Metadata-Version: 2.4
Name: sagan-trade
Version: 2.3.1
Summary: SOTA Quantitative Finance Library: Symbolic Regression, Temporal Fusion Transformers, PINNs, Advanced Portfolio Optimization, and Institutional-Grade Backtesting
Author-email: Sagan Labs <sambit1912@gmail.com>
Maintainer-email: Sagan Labs <sambit1912@gmail.com>
License: MIT
Project-URL: Repository, https://github.com/That-Tech-Geek/sagan-trade
Project-URL: Source, https://github.com/That-Tech-Geek/sagan-trade
Project-URL: Bug Tracker, https://github.com/That-Tech-Geek/sagan-trade/issues
Project-URL: PyPI, https://pypi.org/project/sagan-trade/
Keywords: trading,quantitative-finance,machine-learning,deep-learning,explainable-ai,pinn,temporal-fusion-transformer,tft,symbolic-regression,mean-reversion,ensemble,portfolio-optimization,hrp,risk-parity,black-litterman,almgren-chriss,backtesting,walk-forward,purged-kfold,market-microstructure,hawkes-process,bates-model,limit-order-book,high-frequency,institutional,alpha-generation,factor-investing
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Topic :: Office/Business :: Financial :: Investment
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Typing :: Typed
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# Sagan Trade

> **SOTA Quantitative Finance Library: Symbolic Regression, Temporal Fusion Transformers, PINNs, Advanced Portfolio Optimization, and Institutional-Grade Backtesting**

[![Python](https://img.shields.io/badge/python-3.10%2B-blue)](https://python.org)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)
[![PyPI](https://img.shields.io/pypi/v/sagan-trade.svg)](https://pypi.org/project/sagan-trade/)
[![GitHub repo](https://img.shields.io/badge/GitHub-That--Tech--Geek%2Fsagan--trade-brightgreen)](https://github.com/That-Tech-Geek/sagan-trade)

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 **v2.1.0**, the library natively incorporates:
- **PIN/VPIN Informed Trading** detection (order flow toxicity)
- **Hawkes Trade Arrivals** and **Bates Jump-Diffusion** dynamics
- **115+ research themes** autonomously generated by the **Autonomous Intelligence Network (AIN)**

---

## Quick Start & Installation

```bash
pip install sagan-trade
```

**Verify the installation:**
```python
import sagan_trade
print(sagan_trade.__version__)  # "2.1.0"
```

---

## 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 > 0.95 using basis functions (Polynomial, Fourier, Momentum, LOB Volatility Pressure).

```python
import pandas as pd
from sagan_trade import SymbolicRegressor

regressor = SymbolicRegressor(basis_functions=['poly', 'fourier', 'momentum'])
model_id = regressor.train(target="AAPL", signals=["Close", "RSI", "Volume"])
alpha_signals, formula = regressor.predict()
print(f"Alpha Signals generated via: {formula}")
```

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

---

### 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.

```python
from sagan_trade import analyze_portfolio, visualize_stock_insights

portfolio_df = analyze_portfolio(["AAPL", "MSFT", "GOOG"], quick_mode=True)
print(portfolio_df)

fig = visualize_stock_insights("AAPL")
```

**Key Capabilities:**
- **Heterogeneous Agents:** Simulates 1,000,000+ market participants with varying risk aversions
- **Bootstrapping:** Computes expected market-clearing prices using non-parametric distributions

---

### 3. Asymmetric Convexity Risk Engine (`AsymmetricRiskEngine`)

Non-linear risk management framework inspired by high-frequency market makers. Overrides raw alpha signals when downside tail risk is detected.

```python
from sagan_trade import AsymmetricRiskEngine

risk_engine = AsymmetricRiskEngine(target_vol=0.15, max_drawdown_limit=0.075)
risk_multiplier = risk_engine.get_risk_multiplier(prices_series)
```

**Key Capabilities:**
- **Downside Convexity:** Exponentially scales exposure based on momentum-volatility asymmetry
- **Adaptive Kelly Sizing:** Drawdown-aware fractional Kelly scaling
- **Asymptotic Shield:** Quadratic drawdown protection creates a hard floor on portfolio risk

---

### 4. Informed Trading Detection (`estimate_pin`, `compute_vpin`)

Detect order flow toxicity using Probability of Informed Trading (PIN) and Volume-Synchronized PIN (VPIN) metrics.

```python
from sagan_trade import estimate_pin, compute_vpin, monitor_vpin, compute_order_flow_toxicity

# Estimate PIN from buy/sell counts
pin_result = estimate_pin(buys, sells)
print(f"PIN: {pin_result.pin:.4f}, Alpha: {pin_result.alpha:.4f}")

# Compute VPIN from trade data
vpin_result = compute_vpin(prices, volumes, n_buckets=50)
print(f"VPIN: {vpin_result.vpin:.4f}")

# Real-time monitoring with alerts
status = monitor_vpin(prices, volumes, alert_threshold=0.25)
print(f"Status: {status['status']}, Recommendation: {status['recommendation']}")

# Comprehensive toxicity metrics
toxicity = compute_order_flow_toxicity(prices, volumes)
print(f"Toxicity Level: {toxicity['toxicity_level']}")
```

**Key Capabilities:**
- **PIN Estimation:** MLE-based EKOP model with multiple random restarts
- **VPIN Computation:** Volume-synchronized probability with BVC or tick rule classification
- **Trade Classification:** Bulk Volume Classification (BVC) and Tick Rule methods
- **Real-time Monitoring:** Alert thresholds for WARNING and CRITICAL toxicity levels

---

### 5. Volatility Regime Filter (`VolatilityRegimeFilter`)

Macroeconomic sidecar that acts as a VRP (Variance Risk Premium) proxy, shifting portfolios to cash during contagion regimes.

```python
from sagan_trade import VolatilityRegimeFilter

vol_filter = VolatilityRegimeFilter(vol_window=20, ma_window=120)
regime_signals = vol_filter.generate_signals(prices_series)
# Returns 1.0 (Risk-On) or 0.0 (Risk-Off / Cash)
```

---

### 6. High-Fidelity Backtest Engine (`BacktestEngine`)

Enforces exact transaction fee accounting, portfolio turnover logic, and dynamically allocates positions from alpha signal overlays and risk models.

```python
from sagan_trade import BacktestEngine

backtester = BacktestEngine(
    initial_capital=1000000,
    maker_fee=0.0001,
    taker_fee=0.0003
)

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}%")
```

---

### 7. Advanced Backtesting (`WalkForwardBacktester`, `PurgedKFoldBacktester`)

Walk-Forward Analysis, Purged K-Fold CV, Combinatorial Purged CV, and Monte Carlo backtesting with joblib parallelism.

```python
from sagan_trade import WalkForwardBacktester, BacktestConfig, run_backtest

config = BacktestConfig(train_window=252, test_window=63, n_splits=5)
result = run_backtest("walk_forward", strategy, prices, config=config)
print(result.summary())
```

---

### 8. Portfolio Optimization (`optimize_portfolio`)

Hierarchical Risk Parity, Risk Parity, Black-Litterman, Mean-Variance, Maximum Diversification, and Minimum Variance optimization.

```python
from sagan_trade import optimize_portfolio, efficient_frontier

result = optimize_portfolio(returns, method="hrp")
print(f"Sharpe: {result.sharpe_ratio:.2f}, Diversification: {result.diversification_ratio:.2f}")

frontier = efficient_frontier(returns, n_points=50)
```

---

### 9. Deep Learning Models

#### Temporal Fusion Transformer (`TemporalFusionTransformer`)
Multi-horizon time series forecasting with interpretable attention weights.

```python
from sagan_trade import create_tft_model, TFTConfig

config = TFTConfig(hidden_size=256, num_heads=4, quantiles=(0.1, 0.5, 0.9))
model = create_tft_model(num_static_vars=10, config=config)
predictions = model.predict_median(static_inputs, encoder_inputs, decoder_inputs)
```

#### Physics-Informed Neural Networks (`BlackScholesPINN`, `HestonPINN`)
Option pricing and volatility modeling with PDE-constrained neural networks.

```python
from sagan_trade import create_bs_pinn, create_heston_pinn

bs_model = create_bs_pinn(strike=100.0, option_type="call")
heston_model = create_heston_pinn(strike=100.0, option_type="call")
```

---

### 10. Optimal Execution (`AlmgrenChrissModel`, `TWAPModel`, `VWAPModel`)

Institutional-grade execution algorithms: Almgren-Chriss, Bertsimas-Lo, Obizhaeva-Wang, Gatheral-Schied, TWAP, VWAP, POV, and Implementation Shortfall.

```python
from sagan_trade import optimize_execution, ExecutionConfig, ExecutionModel

config = ExecutionConfig(
    model=ExecutionModel.ALMGREN_CHRISS,
    total_quantity=100000,
    time_horizon=1.0,
    volatility=0.02
)
result = optimize_execution(config)
print(f"Expected Cost: {result.expected_cost:.4f}")
```

---

### 11. Feature Engineering (`FeatureEngine`)

100+ automated features: trend, momentum, volatility, volume, microstructure, cross-sectional, and time-based.

```python
from sagan_trade import create_feature_engine

engine = create_feature_engine()
features = engine.fit_transform(data, target=returns)
importance = engine.get_feature_importance()
```

---

## 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% |

> **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.

---

## CLI Commands

```bash
# Backtest
sagan-backtest --method walk_forward --tickers AAPL MSFT --initial-capital 1000000

# Train deep learning models
sagan-train --model tft --tickers AAPL MSFT GOOGL --epochs 100

# Portfolio optimization
sagan-optimize --method hrp --tickers AAPL MSFT GOOGL AMZN --risk-aversion 1.0

# Serve predictions
sagan-serve --port 8080
```

---

## Optional Dependencies

```bash
# Full development environment
pip install sagan-trade[dev]

# Deep learning (TFT, PINN, attention models)
pip install sagan-trade[torch]

# High-frequency trading extensions
pip install sagan-trade[hft]

# Alternative data
pip install sagan-trade[alt_data]

# Everything
pip install sagan-trade[all]
```

---

## Contribution & Links

- **Repository:** [https://github.com/That-Tech-Geek/sagan-trade](https://github.com/That-Tech-Geek/sagan-trade)
- **PyPI:** [https://pypi.org/project/sagan-trade/](https://pypi.org/project/sagan-trade/)
- **Documentation:** [https://sagan-trade.vercel.app](https://sagan-trade.vercel.app)
- **Author:** Sambit Mishra

## License
[MIT](LICENSE) 2024 Sagan Labs / Sambit Mishra
