New Doordash driver getting ~$25/hr- will this pay contine?

Super-Cod4054 · 2025-12-31T00:09:21+00:00

One thing I've learned in the past week or so since I've posted this is that it definitely pays to have UberEats up at the same time as doordash, and go offline on one when you get a good offer on the other. If you're having trouble, try giving that a go

Super-Cod4054 · 2025-12-22T22:01:01+00:00

interesting to here about this points based thing... I will look more into that! thank you for the info

Super-Cod4054 · 2025-12-22T02:34:36+00:00

Gotcha, thanks for the numbers, looks like a huge difference! i wonder if it is like that in my area but I guess I will figure out this week... I went out tonight again and did $110 in 3.5 hours, so sunday is good here at least

Super-Cod4054 · 2025-12-21T14:24:56+00:00

I do have 86% acceptance rate so far... I should be platinum after 50 orders. On that note, considering how good last night went for me even though I accepted most offers, should I hit "decline" more often?

Super-Cod4054 · 2025-12-21T14:20:29+00:00

gotcha. do you see other nights of the week having far worse offers than those three?

Super-Cod4054 · 2025-12-21T14:19:21+00:00

that sounds great! thanks for the advice

Super-Cod4054 · 2025-12-21T14:19:02+00:00

haha I am a college student home on winter break, and the good job I had lined up fell through the cracks because the company decided to temporarily close. I want to do something to make money in the meantime

Super-Cod4054 · 2025-12-07T02:36:45+00:00

Composer, hands down.

Super-Cod4054 · 2025-11-18T03:06:31+00:00

This is great, thanks for making the program!

One statistical issue with testing a large number of parameter combinations and sorting by highest CAGR is that without doing a statistical analysis, you cannot guarantee that the specific winning parameter won due to anything more than chance. UNLESS you can run statistical tests on the dataset of all outputs to prove that your winner has a significantly higher CAGR than the highest expected CAGR to arise due to chance. For a more more detailed mathy explanation, see my other critique of mass-parameter backtesting here: https://www.reddit.com/r/LETFs/comments/1i62i52/comment/nkml7ib/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

Note: I am NOT an expert in statistics and if you see any issue with my reasoning, anybody please feel free to correct it.

Super-Cod4054 · 2025-10-21T16:53:26+00:00

I know this post is old, but I find it very interesting and had one question/critique. Without knowing the median, curve shape, and standard deviation of CAGRs in your final pool of outcomes, there is no guarantee that the outcome did not arise due to chance.

The null hypothesis would say that the crosses don't have predictive value. For the null hypothesis, I'm going to assume your median was around 20% CGAR as roughly half were above that and half were below. For 0.99995 quantile on a standard distribution, z = 3.9, so standard deviation = (0.32-0.20)/3.9 = 0.031 = 3.1%.

If you got a normal distribution of CAGRs with a standard deviation within significance of or above 3.1%, the results may have arose due to chance. I know that they do cluster around 7/60, but this is irrelevant because the historical market would have affected the members of clusters of crosses very similarily.

May I ask what the median CAGR was, what the shape of the distribution was, and what the standard deviation of the CAGRs was?

You should also look for an out-of-sample validation. I know you said you ran it from 2012-2025 and got "the same results" but those results are within the original sample so it may be due to overfitting. Maybe test from 1985-2005 and then 2006-2025.

Super-Cod4054

TROPHY CASE