Rename core package

LLM.md

@@ -7,12 +7,12 @@ QuantTradeAI is a comprehensive machine learning framework for quantitative trad
 ## Core Architecture
 
 ### Key Components
-- **Data Layer**: `src/data/` - Data fetching, caching, validation
-- **Feature Engineering**: `src/features/` - Technical indicators, custom features
-- **ML Models**: `src/models/` - Ensemble classifiers, hyperparameter optimization
-- **Backtesting**: `src/backtest/` - Trade simulation, performance metrics
-- **Risk Management**: `src/trading/` - Stop-loss, position sizing
-- **Utilities**: `src/utils/` - Metrics, visualization, configuration
+- **Data Layer**: `quanttradeai/data/` - Data fetching, caching, validation
+- **Feature Engineering**: `quanttradeai/features/` - Technical indicators, custom features
+- **ML Models**: `quanttradeai/models/` - Ensemble classifiers, hyperparameter optimization
+- **Backtesting**: `quanttradeai/backtest/` - Trade simulation, performance metrics
+- **Risk Management**: `quanttradeai/trading/` - Stop-loss, position sizing
+- **Utilities**: `quanttradeai/utils/` - Metrics, visualization, configuration
 
 ### Data Flow
 1. Fetch OHLCV data (YFinance/AlphaVantage)
@@ -72,8 +72,8 @@ make test     # pytest testing
 
 ### Data Loading
 ```python
-from src.data.loader import DataLoader
-from src.data.processor import DataProcessor
+from quanttradeai.data.loader import DataLoader
+from quanttradeai.data.processor import DataProcessor
 
 # Initialize components
 loader = DataLoader("config/model_config.yaml")
@@ -87,7 +87,7 @@ df_labeled = processor.generate_labels(df_processed)
 
 ### Model Training
 ```python
-from src.models.classifier import MomentumClassifier
+from quanttradeai.models.classifier import MomentumClassifier
 
 # Initialize and train
 classifier = MomentumClassifier("config/model_config.yaml")
@@ -97,7 +97,7 @@ classifier.train(X, y)
 
 ### Backtesting
 ```python
-from src.backtest.backtester import simulate_trades, compute_metrics
+from quanttradeai.backtest.backtester import simulate_trades, compute_metrics
 
 # Simulate trades
 df_trades = simulate_trades(df_labeled)
@@ -139,7 +139,7 @@ except ValueError as e:
 
 ### Configuration Validation
 ```python
-from src.utils.config_schemas import ModelConfigSchema
+from quanttradeai.utils.config_schemas import ModelConfigSchema
 ModelConfigSchema(**config)  # Validates configuration
 ```
 
@@ -203,7 +203,7 @@ def test_complete_pipeline():
 
 ### Feature Engineering
 ```python
-from src.features.technical import sma, ema, rsi, macd
+from quanttradeai.features.technical import sma, ema, rsi, macd
 
 # Generate technical indicators
 df['sma_20'] = sma(df['Close'], 20)
@@ -213,15 +213,15 @@ macd_df = macd(df['Close'])
 
 ### Risk Management
 ```python
-from src.trading.risk import apply_stop_loss_take_profit
+from quanttradeai.trading.risk import apply_stop_loss_take_profit
 
 # Apply risk rules
 df_with_risk = apply_stop_loss_take_profit(df, stop_loss_pct=0.02)
 ```
 
 ### Performance Metrics
 ```python
-from src.utils.metrics import classification_metrics, sharpe_ratio
+from quanttradeai.utils.metrics import classification_metrics, sharpe_ratio
 
 # Calculate metrics
 metrics = classification_metrics(y_true, y_pred)

Makefile

@@ -1,16 +1,17 @@
 .PHONY: format format-check lint test pipeline
 
 format:
-	poetry run black src/
+	poetry run black quanttradeai/
 
 format-check:
-	poetry run black --check src/
+	poetry run black --check quanttradeai/
 
 lint:
-	poetry run flake8 src/
+	poetry run flake8 quanttradeai/
 
 test:
 	poetry run pytest
 
 pipeline:
 	poetry run quanttradeai train -c config/model_config.yaml
+

README.md

@@ -45,7 +45,7 @@ poetry run quanttradeai evaluate -m models/trained/AAPL
 
 ```
 QuantTradeAI/
-├── src/                    # Core framework
+├── quanttradeai/           # Core framework
 │   ├── data/              # Data loading & processing
 │   ├── features/          # Technical indicators
 │   ├── models/            # ML models & training
@@ -111,9 +111,9 @@ poetry run quanttradeai evaluate -m models/trained/AAPL
 ### Python API
 
 ```python
-from src.data.loader import DataLoader
-from src.data.processor import DataProcessor
-from src.models.classifier import MomentumClassifier
+from quanttradeai.data.loader import DataLoader
+from quanttradeai.data.processor import DataProcessor
+from quanttradeai.models.classifier import MomentumClassifier
 
 # Initialize components
 loader = DataLoader("config/model_config.yaml")
@@ -153,10 +153,10 @@ poetry install --with dev
 poetry run pytest
 
 # Format code
-poetry run black src/
+poetry run black quanttradeai/
 
 # Lint code
-poetry run flake8 src/
+poetry run flake8 quanttradeai/
 ```
 
 ## 📄 License

docs/README.md

@@ -69,7 +69,7 @@ The [Quick Reference Guide](quick-reference.md) includes:
 
 ```
 QuantTradeAI/
-├── src/
+├── quanttradeai/
 │   ├── main.py              # CLI entry point
 │   ├── data/                # Data processing
 │   │   ├── loader.py        # Data fetching and caching

docs/api/README.md

@@ -32,9 +32,9 @@ Complete API documentation for QuantTradeAI.
 
 ### Basic Workflow
 ```python
-from src.data.loader import DataLoader
-from src.data.processor import DataProcessor
-from src.models.classifier import MomentumClassifier
+from quanttradeai.data.loader import DataLoader
+from quanttradeai.data.processor import DataProcessor
+from quanttradeai.models.classifier import MomentumClassifier
 
 # Initialize components
 loader = DataLoader("config/model_config.yaml")
@@ -54,8 +54,8 @@ classifier.train(X, y)
 
 ### Advanced Usage
 ```python
-from src.backtest.backtester import simulate_trades, compute_metrics
-from src.trading.risk import apply_stop_loss_take_profit
+from quanttradeai.backtest.backtester import simulate_trades, compute_metrics
+from quanttradeai.trading.risk import apply_stop_loss_take_profit
 
 # Backtest with risk management
 df_with_risk = apply_stop_loss_take_profit(df_labeled, stop_loss_pct=0.02)

docs/api/backtesting.md

@@ -18,7 +18,7 @@ Simulates trades using label signals.
 
 **Example:**
 ```python
-from src.backtest.backtester import simulate_trades
+from quanttradeai.backtest.backtester import simulate_trades
 
 # Simulate trades with stop-loss and take-profit
 df_with_trades = simulate_trades(df, stop_loss_pct=0.02, take_profit_pct=0.04)
@@ -40,7 +40,7 @@ Calculates basic performance metrics for a strategy.
 
 **Example:**
 ```python
-from src.backtest.backtester import compute_metrics
+from quanttradeai.backtest.backtester import compute_metrics
 
 # Calculate performance metrics
 metrics = compute_metrics(df_with_trades, risk_free_rate=0.02)
@@ -53,7 +53,7 @@ print(f"Max Drawdown: {metrics['max_drawdown']:.4f}")
 
 ### Basic Backtesting
 ```python
-from src.backtest.backtester import simulate_trades, compute_metrics
+from quanttradeai.backtest.backtester import simulate_trades, compute_metrics
 
 # Simulate trades
 df_trades = simulate_trades(df_labeled)
@@ -68,8 +68,8 @@ print(f"Max Drawdown: {metrics['max_drawdown']:.2%}")
 
 ### Backtesting with Risk Management
 ```python
-from src.backtest.backtester import simulate_trades, compute_metrics
-from src.trading.risk import apply_stop_loss_take_profit
+from quanttradeai.backtest.backtester import simulate_trades, compute_metrics
+from quanttradeai.trading.risk import apply_stop_loss_take_profit
 
 # Apply risk management rules
 df_with_risk = apply_stop_loss_take_profit(
@@ -91,7 +91,7 @@ print(f"Max Drawdown: {metrics['max_drawdown']:.2%}")
 
 ### Multi-Asset Backtesting
 ```python
-from src.backtest.backtester import simulate_trades, compute_metrics
+from quanttradeai.backtest.backtester import simulate_trades, compute_metrics
 
 # Backtest multiple assets
 results = {}
@@ -166,7 +166,7 @@ print(f"Average Loss: {losing_trades['strategy_return'].mean():.2%}")
 
 ### Stop-Loss and Take-Profit
 ```python
-from src.trading.risk import apply_stop_loss_take_profit
+from quanttradeai.trading.risk import apply_stop_loss_take_profit
 
 # Apply different risk management scenarios
 scenarios = [

docs/api/data.md

@@ -14,7 +14,7 @@ Handles data fetching, caching, and validation for multiple financial instrument
 
 **Example:**
 ```python
-from src.data.loader import DataLoader
+from quanttradeai.data.loader import DataLoader
 
 # Initialize with default configuration
 loader = DataLoader("config/model_config.yaml")
@@ -97,7 +97,7 @@ DataSource implementation using the yfinance package.
 
 **Example:**
 ```python
-from src.data.datasource import YFinanceDataSource
+from quanttradeai.data.datasource import YFinanceDataSource
 
 # Initialize YFinance data source
 data_source = YFinanceDataSource()
@@ -115,7 +115,7 @@ DataSource implementation for AlphaVantage API.
 
 **Example:**
 ```python
-from src.data.datasource import AlphaVantageDataSource
+from quanttradeai.data.datasource import AlphaVantageDataSource
 
 # Initialize with API key
 data_source = AlphaVantageDataSource("YOUR_API_KEY")
@@ -135,7 +135,7 @@ Processes raw OHLCV data and generates required features for the trading strateg
 
 **Example:**
 ```python
-from src.data.processor import DataProcessor
+from quanttradeai.data.processor import DataProcessor
 
 # Initialize processor
 processor = DataProcessor("config/features_config.yaml")

docs/api/features.md

@@ -17,7 +17,7 @@ Calculates Simple Moving Average.
 
 **Example:**
 ```python
-from src.features.technical import sma
+from quanttradeai.features.technical import sma
 
 # Calculate 20-period SMA
 sma_20 = sma(df['Close'], 20)
@@ -36,7 +36,7 @@ Calculates Exponential Moving Average.
 
 **Example:**
 ```python
-from src.features.technical import ema
+from quanttradeai.features.technical import ema
 
 # Calculate 20-period EMA
 ema_20 = ema(df['Close'], 20)
@@ -55,7 +55,7 @@ Calculates Relative Strength Index.
 
 **Example:**
 ```python
-from src.features.technical import rsi
+from quanttradeai.features.technical import rsi
 
 # Calculate 14-period RSI
 rsi_14 = rsi(df['Close'], 14)
@@ -76,7 +76,7 @@ Calculates MACD indicator.
 
 **Example:**
 ```python
-from src.features.technical import macd
+from quanttradeai.features.technical import macd
 
 # Calculate MACD
 macd_df = macd(df['Close'])
@@ -101,7 +101,7 @@ Calculates Stochastic Oscillator.
 
 **Example:**
 ```python
-from src.features.technical import stochastic
+from quanttradeai.features.technical import stochastic
 
 # Calculate Stochastic Oscillator
 stoch_df = stochastic(df['High'], df['Low'], df['Close'])
@@ -127,7 +127,7 @@ Computes a simple momentum score from multiple indicators.
 
 **Example:**
 ```python
-from src.features.custom import momentum_score
+from quanttradeai.features.custom import momentum_score
 
 # Calculate momentum score
 score = momentum_score(df['Close'], df['sma_20'], df['rsi'], df['macd'], df['macd_signal'])
@@ -149,7 +149,7 @@ Flags days when price breaks above the previous high plus a threshold.
 
 **Example:**
 ```python
-from src.features.custom import volatility_breakout
+from quanttradeai.features.custom import volatility_breakout
 
 # Calculate volatility breakout signals
 breakout = volatility_breakout(df['High'], df['Low'], df['Close'])
@@ -159,7 +159,7 @@ breakout = volatility_breakout(df['High'], df['Low'], df['Close'])
 
 ### Moving Averages
 ```python
-from src.features.technical import sma, ema
+from quanttradeai.features.technical import sma, ema
 
 # Generate multiple moving averages
 periods = [5, 10, 20, 50, 200]
@@ -170,7 +170,7 @@ for period in periods:
 
 ### Momentum Indicators
 ```python
-from src.features.technical import rsi, macd, stochastic
+from quanttradeai.features.technical import rsi, macd, stochastic
 
 # Generate momentum indicators
 df['rsi'] = rsi(df['Close'], 14)
@@ -185,7 +185,7 @@ df['stoch_d'] = stoch_df['stoch_d']
 
 ### Custom Features
 ```python
-from src.features.custom import momentum_score, volatility_breakout
+from quanttradeai.features.custom import momentum_score, volatility_breakout
 
 # Generate custom features
 df['momentum_score'] = momentum_score(

docs/api/models.md

@@ -13,7 +13,7 @@ Voting Classifier for momentum trading strategy using Logistic Regression, Rando
 
 **Example:**
 ```python
-from src.models.classifier import MomentumClassifier
+from quanttradeai.models.classifier import MomentumClassifier
 
 # Initialize classifier
 classifier = MomentumClassifier("config/model_config.yaml")
@@ -178,7 +178,7 @@ xgb_params = {
 
 ### Complete Training Example
 ```python
-from src.models.classifier import MomentumClassifier
+from quanttradeai.models.classifier import MomentumClassifier
 from sklearn.model_selection import train_test_split
 
 # Initialize classifier