4.0 - 1.0 Implement strategy engine foundation with modular components

- Introduced a new `strategies` package containing the core structure for trading strategies, including `BaseStrategy`, `StrategyFactory`, and various strategy implementations (EMA, RSI, MACD).
- Added utility functions for signal detection and validation in `strategies/utils.py`, enhancing modularity and maintainability.
- Updated `pyproject.toml` to include the new `strategies` package in the build configuration.
- Implemented comprehensive unit tests for the strategy foundation components, ensuring reliability and adherence to project standards.

These changes establish a solid foundation for the strategy engine, aligning with project goals for modularity, performance, and maintainability.

tasks/4.0-strategy-engine-foundation.md

@@ -38,19 +38,31 @@
 - **Layered Chart Integration**: Strategy signals and performance visualizations will be integrated into the dashboard as a new chart layer, utilizing the existing modular chart system.
 - **Comprehensive Testing**: Ensure that all new classes, functions, and modules within the strategy engine have corresponding unit tests placed in the `tests/strategies/` directory, following established testing conventions.
 
+## Decisions
+
+### 1. Vectorized vs. Iterative Calculation
+- **Decision**: Refactored strategy signal detection to use vectorized Pandas operations (e.g., `shift()`, boolean indexing) instead of iterative Python loops.
+- **Reasoning**: Significantly improves performance for signal generation, especially with large datasets, while maintaining identical results as verified by dedicated tests.
+- **Impact**: All core strategy implementations (EMA Crossover, RSI, MACD) now leverage vectorized functions for their primary signal detection logic.
+
+### 2. Indicator Key Generation Consistency
+- **Decision**: Centralized the `_create_indicator_key` logic into a shared utility function `create_indicator_key()` in `strategies/utils.py`.
+- **Reasoning**: Eliminates code duplication in `StrategyFactory` and individual strategy implementations, ensuring consistent key generation and easier maintenance if indicator naming conventions change.
+- **Impact**: `StrategyFactory` and all strategy implementations now use this shared utility for generating unique indicator keys.
+
 ## Tasks
 
-- [ ] 1.0 Core Strategy Foundation Setup
-  - [ ] 1.1 Create `strategies/` directory structure following indicators pattern
-  - [ ] 1.2 Implement `BaseStrategy` abstract class in `strategies/base.py` with `calculate()` and `get_required_indicators()` methods
-  - [ ] 1.3 Create `strategies/data_types.py` with `StrategySignal`, `SignalType`, and `StrategyResult` classes
-  - [ ] 1.4 Implement `StrategyFactory` class in `strategies/factory.py` for dynamic strategy loading and registration
-  - [ ] 1.5 Create strategy implementations directory `strategies/implementations/`
-  - [ ] 1.6 Implement `EMAStrategy` in `strategies/implementations/ema_crossover.py` as reference implementation
-  - [ ] 1.7 Implement `RSIStrategy` in `strategies/implementations/rsi.py` for momentum-based signals
-  - [ ] 1.8 Implement `MACDStrategy` in `strategies/implementations/macd.py` for trend-following signals
-  - [ ] 1.9 Create `strategies/utils.py` with helper functions for signal validation and processing
-  - [ ] 1.10 Create comprehensive unit tests for all strategy foundation components
+- [x] 1.0 Core Strategy Foundation Setup
+  - [x] 1.1 Create `strategies/` directory structure following indicators pattern
+  - [x] 1.2 Implement `BaseStrategy` abstract class in `strategies/base.py` with `calculate()` and `get_required_indicators()` methods
+  - [x] 1.3 Create `strategies/data_types.py` with `StrategySignal`, `SignalType`, and `StrategyResult` classes
+  - [x] 1.4 Implement `StrategyFactory` class in `strategies/factory.py` for dynamic strategy loading and registration
+  - [x] 1.5 Create strategy implementations directory `strategies/implementations/`
+  - [x] 1.6 Implement `EMAStrategy` in `strategies/implementations/ema_crossover.py` as reference implementation
+  - [x] 1.7 Implement `RSIStrategy` in `strategies/implementations/rsi.py` for momentum-based signals
+  - [x] 1.8 Implement `MACDStrategy` in `strategies/implementations/macd.py` for trend-following signals
+  - [x] 1.9 Create `strategies/utils.py` with helper functions for signal validation and processing
+  - [x] 1.10 Create comprehensive unit tests for all strategy foundation components
 
 - [ ] 2.0 Strategy Configuration System
   - [ ] 2.1 Create `config/strategies/` directory structure mirroring indicators configuration
@@ -77,7 +89,7 @@
 - [ ] 4.0 Strategy Data Integration
   - [ ] 4.1 Create `StrategyDataIntegrator` class in new `strategies/data_integration.py` module
   - [ ] 4.2 Implement data loading interface that leverages existing `TechnicalIndicators` class for indicator dependencies
-  - [ ] 4.3 Add multi-timeframe data handling for strategies that require indicators from different timeframes
+  - [x] 4.3 Add multi-timeframe data handling for strategies that require indicators from different timeframes
   - [ ] 4.4 Implement strategy calculation orchestration with proper indicator dependency resolution
   - [ ] 4.5 Create caching layer for computed indicator results to avoid recalculation across strategies
   - [ ] 4.6 Add strategy signal generation and validation pipeline