synthetic-datagen

Self-contained synthetic typical_price timeseries generator with plugin architecture.

Trains generative models (VAE, GAN, VAE-GAN) on real EUR/USD typical_price data, then generates realistic but unpredictable synthetic timeseries — critical for DOIN verification.

Programmatic API (Plugin-First)

All plugins have clean programmatic APIs — the CLI is just a wrapper.

# Train
from sdg_plugins.trainer.vae_gan_trainer import VaeGanTrainer
trainer = VaeGanTrainer()
trainer.configure({"window_size": 144, "latent_dim": 16, "epochs": 400, ...})
trainer.train(train_data=["d1.csv", "d2.csv"], save_model="model.keras")

# Generate (e.g. from DOIN evaluator)
from sdg_plugins.generator.typical_price_generator import TypicalPriceGenerator
gen = TypicalPriceGenerator()
gen.load_model("model.keras")
df = gen.generate(seed=42, n_samples=5000)
# → DataFrame with DATE_TIME, typical_price columns

# Evaluate (THE metric — predictive utility from MDSc thesis phase 4)
from sdg_plugins.evaluator.predictive_evaluator import PredictiveEvaluator
ev = PredictiveEvaluator()
ev.configure({"window_size": 144, "eval_epochs": 50})
result = ev.evaluate(
    synthetic=synthetic_df,
    real_train=train_df,     # d4
    real_val=val_df,         # d5
    real_test=test_df,       # d6
)
# result["mae_delta_test"] < 0  → synthetic data HELPS prediction
# result["synthetic_helps_test"] = True/False

# Secondary distribution metrics
from sdg_plugins.evaluator.distribution_evaluator import DistributionEvaluator
dist_ev = DistributionEvaluator()
metrics = dist_ev.evaluate(synthetic=synthetic_df, real=real_df)

CLI Quick Start

# Install
pip install -e ".[dev]"

# Train a VAE-GAN on real data
sdg --mode train --trainer vae_gan_trainer \
    --train_data examples/data/d1.csv examples/data/d2.csv examples/data/d3.csv \
    --save_model examples/models/generator.keras \
    --epochs 400 --latent_dim 16

# Generate synthetic data
sdg --mode generate \
    --load_model examples/models/generator.keras \
    --n_samples 5000 --seed 42 \
    --output_file synthetic_typical_price.csv

# Evaluate: does synthetic data improve prediction? (thesis phase 4)
sdg --mode evaluate \
    --synthetic_data synthetic_typical_price.csv \
    --real_train examples/data/d4.csv \
    --real_val examples/data/d5.csv \
    --real_test examples/data/d6.csv \
    --metrics_file metrics.json

# Optional: use external predictor repo for evaluation
sdg --mode evaluate \
    --synthetic_data synthetic_typical_price.csv \
    --real_train examples/data/d4.csv \
    --real_val examples/data/d5.csv \
    --real_test examples/data/d6.csv \
    --predictor_dir /home/openclaw/predictor \
    --metrics_file metrics.json

# Secondary: distribution metrics only
sdg --mode evaluate --evaluator distribution_evaluator \
    --synthetic_data synthetic_typical_price.csv \
    --real_data examples/data/d4.csv \
    --metrics_file dist_metrics.json

# Optimize hyper-parameters via GA
sdg --mode optimize --trainer vae_gan_trainer \
    --train_data examples/data/d1.csv \
    --population_size 20 --n_generations 50

Architecture

synthetic-datagen/
├── app/
│   ├── main.py              # Entry point & CLI dispatch
│   ├── cli.py               # Argument parsing
│   ├── config.py            # Default configuration
│   ├── data_processor.py    # Data loading, returns, windowing
│   └── plugin_loader.py     # Plugin discovery (entry_points)
├── sdg_plugins/
│   ├── trainer/
│   │   ├── vae_trainer.py        # Pure VAE
│   │   ├── gan_trainer.py        # Pure GAN
│   │   └── vae_gan_trainer.py    # VAE-GAN (recommended)
│   ├── generator/
│   │   └── typical_price_generator.py
│   ├── evaluator/
│   │   └── distribution_evaluator.py
│   └── optimizer/
│       └── ga_optimizer.py
├── examples/
│   ├── data/                # Real typical_price datasets (d1–d6)
│   ├── models/              # Trained models
│   └── config/              # Example JSON configs
└── tests/

Operation Modes

Mode	Description
train	Train a generative model on real typical_price CSVs
generate	Generate synthetic data from a trained model + seed
evaluate	Predictive utility test: does synthetic data improve prediction?
optimize	GA search for optimal hyper-parameters

Output Format

Matches predictor's expected input exactly:

DATE_TIME,typical_price
2020-01-01 00:00:00,1.3007625
2020-01-01 04:00:00,1.2966883333333332

Evaluation Methodology (MDSc Thesis Phase 4)

The real test of synthetic data quality: does it improve prediction?

Step 1: Train predictor on real d4          → MAE on d5, d6 (baseline)
Step 2: Prepend synthetic data to d4        → train same predictor
Step 3: Measure MAE on same d5, d6          → (augmented)
Step 4: Compare: delta = augmented - baseline
        If delta < 0 → synthetic data HELPS → good generator
        If delta > 0 → synthetic data HURTS → bad generator

This is THE metric. Distribution similarity (KL, Wasserstein) is secondary.

Two evaluator backends:

• Built-in (default): lightweight LSTM predictor, fast, good for iteration
• External: runs Harvey's full predictor repo as subprocess, authoritative

Key Design Decisions

• Single feature: typical_price only — no OHLC, no indicators
• Self-contained: trains AND generates — no dependency on feature-extractor
• Returns-based: models log-returns (stationary), reconstructs prices
• Seed-deterministic: same model + same seed = identical output
• Plugin architecture: all components replaceable via entry_points
• 4h periodicity: trains on and outputs 4h interval data directly

Reference Parameters (from MDSc phase_4_2)

Parameter	Value
window_size	144 (24 days @ 4h)
batch_size	128
epochs	400
latent_dim	16
activation	tanh
kl_weight	1e-3
mmd_lambda	1e-2
use_returns	true

DOIN Integration

In DOIN evaluators, each gets a different seed derived from:

seed = hash(commitment + domain + evaluator_id + chain_tip_hash)

Same model + different seed = different but valid synthetic data → optimizer can't predict evaluation data.

Tests

pytest tests/ -v

License

MIT