pymicrostructure is a powerful Python library for simulating and analyzing financial market microstructure. It provides a flexible framework for modeling various market participants, implementing trading strategies, and evaluating market performance metrics.

1. Features
2. Installation
3. Quick Start
4. Documentation
5. Examples
6. Contributing
7. License

• Flexible market simulation framework
• Various trader types (e.g., informed traders, noise traders, market makers)
• Customizable trading strategies
• Comprehensive set of market performance metrics
• Order book visualization tools
• Easy-to-use API for creating and running simulations

• Markets: Implement different market models (e.g., continuous double auction)
• Traders: Model various types of market participants
• Orders: Support for different order types (e.g., market orders, limit orders)
• Strategies: Implement and test different trading strategies
• Metrics: Analyze market efficiency, liquidity, and other performance indicators

To install pymicrostructure, run the following command:

pip install pymicrostructure

Here's a simple example to get you started with pymicrostructure:

from pymicrostructure.markets.continuous import ContinuousDoubleAuction
from pymicrostructure.traders.market_maker import *
from pymicrostructure.traders.informed import *
from pymicrostructure.traders.noise import *
from pymicrostructure.traders.strategy import *

from pymicrostructure.visualization.summary import participant_comparison, price_path
from pymicrostructure.metrics.trader import participants_report

market   = ContinuousDoubleAuction(initial_fair_price=1000)
mm       = BaseMarketMaker(market,
                           fair_price_strategy=OrderFlowMagnitudeFairPrice(window=10, aggressiveness=1),
                           volume_strategy=MaxFractionVolume(fraction=0.1), 
                           spread_strategy=OrderFlowImbalanceSpread(window=5, aggressiveness=10, min_halfspread=3),
                           max_inventory=1000)

informed = TWAPInformedTrader(market)
noise    = NoiseTrader(market, submission_rate=1, volume_size=lambda:np.random.randint(1, 5))

market.run(300)
participant_comparison(market.participants)
price_path(market)

For detailed documentation, please visit our documentation site.

You can find more examples in the examples/ directory of this repository. These examples cover various scenarios and use cases, such as:

• Implementing custom trading strategies
• Analyzing market liquidity
• Visualizing order book dynamics
• Comparing performance of different trader types

We welcome contributions to pymicrostructure! Please see our CONTRIBUTING.md file for details on how to contribute, report issues, or suggest enhancements.

pymicrostructure is released under the MIT License. See the LICENSE file for details.