Futures trading net
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Published
- 1 January 1993
- Authors
- Lonnie Hamm, B. Wade Brorsen, and Ramesh Sharda
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Other
- Task
- Regression
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- tokens
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 5.3 × 10⁹ FLOP
- How it was established
- Hardware
0.3*35*60*60*140000=5292000000=5.29e9 “Training on the data from 1979-1985 took approximately 35 hours” “The neural networks were trained on a 386Dx-25mhz processor” 0.14 MFLOPS taken from this benchmark: https://www.vogons.org/viewtopic.php?t=46350
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Wall-clock time
- 35 hours
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Speculative
Sources
Where this record came from and when it was last checked.
- Reference
- Futures trading with a neural network
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Futures trading net was published by its authors, during January 1993.
It works in the domain of Other, and is recorded as performing the task of regression.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
Training it took a computation budget of roughly 5.3 × 10⁹ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
Futures trading net — common questions
Futures trading net— when was it released?
It was published in January 1993. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Futures trading net— what is it used for?
It works in the domain of Other, and is recorded as handling the task of regression. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Futures trading net— how much compute was used to train it?
Training consumed around 5.3 × 10⁹ FLOP. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
Futures trading net— what GPU do I need to run it?
None. This is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Futures trading net— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
Futures trading net— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.