sagan-trade 0.8.7

Algorithmic trading architecture designed by the Autonomous Intelligence Network (AIN).

Sagan Trade

High-fidelity symbolic mathematical engine and quantitative architecture for institutional alpha generation.

Sagan Trade replaces black-box neural networks with transparent, human-readable mathematical equations discovered via FunctionGemma. It combines the precision of Symbolic Regression with the robustness of Asymmetric Convexity risk management.

As of v0.8.4+, the library natively incorporates mathematical discoveries autonomously generated by the Autonomous Intelligence Network (AIN).

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🏛️ Institutional Benchmarking

Sagan Trade has been rigorously tested across 5 years of historical market regimes, accounting for institutional trading fees and liquidity constraints.

Long-Term Resilience (5-Year Rolling Audit)

Benchmark: 20-Ticker Diversified Portfolio (Tech, Finance, Energy, Consumer).

Metric	Gross of Fees	Net of Fees (5bps)	S&P 500 (B&H)
Annualized Return	33.27%	12.98%	14.50%
Sharpe Ratio	2.11	1.06	0.85
Max Drawdown	-6.91%	-7.30%	-23.90%
Total Cumulative	426.11%	102.46%	96.80%

[!IMPORTANT] Statistical Significance: The symbolic engine achieves a p-value of 0.0182, indicating that its outperformance against legacy TFT-PINN and LSTM models is statistically significant at the 98% confidence level.

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🔬 Core Architecture

1. Symbolic Discovery (FunctionGemma & TCN)

Instead of weight matrices, Sagan discovers market invariants in the form of mathematical expressions using an ultra-fast Temporal Convolutional Network (TCN).

• 30x Faster Inference: Completely replaced legacy LSTMs with dilated causal convolutions, breaking the sequential bottleneck and achieving $O(1)$ hardware-parallel sequences.
• Precision: Fits variables to $R^2 > 0.95$ using basis functions (Polynomial, Fourier).
• Explainability: Every trade is backed by a human-readable formula, e.g., (Close * 0.5) + log(Volume).

2. Asymmetric Convexity Engine

Sagan utilizes a non-linear risk management framework inspired by high-frequency market makers:

• Downside Convexity: Exponentially scales exposure based on momentum-volatility asymmetry.
• Adaptive Kelly Sizing: Drawdown-aware fractional Kelly scaling to ensure capital preservation.
• Asymptotic Shield: Quadratic drawdown protection creates a hard floor on portfolio risk.

3. AIN Volatility Regime Filter (New in 0.8.4+)

Autonomously discovered through the AIN's Grand Synthesis sprint, the Hawkes-Bates Volatility Regime Filter acts as a macroeconomic sidecar that shifts portfolios to cash during contagion regimes by dynamically analyzing the Volatility Risk Premium (VRP). It achieved a validated 1.09 Sharpe Ratio in isolated NIFTY 50 backtests.

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🚀 Quick Start

Installation

pip install sagan-trade

Alpha Generation, Risk Modeling, & Backtesting

This comprehensive quickstart demonstrates the full lifecycle: Symbolic Discovery, Volatility Filtering, Risk Management, and Backtest Execution.

import pandas as pd
from sagan_trade import (
    SymbolicRegressor, 
    AsymmetricRiskEngine, 
    VolatilityRegimeFilter,
    BacktestEngine
)

# 1. Fetch Market Data
data = pd.DataFrame({
    'Close': [...],
    'Volume': [...],
    'RSI': [...]
})

# 2. Symbolic Discovery (FunctionGemma & TCN)
regressor = SymbolicRegressor(basis_functions=['poly', 'fourier'])
model_id = regressor.train(target="AAPL", signals=["Close", "RSI", "Volume"])
predicted_signal, formula = regressor.predict()
print(f"Discovered Alpha: {formula}")

# 3. Macro Regime Filtering (Hawkes-Bates VRP Proxy)
vol_filter = VolatilityRegimeFilter(vol_window=20, ma_window=120)
regime_signals = vol_filter.generate_signals(data['Close'])
print(f"Current Market Regime (1=Risk-On, 0=Cash): {regime_signals.iloc[-1]}")

# 4. Initialize Asymmetric Convexity Risk Engine
risk_engine = AsymmetricRiskEngine(target_vol=0.15, max_drawdown_limit=0.075)

# 5. Execute End-to-End Backtest
backtester = BacktestEngine(
    initial_capital=1000000,
    maker_fee=0.0001,
    taker_fee=-0.0003
)

results = backtester.run(
    prices=data['Close'],
    alpha_signals=predicted_signal,
    regime_filter=regime_signals,
    risk_model=risk_engine
)

print(f"Backtest Sharpe: {results.sharpe_ratio}")
print(f"Backtest Max Drawdown: {results.max_drawdown}")

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🛠️ Components

Component	Responsibility
SymbolicRegressor	High-precision math fitting with iterative $R^2$ optimization.
AsymmetricRiskEngine	Rides upside volatility while aggressively cutting downside tail risk.
VolatilityRegimeFilter	Avoids structural drawdowns via variance targeting and VRP analysis.
BacktestEngine	Rigorous walk-forward evaluation with fee-modeling support.
SaganConfig	OS-level optimization for Turbo/Eco compute profiles.

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License

MIT © 2024 Sagan Labs / Sambit Mishra