Initial commit

.gitattributes

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+*.parquet filter=lfs diff=lfs merge=lfs -text

LICENSE

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+BSD-3-Clause
+
+Copyright (c) 2025 Ilya Snegov (aka Sierra Arn)
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+* Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

README.md

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+---
+tags:
+  - cryptocurrency
+  - bitcoin
+  - btcusdt
+  - ohlcv
+  - candlestick
+  - time-series
+  - financial-data
+  - technical-indicators
+  - machine-learning
+  - trading
+  - finml
+task_categories:
+  - time-series-forecasting
+  - other
+license: bsd-3-clause
+language:
+  - en
+---
+
+# **Financial ML Lab Dataset**
+
+This dataset is a component of the [finml-lab](https://github.com/Sierra-Arn/finml-lab) project, offering raw and preprocessed cryptocurrency (BTCUSDT) 1-hour OHLCV candlestick market data designed for machine learning experiments and educational purposes.
+
+## **Raw Data Source**
+
+The raw data was sourced from [Arthur Neuron's Cryptocurrency Futures OHLCV Dataset 1m 2024](https://www.kaggle.com/datasets/arthurneuron/cryptocurrency-futures-ohlcv-dataset-1m-2024) on Kaggle, distributed under the MIT license.
+
+## **Dataset Structure**
+
+```bash
+├── processed/
+│   ├── test_dataset.parquet    # Test split for model evaluation
+│   └── train_dataset.parquet   # Training split for model training
+├── raw/
+│   ├── BTCUSDT.parquet         # Raw BTCUSDT price data from Kaggle
+│   └── raw_1h_data.parquet     # Aggregated 1-hour candles
+└── samples/
+    ├── raw_1h_data.csv         # Sample of raw data (first 10 records)
+    ├── train_dataset.csv       # Sample of training split (first 10 records)
+    └── data_overview.md        # Detailed description of features
+```
+
+## **License**
+
+This project is licensed under the [BSD-3-Clause License](LICENSE).

processed/test_dataset.parquet

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raw/BTCUSDT.parquet

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raw/raw_1h_data.parquet

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samples/data-overview.md

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+# **Data overview**
+
+## **Notes**
+
+- Each record represents 1-hour candlesticks.
+- Most features were created using the ta-lib technical analysis library.
+- Numerical data in `train_dataset.csv` has already been processed with robust scaling, while categorical features have been label encoded.
+
+## **`raw_1h_data.csv`**
+
+### **Index**
+
+- **datetime** - date and time (in `YYYY-MM-DD HH:MM:SS` format), indicating the timestamp for each record.
+
+### **Features**
+
+1. **open** - opening price for the hourly interval.
+2. **high** - highest price reached during the hour.
+3. **low** - lowest price reached during the hour.
+4. **close** - closing price for the hourly interval.
+5. **volume** - trading volume traded during that hour.
+
+## **`train_dataset.csv`**
+
+### **Index**
+
+- **datetime** - date and time (in `YYYY-MM-DD HH:MM:SS` format), indicating the timestamp for each record.
+
+### **Features**
+
+1. **volume** - trading volume traded during the hour.
+2. **SAREXT** - Extended Parabolic SAR.
+3. **BBANDS_168_lower** - Lower Bollinger Band with 168-hour lookback.
+4. **ADXR_24** - Average Directional Movement Index Rating with 24-hour lookback.
+5. **ADXR_72** - Average Directional Movement Index Rating with 72-hour lookback.
+6. **ADXR_168** - Average Directional Movement Index Rating with 168-hour lookback.
+7. **DX_24** - Directional Movement Index with 24-hour lookback.
+8. **STOCHRSI_D** - Stochastic RSI D-line.
+9. **APO** - Absolute Price Oscillator.
+10. **TRIX_24** - 1-day Rate-of-Change of Triple Smooth EMA with 24-hour lookback.
+11. **TRIX_168** - 1-day Rate-of-Change of Triple Smooth EMA with 168-hour lookback.
+12. **ADOSC** - Chaikin A/D Oscillator.
+13. **HT_DCPERIOD** - Hilbert Transform - Dominant Cycle Period.
+14. **HT_PHASOR_inphase** - Hilbert Transform Phasor Component.
+15. **candles_since_start** - Cumulative count of candles elapsed since the beginning of the dataset.
+16. **day_sin** - Sine component of circular encoding for time of day.
+17. **day_cos** - Cosine component of circular encoding for time of day.
+18. **dayofweek_sin** - Sine component of circular encoding for day of week.
+19. **dayofyear_sin** - Sine component of circular encoding for day of year.
+20. **dayofyear_cos** - Cosine component of circular encoding for day of year.
+21. **target** - Target variable (closing price of the next record).

samples/raw_1h_data.csv

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+datetime,open,high,low,close,volume
+2019-09-08 17:00:00,10000.0,10000.0,10000.0,10000.0,0.002
+2019-09-08 18:00:00,10000.0,10000.0,10000.0,10000.0,0.0
+2019-09-08 19:00:00,10000.0,10357.53,10000.0,10340.12,471.659
+2019-09-08 20:00:00,10340.12,10368.64,10334.54,10351.42,583.271
+2019-09-08 21:00:00,10351.42,10391.9,10324.77,10391.9,689.759
+2019-09-08 22:00:00,10392.59,10412.65,10375.58,10375.58,675.394
+2019-09-08 23:00:00,10375.58,10392.25,10366.57,10391.63,676.206
+2019-09-09 00:00:00,10391.63,10391.63,10391.63,10391.63,0.0
+2019-09-09 01:00:00,10391.63,10391.63,10391.63,10391.63,0.0
+2019-09-09 02:00:00,10391.63,10391.63,10294.29,10296.04,96.462

samples/train_dataset.csv

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