sujinwo/tsie-market-regime-dataset

TSIE: Temporal Signal Intelligence Engine — Market Regime Dataset (IDX)

📊 Overview

This dataset provides a multi-class market regime classification for Indonesian stock market (IDX), designed for time-series modeling using architectures such as Temporal Fusion Transformer (TFT).

The dataset includes:

• OHLCV price data
• Engineered technical indicators
• Temporal features
• Session-aware features (IDX trading hours)
• Multi-class regime labels based on rule-based signal logic

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🎯 Task

Multi-class classification (7 classes)

Each timestep is labeled with a market regime:

Class| Label| Description 0| STRONG SELL| High-confidence bearish breakdown 1| WEAK SELL| Mild bearish movement 2| BEAR TRAP| False breakdown 3| FLAT / NOISE| Sideways / low volatility 4| BULL TRAP| False breakout 5| WEAK BUY| Mild bullish movement 6| STRONG BUY| High-confidence breakout

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🧠 Feature Groups

🔹 Static Features

• "group_id" → stock identifier

🔹 Time-Varying Known Features

(available in future)

• "time_idx"
• "hour", "day_of_week", "day_of_month"
• "hour_sin", "hour_cos"
• "dow_sin", "dow_cos"
• "session" (IDX session)
• "is_session_1", "is_session_2"
• "is_lunch_break"
• "is_opening", "is_closing"

🔹 Time-Varying Observed Features

(observed at each timestep)

• OHLCV: "open", "high", "low", "volume"
• "log_return"
• "roc_5"
• "volume_ratio"
• "rsi"
• "bb_position"

🔹 Observed Categoricals

• "signal_numeric" → (-1, 0, 1)

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🏦 Market Session Encoding (IDX)

The dataset includes trading session awareness:

• Session 1: 09:00–11:59
• Lunch Break: 12:00–13:59
• Session 2: 14:00–15:59

Additional flags:

• Opening period
• Closing period

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⚙️ Labeling Methodology

Labels are generated using:

• Rule-based signal classification
• Price action + volatility + RSI + volume logic
• Inspired by trading heuristics and regime detection

This is not future-leakage-based labeling.

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📦 Dataset Structure

Columns:

• "time"
• "group_id"
• "time_idx"
• features...
• "target" (main label)
• "is_train" (train/validation split)

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🚀 Usage

Load dataset

import pandas as pd

df = pd.read_parquet("tft_dataset_ready.parquet")

Example: TFT (PyTorch Forecasting)

from pytorch_forecasting import TimeSeriesDataSet

dataset = TimeSeriesDataSet( df, time_idx="time_idx", target="target", group_ids=["group_id"], )

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⚠️ Notes

• Dataset is highly imbalanced (dominant "FLAT / NOISE" class)
• Recommended:
  • class weighting
  • focal loss
  • oversampling minority classes

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📈 Intended Use

• Market regime classification
• Algorithmic trading research
• Time-series deep learning (TFT, LSTM, Transformer)

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