-12-30
MMC	165.48	83,236,440,000	24.58	3.99	8.70	2022-12-30
PYPL	71.22	82,401,540,000	35.84	3.05	4.07	2022-12-30
MO	45.71	82,232,290,000	17.50	3.97	(null)	2022-12-30
REGN	721.49	81,384,072,000	15.15	5.94	3.80	2022-12-30

Summary (few sentences)

• This table is the top‑100 market‑cap snapshot constructed by taking each ticker’s most recent price row on-or-before 2022‑12‑31 and ranking by market cap. The largest market caps in this snapshot are AAPL and MSFT (both > $1.7–2.0T in this view), followed by Google share classes and Amazon. Several familiar mega‑caps and defensive large caps (JNJ, XOM, V, TSLA, etc.) appear near the top.
• The dataset includes share‑class duplicates (both GOOG and GOOGL; BRK-A and BRK-B) — be careful if you aggregate exposure by company.
• Many rows show missing (null) trailing multiples, very large or negative P/E values, or extremely large P/S or P/B ratios for certain tickers — these are either genuine accounting/earnings situations or likely data anomalies that need checking.

Notable / unusual observations

• Negative / extreme P/E values: e.g., [BRK-A] & [BRK-B] show very large negative P/E, and [DFLI] / [ACON] show extremely large negative or huge multiples — these often reflect negative trailing earnings, microcap / illiquid adjustments, nonstandard accounting, or data issues; interpret multiples carefully.
• Extremely large P/S / P/B values on a few names (DFLI, ACON) — likely data anomalies or very small denominators (book value or revenue) in the source.
• Null trailing metrics: several large global ADRs and foreign listings (e.g., [TSM], [NSRGY], [NVO], [ASML], [BABA], [SONY], [JD]) have one or more null multiples in this snapshot — possibly due to feed availability or differing reporting conventions.
• Share‑class duplication: GOOG/GOOGL and BRK‑A/BRK‑B both included — when measuring company exposure you may want to collapse share classes.
• Some closing prices look like they may be pre/post‑split or ADR‑adjusted (e.g., [NVDA] at 14.614 in this 2022 snapshot) — confirm whether the price field is adjusted and whether marketCap uses consistent sharesOutstanding at that date.

Practical next steps / checks I recommend

• Verify extreme values and nulls against your raw price/financial feed or company filings (especially DFLI, ACON, and any symbol with absurd multiples).
• Normalize share classes if you want company-level exposures (e.g., combine GOOG + GOOGL; BRK‑A + BRK‑B).
• Confirm how marketCap was computed (price × sharesOutstanding) and whether sharesOutstanding and price are from the same timestamp and currency — mismatches cause incorrect caps.
• If you want, I can: (a) export this table as CSV/Excel, (b) compute sector or country groupings, (c) flag tickers with negative P/E or null metrics, or (d) re-run the snapshot using each ticker’s own latest 2022 row (or strict 2022-12-31 only).

Important reminder

• Double‑check all numbers and unusual metrics against primary data (exchange feed, filings). This is an AI summary of the query results and can miss edge cases or misinterpret feed quirks.

For the new list of stocks in 2022, create this strategy. Every month, rebalance the new 100 stocks by market cap excluding BRK-A and GOOG. Filter to only include

• stocks below the 200 day SMA OR
• we have positions in our portfolio and the positions percent gain is less than 10%

Limit 7. The only condition is every 30 days

Description:
This portfolio implements a monthly rebalancing strategy that focuses on a filtered subset of the top 100 stocks by market capitalization as of 2022. The strategy explicitly excludes BRK-A and GOOG, retaining BRK-B and GOOGL to avoid redundant share classes. The portfolio starts with an initial value of $10,000.
Every 30 days, the strategy evaluates the entire eligible universe of 98 stocks. It then filters these stocks based on a two-part condition:

1. Value/Trend Filter: The stock's current price is below its 200-day Simple Moving Average (SMA). This aims to identify stocks that are currently in a pullback or downtrend relative to their long-term average.
2. Momentum/Loss Aversion Filter: The portfolio currently holds a position in the stock, and that position's percentage gain is less than 10%. This allows the strategy to continue holding positions that are still in early stages of profit or are slightly down, preventing premature selling of potential winners or cutting losses too quickly.
   These two conditions are combined with an "OR" logic, meaning a stock will be considered if it meets either criterion. From the stocks that pass this filter, the strategy selects the top 7 based on their market capitalization (descending order) and rebalances the portfolio to hold these 7 stocks, weighted proportionally by their market capitalization.
   The full list of stocks considered for this strategy (excluding BRK-A and GOOG) includes: AAPL, MSFT, GOOGL, AMZN, BRK-B, UNH, DFLI, JNJ, XOM, V, TSLA, ACON, JPM, TSM, WMT, PG, NVDA, CVX, MA, LLY, META, HD, NSRGY, NVO, PFE, ABBV, MRK, KO, BAC, PEP, AVGO, BABA, ORCL, TMO, ASML, SHEL, COST, CSCO, DHR, MCD, ABT, NKE, TMUS, VZ, NEE, DIS, WFC, ADBE, SCHW, PM, BMY, CMCSA, TXN, UPS, COP, HON, MS, AMGN, T, NFLX, CRM, DE, LMT, UNP, IBM, CAT, QCOM, ELV, LOW, GS, CVS, SBUX, BA, SPGI, AXP, INTU, INTC, GILD, BLK, AMD, ADP, AMT, ISRG, SONY, SYK, TJX, MDLZ, EL, JD, C, PLD, NOC, ADI, AMAT, MMC, PYPL, MO, REGN.
   This approach combines a value/trend-following component with a position management rule, aiming to capture potential rebounds in large-cap stocks while managing existing positions.
   JSON

backtest from 01/01/2023 to today

I'll create a backtest configuration for your "2022 Top 100 Market Cap Rebalance - SMA & Position Gain Filter" strategy from January 1, 2023, to today (August 13, 2025).