FarmingTong/statsmaker

StatsMaker is a powerful Python library designed for market microstructure modeling, statistical analysis, and trading strategy development. It combines probabilistic programming, machine learning, and financial market microstructure theory to provide a comprehensive toolkit for researchers and traders.

Features

Probabilistic programming models based on Uber Pyro
Market microstructure models (order flow, limit order book, market making, etc.)
High-frequency trading and dark pool trading models
Liquidity, volatility, and execution quality metrics
Portfolio optimization and execution strategy optimization
Machine learning and reinforcement learning integration
Data simulation and market replay functionality

Installation

Install statsmaker using pip:

pip install --upgrade statsmaker

Quick Start

Here are some examples demonstrating various features of statsmaker:

1. Order Flow Model

from statsmaker import StatsmakerBase, OrderFlowModel
import torch

# Create a Statsmaker instance
sm = StatsmakerBase()

# Define the order flow model
order_flow_model = OrderFlowModel()
sm.define_model("order_flow", order_flow_model.model)

# Prepare data
data = torch.randint(0, 2, (100,))

# Perform inference
inference_result = sm.infer("order_flow", data)

# Sample from the posterior distribution
posterior_samples = sm.sample("order_flow", inference_result.get_samples())

print(posterior_samples)

2. High-Frequency Trading Model

from statsmaker import HighFrequencyModel
import pandas as pd

# Prepare your market data
market_data = pd.DataFrame({
    'market_returns': [0.001, -0.002, 0.003, -0.001, 0.002],
    'volume': [1000, 1200, 800, 1100, 900]
})

# Create and fit the high-frequency model
hft_model = HighFrequencyModel()
fit_result = hft_model.fit(market_data)

# Make predictions
predictions = hft_model.predict(market_data, fit_result.get_samples())
print(predictions)

3. Portfolio Optimization

from statsmaker import MicrostructurePortfolioOptimizer
import numpy as np

# Prepare return data and microstructure data
returns = np.array([0.05, 0.03, 0.02, 0.04, 0.01])
microstructure_data = {
    'spread': [0.01, 0.015, 0.02, 0.01, 0.025],
    'volume': [10000, 8000, 12000, 9000, 11000]
}

# Create and use the portfolio optimizer
optimizer = MicrostructurePortfolioOptimizer(returns, microstructure_data)
optimal_weights = optimizer.optimize(risk_aversion=2)

print("Optimal portfolio weights:", optimal_weights)

4. Reinforcement Learning Trading Agent

from statsmaker import RLTrader
import pandas as pd

# Prepare your market data
market_data = pd.DataFrame({
    'price': [100, 101, 99, 102, 98, 103],
    'volume': [1000, 1200, 800, 1100, 900, 1300]
})

# Create and train the RL trader
rl_trader = RLTrader(market_data)
rl_trader.train(total_timesteps=10000)

# Use the trained model to make trading decisions
observation = [100, 1000, 0, 0, 0]  # Current price, volume, and other relevant features
action = rl_trader.trade(observation)
print("Recommended action:", ["Buy", "Sell", "Hold"][action])

5. Calculating Microstructure Metrics

from statsmaker.metrics import microstructure_metrics
import numpy as np

# Prepare sample data
bid_prices = np.array([99.5, 99.4, 99.3, 99.2, 99.1])
ask_prices = np.array([100.5, 100.6, 100.7, 100.8, 100.9])
bid_sizes = np.array([100, 200, 300, 400, 500])
ask_sizes = np.array([150, 250, 350, 450, 550])

# Calculate spread and depth
spread = microstructure_metrics.calculate_spread(bid_prices, ask_prices)
depth = microstructure_metrics.calculate_depth(bid_sizes, ask_sizes)

print(f"Average Spread: {spread}")
print(f"Market Depth: {depth}")

Documentation

Detailed documentation can be found at docs.statsmaker.io.

Contributing

We welcome contributions! Please see CONTRIBUTING.md for details on how to get started.

License

StatsMaker is licensed under the Apache License 2.0. See the LICENSE file for more details.

Citation

If you use statsmaker in your research, please cite:

@software{statsmaker2024,
  author = {Jialue Chen},
  title = {statsmaker: A Python Library for Market Microstructure Modeling and Analysis},
  year = {2024},
  url = {https://github.com/jialuechen/statsmaker},
  version = {0.1.0}
}

Contact

If you have any questions or suggestions, please open an issue or contact us directly at contact@statsmaker.io.

Acknowledgments

We would like to thank the Pyro team at Uber for their excellent probabilistic programming framework, which forms the foundation of many models in statsmaker.

FarmingTong / statsmaker