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import pandas as pd
import numpy as np
import logging
import os
from typing import Dict, Any, Optional
from datetime import datetime, timedelta
logger = logging.getLogger(__name__)
def load_data(config: Dict[str, Any]) -> Optional[pd.DataFrame]:
"""
Load market data based on configuration.
Args:
config: Configuration dictionary
Returns:
DataFrame with market data or None if error
"""
try:
data_source = config['data_source']['type']
logger.info(f"Loading data from source: {data_source}")
if data_source == 'alpaca':
return _load_alpaca_data(config)
elif data_source == 'csv':
return _load_csv_data(config)
elif data_source == 'synthetic':
return _load_synthetic_data(config)
else:
logger.error(f"Unsupported data source: {data_source}")
return None
except Exception as e:
logger.error(f"Error loading data: {e}")
return None
def _load_alpaca_data(config: Dict[str, Any]) -> Optional[pd.DataFrame]:
"""Load market data from Alpaca"""
try:
from .alpaca_broker import AlpacaBroker
# Initialize Alpaca broker
alpaca_broker = AlpacaBroker(config)
# Get symbol and timeframe from config
symbol = config['trading']['symbol']
timeframe = config['trading']['timeframe']
# Convert timeframe to Alpaca format
tf_map = {
'1m': '1Min',
'5m': '5Min',
'15m': '15Min',
'1h': '1Hour',
'1d': '1Day'
}
alpaca_timeframe = tf_map.get(timeframe, '1Min')
# Get market data
data = alpaca_broker.get_market_data(
symbol=symbol,
timeframe=alpaca_timeframe,
limit=1000
)
if data is not None and not data.empty:
logger.info(f"Loaded {len(data)} data points from Alpaca for {symbol}")
return data
else:
logger.error(f"No data returned from Alpaca for {symbol}")
return None
except Exception as e:
logger.error(f"Error loading Alpaca data: {e}")
return None
def _load_csv_data(config: Dict[str, Any]) -> Optional[pd.DataFrame]:
"""Load market data from CSV file"""
try:
file_path = config['data_source']['path']
if not os.path.exists(file_path):
logger.error(f"CSV file not found: {file_path}")
return None
# Load CSV data
data = pd.read_csv(file_path)
# Handle both 'timestamp' and 'date' column names
if 'date' in data.columns and 'timestamp' not in data.columns:
data = data.rename(columns={'date': 'timestamp'})
# Ensure required columns exist
required_columns = ['timestamp', 'open', 'high', 'low', 'close', 'volume']
missing_columns = [col for col in required_columns if col not in data.columns]
if missing_columns:
logger.error(f"Missing required columns: {missing_columns}")
return None
# Convert timestamp to datetime
data['timestamp'] = pd.to_datetime(data['timestamp'])
# Sort by timestamp
data = data.sort_values('timestamp').reset_index(drop=True)
logger.info(f"Loaded {len(data)} data points from CSV: {file_path}")
return data
except Exception as e:
logger.error(f"Error loading CSV data: {e}")
return None
def _load_synthetic_data(config: Dict[str, Any]) -> Optional[pd.DataFrame]:
"""Load or generate synthetic market data"""
try:
synthetic_config = config.get('synthetic_data', {})
data_path = synthetic_config.get('data_path', 'data/synthetic_market_data.csv')
# Check if synthetic data file exists
if os.path.exists(data_path):
logger.info(f"Loading existing synthetic data from: {data_path}")
return _load_csv_data({'data_source': {'path': data_path}})
# Generate new synthetic data
logger.info("Generating new synthetic market data")
from .synthetic_data_generator import SyntheticDataGenerator
generator = SyntheticDataGenerator(config)
data = generator.generate_data()
if data is not None and not data.empty:
# Save generated data
os.makedirs(os.path.dirname(data_path), exist_ok=True)
data.to_csv(data_path, index=False)
logger.info(f"Saved synthetic data to: {data_path}")
return data
else:
logger.error("Failed to generate synthetic data")
return None
except Exception as e:
logger.error(f"Error loading synthetic data: {e}")
return None
def validate_data(data: pd.DataFrame) -> bool:
"""
Validate market data quality.
Args:
data: DataFrame with market data
Returns:
True if data is valid, False otherwise
"""
try:
if data is None or data.empty:
logger.error("Data is None or empty")
return False
# Handle both 'timestamp' and 'date' column names
if 'date' in data.columns and 'timestamp' not in data.columns:
data = data.rename(columns={'date': 'timestamp'})
# Check required columns
required_columns = ['timestamp', 'open', 'high', 'low', 'close', 'volume']
missing_columns = [col for col in required_columns if col not in data.columns]
if missing_columns:
logger.error(f"Missing required columns: {missing_columns}")
return False
# Check for NaN values
nan_counts = data[required_columns].isna().sum()
if nan_counts.sum() > 0:
logger.warning(f"Found NaN values: {nan_counts.to_dict()}")
# Remove rows with NaN values
data.dropna(subset=required_columns, inplace=True)
logger.info(f"Removed {nan_counts.sum()} rows with NaN values")
# Check for negative prices
price_columns = ['open', 'high', 'low', 'close']
negative_prices = data[price_columns] < 0
if negative_prices.any().any():
logger.error("Found negative prices in data")
return False
# Check for zero volumes
zero_volumes = data['volume'] == 0
if zero_volumes.sum() > len(data) * 0.5: # More than 50% zero volumes
logger.warning("High percentage of zero volumes detected")
# Check OHLC consistency
invalid_ohlc = (
(data['high'] < data['low']) |
(data['open'] > data['high']) |
(data['close'] > data['high']) |
(data['open'] < data['low']) |
(data['close'] < data['low'])
)
if invalid_ohlc.any():
logger.error("Found invalid OHLC relationships")
return False
# Check timestamp consistency
if 'timestamp' in data.columns:
timestamps = pd.to_datetime(data['timestamp'])
if not timestamps.is_monotonic_increasing:
logger.warning("Timestamps are not in ascending order")
data = data.sort_values('timestamp').reset_index(drop=True)
logger.info(f"Data validation passed: {len(data)} valid records")
return True
except Exception as e:
logger.error(f"Error validating data: {e}")
return False
def add_technical_indicators(data: pd.DataFrame) -> pd.DataFrame:
"""
Add technical indicators to market data.
Args:
data: DataFrame with OHLCV data
Returns:
DataFrame with technical indicators added
"""
try:
df = data.copy()
# Simple Moving Averages
df['sma_20'] = df['close'].rolling(window=20).mean()
df['sma_50'] = df['close'].rolling(window=50).mean()
df['sma_200'] = df['close'].rolling(window=200).mean()
# Exponential Moving Averages
df['ema_12'] = df['close'].ewm(span=12).mean()
df['ema_26'] = df['close'].ewm(span=26).mean()
# MACD
df['macd'] = df['ema_12'] - df['ema_26']
df['macd_signal'] = df['macd'].ewm(span=9).mean()
df['macd_histogram'] = df['macd'] - df['macd_signal']
# RSI
delta = df['close'].diff()
gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
rs = gain / loss
df['rsi'] = 100 - (100 / (1 + rs))
# Bollinger Bands
df['bb_middle'] = df['close'].rolling(window=20).mean()
bb_std = df['close'].rolling(window=20).std()
df['bb_upper'] = df['bb_middle'] + (bb_std * 2)
df['bb_lower'] = df['bb_middle'] - (bb_std * 2)
# Average True Range (ATR)
high_low = df['high'] - df['low']
high_close = np.abs(df['high'] - df['close'].shift())
low_close = np.abs(df['low'] - df['close'].shift())
true_range = np.maximum(high_low, np.maximum(high_close, low_close))
df['atr'] = true_range.rolling(window=14).mean()
# Volume indicators
df['volume_sma'] = df['volume'].rolling(window=20).mean()
df['volume_ratio'] = df['volume'] / df['volume_sma']
# Price momentum
df['price_change'] = df['close'].pct_change()
df['price_change_5'] = df['close'].pct_change(periods=5)
df['price_change_20'] = df['close'].pct_change(periods=20)
logger.info("Technical indicators added successfully")
return df
except Exception as e:
logger.error(f"Error adding technical indicators: {e}")
return data
def get_latest_data(data: pd.DataFrame, n_periods: int = 100) -> pd.DataFrame:
"""
Get the latest n periods of data.
Args:
data: DataFrame with market data
n_periods: Number of periods to return
Returns:
DataFrame with latest n periods
"""
try:
if len(data) <= n_periods:
return data
return data.tail(n_periods).reset_index(drop=True)
except Exception as e:
logger.error(f"Error getting latest data: {e}")
return data
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