本项目在原有MarS基础上新增了基于机器学习的市场预测代理，具有以下特点：
集成了多个技术分析指标用于市场分析：
- RSI（相对强弱指标）
- MACD（移动平均收敛散度）
- 布林带（Bollinger Bands）
- EMA（指数移动平均线）
- 使用LSTM神经网络进行价格预测
- 提供未来3天的价格预测，并附带置信度
- 根据市场状况自动调整预测模型
- 基于预测价格走势自动做出交易决策
- 实现基于置信度的风险管理
- 可自定义交易量和价格阈值
- 创建预测代理：
agent = PredictionAgent(
symbol="BTC-USD", # 交易对
prediction_days=30, # 使用历史数据天数
features=['RSI', 'MACD', 'BB_upper', 'BB_lower', 'EMA'] # 技术指标
)- 运行模拟：
env = Env(exchange, description="Prediction agent simulation")
env.register_agent(agent)
env.push_events(create_exchange_events(config))
for observation in env.env():
action = observation.agent.get_action(observation)
env.step(action)模型会输出未来3天的价格预测，包含预测价格和置信度：
未来3天的价格预测：
Date Predicted_Price Confidence
0 2024-12-20 42155.23 0.950
1 2024-12-21 42890.15 0.825
2 2024-12-22 43102.67 0.700
- Date: 预测日期
- Predicted_Price: 预测价格（美元）
- Confidence: 预测置信度（0-1之间）
- 当预测价格上涨超过2%时，系统自动下达买入订单
- 当预测价格下跌超过2%时，系统自动下达卖出订单
- 每天仅在开盘时进行一次预测和交易决策
- 使用30天的历史数据进行训练
- 模型结构：LSTM神经网络
- 训练轮次：25轮
- 损失函数：MSE（均方误差）
MarS is a cutting-edge financial market simulation engine powered by the Large Market Model (LMM), a generative foundation model. MarS addresses the need for realistic, interactive, and controllable order generation. This paper's primary goals are to evaluate the LMM's scaling law in financial markets, assess MarS's realism, balance controlled generation with market impact, and demonstrate MarS's potential applications.
Below is a high-level overview diagram illustrating the core components, workflow, and potential applications of the MarS simulation engine:
The project now includes an advanced prediction agent that utilizes machine learning for market predictio 8000 n:
-
Technical Indicators: Incorporates multiple technical analysis indicators:
- RSI (Relative Strength Index)
- MACD (Moving Average Convergence Divergence)
- Bollinger Bands
- EMA (Exponential Moving Average)
-
ML-Based Prediction:
- Uses LSTM neural network for price prediction
- Provides 3-day price forecasts with confidence levels
- Automatically adjusts predictions based on market conditions
-
Automated Trading:
- Makes trading decisions based on predicted price movements
- Implements risk management with confidence thresholds
- Executes trades with customizable volume and price thresholds
agent = PredictionAgent(
symbol="BTC-USD",
prediction_days=30, # Days of historical data to use
features=['RSI', 'MACD', 'BB_upper', 'BB_lower', 'EMA']
)We are excited to release the MarS simulation engine along with examples demonstrating its capabilities for market simulation. This release includes:
- mlib: The core engine for generating and simulating financial market orders.
- market_simulation: Example scripts illustrating how to use the MarS engine for market simulations.
The release of the pretrained model is currently undergoing internal review. We will make the model public once it passes the review. We look forward to sharing more features, examples, and applications in the future. Stay tuned for updates!
The code is tested with Python 3.8 & 3.9. Run the following command to install the necessary dependencies:
pip install -e .[dev]🛠️ mlib is a comprehensive library dedicated to market simulation, designed to be user-friendly, allowing users to focus on the design of states and agents.
Behind the scenes, we automatically:
- Refresh the orderbook with incoming orders.
- Update states with pertinent trade information.
- Distribute states and actions considering network and computational latency.
Env is a gym-like interface. Below is an example of how to generate orders using env and a noise agent:
agent = NoiseAgent(
symbol=symbol,
init_price=100000,
interval_seconds=1,
start_time=start_time,
end_time=end_time,
)
env = Env(exchange, description="Noise agent simulation")
env.register_agent(agent)
env.push_events(create_exchange_events(config))
for observation in env.env():
action = observation.agent.get_action(observation)
env.step(action)States are information available to agents, automatically updated with every trade information, including orders, transactions, and orderbook snapshots as defined in trade_info.py.
States are shared by agents and zero-copy during their lifetime, even in environments supporting delayed states.
- Creating a new state is straightforward. Here is an example of creating one that includes all transactions:
class TransState(State):
def __init__(self) -> None:
super().__init__()
self.transactons: List[Transaction] = []
def on_trading(self, trade_info: TradeInfo):
super().on_trading(trade_info)
self.transactons.extend(trade_info.transactions)
def on_open(self, cancel_transactions: List[Transaction], lob_snapshot: LobSnapshot, match_trans: Optional[Transaction] = None):
super().on_open(cancel_transactions=cancel_transactions, lob_snapshot=lob_snapshot, match_trans=match_trans)
self.transactons.extend(cancel_transactions)
if match_trans:
self.transactons.append(match_trans)
def on_close(self, close_orderbook: Orderbook, lob_snapshot: LobSnapshot, match_trans: Optional[Transaction] = None):
super().on_close(match_trans=match_trans, close_orderbook=close_orderbook, lob_snapshot=lob_snapshot)
if match_trans:
self.transactons.append(match_trans)Once a new state is defined and registered with exchange.register_state(state), it will be available when the agent wakes up.
So far, we have defined the following states:
- trans_state contains all transactions.
- trade_info_state contains all trade information.
You can run the run_simulaton.py for a complete example to perform market simulation with a noise agent.
python market_simulation/examples/run_simulation.pyYou can see the price trajectory generated from matching orders by the noise agent as follow:
Note: This example demonstrates the use of MarS to simulate a market with a noise agent. For realistic market simulations, a more comprehensive model, such as the Large Market Model (LMM) in MarS, is typically required.
Users of the market simulation engine and the code should prepare their own agents which may be included trained models built with users’ own data, independently assess and test the risks of the model in a specify use scenario, ensure the responsible use of AI technology, including but limited to developing and integrating risk mitigation measures, and comply with all applicable laws and regulations. The market simulation engine does not provide financial opinions, nor is it designed to replace the role of qualified financial professionals in formulating, assessing, and approving finance products. The outputs of the market simulation engine do not reflect the opinions of Microsoft.
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
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@article{li2024mars,
title={MarS: a Financial Market Simulation Engine Powered by Generative Foundation Model},
author={Li, Junjie and Liu, Yang and Liu, Weiqing and Fang, Shikai and Wang, Lewen and Xu, Chang and Bian, Jiang},
journal={arXiv preprint arXiv:2409.07486},
year={2024}
}