GenMarket is a modular, multi-layer generative market simulation framework designed to evaluate trading strategies and market impact under synthetic yet realistic conditions.
It combines macro regime generation, high-frequency order flow simulation, LOB microstructure modeling, and plug-in execution strategies (e.g. VWAP, TWAP, Dynamic Stop Loss), supporting both controlled experimentation and generative scenario construction.
-
Multi-layer simulation
Top-down generation from macro regimes → order flow → limit order book (LOB). -
Scenario generation
Uses VAE/Diffusion model stubs to create synthetic market conditions beyond historical replay. -
Order flow modeling
Autoregressive event-level generator simulates realistic buy/sell order streams. -
LOB simulation engine
Matches orders with queue logic and customizable liquidity depth. -
Built-in execution strategies
Plug-and-play VWAP, TWAP, and Dynamic Stop Loss strategies for benchmark testing. -
Market impact evaluation
Measures execution price, slippage, impact cost, and profit/loss (PnL). -
Visualization
Interactive plotting of order flow, execution trajectory, and order book depth using Plotly. -
Natural language scenario configuration
Generate simulation configurations from plain English descriptions using OpenAI's GPT API. -
Parallelized simulation
Accelerated order flow generation and LOB simulation using multi-threading.
pip install --upgrade genmarket{
"regime": "volatile",
"volatility": 0.35,
"liquidity": "low",
"lob": {
"levels": 5,
"latency_ms": 10
},
"strategy": {
"type": "vwap",
"params": {
"target_volume": 100,
"time_horizon": 10
}
}
}python examples/run_simulation.pyfrom genmarket import GenMarket
from genmarket.plotting import plot_executions
import json
with open("configs/market_crash_scenario.json") as f:
config = json.load(f)
gm = GenMarket(config)
metrics = gm.run()
print("Simulation Metrics:")
for k, v in metrics.items():
print(f"{k}: {v}")
# Visualize execution result
plot_executions(metrics.get("executions", []))VWAPStrategy– Volume-weighted execution across time horizonTWAPStrategy– Time-weighted slices across fixed intervalsDynamicStopLossStrategy– Trailing or fixed stop loss based on market conditions
You can create your own strategies and add them under genmarket/strategy_plugins/.
Generate simulation configurations from plain English descriptions using OpenAI's GPT API. Example:
from genmarket.nlp_config_generator import NLPConfigGenerator
api_key = "your_openai_api_key"
generator = NLPConfigGenerator(api_key=api_key)
description = "Simulate a high-volatility market with low liquidity. Use a VWAP strategy with a target volume of 1000 shares over a 1-hour period."
config = generator.generate_config(description)
print("Generated Configuration:")
print(config)
# Save the configuration to a file
with open("generated_config.json", "w") as f:
f.write(config)Visualize execution prices, order flow, and order book depth interactively using Plotly.
from genmarket.interactive_plotting import InteractivePlotting
# Example data for execution prices
timestamps = ['2025-04-13 10:00', '2025-04-13 10:01', '2025-04-13 10:02']
prices = [100, 102, 101]
InteractivePlotting.plot_execution_prices(prices=prices, timestamps=timestamps)
# Example data for order book depth
price_levels = [99, 100, 101, 102, 103]
bid_depth = [50, 70, 60, 40, 30]
ask_depth = [30, 40, 50, 60, 70]
InteractivePlotting.plot_order_book_depth(bid_depth=bid_depth, ask_depth=ask_depth, levels=price_levels)Accelerate order flow generation and LOB simulation using multi-threading.
from genmarket.order_flow_generator import OrderFlowGenerator
from genmarket.lob_simulator import LOBSimulator
# Example parameters for order flow generation
order_flow_params = [
{"base_price": 100, "steps": 10},
{"base_price": 200, "steps": 10},
{"base_price": 300, "steps": 10},
]
# Example parameters for LOB simulation
lob_params = [
{"base_bid": 100, "base_ask": 101},
{"base_bid": 200, "base_ask": 201},
{"base_bid": 300, "base_ask": 301},
]
# Initialize generators
order_flow_generator = OrderFlowGenerator(flow_params={})
lob_simulator = LOBSimulator(lob_params={})
# Generate order flows in parallel
order_flows = order_flow_generator.generate_order_flow_parallel(order_flow_params, num_threads=3)
print("Order Flows:", order_flows)
# Simulate LOBs in parallel
lobs = lob_simulator.simulate_lob_parallel(lob_params, num_threads=3)
print("LOBs:", lobs)- Strategy benchmarking under stress scenarios
- Execution cost modeling and market impact analysis
- Synthetic data generation for pre-trade analytics
- Reinforcement learning environment (future support)
- Integrate pretrained diffusion/VAE market generators
- Add LLM-driven natural language scenario parser
- Extend to multi-asset & cross-venue simulation
- Add dashboard interface for real-time simulation control
MIT License. See LICENSE.
@misc{genmarket2025,
title = {GenMarket: A Multi-Layer Generative Market Simulation Framework},
author = {Jialue Chen},
year = {2025},
note = {https://github.com/jialuechen/genmarket}
}