README.md

metadata

pretty_name: Bitcoin SLLM Instruction Dataset
tags:
  - bitcoin
  - finance
  - instruction-tuning
  - sft
license: other
language:
  - en
task_categories:
  - time-series-forecasting
  - text-classification
  - tabular-regression
size_categories:
  - 1K<n<10K

Bitcoin SLLM Instruction Dataset

Daily BTC context transformed into instruction-tuning triples for small LMs.

Schema

• id: unique example id
• task: direction_cls | return_reg | maxdd_cls
• split: train | validation | test (time-based)
• instruction: natural-language task definition
• input: compact JSON string with features (technicals, macro, on-chain, text snippets)
• output: compact JSON string with target(s)
• meta: { "date": "YYYY-MM-DD" }

Targets

• direction_cls: UP (>+2%), FLAT (−2%..+2%), DOWN (<−2%) at t+10; includes r10.
• return_reg: 10-day log return r10_log.
• maxdd_cls: next-10-day max drawdown bucket (low/med/high) with raw maxdd.

Load

from datasets import load_dataset
ds = load_dataset("tahamajs/bitcoin-sllm-instruct")
print(ds)             # DatasetDict with train/validation/test
print(ds["train"][0]) # one example

Notes

• Text is truncated to short snippets to keep token usage low.
• Normalize numeric features at train time using train split stats only.
• Review licensing of underlying sources; this repo may be private.