sagan-trade 0.9.3

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 v0.8.4+, 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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🏛️ 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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🚀 Installation

pip install sagan-trade

For the latest source code, you can clone the repository from GitHub:

git clone https://github.com/That-Tech-Geek/sagan-trade.git
cd sagan-trade
pip install -e .

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📚 Comprehensive Documentation

1. Hawkes Limit Order Book (LOB) Simulator (simulator.py)

Accurate quantitative modeling requires realistic microstructure data. The HawkesLOBSimulator generates tick-by-tick order book updates using a continuous-time Bates Jump-Diffusion process paired with self-exciting Hawkes trade intensity.

• NIFTY50 & Multi-Asset Support: Comes pre-configured with exact market-maker profiles for major indices like NIFTY50, handling specific tick sizes, base depths, and tick volatility.
• Microstructural Realism: Simulates realistic bid, ask, bid_size, ask_size, and dynamic structural features such as Order Flow Imbalance (OFI) and depth_imbalance.

from simulator import HawkesLOBSimulator

sim = HawkesLOBSimulator()
# Simulate 5000 high-frequency ticks for NIFTY50
df = sim.simulate_ticks("NIFTY50", num_ticks=5000)

2. Sagan Mixture-of-Experts (MoE) Architecture (moe_model.py)

To predict ultra-short latency order book dynamics, Sagan Trade uses a Temporal Convolutional Network (TCN) embedded inside a PyTorch Mixture of Experts network.

• Zero Future Leakage: Uses causal dilated convolutions to ensure predictions strictly use historical data.
• State-Dependent Routing: An internal gating network routes market regimes (e.g., flash crashes vs. quiet sessions) to 3 different mathematical "Expert" modules.

import torch
from moe_model import SaganMoEModel

device = 'cuda' if torch.cuda.is_available() else 'cpu'
sagan_model = SaganMoEModel(num_features=5, state_dim=5, num_experts=3).to(device)

3. High-Frequency Backtester & Inventory Control (backtester.py)

Legacy backtesters assume you get filled at the mid-price. The Sagan HighFrequencyBacktester enforces rigid queue priority, latency delays, and exact exchange fee structures (STT, Exchange Transaction Charges, SEBI fees).

• Passive vs. Aggressive: Determines if a trade should cross the spread aggressively (paying taker fees) or wait passively in the queue (earning maker rebates).
• Statutory Friction: Automatically calculates round-trip trading frictions to prevent "suicidal" arbitrage algorithms that lose money on taxes.

from backtester import HighFrequencyBacktester

bt = HighFrequencyBacktester(latency_ticks=2)
results = bt.run_backtest(df_test, sagan_preds, "NIFTY50")

print(f"Net Realized Fees: {results['metrics']['fees_paid']}")

4. Symbolic Regressor & AIN Volatility Filters

Instead of weight matrices, Sagan discovers market invariants in the form of mathematical expressions using FunctionGemma.

• Explainability: Every trade is backed by a human-readable formula, e.g., (Close * 0.5) + log(Volume).
• Hawkes-Bates VRP Proxy: Autonomously discovered through the AIN, this macroeconomic sidecar shifts portfolios to cash during contagion regimes.

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🛠️ Complete Workflow Example

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

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

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

# 2. Symbolic Discovery
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
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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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