Cheaper SPY Alternatives - for Paupers Like Me (Short-Term Plays)

I’ve seen a few TF discussions about tickers that follow SPY closely but have cheaper options for smaller accounts. I think this is useful information so I’ve decided to compile some that I know. If you just want to see the tickers, I’ll put them in the comments below this post.
Please do your own research before trading these, many will be leveraged.

I’ve written some python code for a few metrics that I thought might be useful when comparing tickers. The code is here. I have no formal coding education so I apologise for the formatting. There is also a slim (read: very high) possibility that there are mistakes. I’m pretty sure the correlation coefficient , calculated beta and volume coefficient are accurate though, and they are the most useful.

All metrics (except for volume coefficient) are calculated using monthly adjusted close data from the past 5 years (60 months) sourced from yahoo finance.

Correlation coefficient is a measure of how correlated the ticker is against a benchmark (SPY in this case but you can change that if you copy the code). A value of 1.0 means that the two tickers are perfectly correlated, a value of -1.0 means that the tickers have a perfect negative correlation (inverse), and a value of 0 means there is no linear correlation.

Calculated Beta is a measure of the ticker’s volatility against a benchmark, a beta near 1.0 indicates that the ticker does not deviate from the benchmark, a beta <1 indicates that the ticker is less volatile than the benchmarch, and a beta >1 indicates that the ticker is more volatile than the benchmark. I’ve called this calculated beta as it differs from the beta data on yahoo finance.

Volume coefficient is just the average daily volume over the last month of the ticker divided by the average daily volume of the benchmark. A value of 2 means that the ticker experiences 2x as much average daily volume, a value of 0.5 means that the ticker experiences 0.5x as much daily volume. This is important as it will affect the bid-ask spread.

I dont find these next metrics particularly useful for this but I made them anyway - you can skip to the comments

Novel-alpha is a measure of how well the ticker performs relative to the benchmark ticker. A novel-alpha of 1.2 means that the ticker has performed 1.2x better in percentage gain, a reading of 0.8 means that the ticker performed 0.8x as well etc. I’ve named it novel-alpha so it’s not confused with alpha (α) but it does describe a similar thing.

Novel-gamma is made to be used with novel-alpha. The percentage price increase is plotted against time and the regression line is calculated (line of best fit). The gradient of the regression line for the benchmark is then subtracted from the gradient of the regression line for the ticker, this value then has +1 added so that it centres around 1.0 and not 0.0 to match the other metrics. This can be used in conjunction with novel-alpha to paint a clearer picture of price action. Example below.

Graph 1:
Graph 2:

If you were to just use the novel-alpha values then it would appear that Graph 2 has had better performance but this misses important information. A novel-gamma below 1.0 indicates that there was a sudden rise in the stock price (graph 2), a value above 1.0 indicates that there was a sudden fall in stock price. Remember, this is all in relation to the benchmark, if novel-alpha is not close to 1.0 then you should disregard this interpretation of novel-gamma.


Above are the non-rounded values for QQQ using SPY as the benchmark. You can see that novel-alpha suggests that QQQ performed better than SPY but the novel-gamma is above 1.0 which indicates a steep climb and large drop. I believe this is because QQQ’s 52-week range experienced a 31.4% change and SPY’s experienced a 20.7% change.


Above are the non-rounded values when SPY is compared against itself. The correlation coefficient has a slight inaccuracy but I believe this is due to the magnitude of precision in the way that NumPy calculates covariance (I don’t think this issue is large enough to warrant a new calculation using something other than NumPy). The rest of the values are slightly swayed just because of how floating-point numbers work.

Ideal alternatives should have these values as close to 1.0 as possible. I believe this is theoretically impossible because a cheaper price and high correlation would require a higher beta (I think :yong:) , so there will always be a trade-off. I would say this means that you will always have to assume higher volatility for cheaper prices.



Correlation coefficient: 1.0

Calculated beta: 1.06

Volume coefficient: 0.81

Novel-alpha: 1.24

Novel-gamma: 2.41

Option expiration dates: 3x a week

QQQ is an ETF that tracks the Nasdaq-100. It has 102 holdings and is heavily weighted towards large-cap technology companies.

The sector allocation of QQQ holdings is as follows:

Technology 59.14%
Consumer Services 16.49%
Consumer Goods 9.71%
Health Care 6.07%
Industrials 5.44%
Telecommunications 1.29%
Utilities 1.14%
Non Classified Equity 0.41%
Basic Materials 0.27%

Top 10 holdings:

Apple Inc. AAPL 13.03%
Microsoft Corp. MSFT 10.54% Inc. AMZN 6.41%
Tesla Inc. TSLA 4.56%
Alphabet Inc. Cl C GOOG 3.68%
Meta Platforms Inc. FB 3.51%
Alphabet Inc. Cl A GOOGL 3.48%
NVIDIA Corp. NVDA 3.31%
PepsiCo Inc. PEP 2.00%
Costco Wholesale Corp. COST 1.98%




Correlation coefficient: 0.94

Calculated beta: 4.63

Volume coefficient: 0.58

Novel-alpha: 2.42

Novel-gamma: 11.67

Option expiration dates: 1x a week

SOXL is a 3x daily leveraged ETF exposed to 30 US-listed semiconductor companies. The non-leveraged version is SOXX. SOXL is my personal favourite.

Top 10 holdings:




Correlation coefficient: 0.99

Calculated beta: 1.13

Volume coefficient: 1.01

Novel-alpha: 2.3

Novel-gamma: 6.45

Option expiration dates: 1x a week

Apple designs, manufactures, and markets smartphones, personal computers, tablets, wearables, and accessories worldwide. It also sells various related services. I’m sure I can’t tell you anything about Apple that you don’t already know.



Correlation coefficient: 1.01

Calculated beta: 2.14

Volume coefficient: 0.08

Novel-alpha: 1.31

Novel-gamma: 2.49

Option expiration dates: 1x a week

SSO provides 2x daily leveraged exposure to a market cap-weighted index of 500 large and mid-cap US companies selected by S&P. The volume coefficient of this ticker is low so expect a larger bid-ask spread.

Top 10 holdings:

Apple Inc. AAPL 6.74%
Microsoft Corp. MSFT 5.76% Inc. AMZN 3.55%
Tesla Inc. TSLA 2.25%
Alphabet Inc. Cl A GOOGL 2.08%
Alphabet Inc. Cl C GOOG 1.93%
NVIDIA Corp. NVDA 1.70%
Berkshire Hathaway Inc. Cl B BRK.B 1.61%
Meta Platforms Inc. FB 1.28%
UnitedHealth Group Inc. UNH 1.20%



Thanks for starting this thread, bud.

Might I suggest you rename it to “Cheaper SPY Options Plays”?

Might want to look at leveraged shares too… fngu is good to trade - about triple leveraged spy but cheap and can get options like returns. Currently about 7.15 a share. I’m playing this a little lately

These are the numbers for FNGU. Looks pretty good for shares!

I’ll do a brief write-up on it in a little bit to match the other replies.

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Correlation coefficient: 0.85
Calculated beta: 4.84
Volume coefficient: 0.18
Novel-alpha: 0.68
Novel-gamma: 8.54

Option expiration dates: No option chain

FNGU tracks 3x the daily price movements of an equal-weighted index of US-listed technology and consumer discretionary companies. This security has no options but is low price, volatile and has a semi-high correlation with SPY. The volume coefficient is low but because this would be a stock play and not an options play it doesn’t matter. The underlying is liquid enough for shares to have a tight bid-ask spread.

All holdings:

Alibaba Group Holding Ltd ADR BABA 10.00%
Alphabet Inc A GOOGL 10.00% Inc AMZN 10.00%
Apple Inc AAPL 10.00%
Baidu Inc ADR BIDU 10.00%
Facebook Inc A FB 10.00%
Netflix Inc NFLX 10.00%
NVIDIA Corp NVDA 10.00%
Tesla Inc TSLA 10.00%
Twitter Inc TWTR 10.00%


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Correlation coefficient: 0.93

Calculated beta: 3.71

Volume coefficient: 2.21

Novel-alpha: 1.31

Novel-gamma: 7.85

Option expiration dates: 1x a week

TQQQ provides 3x leveraged exposure to a modified market-cap-weighted index tracking 100 of the largest non-financial firms listed on NASDAQ. The ETF delivers 3x exposure only over a one-day holding period.

The sector allocation of TQQQ is as follows:

Technology 36.27%
Consumer Services 10.69%
Consumer Goods 6.22%
Health Care 3.74%
Industrials 3.61%
Telecommunications 1.00%
Utilities 0.75%
Non Classified Equity 0.29%
Basic Materials 0.16%

Top 10 holdings:

Apple Inc. AAPL 8.51%
Microsoft Corp. MSFT 6.54% Inc. AMZN 4.31%
Tesla Inc. TSLA 2.86%
Alphabet Inc. Cl C GOOG 2.28%
Alphabet Inc. Cl A GOOGL 2.17%
Meta Platforms Inc. META 1.87%
NVIDIA Corp. NVDA 1.78%
PepsiCo Inc. PEP 1.32%
Costco Wholesale Corp. COST 1.28%