209 lines
4.5 KiB
Markdown
209 lines
4.5 KiB
Markdown
# Strategy Framework Architecture
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This document describes the strategy framework design.
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## Strategy Hierarchy
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```
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BaseStrategy (Abstract)
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│
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├──► Technical Strategies
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│ ├──► RSIStrategy
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│ ├──► MACDStrategy
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│ ├──► MovingAverageStrategy
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│ ├──► ConfirmedStrategy (Multi-Indicator)
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│ ├──► DivergenceStrategy
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│ └──► BollingerMeanReversionStrategy
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│
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├──► Ensemble Strategies
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│ └──► ConsensusStrategy
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│
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├──► Other Strategies
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│ ├──► DCAStrategy
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│ ├──► GridStrategy
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│ └──► MomentumStrategy
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│
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└──► CustomStrategy (user-defined)
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```
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## Base Strategy Interface
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All strategies implement:
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```python
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class BaseStrategy(ABC):
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async def on_data(new_data: pd.DataFrame)
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async def generate_signal() -> Dict[str, Any]
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async def calculate_position_size(capital, risk) -> float
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async def start()
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async def stop()
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```
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## Strategy Lifecycle
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```
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1. Initialization
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└──► __init__(parameters)
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2. Activation
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└──► start()
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3. Data Processing
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└──► on_data(new_data)
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└──► generate_signal()
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└──► Trading Engine
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4. Deactivation
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└──► stop()
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```
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## Strategy Registry
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Manages available strategies:
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```python
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StrategyRegistry
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├──► register_strategy(name, class)
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├──► get_strategy_class(name)
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└──► list_available()
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```
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## Multi-Timeframe Support
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Strategies can use multiple timeframes:
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```
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Primary Timeframe (1h)
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│
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├──► Signal Generation
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│
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└──► Higher Timeframe (1d) - Trend Confirmation
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│
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└──► Lower Timeframe (15m) - Entry Timing
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```
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## Strategy Scheduling
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Strategies can be scheduled:
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- **Continuous**: Run on every new candle
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- **Time-based**: Run at specific times
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- **Condition-based**: Run when conditions met
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## Signal Generation
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Signal flow:
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```
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Data Update
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│
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▼
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Indicator Calculation
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│
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▼
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Strategy Logic
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│
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▼
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Signal Generation
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│
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├──► "buy" - Generate buy signal
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├──► "sell" - Generate sell signal
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└──► "hold" - No action
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```
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## Position Sizing
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Strategies calculate position sizes:
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- **Fixed Percentage**: Fixed % of capital
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- **Kelly Criterion**: Optimal position sizing based on win rate
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- **Volatility-Based**: Adjusts based on market volatility (ATR)
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## Advanced Features
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### Trend Filtering
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All strategies can optionally use ADX-based trend filtering:
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```python
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signal = strategy.apply_trend_filter(
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signal,
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ohlcv_data,
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adx_period=14,
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min_adx=25.0
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)
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```
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This filters out signals when:
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- ADX < threshold (weak trend/chop)
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- Signal direction doesn't match trend direction
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### Multi-Indicator Confirmation
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The ConfirmedStrategy requires multiple indicators to agree before generating signals, reducing false signals by 20-30%.
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### Divergence Detection
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Divergence strategies detect price vs. indicator divergences:
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- Bullish divergence: Price lower low, indicator higher low → BUY
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- Bearish divergence: Price higher high, indicator lower high → SELL
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### Ensemble Methods
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ConsensusStrategy aggregates signals from multiple strategies:
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- Weighted voting by strategy performance
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- Minimum consensus threshold
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- Dynamic weighting based on recent performance
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- **Kelly Criterion**: Optimal position sizing
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- **Volatility-based**: Based on ATR
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- **Risk-based**: Based on stop-loss distance
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## Strategy Parameters
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Configurable parameters:
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- Strategy-specific parameters (e.g., RSI period)
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- Risk parameters (position size, stop-loss)
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- Timeframe settings
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- Symbol selection
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## Strategy Execution
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Execution flow:
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```
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Strategy Signal
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│
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▼
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Trading Engine
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│
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├──► Risk Check
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│
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├──► Position Sizing
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│
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└──► Order Execution
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│
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├──► Paper Trading
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└──► Live Trading
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```
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## Strategy Performance
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Performance tracking:
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- Win rate
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- Total return
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- Sharpe ratio
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- Max drawdown
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- Number of trades
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## Extensibility
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Easy to add new strategies:
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1. Inherit from `BaseStrategy`
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2. Implement required methods
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3. Register with `StrategyRegistry`
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4. Configure and use
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