*What makes it different:*

Most trading libraries are either: - Too slow (Backtrader: ~5ms/point, Zipline: ~10ms/point) - Missing advanced math (no quantum finance, chaos theory, optimal execution models) - No AutoML pipeline (QuantConnect has basic ML but no AutoML) - No GPU acceleration

Qantify addresses all of these.

*Performance:* - 0.008ms per data point (50-100x faster than alternatives) - GPU acceleration: 2-3x speedup on compatible hardware - 1767+ comprehensive tests, 100% code coverage - 106K+ lines of code

*Advanced Math Models* (what hedge funds use):on # GARCH volatility modeling from qantify.math.econometrics import GARCHModel garch = GARCHModel(returns) vol_forecast = garch.forecast(horizon=10)

# Heston stochastic volatility from qantify.math.stochastic import HestonProcess, MonteCarloEngine heston = HestonProcess(v0=0.04, theta=0.04, kappa=2.0, sigma=0.3, rho=-0.7) mc = MonteCarloEngine(process=heston) paths = mc.simulate(n_paths=10000, n_steps=252)

# Optimal execution (Almgren-Chriss) from qantify.math.execution import AlmgrenChrissOptimalExecution execution = AlmgrenChrissOptimalExecution( initial_position=10000, time_horizon=timedelta(hours=4), risk_aversion=1e-6 ) optimal_schedule = execution.compute_schedule()

# Advanced cointegration (statistical arbitrage) from qantify.math.stat_arb import JohansenTest, KalmanHedgeRatioEstimator*Unique features* (not found in competitors): - Quantum finance models - Chaos theory applications - Complexity theory - Information theory - Game theory models

*ML/AutoML Pipeline* (end-to-end):thon from qantify.ml import AutoMLRunner, create_features

# Auto-generate 100+ features features = create_features( df, feature_sets=["trend", "momentum", "volatility", "statistical"], windows=(14, 55, 200) )

# Automated model selection with walk-forward validation runner = AutoMLRunner( search="bayesian", # or "grid", "random", "hyperopt" walk_forward=True, # Time-series aware validation test_size=0.25 )

results = runner.run(features, target) # Automatically tests: RF, GBM, XGBoost, LightGBM # Selects best model with proper time-series validation*ML Features:* - Reinforcement learning (DQN, Q-learning, Policy gradients) - Advanced neural networks (LSTM, GRU, Transformers, Attention) - Model drift monitoring - Feature selection (SHAP, mutual information) - Experiment tracking (MLflow, W&B integration)

*Quick example:* from qantify.data import get_candles from qantify.signals import rsi from qantify.backtest.vectorized import run_vectorized

df = get_candles("binance", "BTCUSDT", "1h", limit=1000) df['rsi'] = rsi(df['close'], period=14)

result = run_vectorized( prices=df["close"], long_signal=df["rsi"] < 30, short_signal=df["rsi"] > 70, allocation=0.5 )

print(f"Sharpe: {result.metrics.sharpe:.2f}")*Real benchmarks:* - Backtest speed: < 0.1s (5000 points) vs QuantConnect's ~5-10s - GPU speedup: 2-3x (tested on RTX 3070) - ML accuracy: Market regime 74.3%, Volatility prediction 68.7%

*Why I built it:* I needed advanced math models (GARCH, Heston, optimal execution) combined with modern ML pipelines, all in one library. Existing solutions either lacked the math depth or the ML integration.

*GitHub:* https://github.com/Alradyin/qantify *PyPI:* pip install qantify

I'd love feedback from the HN community. What features would you find most useful? What am I missing?

Built with Python 3.10+, async/await everywhere, modular architecture. Completely open-source and free for personal/research use.