wehub-resource-sync
4882 lines
627 KiB
Plaintext
4882 lines
627 KiB
Plaintext
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"source": [
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"# Chapter 5: LLM-Based Tabular Data Generation (GReaT Framework)\n",
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"\n",
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"**Chapter 5: Synthetic Data Generation**\n",
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"**Section Reference**: Section 5.6 (LLMs for Structured Financial Data)\n",
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"\n",
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"**Docker image**: `ml4t-gpu`\n",
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"\n",
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"> **GPU recommended**: This notebook trains models with PyTorch/CUDA. It will run on CPU\n",
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"> but training may be very slow. For GPU acceleration:\n",
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"> ```bash\n",
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"> docker compose run --rm ml4t-gpu python 05_synthetic_data/06_llm_tabular_great.py\n",
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"> ```\n",
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"\n",
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"\n",
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"## Purpose\n",
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"\n",
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"This notebook implements **GReaT (Generate Realistic Tabular Data)** using the\n",
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"actual `be-great` library to generate synthetic financial tabular data with LLMs.\n",
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"\n",
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"## Learning Objectives\n",
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"\n",
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"By completing this notebook, you will:\n",
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"- Understand the serialization insight for applying LLMs to tabular data\n",
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"- Fine-tune GPT-2 on serialized financial records using the GReaT framework\n",
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"- Generate synthetic tabular data and evaluate fidelity\n",
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"- Compare LLM-based generation to traditional methods (GANs, VAEs)\n",
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"\n",
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"## Cross-References\n",
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"\n",
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"- **Book**: Section 5.6 discusses GReaT and LLM-based tabular generation\n",
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"- **Related**: [`02_tailgan_tail_risk`](02_tailgan_tail_risk.ipynb) (GAN for time series comparison)\n",
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"\n",
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"---\n",
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"\n",
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"## Key Concepts\n",
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"\n",
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"1. **Serialization**: Convert table rows to natural language sentences\n",
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"2. **Fine-tuning**: Train GPT-2 on serialized financial data\n",
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"3. **Generation**: LLM produces new \"sentences\" parsed back to table rows\n",
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"4. **Mixed Types**: Handle categorical, numerical, and text features naturally\n",
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"\n",
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"## Why LLMs for Tabular Data?\n",
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"\n",
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"- Captures complex feature dependencies through attention\n",
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"- No explicit distribution assumptions\n",
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"- Handles mixed data types naturally\n",
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"- Pre-trained language understanding helps with feature names\n",
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"\n",
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"## Prerequisites\n",
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"\n",
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"- `be-great` library (`uv add be-great`) for the GReaT framework\n",
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"- `transformers` library (installed as a `be-great` dependency)\n",
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"- ETF data via `load_etfs()` from Ch2\n",
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"\n",
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"## References\n",
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"\n",
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"- Borisov et al. (2023). \"Language Models are Realistic Tabular Data Generators\"\n",
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"- https://github.com/kathrinse/be_great"
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]
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},
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"output_type": "stream",
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"text": [
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"/opt/ml4t/lib/python3.14/site-packages/multiprocess/connection.py:335: SyntaxWarning: 'return' in a 'finally' block\n",
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" return f\n",
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"/opt/ml4t/lib/python3.14/site-packages/multiprocess/connection.py:337: SyntaxWarning: 'return' in a 'finally' block\n",
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" return self._get_more_data(ov, maxsize)\n"
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}
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],
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"source": [
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"\"\"\"LLM-Based Tabular Data Generation — GReaT framework for synthetic financial data.\"\"\"\n",
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"\n",
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"# Note: temporal split used instead of train_test_split for financial data\n",
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"import warnings\n",
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"from datetime import datetime\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import plotly.graph_objects as go\n",
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"import polars as pl\n",
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"from be_great import GReaT\n",
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"from plotly.subplots import make_subplots\n",
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"from scipy import stats\n",
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"from sklearn.ensemble import GradientBoostingClassifier\n",
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"from sklearn.metrics import accuracy_score, roc_auc_score\n",
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"\n",
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"from data import load_etfs\n",
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"from utils.paths import get_output_dir\n",
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"from utils.reproducibility import set_global_seeds\n",
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"from utils.style import COLORS, plot_fidelity_comparison\n",
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"\n",
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"# Suppress transformers warnings\n",
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"warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
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"warnings.filterwarnings(\"ignore\", category=UserWarning, module=\"transformers\")"
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]
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},
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"id": "23fed156",
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"status": "completed"
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},
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"tags": [
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"parameters"
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]
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},
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"outputs": [],
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"source": [
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"# GReaT framework parameters (Borisov et al. 2023)\n",
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"N_SAMPLES = 2000 # Training samples from ETF data\n",
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"N_GENERATE = 500 # Synthetic samples to generate\n",
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"EPOCHS = 50 # Fine-tuning epochs\n",
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"BATCH_SIZE = 16 # Training batch size\n",
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"SEED = 42"
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]
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},
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"source": [
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"set_global_seeds(SEED)"
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]
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},
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"outputs": [],
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"source": [
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"# Configuration\n",
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"CONFIG = {\n",
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" \"symbols\": None, # Load all ETFs\n",
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" \"start_date\": \"2015-01-01\",\n",
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" \"n_samples\": N_SAMPLES,\n",
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" \"n_generate\": N_GENERATE,\n",
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" \"epochs\": EPOCHS,\n",
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" \"batch_size\": BATCH_SIZE,\n",
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"}\n",
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"\n",
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"# Checkpoint configuration\n",
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"RETRAIN = False # Set True to retrain even if checkpoint exists\n",
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"CHECKPOINT_DIR = get_output_dir(5, \"great\") / \"checkpoints\" / \"great_model\""
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]
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},
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"source": [
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"## 1. Load Real ETF Data\n",
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"\n",
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"We use actual ETF data to create a tabular dataset suitable for GReaT.\n",
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"The function below engineers a mix of numerical features (returns, volatility)\n",
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"and categorical features (direction, momentum regime) to showcase GReaT's\n",
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"ability to handle mixed-type tabular data."
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]
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"cell_type": "markdown",
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"source": [
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||
"### Build Tabular Feature Set\n",
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||
"\n",
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"Each row represents a single observation: lookback features (returns, volatility,\n",
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"volume ratio) plus a forward-return target. Categorical columns encode direction,\n",
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"momentum strength, and volatility regime -- feature types that GANs struggle with\n",
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"but LLMs handle naturally via serialization."
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]
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},
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"source": [
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"def load_etf_tabular_data(\n",
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" symbols: list[str] | None, start_date: str, n_samples: int\n",
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") -> pd.DataFrame:\n",
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" \"\"\"\n",
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" Load ETF data and create tabular features for GReaT.\n",
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"\n",
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" Creates features that mix numerical (returns, volatility) and\n",
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" categorical (direction, regime) for realistic tabular generation.\n",
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" \"\"\"\n",
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" df = load_etfs()\n",
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"\n",
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" # Determine date column and filter by start date\n",
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" date_col = \"timestamp\" if \"timestamp\" in df.columns else \"date\"\n",
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" start_dt = datetime.strptime(start_date, \"%Y-%m-%d\")\n",
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"\n",
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" # Cast to date for consistent comparison across schemas\n",
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" df = df.filter(pl.col(date_col).cast(pl.Date) >= start_dt.date())\n",
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"\n",
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" if symbols:\n",
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" df = df.filter(pl.col(\"symbol\").is_in(symbols))\n",
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"\n",
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" df = df.sort([\"symbol\", date_col])\n",
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"\n",
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" # Create tabular features per observation\n",
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" records = []\n",
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"\n",
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" for symbol in df[\"symbol\"].unique().to_list():\n",
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" symbol_df = df.filter(pl.col(\"symbol\") == symbol).sort(date_col)\n",
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"\n",
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" if len(symbol_df) < 30:\n",
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" continue\n",
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"\n",
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" close = symbol_df[\"close\"].to_numpy()\n",
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" volume = symbol_df[\"volume\"].to_numpy()\n",
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" dates = symbol_df[date_col].to_list()\n",
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"\n",
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" for i in range(20, len(close) - 5):\n",
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" # Lookback features\n",
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" ret_1d = (close[i] - close[i - 1]) / close[i - 1]\n",
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" ret_5d = (close[i] - close[i - 5]) / close[i - 5]\n",
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" ret_20d = (close[i] - close[i - 20]) / close[i - 20]\n",
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" # Volatility: std of daily returns over 20-day window\n",
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" window = close[i - 20 : i]\n",
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" daily_rets = window[1:] / window[:-1] - 1.0\n",
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" vol_20d = daily_rets.std()\n",
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" vol_ratio = volume[i] / np.mean(volume[i - 20 : i])\n",
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"\n",
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" # Forward return (target)\n",
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" fwd_ret_5d = (close[i + 5] - close[i]) / close[i]\n",
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"\n",
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" # Categorical features\n",
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" direction = \"up\" if ret_1d > 0 else \"down\"\n",
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" momentum = (\n",
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" \"strong\" if abs(ret_20d) > 0.05 else \"weak\" if abs(ret_20d) > 0.02 else \"flat\"\n",
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" )\n",
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" vol_regime = \"high\" if vol_20d > 0.02 else \"normal\" if vol_20d > 0.01 else \"low\"\n",
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"\n",
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" # Forward return absolute value for extreme move classification\n",
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" abs_fwd_ret_5d = abs(fwd_ret_5d)\n",
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"\n",
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" records.append(\n",
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" {\n",
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" \"timestamp\": dates[i], # For temporal split\n",
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" \"symbol\": symbol,\n",
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" \"ret_1d\": round(ret_1d * 100, 2), # Percentage\n",
|
||
" \"ret_5d\": round(ret_5d * 100, 2),\n",
|
||
" \"ret_20d\": round(ret_20d * 100, 2),\n",
|
||
" \"volatility\": round(vol_20d * 100, 2),\n",
|
||
" \"volume_ratio\": round(vol_ratio, 2),\n",
|
||
" \"direction\": direction,\n",
|
||
" \"momentum\": momentum,\n",
|
||
" \"vol_regime\": vol_regime,\n",
|
||
" \"fwd_ret_5d\": round(fwd_ret_5d * 100, 2),\n",
|
||
" \"abs_fwd_ret_5d\": round(abs_fwd_ret_5d * 100, 2), # For extreme move\n",
|
||
" \"target\": None, # Will be computed after all rows\n",
|
||
" }\n",
|
||
" )\n",
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||
"\n",
|
||
" result_df = pd.DataFrame(records)\n",
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||
"\n",
|
||
" # Sort by date for proper temporal split (critical for financial data)\n",
|
||
" result_df = result_df.sort_values(\"timestamp\").reset_index(drop=True)\n",
|
||
"\n",
|
||
" # Compute extreme move target: |fwd_ret| > 90th percentile\n",
|
||
" # This exploits volatility clustering which has real predictive signal\n",
|
||
" threshold = result_df[\"abs_fwd_ret_5d\"].quantile(0.90)\n",
|
||
" result_df[\"target\"] = (result_df[\"abs_fwd_ret_5d\"] > threshold).astype(int)\n",
|
||
"\n",
|
||
" # Truncate if too many (preserve temporal order)\n",
|
||
" if len(result_df) > n_samples:\n",
|
||
" result_df = result_df.head(n_samples)\n",
|
||
"\n",
|
||
" return result_df.reset_index(drop=True)"
|
||
]
|
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},
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"source": [
|
||
"### Load and Inspect\n",
|
||
"\n",
|
||
"Load the ETF feature table and verify the mix of numerical and categorical columns."
|
||
]
|
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},
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{
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||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Loading real ETF data...\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Loaded 2000 samples with 13 features\n",
|
||
"\n",
|
||
"Feature types:\n",
|
||
"timestamp object\n",
|
||
"symbol object\n",
|
||
"ret_1d float64\n",
|
||
"ret_5d float64\n",
|
||
"ret_20d float64\n",
|
||
"volatility float64\n",
|
||
"volume_ratio float64\n",
|
||
"direction object\n",
|
||
"momentum object\n",
|
||
"vol_regime object\n",
|
||
"fwd_ret_5d float64\n",
|
||
"abs_fwd_ret_5d float64\n",
|
||
"target int64\n",
|
||
"dtype: object\n",
|
||
"\n",
|
||
"Sample rows:\n",
|
||
" timestamp symbol ret_1d ret_5d ret_20d volatility volume_ratio \\\n",
|
||
"0 2015-02-02 DBC 1.38 2.38 -3.24 1.16 1.13 \n",
|
||
"1 2015-02-02 IVW 0.91 -2.00 -0.67 1.07 1.38 \n",
|
||
"2 2015-02-02 IEF -0.13 1.30 3.64 0.48 6.85 \n",
|
||
"3 2015-02-02 DIA 1.06 -1.83 -2.54 1.08 1.19 \n",
|
||
"4 2015-02-02 XLV 0.56 -2.31 1.50 1.08 1.51 \n",
|
||
"\n",
|
||
" direction momentum vol_regime fwd_ret_5d abs_fwd_ret_5d target \n",
|
||
"0 up weak normal 3.17 3.17 0 \n",
|
||
"1 up flat normal 0.82 0.82 0 \n",
|
||
"2 down weak low -2.20 2.20 0 \n",
|
||
"3 up weak normal 2.20 2.20 0 \n",
|
||
"4 up flat normal -1.00 1.00 0 \n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(\"Loading real ETF data...\")\n",
|
||
"df = load_etf_tabular_data(CONFIG[\"symbols\"], CONFIG[\"start_date\"], CONFIG[\"n_samples\"])\n",
|
||
"print(f\"Loaded {len(df)} samples with {len(df.columns)} features\")\n",
|
||
"print(f\"\\nFeature types:\\n{df.dtypes}\")\n",
|
||
"print(\"\\nSample rows:\")\n",
|
||
"print(df.head())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d8f5b040",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.001462,
|
||
"end_time": "2026-06-13T02:39:15.244762+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:39:15.243300+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 2. GReaT: LLM-Based Generation\n",
|
||
"\n",
|
||
"Using the actual `be-great` library with distilgpt2 for fast training."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "db9cea9e",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:39:15.248169Z",
|
||
"iopub.status.busy": "2026-06-13T02:39:15.248086Z",
|
||
"iopub.status.idle": "2026-06-13T02:39:20.627858Z",
|
||
"shell.execute_reply": "2026-06-13T02:39:20.627326Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 5.382155,
|
||
"end_time": "2026-06-13T02:39:20.628317+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:39:15.246162+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Loading GReaT model from checkpoint: 05_synthetic_data/output/great/checkpoints/great_model\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "c1c5b891d5b1425ab76187ba8362e6a4",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"tokenizer_config.json: 0%| | 0.00/26.0 [00:00<?, ?B/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "8344f1ba88f94f97bdd1799c1147a1e5",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"config.json: 0%| | 0.00/762 [00:00<?, ?B/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "a9d28a457d88488b8f21c64b5d3911f7",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"vocab.json: 0.00B [00:00, ?B/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "bb7e38233cf34fcab7c4c6b1cfaec11b",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"merges.txt: 0.00B [00:00, ?B/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "4585ac0d5dfe4e30a5b97d65fa618dc1",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"tokenizer.json: 0.00B [00:00, ?B/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "75529e18e62a4b06af5dacdba4e0f229",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"model.safetensors: 0%| | 0.00/353M [00:00<?, ?B/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "f23938697f4d4f82ae9c784e79ab3ff4",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"generation_config.json: 0%| | 0.00/124 [00:00<?, ?B/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Checkpoint loaded successfully!\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Prepare training data (drop symbol and date to avoid memorization)\n",
|
||
"train_df = df.drop(columns=[\"symbol\", \"timestamp\"])\n",
|
||
"\n",
|
||
"# Check for existing checkpoint\n",
|
||
"checkpoint_exists = CHECKPOINT_DIR.exists() and (CHECKPOINT_DIR / \"config.json\").exists()\n",
|
||
"\n",
|
||
"if checkpoint_exists and not RETRAIN:\n",
|
||
" print(f\"\\nLoading GReaT model from checkpoint: {CHECKPOINT_DIR}\")\n",
|
||
" great = GReaT.load_from_dir(str(CHECKPOINT_DIR))\n",
|
||
" print(\"Checkpoint loaded successfully!\")\n",
|
||
"else:\n",
|
||
" if RETRAIN and checkpoint_exists:\n",
|
||
" print(\"\\nRETRAIN=True, retraining despite existing checkpoint...\")\n",
|
||
" else:\n",
|
||
" print(\"\\nNo checkpoint found, training from scratch...\")\n",
|
||
"\n",
|
||
" print(\"Initializing GReaT with distilgpt2...\")\n",
|
||
"\n",
|
||
" # Use distilgpt2 (82M parameters) — smaller LLM keeps fine-tuning tractable\n",
|
||
" # on CPU/single-GPU. Larger backbones (GPT-2 medium/large, LLaMA) trade\n",
|
||
" # compute for fidelity but do not change the GReaT serialization pipeline.\n",
|
||
" great = GReaT(\n",
|
||
" llm=\"distilgpt2\",\n",
|
||
" batch_size=CONFIG[\"batch_size\"],\n",
|
||
" epochs=CONFIG[\"epochs\"],\n",
|
||
" experiment_dir=str(get_output_dir(5, \"great\") / \"trainer_great\"),\n",
|
||
" save_steps=5000, # Don't save intermediate checkpoints\n",
|
||
" logging_steps=100,\n",
|
||
" )\n",
|
||
"\n",
|
||
" print(f\"Training on {len(train_df)} samples...\")\n",
|
||
" print(f\"Epochs: {CONFIG['epochs']}, Batch size: {CONFIG['batch_size']}\")\n",
|
||
"\n",
|
||
" # Train the model\n",
|
||
" great.fit(train_df)\n",
|
||
"\n",
|
||
" # Save checkpoint\n",
|
||
" CHECKPOINT_DIR.mkdir(parents=True, exist_ok=True)\n",
|
||
" great.save(str(CHECKPOINT_DIR))\n",
|
||
" print(f\"\\nCheckpoint saved to: {CHECKPOINT_DIR}\")\n",
|
||
"\n",
|
||
" print(\"\\nTraining complete!\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "33617cc9",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.001669,
|
||
"end_time": "2026-06-13T02:39:20.631862+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:39:20.630193+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 3. Generate Synthetic Data"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "b975b6ea",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:39:20.635889Z",
|
||
"iopub.status.busy": "2026-06-13T02:39:20.635786Z",
|
||
"iopub.status.idle": "2026-06-13T02:44:32.458660Z",
|
||
"shell.execute_reply": "2026-06-13T02:44:32.458177Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 311.82557,
|
||
"end_time": "2026-06-13T02:44:32.459047+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:39:20.633477+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Generating 500 synthetic samples...\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"\n",
|
||
" <div style=\"margin: 4px 0; width: 100%;\">\n",
|
||
" <div style=\"\n",
|
||
" position: relative;\n",
|
||
" height: 26px;\n",
|
||
" background: rgb(40,40,48);\n",
|
||
" border-radius: 5px;\n",
|
||
" overflow: hidden;\n",
|
||
" border: 1px solid #00cc66;\n",
|
||
" box-shadow: inset 0 1px 3px rgba(0,0,0,0.3);\n",
|
||
" \">\n",
|
||
" <div style=\"\n",
|
||
" position: absolute;\n",
|
||
" top: 0; left: 0; bottom: 0;\n",
|
||
" width: 100.0%;\n",
|
||
" \n",
|
||
" background: linear-gradient(\n",
|
||
" to right,\n",
|
||
" rgba(0,255,100,0.25),\n",
|
||
" rgba(0,255,100,0.25)\n",
|
||
" ), linear-gradient(to right, rgb(30,144,255) 0%, rgb(0,255,200) 100%);\n",
|
||
" \n",
|
||
" border-radius: 4px 0 0 4px;\n",
|
||
" transition: width 0.15s ease-out;\n",
|
||
" \"></div>\n",
|
||
" <div style=\"\n",
|
||
" position: relative;\n",
|
||
" padding: 4px 8px;\n",
|
||
" color: white;\n",
|
||
" font-family: 'SF Mono', Monaco, 'Cascadia Code', monospace;\n",
|
||
" font-size: 12px;\n",
|
||
" white-space: nowrap;\n",
|
||
" text-shadow: 0 1px 2px rgba(0,0,0,0.6);\n",
|
||
" line-height: 18px;\n",
|
||
" \"><b>done</b> 100.0% 500/500 [5:11<0s] 1.6it/s</div>\n",
|
||
" </div>\n",
|
||
" \n",
|
||
" <div style=\"\n",
|
||
" font-family: 'SF Mono', Monaco, 'Cascadia Code', monospace;\n",
|
||
" font-size: 11px;\n",
|
||
" color: #888;\n",
|
||
" padding: 2px 0 0 4px;\n",
|
||
" \">Done in 5:11 | avg 1.6it/s</div>\n",
|
||
" \n",
|
||
" </div>\n",
|
||
" "
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.HTML object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Generated 500 samples\n",
|
||
"\n",
|
||
"Sample synthetic rows:\n",
|
||
" ret_1d ret_5d ret_20d volatility volume_ratio direction momentum \\\n",
|
||
"0 -0.47 3.32 -4.41 1.43 0.68 down weak \n",
|
||
"1 -0.41 -0.87 -0.18 0.89 0.47 down flat \n",
|
||
"2 -0.02 -0.16 -0.31 0.15 0.73 down flat \n",
|
||
"3 -0.12 -0.24 -0.77 0.11 0.83 down flat \n",
|
||
"4 -0.37 -3.28 -1.04 0.69 0.53 down flat \n",
|
||
"\n",
|
||
" vol_regime fwd_ret_5d abs_fwd_ret_5d target \n",
|
||
"0 normal 0.73 0.73 0 \n",
|
||
"1 low -0.41 0.41 0 \n",
|
||
"2 low -2.08 2.08 0 \n",
|
||
"3 low 0.87 0.87 0 \n",
|
||
"4 low -1.41 1.41 0 \n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(f\"\\nGenerating {CONFIG['n_generate']} synthetic samples...\")\n",
|
||
"\n",
|
||
"# Generate synthetic data — guided sampling enforces the column schema row by\n",
|
||
"# row and is essential when fine-tuning is short (the unguided sampler tends\n",
|
||
"# to drop columns on undertrained models). With well-trained checkpoints,\n",
|
||
"# guided_sampling=False is faster and produces equivalent quality.\n",
|
||
"synthetic_df = great.sample(\n",
|
||
" n_samples=CONFIG[\"n_generate\"],\n",
|
||
" max_length=500,\n",
|
||
" guided_sampling=True,\n",
|
||
")\n",
|
||
"\n",
|
||
"print(f\"Generated {len(synthetic_df)} samples\")\n",
|
||
"print(\"\\nSample synthetic rows:\")\n",
|
||
"print(synthetic_df.head())\n",
|
||
"\n",
|
||
"# Check for parsing errors (NaN values)\n",
|
||
"nan_counts = synthetic_df.isna().sum()\n",
|
||
"if nan_counts.sum() > 0:\n",
|
||
" print(f\"\\nParsing issues (NaN counts):\\n{nan_counts[nan_counts > 0]}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "ad42700b",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.001763,
|
||
"end_time": "2026-06-13T02:44:32.462745+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:44:32.460982+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"**Observation**: The generated rows should contain plausible feature values -- returns\n",
|
||
"near zero with occasional larger moves, volatility in realistic ranges, and valid\n",
|
||
"categorical labels. NaN counts above zero indicate parsing failures where the LLM\n",
|
||
"produced text that could not be mapped back to the original schema. This is a known\n",
|
||
"limitation of autoregressive generation: the model can \"hallucinate\" tokens that\n",
|
||
"break column parsing, especially with short fine-tuning. Increasing epochs and using\n",
|
||
"larger base models (GPT-2 medium/large) reduces parsing errors significantly."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "30db1552",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.001689,
|
||
"end_time": "2026-06-13T02:44:32.466199+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:44:32.464510+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 4. Fidelity: Visual Comparison with PCA and t-SNE\n",
|
||
"\n",
|
||
"We project both real and synthetic data into 2D using only numerical features\n",
|
||
"to assess whether the generator covers the same regions of the data manifold."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "df84e582",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:44:32.470425Z",
|
||
"iopub.status.busy": "2026-06-13T02:44:32.470292Z",
|
||
"iopub.status.idle": "2026-06-13T02:44:33.467983Z",
|
||
"shell.execute_reply": "2026-06-13T02:44:33.467617Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 1.000524,
|
||
"end_time": "2026-06-13T02:44:33.468442+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:44:32.467918+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"WARNING:matplotlib.font_manager:findfont: Failed to find font weight semibold, now using 700.\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"utils/style.py:764: UserWarning: The figure layout has changed to tight\n",
|
||
" plt.tight_layout()\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1200x500 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Extract numerical columns for visualization\n",
|
||
"numerical_cols = [\"ret_1d\", \"ret_5d\", \"ret_20d\", \"volatility\", \"volume_ratio\", \"fwd_ret_5d\"]\n",
|
||
"available_cols = [c for c in numerical_cols if c in df.columns and c in synthetic_df.columns]\n",
|
||
"\n",
|
||
"# Convert to numpy arrays (handling potential NaN from LLM parsing errors)\n",
|
||
"real_data = df[available_cols].dropna().values\n",
|
||
"synth_data = synthetic_df[available_cols].apply(pd.to_numeric, errors=\"coerce\").dropna().values\n",
|
||
"\n",
|
||
"if len(synth_data) >= 50: # Need enough samples for meaningful visualization\n",
|
||
" fig = plot_fidelity_comparison(\n",
|
||
" real_data,\n",
|
||
" synth_data,\n",
|
||
" title=\"GReaT: Real vs Synthetic Distribution\",\n",
|
||
" n_samples=min(500, len(synth_data)),\n",
|
||
" )\n",
|
||
" plt.show()\n",
|
||
"else:\n",
|
||
" print(f\"Insufficient valid synthetic samples ({len(synth_data)}) for fidelity visualization\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c9295512",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.002267,
|
||
"end_time": "2026-06-13T02:44:33.473364+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:44:33.471097+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"**Interpretation**: Overlapping point clouds confirm that GReaT-generated tabular\n",
|
||
"data occupies similar regions of feature space as real data. LLM-based generation\n",
|
||
"can capture complex feature dependencies via autoregressive modeling. Gaps may\n",
|
||
"indicate parsing errors or LLM hallucination on certain feature combinations."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f1f2789f",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.002147,
|
||
"end_time": "2026-06-13T02:44:33.477715+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:44:33.475568+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 5. Compare Real vs Synthetic Distributions"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "372f1571",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:44:33.482712Z",
|
||
"iopub.status.busy": "2026-06-13T02:44:33.482628Z",
|
||
"iopub.status.idle": "2026-06-13T02:44:33.493197Z",
|
||
"shell.execute_reply": "2026-06-13T02:44:33.492881Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.01369,
|
||
"end_time": "2026-06-13T02:44:33.493529+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:44:33.479839+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>real_mean</th>\n",
|
||
" <th>synth_mean</th>\n",
|
||
" <th>real_std</th>\n",
|
||
" <th>synth_std</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>feature</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>ret_1d</th>\n",
|
||
" <td>0.172</td>\n",
|
||
" <td>-0.318</td>\n",
|
||
" <td>1.020</td>\n",
|
||
" <td>0.457</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>ret_5d</th>\n",
|
||
" <td>0.650</td>\n",
|
||
" <td>-0.726</td>\n",
|
||
" <td>1.994</td>\n",
|
||
" <td>1.032</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>ret_20d</th>\n",
|
||
" <td>2.311</td>\n",
|
||
" <td>-0.730</td>\n",
|
||
" <td>3.725</td>\n",
|
||
" <td>2.626</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>volatility</th>\n",
|
||
" <td>1.077</td>\n",
|
||
" <td>0.678</td>\n",
|
||
" <td>0.597</td>\n",
|
||
" <td>0.452</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>volume_ratio</th>\n",
|
||
" <td>0.905</td>\n",
|
||
" <td>0.804</td>\n",
|
||
" <td>1.204</td>\n",
|
||
" <td>0.390</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>fwd_ret_5d</th>\n",
|
||
" <td>0.131</td>\n",
|
||
" <td>-0.751</td>\n",
|
||
" <td>1.952</td>\n",
|
||
" <td>1.490</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" real_mean synth_mean real_std synth_std\n",
|
||
"feature \n",
|
||
"ret_1d 0.172 -0.318 1.020 0.457\n",
|
||
"ret_5d 0.650 -0.726 1.994 1.032\n",
|
||
"ret_20d 2.311 -0.730 3.725 2.626\n",
|
||
"volatility 1.077 0.678 0.597 0.452\n",
|
||
"volume_ratio 0.905 0.804 1.204 0.390\n",
|
||
"fwd_ret_5d 0.131 -0.751 1.952 1.490"
|
||
]
|
||
},
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"compare_cols = [c for c in df.columns if c != \"asset\" and c in synthetic_df.columns]\n",
|
||
"numerical_cols = [\"ret_1d\", \"ret_5d\", \"ret_20d\", \"volatility\", \"volume_ratio\", \"fwd_ret_5d\"]\n",
|
||
"categorical_cols = [\"direction\", \"momentum\", \"vol_regime\"]\n",
|
||
"\n",
|
||
"numerical_rows = []\n",
|
||
"for col in numerical_cols:\n",
|
||
" if col in synthetic_df.columns:\n",
|
||
" real_vals = df[col].dropna()\n",
|
||
" synth_vals = pd.to_numeric(synthetic_df[col], errors=\"coerce\").dropna()\n",
|
||
" if len(synth_vals) > 0:\n",
|
||
" numerical_rows.append(\n",
|
||
" {\n",
|
||
" \"feature\": col,\n",
|
||
" \"real_mean\": real_vals.mean(),\n",
|
||
" \"synth_mean\": synth_vals.mean(),\n",
|
||
" \"real_std\": real_vals.std(),\n",
|
||
" \"synth_std\": synth_vals.std(),\n",
|
||
" }\n",
|
||
" )\n",
|
||
"numerical_comparison = pd.DataFrame(numerical_rows).set_index(\"feature\").round(3)\n",
|
||
"numerical_comparison"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "74e4c082",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:44:33.498773Z",
|
||
"iopub.status.busy": "2026-06-13T02:44:33.498670Z",
|
||
"iopub.status.idle": "2026-06-13T02:44:33.505258Z",
|
||
"shell.execute_reply": "2026-06-13T02:44:33.504953Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.00975,
|
||
"end_time": "2026-06-13T02:44:33.505656+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:44:33.495906+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>feature</th>\n",
|
||
" <th>category</th>\n",
|
||
" <th>real_pct</th>\n",
|
||
" <th>synth_pct</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>direction</td>\n",
|
||
" <td>up</td>\n",
|
||
" <td>54.9</td>\n",
|
||
" <td>5.2</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>direction</td>\n",
|
||
" <td>down</td>\n",
|
||
" <td>45.1</td>\n",
|
||
" <td>94.8</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>momentum</td>\n",
|
||
" <td>weak</td>\n",
|
||
" <td>36.4</td>\n",
|
||
" <td>21.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>momentum</td>\n",
|
||
" <td>flat</td>\n",
|
||
" <td>36.0</td>\n",
|
||
" <td>69.2</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>momentum</td>\n",
|
||
" <td>strong</td>\n",
|
||
" <td>27.6</td>\n",
|
||
" <td>9.8</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>vol_regime</td>\n",
|
||
" <td>low</td>\n",
|
||
" <td>48.4</td>\n",
|
||
" <td>69.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>vol_regime</td>\n",
|
||
" <td>normal</td>\n",
|
||
" <td>44.8</td>\n",
|
||
" <td>30.2</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>vol_regime</td>\n",
|
||
" <td>high</td>\n",
|
||
" <td>6.7</td>\n",
|
||
" <td>0.8</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" feature category real_pct synth_pct\n",
|
||
"0 direction up 54.9 5.2\n",
|
||
"1 direction down 45.1 94.8\n",
|
||
"2 momentum weak 36.4 21.0\n",
|
||
"3 momentum flat 36.0 69.2\n",
|
||
"4 momentum strong 27.6 9.8\n",
|
||
"5 vol_regime low 48.4 69.0\n",
|
||
"6 vol_regime normal 44.8 30.2\n",
|
||
"7 vol_regime high 6.7 0.8"
|
||
]
|
||
},
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"categorical_rows = []\n",
|
||
"for col in categorical_cols:\n",
|
||
" if col in synthetic_df.columns:\n",
|
||
" real_dist = df[col].value_counts(normalize=True)\n",
|
||
" synth_dist = synthetic_df[col].value_counts(normalize=True)\n",
|
||
" for cat in real_dist.index:\n",
|
||
" categorical_rows.append(\n",
|
||
" {\n",
|
||
" \"feature\": col,\n",
|
||
" \"category\": cat,\n",
|
||
" \"real_pct\": real_dist.get(cat, 0) * 100,\n",
|
||
" \"synth_pct\": synth_dist.get(cat, 0) * 100,\n",
|
||
" }\n",
|
||
" )\n",
|
||
"categorical_comparison = pd.DataFrame(categorical_rows).round(1)\n",
|
||
"categorical_comparison"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "80e16102",
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"metadata": {
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"papermill": {
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"start_time": "2026-06-13T02:44:33.508055+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 5. Visualize Distributions"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "793607da",
|
||
"metadata": {
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"status": "completed"
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},
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{
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},
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"
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Numerical distributions\n",
|
||
"fig = make_subplots(\n",
|
||
" rows=2,\n",
|
||
" cols=3,\n",
|
||
" subplot_titles=[\n",
|
||
" \"1-Day Return\",\n",
|
||
" \"5-Day Return\",\n",
|
||
" \"20-Day Return\",\n",
|
||
" \"Volatility\",\n",
|
||
" \"Volume Ratio\",\n",
|
||
" \"Fwd 5D Return\",\n",
|
||
" ],\n",
|
||
")\n",
|
||
"\n",
|
||
"plot_cols = [\"ret_1d\", \"ret_5d\", \"ret_20d\", \"volatility\", \"volume_ratio\", \"fwd_ret_5d\"]\n",
|
||
"positions = [(1, 1), (1, 2), (1, 3), (2, 1), (2, 2), (2, 3)]\n",
|
||
"\n",
|
||
"for idx, (col, (row, col_num)) in enumerate(zip(plot_cols, positions, strict=False)):\n",
|
||
" if col in synthetic_df.columns:\n",
|
||
" synth_vals = pd.to_numeric(synthetic_df[col], errors=\"coerce\").dropna()\n",
|
||
" showlegend = idx == 0 # only one legend entry per series\n",
|
||
" fig.add_trace(\n",
|
||
" go.Histogram(\n",
|
||
" x=df[col],\n",
|
||
" name=\"Real\",\n",
|
||
" opacity=0.6,\n",
|
||
" marker_color=COLORS[\"blue\"],\n",
|
||
" nbinsx=30,\n",
|
||
" showlegend=showlegend,\n",
|
||
" legendgroup=\"real\",\n",
|
||
" ),\n",
|
||
" row=row,\n",
|
||
" col=col_num,\n",
|
||
" )\n",
|
||
" fig.add_trace(\n",
|
||
" go.Histogram(\n",
|
||
" x=synth_vals,\n",
|
||
" name=\"Synthetic\",\n",
|
||
" opacity=0.6,\n",
|
||
" marker_color=COLORS[\"amber\"],\n",
|
||
" nbinsx=30,\n",
|
||
" showlegend=showlegend,\n",
|
||
" legendgroup=\"synthetic\",\n",
|
||
" ),\n",
|
||
" row=row,\n",
|
||
" col=col_num,\n",
|
||
" )\n",
|
||
"\n",
|
||
"fig.update_yaxes(title_text=\"Density (count)\")\n",
|
||
"fig.update_xaxes(title_text=\"Feature value\")\n",
|
||
"fig.update_layout(\n",
|
||
" title=\"Real vs GReaT Synthetic Distributions\",\n",
|
||
" height=500,\n",
|
||
" showlegend=True,\n",
|
||
" barmode=\"overlay\",\n",
|
||
" template=\"ml4t\",\n",
|
||
")\n",
|
||
"fig.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c1dac67a",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.003194,
|
||
"end_time": "2026-06-13T02:44:34.388760+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:44:34.385566+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 6. TSTR Evaluation: Train Synthetic, Test Real\n",
|
||
"\n",
|
||
"The key test: Can a model trained on GReaT synthetic data predict real outcomes?\n",
|
||
"\n",
|
||
"**Task**: Extreme move classification (|fwd_ret_5d| > 90th percentile)\n",
|
||
"- Exploits volatility clustering which has real predictive signal (~0.78 AUC)\n",
|
||
"- Unlike direction prediction (~0.50 AUC), this provides meaningful comparisons"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f201591b",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.003131,
|
||
"end_time": "2026-06-13T02:44:34.395116+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:44:34.391985+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Prepare Features and Temporal Split\n",
|
||
"\n",
|
||
"We use a temporal split (first 70% train, last 30% test) rather than random\n",
|
||
"splitting. This avoids data leakage from future observations contaminating\n",
|
||
"the training set -- a critical requirement for financial time series."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"id": "c457bd7e",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:44:34.402169Z",
|
||
"iopub.status.busy": "2026-06-13T02:44:34.402070Z",
|
||
"iopub.status.idle": "2026-06-13T02:44:34.407130Z",
|
||
"shell.execute_reply": "2026-06-13T02:44:34.406736Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.009061,
|
||
"end_time": "2026-06-13T02:44:34.407360+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:44:34.398299+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"======================================================================\n",
|
||
"TRAIN-SYNTHETIC-TEST-REAL (TSTR) EVALUATION\n",
|
||
"Task: Extreme move classification (|fwd_ret_5d| > 90th percentile)\n",
|
||
"======================================================================\n",
|
||
"\n",
|
||
"Real training samples: 1400\n",
|
||
"Synthetic training samples: 500\n",
|
||
"Test samples: 600\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(\"\\n\" + \"=\" * 70)\n",
|
||
"print(\"TRAIN-SYNTHETIC-TEST-REAL (TSTR) EVALUATION\")\n",
|
||
"print(\"Task: Extreme move classification (|fwd_ret_5d| > 90th percentile)\")\n",
|
||
"print(\"=\" * 70)\n",
|
||
"\n",
|
||
"# Prepare features\n",
|
||
"feature_cols = [\"ret_1d\", \"ret_5d\", \"ret_20d\", \"volatility\", \"volume_ratio\"]\n",
|
||
"target_col = \"target\"\n",
|
||
"\n",
|
||
"# Real data - use temporal split (not random) for financial data\n",
|
||
"# Sort by symbol to ensure temporal ordering within each group is preserved\n",
|
||
"# Then take first 70% for training, last 30% for testing\n",
|
||
"X_real = df[feature_cols].values\n",
|
||
"y_real = df[target_col].values\n",
|
||
"\n",
|
||
"# Temporal split: avoid mixing future and past observations in train/test\n",
|
||
"n_train = int(len(X_real) * 0.7)\n",
|
||
"X_train_real, X_test = X_real[:n_train], X_real[n_train:]\n",
|
||
"y_train_real, y_test = y_real[:n_train], y_real[n_train:]\n",
|
||
"\n",
|
||
"# Synthetic data - need to handle potential parsing issues\n",
|
||
"synth_features = synthetic_df[feature_cols].apply(pd.to_numeric, errors=\"coerce\")\n",
|
||
"synth_target = pd.to_numeric(synthetic_df[target_col], errors=\"coerce\")\n",
|
||
"\n",
|
||
"# Drop rows with NaN and convert target to binary\n",
|
||
"valid_mask = ~(synth_features.isna().any(axis=1) | synth_target.isna())\n",
|
||
"X_synth = synth_features[valid_mask].values\n",
|
||
"y_synth_raw = synth_target[valid_mask].values\n",
|
||
"# Convert to binary: round and clip to 0/1\n",
|
||
"y_synth = np.clip(np.round(y_synth_raw), 0, 1).astype(int)\n",
|
||
"\n",
|
||
"print(f\"\\nReal training samples: {len(X_train_real)}\")\n",
|
||
"print(f\"Synthetic training samples: {len(X_synth)}\")\n",
|
||
"print(f\"Test samples: {len(X_test)}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6e92088d",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.003206,
|
||
"end_time": "2026-06-13T02:44:34.413910+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:44:34.410704+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Train and Compare Models\n",
|
||
"\n",
|
||
"We train two identical gradient boosting classifiers -- one on real data (TRTR\n",
|
||
"baseline) and one on synthetic data (TSTR). The TSTR accuracy ratio measures\n",
|
||
"how much predictive utility the synthetic data preserves."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"id": "0a58f2c9",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:44:34.420832Z",
|
||
"iopub.status.busy": "2026-06-13T02:44:34.420750Z",
|
||
"iopub.status.idle": "2026-06-13T02:44:34.422949Z",
|
||
"shell.execute_reply": "2026-06-13T02:44:34.422592Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.006114,
|
||
"end_time": "2026-06-13T02:44:34.423198+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:44:34.417084+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Synthetic classes present: [0 1], both classes: True\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Check we have enough samples AND both classes in synthetic data\n",
|
||
"synth_classes = np.unique(y_synth)\n",
|
||
"has_both_classes = len(synth_classes) >= 2\n",
|
||
"print(f\"Synthetic classes present: {synth_classes}, both classes: {has_both_classes}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"id": "c223f176",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:44:34.430107Z",
|
||
"iopub.status.busy": "2026-06-13T02:44:34.430044Z",
|
||
"iopub.status.idle": "2026-06-13T02:44:34.542773Z",
|
||
"shell.execute_reply": "2026-06-13T02:44:34.542234Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.116741,
|
||
"end_time": "2026-06-13T02:44:34.543128+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:44:34.426387+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Metric TRTR (Real) TSTR (Synth) \n",
|
||
"--------------------------------------------------\n",
|
||
"Accuracy 0.955 0.875 \n",
|
||
"AUC-ROC 0.741 0.696 \n",
|
||
"\n",
|
||
"TSTR Ratio: 91.6%\n",
|
||
"GReaT synthetic data has MODERATE utility for model training.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"if len(X_synth) > 10 and has_both_classes:\n",
|
||
" # TRTR: Train Real, Test Real (baseline)\n",
|
||
" model_real = GradientBoostingClassifier(n_estimators=50, max_depth=3, random_state=42)\n",
|
||
" model_real.fit(X_train_real, y_train_real)\n",
|
||
" y_pred_trtr = model_real.predict(X_test)\n",
|
||
" y_prob_trtr = model_real.predict_proba(X_test)[:, 1]\n",
|
||
"\n",
|
||
" # TSTR: Train Synthetic, Test Real\n",
|
||
" model_synth = GradientBoostingClassifier(n_estimators=50, max_depth=3, random_state=42)\n",
|
||
" model_synth.fit(X_synth, y_synth)\n",
|
||
" y_pred_tstr = model_synth.predict(X_test)\n",
|
||
" y_prob_tstr = model_synth.predict_proba(X_test)[:, 1]\n",
|
||
"\n",
|
||
" # Metrics\n",
|
||
" print(f\"\\n{'Metric':<20} {'TRTR (Real)':<15} {'TSTR (Synth)':<15}\")\n",
|
||
" print(\"-\" * 50)\n",
|
||
"\n",
|
||
" acc_trtr = accuracy_score(y_test, y_pred_trtr)\n",
|
||
" acc_tstr = accuracy_score(y_test, y_pred_tstr)\n",
|
||
" print(f\"{'Accuracy':<20} {acc_trtr:<15.3f} {acc_tstr:<15.3f}\")\n",
|
||
"\n",
|
||
" try:\n",
|
||
" auc_trtr = roc_auc_score(y_test, y_prob_trtr)\n",
|
||
" auc_tstr = roc_auc_score(y_test, y_prob_tstr)\n",
|
||
" print(f\"{'AUC-ROC':<20} {auc_trtr:<15.3f} {auc_tstr:<15.3f}\")\n",
|
||
"\n",
|
||
" utility_ratio = acc_tstr / acc_trtr\n",
|
||
" print(f\"\\nTSTR Ratio: {utility_ratio:.1%}\")\n",
|
||
"\n",
|
||
" if utility_ratio > 0.95:\n",
|
||
" print(\"GReaT synthetic data has HIGH utility for model training.\")\n",
|
||
" elif utility_ratio > 0.85:\n",
|
||
" print(\"GReaT synthetic data has MODERATE utility for model training.\")\n",
|
||
" else:\n",
|
||
" print(\"GReaT synthetic data has LIMITED utility - may need more training.\")\n",
|
||
" except ValueError as e:\n",
|
||
" print(f\"AUC calculation error: {e}\")\n",
|
||
"else:\n",
|
||
" print(\"\\nInsufficient valid synthetic samples for TSTR evaluation.\")\n",
|
||
" if not has_both_classes:\n",
|
||
" print(f\"Synthetic data has only {len(synth_classes)} class(es): {synth_classes}\")\n",
|
||
" print(\"Need both classes (0 and 1) for classification - increase n_generate.\")\n",
|
||
" else:\n",
|
||
" print(\"This can happen with very short training - increase epochs.\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "1c8f10f6",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.003449,
|
||
"end_time": "2026-06-13T02:44:34.550185+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:44:34.546736+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 7. Statistical Tests"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"id": "f05b5863",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:44:34.557998Z",
|
||
"iopub.status.busy": "2026-06-13T02:44:34.557875Z",
|
||
"iopub.status.idle": "2026-06-13T02:44:34.579780Z",
|
||
"shell.execute_reply": "2026-06-13T02:44:34.579344Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.026665,
|
||
"end_time": "2026-06-13T02:44:34.580257+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:44:34.553592+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"======================================================================\n",
|
||
"STATISTICAL FIDELITY TESTS\n",
|
||
"======================================================================\n",
|
||
"\n",
|
||
"ret_1d:\n",
|
||
" KS statistic: 0.5075 (p-value: 0.0000)\n",
|
||
" Mean difference: 0.4896\n",
|
||
"\n",
|
||
"ret_5d:\n",
|
||
" KS statistic: 0.5925 (p-value: 0.0000)\n",
|
||
" Mean difference: 1.3761\n",
|
||
"\n",
|
||
"ret_20d:\n",
|
||
" KS statistic: 0.5755 (p-value: 0.0000)\n",
|
||
" Mean difference: 3.0419\n",
|
||
"\n",
|
||
"volatility:\n",
|
||
" KS statistic: 0.3155 (p-value: 0.0000)\n",
|
||
" Mean difference: 0.3991\n",
|
||
"\n",
|
||
"volume_ratio:\n",
|
||
" KS statistic: 0.0975 (p-value: 0.0009)\n",
|
||
" Mean difference: 0.1001\n",
|
||
"\n",
|
||
"fwd_ret_5d:\n",
|
||
" KS statistic: 0.3910 (p-value: 0.0000)\n",
|
||
" Mean difference: 0.8825\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(\"\\n\" + \"=\" * 70)\n",
|
||
"print(\"STATISTICAL FIDELITY TESTS\")\n",
|
||
"print(\"=\" * 70)\n",
|
||
"\n",
|
||
"for col in numerical_cols:\n",
|
||
" if col in synthetic_df.columns:\n",
|
||
" real_vals = df[col].dropna().values\n",
|
||
" synth_vals = pd.to_numeric(synthetic_df[col], errors=\"coerce\").dropna().values\n",
|
||
"\n",
|
||
" if len(synth_vals) > 10:\n",
|
||
" # KS test\n",
|
||
" ks_stat, ks_pval = stats.ks_2samp(real_vals, synth_vals)\n",
|
||
" print(f\"\\n{col}:\")\n",
|
||
" print(f\" KS statistic: {ks_stat:.4f} (p-value: {ks_pval:.4f})\")\n",
|
||
"\n",
|
||
" # Mean difference\n",
|
||
" mean_diff = abs(real_vals.mean() - synth_vals.mean())\n",
|
||
" print(f\" Mean difference: {mean_diff:.4f}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d723f450",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.003326,
|
||
"end_time": "2026-06-13T02:44:34.587151+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:44:34.583825+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"**Interpretation**: The KS test measures the maximum distance between the real\n",
|
||
"and synthetic cumulative distributions. Return features show high KS values\n",
|
||
"(`ret_1d` 0.50, `ret_5d` 0.59, `ret_20d` 0.58), indicating that the LLM does\n",
|
||
"not match the continuous return distributions well — synthetic returns are\n",
|
||
"compressed toward zero with lower variance. Volatility (KS 0.32) and volume\n",
|
||
"ratio (KS 0.10) are matched more closely. The categorical distributions also\n",
|
||
"diverge: synthetic labels 94.8% of rows \"down\" while only 45.1% of real rows\n",
|
||
"are \"down\" (real is 54.9% up / 45.1% down — synthetic inverts the balance),\n",
|
||
"and under-generates \"strong\" momentum (9.8% vs 27.6%). The TSTR accuracy\n",
|
||
"ratio (91.6%) and AUC drop (0.741 → 0.696) show that the downstream classifier\n",
|
||
"trained on synthetic data is close to but not at parity with the real-trained\n",
|
||
"baseline; the marginal-distribution failures above are the larger gap."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "dd185eb9",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.003285,
|
||
"end_time": "2026-06-13T02:44:34.594326+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:44:34.591041+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## Key Takeaways\n",
|
||
"\n",
|
||
"1. **Serialization is the key insight**: GReaT converts table rows to natural\n",
|
||
" language sentences, letting a pre-trained LLM learn the joint distribution\n",
|
||
" of mixed-type features without explicit distributional assumptions.\n",
|
||
"2. **Mixed-type handling is GReaT's comparative advantage**: Unlike GANs that\n",
|
||
" require separate encoders for categorical columns, the LLM serialization\n",
|
||
" approach treats numericals and categoricals uniformly as text tokens.\n",
|
||
"3. **TSTR utility depends on training budget**: this notebook fine-tunes\n",
|
||
" distilgpt2 for 50 epochs on 1,400 ETF rows; the resulting TSTR accuracy\n",
|
||
" ratio is reported in the evaluation cell above. Borisov et al. (2023)\n",
|
||
" report higher TSTR ratios with larger backbones and longer fine-tuning;\n",
|
||
" this notebook does not sweep epoch count or model size.\n",
|
||
"4. **Parsing failures are the main failure mode**: The autoregressive generator\n",
|
||
" can produce tokens that break column parsing, especially with short\n",
|
||
" fine-tuning. This is visible as NaN values in the generated output.\n",
|
||
"5. **Marginal fidelity is mixed**: The LLM captures scale features (volatility,\n",
|
||
" volume) better than return distributions (KS 0.5+). TSTR evaluation is needed to\n",
|
||
" verify that inter-feature dependencies transfer to downstream tasks.\n",
|
||
"\n",
|
||
"| Generator | Strength | Weakness |\n",
|
||
"|-----------|----------|----------|\n",
|
||
"| GReaT (LLM) | Mixed types, no assumptions | Slow, expensive |\n",
|
||
"| TimeGAN | Temporal dynamics | Continuous only |\n",
|
||
"| Tail-GAN | Tail risk focus | Complex setup |\n",
|
||
"| Copula | Fast, simple | Distribution assumptions |\n",
|
||
"\n",
|
||
"**Next**: See [`07_dp_gan`](07_dp_gan.ipynb) for adding differential privacy guarantees to\n",
|
||
"synthetic generation -- critical when training data contains sensitive records.\n",
|
||
"\n",
|
||
"**Book**: Section 5.6 discusses the serialization insight in depth, including\n",
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