sagan-trade 1.0.1

Strategic High-Throughput Predictive Trading Engine with iterative R2 fitting and SaganLLM discovery.

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 and cutting-edge limit order book simulations.

As of v0.9.6+, the library natively incorporates mathematical discoveries autonomously generated by the Autonomous Intelligence Network (AIN), including Hawkes Trade Arrivals and Bates Jump-Diffusion dynamics.

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🚀 Quick Start & Installation

pip install sagan-trade

Verify the installation:

import sagan_trade
print(sagan_trade.__version__)

[!IMPORTANT] AIN High-Fidelity Standard: As of v1.0.1, the Autonomous Intelligence Network (AIN) has autonomously enforced rigorous Google Style docstrings and exhaustive static typing across all modules (e.g. BacktestEngine, AsymmetricRiskEngine).

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🔬 Core Architecture & API Reference

1. Symbolic Regressor (SymbolicRegressor)

Instead of opaque weight matrices, Sagan discovers market invariants in the form of mathematical expressions. It fits variables to $R^2 > 0.95$ using basis functions (Polynomial, Fourier, Momentum, LOB Volatility Pressure).

Key Capabilities:

• Completely transparent AI: Every trade is backed by a human-readable formula.
• Built-in technical indicator synthesis (e.g. RSI, Volatility).
• Directly predicts continuous alpha signals mapped to Next-Day Returns.

Usage:

import pandas as pd
from sagan_trade import SymbolicRegressor

# Initialize the regressor with desired basis families
regressor = SymbolicRegressor(basis_functions=['poly', 'fourier', 'momentum'])

# Train on a ticker (auto-downloads data if not provided)
model_id = regressor.train(target="AAPL", signals=["Close", "RSI", "Volume"])

# Predict alpha signals
alpha_signals, formula = regressor.predict()
print(f"Alpha Signals generated via: {formula}")

2. Market Microstructure Insights (simulate_price_range, analyze_portfolio)

Incorporates a Hawkes process MLE estimator combined with heterogeneous agent price expectations to simulate price ranges and generate automated buy/sell signals. This serves as a quantitative pricing engine for intraday and swing horizons.

Key Capabilities:

• Heterogeneous Agents: Simulates 1,000,000+ market participants with varying risk aversions and subjective expectations.
• Bootstrapping: Computes expected market-clearing prices using non-parametric distributions.

Usage:

from sagan_trade import analyze_portfolio, visualize_stock_insights

# Generate actionable signals (BUY/SELL/HOLD) for a basket of stocks
portfolio_df = analyze_portfolio(["AAPL", "MSFT", "GOOG"], quick_mode=True)
print(portfolio_df)

# Visualize price range distributions and risk-aversion interactions
fig = visualize_stock_insights("AAPL")

3. Asymmetric Convexity Risk Engine (AsymmetricRiskEngine)

Sagan utilizes a non-linear risk management framework inspired by high-frequency market makers. It overrides raw alpha signals when downside tail risk is detected.

Key Capabilities:

• 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.

Usage:

from sagan_trade import AsymmetricRiskEngine

risk_engine = AsymmetricRiskEngine(target_vol=0.15, max_drawdown_limit=0.075)

# Calculate defensive risk multipliers for position sizing
risk_multiplier = risk_engine.get_risk_multiplier(prices_series)

4. Volatility Regime Filter (VolatilityRegimeFilter)

Autonomously discovered through the AIN, this macroeconomic sidecar acts as a VRP (Variance Risk Premium) proxy, shifting portfolios to cash during contagion regimes.

Usage:

from sagan_trade import VolatilityRegimeFilter

vol_filter = VolatilityRegimeFilter(vol_window=20, ma_window=120)

# Generates 1.0 (Risk-On) or 0.0 (Risk-Off / Cash) filters
regime_signals = vol_filter.generate_signals(prices_series)

5. High-Fidelity Backtest Engine (BacktestEngine)

Legacy backtesters assume you get filled at the mid-price without market impact. The Sagan BacktestEngine enforces exact transaction fee accounting, portfolio turnover logic, and dynamically allocates positions from alpha signal overlays and risk models.

Usage:

from sagan_trade import BacktestEngine

# Initialize with exact exchange and statutory fees
backtester = BacktestEngine(
    initial_capital=1000000,
    maker_fee=0.0001,
    taker_fee=0.0003
)

# Run full portfolio backtest
results = backtester.run(
    prices=data['Close'],
    alpha_signals=alpha_signals,
    regime_filter=regime_signals,
    risk_model=risk_engine
)

print(f"Sharpe Ratio: {results.sharpe_ratio}")
print(f"Max Drawdown: {results.max_drawdown}%")
print(f"Total Return: {results.total_return}%")

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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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🤝 Contribution & Links

• Repository: https://github.com/That-Tech-Geek/sagan-trade
• PyPI: https://pypi.org/project/sagan-trade/
• Author: Sambit Mishra

License

MIT © 2024 Sagan Labs / Sambit Mishra