Anya AI/ML Trading Engine
=====================
Overview
--------
Anya is an advanced AI/ML trading engine that combines machine learning models with real-time market data to provide predictive analytics and automated trading strategies.
Key Features
-----------
* Real-time market prediction
* Automated trading strategies
* Risk management
* Portfolio optimization
* Market sentiment analysis
* Custom model training
Components
---------
.. toctree::
:maxdepth: 2
architecture
models
strategies
deployment
api
configuration
Getting Started
-------------
1. Installation
~~~~~~~~~~~~~
.. code-block:: bash
pip install anya-engine
2. Configuration
~~~~~~~~~~~~~
Create a configuration file `config.yaml`:
.. code-block:: yaml
api_key: YOUR_API_KEY
model:
type: transformer
params:
layers: 6
heads: 8
data:
sources:
- binance
- coinbase
interval: 1m
3. Basic Usage
~~~~~~~~~~~~
.. code-block:: python
from anya import TradingEngine
engine = TradingEngine.from_config('config.yaml')
engine.start()
# Get predictions
predictions = engine.predict('BTC/USD', timeframe='1h')
# Execute trade
engine.execute_trade(
symbol='BTC/USD',
side='buy',
amount=0.1,
type='market'
)
API Reference
-----------
Core Classes
~~~~~~~~~~
TradingEngine
^^^^^^^^^^^^
The main class for interacting with Anya.
.. code-block:: python
class TradingEngine:
def __init__(self, config: dict):
"""Initialize the trading engine.
Args:
config (dict): Configuration dictionary
"""
def predict(self, symbol: str, timeframe: str) -> dict:
"""Get price predictions for a symbol.
Args:
symbol (str): Trading pair symbol
timeframe (str): Prediction timeframe
Returns:
dict: Prediction results
"""
def execute_trade(self, symbol: str, side: str,
amount: float, type: str) -> dict:
"""Execute a trade.
Args:
symbol (str): Trading pair symbol
side (str): 'buy' or 'sell'
amount (float): Trade amount
type (str): Order type
Returns:
dict: Trade result
"""
Model
^^^^^
Base class for ML models.
.. code-block:: python
class Model:
def train(self, data: pd.DataFrame):
"""Train the model."""
def predict(self, data: pd.DataFrame) -> np.ndarray:
"""Make predictions."""
def save(self, path: str):
"""Save model weights."""
def load(self, path: str):
"""Load model weights."""
Strategy
^^^^^^^
Base class for trading strategies.
.. code-block:: python
class Strategy:
def analyze(self, data: pd.DataFrame) -> dict:
"""Analyze market data."""
def generate_signals(self) -> List[Signal]:
"""Generate trading signals."""
def backtest(self, data: pd.DataFrame) -> dict:
"""Run strategy backtest."""
Configuration
-----------
Environment Variables
~~~~~~~~~~~~~~~~~~
.. code-block:: bash
ANYA_API_KEY=your_api_key
ANYA_ENV=production
ANYA_LOG_LEVEL=INFO
ANYA_DATA_DIR=/path/to/data
Configuration File
~~~~~~~~~~~~~~~~
.. code-block:: yaml
# config.yaml
api:
key: YOUR_API_KEY
secret: YOUR_API_SECRET
model:
type: transformer
params:
layers: 6
heads: 8
dropout: 0.1
data:
sources:
- binance
- coinbase
interval: 1m
features:
- close
- volume
- rsi
strategy:
name: momentum
params:
window: 14
threshold: 0.5
risk:
max_position: 1.0
stop_loss: 0.02
take_profit: 0.05
Deployment
--------
Docker
~~~~~
.. code-block:: bash
docker pull opsource/anya:latest
docker run -d \
-e ANYA_API_KEY=your_api_key \
-v config.yaml:/etc/anya/config.yaml \
opsource/anya:latest
Kubernetes
~~~~~~~~
.. code-block:: yaml
# anya-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: anya
spec:
replicas: 3
selector:
matchLabels:
app: anya
template:
metadata:
labels:
app: anya
spec:
containers:
- name: anya
image: opsource/anya:latest
env:
- name: ANYA_API_KEY
valueFrom:
secretKeyRef:
name: anya-secrets
key: api-key
volumeMounts:
- name: config
mountPath: /etc/anya
volumes:
- name: config
configMap:
name: anya-config
Contributing
----------
See our :doc:`../../CONTRIBUTING` guide for details on how to contribute to Anya.
License
------
Anya is licensed under the MIT License. See :doc:`../../LICENSE` for details.