Update README.md

README.md

@@ -16,12 +16,18 @@ pretty_name: Multi‑Timeframe Market Regimes (HMM‑6) (BTCUSD)
 
 ## Dataset Summary
 
-Labeled crypto market regimes derived from multi‑timeframe OHLCV features and technical indicators (5m, 15m, 1h). Labels come from a 6‑state Hidden Markov Model (HMM). Useful for regime detection and sequence modeling baselines (LSTM/Transformers). **No future leakage**: indicators computed only up to `timestamp` and labels correspond to the regime at that timestamp.
+This dataset provides labeled crypto market regimes derived from multi-timeframe (5m, 15m) OHLCV data and technical indicators.
+Market regimes are inferred using a 6-state Hidden Markov Model (HMM).
 
+The dataset is suitable for market regime detection, regime-aware modeling, and sequence modeling baselines (LSTM / Transformers).
+
+No future leakage: all indicators are computed using information available up to the timestamp, and labels correspond to the inferred regime at that timestamp only.
 ## Source & Licensing
+* **Asset**: BTC (BTCUSD / BTCUSDT – specify exact symbol)
+* **Exchange**: specify exchange (e.g., Binance)
+* **Market**: spot or perpetual futures
+* **Timezone**: UTC
 
-* **Raw data**: describe the exchange(s), symbols (e.g., `BTCUSDT`), and the exact ToS allowing redistribution. Replace this section with precise citations/links.
-* **License**: change the license above if the data source requires a specific license. If redistribution isn’t allowed, provide a script to regenerate labels from the original source instead of shipping raw candles.
 
 ## Files
 
@@ -29,9 +35,9 @@ Labeled crypto market regimes derived from multi‑timeframe OHLCV features and
 .
 ├── README.md
 └── data/
-    ├── train.csv         # chronological split (older → newer)
-    ├── validation.csv    # next chunk after train
-    └── test.csv          # most recent chunk
+    ├── train.csv         # oldest part
+    ├── validation.csv    # middle part
+    └── test.csv          # most recent data
 ```
 
 Optionally provide Parquet equivalents:
@@ -42,90 +48,144 @@ Optionally provide Parquet equivalents:
     ├── validation.parquet
     └── test.parquet
 ```
+All splits are chronological.
+No shuffling is performed.
 
 ## Columns
 
-* `timestamp` \[UTC, ISO8601 or UNIX ms]
-* **Multi‑TF OHLCV (5m, 15m, 1h)**: `open_*`, `high_*`, `low_*`, `close_*`, `volume_*`
-* **Indicators (15m)**: `log_ret_1_15m`, `ema_slope_21_15m`, `price_vs_ema55_15m`, `macd_hist_15m`, `adx_15m`, `atr_norm_15m`, `bb_width_15m`, `realized_vol_20_15m`, `rsi_15m`, `roc_15m`, `stoch_k_15m`, `wick_ratio_15m`
-* **Indicators (5m)**: `log_ret_1_5m`, `ema_slope_21_5m`, `price_vs_ema55_5m`, `macd_hist_5m`, `adx_5m`, `atr_norm_5m`, `bb_width_5m`, `realized_vol_20_5m`, `rsi_5m`, `roc_5m`, `stoch_k_5m`, `wick_ratio_5m`
-* **Indicators (1h)**: `log_ret_1_1h`, `ema_slope_21_1h`, `price_vs_ema55_1h`, `macd_hist_1h`, `adx_1h`, `atr_norm_1h`, `bb_width_1h`, `realized_vol_20_1h`, `rsi_1h`, `roc_1h`, `stoch_k_1h`, `wick_ratio_1h`
-* **Targets**: `state` (0‑5), `regime` (string label, optional), `post_prob_*` (optional soft posteriors per state)
+### Core
+- `timestamp` — UTC timestamp (ISO8601 or UNIX ms)
 
-## Label Semantics
+### OHLCV (contextual)
+- `open_5m`, `high_5m`, `low_5m`, `close_5m`, `volume_5m`
+- `open_15m`, `high_15m`, `low_15m`, `close_15m`, `volume_15m`
 
-```
-0: Choppy High‑Vol
-1: Range
-2: Squeeze
-3: Strong Trend
-4: Volatility Spike
-5: Weak Trend
-```
+OHLCV columns are included for **context and visualization** and are **not required** for training the HMM.
+
+---
 
-* `state` is the integer id.
-* `regime` is the human‑readable label (optional redundant column).
-* If available, include HMM posterior probabilities per state: `post_prob_0` … `post_prob_5`.
+### Technical Indicators (5m)
+- `log_ret_1_5m`
+- `ema_ratio_9_21_5m`
+- `macd_hist_5m`
+- `adx_5m`
+- `atr_norm_5m`
+- `bb_width_5m`
+- `rsi_14_5m`
+- `volume_zscore_50_5m`
+
+### Technical Indicators (15m)
+- `log_ret_1_15m`
+- `ema_ratio_9_21_15m`
+- `macd_hist_15m`
+- `adx_15m`
+- `atr_norm_15m`
+- `bb_width_15m`
+- `rsi_14_15m`
+- `volume_zscore_50_15m`
 
-## Splitting Protocol (leak‑free)
+---
 
-* Sort by `timestamp` ascending.
-* Choose three **contiguous** time blocks: `train` (oldest), `validation` (middle), `test` (most recent).
-* Do **not** shuffle.
-* If training sequence models, consumers should build sliding windows of length `time_steps` (e.g., 64) **within** each split only.
+## Targets
 
-## How to Load
+- `state` — integer HMM state label (0–5)
+- `regime` — human-readable semantic label mapped from `state`
+
+### Regime Semantics
+
+| State | Regime |
+|------:|--------|
+| 0 | Choppy High-Vol |
+| 1 | Range |
+| 2 | Squeeze |
+| 3 | Strong Trend |
+| 4 | Volatility Spike |
+| 5 | Weak Trend |
+
+**Notes**
+- `state` IDs are **model-dependent and arbitrary**
+- `regime` labels are **post-hoc human interpretations**
+- For modeling, use `state` as the target
 
-Python (CSV):
+---
+## Columns
+
+### Core
+- `timestamp` �� UTC timestamp (ISO8601 or UNIX ms)
+
+### OHLCV (contextual)
+- `open_5m`, `high_5m`, `low_5m`, `close_5m`, `volume_5m`
+- `open_15m`, `high_15m`, `low_15m`, `close_15m`, `volume_15m`
+
+OHLCV columns are included for **context and visualization** and are **not required** for training the HMM.
+
+---
+
+### Technical Indicators (5m)
+- `log_ret_1_5m`
+- `ema_ratio_9_21_5m`
+- `macd_hist_5m`
+- `adx_5m`
+- `atr_norm_5m`
+- `bb_width_5m`
+- `rsi_14_5m`
+- `volume_zscore_50_5m`
+
+### Technical Indicators (15m)
+- `log_ret_1_15m`
+- `ema_ratio_9_21_15m`
+- `macd_hist_15m`
+- `adx_15m`
+- `atr_norm_15m`
+- `bb_width_15m`
+- `rsi_14_15m`
+- `volume_zscore_50_15m`
+
+---
+
+## Targets
+
+- `state` — integer HMM state label (0–5)
+- `regime` — human-readable semantic label mapped from `state`
+
+### Regime Semantics
+
+| State | Regime |
+|------:|--------|
+| 0 | Choppy High-Vol |
+| 1 | Range |
+| 2 | Squeeze |
+| 3 | Strong Trend |
+| 4 | Volatility Spike |
+| 5 | Weak Trend |
+
+**Notes**
+- `state` IDs are **model-dependent and arbitrary**
+- `regime` labels are **post-hoc human interpretations**
+- For modeling, use `state` as the target
+
+---
+## Splitting Protocol (Leak-Free)
+
+1. Sort data by `timestamp` (ascending)
+2. Split into **contiguous** blocks:
+   - train → validation → test
+3. Do **not** shuffle
+4. Sliding windows for sequence models must be constructed **within each split only**
+
+---
+
+## How to Load
 
 ```python
 from datasets import load_dataset
-# If you keep the README YAML above, this works without specifying data_files
-ds = load_dataset("<user_or_org>/<repo>")
+
+ds = load_dataset("akashkumar5/Multi_Timeframe_Market_Regimes_HMM6_BTCUSD")
 train = ds["train"]
 valid = ds["validation"]
-# Or explicitly with Parquet
-ds = load_dataset("<user_or_org>/<repo>", data_files={
-    "train": "data/train.parquet",
-    "validation": "data/validation.parquet",
-    "test": "data/test.parquet",
-})
+test = ds["test"]
 ```
 
-PyTorch example (windowing):
-
-```python
-import torch
-import numpy as np
-FEATURES = [
-    "log_ret_1_15m","ema_slope_21_15m","price_vs_ema55_15m","macd_hist_15m","adx_15m","atr_norm_15m","bb_width_15m","realized_vol_20_15m","rsi_15m","roc_15m","stoch_k_15m","wick_ratio_15m",
-    "log_ret_1_5m","ema_slope_21_5m","price_vs_ema55_5m","macd_hist_5m","adx_5m","atr_norm_5m","bb_width_5m","realized_vol_20_5m","rsi_5m","roc_5m","stoch_k_5m","wick_ratio_5m",
-    "log_ret_1_1h","ema_slope_21_1h","price_vs_ema55_1h","macd_hist_1h","adx_1h","atr_norm_1h","bb_width_1h","realized_vol_20_1h","rsi_1h","roc_1h","stoch_k_1h","wick_ratio_1h"
-]
-TARGET = "state"
-WINDOW = 64
-
-def make_windows(table):
-    X = np.stack([table[f] for f in FEATURES], axis=1)
-    y = np.array(table[TARGET])
-    Xw, yw = [], []
-    for i in range(len(X) - WINDOW + 1):
-        Xw.append(X[i:i+WINDOW])
-        yw.append(y[i+WINDOW-1])  # label at window end
-    return np.stack(Xw), np.array(yw)
-```
-
-## Dataset Card Checklist
-
-* [ ] Precise data source + collection dates + timezone
-* [ ] License & ToS compliance
-* [ ] Exact indicator definitions (lookback windows, smoothing, normalization)
-* [ ] HMM configuration (n\_states=6, emissions, training window, seed)
-* [ ] Train/val/test date ranges
-* [ ] Class distribution per split
-* [ ] Known caveats & failure modes
-* [ ] Version tag (e.g., v1.0.0) and changelog
-
 ## Known Limitations
 
 * Technical‑indicator engineered features; not raw trades.