The dataset viewer is not available for this subset.
Exception: ConnectionError
Message: Couldn't reach 'Darrius2020/RL_quant_btc' on the Hub (LocalEntryNotFoundError)
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 268, in get_dataset_config_info
builder = load_dataset_builder(
^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1315, in load_dataset_builder
dataset_module = dataset_module_factory(
^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1207, in dataset_module_factory
raise e1 from None
File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1133, in dataset_module_factory
raise ConnectionError(f"Couldn't reach '{path}' on the Hub ({e.__class__.__name__})") from e
ConnectionError: Couldn't reach 'Darrius2020/RL_quant_btc' on the Hub (LocalEntryNotFoundError)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Dataset Description
These datasets contain financial data related to Bitcoin. They are used to train a reinforcement learning (RL) agent whose objective is to learn how to trade the Bitcoin market.
The agent relies on an architecture inspired by the System 1 / System 2 cognitive model:
System 1: responsible for fast decision-making during trading.
System 2: a pre-trained neural network designed to analyze the market regime and provide macro-level context to improve the decisions made by System 1.
More specifically, System 2 is trained to identify the market regime for the next 24 hours and provide this information to System 1 in order to guide its trading actions.
The data is organized into two main groups of datasets: Macro-State and Micro-State.
Macro-State
The Macro-State group contains data describing the macroeconomic evolution of the Bitcoin market as well as the state of the Bitcoin network.
These datasets are used to train System 2, whose role is to detect market regimes.
The variables in these datasets are obtained from transformations applied to raw on-chain and macro-financial data, such as:
MVRV
NVT
Hash Rate
and other similar metrics.
The goal of these transformations is to extract informative signals about the market structure and dynamics, helping the agent better understand its environment.
Each row corresponds to one day of market history.
The datasets belonging to this group are:
metric_pretrain.csvmetric_train.csvmetric_test.csv
Micro-State
The Micro-State group contains data describing the short-term microeconomic dynamics of the Bitcoin market.
These datasets are used to train System 1, which performs the trading decisions.
Each row corresponds to one hour of market history.
The variables in this group are derived from calculations based on OHLCV data (Open, High, Low, Close, Volume), allowing the extraction of indicators relevant for short-term trading decisions.
The datasets belonging to this group are:
price_train.csvprice_test.csv
Dataset Time Range
The overall market history covered by the datasets spans from 2015/01/01 to 2026/03/03, but the exact time ranges vary depending on the dataset:
metric_pretrain.csv: 2015/01/01 – 2018/07/19
metric_train.csv: 2018/07/20 – 2023/05/31
metric_test.csv: 2023/06/01 – 2026/03/03
price_train.csv: 2018/07/20 – 2023/05/31
price_test.csv: 2023/06/01 – 2026/03/03
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