| --- |
| size_categories: |
| - 1M<n<10M |
| tags: |
| - finance |
| - crypto |
| - BTC |
| --- |
| # 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.csv` |
| |
| - `metric_train.csv` |
| |
| - `metric_test.csv` |
| |
| |
| --- |
|
|
| # Micro-State |
|
|
| The **Micro-State** group contains data describing the **short-term microeconomic dynamics of the Bitcoin market**. |
|
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| 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.csv` |
| |
| - `price_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: |
|
|
| 1. **metric_pretrain.csv**: 2015/01/01 – 2018/07/19 |
| |
| 2. **metric_train.csv**: 2018/07/20 – 2023/05/31 |
| |
| 3. **metric_test.csv**: 2023/06/01 – 2026/03/03 |
| |
| 4. **price_train.csv**: 2018/07/20 – 2023/05/31 |
| |
| 5. **price_test.csv**: 2023/06/01 – 2026/03/03 |
