# AlphaFinance Simulation Environment
## Overview
`AlphaFinance` is a Reinforcement Learning (RL) environment designed to train autonomous agents in portfolio management and trading execution. It draws inspiration from DeepMind's **AlphaZero** and **MuZero** architectures, framing financial decision-making as a sequential game.
## Architecture
### Environment (`AlphaFinanceEnv`)
* **State Space:** A window of market data (Price, Volume, Technical Indicators, Macro Signals).
* **Action Space:** Continuous action space representing the target portfolio weight allocation (e.g., 0.0 to 1.0 for a single asset, or a vector for multi-asset).
* **Reward Function:** Log Returns or Sharpe Ratio (risk-adjusted return).
### Agent (`AlphaAgent`)
* **Actor-Critic:** The agent is designed to use an Actor-Critic architecture (or MCTS in future versions).
* **Policy Network:** Predicts the optimal action (weights).
* **Value Network:** Predicts the expected future reward (Value at Risk / Expected Return).
## Usage
```python
from core.simulations.alpha_finance import AlphaFinanceEnv, AlphaAgent
# Initialize Env
data = load_market_data("AAPL")
env = AlphaFinanceEnv(data_feed=data)
# Run Loop
state = env.reset()
done = False
while not done:
action = agent.act(state)
next_state, reward, done, info = env.step(action)
agent.learn(state, action, reward, next_state)
```
## Future Work
* **Multi-Agent:** Simulate market dynamics with multiple competing AlphaAgents.
* **MuZero Integration:** Implement the model-based planning of MuZero to "imagine" future market scenarios.
* **Real Data:** Connect to `BigQueryConnector` to train on massive historical datasets.