Update README.md
Browse files
README.md
CHANGED
|
@@ -71,3 +71,47 @@ This sample dataset is provided under the CC-BY-4.0 license for research and ana
|
|
| 71 |
---
|
| 72 |
license: cc-by-4.0
|
| 73 |
---
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 71 |
---
|
| 72 |
license: cc-by-4.0
|
| 73 |
---
|
| 74 |
+
|
| 75 |
+
---
|
| 76 |
+
|
| 77 |
+
## 📈 Get the Full Institutional L2 Orderbook Dataset
|
| 78 |
+
|
| 79 |
+
The data provided in this Hugging Face repository is a limited sample. Standard OHLCV candles often lead to model overfitting because they lack the orderbook imbalance and passive limit order features necessary to capture true market microstructure.
|
| 80 |
+
|
| 81 |
+
If you are training Deep Learning models (LSTMs, Transformers) or Reinforcement Learning agents, you need the full, continuous historical depth.
|
| 82 |
+
|
| 83 |
+
You can download the full **12+ months (approx. 96,000 bars per instrument, 10 to 30 depth levels)** directly from our infrastructure.
|
| 84 |
+
|
| 85 |
+
### Download Full Datasets by Asset:
|
| 86 |
+
* [BTC Historical Orderbook](https://imbalancelabs.com/datasets/btc-historical-orderbook)
|
| 87 |
+
* [ETH Historical Orderbook](https://imbalancelabs.com/datasets/eth-historical-orderbook)
|
| 88 |
+
* [SOL Historical Orderbook](https://imbalancelabs.com/datasets/sol-historical-orderbook)
|
| 89 |
+
* [BNB Historical Orderbook](https://imbalancelabs.com/datasets/bnb-historical-orderbook)
|
| 90 |
+
* [XRP Historical Orderbook](https://imbalancelabs.com/datasets/xrp-historical-orderbook)
|
| 91 |
+
* [DOGE Historical Orderbook](https://imbalancelabs.com/datasets/doge-historical-orderbook)
|
| 92 |
+
* [ADA Historical Orderbook](https://imbalancelabs.com/datasets/ada-historical-orderbook)
|
| 93 |
+
* [AVAX Historical Orderbook](https://imbalancelabs.com/datasets/avax-historical-orderbook)
|
| 94 |
+
* [LINK Historical Orderbook](https://imbalancelabs.com/datasets/link-historical-orderbook)
|
| 95 |
+
* [DOT Historical Orderbook](https://imbalancelabs.com/datasets/dot-historical-orderbook)
|
| 96 |
+
* [NEAR Historical Orderbook](https://imbalancelabs.com/datasets/near-historical-orderbook)
|
| 97 |
+
* [SUI Historical Orderbook](https://imbalancelabs.com/datasets/sui-historical-orderbook)
|
| 98 |
+
* [APT Historical Orderbook](https://imbalancelabs.com/datasets/apt-historical-orderbook)
|
| 99 |
+
* [ARB Historical Orderbook](https://imbalancelabs.com/datasets/arb-historical-orderbook)
|
| 100 |
+
* [OP Historical Orderbook](https://imbalancelabs.com/datasets/op-historical-orderbook)
|
| 101 |
+
* [INJ Historical Orderbook](https://imbalancelabs.com/datasets/inj-historical-orderbook)
|
| 102 |
+
* [SEI Historical Orderbook](https://imbalancelabs.com/datasets/sei-historical-orderbook)
|
| 103 |
+
* [TIA Historical Orderbook](https://imbalancelabs.com/datasets/tia-historical-orderbook)
|
| 104 |
+
* [WIF Historical Orderbook](https://imbalancelabs.com/datasets/wif-historical-orderbook)
|
| 105 |
+
* [FIL Historical Orderbook](https://imbalancelabs.com/datasets/fil-historical-orderbook)
|
| 106 |
+
* [LTC Historical Orderbook](https://imbalancelabs.com/datasets/ltc-historical-orderbook)
|
| 107 |
+
* [ETC Historical Orderbook](https://imbalancelabs.com/datasets/etc-historical-orderbook)
|
| 108 |
+
* [ATOM Historical Orderbook](https://imbalancelabs.com/datasets/atom-historical-orderbook)
|
| 109 |
+
* [XLM Historical Orderbook](https://imbalancelabs.com/datasets/xlm-historical-orderbook)
|
| 110 |
+
|
| 111 |
+
### 📚 Quant Research & Tutorials
|
| 112 |
+
We actively publish technical notes on engineering robust crypto models:
|
| 113 |
+
1. [The Overfitting Trap: Why OHLCV Data Ruins Your Machine Learning Crypto Models](https://imbalancelabs.com/research/why-ohlcv-models-fail)
|
| 114 |
+
2. [How to Calculate Realistic Slippage in Python Using Level 2 Data](https://imbalancelabs.com/research/calculating-slippage-level-2-data)
|
| 115 |
+
3. [Pandas vs DuckDB: Querying Crypto Parquet Data in Milliseconds](https://imbalancelabs.com/research/duckdb-vs-pandas-orderbook-analysis)
|
| 116 |
+
|
| 117 |
+
For programmatic REST API access, custom schemas, and enterprise data pipelines, visit **[Imbalance Labs](https://imbalancelabs.com)**.
|